Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism: Social Democratic Governance Practices in Public Higher Education (Educational Governance Research, 19) 3031099958, 9783031099953

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Educational Governance Research 19

Miriam Madsen

Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism Social Democratic Governance Practices in Public Higher Education

Educational Governance Research Volume 19

Series Editors Lejf Moos , Aarhus University, Copenhagen, NV, Denmark Stephen Carney , Roskilde University, Roskilde, Denmark Editorial Board Members Stephen J. Ball, Institute of Education, University of London, London, UK Neil Dempster, Institute for Educational Research, Griffith University, Mt Gravatt, QLD, Australia Olof Johansson, Centre for Principal Development, Umeå University, Umeå, Sweden Klaus Kasper Kofod, Department of Education, Aarhus University, Copenhagen NV, Denmark John B. Krejsler , Danish School of Education (DPU), Aarhus University, Copenhagen, Denmark Romuald Normand, Research Unit CNRS SAGE, University of Strasbourg, Strasbourg, France Marcelo Parreira do Amaral, Institute of Education, Universität Münster, Münster, Germany Jan Merok Paulsen, Teacher Education, Oslo Metropolitan University, Oslo, Norway Nelli Piattoeva, Faculty of Education & Culture, Tampere University, Tampere, Finland James P. Spillane, School of Education & Social Policy, Northwestern University, Evanston, USA Gita Steiner-Khamsi, Teachers College, Columbia University, New York, NY, USA Michael Uljens , Faculty of Education, Åbo Akademi University, Vaasa, Finland

This series presents recent insights in educational governance gained from research that focuses on the interplay between educational institutions and societies and markets. Education is not an isolated sector. Educational institutions at all levels are embedded in and connected to international, national and local societies and markets. One needs to understand governance relations and the changes that occur if one is to understand the frameworks, expectations, practice, room for manoeuvre, and the relations between professionals, public, policy makers and market place actors. The aim of this series is to address issues related to structures and discourses by which authority is exercised in an accessible manner. It will present findings on a variety of types of educational governance: public, political and administrative, as well as private, market place and self-governance. International and multidisciplinary in scope, the series will cover the subject area from both a worldwide and local perspective and will describe educational governance as it is practised in all parts of the world and in all sectors: state, market, and NGOs. The series: - Covers a broad range of topics and power domains - Positions itself in a field between politics and management / leadership - Provides a platform for the vivid field of educational governance research - Looks into ways in which authority is transformed within chains of educational governance - Uncovers relations between state, private sector and market place influences on education, professionals and students. Indexing: This series is indexed in Scopus. Please contact Astrid Noordermeer at [email protected] if you wish to discuss a book proposal.

Miriam Madsen

Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism Social Democratic Governance Practices in Public Higher Education

Miriam Madsen Danish School of Education Aarhus University Aarhus, Denmark

ISSN 2365-9548     ISSN 2365-9556 (electronic) Educational Governance Research ISBN 978-3-031-09995-3    ISBN 978-3-031-09996-0 (eBook) https://doi.org/10.1007/978-3-031-09996-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This is a book on educational governance. It offers several contributions of relevance for scholars in educational governance research. The book’s main contribution concerns quantification practices in educational governance. It provides empirically derived knowledge on how numbers are involved in governing, including how education is rendered quantifiable, how quantification practices embody and enforce particular theorizations of education, how various types of governance instruments deploy quantified data in their governance mechanisms, and how quantification practices construct different temporal, spatial, and social relations. The book furthermore outlines previous educational governance literature on governing by numbers and educational data, and discusses this literature in relation to the empirical case study provided in the book. With this contribution, the book attempts to provide educational governance students and scholars new to the topic of quantification practices and databased governance with a broad introduction to quantification practices in educational governance. However, it also caters to scholars with a more developed interest in governing by numbers by engaging in discussions of how to conceptualize and theorize various aspects of such practices. The book also offers another, more overarching contribution. This contribution highlights how not only global trends of neoliberalism and new public governance but also regional and national traditions of public education, a strong state, and a comprehensive social security and welfare system affect education policy and governance practices in a particular context. The book thus raises a question about the relevance of the critique of neoliberalism that permeates contemporary educational governance literature. In fact, the book suggests that the main trend influencing higher education policy in Denmark and in many other countries is not neoliberalism but rather a social democratic configuration of human capital. The book shows how the human capital theory, as an economic theory applied to education, has become ingrained in public educational governance through measurements of graduate outcomes. The book elaborates how ideas from the human capital theory

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become entangled with global, regional, and national ideas regarding the economy and the state through an analysis of educational-economic relationships embedded in ways of quantifying graduate outcomes. The book furthermore presents ethnographic studies on how students, teachers, and policy makers enact the human capital theory as educational practices. This contribution may be of interest to Danish and Nordic students and scholars in the fields of educational governance, education, and political science, but will hopefully also inspire students and scholars in other contexts to pay due attention to contextual specificities in their studies of educational governance practices. With this contribution, the book invites my fellow empirical researchers and educational thinkers to study how dominant economic theorizations of education permeate educational governance and affect educational practices to bolster our discussions of how to overcome the suppression of educational theories by economics. The third and final contribution concerns different methodological approaches in studies of quantification-based governance practices. The organization of the book in chapters that each highlight various aspects of practices involved in quantification-­based governance also represents a catalogue of methodological approaches. The approaches illustrated in the book draw on the sociology of quantification, the instrumentation approach to public policy, a socio-technical approach to the materiality of numbers, and ethnographic studies of how quantification practices affect. Together with a number of other approaches, also outlined in the book, the illustrated approaches may provide students and scholars who wish to study quantification practices themselves with methodological ideas and analytical concepts. The book will hopefully also inspire educational scholarship on governing by numbers to include more detailed studies of quantification practices. The specificities of quantification practices matter in terms of how governing by numbers takes place. Even though the book is based on new materialist philosophy, which questions many established scholarly practices, my intention here is not to radically challenge conventional understandings of knowledge and research. While new materialist studies provide many cutting-edge and highly thought-provoking examples of how research can be done, and furthermore erode the foundations under conventional approaches, my desire to tell the stories described above supersedes my desire to experiment with new ways of doing research. To me, the project of showing how quantitative practices as agencies co-constitute education in various ways to audiences beyond a sacred few is too important to be shrouded in intricate text. I am aware that this choice is an invitation for critique, and I welcome experimental rewritings of the stories I tell in the book, as well as deconstructions of the categories I use to create order in phenomena that are inherently entangled and dynamic. The book builds on my doctoral dissertation, but provides additional empirical analyses, further analyses of neoliberalism and human capital theory, a completely reworked structure, and a much deeper engagement with various literature and empirical examples from other contexts. Some chapters partly overlap with

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previously published papers, but are embedded in different analytical arguments and theoretical discussions. While chapters can be read individually, the main contribution of the book lies in the compilation of analyses that are interlinked and conjointly show the comprehensive implications of human capital quantification practices in public higher education governance. Happy reading! Aarhus, Denmark

Miriam Madsen

Acknowledgments

The empirical and theoretical work constituting the foundation of this book was conducted as part of two projects. Most of the empirical material and theoretical ideas presented in the book were generated in 2016–2019 as part of my PhD project, which was supported financially by the Graduate School in the Faculty of Arts at Aarhus University. A smaller part of the empirical material, mainly included in Chap. 7, was generated in 2021 as part of an international postdoc project The performative effects of budgets in higher education: A cultural-studies account of how ideas and designs of education are built into budgetary numbers, financially supported by Independent Research Fund, Denmark (grant no. 0162-00038B). The theorization of the governing properties of numbers in Chap. 6 was in part inspired by discussions in the inspiring network Governing educational pasts, presents, and futures with data, led by myself and funded by the Joint Committee for Nordic research councils in the Humanities and Social Sciences (NOS-HS) in 2022–2023 (grant number 122266). Most importantly, writing the book was only possible due to my position as assistant professor in the Danish School of Education, Faculty of Arts, Aarhus University. I appreciate the opportunities that I have been given and value the trust these funds and my university have put in me. I wish to thank my role model and closest colleague, Associate Professor Katja Brøgger, director of the Policy Futures program in the Danish School of Education. I also wish to thank the following colleagues, mentors, and/or sources of inspiration: Professor MSO John B. Krejsler, Professor Dorthe Staunæs, Emeritus Professor Lejf Moos, Associate Professor Helene Ratner, Associate Professor Lise Degn, Associate Professor Nelli Piattoeva, Professor Maarten Simons, Professor Sarah de Rijcke, Professor Berit Karseth, Emeritus Professor Bob Lingard, Professor Sam Sellar, Assistant Professor Mathias Decuypere, Associate Professor Ezekiel Jr. Dixon-Román, Associate Professor Radhika Gorur, Professor Jill Blackmore, Professor Susan Robertson, Head of Department and Professor Laura Bear, Senior Research Fellow Jessica Holloway, Senior Research Fellow Steven Lewis, Associate Professor Malou Juelskjær, Professor MSO David Reimer, Associate Professor Felix Weiss, Head of Department and Associate Professor Pia Bramming, Associate Professor Laura Louise Sarauw, Associate Professor Søren Smedegaard Bengtsen, ix

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Professor Susan Wright, and last but not least Associate Professor Gritt B. Nielsen, my former supervisor. And dear reader, keep an eye out for my junior colleagues Lucas Cone, Maria Louise Hedegaard Larsen, Greta Jimenez, Ronni Laursen, Daniel Malling Blaabjerg-Zederkoff, Camilla Nørgaard, and Ida Cheyenne Martinez Lunde, who are all doing interesting work in the field of educational governance. Thank you to all the above for the collaboration and for your support at various points during the process of writing this book. Thanks to Knud Holt Nielsen for technical assistance and to Simon Rolls for language proof. Special thanks to the people at Danish universities who invited me to observe their work and discuss quantitative and educational practices. Without your time and interest, the book would not have been possible.

Contents

Part I Introduction 1

 The Rise of Outcome Indicators in Educational Governance��������������    3 1.1 The Rise of Outcome Indicators in Danish Higher Education and Beyond ��������������������������������������������������������������������������������������    4 1.2 Contemporary Discussions in the Literature on Governing by Numbers��������������������������������������������������������������������������������������    6 1.3 The Case: Danish Higher Education in a World of Indicators����������    8 1.3.1 Recent Transformations of Higher Education in Denmark���������������������������������������������������������������������������   10 1.3.2 Policy Manifestations of the Agenda Concerning the Relevance of Higher Education��������������������������������������   11 1.3.3 Relevance Indicators and the Humanities����������������������������   13 1.4 Structure of the Book������������������������������������������������������������������������   13 References��������������������������������������������������������������������������������������������������   17

2

Methodological Approaches: Studying Graduate Outcome Metrics and Educational Governance����������������������������������������������������   21 2.1 Framing the Research Object of Numbers����������������������������������������   22 2.1.1 The Performativity of Numbers��������������������������������������������   23 2.1.2 Statistics, Numbers, Data, Indicators, and Metrics��������������   24 2.1.3 A Complementary Approach of Attending to Specificities����������������������������������������������������������������������   27 2.2 Methodological Approaches to the Study of Quantification in Educational Governance ��������������������������������������������������������������   28 2.2.1 Alternative Methodological Approaches������������������������������   29 2.3 Adding a Broader Educational Governance Gaze����������������������������   33 2.3.1 Neoliberalism������������������������������������������������������������������������   34 2.3.2 Neoliberalism and New Public Management�����������������������   35 2.3.3 Neoliberalism and Human Capital Theory ��������������������������   36

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2.3.4 Governing by Numbers and Human Capital in the Social Democratic Welfare State��������������������������������   37 2.4 Studying Education Policy Through Metrics������������������������������������   39 References��������������������������������������������������������������������������������������������������   40 Part II Quantification Practices: Human Capital and the Value of Higher Education 3

 Quantifying Higher Education with Graduate Outcome Metrics������   49 3.1 An Educational Landscape of Graduate Outcome Measurement������������������������������������������������������������������������������������   50 3.2 Danish Graduate Outcome Metrics��������������������������������������������������   52 3.2.1 Graduate Unemployment Statistics��������������������������������������   53 3.2.2 Graduate Income Statistics ��������������������������������������������������   54 3.2.3 Job Match Calculations��������������������������������������������������������   56 3.2.4 Graduate Surveys������������������������������������������������������������������   57 3.2.5 Employer Surveys ����������������������������������������������������������������   59 3.3 The Pursuit of Objectivity and Unambiguity Through Quantification������������������������������������������������������������������������������������   60 3.4 The Crafting of Difference����������������������������������������������������������������   64 3.5 Quantifying Educational Utility in Denmark and Beyond ��������������   67 References��������������������������������������������������������������������������������������������������   68

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Graduate Outcome Metrics and the Economization of Education����������������������������������������������������������������������������������������������   73 4.1 Human Capital Theory and Education����������������������������������������������   75 4.2 Theorizations of Human Capital in the Danish Graduate Outcome Metrics������������������������������������������������������������������������������   76 4.2.1 Education in a Global Economy ������������������������������������������   77 4.2.2 Education in the State-as-Enterprise Economy��������������������   80 4.2.3 Education in the Welfare State Economy������������������������������   81 4.2.4 Education in the Organized and Regulated Labor Market Economy��������������������������������������������������������   83 4.2.5 Education in the Economy of the Workplace������������������������   85 4.3 Economic Valuations of Higher Education ��������������������������������������   88 4.4 Graduate Outcome Metrics and Human Capital Theory������������������   91 4.5 Closing Part II ����������������������������������������������������������������������������������   93 References��������������������������������������������������������������������������������������������������   94

Part III Governance Practices: Indicators, Hierarchical Pressures, and Temporal-­Affective Effects 5

Calculative Governance Instruments����������������������������������������������������  101 5.1 Public Policy Instruments and Graduate Outcome Metrics��������������  103 5.1.1 The Empirical Case of Danish Instruments��������������������������  104 5.2 Typologizing Calculative Policy Instruments ����������������������������������  107

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5.2.1 Performance Measurement Instruments ������������������������������  108 5.2.2 Evidence-for-Policy Instruments������������������������������������������  111 5.2.3 Algorithmic Governance Instruments����������������������������������  112 5.2.4 Nudging Instruments������������������������������������������������������������  114 5.3 Graduate Outcome Metrics in Educational Governance������������������  116 References��������������������������������������������������������������������������������������������������  117 6

 The Governing Properties of Numbers��������������������������������������������������  123 6.1 Aesthetic Data Practices and Data Visualizations����������������������������  124 6.1.1 Data Aesthetics and Visualizations of Danish Graduate Outcome Numbers������������������������������������������������  125 6.2 Relational Properties of Numbers����������������������������������������������������  128 6.2.1 Comparability Across Various Temporal and Spatial Orders����������������������������������������������������������������  130 6.2.2 Competitive and Hierarchical Directions of Governance Pressures������������������������������������������������������  132 6.2.3 Fluid or Tenacious Data��������������������������������������������������������  133 6.2.4 Distribution of Progressive, Adaptive, or Preventive Agency������������������������������������������������������������  134 6.2.5 Temporally Oriented Affectivities Invoked by Data ������������  136 6.3 Governing with the Specificities of Numbers ����������������������������������  138 6.4 Closing Part III����������������������������������������������������������������������������������  140 References��������������������������������������������������������������������������������������������������  142

Part IV Data Reception: Subjectivities and Amplified Resource Inequalities 7

 Subjectivizing Effects of Graduate Outcome Data ������������������������������  147 7.1 Data, Affectivity, and Subjectivizing Effects������������������������������������  148 7.2 Becoming Student with Graduate Outcome Data����������������������������  151 7.2.1 Data-Driven Narratives ��������������������������������������������������������  152 7.2.2 Student Enactments of the Human Capital Narrative ����������  154 7.3 Becoming Teacher with Graduate Outcome Data����������������������������  157 7.3.1 Data Ambivalences and Ambiguities������������������������������������  158 7.3.2 Data Codes of Conduct ��������������������������������������������������������  159 7.3.3 Negotiating with Numbers����������������������������������������������������  161 7.4 Becoming Policymaker with Graduate Outcome Data ��������������������  163 7.4.1 Crafting Politically Feasible Evidence���������������������������������  163 7.4.2 Affected by Data ������������������������������������������������������������������  165 7.5 The Constitutive Potential of Data Design ��������������������������������������  166 References��������������������������������������������������������������������������������������������������  167

8

 Educational Development Effects of Graduate Outcome Metrics������  171 8.1 Studying Educational Effects of Governance Practices��������������������  173 8.2 Increased Imbalances of Educational Resources Across Academic Areas of Studies ��������������������������������������������������������������  175

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8.2.1 Effects on Educational Quality ��������������������������������������������  176 8.2.2 Effects of Quantifying Policy: Changed Knowledge Structures and Resource Allocations������������������������������������  178 8.3 A Rise in Labor Market Transition Activities����������������������������������  179 8.3.1 Effects of Incentivizing via Quantification: Distorted Educational Priorities������������������������������������������������������������  181 8.4 Transitions Towards More Generic Study Programs������������������������  182 8.4.1 The Bias of Generic Skills����������������������������������������������������  184 8.4.2 A Vocabulary of Skills����������������������������������������������������������  186 8.4.3 Effects of Quantifying Educational Concepts: Constituting Educational Knowledge and Values ����������������  187 8.5 The Link Between Governance Practices and Educational Practices������������������������������������������������������������������������  188 8.6 Closing Part IV ��������������������������������������������������������������������������������  189 References��������������������������������������������������������������������������������������������������  190 Part V Conclusion 9

Governance Hybridity and Its Implications for Education and Research on Educational Governance��������������������������������������������  195 9.1 Governing with Graduate Outcome Indicators in Danish Higher Education������������������������������������������������������������������  196 9.1.1 The Techno-scientific Rationale of Governing with Quantification���������������������������������������������������������������  197 9.1.2 A Governance Ideal of Planning������������������������������������������  198 9.1.3 Quantification and Managerial Soft Governance������������������  199 9.1.4 New Modes of Governing with Numbers ����������������������������  201 9.1.5 Governing for the Benefit of the Collective��������������������������  202 9.2 From Convergence to Hybridity ������������������������������������������������������  203 9.2.1 Danish Governing with Numbers as a Dynamic Hybrid�������������������������������������������������������������������  204 9.3 Implications for Danish Higher Education ��������������������������������������  206 9.3.1 The Tension in Responses to the Correspondence Approach����������������������������������������������������  206 9.3.2 Humanities Impoverished ����������������������������������������������������  208 9.4 Methodological Epilogue: Implications for Studies in Educational Governance ��������������������������������������������������������������  209 References��������������������������������������������������������������������������������������������������  210

Part I

Introduction

Chapter 1

The Rise of Outcome Indicators in Educational Governance

The Danish economy is facing a major challenge in terms of growth. In recent years, Denmark has lost ground compared to the wealthiest OECD countries. Since 1970, Denmark has on several occasions been among the five wealthiest countries in the OECD – most recently in 1998. However, since then, the wealthiest countries have outpaced Denmark. One reason for this is that countries like USA, Sweden, the Netherlands, Germany, and Great Britain have had stronger growth in productivity than Denmark since the mid-1990s. If this weak growth in productivity continues, there is a considerable risk that Denmark will fall further behind other wealthy countries and therefore face difficulties in maintaining private and public welfare of a high international standard. (the Danish Government, 2012)

In 2014, while I was completing my master’s degree in education and thus spent time at a university, a major shift took place in Danish higher education policy. The first big shock came in 2014 with the publication of a report with the rather technical title Analysis Report 4. Education and Innovation (the Productivity Commision, 2014). The report was produced by the national Productivity Commission (the Danish Government, 2012), comprising a group of nine experts appointed by the Danish government to review all the major drivers of productivity in Denmark, map important barriers, and make recommendations for both the private and public sectors. The document describing the terms of reference for the commission’s work outlined the importance of this task with the paragraphs quoted above. When the report on education was published, it presented a number of analyses showing that some areas of studies were characterized by a high level of graduate unemployment and low graduate income. The report stated that the massive growth in the number of graduates within these areas of studies, which had taken place during the massification of Danish higher education in previous decades, thus constituted a loss of productivity for Denmark. The commission proposed structural reforms of the Danish higher education system to put a stop to the growth of areas of studies with low productivity gains and making it more attractive for students to enroll in programs with high productivity gains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_1

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1  The Rise of Outcome Indicators in Educational Governance

As a master’s student in education, and part of the Faculty of Humanities at the University of Copenhagen, I experienced first-hand the startled reactions and agitated debates among academics and students in response to the report. Humanities environments at the universities felt under attack, and with good reason: The Ministry of Higher Education and Science responded to the report by instantly appointing a Committee on Quality and Relevance in Higher Education (2014a, b). And only a few months later, the minister announced a new policy initiative called the Resizing Model (Ministry of Higher Education and Science, 2014; Nielsen, 2014), introduced to regulate graduate production in areas of studies with high and persistent graduate unemployment rates. Most of the regulated programs were within the humanities. While the political focus on graduate outcomes of education had already begun a few years earlier (it was mentioned by the students I interviewed in the fall of 2013 for an exam project on how they responded to political agendas into consideration in the choices they made during their studies), the year 2014 marked the first time Danish higher education policy explicitly addressed graduate outcomes. These policies were all framed by the Danish concept of higher education relevance, program relevance, or labor market relevance. Today, almost a decade later, ‘relevance’ remains a dominant agenda in Danish higher education policy, and the policy initiatives introduced in this regard in 2014 and 2015 are still in place. Even more importantly, however, ‘relevance’ has become a mainstreamed part of governance and administration practices throughout the Danish higher education sector. This mainstreaming has predominantly been orchestrated via graduate outcome indicators.

1.1 The Rise of Outcome Indicators in Danish Higher Education and Beyond Quantification practices have played a major role in mainstreaming the agenda of higher education relevance in Denmark. Like most other aspects of society (Mau, 2019), universities today are subject to comprehensive quantification procedures. Not only are higher education systems, institutions, and programs described in terms of the quantities of their students, staff, and monetary inputs, a range of processual measures, such as study intensity, dropout rates, achieved learning outputs, perceived teaching quality, studies abroad, and extracurricular participation, have also become quantified. Furthermore, higher education is measured in terms of graduate employment and economic and social gains—in other words, the outcomes of education. While students, teachers, and expenses have been counted for many decades, the measurement of educational outcomes constitutes a particular, relatively new, and continuously developing technique in higher education governance. This type of measurement does more than merely count. The production of a measurement of higher education relevance involves complex conceptual and

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methodological work—work that is highly constitutive for the measurement’s results. The measurement of relevance is thus far from trivial. We can understand ‘relevance’, as well as other policy concepts like ‘quality’, ‘productivity’, or ‘efficiency’, as an abstraction. Abstractions are abstract sets of concepts used to talk about the economy, nations, citizens, and cultures in ways that perform particular versions of these phenomena. According to Lindblad et  al. (2018a), who proposed and theorized the concept, contemporary societies are thought through such abstractions. Thinking through abstractions implies a distancing of the self from the abstracted phenomena. Quantified data often refer to abstractions of phenomena, and thus quantification practices contribute to the production of distance between policy or governance and everyday life. However, at the same time, quantification practices rematerialize the measured phenomena by embodying the abstractions in particular metrics. Even so, governing by numbers in education is arguably about improving the measured abstractions, rather than about improving educational practices (Popkewitz & Lindblad, 2018: 210). In other words, indicators, including indicators for the relevance of higher education, have emerged as an inevitable part of the everyday governance and management of higher education. Besides its role in policy processes, quantitative information has become an integral part of a wide variety of governance and administration practices. These encompass ongoing calculation and estimation processes, the distribution of data sets, recurring surveys, various meeting and report-writing procedures, resource allocation, strategic efforts to define targets and indicators, and a number of local initiatives aimed at improving the numbers. Indicators for educational outcomes are of particular interest to a variety of actors, including individuals, states, and international organizations, as the outcomes are closely related to the potential private and national economic benefits of education, as well as to the positive gains from education more broadly. For example, indicators for graduate unemployment are used in quality assurance, in university accreditation, in university funding, and in performance management contracts between universities and the Ministry of Higher Education and Science. Universities often make strategic decisions with a view to improving graduate unemployment rates and the status of the humanities as an area of studies with poor job prospects has become solidified through the circulation of graduate unemployment statistics in the public sphere. At the same time, educational outcomes constitute a complex and multifaceted phenomenon that can be quantified through a variety of different approaches, and these approaches all have specific implications for educational design, educational subjectivities, and educational governance. Numbers affect social practices in educational governance. Educational data enable easily readable and assessable comparisons of individual student performances, public services, and, at the global level, nation states. Due to their numerical character, data always appear to provide clear and accessible knowledge. When numbers enter the room in any educational governance or management context, they often do so with great authority. However, specific educational metrics also affect the formation of contemporary educational thinking. They promote specific theorizations regarding cause and relationships in education, as well as specific educational values and ideas about the purpose of

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education. These metrics play a highly influential role in a range of different practices that conjointly have a huge impact on educational development and the lives of people involved in teaching as well as the governance of higher education institutions, including students, parents, university teachers, managers, business partners, employers, and policymakers. The construction of quantitative data thus appears to be an important technology of governance (Piattoeva, 2015) in Denmark, as is the case in most countries. This book is about quantification practices in public higher education governance, studied through the case of Danish graduate outcome metrics and their role in policy and governance. The case is continuously brought into dialogue with the international literature within the field of governing by numbers in education (Grek, 2009; Piattoeva & Boden, 2020; Rose, 1991), also sometimes phrased as studies of governance practices using educational data. The literature shows that data are considered invaluable in all sorts of governance and administration practices, including local and national policy decisions, financial priorities and allocations, the development of public services, and even the management of individual staff. In conclusion, governing through and with educational data is an important type of contemporary governing practice. The case study presented in this book in many ways confirms the conclusions from the existing literature on governing by numbers. However, the Danish case also speaks to some of the discussions currently emerging within the literature on governing by numbers and educational governance more broadly.

1.2 Contemporary Discussions in the Literature on Governing by Numbers The book sets out to provide a structured overview of the educational governance literature on quantification and data, as well as to add to this literature by proposing new research questions, concepts, analytical frameworks, and topics for discussion alongside those already familiar in this field of research. The Danish case of graduate outcomes constitutes the empirical backbone of the analyses in the book. The book-length single case study enables a comprehensive analysis of interlinked practices and their effects, from specific quantitative methodology practices to their implications in the realm of education. However, each analysis of the Danish case is carefully related to international literature and in some cases to empirical examples from other national and international contexts as well. This comparison of the Danish case to other cases highlights both analytical discussions and empirical differences, contributing to the existing literature. While our knowledge about educational data is by now extensive, a few discussions are starting to arise, both in relation to the methodological question of how best to study governing by numbers and in relation to the overall conclusions on educational governance drawn in many earlier studies. Starting with the latter, I see two major discussions to which this book speaks. First, and most clearly, the

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dominant interpretation of governing by numbers as a neoliberal governance practice is now being questioned in the literature. Previously, many educational governance scholars have interpreted empirical cases of performance management, ranking, and quality assurance as neoliberal practices. This is in particular the case for scholars based in Anglosphere contexts (e.g., Baird & Elliott, 2018; Boden & Nedeva, 2010; Keddie, 2016; Naidoo, 2018; Ranson, 2003; Shore & Wright, 2015) but also many scholars based in the Nordic countries (Hammer, 2010; Rinne, 2021; Telhaug et al., 2006). Meanwhile, several scholars have recently begun to question whether this is necessarily the case (Bürgi & Tröhler, 2018; Gurova, 2018; Maroy & Pons, 2019). As I will explain in Chap. 2, this discussion can partly be framed in terms of two different conceptualizations of neoliberalism, but not entirely. This book adds to these accounts questioning whether neoliberalism is as universal a phenomenon as it appears in much educational governance literature. The second general discussion on educational governance that this book speaks to relates to the apparently global contemporary understanding of education as human capital. While educational governance scholars have rightfully observed the global spread of this understanding (Henry et al., 2001; Kristensen, 2007; Nielsen, 2015; Sellar, 2015), leading to recent developments like the proliferation of shadow education (Kim & Jung, 2021) and the capitalization of positive psychological traits (Sellar & Zipin, 2019), these accounts also often describe the economization of education implicit in the concept of human capital as imbricated in neoliberalism. Meanwhile, the individualization and commodification of education often discussed in literature on neoliberalism in higher education, and the resulting consumer subjectivity of students in some contexts (Brooks et al., 2016; Saltmarsh, 2011), is not the only existing or even the dominant manifestation of human capital thinking when considered in a global perspective. This book questions the universality of human capital manifestations as necessarily neoliberal and proposes a much more differentiated approach to human capital thinking in education policy. Building on this contribution, the book argues that, as critical educational governance scholars outside the heartlands of neoliberalism, we should perhaps consider human capital thinking an even bigger threat to educational ideas than neoliberalism. Both of these discussions relate to the question of how policies and governance practices ‘travel’ or influence each other globally. Many accounts tell stories about global trends affecting local educational governance, including the spread of human capital theories on education and neoliberal practices of marketization and accountability from Anglosphere countries to the rest of the world, with the result that these ideas and practices slowly but surely overtake traditional local practices in educational governance (e.g., Krejsler & Moos, 2021). This book questions the fatalistic and forceful narratives of local decline resulting from invasive Anglosphere ideas inscribed in much recent educational governance literature, and thereby positions itself within a growing body of literature critical of this narrative (Maroy & Pons, 2019; Neave, 2009; Sobe, 2015). However, the book does not analyze how ideas spread, but merely discusses how we might theorize the specificities of educational governance found in the Danish case.

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While these discussions concern major questions within educational governance, the book also speaks to a methodological discussion more closely related to governing by numbers. Most literature on governing with numbers, or educational data, mainly focuses on the socio-material practices of human beings that construct and engage with data as a generalized object. This includes both what has been termed data ‘reception studies’ (Sellar & Lingard, 2018) and some more STS-inspired studies of how data are crafted and distributed (Carvalho, 2014). With this book, I suggest that we can further hone our understanding of social practices if we enrich studies of social practices centered on educational data with statistical-technical studies of the specific practices of operationalization, calculation, and categorization embedded in particular educational data. The book thus adopts a methodological shift from educational data as a generalized object to specific educational data in the form of graduate outcome data. This enrichment, I suggest, offers a methodological perspective on governing by numbers that enables a deeper understanding of the ways specific quantification practices affect how educational data ‘work’ in educational governance and how they affect the realm of education. I furthermore suggest that the substantial discussions of neoliberalism and human capital theory and the methodological discussion regarding the importance of quantitative properties of educational data are interlinked. The methodological approach of using statistical-technical studies of numbers themselves as a jumping-off point makes it possible to transcend the study of whether governing by numbers in general can be termed capitalist or neoliberal, and instead study precisely which micropractices can be defined as drawing on human capital theory or neoliberal thinking, and in what ways.

1.3 The Case: Danish Higher Education in a World of Indicators As already stated, the use of quantification in education is not new. Statistics have been an important tool for governing for more than a century (Desrosières, 1998). However, in the field of education, the global history of governing with numbers began after the Second World War, with countries in the West pursuing greater educational outputs in an attempt to bolster the fragile peace and reinforce national defenses by educating their populations (Bürgi & Tröhler, 2018: 77). Under pressure from the USA and with support from the UK, global society became interested in developing indicators suitable for quantitative and international comparative studies (Henry et al., 2001: 87; Krejsler & Moos, 2021: 135). The early work took place in organizations such as the International Association for the Evaluation of Educational Achievement (IEA), which started developing large-scale assessments as early as the 1960s (Lindblad et al., 2018b: 3). The Organisation for Economic Co-operation and Development (OECD) also became engaged in developing

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indicators from the 1970s, but with a particular interest in statistics and quantitative analysis of the utilization and management of resources (Henry et al., 2001: 86). During subsequent decades, this work resulted in the establishment of the global and quantified space of education that we know today: In short, the 1990s saw some remarkable shifts in the development of educational indicators within the OECD: from philosophical doubt to statistical confidence; from covering some countries to covering most of the world; from a focus on inputs to a focus on outputs; and from occupying an experimental status to being a central part of the Organization’s educational work. (Henry et al., 2001: 90)

While the OECD’s large-scale assessments, commonly referred to as PISA, which have measured the skills of 15-year-olds since the early twenty-first century (Lindblad et al., 2018b: 4), are probably the most famous and scrutinized example in educational governance studies, the transnational focus on comparable indicators has not only resulted in large-scale assessments. Administrative data, for example, based on transnational categorization standards like International Standard Classification of Education (ISCED) and International Standard Classification of Occupations (ISCO) (Gorur, 2018; Statistics Denmark, 2011), have also continuously been collected, implying the enrollment of participating countries in comprehensive statistical practices that also affect the intra-national governing of education. In Denmark, the quantification of education initially met some degree of cultural resistance. In the mid-1990s, the Danish Minister of Education at the time initially expressed doubts that something as complex as education could be measured in a meaningful way. The Danish educational tradition would entail an emphasis on the measurement of non-cognitive skills rather than learning outputs, he argued (Appel et  al., 2013: 40–45). The practice of producing quantitative and comparative data, originally promoted by the USA and the UK, was thus perceived as clashing with Danish educational traditions. Meanwhile, the pressure on the Ministry of Education was not only (and possibly not even primarily) external. According to scholars in comparative economic policy, Denmark has since the 1980s adopted a national governance tradition relying on economic expertise and econometric analyses in policy development (Campbell & Pedersen, 2014), and the pressure from the Ministry of Finance for other ministries to increase their use of quantitative analysis continues today. Either way, by the end of the 1990s, the Danish government also started to govern education through the use of quantitative indicators of learning outputs (Appel et al., 2013). The global development of indicators did not only include the primary and secondary school level, but also higher education and the wider impact of education on society (Henry et al., 2001: 86). In particular, the OECD publication Education at a Glance, published since 1992, first biennially and then annually (Bürgi & Tröhler, 2018), includes indicators concerning higher education inputs and outputs, as well as the economic and social outcomes of education. These indicators are based on nationally generated data and have thus required the compilation of national statistics in the participating countries, as well as some degree of standardization of the data in order to ensure comparability across the OECD countries. Besides transnational comparisons, the OECD has also conducted country reviews on higher

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education systems (as well as other parts of national education systems), with the review in Denmark published in 2005. These reviews have further promoted the use of indicators regarding the impact of higher education on society, even though the 2005 report still mainly includes input and output indicators rather than outcome indicators (OECD, 2005).

1.3.1 Recent Transformations of Higher Education in Denmark Parallel to the international and national rise in the use of outcome indicators, the Danish university sector was subject to several extensive reforms of public governance. First, a major revision of the Danish University Act in 2003 transformed universities and other higher education institutions from a large number of institutions of varying size that were directly embedded in the state bureaucracy but primarily governed by academic hierarchies, instead creating fewer but larger quasi-corporate organizations run by professional managers (Wright & Ørberg, 2017: 76). Around the time of the enactment of this legislation, Danish higher education institutions were merged into three types of institutions, all with a size considered suitable for the new types of governance. At the time of writing, Denmark has eight universities (providing academic bachelor’s and master’s degrees), seven university colleges (primarily providing professional bachelor’s degrees, such as for teachers and nurses), and eight business academies (providing practically oriented 2-year programs). As part of the separation of the universities from the state, higher education became subject to the ‘aim and frame steering’ (Wright & Ørberg, 2017) that also characterizes other parts of the Danish public sector in line with ideas from new public management. As in the other Nordic countries, the Danish higher education sector remains almost entirely public (Rinne, 2021), and thus the state could not fully relinquish its control of the universities, even though they were no longer part of the state bureaucracy. Second, parallel with this development, Denmark relatively rapidly implemented the standardizing templates from the Bologna Process (Brøgger, 2019). The Bologna Process was initiated in 1999 by the European Ministers of Education. The aim of the Bologna Process was the establishment of a European Higher Education Area, which is now reality. The instruments used to create this common higher education area involved the implementation of a number of standards across the countries participating in the process. These instruments included easily readable degrees, a two-cycle structure, ECTS credits, mobility programs, a quality assurance collaboration, and the promotion of European dimensions in higher education (European Ministers of Education, 1999). Like the reform of the University Act, the implementation of the Bologna standards in Danish higher education has framed higher education policy in recent decades.

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1.3.2 Policy Manifestations of the Agenda Concerning the Relevance of Higher Education With only eight universities and a manageable total number of higher education institutions, the Danish higher education sector is characterized by a relatively high level of coherence. Compared to other countries, the universities are furthermore relatively homogeneous. This includes a degree of uniformity in their implementation of national policies in institutional procedures and practices, such as the use of indicators. Through the reform of the University Act and the implementation of the Bologna Process, the abstraction of relevance has come to play an important role, both as a rhetorical figure in political discourse and as a specific set of policy and governance practices. Relevance gradually became one of the performance management targets implied in the ‘aim and frame steering’, and it was also enforced through the implementation of the Bologna standards. The Danish government used the implementation of qualification frameworks in 2003 and 2007 to deploy a narrow ‘Fordist’ imaginary of the labor market, emphasizing labor market outcomes of higher education and ignoring the social and democratic outcomes also mentioned in the Bologna documents (Sarauw, 2012). The Bologna Process also led to the introduction of accreditation in Danish higher education (in line with the guidelines provided by the European Association for Quality Assurance in Higher Education (ENQA) established in 2004), which from 2007 was carried out by the Danish Accreditation Institution with relevance as one of the accreditation criteria. Thus, the relevance agenda had already begun to emerge in Danish higher education policy during the first decade of the new millennium. In the 2010s, this agenda was brought to the forefront and furthermore framed by a particular economic emphasis, perhaps in response to the increasing public expense of funding higher education. The reinforcement of the relevance agenda initially materialized as a series of commissions and committees directed towards the development of Danish higher education (and in some cases other areas of the public sector as well). The first in this series was the Productivity Commission [Produktivitetskommissionen], mentioned in the introduction to this chapter. The commission, which was appointed by the government in 2012 and finished its work in 2014, was tasked with mapping and analyzing productivity levels in Denmark and recommending initiatives that would improve the nation’s productivity. The commission’s work covered a range of topics, including education (the Productivity Commision, 2014). Already before the Productivity Commission had published its report on education, the Ministry of Higher Education and Science had appointed the Committee on Quality and Relevance in Higher Education [Udvalg for Kvalitet og Relevans i de Videregående Uddannelser] to look more specifically at the quality and relevance of higher education, as well as the coherence of the higher education system. This committee produced two reports: one focusing on the educational system of the future (Committee on Quality and Relevance in Higher Education, 2014b) and the other one on the excellence of Danish higher education (Committee on Quality and Relevance in Higher Education, 2014a).

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Over the following years, a range of initiatives proposed by this committee were implemented, including the 2014 Resizing Model [Dimensioneringsmodellen or Dimensionering af de videregående uddannelser], which was a calculative and regulatory model implemented to cap the number of graduates in areas of study with a ‘systematic and striking excess unemployment’ (Ministry of Higher Education and Science, 2014). The implemented initiatives also included the 2015 initiative Education Zoom, which is a website aimed at potential students that presents transparent information about degree programs, including average salaries and unemployment rates among graduates (Ministry of Finance, 2014). At this point in time, relevance had become one of the cornerstones of contemporary Danish higher education policy. Along with the other main policy initiative implemented in this period, called the Study Progress Reform [Studiefremdriftsreformen] (Nielsen & Sarauw, 2017), the relevance agenda sought to ensure the efficiency in and of Danish higher education (as I will argue throughout the book). In 2017, the Ministry appointed yet another committee, this time with a sole focus on university education. The Committee on Better University Programs [Universitetsudvalget or Udvalg om bedre universitetsuddannelser] published its report in 2018 (Committee on Better University Programs, 2018). This committee’s task was to develop more specific solutions to the initiatives recommended by the Productivity Commission and the Committee on Quality and Relevance. Recently, a Reform Commission [Reformkommissionen] has produced a report with further reform recommendations (the Reform Commission, 2022). Thus, the series of reports, starting from 2014 and still continuing at the time of writing, progressed by gradually focusing more narrowly on university education and by proposing the implementation of increasingly specific initiatives. The published reports also gradually solidified university programs within the humanities as a problem area for Danish higher education. The humanities were considered insufficiently relevant. The discussion of the relevance of university education, and in particular of university education within the humanities, engaged several non-government agencies, who produced a range of reports similar to those compiled by the commissions and committees (e.g., Dansk Magisterforening in DAMVAD, 2015; Danmarks Evalueringsinstitut, 2017; Danske Universiteter, 2013; Kraka – Danmarks uafhængige tænketank, 2014; The Rockwool Foundation in Skaksen & Andersen, 2018; the Think Tank DEA, 2016). The Danish Confederation of Industry (DI) and other lobby organizations produced policy proposals to improve the quality (including the ’relevance’) of university education (Dansk Industri, 2012; Dansk Industri & Akademikernes Centralorganisation, 2009), while the universities’ own association, called Danish Universities, defended their value in yet another series of reports and memorandums (Danske Universiteter, 2012, 2013).

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1.3.3 Relevance Indicators and the Humanities Danish policy on the relevance of higher education thus developed through these events. The relevance agenda is linked to transnational notions of employability, but has materialized in a distinct way in Denmark (Sarauw, 2012). Here, indicators came to play a big role, for example, in the Resizing Model and Education Zoom initiatives. The Danish abstraction of relevance, measured via graduate outcome indicators, thus comprises a relevant case for studies of how particular quantification practices in public sector governance become constitutive for educational practices. The case is mainly focused on universities, and the analyses drawing on ethnographic fieldwork focus even more narrowly on humanities departments within the universities. The humanities have been particularly and significantly affected by the policies addressing relevance, which also means that the impact of these policies varies across the eight universities, of which three are mono-faculty universities with a low number of humanities programs (if any) and are in general less burdened by poor graduate outcome indicators than the five multi-faculty universities. Furthermore, while one mono-faculty university and some health and technical science faculties in multi-faculty universities are dominated by research activities (Banghøj et  al., 2021), all the humanities faculties are dominated by educational activities and thus highly affected by higher education reform and governance practices. As the book will show, the policies addressing relevance have had far-reaching effects in these areas of Danish higher education.

1.4 Structure of the Book The book is comprised of five parts: an introduction, three parts presenting empirical analyses, and a conclusion. The chapters in the main body of the book, encompassing the empirical analyses, are structured as a series of complementary analyses of the Danish graduate outcome indicators and their use in educational governance. Across these analyses, questions concerning how the Danish graduate outcome quantification practices relate to neoliberalism and human capital theory will be discussed. The discussion of these questions is mainly placed at the end of the three parts (Chaps. 4, 6, and 8) and is wrapped up in the conclusion (Chap. 9). The six analytical chapters follow more or less the same structure. Each chapter includes an introduction, framing the research questions and problems addressed in that chapter. This is followed by outlines of key international literature, theoretical concepts, and methodological approaches relevant to that chapter. The main body of the chapter then describes and analyzes the Danish case of graduate outcome metrics, sometimes drawing on various concepts from previous studies, and other times proposing new concepts or typologies. Finally, the empirical and analytical findings are related to the broader literature and where applicable to empirical examples

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from other contexts. As such, each chapter attempts to outline what the international research field can learn from the Danish case in relation to the questions and problems addressed in the chapter. The six analytical chapters, as well as the other chapters in the book, can largely be read independently of one another, but include cross-references as certain descriptions that are necessary for the argumentation in several chapters are only described in detail the first time they occur. Ideally, the book should be read in its entirety, as what makes it unique is the combination of the various analyses. The journey from quantification practices over governance practices to educational practices provides an insight into the implications of governing by numbers for higher education that does not become apparent when reading individual chapters in isolation. Part I of the book, the introduction, includes this framing of the book as well as a second chapter presenting key concepts and methodologies used in the book. Chapter 2 first outlines various conceptualizations of ‘numbers’ in the ‘governing by numbers’ literature and argues for a conceptualization of the research object as ‘metrics’, drawing on the agential realist concept of ‘apparatus’. Next, the chapter assembles the methodological approaches used across the different parts of the book, as well as other methodological approaches used in studies on governing by numbers in education, and discusses the need for analyses that engage with specific quantification practices and specific governance contexts. The individual methodological approaches are presented throughout the book in the chapters where they are applied. Finally, Chap. 2 introduces a number of key concepts characterizing grand narratives in educational governance research and frames the main analytical thread that runs through the book. Part II includes analyses on quantification practices and encompasses Chaps. 3 and 4. Chapter 3 begins by analyzing how various aspects of higher education, and in particular graduate outcomes, are quantified nationally and internationally. With inspiration from the sociology of quantification (Berman & Hirschman, 2018; Espeland & Stevens, 1998; Fourcade, 2016), this chapter addresses not only how various conventional quantification practices are used to provide objective knowledge, but also how specific quantification practices embodied in the Danish graduate outcome metrics constitute their production of particular differences. These include differences across measured entities in the education sector, as well as differences across conceptual operationalizations of graduate outcomes and thereby proxies of the political abstraction of relevance in Danish higher education policy. The chapter shows how a particular differentiation according to academic disciplines, as well as a particular conceptualization of graduate outcomes as labor market outcomes, dominates the Danish higher education sector. While these configurations of higher education are also found in quantification practices in other countries and contexts, these contexts also include a wider variety of configurations that are not found in Denmark. As such, the Danish way of quantifying graduate outcomes and higher education is quite narrow. Continuing to draw inspiration from the sociology of quantification (Merry, 2016), Chap. 4 analyzes how the graduate outcome metrics analyzed in Chap. 3 draw on human capital theorizations of higher education in various ways. The

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chapter outlines human capital theory and different approaches to educational policy based on this theory throughout history and globally. These outlines constitute the backdrop for the analysis of several educational-economic relationships referred to by the graduate outcome metrics, some of them related to the global economy of capitalist competition between nation states and others related to regional and national traditions of a strong welfare state and a highly organized labor market. The economically wired educational thinking that constitutes Danish graduate outcome measurement is in other words conditioned by the local governance context. The theorizations of education embedded in the Danish graduate outcome metrics emerge as criteria in higher education valuation devices. Together with the particular ways of differentiating education analyzed in Chap. 3, these economically founded criteria for valuations of higher education enable a devaluation of in particular the humanities. Part III of the book moves on from the specificities of graduate outcome quantification practices and instead focuses on governance practices involving graduate outcome indicators. First, Chap. 5 addresses quantitative data as techniques in different public policy instruments used to govern education. The chapter shows how the Danish graduate outcomes play different roles in different instruments, including the role as a performance indicator, the role as evidence in policymaking, the role as data inputs for algorithmic governance practices, and the role as probability data provided publicly to nudge potential students to make particular educational choices. The instrumentation approach to public policy (Brøgger & Madsen, 2021; Lascoumes & Le Gales, 2007) is used to analyze how the instruments and their use of quantified information establish particular modes of governance that mobilize the higher education sector in different ways. The analysis of instruments shows that marketization plays a limited role in governing with numbers within Danish higher education, and that the use of numbers in governance thus does not always reflect neoliberal governance ideals. Next, Chap. 6 takes on a more explorative socio-material approach, seeking to compile and develop various concepts relevant for studying the material properties of numbers. The material properties of numbers are tentatively conceptualized as encompassing their aesthetic and visual attributes, as well as their spatializing and temporalizing properties, both affecting how numbers affect. While aesthetic and visual attributes reflect how educational data are packaged and projected for particular audiences, the spatializing and temporalizing properties define how educational data invite particular kinds of comparisons, evoke particular kinds of governing pressures, enable different ways of dealing with data, evolve in particular ways, and facilitate particular temporal-affective orientations among actors governed by data. By providing these conceptualizations, the chapter invites further conceptual elaboration in tandem with studies of particular quantification practices and numbers with particular properties. In relation to the Danish graduate outcome data, the chapter shows how data gain very different attributes when designed for student consumption rather than administrative use, but also that most of the data evoke hierarchical rather than competitive pressures. It furthermore shows that the different ways that numbers are propertied in Danish higher education governance

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entail a tension between calls for progressive educational development and calls for adaptation to structural characteristics of the Danish labor market. Part IV of the book moves on from governance practices to explore the performative effects of graduate outcome data reception, beginning in Chap. 7 with a focus on subjectivizing effects. Drawing on ethnographic fieldwork in humanities departments in three Danish universities, as well as interviews with national policy actors, the chapter shows how various actors engage with the Danish graduate outcome data and are affected by the data. Humanities students, who encounter the data in the media, express fears and doubts in relation to their choice of a humanities study program and develop a range of different strategies to emerge as subjects with a sufficient amount of capital, despite their programs being assessed as not very relevant for the labor market. Humanities teachers express ambiguity and ambivalence towards graduate outcome data, actively attempting to resist the economic narratives of education that are embedded in human capital quantification practices. At the same time, however, they find it difficult to argue against the objectivity of the data and against the naturalized imperative of the importance that graduates are able to find a job. Educational leaders express these same ambiguities, but are simultaneously forced to respond actively to poor data in order to maintain a subjectivity as a responsible leader. Meanwhile, these leaders also find ways of negotiating data and thereby tell different stories than those promoted by particular aesthetic and visualization practices. Finally, policymakers express not only a high dependence on objective and indisputable data in policymaking, but also the power of data to enforce political action. Chapter 8 focuses on educational enactments of quantification practices and thereby on the performative effects of human capital governance practices on educational development. Again drawing on ethnographic fieldwork, the chapter shows how the negative stratifications of the humanities, combined with incentive-based governing according to these stratifications, imply a lowering of educational standards, program closures, and an erosion of discipline-specific content in the humanities due to reduced resources, distorted priorities towards labor market transition activities, and the promotion of generic skills. This chapter thus demonstrates how quantification and governance practices developed to level out differences in educational quality risk reinforcing these differences. Part V presents conclusions relating to each of the three contributions offered by the book (see Preface). First, the characteristics of governing with numbers in Danish higher education are summarized and discussed in terms of globally dominant neoliberal ideas, as well as heritage from Cold War logics and the Nordic welfare state. Second, the chapter discusses how we can understand the specific combination of different modes of governance, which is not consistent with a model of convergence, via the concept of hybridity. Third, the chapter sums up the implications of these practices for Danish higher education. Finally, the conclusion includes a methodological epilogue, highlighting the need for further comparative studies of governing with numbers.

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Danske Universiteter. (2012). Universitetsuddannelsernes værdi. Retrieved from https://dkuni.dk/ publikationer-­og-­notater/universitetsuddannelsernes-­vaerdi/ Danske Universiteter. (2013). Akademikernes arbejdsmarked. Retrieved from https://dkuni.dk/wp-­ content/uploads/2017/10/akademikerenes-­arbejdsmarked-­230513.pdf Desrosières, A. (1998). The politics of large numbers: A history of statistical reasoning. Harvard University Press. Espeland, W. N., & Stevens, M. L. (1998). Commensuration as a Social Process. Annual Review of Sociology, 24(1), 313–343. https://doi.org/10.1146/annurev.soc.24.1.313 European Ministers of Education. (1999). The Bologna declaration of 19 june 1999: Joint declaration of the European Ministers of Education: The European higher education area. S.l. Fourcade, M. (2016). Ordinalization: Lewis A. Coser memorial award for theoretical agenda setting 2014. Sociological Theory, 34(3), 175–195. https://doi.org/10.1177/0735275116665876 Gorur, R. (2018). Standards. Normative, interpretive, and performative. In S.  Lindblad, D. Pettersson, & T. S. Popkewitz (Eds.), Education by the numbers and the making of society: The expertise of international assessments. Routledge. Grek, S. (2009). Governing by numbers: The PISA ‘effect’ in Europe. Journal of Education Policy, 24(1), 23–37. https://doi.org/10.1080/02680930802412669 Gurova, G. (2018). Soviet, post-Soviet and neo-liberal: Governing Russian schools through quality assurance and evaluation. Policy Futures in Education, 16(4), 398–415. https://doi. org/10.1177/1478210317743648 Hammer, S. (2010). Governing by indicators and outcomes: A Neo-liberal governmentality? In A. R. Saetnan, H. M. Lomell, & S. Hammer (Eds.), The mutual construction of statistics and society (p. 299). Routledge. Henry, M., Lingard, B., Rizvi, F., & Taylor, S. (2001). The OECD, globalisation and education policy. IAU. Keddie, A. (2016). Children of the market: Performativity, neoliberal responsibilisation and the construction of student identities. Oxford Review of Education, 42(1), 108–122. https://doi. org/10.1080/03054985.2016.1142865 Kim, Y.-C., & Jung, J.-H. (2021). Theorizing shadow education and academic success in East Asia: Understanding the meaning, value, and use of shadow education by East Asian students. Taylor and Francis. Kraka – Danmarks uafhængige tænketank. (2014). Arbejdsmarkedet for nyuddannede magistre. Retrieved from København. Krejsler, J.  B., & Moos, L. (2021). Danish  – and Nordic  – School policy: Its Anglo-American connections and influences. In J. B. Krejsler & L. Moos (Eds.), What works in Nordic School policies? Mapping approaches to evidence, social technologies and transnational influences (pp. 129–151). Springer. Kristensen, J. E. (Ed.). (2007). Ideer om et universitet: det moderne universitets idehistorie fra 1800 til i dag. Aarhus Universitetsforlag. Lascoumes, P., & Le Gales, P. (2007). Introduction: Understanding public policy through its instruments? From the nature of instruments to the sociology of public policy instrumentation. Governance, 20(1), 1–21. https://doi.org/10.1111/j.1468-­0491.2007.00342.x Lindblad, S., Pettersson, D., & Popkewitz, T. S. (2018a). Education by the numbers and the making of society: The expertise of international assessments. Routledge. Lindblad, S., Pettersson, D., & Popkewitz, T.  S. (2018b). Getting the Numbers Right. An Introduction. In S.  Lindblad, D.  Pettersson, & T.  S. Popkewitz (Eds.), Education by the Numbers and the making of society: The expertise of international assessments. Routledge. Maroy, C., & Pons, X. (2019). Accountability policies in education: A comparative and multilevel analysis in France and Quebec (Vol. 11). Springer. Mau, S. (2019). The metric society. Polity Press. Merry, S. E. (2016). The seductions of quantification: Measuring human rights, gender violence, and sex trafficking. University of Chicago Press.

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Ministry of Finance. (2014). Agreement on a Growth Kit 2014 [Aftale om en vækstpakke 2014]. Retrieved from https://www.fm.dk/nyheder/pressemeddelelser/2014/07/ aftale-­om-­en-­vaekstpakke-­2014 Ministry of Higher Education and Science. (2014). Description of the Sizing Model [Beskrivelse af dimensioneringsmodel]. Retrieved from https://ufm.dk/uddannelse/videregaende-­uddannelse/ dimensionering/beskrivelse-­af-­dimensioneringsmodel.pdf Naidoo, R. (2018). The competition fetish in higher education: Shamans, mind snares and consequences. European Educational Research Journal, 17(5), 605–620. https://doi. org/10.1177/1474904118784839 Neave, G. (2009). The evaluative state as policy in transition: A historical and anatomical study. In R. Cowen & A. M. Kazamias (Eds.), International handbook of comparative education (1st ed.). Springer Netherlands. Nielsen, S. C. (2014, September 23). No youth should end up as MSc Useless [Ingen Unge Må Ende Som Stud. Ubrugelig.]. Politiken. Nielsen, G. B. (2015). Figuration work: Student participation, democracy and university reform in a global knowledge economy. Berghahn Books. Nielsen, G.  B., & Sarauw, L.  L. (2017). Tuning up and tuning in. How the European Bologna process is influencing students’ time of study. In S. Wright & C. Shore (Eds.), Death of the public university?: Uncertain futures for higher education in the knowledge economy (p. 338). Berghahn Books. OECD. (2005). Reviews of National Policies for Education: University Education in Denmark. Retrieved from https://www.oecd-ilibrary.org/education/reviews-­of-­national-policiesfor-education-university-education-in-denmark-2005/university-education_978926400 9745-8-en Piattoeva, N. (2015). Elastic numbers: National examinations data as a technology of government. Journal of Education Policy, 30(3), 316–334. https://doi.org/10.1080/02680939.2014.937830 Piattoeva, N., & Boden, R. (2020). Escaping numbers? The ambiguities of the governance of education through data. International studies in sociology of education, 29(1-2), 1–18. https://doi. org/10.1080/09620214.2020.1725590 Popkewitz, T.  S., & Lindblad, S. (2018). Statistics reasoning, governing education and making differences as kinds of people. In S. Lindblad, D. Pettersson, & T. S. Popkewitz (Eds.), Education by the numbers and the making of society: The expertise of international assessments. Routledge. Ranson, S. (2003). Public accountability in the age of neo-liberal governance. Journal of Education Policy, 18(5), 459–480. https://doi.org/10.1080/0268093032000124848 Rinne, R. (2021). The Nordic social democratic regime in education colliding with the global Neo-Liberal regime. In J. B. Krejsler & L. Moos (Eds.), What works in Nordic school policies? Mapping approaches to evidence, social technologies and transnational influences (pp. 153–172). Springer. Rose, N. (1991). Governing by numbers: Figuring out democracy. Accounting, Organizations and Society, 16(7), 673–692. https://doi.org/10.1016/0361-­3682(91)90019-­B Saltmarsh, S. (2011). Economic subjectivities in higher education: Self, policy and practice in the knowledge economy. Cultural studies review, 17(2), 115–139. Sarauw, L. L. (2012). Qualifications frameworks and their conflicting social imaginaries of globalisation. Learning and Teaching: The International Journal of Higher Education in the Social Sciences, 5(3), 22–38. https://doi.org/10.3167/latiss.2012.050302 Sellar, S. (2015). A strange craving to be motivated: Schizoanalysis, human capital and education. Deleuze Studies, 9(3), 424–436. Sellar, S., & Lingard, B. (2018). International large-scale assessments, affective worlds and policy impacts in education. International Journal of Qualitative Studies in Education, 31(5), 367–381. https://doi.org/10.1080/09518398.2018.1449982

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Sellar, S., & Zipin, L. (2019). Conjuring optimism in dark times: Education, affect and human capital. Educational Philosophy and Theory, 51(6), 572–586. https://doi.org/10.1080/0013185 7.2018.1485566 Shore, C., & Wright, S. (2015). Audit culture revisited. Current Anthropology, 56(3), 421–444. https://doi.org/10.1086/681534 Skaksen, J. R., & Andersen, T. M. (Eds.). (2018). Returns from education: The societal and individual rationale [Afkast af uddannelse: det samfundsmæssige og individuelle rationale] (1. udgave ed.). Kbh.: Rockwool Fondens Forskningsenhed. Sobe, N. W. (2015). All that is global is not world culture: Accountability systems and educational apparatuses. Globalisation, Societies and Education, 13(1), 135–148. https://doi.org/10.108 0/14767724.2014.967501 Statistics Denmark. (2011). DSCO-08  in the employment statistics. 2nd edition [DISCO-08 i lønstatistikken. 2. udgave]. Retrieved from https://www.dst.dk/da/Statistik/dokumentation/ nomenklaturer/disco-­08-­i-­loenstatistikken Telhaug, A. O., Mediås, O. A., & Aasen, P. (2006). The Nordic model in education: Education as part of the political system in the last 50 years. Scandinavian Journal of Educational Research, 50(3), 245–283. https://doi.org/10.1080/00313830600743274 the Danish Government. (2012). The productivity commission. Terms of reference [Produktivitetskommissionen. Kommissorium]. Retrieved from https://produktivitetskommissionen.dk/media/18704/produktivitetskommissionens-­kommissiorium.pdf the Productivity Commision. (2014). Analysis Report 4. Education and Innovation [Analyserapport 4. Uddannelse og innovation]. Retrieved from Copenhagen: http://produktivitetskommissionen. dk/media/162592/Analyserapport%204,%20Uddannelse%20og%20innovation_revideret.pdf the Reform Commission. (2022). New roads to reform 1 [Nye reformveje 1]. Retrieved from https://reformkommissionen.dk/udgivelser/nye-­reformveje-­1/ the Think Tank DEA. (2016). University graduates’ transition to the labor market [Universitetsuddannedes vej ud på arbejdsmarkedet]. Retrieved from https://dea.nu/sites/dea. nu/files/universitetsuddannedese_vej_ud_paa_arbejdsmarkedet.pdf Wright, S., & Ørberg, J. W. (2017). Universities in the competition state: Lessons from Denmark. In S. Wright & C. Shore (Eds.), Death of the public university?: Uncertain futures for higher education in the knowledge economy (p. 338). Berghahn Books.

Chapter 2

Methodological Approaches: Studying Graduate Outcome Metrics and Educational Governance

As most studies on educational data take for granted the value of data as a tool for improvement and are mainly interested in how to optimize this tool and make best use of it, critical studies of governing by numbers represent a minority. However, when new tools are introduced and new practices emerge, they should be critically interrogated and discussed. The kinds of governing that take place via numbers, indicators, data, or metrics are constitutive of social relations in public governance, of the subjectivities of students, teachers, and managers, and of the changes in educational thinking and design characterizing our time. Thus, they are part of the fabric of our present and future societies. Meanwhile, the tools we deploy in our critical interrogations are also of great importance. Our conceptualizations and analytical approaches frame what kinds of critique we are able to produce. While governing by numbers in education is still a relatively new and emerging research field, it is already characterized by a number of methodological strands and a few major points of discussion. In this chapter, I will outline different methodological approaches to studies on governing by numbers specifically and educational governance more broadly. These two areas of educational governance studies reflect the book’s two overarching research questions: How does governing by numbers take place in the case of Danish graduate outcome indicators? And how are these practices related to major trends in educational governance, including neoliberalism, new public management, and human capital theory? This chapter will hopefully provide methodological inspiration for students and scholars studying quantification practices in educational governance and furthermore inspire further methodological discussions regarding how to conduct such studies.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_2

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2.1 Framing the Research Object of Numbers In our everyday understanding of numbers, we perceive them as representations of reality—for example, of the excellence of a university, of the learning outputs of a group of students, or of the number of graduates in employment. These representations provide us information about various educational entities, such as students, programs, departments, schools or universities, and countries. Much of the literature on data and data use in education builds on this understanding of numbers. One strand of research is the school effectiveness literature, which is particularly strong in Anglosphere contexts where data has been used in educational governance, management, and development for decades (Prøitz et al., 2017: 46). Scholars contributing to this strand of literature are interested in how data use in schools can be improved and in the barriers preventing this from happening (e.g., Coburn & Turner, 2011; Schildkamp & Kuiper, 2010). Another strand of research that also perceives numbers as representations of reality includes studies discussing the technical or methodological quality of measurement systems, such as national test systems (Prøitz et al., 2017: 48). These studies dominate in contexts where the use of data in educational development is a recent phenomenon, such as the Scandinavian countries, but also encompass transnational measurements like PISA (e.g., Feniger & Lefstein, 2014; Goldstein, 2017). This book adopts a different perspective on numbers—a perspective shared by the majority of scholars working in the critical ‘governing by numbers’ strand of research. Here, numbers are regarded as part of the fabric of contemporary governance, or in other words as powerful technologies of governance (Piattoeva, 2015), and thus as more than mere tools that can be utilized for improvement. This perspective is part of a wider recent development in educational governance studies, which replaces realist research questions that seek to identify policy initiatives and implementation models that can solve the world’s problems with critical studies of the forces and structures that constitute policy and governance (Webb & Gulson, 2015). From this perspective, it becomes interesting to study not what numbers say, but rather what they do (Sellar & Lingard, 2018), which is much more than simply displaying facts. Therefore, a conceptualization of numbers as representations of reality, or as epistemological and practical tools through which we can grasp other objects, is not sufficient for this book. Instead, the book frames numbers themselves as objects. The following section will introduce a theoretically informed conceptualization of numbers based on agential realism and furthermore discuss how to frame numbers as a research object in dialogue with the broader governing by numbers, educational data, and audit culture literature.

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2.1.1 The Performativity of Numbers One way of conceptualizing numbers as research objects is to understand them as agencies (Barad, 2007). With this conceptualization, the focus of research and analysis becomes the performative properties of numbers rather than their representational properties (Barad, 2003; Brøgger & Madsen, 2021). The shift from representational to performative conceptualizations of numbers can be understood through the notion of onto-epistemology, coined by Karen Barad (2007). While the ontology of a research object (or any object) is usually perceived as separate from the epistemological ways of gaining access to the object, the notion of onto-­ epistemology entails that ontology and epistemology emerge from the exact same process. In this understanding, knowledge practices are also practices of worlding (Staunæs et al., 2018), through which reality is constantly produced and re-enacted. As Katja Brøgger writes, ‘in onto-epistemological processes, the realizations of the world cannot be distinguished from the ontologies of the world’, because knowledge practices do not merely produce knowledge, but are also ‘processes that produce ontological effects by bringing into being certain realities’ (Brøgger, 2018: 357). Knowledge practices (as well as a range of other practices) delineate entities (Madsen, 2021a, b), produce matters of concern (Gorur, 2013; Latour, 2004), and define social problems and their solutions (Merry, 2016). The enacted realities that follow from knowledge practices are real, understood as part of the iterative yet dynamic production and reproduction of social and material relations in the world. Barad (2007) calls her theory agential realism with reference to a reality that is constantly produced through agentic processes. It is in this particular sense of agency that numbers can be understood as agencies and thus as performative in the production of the world. From a performative perspective, numbers in educational governance affect the ongoing dynamically produced ontologies of education. The ontologies produced include the ontologies of teachers, students, curricula, schools, education systems, and the value of education. Numbers may, for example, render these a vulnerable child (see, e.g., Riberi et al., 2021), a child at risk (see, e.g., Ratner, 2020), a well-­ performing teacher (see, e.g., Lewis & Holloway, 2019; Sorensen & Robertson, 2020), an effective school (see, e.g., Lewis, 2018), an expensive education system (as in the Education at a Glance series), or a bad investment (see Chap. 4). Ontologies are not merely discursive; they are discursive-material and materialize in, for example, test scores, textbook systems, pedagogies, subjectivities, renovation priorities, job prospects, state finances, and much more. The effects of the performativity of knowledge practices thus reach far beyond the formation of knowledge and conceptions. In tandem with a range of other practices, such as legal, administrative, financial, and pedagogical practices, knowledge practices of quantifying education constitute education and educational practices in fundamental ways. With this theorization of numbers as performative, it becomes relevant to study them in this capacity, as performative agencies that affect in various ways. There is a long tradition for such studies in a number of research fields, including the

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sociology of quantification (Berman & Hirschman, 2018; Espeland & Stevens, 1998; Merry, 2016), the sociology of public statistics (Alonso & Starr, 1987; Desrosières, 1998; Porter, 1996; Rose, 1999), organization studies (Miller & Power, 2013; Power, 1999; Redden, 2019), educational studies of governing by numbers (Gorur, 2018a; Grek, 2009; Hartong & Piattoeva, 2021; Lindblad, Pettersson, & Popkewitz, 2018; Lingard & Sellar, 2013; Piattoeva, 2015; Piattoeva & Boden, 2020; Sellar & Lingard, 2018), and new materialist studies of numbers (de Freitas et al., 2016; Dixon-Román, 2016a, b, 2017). Most of these studies have provided valuable insights into how numbers or data work in governance. However, they frame numbers in educational governance in slightly different ways.

2.1.2 Statistics, Numbers, Data, Indicators, and Metrics The different framings of numbers in educational governance studies entail different research interests and possible conclusions. The earliest studies on numbers in governance concerned the rise in the production and use of population statistics such as censuses and surveys of an entire population (Alonso & Starr, 1987; Desrosières, 1998; Porter, 1996). Some of these studies show how numbers become governance technologies of control (Rose, 1999: 12) and of trust (Desrosières, 1998; Porter, 1996). Numbers depoliticize policy areas by implying that problem prioritization and resource allocation can be thought of as mechanical processes (Rose, 1999: 189–199), whereby the legitimacy of a policy and general trust in governance across vast distances comes to rely on the objectivity of numbers rather than the subjective opinions of weak politicians (Espeland, 1997; Porter, 1996). Numbers, or more specifically statistics, also turn groups of people into populations, enabling the governing and self-governance of these populations (Rose, 1999). Hence, numbers seem to be constituents of democratic governance (Rose, 1991, 1999: 200). In these early studies, numbers thus appeared as technological devices that those in power use, intentionally or not, to enable certain forms of legitimacy and promote trust in governance. This conceptualization of numbers entails their role as an instrument or object in the hands of those governing, without any emphasis on what the numbers show. More recent studies conceptualize numbers as ‘inherently interpretative, fluid and amorphous’ (Piattoeva & Boden, 2020: 14), and thereby as sites for political negotiation in terms of what should be measured and how. While this conceptualization includes the abovementioned early studies as part of its foundations, it redirects the emphasis in relation to numbers from their role in governance to their role in power struggles. Numbers thereby cease to be technologies in the hands of the state or government elites per default and instead become destabilized expressions of current societal structures (Piattoeva & Boden, 2020). Regardless, when discussing how to frame the research object, it is also important to note that the term ‘number’ directs attention to the immediate numerical character of the information produced by metrics—or in other words, to the materiality of the information.

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Other scholars prefer to conceptualize the object of study as data. This conceptualization in part implies a more narrow research object and in part a different conceptual focus. The term data emphasizes the referential function of numbers by which the reality described by the data is made visible in a particular way. Data studies in education typically study input, output, and outcome data produced via collections and calculations of administrative data (Ratner & Ruppert, 2019) or via testing (Gorur, 2013; Lingard et al., 2016; Ratner, 2020), and sometimes involving a combination of these into rankings (Espeland & Sauder, 2016). Data studies thus transcend the conceptualization of numbers as instruments or objects of governance used by people, and rather conceptualize them as involved in a complex cycle of data collection, comparison, and attempts to improve the measured reality (or, in some cases, merely attempts to improve the data). Studies that frame numbers as data encompass both data construction studies and data reception studies. A number of data construction studies are inspired by Science and Technology Studies and Actor-Network Theory, studying the processes involved in establishing and maintaining data. For example, Gorur shows how PISA data are constructed, partly through various international negotiations among actors with different levels of power and capacities to influence how student skills should be measured. As a result, the PISA test is a better match for some educational systems than others (Gorur, 2012). Ratner and Ruppert show the kinds of ‘aesthetic practices’ that are required to make messy context-ridden or dispersed data uniform and ready for comparison and distribution via, for example, government data portals (Ratner & Ruppert, 2019). Despite focusing, respectively, on political and aesthetic production processes, these studies collectively show how data do not represent the world in an unproblematic way, but rather relate to the realities they refer to in complex ways that need to be crafted and maintained. Examples of data reception studies, meanwhile, focus on ‘catalyst data’ that catalyze change in schools, municipalities, or states (Lingard et al., 2016), on data visuals that affectively impact countries and people by motivating them to improve their efforts (Brøgger, 2018), and on comparative test data that encourage schools to direct their activities towards desired futures (Lewis, 2018). In these studies, the focus is on the interplay between the data, the policy or performance targets they are supposed to measure, and the people in charge of reaching these targets. Different traditions thus draw on different terminologies in their framing of the research object. In this book, I add a further way of framing the object of analysis, namely, as metrics. The framing of the research object as metrics involves a shift towards a processual focus on systems of categorization, simplification, translation, commensuration, calculation, comparison, standardization, visualization, distribution, and processing. These systems include the ‘objects’ (numbers, data, or indicators) that are produced in the process, but the framing remains open to different materializations of this object. For example, an analysis of a metric can include analyses of how a number is turned into an indicator when it is put to use in a certain way. An analysis of metrics turns the definition of the object into an empirical and analytical rather than a conceptual or merely terminological question, and furthermore embraces different terminological practices in different empirical contexts.

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In my conceptualization of metrics, I draw on Barad’s notion of ‘apparatus’ (Barad, 2007). Building on the work of Foucault, Butler, and Bohr, Barad conceptualizes apparatuses as specific material-discursive practices or materializations of theoretical concepts that draw boundaries and produce differences. Through these practices, apparatuses are formative of matter and meaning and productive of, as well as part of, emerging phenomena (Barad, 2007: 146). The world is constantly produced by agential cuts that performatively determine relations of unity and separation, sameness and difference, proximity and distance, simultaneity and subsequence. With this conceptualization, I understand metrics as quantification practices that draw boundaries via categorizations and hierarchies while simultaneously materializing and realizing theoretical ideas in measurements of concepts and relations. Through these practices, metrics constitute what they measure, and are therefore also inseparable from the measured phenomena. Based on this conceptualization, facts displayed in numbers in educational governance only refer to a reality if one takes the practices that produce them into account. Meanwhile, Barad’s conceptualization of apparatuses encompasses far more than metrics, as Barad’s philosophy includes not only social phenomena, but also worldings down to the atomic level. Furthermore, many other kinds of apparatuses besides metrics are involved in the production of a social phenomenon like education, including the enlightenment ideal of the Western world, specific educational theories, ways of organizing society and families, economies, nation states and their internal organization, legal regulations, governance instruments, geographical arrangements and infrastructures, and much more. The mutual relationship between all these apparatuses is what constitutes education. Barad calls such relationships intra-actions (Barad, 2007), thereby indicating that the related entities are not separable but ontologically entangled. As a phenomenon like education is co-constituted in intra-actions involving a range of apparatuses that also co-constitute each other in the process, metrics do not constitute what they measure alone. The conceptualization of metrics as apparatuses embedded in wider entanglements of apparatuses entails that metrics do not have deterministic performative effects, but rather create fields of possibilities via their onto-epistemological processes of measurement (Barad, 2003). It also entails that metrics in educational governance should be analyzed in relation to other important apparatuses, such as the instruments of governance and ideological frameworks in which they are embedded. The study of metrics as processes enables an analytical turn away from a focus on the human actors working with numbers that often dominates educational governance studies. While some scholars emphasize the value of directing attention towards the social processes surrounding quantification practices (e.g., Beer, 2017), including the production, packaging, and use of data, I find that the operations of metrics are themselves important in understanding their impact on governance practices. Even though human actors have been involved in designing metrics to work in particular ways and in implementing governance practices that imbue metrics with particular properties with particular effects, I argue that metrics also operate independently, beyond the moment of design and implementation. Metrics are durable and data are reused and only developed through slow incremental processes because

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it is costly to produce new data (Merry, 2016: 6–7). They thus often continue to operate even if the political will behind their creation is no longer active. However, this book will not resolve the discussion on how to theorize the agency of metrics in relation to the agency of human beings.

2.1.3 A Complementary Approach of Attending to Specificities As outlined above, the different framings of the research object—as statistics, numbers, data, indicators, or metrics—entail different research focuses and results. When framing the object as numbers, the material and essentially numerical properties of numbers are brought into focus (Piattoeva, 2021). The notion of statistics has Foucauldian connotations and furthermore focuses the object on the macro-level of society. In turn, framing the object as data emphasizes the research object in its context of accountability and comparability, often also with an interest in the micro-­ processes that surround data, involving various human actors as well as IT and communication systems. The term indicator suggests a context of performance management. Finally, metrics is a term that frames the object as agential practices that produce differences and materialize theoretical concepts. All of these framings are analytically valuable. Thus, the question is how to navigate these different framings. I suggest that we might perceive of these framings as analytically complementary. I use ‘metric’ as an analytical concept that opens for analyses of how the world is realized (in the onto-epistemological sense of the word) through processes of categorization, conceptualization, simplification, translation, commensuration, calculation, comparison, and standardization. This framing thus allows for a set of analyses exploring the kind of world that is created in quantification practices. I use ‘numbers’ when I wish to highlight the function of numbers in (instruments of) governance and thus to study governance practices involving numbers. This framing opens the question of different governance configurations of numbers, such as statistics, indicators, or evidence, which I consider an empirical question and the configuration thus an analytical result. Finally, I use ‘data’ when I study the reception of numbers among educational actors. In these contexts, data become a blackboxed object that is handled by actors, almost like an artifact. Furthermore, while this brief review on how different strands of research frame the study of numbers or data reveals a range of important general aspects concerning the use of numbers in (educational) governance and administration, I argue that an adequate analysis of the performative effects of numbers needs to distinguish between different numbers, data, or metrics and their effects. A differentiated analysis will allow for a more comprehensive analysis of the performative effects of specific numbers and thereby enable discussions on how we as societies wish to measure teaching quality, learning outputs, and educational outcomes. The importance of studying numbers in their specificity is brilliantly illustrated by Merry in the book The Seductions of Quantification: Measuring Human Rights, Gender

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Violence, and Sex Trafficking (2016), where she analyzes how different indicators are constructed, and how different constructions imply different underlying social theories and thereby also particular ways of addressing the problem (Merry, 2016: 45). With indicators as her object of analysis, she inscribes numbers in the context of governance in which they are used to compare countries, thus emphasizing a particular aspect of the numbers like the studies mentioned above. Meanwhile, her comparative approach, where she analyzes different indicators separately, including the context of their construction and use, as well as their technical design, proves that numbers have specific effects beyond the general effects of numbers as such, for example, on the understanding of problems and solutions. She powerfully shows that it matters how numbers are designed. With my framing of the research object as metrics and conceptualization of metrics as apparatuses, my work intersects with an emerging research approach called the ‘cultural studies of quantification’ (Dixon-Román, 2016a, b), which suggests that we need to ‘imbue numbers with ontology’ rather than conceive of numbers as a ‘simple signifier representing the measurable social world’ (Dixon-Román, 2016a, b: 483). This approach asks us to re-entangle the barren numbers circulating in educational governance practices with the techniques, political ideas, cultural practices, and histories from which they have emerged (Dixon-Román, 2016a, b: 484; Lather, 2016), and with the agencies and materialities that operate within them (de Freitas et al., 2016: 432). The framing of numbers as metrics is particularly advantageous when seeking to imbue numbers with ontology as it invites studies of practices embedded within numbers rather than processes around numbers. The analysis of these practices has enabled studies of the performativity of quantitative practices in educational governance as a matter of specific practices rather than the overall use of data in governance. The analytical task of studying metrics using this approach involves constant particularizing efforts. Through the conceptualization of numbers as metrics and methodological insistence on constantly referring back to specific quantification practices involved in particular metrics (despite the lack of immediate relevance for many readers), the book will contribute with a more elaborate analysis of how governance practices affect educational practices than would otherwise be possible.

2.2 Methodological Approaches to the Study of Quantification in Educational Governance The book’s methodology for studying governing by numbers builds on the complementarity of the different framings of numbers described above. The analysis of governing by numbers includes three overall analytical moves, each constituting one part of the book: a study of quantification practices embedded in metrics (part II), a study of governance practices relying on numbers (part III), and a study of data reception (part IV). Each part is comprised of two chapters, representing two

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different approaches to how the aspect of governing by numbers covered in that part can be studied analytically. In total, the book thus includes six different approaches to the study of governing by numbers from a performative perspective. Furthermore, they enrich each other by providing insights on the specificity of one aspect of governing by numbers to the analysis of another aspect. The compilation of different approaches is thus a methodological benefit in itself. Table 2.1 specifies these different approaches, including the overall research question, analytical questions, key concepts, empirical approaches and methods, and examples and sources related to each approach. While most of the approaches will be familiar to many readers, the approaches used in Chaps. 6 and 8 represent attempts to draw attention to new research agendas in the study of educational governance with numbers. The examples and sources included in the table are provided to point the interested reader in the direction of further inspiration and are by no means exhaustive. The analysis resulting from these approaches focuses on mapping mechanisms and logics involved in quantification practices and governance practices, as well as their effects, with the aim of showing what kind of reality those practices produce. These are critical aspects of governing with numbers. They invite the reader to join a discussion of how we as societies can change our practices to avoid the (arguably at least partially dystopian) realities that this book and other studies present. The analyses of these aspects of governing by numbers are not critical of specific actors; there is no suggestion that the effects of the quantification and governance practices are attributable to political interests in privileging some parts of the education system and weakening others (Decuypere & Simons, 2016; Madsen, 2020). They are also not ideological: They do not oppose economic thinking or quantitative practices in education as such, but rather the effects of specific practices associated with economic thinking and quantitative practices. They are hopefully mappings that will inspire new practices, while not ignoring that current realities cannot be reversed.

2.2.1 Alternative Methodological Approaches Meanwhile, the approaches that I have deployed in this book are by no means the only approaches available for studying the use of data in educational governance. Educational governance research encompasses a range of other approaches. Scholars have studied data in the sites where they are crafted and shown how specific aesthetic practices of elicitation, selection, and cleaning are constitutive in the production of comparable data (Ratner & Ruppert, 2019), how particular scales are designed to accommodate specific political purposes (Ratner, 2020), and how specific political processes of negotiation and consensus are constitutive in quantitative practices of ranking (Gorur, 2012). These and other scholars have also studied the data infrastructures connecting various locations in public governance hierarchies and allowing data to travel, both intra-nationally (Fenwick & Edwards, 2014; Gorur,

Research question How is a given phenomenon quantified, and how do these quantification practices configure the social world?

What social theories are embedded in a given set of metrics?

What is the role of a given set of metrics in public policy and governance?

Approach Chapter 3 Sociology of quantification

Chapter 4 Sociology of quantification

Chapter 5 Instrumentation approach to public policy

Key concepts Metrics, objectivity, commensuration, categories, scales, aggregations, differences, proxies

Examples and sources Fourcade (2016), Espeland and Stevens (1998), Williamson and Piattoeva (2019), Ratner (2020), Merry (2016), and Sorensen and Robertson (2020) How are measured key Proxies, theorizations/ Document studies of the Merry (2016), concepts operationalized? social theories, relation between (a) Williamson and Which theoretical relations configurations method descriptions, Piattoeva (2019), are assumed between material manifestations Madsen (2021a), concepts? What are the of metrics, and Popkewitz and histories of these theories? interpretive text in Lindblad et al. How is education reports and (b) scientific/ (2018), and Berman configured in these popular/political and Hirschman theories? discourse (2018) What policy instruments Policy instruments, Document studies and Lascoumes and Le are the analyzed numbers performance ethnographic studies of Galès (2007), Le part of? What social measurement and policy instruments, Galès (2016), relations do the instruments management, evidence-­ including legislation, Redden (2019), establish? What roles do based policy, algorithmic political agreements, and Decuypere et al. the numbers play in those governance, nudging material manifestations (2014), Piattoeva instruments? of the instruments in (2015), Brøgger governance practices (2018), and Brøgger and Madsen (2021) Analytical questions What are the key metrics? Which modes of quantification are used? How do these practices produce objectivity, meaning, and difference?

Empirical approach and methods Mappings and document studies of method descriptions and material manifestations of metrics

Table 2.1  Overview of analytical approaches to the study of quantification in educational governance adopted throughout the book

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How do specific material and relational properties of numbers affect the ways they govern?

How do quantification practices affect subjectivation processes?

How do quantification practices affect educational practices?

Chapter 6 Socio-material studies of numbers

Chapter 7 Subjectivation studies

Chapter 8 (Not yet developed as a coherent approach)

How do educational practices change in ways that refer back to specific quantification practices? What are the implications of those changes?

In what material forms do the numbers appear? How are they visualized? What temporalities and spatialities do the numbers enact? How do these properties affect human beings? How do various actors engage with the data? How do they respond? What are the implications of those responses and the changes they entail? Educational thinking and design, pedagogical practices, performative effects

Subjectivity, affectivity, performative effects

Data aesthetics, data visualizations, data shadows, comparability, temporal and spatial orders, fluidity, distribution of agency and affectivity

Ethnographic studies, including observations of educational practices and interviews with actors involved in those practices

Ethnographic studies, including observations of and interviews with actors engaging with data

Document and ethnographic studies of method descriptions and material manifestations of numbers

Dixon-Román (2016a, b, 2017), Staunæs and Conrad (2020), Lewis (2018), Hardy and Lewis (2017), and Adams et al. (2009) Riberi et al. (2021) and Espeland and Sauder (2016)

Piattoeva (2015, 2021), Ratner and Ruppert (2019), Brøgger (2018), Williamson (2016), and Gorur (2018a)

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2013; Hartong, 2018; Ratner & Gad, 2018) and internationally (Carvalho, 2014; Ratner, 2020; Williamson, 2016). Categorical standards constitute one type of data infrastructure, itself also crafted through continuous processes of development and adjustment (Gorur, 2018b). Studies of data production and data infrastructures emphasize the practices involved in initially establishing data as powerful technologies of educational governance (Piattoeva, 2015). Other scholars have emphasized how data are used by various actors, typically in national or subnational policy contexts (Cort & Larson, 2015; Grek, 2009, 2017; Lingard & Sellar, 2013; Moreno-Salto & Robertson, 2021; Sellar & Lingard, 2018), but also at school level (Lewis, 2018). These studies show how transnational educational data impact national policy reform and local strategies through complex, affectively wired processes (Webb & Gulson, 2012; Webb et al., 2019) that involve not only national policymakers, but also the media and a wider public of parents, educational professionals, and other interest groups. While these studies are characterized by a dominant interest in international large-scale assessments, they also mainly focus on the social and political processes following the publication of a set of data, for example, a new PISA report. In addition, one strand of research is specifically interested in how teachers make use of data in decision-making aimed at improving their teaching, thus conducting studies of micro-processes of teacher data reception (Coburn & Turner, 2012; Hardy, 2021; Little, 2012) and of the resulting changes in the teacher profession (Lewis & Holloway, 2019; Spina, 2017). Other anthropological studies, drawing on the concept of governmentality, have analyzed the proliferation of ‘audit culture’ in academia and schools, where ‘principles and techniques of accountancy and financial management are applied to the governance of people and organizations’ (Shore & Wright, 2015b: 24). These studies emphasize the culture surrounding quantification practices and other audit practices, including the transformation of academic subjectivities into what Shore and Wright call ‘self-actualized auditable individuals’ as a consequence these practices (2000: 78). To these scholars, ‘audit culture’ and its seemingly legitimate concern with improving the quality and efficiency of universities is nothing more than a new and surreptitious form of coercive power structure, with costly and damaging effects on the work being done at such institutions (Shore & Wright, 2000: 85). A final group of studies that I feel is worthy of mention explore how quantification practices such as tests are used in the crafting of statistical populations and thereby numerical objects of governance (Popkewitz, 2018; Popkewitz & Lindblad, 2018; Ratner, 2020). These studies analyze social norms embedded in quantification practices, much like the analysis of theorizations embedded in metrics in Chap. 4 of this book, but with a focus on inclusion and exclusion processes of statistical populations and the fabrication of particular kinds of people instead of configurations of education. The scholars producing these studies draw explicitly on Foucauldian concepts of discipline, risk, and biopolitics. All of these approaches offer important ways to generate new knowledge about quantification practices in educational governance. While I find the combination of the six approaches presented in Table 2.1. analytically fruitful, other combinations might also prove fruitful. Hence, a main argument of this book concerns the value

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of combining several approaches in a single study. In particular, the combination of approaches from the sociology of quantification and approaches from educational governance studies, broadly speaking, is valuable, as it allows a degree of analytical precision not found in studies of governance with numbers that do not include analyses of specific quantification practices. Lines of analysis that show how a particular way of stratifying education has implications for imbalanced resources and differences in norms and standards for educational practice, or that show how a particular theorization of education with context-specific historical roots is embedded in specific quantification practices are important in the development of nontrivial educational governance conceptualizations and theorizations. An increased analytical specificity is crucial for contextualized educational governance knowledge, also when taking general trends of convergence into account. The ongoing production of reality following from an agential realist philosophy only becomes visible in detailed studies of specific practices.

2.3 Adding a Broader Educational Governance Gaze Above and across the studies of quantification practices, the book also addresses broader questions regarding global trends in educational governance. Educational governance in the contemporary Western world and beyond is often analyzed via a number of supposedly global frameworks. These include capitalism, neoliberalism, and new public management. These labels are used to describe particular ideologies or ideals promoted by particular actors, as well as particular practices or ways of organizing society that have dominated particular historically and geopolitically demarcated eras of social life, and are often used to diagnose empirically studied practices in educational governance (Bacevic, 2019). Meanwhile, some scholars are also beginning to question the adequacy of such labels as descriptors of empirical instances of governance practices, including the historical-geopolitical reach of these concepts. In other words, one might ask whether some practices that are often considered neoliberal, for example, could also be interpreted as materializations of other systems of thinking, such as the emerging theorization of a Cold War grid of thinking (Buchardt, 2020; Bürgi & Tröhler, 2018). In addition, some scholars suggest that we need to pay attention to the variations in the appearance and dominance of each of these systems of thinking across national contexts (e.g., Rinne, 2021), thereby raising the question of whether specific modes of governance, such as quantification, are always wrapped in, for example, capitalist or neoliberal ideologies and governance ideals. This book adds to the emerging body of literature questioning the global convergence of governance ideas and practices. Specifically, the book discusses (a) whether governing by numbers is always neoliberal and (b) whether human capital thinking in education is always neoliberal. These questions are addressed via the close-up empirical studies described above, whereby the detailed analysis of how graduate outcome metrics and numbers configure and govern education is

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methodologically assigned as the site in which neoliberal ideas or human capital thinking becomes visible. With this close-up approach, the analysis will not produce uniform conclusions, determining whether Danish governance with graduate outcome indicators is neoliberal. Rather, the analysis will map the specific composition of elements from various systems of thinking and in Chap. 9 discuss how to understand the co-presence of these different elements. The following section will provide an outline of how I understand key concepts in the analysis of global (or not so global) trends in education governance and their mutual relation. First, I will discuss neoliberalism, then its relationship to new public management and human capital theory, before finally contrasting it with different lines of thought in governance, including the social democratic welfare state, the Nordic model of education, and a Cold War grid of thinking. Conjointly, these frameworks constitute a frame of reference for the book’s analyses of quantification and governance practices. I will return to a more elaborate discussion on the role of these frameworks—and how they intertwine in the Danish context—in Chap. 9.

2.3.1 Neoliberalism Neoliberalism has been defined in multiple ways within educational governance research. It has even been described as a multiple phenomenon (Cannella & Koro-­ Ljungberg, 2017). However, in this book, I will refer to this multiplicity as different versions or definitions of neoliberalism. Neoliberalism has been described as a particular mode of public governance or state-crafting that is based on the separation of private property and public power, or of the economy and politics (Wood, 2002: 169), and thus predominantly operates outside the public sector. According to Cahill and Konings (2017), neoliberalism rests on three intellectual planks: anti-rationalism, neoclassical economics, and public choice theory. The anti-rationalist disposition entails that no human being is able to determine the optimal decision, including those in power, which is why it should be left to the liberty of individuals to make choices, rather than to the state. The ‘Chicago School’ or neoclassical economics disposition entails that individuals can be understood as rational, self-interested, utility-maximizing actors, who via free markets are able to ensure optimal decision-making and societal progress. Finally, public choice theory entails that these characteristics of human beings also apply to politicians and bureaucrats, who are interested in optimizing their electoral prospects or budgets and thus tend to do what is best for themselves rather than for the collective. As a result, neoliberalism has a preference for organizing society in free markets, considered capable of ensuring both economically efficient outcomes and a morally desirable preservation of liberty (Cahill & Konings, 2017: 32). In terms of public governance, neoliberalism implies a minimization of the state, as well as skepticism towards the intentions of policymakers and bureaucrats, who should be held accountable for their actions. Thus, while accountability measures are considered unnecessary for the market, as such measures are built into market

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mechanisms, the public sector should be subject to such accountability measures (Wood, 2012: 317). In relation to education and quantification, neoliberalism has been presented as the explanation for the emergence of quantified and comparative knowledge in educational governance, as well as for the introduction of marketization and privatization modes of governance in education (Naidoo, 2018; Shore & Wright, 2015a). Neoliberalism thus constitutes a historical-geopolitical framework that unlocks trends in the governing of education based on particular economic ideas. Since these philosophical ideas were first developed, neoliberalism has been theorized in various ways (Jensen & Olsen, 2020). One important theorization is ‘neoliberalism as governmentality’, as suggested by Foucault (2009) and Rose (1999). In this theorization, neoliberalism is about promoting a responsible, entrepreneurial, and competitive self that can be governed at a distance, for example, through calculability. Another theorization is ‘neoliberalism as a political program of the elite’ (Harvey, 2011). In this theorization, neoliberalism is a deliberate strategy to promote economic structures that benefit the ruling class, masked by a rhetoric of freedom and liberty. Both these theorizations add questions of power to the economic theories and organization models that represent neoliberalism in its philosophical form. While both theorizations add important analytical aspects to neoliberalism, they also shift the focus from governance instruments and arrangements to subjectivities and politics, respectively. For the purpose of this book, I will thus analyze governance practices as neoliberal if they involve marketization and competition, or if they draw on ideas from anti-rationalism, neoclassical economics, or public choice theory.

2.3.2 Neoliberalism and New Public Management Another major governance trend, which has dominated educational governance research for decades, is new public management (see, e.g., Gunter et al., 2016). New public management can be described as a number of specific practices used in the public sector. These practices include evaluations based on performance, a preference for disaggregated organizational forms, widespread use of contracts as a coordination device, the promotion of market-type mechanisms in public governance, and an emphasis on the users of public services, who are configured as consumers (Pollitt & Bouckaert, 2011: 10). Quantification practices can thus be characterized as fundamental to new public management, as they measure performance and are used in both contractual devices and marketization instruments. Neoliberalism is often considered the ideological foundation of new public management. However, it is evident from comparative studies that the implementation of new public management varies considerably between countries (Gunter et  al., 2016; Pollitt & Bouckaert, 2011). The differences are attributed to cultural and historical differences in public administration that have affected how new public management has been translated into practices by different states (Gunter et al., 2016). These differences are framed in a number of ways in the literature. For example,

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Neave (2009) has described how accountability policies in Europe have been characterized by two different discourses: an economically oriented neoliberal discourse and a participatory democracy discourse that emphasizes decentralization and local influence on service provision. Maroy and Pons (2019) have demonstrated empirically how France and Quebec are characterized by neo-statist accountability policies, focusing on the delivery of a public service for all through regulatory and strategic interventions, rather than neoliberal policies of market information. Similarly, both Kipnis (2008) and Gurova (2018) have shown how presumably neoliberal techniques of performance measurement can also be analyzed as having deep roots in socialism. Within the literature on new public management, there are also accounts that view national differences in the implementation of management reforms as a product of different strands within what must be seen as an essentially non-coherent collection of tools and techniques that make up new public management (Pedersen & Löfgren, 2012). While new public management is often considered as linked to neoliberal governance ideals, several scholars thus highlight how different intellectual streams feed into new public management. Some new public management instruments and some ways of implementing new public management draw on a managerial and perhaps more pragmatic way of thinking, rather than neoliberal, market-oriented ideas. The managerial strand of new public management focuses on leadership and motivation (Pollitt & Bouckaert, 2011), for example, by implementing command-and-control instruments in new, more subtle forms (Kurunmäki et  al., 2016; Pedersen & Löfgren, 2012). These include accountability and audit instruments, as well as an extensive use of indicators. The occurrence of quantification practices in higher education governance can thus be interpreted in various ways, as either neoliberal or managerial/neo-statist instances of new public management, depending on the instruments in which these practices appear.

2.3.3 Neoliberalism and Human Capital Theory A third major governance trend within contemporary society is capitalism. Capitalism refers to a particular way of organizing the economy that involves the accumulation of capital through an increase in the productivity of labor, thereby producing a surplus, rather than through coercive means of extracting value from politically inferior subjects by forcing them to work harder or for longer. The accumulation of capital is one of the preconditions of capitalism, because it allows for a division of labor that contributes to increases in productivity (Wood, 2002). Understanding phenomena like higher education through the notion of capitalism often implies analyses of how higher education institutions become entwined with private businesses (Cone & Brøgger, 2020; Cone & Moos, 2022; Verger et  al., 2016), or even how academic degrees become market commodities, in some cases provided by for-profit actors and in others simply viewed as an export commodity (Robertson, 2017a, b).

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Meanwhile, in (almost) exclusively public education systems like Danish higher education, capitalism plays a more subtle role in the sense that education is understood as an investment in the human capital of the population. In this context, education is considered a means of increasing productivity, as laid out in human capital theory (e.g., Becker, 1976, 1993). Through the idea of ‘human capital’, education becomes economized as an asset for both the nation, strengthening its position in the global knowledge economy, and for the individual graduate. Thus, while it is probably a conceptual stretch to call governments capitalist, in the sense that they are exploiting the extra productivity of the national labor force achieved through education for profit, we can certainly say that they are capitalizing on education for the benefit of the nation state. Capitalism is closely connected to neoliberalism—some scholars even describe neoliberalism as the currently dominant form of capitalism (Bacevic, 2019). The often-claimed close connection between neoliberalism and human capital theory is particularly visible in relation to the individual graduate. For example, Mintz (2021) argues that education has been transformed from a public to a private good, pursued by individuals who wish to enhance their value in the labor market. Likewise, Saltmarsh (2011) analyzes how higher education and pedagogical encounters are increasingly configured in terms of economic exchange, with students as customers that expect a certain output from their investments in education. While these analytical points are probably at least somewhat relevant across national contexts when viewed through the governmentality-inspired notion of neoliberalism, emphasizing competitive and calculative attitudes among students, they appear less relevant in contexts of free public higher education when viewed through the economic historical-­philosophical notion of neoliberalism to which I refer. The inclusion of philosophical ideas concerning human capital thus enables analyses of this way of thinking permeating higher education governance and policy beyond the idea of the economically maximizing, competitive, and entrepreneurial subject.

2.3.4 Governing by Numbers and Human Capital in the Social Democratic Welfare State With these critical stances towards the global reach of neoliberal thinking, one might ask how contemporary practices of governing by numbers and human capital policies should be understood, if not as a product of global convergence towards neoliberal societies. The answer might be that governing by numbers and human capital theory are fully capable of serving other governance regimes than neoliberalism, based on other intellectual ideas and values. In the case of the Danish graduate outcome metrics, we might turn to the social democratic welfare state (Esping-Andersen, 1990), the Nordic model of (higher) education (Rinne, 2021), and a desire for planning and optimization rooted in the Cold War (Bürgi & Tröhler, 2018).

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The social democratic welfare state and the Nordic model of education are often characterized through the figure of the Nordic model. This model is contested and arguably conceals important differences between the Nordic countries, as well as important similarities between Nordic and other countries (Kettunen, 2012: 30). Nevertheless, it is a commonly used framework in the analysis of educational governance in the Nordic context. The Nordic model refers to a general governance model characterized by more fundamental principles, such as a strong state and strong public institutions combined with market forces and international cooperation (Telhaug et  al., 2006: 262). The Nordic model is furthermore defined by an egalitarian tradition in which the state historically was not considered a threat to the people, but rather a protector of the people from privileged and patriarchal forces within society (Kettunen, 2012: 23). The notion of the state as a protector rather than a threat is key in understanding the difference between a Nordic tradition and the neoliberal thinking developing in other contexts (Cahill & Konings, 2017). The social democratic welfare model as a concept refers to a state model of social solidarity and social security (Kettunen, 2012: 23) and thereby of collective risk-sharing (Andersen et al., 2007) based on consensual ideas regarding the common good (Prøitz & Aasen, 2018). The theorization of this welfare model emerged from Esping-Andersen’s (1990) typology, which contrasted the social democratic welfare model with liberal and conservative welfare models found elsewhere in Europe and the Western world. While this typology has since been discussed and developed (Emmenegger et al., 2015), the concept of the social democratic welfare state still prevails. In contrast to the liberal and conservative models, the social democratic welfare state is universal and comprehensive with a significant redistribution and characterized by publicly provided services, strong labor unions, and massive investments in education and research (Andersen et al., 2007). The Nordic model of education thus belongs ideationally to the social democratic welfare model with its inclusion of publicly funded comprehensive education systems characterized by values such as equity, participation, and welfare (Buchardt et al., 2013: 23; Prøitz & Aasen, 2018: 216). These values typically materialize in schools without tracking and with close relations to the local community (Skedsmo et al., 2021). The Nordic university model similarly refers to publicly funded university systems with no student fees and with homogeneous and non-hierarchized institutions promoting democracy and equality (Rinne, 2021: 161). Public education plays an important role in the Nordic welfare model. The provision of high-­ quality public education has been crucial in the development of equal citizenship, social justice, and a skilled labor force, all of which are prerequisites for the Nordic welfare state model (Buchardt et al., 2013: 19–21). The Nordic education systems are thus both rooted in the national economy and national culture (Rinne, 2021: 154). Public education has been foundational for the historical normalization of wage work, which became the norm in return for universal social rights, including the right to attend public education and to social security (Kettunen, 2012: 24). While Nordic education systems have become entangled with the discourse of global competition (Imsen et al., 2017: 569) and furthermore affected by neoliberal governance practices, including accountability regimes (Imsen et al., 2017; Skedsmo

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et al., 2021), the decentralization of responsibility to individual institutions (Krejsler & Moos, 2021: 140), and performance management (Telhaug et al., 2006: 263), the opposition between the Nordic welfare model and these practices often claimed by educational scholars is debatable, at least in relation to higher education. The critical stance towards a dichotomizing between Nordic or European models of education and neoliberal governance practices is shared in literature on indicators (Bürgi & Tröhler, 2018; Le Galès, 2016). As an emerging alternative, several scholars theorize quantification practices in educational governance as materializations of a Cold War grid of thinking rather than of the spread of neoliberalism. According to Bürgi and Tröhler (2018), the early international education indicators were developed as ways of first forecasting, then planning, and finally managing education in order to maximize output. With manpower as a key resource in the Cold War, education became an important area of improvement. Ideas from both sides of the East/West divide regarding the use of quantitative indicators to optimize society developed into a particular mode of governance, focused on planning and management. The Cold War grid of thinking can also serve as a loose conceptualization of how ideas of human capital have flourished in non-neoliberal contexts. The discourse of global competition and the desire to regulate and manage the labor force can be seen as an extension of the desire to maximize the output of education (Buchardt, 2020; Bürgi & Tröhler, 2018). While the national and regional human capital armament in the Cold War (now re-emerging in Europe) was inscribed within a discourse of a technological war, where security and peace were at stake, the post-Cold War human capital armament is inscribed within a discourse of an economic war, where prosperity and standards of living are at stake. Despite the difference in threats, the approach of developing a skilled labor force to ensure technological progress is very similar across these different contexts of global competition. The Cold War theorization thus offers another model that may both serve as an analytical tool and challenge the neoliberalist analysis of governing with numbers, while also qualifying the capitalist analysis of the quantification of graduate outcomes in the Danish case.

2.4 Studying Education Policy Through Metrics With this chapter, I have outlined several different approaches to the study of governing by numbers, including different framings of the research object and different analytical methodologies. The chapter thus serves as an introduction to methodological approaches in studies of governing by numbers, not just relevant for the readers of this book, but also for scholars and students new to the field. Meanwhile, the chapter also makes new contributions to research on governing by numbers, including a conceptualization of numbers as metrics, an emphasis on the importance of including number specificities in studies of governing by numbers, an underlining of the analytical value of combining different methodological approaches in a single study, and a framework for analyzing practices of governing by numbers as materializations of different ideas about governing and the state.

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While each approach individually supports analytical findings, another set of findings emerges when the approaches are combined. The empirical study of quantification practices also contributes analytical perspectives relevant to the broader field of educational governance studies. While educational data remain a particular niche within educational governance studies, being just one of many possible points of interest, the methodologies proposed in this chapter are of relevance for a wider range of studies. Scholars who study education policy may have noticed that the book provides not only insights into quantification practices in educational governance but also into contemporary Danish higher education policy and its focus on graduate outcomes. The point I wish to make here is that metrics used in public policy constitute an entry point for policy analysis. Especially the methodological approaches of mapping the quantification practices used in policy and studying how they operationalize, hierarchize, and theorize educational phenomena, illustrated in Chaps. 3 and 4, are useful as approaches to policy analysis. In that sense, the book’s methodology can be read as a materiality-focused elaboration of other methodologies for policy studies (e.g., Bacchi, 2009; Lascoumes & Le Gales, 2007). It can be understood as one way of analyzing the material form of a policy in cases where that form is (at least partially) numerical (Piattoeva, 2021). The study of policy metrics could thus constitute a separate approach in policy studies, resting on a conceptualization of metrics as sites of policy negotiation and policy materialization. The metrics used in public policy are indicative of the priorities, problematizations, differentiations, governance rationalities, and educational thinking that make up education policy—sometimes even more than policy texts. They are often built into the core of policies, either as their foundation or as a key part of how they operate. Importantly, they also play a significant role in defining how these policies are enacted in managerial and educational contexts (Ball et al., 2012). With this framing in place, it is now time to move on to the analysis.

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Part II

Quantification Practices: Human Capital and the Value of Higher Education

Chapter 3

Quantifying Higher Education with Graduate Outcome Metrics

Neoliberal and capitalist ideas have come to play an increasing role in higher education governance. In graduate outcome metrics, these ideas become intertwined and together produce a powerful tool used to determine the societal value of various subsections of higher education. On the one hand, neoliberal ideas of the market as the ultimate truth-teller underlie the methods for measuring graduate outcome metrics. The behavior of the labor market, in terms of how the value of various types of higher education graduates is estimated in recruitment processes and via salaries, provides genuine knowledge regarding the actual value of these graduates, it is assumed. Besides data on market behavior, the market opinion is also conjured up in surveys, where either employers (as representatives of the labor market) or students (as first-hand higher education ‘customers’) are asked to assess higher education as a product. Evidently, such assessments by the market overrule the assessments of educational value conducted by higher education institutions themselves, for example, in the form of quality assurance practices. On the other hand, the idea of human capital as an asset in the competitive global economy provides the substance that is measured by these metrics. Higher education is ascribed value in terms of its utility for the economy, most often via its utility in workplaces, and is thus considered an investment in the productivity of graduates. However, close study of specific graduate outcome metrics shows that these trends are not all-embracing, at least not in Denmark. Rather, as this chapter will show, neoliberal ideas of the market as the ultimate truth-teller are accompanied by techno-scientific ideas of objectivity and unambiguity. In addition, as Chap. 4 will show, twisted versions of capitalist ideas of human capital have materialized in Denmark, transformed into collectivist and public models of welfare and labor rights. As such, any analysis highlighting neoliberalism and capitalism as the This chapter draws partly on empirical analyses and theoretical arguments previously published (Madsen, 2021). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_3

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mindsets that define contemporary higher education governance with numbers is too narrow and simple. Part II of the book studies the measurement methods and substance of graduate outcome metrics in order to analyze the neoliberal and capitalist thinking embedded in these quantification practices, as well as other ideas that dominate the quantification practices of Danish graduate outcome metrics. The present chapter explores different approaches to the quantification of higher education outcomes and discusses the potential implications inherent in these approaches for the differentiation of higher education and the promotion of educational ideas founded in economics. The chapter begins by looking at the broader international context of educational outcome measurement, before diving into the empirical case of the Danish higher education system, which has adopted a number of metrics with different approaches to the quantification of graduate outcomes. A brief description of the characteristics of each metric is followed by an analysis of how they, as well as metrics found in other contexts, quantify educational utility as objective and unambiguous information. This part of the chapter shows how the metrics are not only based on neoliberal thinking, but also draw on a techno-scientific paradigm. The chapter shows how the various metrics produce difference and operationalize graduate outcomes in different ways, thereby addressing the importance of the specificity of particular quantification practices. The wider implications of the specificities of the metrics and quantification practices analyzed in this chapter will be a recurring topic throughout the rest of the book. Thus, the measurement details provided in this chapter are foundational for subsequent analyses. The chapter is written with a nonspecialist reader in mind in order to allow scholars and students of society, culture, and education to obtain a clear understanding of the described quantification practices and their implications.

3.1 An Educational Landscape of Graduate Outcome Measurement Educational outcomes have been measured across European and Anglosphere higher education systems for decades. Numerous actors are involved in these measurements, including universities, government agencies, independent bureaus and think tanks, consultancy corporations, researchers, and international organizations. One example of an influential actor involved in the measurement of educational outcomes is the OECD, which has delivered annual comparative analyses of its member states’ education systems in a publication titled Education at a Glance since 1992 (Bürgi & Tröhler, 2018; Simola et al., 2011). This publication typically includes between 20 and 30 indicators that cover various aspects of education systems, including indicators addressing the output of educational institutions and the impact of learning (OECD, 2020). Within this category of indicators, the OECD compares the education level of adults, their transition from education to work, the

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relation between education and labor market participation, the earning advantages from education, the financial incentives to invest in education, the social outcomes of education, and the equality of educational participation across nation states. Other examples of key actors in relation to the measurement of educational outcomes include national statistics agencies, such as the Higher Education Statistics Agency (HESA), which is the government-appointed Designated Data Body for higher education in England, or the Social Research Centre, which hosts the Quality Indicators for Teaching and Learning (QILT) initiative funded by the Australian government. Both these actors provide comprehensive national statistics and compile reports on graduate outcomes. On the HESA website, statistical bulletins presenting data on graduate activities (e.g., employment status), graduate salaries, and graduates’ reflections on their activities are publicly available, including information on the entire higher education system, on various segments of the student or graduate population, and on each higher education provider (HESA, 2020). In a similar way, the QILT initiative produces graduate outcome surveys, showing graduate employment, skills utilization, salaries, entry to further studies, and graduate satisfaction among Australian graduates, both at the national level (QILT, 2019) and the provider level. Universities can use the provider-level information as part of their quality assurance procedures and in the promotional material they produce to recruit students (see, e.g., Deakin University, 2019). Similar information is available in the USA, where the National Center for Education Statistics provides statistics to the US Department of Education, for example, on employment rates and graduate wages across various types and levels of education (NCES, 2021), and in Finland, where Statistics Finland publishes employment data for graduates across various degree levels, providers, and geographical regions (Statistics Finland, 2018), as in many other countries. Further examples of agencies engaged in the measurement of educational outcomes include the European Commission, which has conducted surveys on, for example, employers’ perception of graduate employability (Directorate-General for Employment Social Affairs and Inclusion, 2010). This survey included questions regarding the importance of various skills, both in relation to employers’ current experiences and future expectations, as well as questions regarding the skill levels of the graduates employed by the respondents. The approach of conducting employer surveys to measure educational outcomes is also characteristic of much quantitative research on graduate employability, such as a study conducted in Spain by Marzo-­ Navarro et al. (2009). However, the body of graduate employability research also includes graduate surveys exploring the complex relationships between a host of educational trajectories and characteristics on the one hand and the ‘world of work’ on the other (for example Teichler, 2007). Graduate surveys are also found in governmental contexts, for example, in Norway (NIFU, 2018) and, as detailed below, in Denmark. Across these actors and the data they publish, there is a common interest in the utility of education in the broader context of society and life. The quantified information can help a potential student determine whether or not it will pay off to enroll in higher education, and, if so, in what kind of program, at which university, and

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within which field of study. The information thus supports higher education institutions in branding their products, besides being useful in the management and development of education. The quantified information can furthermore assist national governments in deciding whether and how public higher education funding should be prioritized. Finally, it allows international organizations like the OECD to assess whether education is an efficient investment to strengthen the economy. These purposes all reflect the utility of education beyond the context of education itself. While graduate outcome metrics found in different contexts embody different approaches and ideas, and furthermore frame themselves in diverse ways, for example, as employability or impact of learning measurements, they all measure higher education in terms of its utility beyond itself, and it is therefore relevant to compare and study such metrics more closely.

3.2 Danish Graduate Outcome Metrics The Danish higher education context, which is characterized by its relative homogeneity and uniformity in terms of higher education quality indicators, as well as a strong coherence between national policies and institutional procedures, has seen the implementation of broadly speaking five different types of measurement of graduate outcomes. These include graduate unemployment statistics, graduate income statistics, calculations on job match, graduate surveys on the utility of graduate skills and general satisfaction, and employer surveys on graduate skills and general employer satisfaction. The categorization of the five types of metrics builds on my understanding of metrics as apparatuses that can share important commonalities, and thereby constitutes a ‘type’, even though various metrics within the same category also have different features. The metrics are categorized according to the overall method of data collection, as well as their conceptualization of graduate outcomes. The different conceptualizations can be unlocked by examining how relevance is operationalized methodologically in the graduate outcome metrics, or, in other words, by determining what kind of variable (and thereby data) is afforded the status as a proxy (Gorur, 2013) for relevance. The construct measured by a metric, such as the graduate outcome of a particular degree program, requires translation into a specific variable in order to become measurable. The nature of that variable—that is, the data that stand as a proxy for the measured phenomenon—is an essential part of the information the metric produces and plays a crucial role in the ontology of the resulting indicators. As subsequent chapters will show, the proxy data affect the educational thinking embedded in the policy and management initiatives, as well as revisions of program designs and other developments within the higher education sector, that revolve around the metrics. The analyzed metrics conceptualize relevance in different ways, albeit all related to the utility of education in the labor market. The metrics draw on very different data sources to address the politically formulated abstraction of labor market

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relevance (see Chap. 1). They also all render the opinion of the market that ‘buys’ the ‘product’ of higher education (i.e., workplaces, employers, or graduates themselves) as knowledge that constitutes a public critique of the higher education sector (Raffnsøe et al., 2022). While some of the included metrics were developed by the Danish Ministry of Higher Education and Science as part of policy initiatives on ‘program relevance’ launched in 2014–2015, others have a longer and more entangled history at specific universities prior to their adoption by the ministry. The job match metric was developed by a non-governmental think tank (Junge et al., 2012; the Think Tank DEA, 2016). As outlined in Chap. 1, I conceptualize metrics as apparatuses in my analysis. This conceptualization comes with an interest in how metrics set boundaries, produce differences, and determine objects. In other words, I will analyze how metrics categorize, classify, commensurate, quantify, calculate, translate, objectify, compare, and simplify. To tease out all these practices of the Danish graduate outcome metrics, I will go into the specificities of each metric. The analysis is based on a reading of the metrics as they materialize in questionnaires, calculation models, definitions, assessment criteria, and reports. Metrics are typically described in method sections or method documents or in guidelines and principles, and their results are displayed in spreadsheets, reports, and data packages. Method sections in reports or separate method documents constitute key empirical material for the analysis of metrics as apparatuses because they make explicit (some of) the ways in which the metrics classify, calculate, document, commensurate, rank, assess, and so forth. In the following, the metrics are first introduced one by one and thereafter compared to each other and to examples from other contexts.

3.2.1 Graduate Unemployment Statistics The first category of graduate outcome metrics that I will introduce is graduate unemployment statistics, which in Denmark covers several specific metrics, all of which are developed by the Ministry of Higher Education and Science with regular updates published on the ministry’s website. The most important graduate unemployment metric is called Current Unemployment [Aktuel Ledighed]. It has become the most commonly used metric due to certain statistical advantages over the previously used metric Employment of Graduates and because it (as I will unfold in Chap. 5) received more public attention and has had a larger impact than the Graduate Employment Rate, which is a metric used as part of the mechanism for allocating funding to universities for teaching activities. The Current Unemployment metric calculates a quarterly unemployment rate for graduates based on their registrations in a system used to apply for public unemployment benefits. The unemployment rate is defined as the extent to which a graduate is reported as unemployed during a particular quarter of the year. If a graduate applies for full-time unemployment benefits for 1 of 3 months during a given quarter, the unemployment rate of the graduate will be calculated as 33%. Similarly, if a graduate is registered as employed

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in a part-time position equivalent to 80% of a full-time position for all 3 months, the unemployment rate of the graduate will be calculated as 20% (Danish Agency for Higher Education, 2014). The unemployment rate thus indicates the (full or partial) lack of employment of the graduate with a single number, covering both part-time and partial employment and unemployment. These data are extremely fine-grained, as unemployment benefits are calculated in terms of the number of hours worked in a given week subtracted from the standard working week of 37  h. The graduate unemployment rate can thus be calculated very accurately based on these data. The use of unemployment benefits data makes this metric stand out from other graduate unemployment metrics that use occupational registration data. With the use of applications for unemployment benefits, rather than occupational registration, as a proxy of educational utility, the graduate unemployment rate conceptualizes relevance specifically as the ability to sustain themselves financially without dependence on public benefits. The measurement of graduate unemployment in the 4th to 7th quarters after graduation configures higher education as something that is valued in relation to the transition into the first post-graduation job and early career, rather than the career achievements of the graduate throughout a long working life. Unemployment data for individual graduates are aggregated in various ways, for example, according to university, program type, or individual program, allowing data to be configured as information on these aggregate units of measurement. One university has a higher unemployment rate than another; university graduates have higher unemployment rates than graduates from business academies; and history graduates have higher unemployment rates than medicine graduates. The most often used aggregate units of measurement are the individual programs, the broader areas of study (i.e., the humanities, social sciences, natural sciences, technical sciences, and health sciences), and a clustering of individual programs originally proposed by the Committee on Quality and Relevance in Higher Education (Committee on Quality and Relevance in Higher Education, 2014a, b), which I will return to in Chap. 5. By aggregating the data for individual graduates into various units of measurement, the metrics enable direct, numerical comparisons and benchmarking of unemployment rates between programs, areas of study, or program clusters.

3.2.2 Graduate Income Statistics The second category of graduate outcome metrics, graduate income metrics, is used to describe the financial gains from education. In a series of recent policy reports produced by various commissions, committees, and independent think tanks, economic earnings are measured in different ways and with different purposes. For example, the Committee on Better University Programs (2018), which was a committee appointed by the Ministry of Higher Education and Science, included an annual graduate income measure as an indicator in the section of their report under the heading The value of university education for society. The annual graduate income metric measures the annual income of graduates. The report states that the

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annual income data were generated as the total value of income from employment and pension schemes paid by employers, including net profits from self-­employment and fringe benefits (Committee on Better University Programs, 2018: 98). These incomes are all measured in monetary values. In a line graph included in the report (see Fig. 3.1), the income data for master’s degree graduates are aggregated into the five broader fields of study mentioned above in order to make them comparable. The data include a longitudinal aspect, plotting income across the first 10 years after graduation. Thus, each line shows the development in average annual income for graduates in a particular field of study over 10 years. While the figures for annual income describe the average income across the entire population of graduates, they only include the income of graduates in employment, even though the population is composed of both employed and unemployed graduates, as well as graduates outside the labor force. Examples of graduates outside the labor force include graduates on parental leave that is not paid by employers and graduates with chronic diseases and disabilities who are eligible for social benefits and unable to work (Committee on Better University Programs, 2018: 99). By only including income from employment in the calculation, graduates receiving social benefits (including parental leave) or unemployment benefits will reduce the average annual income for their area of study as any income in the form of social benefits is not counted. In contrast to the unemployment benefits data, this data conceptualizes relevance as earnings gained. The higher the income, the higher the utility of the degree. Graduates with a PhD and graduates resident outside Denmark are not included in either the income calculation or the graduate population of this metric. Other graduate income metrics measure the lifetime income of graduates, aggregated according to educational level and field of study. These metrics describe the average total individual earnings of graduates from a particular type of education,

Fig. 3.1  Graduate income statistics copied from Committee on Better University Programs, 2018: 99. The graphs are referring to the five broader areas of studies. SAMF social sciences, HUM the humanities, TEK technical sciences, NAT natural sciences, SUND health sciences; and finally all graduates together

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deducting lost earnings during periods of study. The graduate lifetime income metric conceptualizes relevance as the personal financial gain when graduating with a degree from higher education compared to another type of qualification (e.g., from vocational training) or to entering the labor market directly at the post-secondary level. When the public costs of educating a graduate are subtracted from the lifetime income, another metric is formed, showing the national returns of public funding investments in various types of education. Here, the proxy of relevance is the directly comparable overall economic advantage for society of investing in various types or areas of higher education.

3.2.3 Job Match Calculations The next graduate outcome metric is provided by the independent, non-profit Danish think tank DEA, who have published a report on the transition from university studies to the labor market (the Think Tank DEA, 2016). This report analyzes the 2001–2013 cohorts of graduates in Denmark in terms of the match between their qualifications and job. In the report, educational utility is quantified as a vertical match (or hierarchical match) between the degree level and job level (as opposed to a horizontal match between area of study and area of work). Two job match metrics are used to determine the appropriateness of the job level. The academic match measure draws on the DISCO classification of all employees in Denmark according to their job titles. This classification is the Danish version of the International Standard Classification of Occupations (ISCO), developed by Statistics Denmark. It includes 563 occupational groups, for example, administrative management in the public sector or ordinary office work (Statistics Denmark, 2011). These occupational groups are sorted according to the equivalent educational level, based on the typical educational level of employees within each group (Skaksen & Andersen, 2018: 104). The category administrative management in the public sector, for example, is defined as an occupational group that requires a master’s degree, while ordinary office work only requires secondary-level qualifications. Statistical data are collected for workplaces with 10 or more employees, which are required to submit data on their employees. Based on these statistical data, the share of graduates who are classified in occupational groups with educational requirements equivalent to a master’s degree is calculated 1 and 5 years after graduation (Junge et al., 2012: 13; the Think Tank DEA, 2016: 13). The other measure, wage match, calculates the average wage of all employees in an occupational group with any type of higher education degree and compares the wage of the university graduate with this average. If the university graduate has a wage that is at least 10% above the average of all higher education employees in the category, there is a wage match. Or, in other words, if the salary is more than 10% above the average, the employer is assumed to be willing to pay the graduate for the extra skills achieved from the master’s degree, because those skills are required in the job (Junge et al., 2012: 13; the Think Tank DEA, 2016: 13).

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Both measures use a ‘realized match’ method and thereby describe a ‘market equilibrium’ rather than an absolute definition of match (Skaksen & Andersen, 2018: 104). Thus, the relative position of the graduate compared to the rest of the labor market in a similar field becomes the proxy of relevance. If you earn more than people in the same category of occupation with a lower degree, or if you belong to a category typically occupied by graduates of the same educational level as you, your job matches your degree. If there is a match between job and degree, your degree has utility for you. This metric conceptualizes gains from education in terms of achieving employment in specific occupations that represent a higher skill level than others. The relevance of education is thus conceptualized as the ability to gain a better (e.g., more skilled and highly paid) job than others. The DEA report combines the two different measures of match so that only graduates that match neither via the academic match method nor the wage match method are considered to be in the mismatch category. The DEA report looks at area of study, gender, age, children, immigrant status, and employment sector as possible characteristics of mismatched graduates (the Think Tank DEA, 2016: 37). The findings show that the subpopulations of graduates above 30 years of age, graduates who immigrated to Denmark or are descended from immigrants, and (humanities) graduates working in the public sector are over-represented in the mismatch subpopulation, while neither gender nor parental status makes a difference to the proportion of mismatched graduates.

3.2.4 Graduate Surveys While the three categories of metrics listed above are based on administrative data, the remaining two categories of metrics are based on survey data from graduates and employers, respectively. In Denmark, most universities conduct periodic surveys based on questionnaires distributed to recent graduates, typically from the previous five cohorts. These surveys map comprehensive information on graduates, including their employment status, their first job, their transition into the labor market, possible reasons for being outside the labor market, area of study and skills in relation to job, activities during their studies, and factors promoting access to the labor market (see, e.g., Roskilde Universitet, 2013: 3–4). Additionally, in 2017, the Ministry of Higher Education and Science in addition introduced a graduate survey that, while less comprehensive than those conducted by individual universities, is conducted at the national level. Both the university surveys and the ministry survey include a section on the applicability of the graduate’s university degree in the context of their current position. Applicability is measured in terms of graduate skills and overall satisfaction with the degree in light of their transition into the labor market. Quantification by self-categorization is a key part of the survey metric. When filling out the questionnaire, the graduates are asked to categorize themselves according to sets of predefined categories in the survey, both in terms of their

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background characteristics and their experiences and opinions. The surveys thus produce knowledge on the experiences and opinions of the respondents, based on a number of questions. Together with the background characteristics, the responses to these questions are transformed into a data set where each response is coded with a respondent ID. This allows a range of different aggregations across the data set to be compiled, compared, and reported. Background characteristics typically include area of study, graduation year, employment rate, and parental status. These variables are used to aggregate the survey results in various combinations. The graduates are, for example, asked what skills [kompetencer]1 they use the most in their job, what skills they did not acquire during higher education but which are needed by their employer, and how they assess the coherence between the skills achieved in their degree program and those needed by the employer. The graduates can answer such questions by selecting a number of skills from a predefined list. They are also asked to assess to what extent the study program has equipped them for their current job, and thus their overall satisfaction with the program in relation to their current position in the labor market. This type of question is typically measured on a scale from one to five, also known as a Likert scale. As Fig.  3.2 shows, the master’s degree program in history at Roskilde University scores 3.67 on the question of how well the degree program has equipped its graduates for their

Min uddannelse har rustet mig till mit job Meget uenig 1

Meget enig 3.65

5

Fig. 3.2  The figure shows the indicator ‘The extent to which the degree has equipped the graduates for their job’ from the national graduate survey for the master’s degree in history at Roskilde University. (Screenshot from Uddannelsesguiden, 2018)

 The English word ‘skills’ can have both a narrow and a broader meaning. The narrow meaning can be translated into the Danish word ‘færdigheder’, which could, for example, be ‘foreign language skills’, ‘IT skills’, or ‘oral dissemination skills’ (these examples are taken from the Roskilde University case). The broader meaning is the sense of the word that I will be using throughout the chapter (unless otherwise specified), which includes such narrow skills, but also knowledge, responsibility and independence, and the ability to successfully apply knowledge and (narrow) skills in a given situation. This definition is in line with the official definition from the Danish Ministry of Higher Education and Science (Ministry of Higher Education and Science, 2013). By translating the Danish word for competencies into skills, understood in the broader sense, I want to emphasize the particular sense of the word related to the utility of the skills in work life or other contexts outside education, which is a tint also connoted by the Danish word [kompetencer]. A translation into the English competencies would risk losing the utility connotation and instead connoting the professional state of being competent, while in Danish university governance, skills [kompetencer] are often talked about as units detached from the person. 1

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jobs. Via these data, the graduate survey metrics produce knowledge regarding the correlation between, for example, the area of study, occupational sector, age, or gender of graduates on the one hand and their experiences of and opinions regarding the match between achieved and required skills on the other hand. The methodological choice of selecting graduates as respondents indicates that relevance is conceptualized as the alignment of educational content (assessed both in terms of the separate skills and holistically) with a job context that was not yet defined at the time of study. The use of a specific list of skills as response categories for questions concerning the match between education and job provides a systematic means of connecting educational content to job context, as the categories are likely recognizable as related, although not identical, to elements in both contexts. The lists of skills embedded in the various survey metrics thus structure the responses, thereby becoming an important part of the ontology of the numbers produced by the metrics.

3.2.5 Employer Surveys The final category of graduate outcome metric found in the Danish context, employer surveys, does not (yet) exist on a national level. However, such surveys are conducted by at least one university, namely, DTU, the Technical University of Denmark (DAMVAD Analytics, 2016), and are thus relevant to include. The DTU employer survey is based on semi-structured qualitative interviews with 50 employers. The employers are all day-to-day managers of graduates from DTU. The responses are coded thematically and analyzed via quasi-quantitative tallies of different types of responses to the questions asked. The interview questions address the general characteristics of the company, its demand for engineering graduates (reflecting the academic profile of DTU) and, in particular, what skills these graduates need to have, experiences of the value of DTU graduates for the company, and opinions on how well DTU educates its graduates. The questions resemble the questions asked in the graduate surveys, likewise seeking to compare the required skills and personality traits with those attained during education and to assess the overall satisfaction with graduates from DTU. The proxy of relevance in the employer surveys is quite similar to that of graduate surveys. In comparison to the graduate survey questionnaire, however, the employer interview guide allows more detailed responses. Based on the coding of the responses, patterns of correlation can nevertheless be identified, such as a correlation between company size and requirements for generalist business knowledge versus specialist engineering knowledge. In the reports compiled by the university, these correlation trends are described and documented through the use of selected interview statements, including anonymized indications of the business sector within which the specific employer operates, which support and provide greater detail concerning the general trends. The correlation trends and quasi-quantitative practices make the employer survey a borderline-quantitative metric.

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3.3 The Pursuit of Objectivity and Unambiguity Through Quantification With the five categories of metrics, which comprise a larger number of specific metrics, a number of approaches to the quantification of graduate outcomes are available. One might argue, as many have, that the plurality of methods of measurement constitutes yet another proof that metrics can be designed to accommodate a specific result, and that the resulting numbers are thus merely social constructions with no real existence. However, I do not consider this plurality a proof of the weakness of individual metrics of graduate outcomes. Rather, the variety of metrics both entails contestations of the optimal way of quantifying graduate outcomes, including the relevance and robustness of data and calculative practices, and complementary approaches constituting a catalogue of information on the utility of higher education. While the variety of metrics reveals the lack of a single, stable convention for how to quantify, it also strengthens the abstract concept of relevance, as the strengths of one approach may make up for the weaknesses of another approach. For example, the unemployment rate metric builds on a very robust, coherent, fine-­ grained, and transparent data set that is highly regulated, but it has also been criticized for its relatively vague concept of relevance in terms of employment. While graduate employment is considered an important matter for higher education, also among those who are critical of the metrics currently in use, employment statistics cannot stand alone as an indicator of relevance. In my interview with an official from the Ministry of Higher Education and Science, he talked about the need to add different types of numbers and thereby provide a bigger picture, because the relevance of higher education is a matter of more than employment: Having said that [we find graduate employment figures more solid than graduate income numbers], it is clear that relevance is about more than employment, and for quite some time we have wanted better knowledge on… if the graduate gets a job, those skills that the person has gained—are they, then, relevant for the job the person gets? … That is very difficult to assess. You can ask the employers; you can ask the graduate herself… There are no solid numbers showing if you are relevant or not, but with Education Zoom we have taken a step closer, asking questions like: Is there an experience of compliance between the skills gained in the study program and those experienced in demand within the labor market? So, in my opinion we are a step closer, but we would still not claim that we are able to measure relevance 1:1. (Interview with ministry official, March 2017)

In the passage above, the ministry official distinguishes between graduate employment statistics, graduate income statistics, and survey data from Education Zoom (the national graduate survey) on graduates’ (and employers’) experiences of the adequacy of their degree in relation to their job functions as different measures, each with their own strengths. As he explains, none of these numbers can be used in isolation as a robust measure of relevance, but a survey-based approach comes closer to this goal. The surveys transform the opinions and experiences of graduates and employers into a quantifiable or quasi-quantifiable linear relation between two contexts (education and job) by using the graduate or employer as a measuring instrument (Muniesa, 2014: 86), providing an indication of educational utility. By

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using responses from graduates and employers as proxies for educational utility, graduates and employers appear to be considered legitimate assessors of educational utility and capable of bridging the contexts of education and job, based on their positions in the job contexts from where the utility can be assessed. In the interview, it became clear that employers are seen as having greater legitimacy than graduates in the assessment of educational utility; however, the logistical difficulties in conducting employer surveys in relation to specific educational units are considered too great for such surveys to be a useful tool in educational governance. Meanwhile, as decades of research on evaluation have shown, opinions and experiences are formed in complex ways and often depend on factors other than those explicitly measured. For example, there is a strong correlation between students’ evaluations of teaching quality and their assessments of the teacher’s personality (Bedggood & Donovan, 2012; Clayson & Haley, 1990). A similar influence of factors unrelated to the relevance of the skills acquired by the graduate may occur in graduate and employer surveys, thereby rendering the knowledge produced by these surveys. To give another example, the job match metric offers a stronger concept of relevance than the graduate unemployment metrics as it endorses not just any type of employment, but employment equivalent to a university degree. However, it can be argued that this metric rests on a weaker data set, as the occupational categories of graduates are registered by employers, who may interpret the categories differently, and furthermore only includes data from workplaces with ten or more employees. Overall, however, the quantification procedures of the metrics render the produced information trustworthy. All the quantification practices and approaches involve standardized procedures that are necessary for achieving the objectivity of the produced utility indicators. As a number of scholars interested in objectivity point out, objectivity is a result of various practices rather than an external parameter that can be used to judge knowledge (Daston & Galison, 2007), and is thus an achievement (Yarrow, 2017: 115). The standardized procedures involved in the quantification of qualities such as the relevance of a university degree are ‘indispensable to objectivity-making’ (Williamson & Piattoeva, 2019: 67). Objectivity relies on mechanical representation, emotional detachment, automated procedures, and the belief in an independent reality (Daston & Galison, 2007: 29). These practices are particularly characteristic of those statistical metrics where the uniform sorting procedures used by Statistics Denmark ensure the high credibility of statistical data such as employment and income statistics (Desrosières, 2001: 346). The transparency of the graduate and employer surveys is more questionable, with a range of different factors and events outside university education and a certain amount of emotional attachment potentially affecting responses. The objectivity of survey data instead relies on the standardized procedures of the measurement tool. These procedures include the uniformity of the questions asked, of the response categories, and (in the case of the employer survey interviews) of the coding of the qualitative data. Furthermore, the metrics all draw on conventions that have emerged from processes of negotiation and standardization, as various studies of other cases have

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already shown (Gorur, 2018; Merry, 2016; Ratner & Ruppert, 2019; Williamson & Piattoeva, 2019). These conventions include general quantitative methodological conventions, such as the definition of a significant difference, and specific conventions on how to measure a particular phenomenon. For instance, many of the same skills recur across graduate surveys for all university degrees in Denmark. A graduate survey from Roskilde University has the longest list, with 23 skills, but 15 of these skills are found in different variations in all the other survey examples I have come across since 2003. The ability to think creatively and innovatively, for example, is among the response categories in the Roskilde University survey, while creativity and innovation (Københavns Universitet, 2013), the ability to work creatively and innovatively (Aspekt R&D, 2013; Capacent Epinion, 2007; Aalborg Universitet, 2018), and creative and innovative skills appear in other graduate surveys (Danish Agency for Higher Education and Science, 2018). Another example is the inclusion of categories concerning to organize and manage project processes (Roskilde Universitet, 2013), to work project-oriented (Københavns Universitet, 2013), the ability to work project-oriented (Aspekt R&D, 2013; Capacent Epinion, 2007; Aalborg Universitet, 2018; Aarhus Universitet, 2017), interdisciplinary and project-­ oriented skills (Danish Agency for Higher Education and Science, 2018), and project-­oriented work (Udvalg om bedre universitetsuddannelser, 2018). Only the graduate survey at DTU, the Technical University of Denmark, deviates from this pattern, perhaps due to its origin at a single-faculty university where generic skills across programs can be more specifically formulated than is possible at multi-­ faculty universities. The length of the Roskilde University list can similarly be seen as reflecting a specific university profile, since a number of university-specific skills appear in the Roskilde list but are not included in graduate surveys at the other universities, including political understanding and to produce academically founded knowledge. Notwithstanding these exceptions, the skills included in graduate surveys in Denmark have become a relatively standardized convention as to how graduate outcomes operationalized as skills can and should be described. While the quantification practices thus ensure the objectivity of the information produced, the specific quantification practices adopted by most metrics furthermore achieve unambiguity. The calculation of averages across graduate populations implies a leveling out of differences or deviations among graduates. Calculations of averages and mean values represent methods deployed to identify the typical value across the data set, such as the typical unemployment rate, the typical annual graduate income, or the typical overall satisfaction with the utility of a particular university degree. Through average and mean calculations, any variance across the data set becomes invisible, and none of the metrics include variance calculations to complement the information on the typical data value. However, some of the metrics use other calculations than averages. For example, while the job match metric calculates and compares the average salaries of master’s degree graduates and other graduates within an occupational group, the result of the match metric builds on a calculation of the distribution of graduates across the binary categories of match and mismatch. With such a distribution calculation, the variance becomes visible. Nevertheless, the binary distribution achieves the same result of unambiguity as an average or mean

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calculation in terms of a single, directly comparable number. It is easy to compare the mismatch of Danish humanities graduates of 26.5% with the much lower mismatch of Danish graduates within natural sciences of 12.1% (see Fig. 3.3, which is an example of how different categories of graduates are made comparable in the job match metric report). Metrics that include distribution calculations across a larger number of categories, such as the older employment metric Employment of Graduates, have proven less useful for higher education governance. This metric includes five mutually exclusive categories concerning occupational status: (1) graduates enrolled in education, for example, at doctoral level; (2) graduates employed in Denmark; (3) graduates who emigrated; (4) unemployed graduates in Denmark; and (5) graduates who are not available to the Danish labor market (the Danish Agency for Science and Higher Education, 2018). Due to the sorting of data according to five categories, this metric has proven unable to produce unambiguous information, which may explain why it has largely been replaced. With the quantification of graduate outcomes, an objective and unambiguous single number has replaced local knowledge about graduates’ achievements in the labor market in terms of what counts as legitimate knowledge. The standardized calculations performed by economists and statisticians remote from educational settings have superseded the insights of university teaching staff regarding the utility of higher education. The quantification practices have thus resulted in a fundamental shift in the relative influence of different professionals on higher education governance. Meanwhile, even though the metrics produce knowledge based on (labor) market assessments and can thus be considered imbued with neoliberal thinking, this shift in terms of who is knowledgeable and what counts as knowledge is not merely a product of neoliberalism. The pursuit of objective and unambiguous knowledge can equally be seen as the result of a growing dominance of technical-­ scientific and cognitive-instrumental rationalities and practices in education (Skedsmo et al., 2021; Williamson & Piattoeva, 2019). The objectivity of educational data is important for the legitimacy of data as powerful truth-tellers. It grants both the data and their proponents, such as policymakers, their relative strength compared to local knowledge and the experiences of professionals and students actively involved in educational practices (Tröhler & Maricic, 2021). This also applies to the local knowledge of employers, such as employer representatives on

Fig. 3.3  A table showing the share of Danes and immigrants or descendants of immigrants in mismatching jobs for each of the five broader areas of study (humanities, social sciences, technical sciences, natural sciences, and health sciences). (Copied from the Think Tank DEA, 2016, p. 40)

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university advisory boards, which the government official did not consider an equally valid source of information as the various surveys and statistics.

3.4 The Crafting of Difference Importantly, however, the various graduate outcome metrics quantify in different ways, and the particular methodological approach of each metric has important effects beyond the objectivity and unambiguity effects of quantification. The five types of graduate outcome metrics produce similarity and difference between various entities in different but overlapping ways. The outputs of the metrics are numbers (or, in the case of the employer survey, trends) that indicate the outcomes of graduates from a particular university, the utility of a particular program or area of study, or the gains from a particular level of education. These numbers can be compared and thus reveal which institutions, programs, or educational levels have greater or lesser utility for the individual and for society. Through these comparisons, differences and similarities across the educational sector emerge. The metrics play a role in constituting the university sector as a tiered field. The precise methodological operations involved in producing difference can be unlocked with inspiration from ideas found in the sociology of quantification (Berman & Hirschman, 2018). One of the main figures here is Marion Fourcade, who has introduced the notion of ordinalization and discussed various modes of quantification (Fourcade, 2016; Sorensen & Robertson, 2020). Fourcade distinguishes between three different modes of relating entities to each other. The first is nominal judgment, where entities are sorted according to their kind. The second is cardinal judgment, where entities are counted and categorized according to numerical values. The third is ordinal judgment, where elements are ordered according to their relative value. In the current educational landscape, there is a trend of intersections between cardinal and ordinal principles of organization, which can be seen in the emergence of numerical rankings of education systems, institutions, and programs. With this intersection, the ordering according to relative value becomes numerical. This trend can also be seen in the 0–100% ratio scale design of the graduate unemployment rate, which enables the comparison of educational units based on a single number. Thus, the intersection of the ordinal and the cardinal (Fourcade, 2016) creates a scale that clearly shows which degree program is better. However, ordinality implies a specific form of difference between the ranked entities. While nominality assumes fundamental uniqueness, ordinality assumes fundamental equivalence of different entities (Fourcade, 2016: 188). In other words, when comparing graduate outcomes of study programs, the comparison rests on the assumption that all study programs are equivalent. Through equivalence, they become commensurate. As suggested by Espeland and Stevens (1998), the commensurability of different entities implies that they are configured as comparable on a common scale, stripped of all other properties than those that fit this scale.

3.4  The Crafting of Difference

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Commensuration processes transform qualities into quantities—sometimes easily and sometimes in more cumbersome ways. In the process, some information is discarded while other information is re-organized into new forms (Espeland & Stevens, 1998: 317–318). Commensuration ‘simplifies in two ways: by making irrelevant vast amounts of information, and by imposing on what remains the same form—a shared metric’ (Espeland & Sauder, 2007: 17). The properties that are made commensurable in the metric make other categorizations of difference irrelevant and thus invisible. The inherent assumption of equivalence and commensurability in quantitative orderings of entities makes difference a relation between entities that are configured as essentially equivalent and only different as a result of circumstances, rather than the kind of essential difference that would have been assumed by a nominal categorization. The assumption of equivalence is ingrained in the liberal paradigm of the contemporary Western world—a paradigm that comes with an egalitarian promise that everyone can in principle obtain any position (Fourcade, 2016: 182–183), also known as meritocracy. Based on this assumption, the difference between quantified and thereby comparable units of measurement is understood as conditional rather than given. In principle, the difference in numerical values could have turned out differently. The various graduate outcome metrics rely on the basic equivalence of graduates in respect of their success in the labor market. While graduates are typically considered nominally different in some respects (e.g., gender, ethnicity, race) and cardinally different in other respects (e.g., age, years of education, number of children), they are assumed to be equivalent in terms of their educational performance, graduate income, and educational experience. Otherwise, it would not make sense to aggregate data on these properties of the graduates and calculate averages to measure and compare educational entities. The aggregations of data on equivalent graduates and the calculations of averages across the aggregate graduate population furthermore imply that universities, areas of study, types of education, and individual programs are also made equivalent and commensurable, for example, according to their utility. Hence, the numerical outputs of the various graduate outcome metrics become expressions of conditional or behavioral differences between these entities, rather than of essential differences. If one university scores higher than another on graduate income, this difference is understood as a consequence of different actions or stances of these universities. The difference can be overcome if the lower-scoring university acts differently. However, whenever educational units (e.g., institutions, areas of study, programs) are compared according to a single metric such as the graduate unemployment rate by aggregating and calculating averages for their graduates, other differences between graduates are simultaneously ignored. Comparing the unemployment rate of a program to that of another program assumes that the difference in average between the graduates is related to the program and not to either differences in graduates’ geographical, socioeconomic, ethnic, cultural, or previous educational backgrounds, differences in their sex or age, or differences in their historical or contemporary life conditions. The metric organizes the data according to one difference and turns a blind eye to all other differences in the data.

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In most cases, the graduate income and graduate unemployment metrics are strictly confined to aggregations according to educational units, while graduate and employer surveys tend to aggregate the data in a range of ways, for example, according to occupational status, sex, and educational background. The varied aggregation according to the wide range of background characteristics included in a graduate survey produces a detailed and complex map of patterns of difference in the responses. The survey reports judge the importance of differences in relation to significance. Significance is a statistical notion based on a calculation of the randomness of correlations (with a somewhat arbitrary and conventional limit between significant and non-significant, as Porter notes; 1996: 212). If a particular survey response and a particular background characteristic seem to be correlated, the calculation of significance intervals will show to what extent the correlation could be random. However, like the metrics based on statistical data, the analyzed graduate surveys (e.g., Roskilde Universitet, 2013) appear to be mostly interested in educational units, such as areas of study. Education is thus singled out as the main cause of difference between graduates, with only vague indications of the influence of other graduate characteristics across the studied metrics. Meanwhile, while both the DEA report (the Think Tank DEA, 2016) and the institutional graduate surveys do in some cases include social background and labor market circumstances in their quantitative constructions of difference, one factor appears completely absent from any quantifications of graduate outcomes: the workplaces of the graduates. Even though all the metrics conceptualize graduate outcomes in terms of the current labor market context (e.g., a particular job or unemployment) of the graduate, this context is not taken into consideration by the metrics and thus not allowed to be a factor of difference. As the differences between companies are ignored, and thereby also the differences between the labor market contexts in which a salary is earned or a skill is assessed, the metrics provide no information on whether the differences between graduate incomes correlate with different workplaces, and thus no information on whether such correlation can explain differences between educational units. Only the employer survey breaks this silence of the other metrics by providing information on, for example, differences between the skills demanded by large and medium/small businesses (tentatively indicating that such differences may actually be important). However, besides this exception, the Danish graduate outcome metrics are by and large occupied with the crafting of differences between educational units, with an emphasis on types of education, areas of study, and individual programs. The production of difference involved in quantification practices is in other contexts often analyzed as performative in the creation of inequality (Espeland & Sauder, 2016; Naidoo, 2018). Quantification practices do indeed contribute to the creation of a tiered field of academic areas or, as is most common in Anglo-American countries, universities. The construction of independent variables concerning the university sector, combined with ordinal dependent variables on graduate outcomes, enables a direct numerical comparison of programs, universities, or areas of study that affects how they are valued among educational stakeholders and the general public. Through numerical comparisons, the measured entities are held accountable

3.5  Quantifying Educational Utility in Denmark and Beyond

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for their performance. From this perspective, the production of commensurability and the intersection of cardinal and ordinal judgment can be understood as neoliberal practices, creating a tiered higher education market.

3.5 Quantifying Educational Utility in Denmark and Beyond As the chapter has shown, the proxies or operationalizations of relevance, and thereby the materialized conceptualizations of the object of measurement, vary highly across the five categories of metrics and the specific metrics included in each category. When combined with the crafted differences between educational units, particular dominant correlations emerge. The capacity of graduates to become financially self-sustaining emerges as dependent on their area of study or the specific program they attended. The degree to which it is worthwhile to attend higher education in terms personal financial gain and the return on public investments in higher education emerges as dependent on type and area of study. A graduate’s relative position in the labor market in terms of type of occupation emerges as dependent on area of study. The relevance and adequacy of the skills acquired in education emerges as dependent on area of study and the particular program attended. And the overall value of employees to a given company emerges as dependent on the specific design of university programs. In turn, labor market outcomes are considered not to be highly dependent on factors such as graduates’ cultural and socioeconomic backgrounds; the discriminatory patterns of labor markets and societies in relation to demographics such as race, ethnicity, gender, and age; the varying academic achievements of graduates; the personality traits of individual graduates; or the organizational cultures at the workplaces where graduates are employed. Such factors are rendered invisible by the assumptions of equivalence and the averages calculated by the metrics. The correlations between education and labor market outcomes are thus precise results of the quantification practices embedded in the metrics. These practices produce the meaning of the relevance of university education. In comparison to the examples of measurements used in international and other national contexts mentioned at the beginning of this chapter, the Danish metrics appear to stand out in several ways. First, in the Danish case, labor market outcomes seem to be understood in narrow terms as a result of higher education, while a wider range of factors are included in other contexts. For example, the HESA bulletin also considers social structures like sex, age, disability, and ethnicity in relation to labor market outcomes (HESA, 2020). This difference means that, while the UK statistics call for governance strategies focusing not only on education but also broader social patterns of labor market participation and success, the Danish metrics strongly support tight governance of higher education institutions and activities. Second, the Danish graduate outcome metrics exclusively conceptualize the relevance of higher education in terms of labor market outcomes. The Danish notion of relevance is thus more narrowly oriented towards the labor market than the notions adopted in the

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Bologna documents, such as the qualifications frameworks (Sarauw, 2012) and the European Association for Quality Assurance in Higher Education standards and guidelines (Brøgger & Madsen, 2021; Degn et  al., forthcoming). In such documents, the relevance of education is also gauged in terms of its ability to foster democratic values and strengthen social cohesion. The Danish metrics are furthermore more narrow than the indicators in the OECD’s Education at a Glance, where educational outcomes also include aspects like health (OECD, 2016), experiences of and attitudes towards bullying, and democratic efficacy and interests (OECD, 2020). The different policy contexts of quantification are thus intertwined with the specific ways in which the objects of measurement are conceptualized. The Danish policy context has been successful in producing a close, almost causal, relationship between education and labor market outcomes, and furthermore positioned this relationship as crucial for educational thinking, and thereby also for educational policy, governance, and management. This relationship is also produced in all the other contexts of measurement mentioned here, but it is only in the Danish context that it is considered in isolation. In conclusion, the quantification practices involved in measuring the utility of education affect the kind of educational thinking that is promoted. The very practice of quantifying alters the relative status of various actors in the politics of university education. The objectivity and unambiguity provided by standardized quantification practices, endorsed for their techno-scientific character, overrides the professional knowledge of university teachers. Nevertheless, as the close study of the Danish graduate outcome metrics in comparison to other national and international examples has shown, the specific approaches adopted to the quantification of graduate outcomes also matter. In the Danish case, these approaches all enact neoliberal thinking, perceiving the market as the only rational actor (Cahill & Konings, 2017) and rendering the market’s opinion the most highly valued type of knowledge. The metrics are thus predominantly based on neoliberal ideas, while also drawing on a techno-scientific paradigm.

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Chapter 4

Graduate Outcome Metrics and the Economization of Education

Educational metrics are important constitutive agencies in the formation of contemporary educational thinking. Metrics do not only produce objectivity, unambiguity, and difference in particular ways; their quantitative practices also promote specific theorizations concerning cause and effect relations in education, as well as specific educational values and ideas about the purpose of education. These theorizations, values, and ideas are important as they are accompanied by particular definitions of and solutions to perceived problems, which are readily available for use in educational governance and management practices (Lingard, 2021; Merry, 2016). Essentially, the educational thinking promoted by graduate outcome metrics builds on economic ideas from human capital theory. The assessment and valuation of education offered by these metrics is thus conducted from an economic rather than a pedagogical perspective. In effect, educational governance and management appears to be preoccupied with solving economic problems regarding the human capital of the population rather than solving pedagogical problems. Human capital theory has guided educational policy in international organizations like the OECD and the World Bank for decades (Gorur, 2018; Henry et al., 2001). Its ideas are now the dominant paradigm shaping educational thinking among influential actors, including states investing public funding in education systems, private companies investing capital in their employees’ lifelong learning, and young people and their families investing time, and in many countries also money for indebting tuition fees, in livable futures. These investments are warranted by core ideas from human capital theory that associate education with the promise of growth and prosperity for both nation states fighting to be competitive in the global economy and for individuals around the world. The success of education as an enterprise and increasing educational level among populations are thus affected by this rationale and the persuasiveness of optimistic promises embedded in human capital theory (Sellar & Zipin, 2019), captured in the phrase the educational gospel (Rinne, 2021: 156). Meanwhile, the success of education as an enterprise also entails a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_4

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growing focus on education systems around the world and their ability to fulfill these promises. The introduction and continuous development of graduate outcome indicators are, at least partly, a result of this attention. The measurement and monitoring of education systems, providers, and programs in terms of the degree to which they enable graduates to gain employment, high salaries, and attractive high-skilled jobs, as well as whether they are aligned with the skills needs of employers, draws directly on the educational thinking that configures education as a pathway to economic growth. While human capital is a complex phenomenon that is difficult to measure and correlate with economic outcomes, the utility of higher education in the labor market serves as one empirical setting where the supposed mechanisms of human capital play out in practice. The graduate outcome metrics presented in the previous chapter can thus be seen as different attempts to measure how various educational units realize the promises of human capital. These measurements are used as a means of identifying unrewarding educational investments and ineffective flaws in the educational enterprise (Henry et al., 2001: 101). However, with the conceptualization of metrics as apparatuses in mind (see Chap. 2), the graduate outcome metrics not only measure human capital but also reinforce the human capital educational thinking that they draw on, which has direct implications for educational governance. This chapter builds on the conclusions from the previous chapter regarding how specific quantification practices constitute the production of difference, as well as conceptualizations of educational relevance, by showing how these quantification practices also embody particular educational theories, values, and ideas. As the analysis will show, the Danish graduate outcome metrics introduced in the previous chapter theorize education as human capital in slightly different ways. These theorizations entail divergent implications for educational thinking, and thus for educational governance, educational development, and the subjectivities of the people inhabiting education systems. Besides capitalist and neoliberal educational-­ economic thinking, we also find theorizations that rely heavily on social democratic and Nordic models of society. When combined with the particular ways of differentiating education that characterize graduate outcome metrics in specific contexts, human capital theory constitutes a powerful tool for determining the value contribution of particular educational units, and thereby a tool for problematizing specific parts of the education sector. The chapter begins with an outline and discussion of the relation between human capital theory and education. The chapter then analyzes how this relation plays out in the Danish graduate outcome metrics. The metrics are grouped in a slightly different way than in Chap. 3, with graduate income split into annual income metrics and net lifetime income metrics, while the analyses of the graduate and employer surveys are collapsed into a single analysis. This is followed by a discussion focused on how the configuration of education that is embedded in the metrics constitutes a particular problematization of higher education. The chapter thus follows up on the analysis of quantification practices in Chap. 3 by highlighting how such practices are not merely a technical matter, but also a matter of educational theorizing.

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4.1 Human Capital Theory and Education Human capital theory (see, e.g., Becker, 1976, 1993) has proven to be an important ideational framework for linking education to economy. Rooted in this theory, education has continuously been on the agenda of transnational economic actors like UNESCO, the OECD, and the World Bank (Gorur, 2018; Klees et  al., 2012; Sorensen & Robertson, 2020), and with the Lisbon Treaty and the Bologna Process, the EU reinforced the inscription of education into the narrative of a globally competitive economy (Brøgger, 2019: 60 ff). Adherence to the human capital theory entails seeing investments in education as also being investments in the economy. Human capital theory conceptualizes knowledge, skills, and values in a specific way, emphasizing the output of education and training (and more) as a human capital because it is not possible to separate the knowledge, skills, and values obtained in education from the person. The knowledge, skills, and values are capital, because they are assets that can be utilized for profit: [It] is fully in keeping with the capital concept as traditionally defined to say that expenditures on education, training, medical care, etc., are investments in capital. However, these produce human, not physical or financial, capital because you cannot separate a person from his or her knowledge, skills, health, or values the way it is possible to move financial and physical assets while the owner stays put. (Becker, 1993: 16)

With the conceptualization of knowledge, skills, and values as capital, human capital theory seeks to explain how the value of labor can vary from person to person and from one situation to another. The concept of human capital theorizes this difference in productivity as a result of productivity enhancement and relates it to education (Bol, 2013; Sellar & Zipin, 2019). Through the accumulation of education and training, a person will be able to produce more value in the same time, at least in principle. The concept thus conceptualizes the value contribution of education to the labor market and thereby to individual earnings, company profits, and the competitiveness of national economies. The theory aligns with meritocratic ideas (Young, 1996) that consider educational and occupational awards a product of obtained merits. It also aligns with much of the graduate employability literature, which is inherently occupied with identifying the types of skills that are most highly valued by employers in order to support the design of higher education programs aligned with these skills (see, e.g., Ayala Calvo & Manzano García, 2020; Caballero et al., 2020; Harvey, 2001; Helyer & Lee, 2014; Pool & Sewell, 2007; Tymon, 2013). The theory’s arguments are largely based on quantitative empirical findings concerning the relation between education and income (Becker, 1993: 17–21). However, the exact mechanics behind this relation are not conclusively determined and are thereby open to interpretation and ongoing theorization. Over the years, different understandings of human capital have dominated the political discourse. While human capital theory was previously associated with the macro-level link between educational expenditure and economic outcomes, the political discourse has in recent decades framed the theory as a micro-level theory emphasizing the value of specific skills of individuals (Henry et  al., 2001: 99; Nielsen, 2015: 172). This

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displacement of human capital theory also involves a shift from the previous focus on the educational level of the population (Gorur, 2018; Lingard, 2021: 341) to a focus on the utility of the specific skills obtained by the individual during education. The investments made by states and individuals in human capital have thus gradually changed from investments in higher levels of education to investments in the right kind of education. Meanwhile, ideas about what constitutes the right kind of education have also changed over time: from job-specific skills towards more flexible skills, varying from technology skills to life, career, and learning skills, commonly framed as twenty-first-century skills (see, e.g., Partnership for 21st Century Skills, 2002; World Economic Forum, 2016). The theory thus serves as an umbrella for a host of more specific educational theorizations, which the Danish case of graduate outcome metrics also illustrates.

4.2 Theorizations of Human Capital in the Danish Graduate Outcome Metrics Human capital theory is intimately woven into the Danish graduate outcome metrics. Based on the analysis in the previous chapter, showing that the Danish graduate outcome metrics are strictly occupied with labor market outcomes in contrast to graduate outcome metrics in other contexts that also encompass democratic and social outcomes, this chapter will show how the Danish graduate outcome metrics furthermore promote a relatively narrow understanding of education as an activity enhancing human capital. While the graduate outcome metrics in the Danish case all relate to the overall idea of education as human capital, the metrics configure human capital theory and theorize the relationship between human capital, education, and the labor market in different ways. In other words, the metrics embody the concept of human capital in specific, yet implicit, ways, closely related to their quantification practices. As discursive-material apparatuses, metrics can be understood as physical arrangements that embody theoretical concepts in matter and material practices (Barad, 2003: 819, 2007: 129). When metrics categorize, calculate, and compare, they theorize the measured phenomenon, as well as actors and other entities involved in the measurement, in specific ways (Dixon-Román, 2016: 163). A metric is thus a materialization of a theory about the measured phenomenon, but a materialization that also co-constitutes this theory. Importantly, the particular theorization articulated by a metric plays a crucial role in the performative effects of that metric. As Brøgger argues, governing technologies (such as metrics) reconfigure reality and thereby ‘do not simply offer different perspectives on a pre-established reality’ (Brøgger, 2018: 357). When a metric theorizes a phenomenon, such as when higher education is theorized through the concept of human capital, the metric also promotes a particular understanding of the world, which may affect actors and other practices. For example, theorizations imply particular responses or actions from

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political actors engaging with the indicators (Merry, 2016: 20, 45). They shape individual and institutional actions and decisions and furthermore discursively-­ materially enable and constrain what it is possible to do and think. It is thus key to understanding the specificities of the theorizations or theoretical concepts embedded in metrics and their embodiment in quantification practices when studying the performative effects of specific metrics, even though the political and epistemological choices involved in quantification are no longer visible when the quantification practices stabilize and when the numbers start to travel into other contexts (Berman & Hirschman, 2018: 260). The theorizations embedded in the Danish graduate outcome metrics draw on and embody different concepts of human capital. The empirically derived human capital concepts refer to different ways of connecting education and labor market. Through these educational-economic relationships, education is framed in relation to different economies in which human capital circulates. The human capital concepts configure the capital of human capital, and thereby the role of higher education in the development of human capital, differently from each other. Some of them furthermore attach the concept of human capital to other theories and ideas. The empirical case of the Danish graduate outcome metrics frames human capital in relation to a globally competitive economy, a public sector economy, a welfare state economy, a labor market economy, and a workplace economy (see Table 4.1). Each graduate outcome metric embodies a concept of human capital that relates to one of these economies. The theorization embedded in a particular metric thus draws meaning from the particular governance contexts (or economies) with which it is entangled and from which it emerges (Madsen, 2022), subsequently affecting educational thinking and governance practices across the university sector. In the following, each of the aforementioned concepts of human capital will be described and analyzed and then discussed in relation to how they ascribe value to higher education.

4.2.1 Education in a Global Economy The first educational-economic relationship is the global economic order of competition between nation states, in which economic growth is the underlying ideal. The capitalist global competition materializes in graduate income metrics. Growth in the annual income of a graduate contributes to growth in the GDP, which is an indicator of the economic activity and progress of a nation, and thus potentially of the wealth and competitive strength of that nation. Graduate income is thus valuable by virtue of its productive role in ensuring the economic position of a nation within a global geopolitical struggle. Education is assumed to enhance the productivity of graduates and thereby increase their income and their contribution to the economy. Importantly, however, in the global economy, the nation is not primarily concerned with the actual balance of the state finances, but rather with the nation’s stock value of human capital and thereby its potential competitiveness (Sellar, 2015). The human capital provided by education is thus a national asset. In other words, graduate income

Formal Labor obligations and qualifications awards

Employee adequacy

The organized and regulated labor market

The workplace

The theorizations are analytically derived

Specific skills + all-round experience

Resources

Collective risk-sharing

The welfare state

Productivity

State-as-­ enterprise

Concept of human capital Productivity

The public sector

Economy The global competition between nations

Educational-­ economic relationship Capitalism The role of education in human capital formation Education enhances the productivity of graduates

Financial return on state Additional years of investments education improve the lifetime earnings of graduates and thereby the state finances Job security and Higher education ensures The graduate self-sustainability unemployment the economic self-­ rate sustainability of graduates Higher education The job match Societal contract of metric individual benefits from qualifications ensure educational investment individual prosperity (employment, higher job status, and adequate salary) Graduate and Profit Education supplies employer surveys companies with demanded skills and with graduates equipped to perform the job Net lifetime income of graduates

Associated Benefits from human metrics capital Graduate income Competitiveness metrics

Table 4.1  Human capital theorizations of education embedded in the Danish graduate unemployment metrics

Supply and demand Over-­ education

Skills

National

National

Local

Attached theories and concepts Level National-­ Wage global formation Supply and demand National Investment

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metrics serve as an indicator of the productivity and thereby competitiveness of the nation, rather than denoting actual income. The quantification of competitiveness, via graduate income, indicates a seemingly limitless potential for economic growth. In the metrics, the variable of graduates’ annual or lifetime income is measured on an infinite scale and includes data values ranging from zero to the highest obtained value in a given data set, unrestricted by any upper limit. It is implied in the scale that a higher income represents a higher productivity. This infinite scale thus implies an ideal of never-ending growth. The income metrics calculate average graduate income across various aggregations, most often programs or areas of studies. Higher education is thus measured in terms of which programs or areas of studies most significantly contribute to greater growth, which is the imperative of the global competitive economy. As already mentioned, the differences between programs or areas of studies in terms of graduate income are explained as differences in graduate productivity. The enhancement of productivity enabled by higher education is thus considered a function of a program’s subject area. This explanation is, for example, found in the report from the Committee on Better University Programs (2018), which is one of the publications that includes a graduate income metric. According to this report, annual income is a product of a combination of three measures: employment frequency, working hours, and hourly pay (Committee on Better University Programs, 2018: 98). Employment frequency and working hours (quantified as ‘official’ or paid working hours (Statistics Denmark, 2019) and thereby excluding any unpaid overtime) indicate the amount of work done by graduates within a subpopulation, while hourly pay indicates the productivity within a ‘work unit’ (1 h). The report measures hourly pay as a wage gain from higher education participation (Committee on Better University Programs, 2018: 105), indicating that a graduate with a 5-year university degree is expected to earn more per hour than a graduate without a university degree (e.g., a graduate from a 2-year business academy program). The logic behind hourly wage as an indicator of productivity is explained using the wage formation theory, as presented, for example, by the Productivity Commission (2014). This theory relies on the assumption that employers pay their employees according to productivity. The employer will not pay the employee any more than they are worth in productivity, but the employer will have to pay that amount to ensure that the employee does not find another job (Produktivitetskommissionen, 2014: 39). Productivity (and wages) also depends on supply and demand. On the one hand, if there is a scarcity of graduates with particular skills, their productivity will in principle be higher because they will be employed in specialized positions where their skills are needed. This scarcity is also reflected in the wages, with employers competing with one another for specialized skills by offering higher salaries. On the other hand, if there is a surplus of graduates with particular skills, they will be employed in less specialized positions and therefore not utilize their skills to the same extent as workers with scarce skills, which in turn means that employers are not willing to pay as much for their work (Produktivitetskommissionen, 2014: 40). Thus, there are two reasons why hourly pay reflects the productivity of the graduate: First, employers are willing to pay a

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premium for particular skills because they increase productivity; and second, employers are willing to compete for certain graduates by offering a higher salary, whereby graduates with particular skills end up in those jobs where the skills have the greatest utility. While these mechanisms are only considered fully functional in private sector jobs, as the public sector is more strictly regulated, they are regarded as important mechanisms of a well-functioning market. This concept of human capital is thus a market-based concept, where value and scarcity are rewarded. In summary, the capitalist concept of human capital configures the capital obtained from education as education’s ability to increase the productivity of graduates. The market value of education is in Denmark considered dependent on area of study, whereby the educational choices of potential students become a matter of national importance. Education that correlates with high productivity and skills that are relatively scarce in terms of labor market demands becomes especially valuable. The programs that ensure graduates a higher income serve as the benchmark for other programs. However, the benchmark is not a limit, since growth in principle is limitless, and thus a continuous raising of the benchmark is ideal.

4.2.2 Education in the State-as-Enterprise Economy The second educational-economic relationship refers to the public sector economy, which materializes in the net lifetime income metric. In this metric, the public costs of education, including both the direct costs of universities and student-grant schemes and the indirect costs of lost taxes during the extra years of education, are subtracted from the lifetime income of graduates in order to show the return on investment of various types of education. The return on investment measure shows the revenue for the state when investing tax money in different kinds or levels of education. The net lifetime income metric thus shows the advantage of prioritizing the funding of education compared to other means of stimulating continuous growth in GDP. In this economy, each additional year of public education is expected to result in an increased graduate contribution to the nation’s economic growth in order to be worthy of public investment. The utility of university education in terms of increased productivity as a result of education should as a minimum exceed the lost productivity of graduates due to less years in the labor market over a lifetime. The economic contribution of graduates justifies the public funding of higher education in this state-as-enterprise economy. The calculation of the return on investment shows that education is considered valuable as a public sector investment rather than as a welfare expenditure and public good. One could argue that this educational-economic relationship reflects neoliberal ideas emerging during the last decades of the twentieth century, in line with analyses on higher education cutbacks across Europe (Landri et al., 2017; Nixon, 2017). In neoliberalism, the welfare state is considered a threat to market forces, which should ideally permeate all aspects of life. State-driven enterprises such as education are met with distrust, as neoclassical economic ideas necessitate an

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understanding of all individuals, including public sector bureaucrats, as self-­ interested utility-maximizing agents (Cahill & Konings, 2017). The neoliberal analysis of public spending thus entails that public sector staff and managers will seek to increase their budgets while concealing the presence of considerable slack (e.g., Migué & Bélanger, 1974). The introduction of neoliberal ideas within public governance in countries such as the UK and the USA has led to comprehensive privatization and austerity programs, including welfare cuts, for the benefit of greater individual autonomy and free-functioning market forces (Cahill & Konings, 2017). Meanwhile, these ideas did not take root in Denmark, where there are no cut-and-dried austerity policies in higher education, many welfare programs still exist, and privatization initiatives have been relatively limited, especially in education. Instead, we can understand the parsimony of the public sector, which has certainly become the norm and which materializes in the net lifetime income metric, as an enactment of a state-as-­ enterprise economy. I do not consider this a neoliberal economy in the case of Denmark, but rather a hybrid of public sector capitalism and a Cold War grid of thinking, where educational investment is understood as a way of optimizing the public sector and ensuring an economic surplus available for investment in welfare. Either way, the old narrative of education as valuable purely as a means of social mobility and as a welfare asset, which appears to be the implicit contrast to the parsimoniously governed education sector of today, has been damaged. In a national context like Denmark, where higher education is still a public sector enterprise, the prioritization of the public sector budget results in public scrutiny of higher education.

4.2.3 Education in the Welfare State Economy While the capitalist and public sector theorizations of education are probably quite familiar for most readers, there are also other economies that entangle with the concept of human capital in the Danish context. One is the welfare state and its redistributive economy, which provides a safety net and reduces inequality among citizens. While some scholars have claimed that the welfare state model has been replaced by a competition state model (Cerny, 2000), also in Denmark (Pedersen, 2011), I maintain that the welfare state still exists – and has perhaps even seen a revival since these works were published – possibly in coexistence with the competition state. At least, I see clear evidence of social-democratic welfare state ideas in the literature on the Nordic model of education. The generalization of wage work and the aim of full employment are core to the Nordic welfare state model that also characterizes Denmark, as these features are foundational for the sharing of economic risks via social security structures (Andersen et al., 2007). The welfare state builds on an ‘individual-state social contract’ of moral duties, obliging citizens, and especially resourceful graduates, to work (Kettunen, 2012: 24; 31). This sense of

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moral duty is alive and well in the Danish public discourse regarding unemployed university graduates. The welfare state economy materializes in the graduate unemployment rate, reenacting a collective risk-sharing model according to which university graduates should contribute to rather than require support from the redistributive economy. A low unemployment rate is important for the redistributive economy, seen from a welfare state perspective, as it ensures an improved balance in the redistribution of wealth implicit in universal welfare states (Esping-Andersen, 2013; Kettunen, 2012). With a lower unemployment rate, a larger proportion of the taxpayers contribute to the welfare state, while a smaller proportion are supported by social benefits. In the economic structure of the welfare state, indicated by the expenses and incomes of social benefits and taxes on the state budget, job security is a major priority. Human capital is conceptualized as resources that should be exchanged for a sufficient level of income to sustain the employees economically and enable them to contribute to the welfare state by paying taxes. The welfare state economy is directly quantified in the graduate unemployment rate, as this draws on data on the number of graduates applying for unemployment benefits and thereby directly measures whether or not graduates are financially self-­ sufficient. The welfare state is thus interested in the capacity of graduates to support themselves rather than their wealth, and thereby materializes in a metric that measures higher education’s ability to ensure graduate employment. The graduate unemployment rate furthermore builds on quantification practices that embody a different logic of improvement than graduate income metrics. In contrast to the latter, the graduate unemployment rate measures unemployment on a scale from 0% to 100%, where 0 represents the optimal value on the scale. This means that the absence of unemployment – in other words, full employment – is the lower limit of the scale, and that any rate above zero in principle indicates a flaw in the utility of the measured educational unit (e.g., a particular program, area of studies, or provider), even though, in practice, a degree of transitional or frictional unemployment is always expected. The graduate unemployment metric thus includes an absolute target of zero percent, representing the optimal match between higher education and the labor market, and thereby higher education playing a healthy role in the welfare state. While the welfare state logic is different from the capitalist logic of competition between nations, they share the overall conceptualization of human capital obtained from education as dependent on educational choice. Both the graduate unemployment rate and the annual graduate income metric frame a graduate’s human capital as a product of their degree, thus configuring the degree as a unity. As the data are often aggregated into clusters of related programs or broad areas of studies (the humanities, social sciences, natural sciences, technical sciences, and health sciences), the human capital of graduates seems to be constituted by the area of studies in which they have completed their degree. The framing of educational human capital as constituted by disciplines or broader areas of study perceives programs as essentially constant regardless of their specific design. In this configuration, a philosopher is a philosopher, and an engineer is an engineer.

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Furthermore, like the capitalist concept of human capital, the welfare state concept of human capital draws on the theory of supply and demand, albeit in a more structural than market-based version. When supply and demand are conceptualized in relation to the welfare state, unemployment indicates a mismatch between the supply (the number of graduates available) and demand (the need for graduate labor among employers) (Committee on Quality and Relevance in Higher Education, 2014b; also see the analysis by Wieling and Borghans, 2001, for an example). A relatively high graduate unemployment rate for a particular area of studies or a program indicates an oversupply of graduates from that area of studies or program (Teichler, 2007: 15). If the supply of graduates with this disciplinary profile falls or the demand increases, the unemployment rate will be reduced. The respective capital of philosophy and engineering graduates is determined by the need for philosophers and engineers, rather than by the specific skills of these philosophers and engineers. The welfare state economy ascribes education the role of preparing graduates for specific jobs, much like the relation between education and occupation in professions where educational programs qualify graduates for entry, such as law, medicine, or theology. From a welfare state perspective, unemployment thus emerges as a structural problem of oversupply within certain areas of studies that can be resolved through better planning and coordination. The concept of human capital implied in the welfare state economy is determined by the scarcity or abundance of graduates with broadly the same disciplinary background, and the human capital of students can thus be improved by enrolling in programs representing a disciplinary background that is in high demand within the labor market or where there is very little competition. If the demand for graduates from a given area of studies equals or exceeds the supply of graduates, this area of studies provides sufficient human capital for a graduate to obtain job security. Hereby, this concept of human capital contains the possibility of an optimal match of supply and demand for the benefit of state finances in the context of the welfare state, and it calls for continuous optimization and coordination of the graduate supply in response to the demand at a given time.

4.2.4 Education in the Organized and Regulated Labor Market Economy A fourth educational-economic relationship links higher education utility to the relation between the graduate as a worker and the labor market. The economy of this relationship is the relatively structured and regulated labor market characterizing the Danish context, in which qualifications both give access to specific jobs and trigger specific contractual rights such as titles, salaries, and work conditions. The concept of qualification has a long history (Illeris, 2009), but in Danish labor market discourse, partly due to the highly unionized and regulated history of the Danish labor market, qualification has become a formal concept denoting the prerequisites

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a candidate possesses for a specific job, including educational background and seniority, measured as educational level and number of years of relevant work experience. While the concept of qualifications is strongly linked to professions with monopolized access dependent on educational certificates (Larson, 2018), it has spread to form a general qualification-occupation nexus with the expectation of a progressive relationship between qualification levels and corresponding job functions, status, and salary. The progressive and hierarchical concept of qualifications, which is apparent in schematic and hierarchical systems such as qualification frameworks (e.g., Ministry of Higher Education and Science, 2008) and the ISCED and DISCO classifications (Gorur, 2018; Statistics Denmark, 2011), constitutes a different concept of human capital than those outlined above, which respectively emphasize the graduate’s productivity and resources. By contrast, the qualification conceptualization of human capital does not consider a graduate’s capital to be a product of disciplinary background, but rather the academic level of their degree. This conceptualization is in line with a traditional understanding of human capital as a matter of the educational level of the population (Gorur, 2018: 103; Henry et al., 2001: 99), as described in the introduction to this chapter. The economy of qualifications in the labor market is embodied in the job match metric, which measures the relative position of university graduates to other employees. The quantification practices of the job match metric and the DISCO classifications upon which it draws (see Chap. 3) configure occupations as fixed categories representing a particular skill level. A mismatch between academic level and job status or salary indicates over-education, where people have educated themselves more than is necessary to fulfill the requirements of their occupation (see Barone and Ortiz (2011) for a European cross-national analysis). In this educational-­ economic relationship, a lack of correspondence between qualifications and occupations represents a fundamental problem of a skewed education system, where formal qualifications, defined by years of education, do not actually qualify graduates for corresponding jobs. When the occupations of university graduates with 5 years of higher education do not offer a higher status or salary than the occupations of graduates with a professional bachelor’s degree and 3–4  years of higher education, or of business college graduates with 2 years of higher education, the university sector has failed to deliver on its promise. The opinion that the Danish universities have failed some of their graduates, who should be entitled to expect a particular job status and salary level in the labor market, was expressed by the Minister of Higher Education and Science at the launch of a policy initiative in relation to the graduate outcome metrics, which will be introduced in the next chapter: A number of programs deliver more graduates than can be found relevant jobs. Unfortunately, that means that far too many face the following brutal message when they enter the labor market: Sorry, we don’t need the knowledge and the skills that you have worked so hard to achieve. That’s not good enough [translated from: Det kan vi ikke være bekendt]. …

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This is not just about money and numbers. It is about young people who embark on their future life with a huge, crushing defeat. It is about dreams that are shattered before they even begin. And it is about people whose degrees do not give them the opportunities that they had expected – and may even have been promised. (S. C. Nielsen, 2014, my translation)

As the Minister writes, there are moral obligations associated with young people’s participation in higher education, indicated by the insufficiency of educating young people without being able to fulfill their legitimate expectations of a corresponding work life. Students are right to expect an increase in opportunities based on their educational level. And as the rest of the passage from the minister’s media statement states, the universities should take the labor market into account when admitting students, and they therefore carry a degree of moral responsibility for failing young people who cannot find a suitable job following graduation. A key word in the quote from the minister is relevant jobs. The theorization of education embedded in the economy of the unionized, regulated, and structured labor market exceeds the market-based concept of human capital as a productivity booster that can be capitalized in the labor market. The Danish labor market offers a more structured relationship between education and labor market. The job match measure furthermore differs from the graduate employment statistics in the sense that mere employment is not enough to constitute sufficient human capital. Employment aligning level of qualifications with type of occupation is required.

4.2.5 Education in the Economy of the Workplace While the economies presented so far are organized at the global and national levels, the final economy is local: the economy of the workplace. Here, companies hire employees based on the skills they are expected to possess, whereby universities become providers of skills and responsible for delivering the skills needed by employers. Businesses require the right amount and type of skills to increase their profit and thereby contribute to the national economy. The workplace economy is thus closely linked to the nation’s performance in the competitive global economy and based on the model of the private sector workplace. As individual workplaces and their skill needs are difficult to measure statistically and with administrative data, the economy of the workplace is entangled with a narrative concerning employee adequacy, surveying how well-prepared graduates see themselves as being for a specific job. As already described in Chap. 3, this narrative draws on the legitimacy of the graduates and employers actually living the link between education and labor market in the context of a specific workplace. The narrative can be understood as embedded in an affective economy of experiences of graduate adequacy in their encounters with the labor market, including associated affects such as confidence, success, satisfaction, acceptance, doubt, failure, embarrassment, and/or rejection, in the perspective of the graduate employee or the employer responding to the survey.

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The educational-economic relationship of workplaces thus builds on a concept of skills. The skills-based concept of human capital materializes in both graduate and employer surveys. Both surveys include questions on the match between skills acquired in education and skills required by employers. The universities are in other words held accountable for the quality of their graduates, both by graduates themselves and by their employers. The responsibility of universities thus encompasses a responsibility towards employers in terms of delivering the right skills as well as a responsibility towards graduates in terms of equipping them with the skills that enable them to fulfill their employers’ expectations. When graduates and employers are asked to estimate the importance of a particular skill in the graduates’ degree program and the need for that skill in the workplace, and perhaps to compare the two, it expresses the expectation of a close alignment between educational content and job requirements. The more programs are aligned with labor market needs, the better. In the national graduate survey, conducted by the Ministry of Higher Education and Science, this alignment is measured via an overall assessment by the respondents. However, in some of the graduate surveys conducted by universities, the assessment is more specifically related to each individual skill. These surveys ask, for example, if the skills acquired during a program have proven redundant in the workplace (Roskilde Universitet, 2013), or to what extent a specific skill has been obtained during education and to what extent this skill is relevant within the labor market, thereby enabling an immediate visual comparison of the alignment of education and labor market needs (DAMVAD Analytics, 2016). In a similar vein, the employer survey asks employers questions about which skills they require of employees and to what extent the university’s graduates possess these skills (DAMVAD Analytics, 2016) (Fig. 4.1). In contrast to the ideas of educational capital as constituted by the disciplinary field or academic level of the degree, the skills-based concept of human capital indicates that educational capital is made up of separate and distinguishable FIGUR 5.3 Generelle kompetencer, evnen till at anvende teknish-videnskabelige teorier og metoder Uddannelse Arbejde

53%

38%

40%

I h Øj grad

35%

I nogen grad

I mindre grad

Slet ikke

7% 20%

3%

Ved ikke

Kilde: DAMVAD Analytics 2015 Note: N=1.361. Begrebet “Uddannelse” i venstre kolonne dækker over, om dimittenderne har opnáet kompetencen gennem deres uddannelse, mens begrebet “Arbejde” beskriver, om denne specifikke kompetence er relevant pá arbejdsmarkedet.

Fig. 4.1  Survey data copied from DAMVAD Analytics (2016), p.  37. The upper bar shows whether the graduate has obtained the skill during education, while the lower bar shows whether the skill is relevant in the labor market. The response categories are (from left to right) ‘to a large extent’, ‘to some extent’, ‘to a lesser extent’, ‘not at all’, and ‘don’t know’. The skill displayed in this figure is ‘General skills, the ability to apply technical-scientific theories and methods’

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elements. This idea of human capital as skills implies that skills can be acquired independently of each other and combined in various ways, perhaps based on strategic considerations regarding the value of different skills for a specific labor market context and/or at a specific time and place in history. In relation to the surveys, the idea of separate elements materializes in the response categories, taking the form of a list of skills. For example, the skills included in a Roskilde University survey ranged from IT skills and foreign language skills, over theoretical knowledge within one’s area of study and oral dissemination skills, to the ability to collaborate across disciplines and the ability to organize one’s work and meet deadlines (Roskilde Universitet, 2013). This list of skills encompasses what may be termed academic and professional skills as well as personality traits and personal capabilities and is relatively common across most graduate surveys. The consultancy agency that developed and conducted the survey for the Technical University of Denmark (DTU) adopted a more theoretically informed concept of skills, divided into core disciplinary skills, generic engineering skills, and personal skills. The generic engineering skills category was further divided into eight skills, such as the ability to use analysis and modeling to develop relevant models, systems, and/or processes to solve technological problems, while the personal skills category was further divided into six skills, such as the ability to orally disseminate achieved results in a structured and clear way (DAMVAD Analytics, 2016). The surveys operationalize the skills obtained as part of a university degree program in different ways, partly because the operationalization can become more specific at a mono-faculty university like DTU.  Nevertheless, the surveys all adhere to the human capital idea of individual skills as a form of capital that can attain greater or lesser value in the labor market. With the framing of educational human capital as separable skills, the skills become entities that can be combined in various ways in the design of a higher education program. In the Danish graduate surveys, particularly the national graduate survey and surveys at multi-faculty universities, the skills that are included as items or response categories are the same for respondents across all fields of higher education. Thus, the generic skills included can in principle become relevant in relation to all areas of study, no matter the disciplinary content. These skills fulfill contemporary ideas of valuable skills as flexible, lifelong learning skills, such as project management and IT skills, rather than discipline- and profession-specific skills. Skills are hereby configured not as related to a particular discipline, but rather as a common pool of components that can be assembled in various ways, regardless of the degree program (in line with Committee on Quality and Relevance in Higher Education, 2014a: 29–30, where it is argued that the labor market needs more generalists and fewer specialists). For example, only 2 of the 23 skills included in the Roskilde University graduate survey (2013) concern the area of study, namely, theoretical knowledge within one’s area of study and general methodological skills within one’s area of study. The generic nature of most of the included skills indicates that programs mainly differ in terms of a distinct composition of skills drawn from a common pool and only minimally in terms of their distinct knowledge and methodologies. The skills taught by a program are interchangeable.

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While the already introduced educational-economic relationships mainly configure the value of the human capital produced by education as dependent on the choice of a particular program, the economy of the workplace configures the content of higher education degrees as adjustable and constantly open to improvement; as such, the value is not predetermined by the discipline. This configuration calls for the ongoing development of the educational content of programs of every kind in order to provide graduates with the skills that are in demand at a given time, indicated by aggregations of the specific experiences of graduates and employers.

4.3 Economic Valuations of Higher Education Across all the described educational-economic relationships, the general foundation for and performative achievement of the metrics is a theorization of higher education as valuable in terms of its provision of capital to individuals and societies. The metrics measure and express to what extent different educational units (programs, areas of studies, and educational providers) contribute to the production of human capital, thereby enabling comparisons according to the abstraction of the relevance of higher education. Since these metrics, as well as the quantified, objective, and unambiguous information they produce, are discursively dominant in public debates, the metrics become valuation devices, offering persuasive problematizations of higher education. Metrics thus play an important role in valuation processes concerning higher education. The notion of valuation is a concept drawn from valuation studies. It involves a ‘shift in subject matter from value (or values) to valuation, considered explicitly as an action’ or ‘some sort of performance’ (Muniesa, 2012: 25–26). This shift implies that the value of an object or the values of a person cannot be understood as an essential, inherent, and fixed characteristic of the object or person. Rather, value and values are constantly enacted through ongoing iterative practices and, through these practices, attributed to the object or person (Muniesa, 2012: 32). The shift implies a redirection of analysis towards the processes of valuation rather than the determination of something’s value or someone’s values. These processes can involve ‘valuation devices’ such as calculation models, professional evaluators, or evaluation or measurement procedures (Asdal, 2015; Muniesa, 2012: 33). As valuation devices, the graduate outcome metrics build on economic values. Graduate outcome metrics, which are not unique to the Danish context, largely valuate higher education in terms of labor market demand (Boden & Nedeva, 2010: 46). Meanwhile, analysis of the educational-economic relationships referred to by the Danish graduate outcome metrics implies that the metrics valuate higher education based on different configurations of education and different configurations of what constitutes the capital provided by higher education. Starting with the job match metric, the capital provided by higher education is here constituted by the level of qualifications and thus the number of years of education. The different quantitative values of how the acquired qualifications are correctly exchanged for

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access to a corresponding occupation indicate that not all areas of studies live up to the societal promise of rewarding educational qualifications with an appropriate job. In turn, both the graduate income and graduate unemployment metrics, which respectively conceptualize human capital as productivity and resources, configure the human capital of graduates as constituted by their areas of studies or degree programs—or in other words, the disciplinary field of their degree in its entirety. The discipline is defined holistically by a particular set of skills that constitutes the discipline and corresponding profession. Finally, the graduate and employer surveys configure the capital of graduates as separate, distinguishable, and often generic skills that can be combined in various ways as part of a higher education program. This configuration of education is thus more fluid than the disciplinary configuration, as programs can be redesigned to enhance the human capital of graduates. These different configurations show that education is both cardinally and nominally valuated (Fourcade, 2016). First, degrees are considered different in quantity, as reflected by the concept of qualifications and levels of education: The higher the level of a degree, the better. Second, degrees are considered different in kind, as reflected by the areas of studies and programs representing different disciplinary content. And third, skills are both considered different in quantity and kind, but in an entangled way where the combination of taught skills constituting a program can be adjusted both in terms of their volume and in terms of adding or subtracting particular skills. Each of these three configurations of human capital represents a particular concept of ‘relevance’. Historically, human capital policy has changed according to these lines, from previously being primarily concerned with boosting the overall educational level of the general population to a more recent strategic concern with boosting specific kinds of skills or disciplines at the expense of others (Henry et  al., 2001). This development is also apparent in Danish education policy, where previous targets of increasing the educational level of the population and reducing the proportion of a youth cohort leaving school without upper secondary or vocational qualifications have been replaced by graduate outcome metrics that measure education in terms of specific skills and areas of studies. This reflects a degree of skepticism among policymakers regarding the value of continuing to increase educational levels within the population without focusing on the skills profile of graduates. The limited use of the job match metric, which conceptualizes human capital in terms of graduate qualification levels, perhaps reflects this historical development. This development and the particular interest in ensuring that Danish students obtain the right kinds of degrees and skills imply a specific contemporary modus of valuation of higher education. As the valuation devices configuring human capital according to disciplinary fields are influential in both educational governance and the broader public debate on the relevance of higher education, they contribute to a vertical valuation of subject matter that is characterized by a condemnation of the humanities in particular and a glorification of STEM programs, especially in the fields of computer science and engineering. According to the graduate income and graduate unemployment metrics, disciplines must be in demand within the labor

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market to be considered valuable, and the humanities appear to be less in demand than other areas of studies, according to the parameters set up by the metrics, i.e., high graduate income and full employment. In addition, to be considered valuable, it must be possible to capitalize the skills of graduates in the economy of workplaces. Even when actors who identify with study areas considered less valuable attempt to restore value, such as when academics within the humanities argue for the societal value of cultural knowledge and understanding, these arguments are often presented in economic terms through claims regarding the capital value of humanist knowledge and skills (Madsen, 2015). For example, the humanist businessman Jon Kyst wrote the following in the Danish newspaper focusing on financial and business news Børsen in 2014: I believe that the only thing that can counter this suspicion [that the humanities do not provide sufficient income and that the programs can be considered a form of financial subsidy for the cultural and creative sectors] is specific examples of the opportunities that these programs can provide within the labor market. According to my annual tax assessment, I earned 1,096,531 DKK in 2013. My preliminary income assessment for 2014 tells me that this year I will accumulate 1.2 mill. DKK in personal income. I studied Russian and English at the Faculty of the Humanities at the University of Copenhagen and have always used my degree. I have never been unemployed or received any social benefits. I have twice been part of companies awarded Børsens Gazellepris [an award given to companies displaying significant growth]. In the space of six years, a fellow student in Russian and I have created a company with a revenue of more than 20 mill. DKK without any support from investors or counseling from people with a business degree. (Kyst, 2014)

The number of references to economic matters in this excerpt indicates that the valuation of education, including the humanities, is dominated by economic values, even when argued by actors from within the humanities themselves. The value of the author’s degree in Russian and English is documented in terms of how it has enabled the generation of income and prevented unemployment. The valuation of higher education according to disciplinary content and skills, as I have demonstrated in both this and the previous chapter, appears to represent one among several possible modes of valuation. It furthermore rests on a debatable theory on the connection between higher education and the labor market. Human capital theory and its overall concept of education as a productivity-enhancing enterprise is not the only possible way of explaining this connection. In sociology, we find the theoretical position known as signaling theory, which emphasizes how education should be understood as a positional good rather than as a productivity-enhancing enterprise in itself. This theory understands educational credentials as a signal of trainability that shows employers how easily the graduate can be trained for the specific job (Becker, 1993: 19; Bol, 2013: 23–24; Tomlinson, 2018), or, as suggested by Brown and Souto-Otero (2020), merely a signal of job readiness used by employers to screen candidates. Here, education is not attributed the role of immediately enhancing the productivity of the graduate through the provision of specific knowledge and skills. Education is instead seen as enhancing the relative position and thereby value of the graduate in comparison to other job candidates, and this value can be exchanged for a position in the labor market, from which the graduate can obtain the knowledge, skills, and values that are needed for a particular job. The

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relation between education and labor market value thus lies in the signal or reputation of the degree, rather than in its actual content. With the idea of education as a positional good in itself (Marginson, 2006), rather than as a provider of human capital, the low-income and high unemployment numbers characterizing humanities graduates can be understood differently, as a matter of the reputation of this area of studies. This theorization of the relation between education and the labor market enables an understanding of graduate outcome metrics as not only representing the differences in graduate outcomes across different disciplinary areas, but also themselves actively enhancing these differences by contributing to the tarnished reputation of the humanities. This theory also better explains the valuations of higher education according to providers in many countries, including Denmark where such valuations are however of less importance than the disciplinary valuation. The value added to the graduate by the educational provider can more meaningfully be ascribed to the better reputation of some (elite) universities compared to others than to a consistently greater enhancement of graduate productivity provided by these universities. The human capital narrative that characterizes Danish higher education policy, and the specific theorizations of education upon which it draws, may thus enact an illusory understanding of the current valuation devices as fair and objective, but may also be understood as highly politicized and even damaging for some areas of studies.

4.4 Graduate Outcome Metrics and Human Capital Theory The analysis of graduate outcome metrics has demonstrated that while human capital theory dominates educational thinking across the world, it has been transformed and dispersed over time, becoming entangled with specific quantification practices, educational and economic ideas, and economies of education. As the basic mechanics of how education provides students with human capital are open to interpretation, the nature of the relevance of higher education remains contested and subject to different policy and management initiatives. The educational theorizing enabled by human capital theory is thus not completely homogeneous, but allows for several orientations that represent theories, values, and ideas embodied in different quantification practices. Importantly, the five economies with which human capital theory is entangled are themselves entangled and interdependent. In practice, the welfare state not only depends on job security, but also income, as the income from employment determines the state’s revenue from income taxes. The competitive nation, in turn, depends on the welfare state to maintain the productivity of the workforce, even in periods of frictional unemployment or economic crisis. Furthermore, while the public sector is a system of its own, public sector priorities are aligned with the priorities of the capitalist competitive nation and the welfare state. Finally, improvements in graduate satisfaction with programs might reflect improvements in graduate

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productivity and job security. These five configurations of human capital theory are thus distinguished by different logics or rationales rather than representing distinct parts of society. These logics play an important role in shaping governance and management practices, including quantification practices. This chapter’s primary contribution to human capital theory is its analysis of how human capital appears to be conceptualized in various ways in relation to education. Particularly, the difference between capital constituted as the level of a degree, the discipline of a degree, and the composition of skills provided throughout the degree has generally been overlooked in the literature on human capital theory. This literature generally frames human capital as the knowledge, skills, and in some cases personality traits possessed by a population or individual (Higdon, 2016; Sellar & Zipin, 2019; Tomlinson, 2010). Skills in particular have been central to this concept of human capital, as they appear as ‘manifestations of human behavior in manageable and transferable components’ (Simons, 2007: 444, my emphasis). However, the analysis has shown that especially the configuration of capital as constituted by the supply and demand of graduates from specific disciplines dominates how educational relevance is perceived at the macro-economic level within Danish higher education policy. These two different ways of quantifying the human capital obtained in education are to some extent contradictory and certainly entail very different governance practices and different directions for educational thinking and development. The political and public dominance of the disciplinary configuration implies that especially the humanities are highlighted as having low relevance in the Danish context. The valuation of higher education plays out differently in other contexts, as the produced differences are structured differently. Metrics in the UK and Australia, for instance, enable a problematization of certain higher education providers and their ability to provide their graduates with sufficient human capital to prosper (HESA, 2020; QILT, 2019). The US problematization of what are referred to as academic fields of studies (NCES, 2021), in turn, partly resembles the Danish problematization of disciplines with no clear link to particular professions and thereby not offering graduates a smooth transition into the labor market. Quantification practices like these document problems of graduate unemployment and income inequality, but may also reproduce and amplify these problems. The analysis of the different concepts of human capital has furthermore shown how these problematizations are linked to different economies. In order to tease out these problem definitions, there is a need for sensitivity regarding the logics at play in a particular governance context. Human capital is typically considered part of a capitalist world, where education contributes to the enhancement of productivity in combination with businesses who employ graduates with particular skills in order to increase profits. However, in the Danish context, the strong public sector, the universal welfare state, and the tradition of a highly regulated and unionized labor market also have to be taken into consideration when considering the evolution of the concept of human capital in higher education policy. This finding might inspire similar studies in other contexts. The public sector configuration can be relevant in education systems where the providers are mainly or exclusively public (like the

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Danish case). The welfare state perspective might be relevant in universal welfare states (or welfare states that build on this model, even if it may be eroding) and may play out differently in other types of welfare states. Likewise, the labor market perspective might be relevant in countries with similarly regulated and structured labor markets. With these elaborations of human capital theory in mind, as it plays out as educational-economic relationships in a specific governance context, the graduate outcome-based valuation and problematization of higher education, including the condemnation of the humanities and the glorification of STEM programs, are shown to rest on ideas that are not universal and value-neutral, but politically and historically constituted by dominant societal and state traditions.

4.5 Closing Part II In the introduction to Chap. 3, at the start of Part II on quantification practices, I stated that graduate outcome metrics intertwine with neoliberal and human capital ideas: The former suggest that the market for higher education graduates produces the most reliable assessment of the value of higher education, while the latter constitute the substance of this assessment. While I have added empirical substance to this statement throughout the two chapters in this part of the book, I have also modified it in two ways. First, I have shown that the market alone is not a reliable source of knowledge regarding the value of higher education; the reliability of the knowledge produced by graduate outcome metrics also relies on techno-scientific ideas concerning how to attain objective and unambiguous knowledge. Second, I have shown how human capital theory is enacted in several different ways by the Danish graduate outcome metrics, some of which draw the idea of human capital into welfare state and regulated labor market thinking, emphasizing the population as a collective and the graduates as members of this population entitled to particular kinds of jobs. These modifications of the theory transgress the maxim of education as an individual responsibility found in many other contexts, and partly replace it with moralities of responsibility towards, and rights within, the state. Moving forward from this conclusion, there appear to be many additional aspects that caution against seeing the graduate outcome metrics as evidence of neoliberal thinking permeating Danish higher education policy. Despite the fact that theories and concepts like supply and demand, return on investment, and skills are typically associated to the market, these phenomena appear to serve a different purpose than a free market in the Danish case. Instead, they serve to optimize the public sector to match the collective pool of jobs required for the economy by ensuring a suitable supply of graduates, sensible investment of public funds, and skills that ensure an adequate labor force. This purpose aligns with the techno-scientific paradigm. Importantly, this optimization is a goal pursued by the state – rather than something that is left to the labor market. Thus, the use of data reflecting market preferences, as seen in the graduate outcome metrics, becomes a vehicle for another agenda than neoliberalism within higher education.

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Throughout the two chapters included in Part II on quantification practices, I have drawn on a range of methodological and analytical concepts and approaches, including the concepts of proxy, objectivity and unambiguity as achievements, ordinal versus nominal judgment, commensuration, and valuation. All these analytical concepts emphasize in various ways how things are related to each other through quantification. Together, they enabled an analysis of the Danish higher education landscape in comparison to the landscapes found in other countries. Meanwhile, I added the more substance-oriented analytical concept of educational theorization in this chapter in order to also grasp the relations between phenomena in the world (such as education, economy, and society) as they are understood and reproduced in quantification practices. With these analytical approaches, I have come closer to the numbers or ‘data’ used in the context of my case study than most other studies on educational data. Based on this foundation, I will now move on to Part III where I analyze governing practices using educational data without glossing over these data. The specificities of the quantification practices embedded in the graduate outcome metrics will continue to play a major part in the story I tell in this book, as they affect both governing practices, educational subjectivities, and, not least, the economic conditions framing Danish higher education programs. However, while no metrics are left behind, the book will from now on primarily focus on telling a story about the graduate unemployment metrics, which dominate governance practices in Danish higher education. Part III will show how.

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Produktivitetskommissionen. (2014). Analyserapport 4. Uddannelse og innovation. Retrieved from Copenhagen: http://produktivitetskommissionen.dk/media/162592/Analyserapport%20 4,%20Uddannelse%20og%20innovation_revideret.pdf QILT. (2019). 2018 Graduate Outcomes Survey. National Report. Retrieved from https://www. qilt.edu.au/docs/default-­s ource/default-­d ocument-­l ibrary/qilt-­g os-­n ational-­r eport-­2 018. pdf?sfvrsn=23f7ec3c_0 Rinne, R. (2021). The Nordic social democratic regime in education colliding with the global neo-liberal regime. In J.  B. Krejsler & L.  Moos (Eds.), What works in Nordic school policies? Mapping approaches to evidence, social technologies and transnational influences (pp. 153–172). Springer. Roskilde Universitet. (2013). MASTERsurvey 2012 [KANDIDATundersøgelsen 2012]. Retrieved from http://www.e-­pages.dk/roskildeuniversitet/195/html5/ Sellar, S. (2015). A strange craving to be motivated: Schizoanalysis, human capital and education. Deleuze Studies, 9(3), 424. Sellar, S., & Zipin, L. (2019). Conjuring optimism in dark times: Education, affect and human capital. Educational Philosophy and Theory, 51(6), 572–586. https://doi.org/10.1080/0013185 7.2018.1485566 Simons, M. (2007). The ‘renaissance of the university’ in the European knowledge society: An exploration of principled and governmental approaches. An International Journal, 26(5), 433–447. https://doi.org/10.1007/s11217-­007-­9054-­2 Sorensen, T. B., & Robertson, S. L. (2020). Ordinalization and the OECD’s governance of teachers. Comparative Education Review, 64(1), 21–45. https://doi.org/10.1086/706758 Statistics Denmark. (2011). DSCO-08  in the employment statistics (2nd ed.) [DISCO-08 i lønstatistikken. 2. udgave]. Retrieved from https://www.dst.dk/da/Statistik/dokumentation/ nomenklaturer/disco-­08-­i-­loenstatistikken Statistics Denmark. (2019). Concepts in the work hours calculation (ATR) [Begreber i arbejdstidsregnskabet (ATR)]. Retrieved from https://www.dst.dk/da/Statistik/emner/ arbejde-­indkomst-­og-­formue/beskaeftigelse/arbejdstidsregnskab Teichler, U. (2007). Does higher education matter? Lessons from a comparative graduate survey. European Journal of Education, 42(1), 11–34. https://doi.org/10.1111/j.1465-­3435.2007.00287.x Tomlinson, M. (2010). Investing in the self: Structure, agency and identity in graduates’ employability. Education, Knowledge & Economy, 4(2), 73–88. Tomlinson, M. (2018). Employers and universities: Conceptual dimensions, research evidence and implications. Higher Education Policy. https://doi.org/10.1057/s41307-­018-­0121-­9 Tymon, A. (2013). The student perspective on employability. Studies in Higher Education, 38(6), 841–856. https://doi.org/10.1080/03075079.2011.604408 Wieling, M., & Borghans, L. (2001). Discrepancies between supply and demand and adjustment processes in the labour market. Labour, 15(1), 33–56. https://doi.org/10.1111/1467-­9914.00154 World Economic Forum. (2016). The future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution. Retrieved from http://reports.weforum.org/future-­of-­jobs-­2016/ preface/ Young, M.  D. (1996). Introduction to the transaction edition. In The rise of the meritocracy (2 printing ed., pp. xi–xvii). Transaction Publishers.

Part III

Governance Practices: Indicators, Hierarchical Pressures, and Temporal-­Affective Effects

Chapter 5

Calculative Governance Instruments

Educational data or indicators are influential because of their role in educational governance and management practices. Metrics and the information they produce have become an ingrained part of a wide variety of governance practices, including performance management, policymaking, and quality assurance, and play a pivotal role in resource allocation, economic incentives, and nudging instruments. In and beyond educational research, these types of governance practices have been termed ‘governing by numbers’ (Grek, 2009; Piattoeva & Boden, 2020; Rose, 1991). Several educational scholars have shown an interest in how governing by numbers affects both policy and governance. Most of these studies focus on the OECD’s establishment of a global educational landscape of comparable data through measurements such as PISA and TIMMS. For example, educational scholars study different political and public perceptions of and reactions to PISA results (Grek, 2009), the ‘socialization’ of education systems according to the OECD assessments (Grek, 2017), and how policy reforms in different school systems are affected by international and national testing and accountability regimes (Hopmann, 2008; Krejsler, 2018; Lingard et al., 2013, 2016). In general, these studies are designed to study the relations and interactions between transnational institutions, such as the OECD and the EU, and national school systems. They thus emphasize the transnational impact on national education policy and the dominant narrative of education as a means of obtaining a position of strength in the global knowledge economy. In addition, higher education literature on educational governance has shown an interest in quantification practices, in particular in the form of rankings, but also in performance indicators more broadly. Here, quantification is often associated with marketization (Blackmore, 2009; Robertson, 2017) and competition (Naidoo, 2018). The overall narrative in this literature frames numerical comparisons as a technology encouraging competition among educational providers in an organizational field, country, or global educational market. Educational providers are incited to compete for status and popularity among various stakeholders (i.e., potential © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_5

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students, potential staff, and funding agencies) and thereby for the resources that these stakeholders bring to the university (i.e., enrollment, contracting, and grants). As a result, numerical comparisons not only express the relative positions of educational providers according to the scale of a metric but also reinforce the upward or downward movement of providers by directly affecting their status and popularity and indirectly affecting their resources and thus their ability to improve their performance and position on the scale (Espeland & Sauder, 2016). The amplification of quantification practices in education thus appears as a sign of neoliberal marketization and capitalization trends in higher education governance. Meanwhile, the dominance of the transnational narrative in education studies and the marketization narrative in higher education studies conceals the range of governance practices in which educational metrics play a role and furthermore dampens interest in the differences between these governance practices and their effects on educational practices. While some of the most studied governance practices involving metrics in the field of education are brought about by transnational organizations such as the OECD, other actors such as national governments, public agencies, and universities themselves also engage in governance and management involving metrics. Furthermore, various actors outside the national state and public sector hierarchies, such as private corporations or media, may also install educational governance by publishing rankings or other quantified information that has governing effects (Espeland & Sauder, 2016). These governance practices may be confined to specific national or institutional settings, or they may span various spatial contexts in a globalized world. They may entail marketization, but this is not always the case. There is thus a need for a more detailed theorization of the role of metrics in educational governance. In addition to the quantification practices and educational theorizations analyzed in Chaps. 3 and 4, quantification-based governance and management practices also have implications for the characteristics of the numerical information produced by the metrics applied. The third part of this book focuses on the governing capacities of number. Part III begins with this chapter, which outlines four overall types of instruments in which the calculative practices of the Danish graduate outcome metrics play an important role, characterizing these instruments, their mechanisms, and the functions of indicators or data. Through this outline, the chapter assembles knowledge on quantification-based governance instruments and thereby offers an overview in the shape of a tentative instrument typology. As the chapter shows, the instruments deployed in relation to the studied case mostly differ from those kinds of new public management instruments that draw on neoliberal thinking. However, in order to understand the difference between neoliberal instruments that instill competition among higher education actors and the majority of the Danish instruments, Chap. 6 will return to the material and relational properties of numbers to reveal their governing capacities. The analysis in Chap. 6 thus supplements and refines the analyses of governance instruments presented in this chapter.

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5.1 Public Policy Instruments and Graduate Outcome Metrics As already indicated, one way of understanding how metrics operate when they are incorporated in governance practices is to turn to the instruments of governing. While a range of such instruments have been labelled and categorized by political science scholars, for example, in the literature on new public management and beyond (Dunleavy et  al., 2006; Pollitt & Bouckaert, 2011), the literature on the specific character of calculative governance instruments is dispersed and has mainly described the historical development of statistics in governance (Desrosières, 1998; Porter, 1996; Scott, 1998), the role of numbers in political processes and democracy (Rose, 1991, 1999), and the social effects of introducing calculative practices in management (Miller & Power, 2013; Power, 1999), including in higher education (Shore & Wright, 2015; Strathern, 2000). An analysis of the role played by metrics in relation to specific instruments of governing will contribute to this literature by exploring the different ways metrics operate in different types of instruments. In 2007, Pierre Lascoumes and Patrick Le Gales introduced what they called the instrumentation approach to the study of public policy. With this approach, they argued that policy is not only defined by its content, and thereby the articulated intentions behind the policy, but also by its instrumentation. In their understanding, public policy instruments embody a particular ‘theorization of the relationship between the governing and the governed: every instrument constitutes a condensed form of knowledge about social control and ways of exercising it’ (Lascoumes & Le Gales, 2007: 3). This means that policy instruments can be analyzed in terms of the governance relations they seek to establish and the assumptions constituting the appeal of these relations. Furthermore, Lascoumes and Le Gales emphasized that public policy instruments produce specific effects that are not merely identical to the explicitly stated aims of the policy. It is also important to study these effects. Based on these understandings, they proposed a definition of policy instruments as technical and social devices that organize specific social relations and a definition of policy instrumentation as the questions related to the choice and use of specific instruments, including the theorization of governance behind a specific choice of instrument (Lascoumes & Le Gales, 2007: 4, 11). The Danish graduate outcome metrics are used in various governance instruments. Some of these can be defined as soft informal governance instruments, such as when a nongovernmental agency publishes a report based on quantitative information on the relevance of higher education, and the conclusions are distributed via the media or used in lobbying activities (see, for example, the Think Tank DEA, 2016). However, most such practices are part of the formal public governance and administration of the higher education sector. The graduate outcome metrics analyzed in Chap. 3 thus play a role in all of the Danish governance instruments that I will now introduce, with the graduate unemployment metric used consistently in combination with various other metrics. Only the job match metric is not used in any public governance instruments, only playing a role in the soft informal governance aimed at influencing policymakers.

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This chapter will analyze each of the formalized public policy instruments in order to show how metrics serve as calculative devices in different types of instruments. The analyzed policy instruments include a contract instrument, a funding instrument, an accreditation instrument, an expert committee instrument, a regulatory algorithm, and a nudging instrument. The analyses draw on several empirical manifestations of each of these policy instruments, including policy documents, contracts, press releases, method descriptions, ministry websites, spreadsheets, university procedural descriptions, guidelines, accreditation reports, and other reports. These different materialities of the policy instruments are part of their ontological specificities, adding structure, functionality, purpose, and an audience to the instruments. The documents are supported by interviews with two government officials, one involved in accreditation and one in the development of the instruments called the Resizing Model and Education Zoom, as well as an interview with an expert from one of the expert committees. With an analysis primarily based on document material, the main analytical focus is on how the policy instruments function and affect governance relations. An analysis of specific empirical effects of the instruments in particular educational contexts will be presented in subsequent chapters.

5.1.1 The Empirical Case of Danish Instruments Public governance of higher education via graduate outcome metrics in Denmark ranges from hard to soft modes of governance and incorporates the graduate outcome metrics in various types of instruments. The first Danish governance instrument drawing on the graduate outcome metrics is the strategic framework contracts between the Ministry of Higher Education and Science and the individual institutions. The current contracts replaced the previous development contracts in 2018. Whereas the development contracts included 3–5 mandatory objectives, complemented by a similar number of voluntary institution-specific objectives, the strategic framework contracts only include voluntary institution-specific objectives. In both cases, the objectives and indicators are defined through a process of negotiation and agreement with the ministry. The contracts run for a number of years, currently four, during which period the institution commits to improve its performance in certain areas as defined in the concomitant quantitative indicators. The second governance instrument that deploys a graduate outcome metric as a calculative device is the performance-based funding of teaching in higher education. The current funding system is the result of a political agreement in 2017 (Ministry of Higher Education and Science, 2017) and has been in effect since January 2019. The system includes both a basic grant (approximately 25% of the total funding), an activity-based grant calculated by multiplying the number of full-­ time equivalent students with a politically defined rate per student (approximately 65% of the total funding), and a performance-based grant that includes both a student completion indicator and a graduate employment indicator (approximately 11% of the total funding). Performance-based funding according to graduate

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employment rates is thus only part of the funding instrument, which also serves other purposes such as improving efficiency and quality. The graduate employment indicator represents up to 5.56% of the funding of teaching; as teaching funding only comprises part of the funding received by universities, and as all universities have so far received at least 50% of the funding allocated via the graduate employment performance indicator, this component makes a relatively small difference to the total amount of funding received by universities. Nevertheless, this funding instrument has a tangible economic incentive. The third governance instrument is the accreditation of higher education institutions and their quality systems. In Denmark, higher education institutions are responsible for their own quality assurance policies, procedures, and practices. However, every 6 years, the quality assurance processes conducted by the institutions are audited and accredited externally by the Danish Accreditation Institution in accordance with the international standards and guidelines from the European Association for Quality Assurance in Higher Education (Brøgger, 2019; Brøgger & Madsen, 2021a; ENQA, 2015). A panel of peers prepares and makes a recommendation regarding accreditation of universities. The accreditation panel is supported in this task by the Danish Accreditation Institution (the public agency assigned to administer accreditation procedures in Denmark), which appoints the panel, compiles a draft report, ensures the legality of the process, and organizes the panel’s work. The final decision on accreditation is made by an Accreditation Council (the Accreditation Act. Act no. 173 of 02/03/2018, 2018). This decision, which can have three different outcomes (positive, conditional, or refusal), has a huge impact for higher education institutions as it determines their status, including whether they are allowed to conduct their own quality assurance procedures rather than the Danish Accreditation Institution accrediting each degree program individually. The assessment is based on codes of conduct for good-quality assurance practice in the Guide to institutional accreditation (the Danish Accreditation Institution, 2013), including how to conduct quality assurance by using key figures on, for example, graduate unemployment. The codes of conduct work by establishing specific procedures at the institutional level. The institutions are thus evaluated in terms of their ability to identify and solve problems through a systematic flow of information. The flow of information is seen as a prerequisite for discussions in various formal bodies within the university, as well as for the responsibilization of university management in relation to program quality (the Danish Accreditation Institution, 2013: 20–21). In practice, the institutional procedures typically involve a series of activities, including discussions, reports, action plans, and approvals, all based on various indicators. The Danish guidelines require universities to use indicators that cover particular aspects of quality and define threshold values for satisfactory performance but leave the definition of specific indicators and thresholds to the universities. The accreditation instrument thus obliges the institutions to design quality assurance systems in which the graduate unemployment metric has to be used as a quality indicator. The next governance instrument found in the Danish context is the appointment of expert commissions and committees that produce analyses and recommendations for the government or for specific ministries. In recent years, such commissions and

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committees have played an important role in the development of policy in Denmark (Campbell & Pedersen, 2014). This instrument differs from the others through its location at the national level. As such, it is not directly used to govern higher education institutions but instead addresses the government by proposing policy initiatives. The sociotechnical arrangement of this instrument involves the appointment of a number of experts, typically including economists, representatives of the business and higher education sectors, and other prominent figures of relevance for the topic to be addressed by the expert commission or committee. The experts participate in a series of meetings; between these meetings, a secretariat comprising government officials appointed from various relevant ministries compile analyses based on requests from the experts. Expert commissions and committees typically result in the publication of one or more comprehensive reports, presenting analytical findings and a list of recommendations. These recommendations often include various policy initiatives, which may be described in relatively high detail. The recommendations are presented at a press conference and thus usually receive some publicity. Following publication, it is up to the relevant ministries and politicians to decide how to proceed with the recommendations. In recent Danish higher education governance, three expert commissions and committees have been appointed: the Productivity Commission (2014), the Committee for Quality and Relevance in Higher Education (Committee on Quality and Relevance in Higher Education, 2014a, b), and the Committee on Better University Programs (2018), all incorporating data from a number of graduate outcome metrics. While the Productivity Commission was appointed by the government and assigned to look at a broad set of factors influencing national productivity, of which education was only one, the two committees were appointed by the Ministry of Higher Education and Science and thus focused specifically on higher education. The final two governance instruments represent policy initiatives that were developed as part of a financial plan called Growth Kit 2014 [Vækstpakke 2014] aimed at stimulating economic growth in Denmark (Ministry of Finance, 2014) and inspired by the recommendations from the Productivity Commission (2014) and the Committee on Quality and Relevance in Higher Education (2014b). The first of these policy initiatives was a regulatory instrument called the Resizing Model [Dimensioneringsmodellen], launched in 2014, which is used to govern the maximum student enrollment in study programs with high graduate unemployment rates. This instrument is directly based on the Current Unemployment metric. The second policy initiative that was described in Growth Kit 2014, and the final instrument presented in this chapter, was the website called Education Zoom [Uddannelseszoom], launched spring 2015. Education Zoom is a website on which all higher education degree programs can be compared according to a range of indicators. It is intended to aid potential students in selecting a degree program for their future studies. Some of the included indicators are based on statistics, while others are based on student and graduate surveys that were added to the instrument in 2017. The indicators used to compare programs include the graduate unemployment rate, graduate income statistics, the percentage of entrepreneurs among graduates, the distribution of public and private sector employment, the demand for and utility

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of skills acquired during studies, and average working hours. When using the website, potential students can select up to three specific study programs for comparison to support them in making informed choices. For example, they can compare similar programs at different universities, or they can compare completely different programs at the same university (Ministry of Higher Education and Science, 2015, 2018).

5.2 Typologizing Calculative Policy Instruments Lascoumes and Le Gales propose a typology of five different types of policy instruments, each defined by a particular relationship between the governing and the governed, and a particular type of legitimacy (Lascoumes & Le Gales, 2007: 12). • Legislative and regulatory instruments, which theorize the state as a social guardian that takes care of the population, legitimized through representative democracy, which provides a mandate to define what is in the population’s best interest. The Resizing Model may be considered an example of a regulatory instrument. • Economic and fiscal instruments, which are essentially a subcategory of legislative and regulatory instruments, building on the same concepts, but specifically oriented toward the economic interests of the population regarding the production and redistribution of wealth, as well as the economic efficiency of society. The performance-based funding model is an example of an economic instrument. • Agreement- and incentive-based instruments, theorizing the state as a noninterventionist, coordinating, mobilizing, and integrative state that ensures the autonomy of the governed as well as the reciprocity of benefits and sanctions. This type of instrument draws its legitimacy from the involvement of actors. Strategic framework contracts are a relevant example of such instruments. • Communication- and information-based instruments, which theorize a governance relation of the public authorities as obliged to provide the general public with information regarding their actions, legitimized through notions of accountability and transparency in decision-making. Both the expert committee instrument and the website Education Zoom can be categorized as examples of communication- and information-based instruments. • De jure and de facto standards and best practices, which theorize governance as a matter of actors adjusting to standards and best practices, legitimized via the scientific-technical and the democratically negotiated foundations of the standards, and via the competitive pressure of market mechanisms created by these standards and best practices. The accreditation instrument is an example of an instrument that works through best practices. Lascoumes and Le Gales label the agreement- and incentive-based instruments, the information- and communication-based instruments, and the standards and best practice instruments ‘new public policy instruments’ (Lascoumes & Le Gales, 2007: 13), in line with the notion of soft governance (Lawn, 2011). An emerging body of studies in the field of education suggests that metrics are often involved in

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new or soft modes of governance in the form of instruments that function through tangible economic incentives or sanctions or through affective economies (see, for example, Brøgger, 2016a; Brøgger & Staunæs, 2016; Kure et al., 2021). These types of instruments rely on comparisons across units and/or across time (Novoa & Yariv-­ Mashal, 2003; Simons, 2014) and on a more or less formalized circulation of comparative information that involves a silent, implicit authority in which power becomes invisible (Piattoeva & Boden, 2020: 6). Through the circulation of comparative information, institutions, individuals, and other actors are responsibilized for their performance (Miller, 2001; Miller & Power, 2013) and incited to develop particular strategies in line with the information (Ozdil & Hoque, 2019). However, Lascoumes and Le Gales also emphasize that instruments are in practice heterogeneous and may be a compound of several of the listed types; furthermore, two instruments that are labelled identically may not theorize governance relations in exactly the same way or share the same scope (Lascoumes & Le Gales, 2007: 6). As the presentation of the six Danish instruments has shown, metrics are involved in both soft or new policy instruments and in more traditional, regulatory instruments. Governing by numbers may involve a broader range of modes of governance than suggested by the body of literature listed above. The typology proposed by Lascoumes and Le Gales thus does not provide sufficient support when categorizing instruments that draw on calculative techniques and devices. Instead, I will distinguish between performance measurement instruments, evidence-for-policy instruments, algorithmic governance instruments, and nudging instruments as four types of governance instruments based on calculative practices. This distinction may not be comprehensive for empirical contexts beyond the Danish case but encompasses the governance instruments described above.

5.2.1 Performance Measurement Instruments The performance management contracts, the performance-based funding system, the accreditation system, and the Education Zoom website all function as soft governance instruments. Each of these instruments draw on a performance measurement mode of governance (Redden, 2019) that seeks to responsibilize higher education institutions. The instruments position the institutions as strategic self-­ governing entities responsible for their own enterprise however held accountable for their public service delivery by the various instruments according to a set of indicators. The performance measurement mode of governance is typical for the logic of new public management in a northern European context, where the state in general plays an active role in governing, often through incentives (Pollitt & Bouckaert, 2011). The performance management logic in this context combines a governance model of universities as independent service enterprises, which materialized in the quasi-corporate formation of eight large self-governing institutions through the University Act of 2003 (Ministry of Higher Education and Science, 2019), with a

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vision of universities as an instrument for national authorities (de Boer & Maassen, 2020). The four performance measurement instruments work in different ways. The strategic framework contract instrument, which is the instrument that most clearly resembles a performance management instrument, constitutes a type of soft governance instrument that builds on its sociotechnical capacity to instill strategic ambitions, and thereby a will to do what is required, in the governed institutions (Brøgger, 2016b). As an agreement-based instrument (Lascoumes & Le Gales, 2007), the contract instrument enables a governance relation between the ministry and the individual institution characterized by delegation and institutional responsibility, thus highlighting the strategic independence and capacity of the top-level management at the institutions (Kure et al., 2021). It also frames the governance relation as based on autonomy, albeit previously directed by ministry-defined mandatory objectives and constantly guided by broader national policy agendas for higher education (Brøgger & Madsen, 2021b; Degn et al., forthcoming; Ørberg & Wright, 2019). The funding instrument constitutes another soft governance instrument, which functions through an economic incentive. With this type of instrument, the government refrains from enforcing specific priorities on the institutions and instead governs institutions by letting them decide themselves whether they want to improve their performance and how. This institutional freedom is evident in the following introduction published in conjunction with the launch of the new funding system: The funding system simultaneously supports greater managerial capacity and ongoing strategic prioritization at the institutions. (the Ministry of Higher Education and Science, 2019, my translation)

Graduate employment rates, which are used as part of the funding instrument, are calculated at the institutional level rather than at program level, whereby the universities rather than individual programs are responsibilized for their outcomes. The accreditation instrument works through peer evaluation of the higher education institutions’ adherence to norms and criteria for good-quality assurance practice. As an instrument, the accreditation system installs a governance relation of peer evaluation, which indicates that accreditation is a standards and best practice instrument (Lascoumes & Le Gales, 2007). Importantly, it is the quality assurance process rather than the quality itself that is assessed by the accreditation panel. As a result, quality is not assessed by looking at the quantitative information in the key figures alone. Instead, the key figures are configured as momentary indicators of the state of a program – indicators that the institution is required to address in specific ways through its quality assurance procedures. The accreditation system, despite its apparent freedom and delegation of responsibility to institutions, thus becomes an instrument that responsibilizes universities to establish their own internal soft governance practices in line with political priorities (Brøgger & Madsen, 2021a). Finally, the Education Zoom instrument functions as a performance measurement instrument that penetrates higher education institutions and their programs, making their performance visible to the public through the display of intimate data (Gorur, 2018). Katja Brøgger has described the comparability feature of Education

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Zoom as a ‘major exposure of the performance of each university … and their specific educational programs’ (Brøgger, 2018: 361). Brøgger highlights the data visuals and their role in this exposure, resulting in ‘tremendous peer pressure’ among teachers and coordinators at the universities (Brøgger, 2018: 362). This peer pressure adds a governance relation of public shaming and faming (Brøgger, 2016a) to the formal bureaucratic relations of the other performance measurement instruments and thereby contributes in a different way in persuading institutions to enroll in the governance of these numbers and pursue better performance according to the measurement criteria of this particular apparatus. Through these instruments, the institutions are held accountable, respectively, by the ministry, the funding model, their peers representing the accreditation institution, and potential students as part of the general public. All four instruments establish a governance relation that emphasizes institutional freedom and responsibility but with externally defined priorities. They all promote the adoption of nationally defined priorities as part of the universities’ strategic ambitions: The contractual instrument governs their strategic priorities and ambitions through contractual commitment; the funding instrument governs their priorities through an economic incentive; the accreditation instrument governs their priorities and actions through horizontal external control; and Education Zoom governs their priorities through public exposure. This double directionality of the instruments, simultaneously establishing institutional autonomy and ensuring centrally defined priorities, is explicitly highlighted by the ministry, here in relation to the contract instrument: The purpose of the framework contract is to define important strategic objectives in relation to the core tasks of the institution. The contract should help set the direction of the development of and priorities within the individual institution and thereby make visible the contribution of the institutions to the fulfillment of important societal goals. (the Ministry of Higher Education and Science, 2020, my translation)

As the quote shows, institutional development and priorities are not entirely open but need to be directed by a contract and defined in relation to societal goals. In all the instruments, the nationally defined priorities include the relevance of programs for society and the labor market. In the previous development contracts, the utility of higher education within the labor market appeared to be a particularly important goal for society, as it was often a mandatory objective, with the graduate unemployment rate typically used as a quantitative indicator for measuring relevance. While no longer mandatory, all universities still address the relevance objective in some form in their performance contracts, and regardless of the autonomy of universities in terms of defining their own indicators in the current model, five of the eight universities still use some version of the graduate unemployment rate in their contracts, typically combined with more controllable input and activity measures. The performance funding instrument likewise includes relevance for the labor market as a societal goal through the measurement of graduate unemployment. The accreditation instrument includes an accreditation criterion stating that the institution should have ‘a practice which ensures that new and existing programs reflect the needs of society and are continually adapted to societal development and the

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changing needs of the Danish labor market’ (the Danish Accreditation Institution, 2013: 16) by processing data from graduate surveys and graduate unemployment statistics. While the universities are free to define the exact measurement used to produce a particular indicator, most universities have aligned their indicators with utility metrics also found in other governance contexts, including the graduate unemployment rate. Education Zoom expresses relevant societal goals indirectly through the choice of data presented on the website, which include graduate unemployment rate and graduate income statistics. The societal goal of relevance for the labor market is made explicit in the performance indicators used in these governance instruments. In the contract instruments, the indicators materialize as strategic performance indicators, defining the future direction to be followed by the university and describing the distance between the future state of the university and its current state. The quantitative objectives bind the institution to follow a particular direction in its decisions and internal management during the period of the contract. In the funding and accreditation instruments, they materialize as standard-setting performance indicators that are used to determine when performance is sufficiently high. In the funding instrument, this is combined with an algorithmic model defining the economic penalty for not living up to politically defined performance objectives. Also, in Education Zoom, the indicators materialize as a set of indicators that can be dynamically ranked and highlight the relative relevance of various programs. The performance measurement mode of governance thus configures the numbers produced by the metrics as indicators used to govern the strategic priorities of what are otherwise presumed to be autonomous institutions. In both the contract and the accreditation instrument, the apparent freedom for universities to select their own measures is constrained by the appeal of standardized statistical indicators and assessment procedures, partly due to the involvement of external actors (Shore & Roberts, 1995: 12) and partly due to the desire to minimize the number of different indicators. The indicators are thus in most cases de facto standards. In principle, the configuration of the indicator grants it a status as an outcome of the effort of the institutions, even though the phenomena measured by the indicator, such as graduate unemployment, to some extent lie beyond the institution’s control (Kure et al., 2021).

5.2.2 Evidence-for-Policy Instruments While the performance management instruments govern the priorities and ambitions of institutions, the politically appointed expert commissions and committees, through their production of reports on the relevance of higher education, influence the priorities and ambitions of the government. However, these expert commissions and committees are appointed by the government itself and thus serve as a legitimization of policy intervention. The reports produced by the expert commissions and committees include analyses that serve as evidence for the policy recommendations also included in the reports (Committee on Better University Programs, 2018;

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Committee on Quality and Relevance in Higher Education, 2014a, b; the Productivity Commision, 2014), and these analyses build on quantitative information on higher education utility based on the various graduate outcome metrics presented in the previous book chapters. For example, the report from the Productivity Commission includes bar charts on the average graduate unemployment rate in 1991–2011 across the five overall areas of studies at universities; graduate unemployment within different areas of the humanities; the wage gain from higher education in a range of OECD countries and across the five areas of studies and the eight universities; the wage gain among graduates with a program requiring high school mathematics; the graduate income of graduates with various numbers of hours of teaching per week; and the share of university graduates working in unskilled jobs. The report also includes quantitative information on a range of other aspects of education, including expenses and the educational level of the population, as well as information on other parts of the education system than higher education (the Productivity Commision, 2014). Both the graduate outcome metrics and the other quantitative information included in the reports support the arguments and recommendations of the expert commissions and committees and thereby serve as a legitimizing knowledge foundation for political decisions, in line with recent trends toward evidence-based policymaking that primarily draws on technocratic sources of evidence (Carusi et al., 2018; Carvalho, 2014; Karseth et al., 2022; Steiner-Khamsi et al., 2020). The metrics provide data input for decision-making (Barber & Ozga, 2014), much like the function of the PISA studies published by the OECD (Carvalho, 2018; Grek, 2009). However, as already mentioned, the media and the university sector usually pay attention to the reports when they are published, and, from that point on, facts and bar charts circulate into an uncountable number of other locations where they become a knowledge foundation for decision-making. Through these less controllable practices, the governance relations constituted by the expert commission and committee instrument not only involve the specific policy recommendations proposed by the experts but also a production of knowledge that may more subtly affect numerous decisions throughout the higher education system, as well as public opinion and the general impression of the higher education system among the population. The metrics that are promoted through the media and picked up by educational actors thus obtain an authoritative status and indirectly produce governance effects on the entire sector.

5.2.3 Algorithmic Governance Instruments The Resizing Model, and in part the performance-based funding instrument, can be described as instruments of algorithmic governance. Here, the quantitative information provided by graduate unemployment metrics again does not serve as an indicator but as data input for an algorithm. The instruments install an automated type of decision-making (albeit in practice often supported by manual processes performed

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by university and ministry employees, as my conversations with such employees testify), working through politically defined criteria. Through automated decisionmaking, the instruments enable the government to distance itself from specific policy decisions, as the decision-making power is delegated to algorithms and depends on objective data input. Both these algorithmic instruments function through thresholds defined in relation to the average unemployment rate within a larger population. The calculation model of the funding based on graduate employment draws on the Occupation of Graduates metric and includes graduates in employment and unemployment while leaving out graduates living abroad, graduates in education, and graduates outside the labor market (for example, graduates on parental leave or sick leave). The formula divides the number of graduates in employment with the graduates in employment and unemployment combined, calculated in the 12th to 23rd month after graduation. The resulting graduate employment rate is then compared to the national average employment rate among 16–66-year-olds, subtracting a coefficient representing the frictional unemployment typical for graduates as well as the economic conditions more broadly. If the graduate employment rate minus the coefficient is lower than the national average, it automatically affects the allocation of resources to universities. The performance-based funding related to graduate employment, as in other countries, is merely one part of a complex funding formula defining the allocation mechanism (Landri et al., 2017). The Resizing Model includes a slightly more complicated calculation. The model allocates a cap on student admission for programs that the model defines as having ‘systematic and striking excess unemployment’ (Ministry of Higher Education and Science, 2014a). The model is based on clusters of study programs, grouped to represent areas of studies that are presumed to share the same labor market. For example, the group of degree programs named Classical Humanities includes philosophy, history, Danish, different types of archaeology, and religion. Another example is Physics and Chemistry, including programs like astronomy, biophysics, geophysics, and medical chemistry (Ministry of Higher Education and Science, 2014b). Through this clustering, related programs are measured and regulated as a whole to prevent a reduction of graduates from one program being countered by an increase in graduates from another program feeding into the same labor market. The model works through three steps of calculations. In the first step, the model selects groups of study programs with a ‘systematic and striking excess unemployment’ for (down)sizing. ‘Striking excess’ is here defined as more than two percentage points above the average unemployment rate of all higher education graduates; ‘unemployment’ is defined as the unemployment rate in the 4th to 7th quarter after graduation; and ‘systematic’ is defined as a striking excess unemployment in 7 out of 10  years of measurement. The assessment of ‘striking excess unemployment’ builds on a relative calculation, where the clusters of study programs are compared to each other rather than to an absolute number. When a cluster systematically has a graduate unemployment rate 2.0 percentage points or more above the general average, a ‘cap on enrollment’ is triggered. The second step of calculation in the model determines the cap on enrollment, where 2.0–4.9 percentage points of excess

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unemployment equals a 90% cap (or a 10% cut); 5.0–7.5 percentage points equals an 80% cap; and more than 7.5 percentage points equals a 70% cap. Finally, the third step of calculation distributes ‘resizing’ from the (national) clusters to the individual institution (Ministry of Higher Education and Science, 2014a). The policy initiative furthermore implies a governance relation of centralized governance of higher education institutions for the benefit of society. In the policy process surrounding the development of the policy initiative, the decision to develop a centralized Resizing Model was considered controversial, because the universities generally have the power to make decisions regarding enrollment numbers. Meanwhile, the government (and the government official that I interviewed) argued that the universities had already had their chance to solve the problem and had failed to do so: What you need to remember is that the universities had the opportunity forever to do it themselves. They can enroll as many as they want. Now there is just a maximum, based on unemployment numbers. If they rise too much, systematically, then we add a maximum. (Interview with ministry official, March 2017)

The Resizing Model thus builds on a governance relation of a ministry keeping higher education institutions in check. In general, the lack of action from the universities has been considered a result of the funding system, which at the time of the launch of the Resizing Model was purely activity based. The universities were presumably more interested in maximizing their public funding through continuous growth in the student population than ensuring low rates of graduate unemployment (DAMVAD et al., 2011). The policy instrument thus configures universities as suboptimizing agents that require external regulation. The Resizing Model works as a regulatory instrument (Lascoumes & Le Gales, 2007) that counters the previous funding system, which in general was considered useful, but also characterized by a biased incentive toward mindless growth, thereby necessitating centralized regulation. Both these instruments thus serve as algorithmic automated decision-making devices (Yeung, 2017) that combine quantitative standards for higher education with regulation. The calculative models are on the one hand embedded in algorithmic governance practices, which imply that the funding decision and the enrollment cap are constructed as depoliticized and automated decisions based on objective measures (Yeung, 2017: 121). On the other hand, the selection of the graduate outcome metrics used to calculate the allocated funding and enrollment cap is highly politicized and imposes specific strategic priorities on the universities.

5.2.4 Nudging Instruments Finally, Education Zoom works not only as a performance measurement instrument but also as a nudging instrument. It thus both serves as a digitalized transparency instrument that exposes the performance of public sector providers to the general

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public and as a nudging instrument that seeks to affect the parameters used by potential students in their selection of higher education programs. A nudging instrument can be defined as a ‘choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives’ (Thaler & Sunstein, 2008: 6). It is a form of regulation that affects the decision-making environment in ways designed to accommodate behaviors favored by the choice architect (Yeung, 2017: 121). While Education Zoom is not an advanced algorithmic ‘hypernudge’ instrument (Yeung, 2017) but merely an instrument of database governance (Ruppert, 2012; Williamson, 2016), the information provided on the website still seeks to influence potential students via data. The website and the displayed data ‘partly simplifies the complex, but at the same time partly closes the open’, directing the practices of actors (in this case students) in an indirect and sophisticated way (Hammer, 2010: 91). The website thus provides a governed form of transparency (see also Piattoeva, 2015). Education Zoom seeks to nudge students in the direction of educational choices that are considered better for both the state and the individual students. Before the launch of the website, the Minister talked about the students as rational selectors, making educational choices with both their ‘hearts’ and their ‘minds’ but needing the proper information to make the right choices (Nielsen, 2014). The instrument appears to be an attempt to encourage potential students to make more rational education choices, thereby seeking to overcome the apparent mismatch between their desires and their interests (Sellar, 2015) through the availability of transparent information. If information on graduate unemployment is available to the potential students, the rationale is that they will seek study programs without an oversupply to increase their attractiveness within the labor market. The instrument thus frames student interests as educational choices that ensure employment, high income, and high-status jobs. Meanwhile, the policy instrument assumes that a potential student is a homo economicus, or a rational human being characterized by a maximizing behavior (Becker, 1976: 5–14; Berg & Gigerenzer, 2010; Goldthorpe, 1998, 2000; Teichler, 2007), but it has previously been shown that students do not always make the right choices in the eyes of the government (as also discussed in the economic literature in Denmark and internationally; see, for example, Glocker & Storck, 2014; Kirkeboen et al., 2016; Skaksen & Andersen, 2018). They thus appear to be in need of governance through nudging. The role of the data in Education Zoom as a nudging instrument is more than to expose the performance of universities and programs. The displayed data can be understood as a modulating device that adjusts or transforms courses of actions, for example, the choices made by potential students, by enabling a constant observation of patterns. Modulatory governance is directed toward the pattern, or ‘the regularities of the aggregate effects of individual bodies’ (Clough, 2007: 19), such as the relation between certain choices and certain outcomes, or between certain populations and certain risks, rather than toward the subject (Clough, 2007: 19–21; Savat, 2009: 48–51). Through an ongoing observation of negative patterns of the past and present, these patterns and events can be anticipated and thus prevented in the future

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(Savat, 2009: 48–51). Thereby, subjects, and especially their futures, become malleable (see also Adams et al., 2009: 256). As Savat states: The moment when a person’s weaknesses and strengths, likely diseases and resistances, likely failures and desires can be predicted, is effectively the moment one can order those persons in advance. (Savat, 2009: 49)

Education Zoom can be understood as a policy device that ‘orders’ or promotes a specific kind of graduate in advance. According to Savat, the production of continuous data on likely futures enables the observers, who are often the subjects themselves, to steer clear of potential dangers such as personal failures. Graduate unemployment is an example of such a failure. When the graduate unemployment statistics are produced and distributed to potential students through Education Zoom, the students are encouraged to observe and thereby avoid the pattern of graduate unemployment by choosing a different area of study where the risk of unemployment is lower. The data thus serve as indicators of probabilities (Desrosières, 1998: 59; Hacking, 2006) in this type of instrument. The injunction of avoiding negative patterns or probabilities through self-adjustment becomes a very interesting and subtle mode of governing through ‘a technologically dispersed education/ training in self-actualization and self-control at the preindividual, individual, communal, national, and transnational levels’ (Clough, 2007: 21). Nudging instruments govern by circulating or modulating affect (Clough, 2007: 20) in the form of anticipated futures. The availability of comparable and probabilistic statistics with dramatically different numbers enrolls potential students into a control relation to their futures by entrusting them with the responsibility for their future labor market success or ‘failure’. Nudging instruments can thus be characterized as using numbers to conduct a form of intimate regulation (Gorur, 2018: 91) of the population.

5.3 Graduate Outcome Metrics in Educational Governance The analysis of how metrics serve as calculative devices in various policy or governance instruments has shown that metrics play different roles, depending on the type of instrument they are embedded in. In the Danish case, these roles include strategic performance indicators directing institutional priorities, data as evidence for policymaking, data inputs for algorithmic decision-making, and probability data for decision-making. Each of these roles entail a specific kind of governance relation and concept of governance in terms of the responsibilization and accountabilization of institutions within a space of societal-political priorities, a legitimization of future policy decisions through knowledge, an interventionist regulation of institutions not living up to their responsibilities, and a modulation of data-­consuming students in their educational choices. This analysis of the role of metrics contributes to the instrumentation literature in educational governance by adding an explicit focus on instruments using calculative practices to the existing cases (see, for example, Carvalho, 2014; Decuypere et al., 2014; Williamson, 2016).

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The analysis furthermore shows that marketization instruments are nearly absent in the analyzed Danish case. Only Education Zoom partly resembles a marketization instrument but only via an indirect market sustained by the economic incentive of activity-based funding. In relation to the international literature on educational governance, the lack of marketization instruments may appear surprising. However, most of the international literature is produced in Anglo-American contexts where other governance instruments have been deployed during recent decades than those deployed in Denmark. In the UK, for example, where graduate employability has been on the political agenda for at least a decade longer than in Denmark, the state has governed universities through the marketization of student choice combined with tuition-fee funding (Boden & Nedeva, 2010: 45; Tomlinson, 2012: 409). These policy trends have been followed up by the ‘transparency revolution’ of the 2016 Higher Education and Research Bill, ‘enabling students to make informed choices between institutions and courses that meet employers’ needs’ (Department for Business Energy & Industrial Strategy, 2016), with clear parallels to the Danish Education Zoom, characterized by marketization mechanisms and competition. However, unlike the Danish case, the UK has not implemented direct regulations on the graduate supply through admission caps for selected degree programs, as in the Danish case with the Resizing Model.1 Thus, the UK incentives are to a greater extent directed toward student choice and are in that sense more clearly market based, while the Danish policies combine marketization with a direct, legal regulation of higher education institutions and their discipline-specific supply of graduates, as well as instruments responsibilizing institutions to align themselves with government priorities. Finally, the Danish case shows that governance via metrics takes place in a range of other contexts and through very different instruments than those used by transnational organizations, such as the Education at a Glance reports. Studies on the use of metrics or data in educational governance thus need to be highly sensitive to the exact governance mechanisms at play, rather than merely subscribing to the transnationalization or marketization narratives outlined at the beginning of this chapter.

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Ørberg, J. W., & Wright, S. (2019). Steering change – Negotiations of autonomy and accountability in the self-owning university. In S. Wright (Ed.), Enacting the university: Danish university reform in an ethnographic perspective (Vol. 53). Springer. Ozdil, E., & Hoque, Z. (2019). Accounting as an engine for the re-creation of strategy at a university. Accounting and Finance (Parkville), 59(3), 1747–1768. https://doi.org/10.1111/acfi.12445 Piattoeva, N. (2015). Elastic numbers: National examinations data as a technology of government. Journal of Education Policy, 30(3), 316–334. https://doi.org/10.1080/02680939.2014.937830 Piattoeva, N., & Boden, R. (2020). Escaping numbers? The ambiguities of the governance of education through data. International Studies in Sociology of Education, 29(1–2), 1–18. https:// doi.org/10.1080/09620214.2020.1725590 Pollitt, C., & Bouckaert, G. (2011). Public management reform: A comparative analysis (3rd ed.). Oxford University Press. Porter, T. M. F. (1996). Trust in numbers: The pursuit of objectivity in science and public life (2 printing ed.). Princeton University Press. Power, M. (1999). The audit society: Rituals of verification (Paperback edition, reprinted ed.). Oxford University Press. Redden, G. (2019). Questioning performance measurement: Metrics, organizations and power. SAGE. Robertson, S. L. (2017). Making education markets through global trade agreements. Globalisation, Societies and Education, 15(3), 296–308. https://doi.org/10.1080/14767724.2017.1345408 Rose, N. (1991). Governing by numbers: Figuring out democracy. Accounting, Organizations and Society, 16(7), 673–692. https://doi.org/10.1016/0361-­3682(91)90019-­B Rose, N. (1999). Powers of freedom: Reframing political thought. Cambridge University Press. Ruppert, E. (2012). The governmental topologies of database devices. Theory, Culture and Society, 29(4–5), 116–136. https://doi.org/10.1177/0263276412439428 Savat, D. (2009). Deleuze’s Objectile: From discipline to modulation. In M. Poster & D. Savat (Eds.), Deleuze and new technology (p. 275). Edinburgh University Press. Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed. Yale University Press. Secretary of State for Business Innovation and Skills. (2011). Higher education: Students at the heart of the system. Retrieved from https://assets.publishing.service.gov.uk/government/ uploads/system/uploads/attachment_data/file/31384/11-­944-­higher-­education-­students-­at-­ heart-­of-­system.pdf Sellar, S. (2015). A strange craving to be motivated: Schizoanalysis, human capital and education. Deleuze Studies, 9(3), 424–436. Shore, C., & Roberts, S. (1995). Higher education and the panopticon paradigm: Quality assurance as ‘disciplinary technology’. Higher Education Review, 27(3), 8–17. Shore, C., & Wright, S. (2015). Audit culture revisited. Current Anthropology, 56(3), 421–444. https://doi.org/10.1086/681534 Simons, M. (2014). Governing through feedback: From national orientation towards global positioning. In T.  Fenwick, E.  Mangez, & J.  Ozga (Eds.), Governing knowledge: Comparison, knowledge-based technologies and expertise in the regulation of education (pp.  155–171). Routledge. Skaksen, J.  R., & Andersen, T.  M. (Eds.). (2018). Returns from education: The societal and individual rationale [Afkast af uddannelse: det samfundsmæssige og individuelle rationale] (1udgave ed.). Rockwool Fondens Forskningsenhed. Steiner-Khamsi, G., Karseth, B., & Baek, C. (2020). From science to politics: Commissioned reports and their political translation into White Papers. Journal of Education Policy, 35(1), 119–144. https://doi.org/10.1080/02680939.2019.1656289 Strathern, M. (2000). Audit cultures: Anthropological studies in accountability, ethics and the academy. Routledge. Teichler, U. (2007). Does higher education matter? Lessons from a comparative graduate survey. European Journal of Education, 42(1), 11–34. https://doi.org/10.1111/j.1465-­3435.2007.00287.x

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Thaler, R.  H., & Sunstein, C.  R. (2008). Nudge improving decisions about health, wealth, and happiness. Yale University Press. the Accreditation Act. Act no. 173 of 02/03/2018. (2018). Ministry of Higher Education and Science. the Danish Accreditation Institution. (2013). Guide to institutional accreditation. Retrieved from https://akkr.dk/wp-­content/filer/akkr/Korekturl%C3%A6st-­og-­godkendt_Vejledning-­om-­ institutionsakkreditering-­endelig_godkendt.pdf the Ministry of Higher Education and Science. (2019). The higher education funding system [Bevillingssystemet for de videregående uddannelser]. Retrieved from https://ufm.dk/uddannelse/videregaende-­uddannelse/institutionstilskud/ nyt-­bevillingssystem-­for-­de-­videregaende-­uddannelser the Ministry of Higher Education and Science. (2020). Strategic framework contracts [Strategiske rammekontrakter]. Retrieved from https://ufm.dk/uddannelse/videregaende-­uddannelse/ universiteter/styring-­og-­ansvar/strategiske-­rammekontrakter the Productivity Commision. (2014). Analysis report 4. Education and innovation [Analyserapport 4. Uddannelse og innovation]. Retrieved from Copenhagen: http://produktivitetskommissionen. dk/media/162592/Analyserapport%204,%20Uddannelse%20og%20innovation_revideret.pdf the Think Tank DEA. (2016). University graduates’ transition to the labor market [Universitetsuddannedes vej ud på arbejdsmarkedet]. Retrieved from https://dea.nu/sites/dea. nu/files/universitetsuddannedese_vej_ud_paa_arbejdsmarkedet.pdf The University Act. LBK nr 778 af 07/08/2019. (2019). Tomlinson, M. (2012). Graduate employability: A review of conceptual and empirical themes. Higher Education Policy, 25(4), 407–431. https://doi.org/10.1057/hep.2011.26 Williamson, B. (2016). Digital education governance: Data visualization, predictive analytics, and ‘real-time’ policy instruments. Journal of Education Policy, 31(2), 123–141. https://doi.org/1 0.1080/02680939.2015.1035758 Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713

Chapter 6

The Governing Properties of Numbers

When numbers enter any educational governance or management context, they often do so with great authority. Numbers are brutally clear and at the same time frustratingly impenetrable. While they offer readily available comparisons, they are also cloaked in a shroud of mystery in terms of the work they perform on human-to-­ human relations. Their specific statements and limitations are often cloaked in much more bold interpretations, and their affective powers are opaque. Nevertheless, numbers have governing effects, even though the traces of how they govern often remain concealed (Brøgger, 2018: 359). One reason for the impenetrable and mysterious, and thereby authoritative, character of numbers may be our relatively limited everyday and analytical vocabularies for describing their governing potential. Examining the few available analytical vocabularies, they reveal that numbers are not just numbers. Numbers in educational governance obtain different ontologies depending on their material and relational properties and on the governance and management practices, as well as the educational practices and educational thinking, with which they are entangled. The material and relational properties of numbers matter, as they affect how numbers affect actors engaging with them. The properties of numbers are in other words performative in the sense that they contribute to the crafting of new realities (Brøgger & Madsen, 2021). These new realities encompass reworked educational ontologies (as analyzed in Chap. 4); reconfigurations of the ontologies of universities, students, academics, policymakers, and so forth (analyzed in Chaps. 7 and 8); and reworked modes of governance (analyzed and discussed in Chap. 9). The literature offers a range of general characteristics of numbers that enable various modes of governing, including governance at a distance, processes of subjectification, processes of responsibilization, and/or the linking of various actors into specific relations of power (Carvalho, 2014; Miller & Power, 2013; Porter, 1996). However, it is not an easy task to grasp the governing properties of specific numbers. The literature does not offer readily available conceptualizations of how © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_6

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the specific properties of numbers affect how they govern. A tentative conceptualization could frame the governing properties of numbers as a combination of their aesthetic and visual attributes and their relational properties—or, in other words, how numbers are entangled with, and set in relation to, each other in specific data sets. The aesthetic and visual attributes of numbers, as well as their relational properties, are the topic of this chapter. The first section explores aesthetic data practices and data visualizations, outlining key concepts in the existing literature and briefly analyzing examples of the aesthetics and visualizations of the higher education graduate outcome numbers. Next, the chapter will add to the previous literature by thoroughly analyzing the relational properties of the graduate outcome data, including ways of connecting numbers spatially and temporally. This exploratory analysis, which constitutes the main body of the chapter, contributes through the development of an additional analytical vocabulary for the analysis of governing properties of numbers. The chapter thus seeks to bring together and add to dispersed concepts conjointly defining important governing aspects of governing with numbers.

6.1 Aesthetic Data Practices and Data Visualizations Despite the neutral and natural aura typically attributed to data, a series of reworking processes are required for data to emerge in their displayed form. Ratner and Ruppert (2019) distinguish between sites of data production and sites of data projection and thereby also between aesthetic practices and data visualizations as practices reworking data (Ratner & Ruppert, 2019: 3). Aesthetic practices refer to the operations through which data are given a desired form and thus made ready for projection. The specific aesthetic practices involved in making data ready for projection result in what we might term a particular type of data aesthetics, for example, designed to make data accessible to specific audiences (Williamson, 2016: 129). These practices involve bringing data from dispersed sites into relation, for example, by addressing differences between data sets in metadata and thereby containing these differences away from displayed data or by cleaning data to remove absent, inaccurate, and indeterminate data (Ratner & Ruppert, 2019). The elasticity of a number, in terms of its flexibility and amenability to different calculations and recalculations, is also an important aspect of its aesthetics (Piattoeva, 2015). The desired data aesthetics of a particular set of data are closely related to the visualization designs deployed in a specific context. Data visualizations expose particular aspects of data that promote particular arguments and explanations (Brøgger, 2018; Williamson, 2016: 131). Data visualizations can thus be understood as techniques with governing effects, such as incentivizing effects that mobilize actors to improve their performance (Brøgger, 2018: 360–362). Data in educational governance are often visualized in the form of dashboards that ‘render invisible the underlying data and the various algorithmic and statistical techniques performed on it,

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while rendering visible particular representations of data’ (Williamson, 2016: 131). This often involves communicative techniques like scorecards and graphs, deploying magnitudes, colors, and symbols to expose particular aspects of data (Brøgger, 2018). The design of dashboards and visuals enables some aspects of data to remain hidden while others are highlighted (Leonelli et al., 2017). The use of magnitudes, colors, and symbols furthermore installs affective economies through which affects of not only honor and shame (Brøgger, 2018) but also hopes and fears (Adams et al., 2009) are distributed. Through these affective economies and affectively wired techniques, visualizations emerge as practices with governing effects that conceal their own traces (Brøgger, 2018: 359). Both aesthetic practices and visualization practices thus add important material attributes to numbers. These attributes are performative in the sense that they have governing effects. This is also the case for the numbers produced by the Danish graduate outcome metrics. In the following section, I will highlight some aesthetic and visual attributes of the graduate outcome data that affect the governing of higher education with numbers in Denmark.

6.1.1 Data Aesthetics and Visualizations of Danish Graduate Outcome Numbers The data aesthetics and visualizations of the Danish graduate outcome numbers vary, depending on the audiences they are designed for. For example, the spreadsheets used to disseminate graduate unemployment rates, both in relation to performance funding (the Graduate Unemployment Rate metric) and in relation to the Resizing Model (the Current Unemployment metric, see Chap. 3), require a streamlined data aesthetics in which all cells in the spreadsheet contain a data value, even if the value is sometimes hidden by an asterisk, indicating that the data value is based on less than ten graduates and thus made invisible for data protection purposes. If all cells do not contain a data value, the calculations of averages and thresholds performed by the spreadsheets will cease to function. Furthermore, the comprehensive dissemination is a prerequisite for the required elasticity of the data, indicative of the role of the Danish Ministry of Higher Education and Science as a provider of official data to other parts of the Danish higher education sector. The bureaucratic and technical spreadsheet format of the data signals that the data are raw and thereby implies that they are neutral and not manipulated, intended for administrative use in the sector. By contrast, the aesthetic data practices in the institutional quality assurance systems transform graduate unemployment rates into binaries, thereby removing the elasticity of numbers. The nonelasticity of numbers is important in quality assurance, as performance indicators should be unambiguous and nonadjustable if they are to serve as instruments of control and judgment. Besides aesthetic data practices of nonelasticity, the production of binaries also requires thresholds for

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sorting the numbers. The binary attributes of the numbers allow them to be visualized via color coding and symbolic coding. The threshold of an indicator is often marked by the institutions using red or green to indicate whether or not there is a problem. In the case of the University of Southern Denmark, displayed in Fig. 6.1, the indicators are also coded using the symbols of a green tick or a ‘red light’, as it is commonly referred to at the university. The alerting red light amplifies the mobilizing power of a numerical value above a threshold and motivates academic staff responsible for a program to improve their performance and thereby achieve the reassuring green tick, indicating a completed task (Brøgger & Staunæs, 2016). At other universities, a third, yellow boundary category of indicators is included, warning academic staff to avoid falling into the red category. The color-coded and symbolic visualization of program quality data is a powerful management tool that affects academics even if they principally reject quantitative indicators as useful in the development of educational practices (see also Chap. 7). As a third example, Education Zoom deploys even more radical data aesthetics of simplification than that deployed in quality assurance. In contrast to the information provided by the Current Unemployment metric as it appears in the ministerial spreadsheets, the unemployment numbers displayed on the Education Zoom website are calculated as an average of the latest 3 years of data, both making the numbers more statistically robust in the sense that the population size is increased to 3 years of graduates and making the programs directly comparable with a single indicator (or, as in the case of graduate unemployment, an indicator on graduate unemployment and an indicator on long-term unemployment; see Fig. 6.2).

Fig. 6.1  Green ticks and red lights marking five key figures for a program at the University of Southern Denmark

Nyuddannede 10 är after endt uddannelse

29% 1%

Nyuddannede

3%

10 är after endt uddannelse

1%

Fig. 6.2  The bar chart, which is a screenshot from ‘Education Zoom’ (Uddannelsesguiden, 2018), shows the unemployment rates for ‘recently graduated’ and ‘10 years after graduation’ for two study programs

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The calculation of 3-year averages for graduate unemployment erases the development of unemployment rates from graduation year to graduation year, making the numbers appear more constant. The data displayed by Education Zoom are thus extracted from the Current Unemployment metric and reworked into an even more simplified form to accommodate the needs of potential students. In a similar vein, graduates’ assessments of how well the program has equipped them for the labor market, based on qualitative response categories, are recalculated into an average numerical value, thereby hiding the methodology of the Likert scale for the benefit of achieving a single number. It seems as if numbers made for potential students require different data aesthetics than numbers made for university management and administrative staff. The numbers are projected in the form of a dashboard with bar charts on what feels like a very bare website, thereby stripping the easily comparable numbers from potentially distracting elements. These visualization practices shed light on the simple numerical differences in graduate unemployment across areas of study, while casting a shadow on any contextual information that could add meaning to these differences (Leonelli et al., 2017). Both the spreadsheets and Education Zoom share a visualization practice of customized data sets that invite their audience to play the role of data analysts (Decuypere et al., 2014; Williamson, 2016). In the Current Unemployment spreadsheet, the data displays are customizable via a complex network of links to different tabs. These tabs enable displays centered on differences between types of higher education programs (business academy programs, university college programs, and university programs), between institutions, between the five broader areas of studies (the humanities, social sciences, natural sciences, technical sciences, and health sciences), or between individual programs. For Education Zoom, the visualization techniques allow for even greater customization of data displays, as visitors to the website can simply search for and select up to three specific programs to generate a dashboard with directly comparable numbers. As Williamson (2016) states, the co-­ creation of data enabled by these interactive visualization techniques elides the distinction between expert and popular knowledge and thereby configures the audiences for data as rational actors that base their decisions on factual knowledge. Meanwhile, the binary, nonelastic data in quality assurance systems configure audiences of academic staff as manageable subjects, required to adjust their practices in line with the numbers. The Danish graduate outcome numbers thus illustrate how different data aesthetics and visualization techniques are required in order to package data for different imagined audiences with different preferences, different data literacy capacities, and different interests that might need to be supported or contained. Through these techniques, the numbers are attributed differently in ways that enable them to perform various kinds of affective work on the audiences who actually encounter the data.

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6.2 Relational Properties of Numbers Meanwhile, there is more to the governing properties and materiality of numbers than their visual and aesthetic packaging. The numerical values themselves add governing capacity to numbers. A graduate unemployment rate of 25% governs differently than an unemployment rate of 8%. The difference between these numbers produces stratifications that mobilize political and personal decision-making. Similarly, a numerical target of 95% governs differently than a numerical target of 85%, if last year’s performance was 83%. This difference produces affectivities that mobilize resignation or effort. In order to fully understand the governing potential of a numerical value, I suggest that we need theorization of the relational properties of numbers. Numbers hardly ever stand alone; they are almost always part of a data set that allows for comparison, enabling actors to relate a given number to other numbers. Through the lens of agential realism (Barad, 2007), we can theorize a data set, or even a consecutive measurement referring back to a baseline, as the entanglement from which individual numbers obtain their matter and meaning. The relational ontology proposed by agential realism and other new materialist or sociomaterial theories, I suggest, thus not only provides a framework for the ontology of human and more-than-­ human entities interacting with each other but also for the ontology of quantitative entities that are mutually related to each other. In addition to the format of the number, such as the number of digits or the level of aggregation, the position of the number compared to other numbers also affects its meaning. The relations between numbers are either organized spatially, as relations between comparable spatial or organizational units of measurement, or temporally, as relations between different points in time, or both spatially and temporally. In agential realism, time and space are understood as enacted in the agential cuts performed by apparatuses. Barad rejects the notions of space as a container and of time as a collection of preexisting points with evenly spaced intervals. Instead, she suggests that spatiality and temporality are ‘iteratively reconfigured in the materialization of phenomena’ (Barad, 2007: 234). The reworked notions of space and time proposed by Barad are particularly important in the study of metrics, as measurement relies heavily on conceptions of space and time as constants or as variables with fixed properties. From an agential realist philosophical point of view, we need to instead understand metrics as apparatuses that enact, materialize, and promote particular spatial-temporal relationalities. Spatiality and temporality are aspects of data that have been addressed in the literature on educational governance. Beginning with spatiality, the notion of topological spatialization has been influential in recent educational governance research as an analytical tool for understanding the influence of transnational organizations, such as the OECD, on national and local educational contexts (Hartong & Piattoeva, 2021; Lewis & Lingard, 2015; Ratner, 2020b; Ruppert, 2012). The notion of

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topological spatialization refers to a spatial concept that transcends traditional scales such as the transnational, national, and local, as it focuses on how policy has reach and is influential across these scales and physical distances (Hartong, 2018). In this literature, spatial data infrastructures provide the means of creating topological closeness across distances and inscribing local populations into wider populations, such as the European (Ratner, 2020a). Temporality has only more recently begun to emerge as a separate analytical concept in the understanding of education policy. The literature is scarce, but in a paper that seeks to put temporality on the agenda of policy analysts, Lingard (2021) emphasizes four foci of a temporally sensitive approach to education policy: the changing historical concepts of policy, the histories of policies, the temporal construction produced by policies, and the changing spatio-temporalities and time-­ spaces following from globalization. Like the concept of topological spatialization, and like other important emerging contributions on education policy and time (Decuypere & Vanden Broeck, 2020; Lingard & Thompson, 2017), most of these foci relate to the temporal reach and influence of policy, which are important aspects of policy studies, but of less direct relevance to the study of the governing properties of numbers. Meanwhile, the temporal and spatial constructions produced by policies constitute an important focus in relation to the governing capacities of numbers, as these constructions depend on material-discursive practices such as quantification practices. The spatial-temporal relationalities that are embedded in numbers affect how numbers affect actors engaging with them. Specific properties of numbers enable them to coproduce temporal and spatial effects such as imagined communities and common pasts (Piattoeva & Tröhler, 2019), future risk and timely management (Madsen, 2022), or hopeful gazes toward the future and neglect of the past (Lingard, 2021: 247–248). The specific quantification practices implied in the production of a number, including the scales used for comparison, produce temporal and spatial distinctions, for example, between past/present performance or between Danish/ another country’s performance (Ratner, 2020a: 221; 229), and these distinctions are part of the powers of numbers. In addition, temporal properties of numbers affect the configuration of possibilities for change, which are core to the governing capacities of numbers. The following sections will address these properties of data by analyzing the Danish graduate outcome metrics in relation to their comparability, directionality, and fluidity, their stratifying abilities, their distribution of agency, and the temporally oriented affectivities they produce. Through this analysis, these sections offer empirically derived analytical concepts for the analysis of the governing properties of numbers beyond the instrumentation analyzed in Chap. 5.

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6.2.1 Comparability Across Various Temporal and Spatial Orders As already indicated, the relations between numbers are constitutive of the comparisons enabled by quantification practices. These practices typically render either time or space quantifiable, either as constants against which other variables can be made commensurable or as categories or scales that other variables can be compared across. In cross-sectional data, time is held constant, enabling comparisons of different spatial entities at the same point in time. In time-series data, spatial markers are held constant, enabling longitudinal comparisons of the same spatial entity across time. While comparison across spatial units requires a division of spatial units as clearly demarcated and mutually exclusive, this division is fundamentally a qualitative or nominal separation into equivalent but different entities. In turn, comparisons across time rely on a quantified understanding of time as separable into intervals of equal sizes in order to render, for example, 1 year comparable to another year. Only multivariable quantitative analysis enables the simultaneity of temporal and spatial relations. If we turn to the Danish graduate outcome numbers, most of them enable cross-­ sectional comparisons, holding time as a constant while creating an order of differences across space. The numbers in the instruments produce spatial differences in different ways. The calculation of graduate unemployment in the funding instrument compares universities, whereby the universities are territorialized as spatially calculable entities (Miller & Power, 2013). Universities as spatial entities are today primarily marked by their organizational boundaries rather than by the geographical boundaries that previously defined them and that are reflected in the names of many universities. The comparison performed in the funding instrument is conducted across data demarcated by a single calendar year, and this alignment of the data is what makes them objectively comparable and fit for unbiased decisionmaking in educational governance. The temporal-spatial materiality of the numbers embedded in the funding instrument thus reproduces not only a spatial order of difference between the compared organizations but also a serial-temporal order of years. The serial temporality of years is quite common across cross-sectionally comparable numbers. The accreditation instrument, the Resizing Model, and the Education Zoom website all also align data according to calendar years. However, these data render study programs rather than universities as spatial entities that can be compared. Again, this spatiality is not geographical but rather organizationalspatial, territorializing programs as commensurable (Espeland & Stevens, 1998) and clearly demarcated organizational entities fit for comparison and thereby governance. Finally, the numbers included in commission and committee reports typically enable cross-sectional comparisons that enact five areas of studies (the humanities,

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social sciences, natural sciences, technical sciences, and health sciences) as spatially demarcated in an organizational-academic sense. The consolidation of the five areas of studies as a dominant spatiality of Danish higher education is largely an achievement of these reports and similar reports from private bureaus and nongovernmental think tanks. This spatial order thus also represents an ontological reconfiguration of the sector. Besides this primary mode of comparison, the reports also compare countries, thereby contributing to the spatiality of a Danish territory of governance and furthermore include multivariable comparisons across both time and space. Especially the comparability of subject areas is interesting, whether as broader areas of study or as individual study programs, as it promotes a stratification of subject areas. This stratification represents an important governing property of the graduate outcome data and has implications that will be more closely analyzed in subsequent chapters. The enacted spatial order of programs is a very tangible ontological reconfiguration of the university sector, resulting from the quantification practices that emerged in Denmark in recent decades with these governance instruments. By contrast, the dominant UK and Australian quantification practices promote the stratification of educational providers (Marginson, 2006), thereby reinforcing the stratification traditions in these countries (Marginson, 2016; Naidoo, 2018). The university’s brand thus appears to be of greater importance in these countries than in Denmark, where the prestige of the area of studies appears the most important factor in stratification processes.1 In contrast to these numbers, the numbers embedded in contract instruments purely enable longitudinal comparisons between a baseline measurement and a result at the end of the contract period. Here, the spatiality of the university as an organizational unit is held constant, entailing that the university is not compared to other universities but merely to its own previous performance, thereby enacting universities as independent and strategic organizational units responsible for their own enterprise. This form of comparison does not produce spatial stratifications but rather a temporal order of progress and decline, which may be considered positive or negative depending on the character of the data.

 Other examples include US metrics that emphasize areas of studies, like the Danish case, but divided into occupational fields of study and academic fields of study, creating a different stratification, according to the purpose of education rather than disciplinary field, whereby academic fields of study stand out as less useful than occupational fields (NCES, 2021). Besides these stratification patterns, there is also a sign in the UK case of a promotion of the stratification of individual graduates, as the graduate outcomes are also aggregated in ways that highlight the educational achievements of the graduates. This stratification entails a meritocratic understanding of graduate outcomes as dependent on the skills of the individual graduates—an understanding not found in the Danish metrics. As a final example, the OECD reports Education at a Glance unsurprisingly promote the stratification of member states in terms of their education systems (OECD, 2020). 1

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6.2.2 Competitive and Hierarchical Directions of Governance Pressures Temporally and spatially organized comparisons influence the governance pressures enacted by numbers. These governance pressures may have different directions, like vectors. In simple terms, cross-sectional comparisons enacting spatialities of organizational units enable competitive peer pressure that is not enabled by longitudinal comparisons. Cross-sectional comparisons invite a sideways glance that motivates in different ways than longitudinal comparisons and their invitation to look ahead. Meanwhile, the instruments drawing on cross-sectional comparisons are often entangled with other relational structures than purely cross-sectional comparison that modify the direction of the governance pressure. While commission and committee reports, university data packages, and Education Zoom all enable direct comparisons of spatial entities, several of these instruments also include comparisons against a threshold. Like the numbers themselves, the thresholds are defined in and by calculative models. In other words, the important relation is that between a number and a (numerical) threshold and not that between two comparable numbers. In the accreditation instrument, thresholds are local and defined by the individual universities. Some universities have defined their thresholds as an absolute value, but most calculate them as a benchmark defined in relation to the average national performance. In other instruments, including the Resizing Model and the funding instrument, both of which use a single national threshold for algorithmic decision-­ making, the thresholds are also defined as benchmarks or relative values: in the Resizing Model as the national average graduate unemployment rate and in the funding instrument as the national average unemployment rate of the entire population. A spatial comparison against a benchmark entails a hierarchical pressure rather than purely competitive peer pressure. Hierarchical pressures are constituted by penalties from above, either in the form of managerial decisions regarding programs that continuously perform unsatisfactorily in the accreditation instrument or in the form of automatically reduced funding or enrollment allocations in the funding and Resizing Model instruments, respectively. While these instruments produce hierarchical pressures via procedures and processes that are built into the instruments, hierarchically directed pressures on the institutions may also appear in instruments that do not directly govern them, such as the commission and committee reports. As such reports embed cross-sectional comparisons in a context of policy recommendations, they result in hierarchical pressures on the compared entities, who may fear the political reforms initiated on the basis of the included cross-­ sectional comparisons. Potential students also face hierarchical pressures. The numbers displayed on the Education Zoom website place a moral obligation on students to make a rational educational choice, both for the sake of themselves and for the nation. Potential students are enacted as (self)calculable persons (Rose, 1999: 213–214) that take future risks into account and seek to avoid unemployment for the benefit of the

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collective, in line with the welfare state economy analyzed in Chap. 4. The hierarchical pressure in this case is thus not related to performance but to educational choices in the presence of the provided information, showing what the state considers a good educational choice. The contract instrument does not imply a competitive pressure, as it works through longitudinal rather that cross-sectional comparisons. Instead, the contract instrument can be understood as instilling a strategic pressure, encouraging the institution to envision a better future. This strategic pressure is thus directed temporally rather than spatially. Meanwhile, it is also partly a hierarchical pressure, as the ministry is involved in defining the strategic ambitions of each institution and may introduce sanctions if an institution does not reach the quantitatively defined targets. Together, the different modes of comparison install a variety of pressure directionalities that are entangled in complex and specific ways. The directions of governance pressures are both spatial (including competitive and especially hierarchical penalizing or moral pressures) and temporal (including strategic pressures). The competitive pressures enabled by the Danish graduate outcome data are relatively vague compared to the hierarchical pressures and their penalizing procedures. It is unclear what the spatialized entities (universities, programs, and areas of studies) are competing for, besides intangible assets like status and esteem. Only the Education Zoom website installs a purely competitive pressure on the measured programs, constituted by the marketization forces of students selecting programs to enroll in and thereby bringing funding to the host institution. Whether other policy instruments also instill competitive attitudes among academics and students from various areas of studies is another matter.

6.2.3 Fluid or Tenacious Data An analysis of comparability and the directionality of governance pressures maps relations between numbers and thus constitutes a description of the relational properties of a number in a single plane. However, numbers also obtain particular governing properties via their relation to themselves or in other words via their dynamic properties. Numbers can be fluid or tenacious. Number fluidity is not the same as elasticity (Piattoeva, 2015), as fluidity refers to the ability of a future version of numbers to be manipulated, or at least affected, while elasticity refers to the ability of already existing data to be the object of recalculations. Fluidity or malleability rests on the idea of fundamental equivalence and implies that measured entities should be (equally) able to move up and down the ranking order produced by the numerical calculations (Fourcade, 2016: 185). When numbers are fluid, they are thus considered relatively sensitive to the actions of actors constituting the measured units. Like the directionality of governance pressures, the dynamism of numbers differs depending on the nature of the instrument in which they are embedded.

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For example, the numbers presented in performance contracts, the funding algorithm, and quality assurance data packages are assumed to be fluid. In these instruments, the numbers are considered malleable or moveable and thus possible to manipulate. Performance data call for action (Winthereik & Jensen, 2017: 260). In quality assurance, a program is considered capable of change, improving an unsatisfactory program indicator through ongoing development and the implementation of initiatives institutionalized in action plans. The same progressive logic is foundational for the performance contracts. The fluid character of such numbers is a core principle in the idea of improvement through incentive structures, such as in the performance funding instrument. The actions of the measured universities and programs are reflected in the numbers, and low scores thus reflect poor effort rather than essential characteristics of the measured units. The contracts, funding, and quality assurance instruments are thus effective in instilling a sense of responsibility in the measured units. By contrast, the data disseminated in commission and committee reports and on the Education Zoom website, as well as the data that are processed by the algorithmic calculation of the Resizing Model, are not considered fluid and malleable. Rather, these numbers appear to be characterized by a tenacious dynamism. They express structural matters of fact. The structural and tenacious materiality of the numbers in these contexts is underlined by the historical dimension included in both the Resizing Model and several reports. For example, the assessment of ‘systematic excess unemployment’ in the Resizing Model is configured as long-term calculations spanning 10 years, indicating that the model addresses persistent long-term unemployment problems. The structural misfit calculations still rest on a fundamental principle of equivalence, but, here, a high graduate unemployment rate does not indicate a bad performance or low effort but rather a misfit between the production of graduates and the needs of society, which are considered relatively stable over time.

6.2.4 Distribution of Progressive, Adaptive, or Preventive Agency The type of dynamism affects the kind of agency the numbers distribute to actors. Poor numbers demand improvement in subsequent versions of the numbers. Meanwhile, the conditions for improvement that are embedded in numbers differ. The dynamism of numbers determines whether human beings can trigger changes in the numbers over time or are instead called upon by the numbers to change themselves. The dynamism of numbers thus has specific governance implications in terms of agency and responsibility. The distributed agency in the analyzed instruments is either progressive, adaptive, or preventive. Performance measurement instruments attribute number fluid properties that entail simultaneous improvement of actors and numbers. Thus, data series will in principle show progression alongside progressive actions within the measured organizational units. The distribution

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of a progressive agency is core to the governing properties of performance indicators, which are considered highly malleable through effort. The distribution of progressive agency by performance indicators constitutes one of the biggest difficulties when governing with numbers in education, especially with graduate outcome numbers. The measurement temporality of graduate outcome data and the temporality of initiatives and effects in education simply do not fit the progressive temporality of performance measurement. First, graduate outcome data are by their nature characterized by a significant measurement delay, as graduates need time in the labor market before it makes sense to measure their success rate. A current data set most often concerns graduates who graduated 2 or 3 years ago. This measurement delay is a result of the definition of unemployment as measured in the 4th to 7th quarter after graduation and of an additional period of time used for the processing of the measurements. Second, education as an enterprise is characterized by a significant effect delay, as it takes years for the effects of a change in educational practices to become apparent. In Danish higher education, it takes at least 5 years to produce a graduate from initial enrollment to graduation and at least 2 years for someone with a bachelor’s degree to complete a master’s degree and become a graduate. The effect delay implies that the effects of any educational initiative aimed at improving the performance indicators that targets students at a particular point in their studies will not have an impact on the numbers until the these students complete their studies. The combined measurement delay of graduate outcomes and effect delay of education means that it can take up to 5 years for an educational initiative to affect a graduate cohort plus an additional 2–3 years for its effect to be measured. While the annual performance measurement cycles characterizing the instruments and the progressive agency distributed by performance indicators entail an assumption of cause and effect, where initiatives are expected to improve subsequent performance data, this is literally never the case when it comes to graduate outcomes. The performance data are encumbered with a significant delay. This delay reduces the motivational mechanism of the performance measurement instruments, including both the quality assurance practices involved in the accreditation instrument and the performance-based funding and strategic framework contract instruments. The delay weakens the motivational mechanisms and economic incentives of performance measurement as universities and teachers are left with little opportunity to improve their performance in the immediate future. The annual temporality of the performance data and their distribution of a progressive agency constitute a fragile mode of governance. The agency distributed by numbers is very different in contexts where the numbers are considered structural and tenacious. Instead of a progressive agency, the numbers here distribute an adaptive agency, entailing that institutions and other actors need to adapt to the facts displayed in the numbers before the numbers will change. Instead of a simultaneity of progression (or regression) in the numbers and in the performance of actors, numbers here indicate an imbalance that can only be resolved through adaptation by actors. The requirement for adaptation is, for example, evident in the policy recommendations presented in the 2014–2017 expert commission and committee reports, which primarily included adaptive policies like the

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Resizing Model and a website to support transparency that would later materialize as Education Zoom. Both these policies sought to adjust graduate production to match the documented labor market demands, understood as a structural property of Danish society. Finally, the Education Zoom data used to govern students attribute them with a preventive agency. Instead of merely indicating the state of affairs, either in terms of an indicator, a baseline measurement, or a result, the numbers on the website are configured as a means of predicting the distant future based on projections of past patterns in the data into the future. This prospective function of the numbers relies on the notion of probability (Desrosières, 1998: 59; Hacking, 2006), which in turn relies on frequencies and the idea that a higher frequency in historical-statistical cases can be interpreted as a higher chance that the specific event will occur in the future (Hacking, 2006: 43–53). Probability thus serves as a core statistical concept aimed at mitigating future societal and individual risks. It implies that statistics can be used to generate speculative forecasts of a future that is always uncertain (Adams et  al., 2009). Potential students are encouraged to project past statistics into the future in order to be able to manage risk and control their own futures and thereby the future of society. Unlike the instruments distributing a progressive or adaptive agency, the website does not ask students to engage with the problems through initiatives focusing on improvement or adaptation but rather calls for them to prevent a repetition of negative patterns shown in the numbers. By way of the distribution of a preventive agency, this instrument governs students to be cautious and alert prior to and throughout their studies.

6.2.5 Temporally Oriented Affectivities Invoked by Data The temporal and spatial entanglement and the dynamism of numbers entail particular capacities to invoke different kinds of temporal orientations, and thereby also temporally oriented affectivities, within the governing and governed actors. Most of the instruments work through either reactive or responsive temporal orientations, implying that the quantitative information provided by utility metrics requires a reaction or a response. The funding system and Resizing Model instruments, which both represent algorithmic governance practices, simply react to differences in the numbers by automatically allocating funding or a graduate cap according to these differences. The reactive temporal orientation of an algorithm involves simultaneity of measurement and reaction. These orientations of the algorithmic instruments are imbued with government affectivities of justice, as the algorithmic devices dislocate the immediate affectivity of disappointment related to poor performance through penalties. Meanwhile, the politically defined mechanisms of the algorithmic devices are a matter of impassioned debate among the governed, especially in the case of the Resizing Model, which has been criticized and met with contempt and suspicion among academics working in the areas most affected by the instrument (as the subsequent chapters will show).

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The quality assurance systems and the politically appointed expert commissions and committees instead invoke a temporal orientation of responsiveness. These instruments do not dictate an automatic reaction but require responses from the actors involved (including university managers and staff or policymakers) when a problem is identified. This responsiveness is embedded in the governance instruments as requirements for action plans and as policy recommendations that address the identified problems. As the responsiveness relates to problems, it is imbued with affectivities of concern. However, while the accreditation instrument fosters concern within the institutions, sustained by the soft-governance instruments that the institutions have themselves implemented, the expert commission and committee instrument mainly fosters concern among policymakers and within the wider public, leaving institutions and academics with a sense of resignation and a fear of the reforms that policymakers might impose on them. The contractual instrument differs from these reactive and responsive instruments by being temporally oriented toward the future rather than the past and present. It invokes a visionary and strategic temporal orientation imbued with affectivities of optimism related to future progress and improvement and with promises of satisfaction and pleasure as the institution achieves these ambitions. The orientation toward the future entails that the institution implicitly distances itself from its present version, which emerges as flawed and unsatisfactory in comparison to the envisioned future. The multiple temporal-material properties of Education Zoom also entail multiple temporal orientations for the governed. While the instrument invokes temporal orientations of responsiveness and concern for institutional actors, much like the quality assurance systems, it invokes anticipatory temporal orientations for students. Vincanne Adams et  al. (2009) conceptualize anticipation as an affective-­ temporal state caused by the predictable uncertainty of the future. The possibility of predicting the uncertain future invokes anticipation as affective states or conditions that ‘interpellate, situate, attract, and mobilize subjects’ (Adams et al., 2009: 249). Graduate outcome data affect students by engaging them in the anticipation of a distant future of employment (Guyer, 2007; Nielsen & Sarauw, 2017: 165). When graduate outcome data are circulated among students, they produce possible futures that are enacted as an extension of the statistically measured past (Nielsen & Sarauw, 2017: 167), and these futures are ‘lived and felt as inevitable in the present, rendering hope and fear as important political vectors’ (Adams et  al., 2009: 248). Anticipation comes with necessity and moral demands: Anticipation, as a lived condition or orientation, gives speculation the authority to act in the present. Anticipatory regimes offer a future that may or may not arrive, is always uncertain and yet is necessarily coming and so therefore always demanding a response. Anticipatory regimes in their specificity can conjure many versions of the future, but what all speculations share is the orientation towards and claim to the future as that which matters. Anticipation is not just betting on the future; it is a moral economy in which the future sets the conditions of possibility for action in the present, in which the future is inhabited in the present. Through anticipation, the future arrives as already formed in the present… (Adams et al., 2009: 249)

Through anticipation, the conditions for present actions (such as choices) are set. Hence, anticipation provides a political potential for governance. Education Zoom

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incorporates anticipatory mechanisms by providing forecasts and predictions and produces affective states of anticipation among students. Anticipatory temporal orientations can involve affectivities of aspiration and yearning for one’s future, as well as affectivities of nervous anxiety. Indeed, the entanglement of Education Zoom with the financial plan and the analyses provided by the Productivity Commission (2014) and the Committee for Quality and Relevance in Higher Education (2014) appears to connect the instrument to affective stances of alert, warning potential students against applying for programs with low probabilities of providing them with secure employment and a high salary. This affectivity is enhanced by the single action associated with the instrument—the choice of program—which indirectly indicates that the potential student’s entire future is determined by this choice.

6.3 Governing with the Specificities of Numbers In summary, I argue that affectivities of concern, dissatisfaction, anxiety, and fear, as well as optimism, hope, and promises of pleasure, can be understood as governance mechanisms that are woven into the quantitative fabric of numbers, and in particular into the fabric of relations between coinciding and consecutive numbers. Together with the sideway, upward, and forward glances encouraged by different governance instruments and their use of numbers, an analysis of these governance mechanisms adds important insights to what can be gained from the instrumentation approach deployed in Chap. 5. In other words, governing with numbers is not merely about the instruments used to govern, such as performance measurement, algorithmic governance instruments, or nudging instruments, but also about the properties of the numbers used in these instruments. This chapter has sought to outline a vocabulary for studies of the governance properties of numbers. As argued, these governance properties include both the aesthetic and visual attributes and the relational properties of numbers. A number of useful concepts for the analysis of material properties of numbers already exist in the literature: data aesthetics (Ratner & Ruppert, 2019), number elasticity (Piattoeva, 2015), color coding and other symbolic coding (Brøgger, 2018; Brøgger & Staunæs, 2016), and data shadows (Leonelli et al., 2017). In addition to these attributes, the chapter has presented a relational theorization of numbers as constituted by their relations to other numbers (including thresholds and benchmarks) and to consecutive versions of themselves, thereby highlighting governing pressures and distributed agency as important governing properties of numbers. The common characteristic of these attributes and properties is that they cannot be analyzed without taking into consideration the quantification practices that constitute the numbers, as well as the instruments of governance in which the numbers are embedded. An overview of the Danish governance instruments and the governing properties of the numbers embedded in these instruments can be seen in Table 6.1. The table

Education zoom

Expert commission and committee reports The resizing model

Performance contracts Performance-­ based funding Quality assurance

Cross-sectional comparison

Cross-sectional relative benchmark comparison

Cross-sectional absolute threshold or relative benchmark comparison Cross-sectional comparison

Cross-sectional relative benchmark comparison

Mode of comparison Longitudinal comparison

Penalizing-hierarchical pressure

Mode of governance pressure Strategic pressure (+ hierarchical pressure) Penalizing-hierarchical pressure

Spatiality of programs Temporality of historical continuity Spatiality of programs Temporality of the future Competitive pressure for institutions Moral-hierarchical pressure for students

Penalizing-hierarchical pressure

Spatialities of areas of Affective-hierarchical studies, nations, etc. pressure Temporalities of progress and decline

Spatial/temporal orders Temporality of progress and decline Spatiality of universities Serial temporality Spatiality of programs Serial temporality

Table 6.1  Temporal-spatial material properties of graduate outcome data

Progressive Reactive

Distributed agency Temporal orientations Progressive Visionary

Tenacious-­ structural

Tenacious-­ structural

Tenacious-­ structural

Responsive

Adaptive Reactive via regulation Preventable Responsive for via nudging institutions Anticipatory-alerting for students

Adaptive via policy

Fluid, malleable Progressive Responsive through effort

Dynamism of numbers Fluid, malleable through effort Fluid, malleable through effort

6.3  Governing with the Specificities of Numbers 139

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presents a comprehensive overview of graduate outcome metrics used in Danish higher education. Other specific characteristics will most likely emerge if we turn to other higher education governance instruments, to other parts of the educational sector or public governance more broadly, or to higher education governance in other contexts than the Danish. The six instruments deploying graduate outcome metrics in the Danish context are primarily characterized by hierarchical (and sometimes strategic) pressures and only in the case of Education Zoom by pure competitive pressures. Meanwhile, the public’s access to Education Zoom, as well as the simplifying aesthetic and visual practices that characterize the website, reinforces its affective impact as an instrument enforcing competitive pressure, even though it represents an exception in Danish higher education governance. The contrasts across the Danish graduate outcome data make the power of data aesthetics and visualizations very clear. There are both instruments based on an assumption that the graduate outcome data are malleable and instruments based on an assumption that the data are tenacious, representing historical-structural continuity. These two assumptions furthermore relate to the same data, namely, the graduate unemployment rate, thereby creating tension in Danish higher education policy and governance. While heads of programs struggle to improve their numbers (as the following chapter will show), they also face continuous external intervention, challenging their motivation to take action themselves. The Danish case thus constitutes an example of how the material properties of numbers in governance may not be coherent but rather contribute to common difficulties when governing with numbers.

6.4 Closing Part III Part III of the book set out to study the governance practices in which the Danish graduate outcome metrics are embedded. While this involved a shift in analytical glance, from the metrics to instruments of governance, it eventually brought us back to the metrics. The study of governance practices presented in Chaps. 5 and 6 departed built on the work of Lascoumes and Le Gales (2007), but with a focus on how instruments and numbers govern rather than on how they theorize and legitimize governance. To my knowledge, this theorization of the governing properties of numbers as a product of their entanglement with other numbers constitutes an original and relevant contribution to the research on governing by numbers, indicating that governing with numbers is not just constituted by the presence of numbers but also by the specificities of these numbers. Meanwhile, both the instrumentation analysis and the analysis of material and relational properties of numbers have contributed to the discussion of governing with numbers in relation to neoliberalism. As shown in Chap. 5, performance measurement is related to accountability and corporatization rather than marketization

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in the Danish case. With the separation of universities from the bureaucracy of the state and their reconfiguration as large, self-governing corporations, enacted through the University Act of 2003, the universities became susceptible to wide-ranging accountability measures (Ørberg & Wright, 2019), including performance contracts, performance-based funding, accreditation, and public transparency in the shape of the Education Zoom website. While these accountability measures can be considered examples of new public management instruments, drawing on the neoliberal distrust of bureaucrats (Cahill & Konings, 2017), they are not (with the exception of Education Zoom) marketization instruments. The state plays a crucial role in defining the strategic priorities of the universities; as such, they are not really allowed to diversify and face success or failure in an open market. Instead, performance measurement is deployed to achieve uniformity across the sector, instilling strategic and hierarchical pressures, adaptive agency, and affectivities of concern, fear, and anxiety, rather than competitive pressures and affectivities of rivalry. The distribution of progressive agency by some of the performance measurement instruments substantiates the presence of new public management instruments but in a managerial form rather than a competitive and neoliberal form (Pedersen & Löfgren, 2012). Algorithmic governance cannot be considered an example of neoliberalism. The use of automated decision-making instead draws on the optimization logic from the Cold War grid of thinking (Bürgi & Tröhler, 2018) in combination with ideas from what is known as the Digital Era Governance paradigm (Dunleavy et  al., 2006). Also, while the nudging instrument can be considered as providing support for students as rational consumers, it can also be analyzed as a negation of this neoliberal idea of the rational consumer, as it considers the market of potential students incapable of making a rational choice and instead promotes a morally wired image of what the state considers a rational choice. Thus, an analysis of educational governance drawing on an economic and historical notion of neoliberalism, rather than on a governmentality-oriented theorization, would not deem Education Zoom neoliberal but instead as an example of a policy of optimization and coordination in a new, subtle guise. The various analyses presented in part III challenge the idea that governing by numbers is inherently neoliberal. Meanwhile, the governance practices certainly affect the university sector. Especially the algorithmic governance instrument of the Resizing Model and the nudging instrument of Education Zoom have had profound effects on parts of the education sector, both in terms of the subjectivities of students and teachers, and the economic conditions for the production of high-quality education, in particular within the humanities. In part IV, I will focus on the performative effects of the graduate outcome metrics in their reception by various actors in higher education.

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Part IV

Data Reception: Subjectivities and Amplified Resource Inequalities

Chapter 7

Subjectivizing Effects of Graduate Outcome Data

Calculative governance practices affect the lives of people involved in higher education institutions and their governance, encompassing students, parents, teachers, managers, business partners, employers, and policymakers. These people’s understandings and decisions in relation to higher education are potentially changed by encounters with data, with consequences for both their life experiences and actions. While educational data serve as important tools in educational governance and administration, where they are used to evaluate and optimize education as an enterprise that benefits society, they are thus not innocent. As the literature has shown, evaluation and optimization are not the only effects of using data in educational governance; in other words, there are other effects than those explicitly identified as the reasons for developing and deploying such data. Hence, educational data significantly affect the educational realities that they are put into the world to describe. This chapter explores the different ways that educational actors engage with data. The effects of data reception, which is the focus of inquiry here in Part IV of the book, also include data’s impact on educational development, which is the topic of Chap. 8. The claim that educational data affect people is not new. Several scholars have shown how data affect the desires and behaviors of policymakers (Grek, 2009), educational leaders (Espeland & Sauder, 2007; Lewis, 2018; Lingard & Sellar, 2013), teachers (Shore & Wright, 2000), students (Espeland & Sauder, 2016; Keddie, 2016), and the wider public (Sellar & Lingard, 2018). However, the combination of these studies’ shared focus on how data affect and a detailed analysis of specific educational data, such as provided in the previous chapters, opens a new analytical space. It allows a tracing of the effects of the specific stratifications, educational-­economic theories, governance mechanisms, aesthetics and visualizations, and governing properties embedded in and enabled by particular data on the This chapter draws partly on previously published empirical analyses and theoretical arguments (Madsen, 2021). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_7

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people engaging with them. This type of analysis is relevant to both governance and democratic debate as it invites discussions on the advantages and disadvantages of specific quantification practices, not only in technical-statistical and political-­ managerial terms but also in terms of human well-being. Previous scholarship, for example, by Marilyn Strathern (2000) and Chris Shore and Susan Wright (2000), has analyzed the proliferation of what they call an audit culture in academia. Here, ‘the principles and techniques of accountancy and financial management are applied to the governance of people and organizations’ (Shore & Wright, 2015b: 24), thereby transforming academic subjectivities into what they call self-actualized auditable individuals (Shore & Wright, 2000: 78). The calculative practices involved in producing educational data are part of this audit culture. Shore and Wright see audit culture and its seemingly legitimate concern with improving the quality and efficiency of the university as nothing more than a new form of coercive power structure in disguise, with costly and damaging effects on academic work (Shore & Wright, 2000: 85). By comparison, the analysis presented in this chapter is more interested in how people engage with data in various ways. It builds on these earlier studies but adds a new materialist understanding of becoming that emphasizes the various ways educational actors enact subjectivities provided by graduate outcome data, as well as sensitivity toward the specificities of these data rather than the effects brought about by data in general. In relation to the book’s overall storyline, the chapter will also analyze how human capital theory and neoliberal thinking penetrate the subjectivities of students and teachers, summarized in the discussion that closes Part IV (see Chap. 8). The chapter first introduces contemporary approaches to the study of how data affect humans. Next, it presents ethnographic reports and analyses of the effects on students, teachers, educational leaders, and policymakers in higher education. As these analyses will show, living with graduate outcome data affects actors both in general ways characteristic of engagement with any type of data and in specific ways closely related to the specificities of the Danish graduate outcome data. The analysis furthermore shows how students, teachers, and educational leaders find different ways of engaging with the data and that policymakers, who are usually considered powerful and free to follow their own interests, are also affected and constrained by data. Living and engaging with graduate outcome data is thus demanding for all involved parties.

7.1 Data, Affectivity, and Subjectivizing Effects Different bodies of literature offer different perspectives on how human beings engage with data. The performance management literature is mostly occupied with either the effectiveness or performative effects of different calculative practices as seen from a management perspective. In this literature, calculative practices are understood as instruments of authority or incentive that are implemented to ensure staff’s motivation to improve their work (e.g., Holm, 2018; Pollitt, 2013). In

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general, the performance management literature considers a rational use of measurement data appropriate, but cases of ceremonial and symbolic use of data (Argento et al., 2020: 10) and cases of ambiguous and uncertain data resulting in unpredictable effects (Boedker et al., 2020) are also reported. Specifically, it is considered difficult to manage universities through performance measurement as they are hybrid organizations characterized by a mix of private and public ownership, by goal incongruence and competing institutional logics, by a multiplicity of funding arrangements, and by a mix of public and private forms of financial and social control (Vakkuri & Johanson, 2020: 36). These conditions lead to an abundance of measurements, thereby limiting the positive effects of performance measurement. Studies in the performance measurement literature thus often seek to understand why people do not engage with measurement systems in the intended ways. Educational governance literature is also concerned with measurement systems and unintended effects but often based on an implicit ideal of reducing the use of data in educational governance rather than an ideal of rational use of data. Sam Sellar and Bob Lingard use the notion of catalyst data to conceptualize the effects of large-scale assessments like PISA on national or post-national policy contexts. This notion of catalyst data emphasizes the value of the data produced in these assessments as a matter of what they do, in terms of catalyzing effects, rather than what they show. When performance data enter a particular context, they ‘provoke a reaction’ and open windows for policy reform (Sellar & Lingard, 2018: 368). However, not all actors engage with data in intended ways. For example, Lingard and Sellar examine the enacted effects of high-stakes testing in three Australian states. The kinds of effects they point to are perverse political effects, such as attempts to game the system by setting targets that ensure successful outcomes and thereby federal funding of state education systems (Lingard et al., 2016: 79, 82). There are a range of studies that point toward the perverse (Baird & Elliott, 2018; Shore & Wright, 2015a: 426), unintended, or constitutive (Dahler-Larsen, 2007; Rijcke et  al., 2016) effects of performance measurement in various educational contexts. In addition, much contemporary educational research on educational data seems to be very interested in how data produce particular affectivities that work upon human beings and make them act in specific ways. For example, Espeland and Sauder studied the effects of rankings and showed how they create anxiety, fear, and concern and furthermore how these affectivities caused educational leaders to mimetically reflect the priorities of the rankings in internal budgetary priorities in order to improve their ranking (Espeland & Sauder, 2016: 2–4). They label these rankings ‘engines of anxiety’ (Espeland & Sauder, 2016). Similarly, other studies have shown how data affect populations of parents and educational professionals, perhaps even intentionally. According to these studies, both politicians and the media frame data affectively and rhetorically to encourage specific interpretations (Sellar & Lingard, 2018; Webb & Gulson, 2012). Color-coding technologies can also be utilized to promote specific affective effects, motivating actors to engage in efforts to adapt and improve their performance. Governance that uses color-coding technologies accelerates change processes through naming, shaming, and faming

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(Brøgger, 2016; Brøgger & Staunæs, 2016). The colors incite participating actors ‘to move from the “reddish alert colors” to the calmer green nuances indicating success and hopefully a better sense of oneself’ and keep actors ‘stretched between the potential embarrassment of shame and the potential thrill of fame’ (Brøgger & Staunæs, 2016: 231). However, a focus on affectivity does not enable sufficient understanding of how human beings are affected by data (Madsen, 2021). Data may entail various subjectivizing effects, including both affectively wired intensities that affect human bodies and consciously wired thoughts and strategic actions. The agential realist notion of the radical co-constitution of various entangled elements, including entanglements of humans and data, entails that data cannot simply be understood as produced by human beings nor can human beings simply be understood as effects of the discursive-­material work performed by data. Both data and human beings emerge from materialization processes through which a myriad of other forces co-constitute one another. These materializations processes include, for example, the becoming of (the subjectivities of) human beings (Højgaard & Søndergaard, 2011; Lunde, 2021) as they emerge in their entanglement with metrics, including both quantitative practices of producing difference and objectivity, economic-educational theories, and the instrumental capacities and materialities in which metrics are embedded. The co-constitutive forces are nondeterministic, enabling and constraining human beings in complex ways in combination with a range of other forces, which may all be picked up by human beings in various ways (Lupton, 2018). Nevertheless, data are indeed one of the forces affecting subjectivities, and this particular force works through particular affectivities and rationalities, including particular codes of conduct regarding how to be rational and affected in appropriate ways, that human beings may then adopt and enact. In the language of agential realism, subjectivizing effects can thus be understood as the ways organizations, nation states, public agencies, institutions, and individuals accept the invitations embedded in metrics to navigate or govern themselves in accordance with the data. Through their embedded configurations, metrics enable certain responses from different actors and thereby invite policymakers, managers, teachers, and students to navigate or govern themselves and others in particular ways (just as these actors invite the fabrication of particular forms of metrics). This chapter analyzes what kinds of educational subjectivities the graduate outcome metrics enable and how these are picked up and enacted by students, teachers, and policymakers in higher education. The analysis of enacted subjectivities draws on ethnographic fieldwork conducted over 18 months in 2016–2018, where I made repeated visits to three Danish universities where I followed their efforts to make their programs more relevant to the labor market—something I will return to in Chap. 8. During these visits, I attended 60 activities, including meetings and special ‘employability’ events where leaders, managers, administrative staff, teachers, students, and advisory board members participated in different constellations and with various purposes. Besides these events, I also conducted 41 interviews with teachers, employer representatives, managers, students, and administrative staff and 12 more informal conversations with managers and administrative staff (some of which

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overlapped with those interviewed). This part of my fieldwork took place at the humanities departments and faculties of the three universities, because this academic field is highly affected by the metrics—at least in the sense that the humanities often stand out by achieving poorer results in the measurements than other areas of studies. I furthermore interviewed representatives of four key national policy stakeholders: the Danish Ministry of Higher Education and Science, the Danish Accreditation Institution, a government-appointed commission, and a lobby organization. While the fieldwork was mainly designed to study educational development based on graduate outcome data, it also provides a rich body of material for understanding how educational subjectivities are affected by data. My encounters included numerous conversations about graduate outcome data and how they affect people, whether informal conversations before, during, or after meetings and events or during formal interviews. These conversations could be about others e.g., teachers and managers often talked about the effects on students) or about themselves (e.g., students often described themselves as worried, in doubt, deliberately indifferent, and so forth). However, my entanglement within the field also allowed me to observe subjectivizing effects myself through thoughts shared among students and the decisions they made as their studies progressed. Throughout the chapter, I will include observations from meetings and interview statements (all translated by me from Danish to English). The analysis is organized around students, teachers and educational leaders, and policymakers as key educational actors affected by data. As such, it does not include other relevant groups such as secretaries or external actors like employer representatives, despite their importance with regard to educational governance and design.

7.2 Becoming Student with Graduate Outcome Data During my fieldwork, I became acquainted with at least 25 students enrolled in humanities programs. I met some of these students several times during the eighteen months of my fieldwork, as they were elected as student representatives on one of the boards of studies at the universities, whose meetings I attended. Besides getting to know these students via my observations at meetings and through brief conversations during coffee breaks, I also participated in a special meeting assembly with several boards of studies and furthermore in a student representative meeting with participation of students from several boards of studies. On both of these occasions, I was able to observe the students speaking to each other in both a formal setting and during the lunch break. As elected student representatives, these students had a special interest in the governance of their programs but also occasionally shared their personal thoughts and approaches to being a student. Besides these elected student representatives, I also interviewed 10 students and observed 4 events involving a total of approximately 75 students: a career event, two ‘Student Development Dialogues’ focusing on the studies and career opportunities of a small group of

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students, and a student-initiated meeting for exchanging thoughts and ideas regarding which program specialization to choose. The ethnographic material thus includes both observations of students talking informally to each other and to teachers and more structured interviews with students regarding their thoughts and actions related to the labor market and their engagement with graduate outcome data. While the policy instrument Education Zoom, which is the only instrument that is directly aimed at governing students, is actually a tool for potential students, the analysis will show that students already enrolled in humanities programs are also affected by this instrument and the graduate outcome data more generally. Data narratives circulated in the media cause doubt and anxiety among students. To overcome these narratives and make the humanities livable, the students make efforts to adapt in pursuit of more positive positions in the narratives. In other cases, they construct alternative narratives through which they can obtain more positive positions. As the analysis will show, living with graduate outcome data is something that humanities students find demanding.

7.2.1 Data-Driven Narratives In general, the students that I encountered during my fieldwork did not spend much time engaging directly with educational data. Of course, those elected as student representatives had a formal role in the quality assurance procedures and were presented with key figures on graduate unemployment as part of this work. Furthermore, a few students told me that they had used Education Zoom before or during their studies, mainly to compare graduate unemployment rates and graduate income rates across programs. Nevertheless, the students that actually looked at graduate outcome data were a minority among the students with whom I spoke. Many students stated that they had not used the website and were not interested in looking at the data presented there. However, all the students in my material were aware of the relatively high unemployment rate among humanities graduates, which they had learned about in the media. Their main brush with graduate outcome data was thus not through direct engagement with the data itself but rather through encounters with negative narratives supported by the data, whose existence they thereby became aware of. The circulation of in particular the graduate unemployment data and its effects were highlighted by one of the heads of studies that I interviewed during my fieldwork: Some of these concerns [regarding the relevance of their area of study] are prevalent among our students, so the political discussions or discourses spill over. I mean, the young students and in particular their parents also read the newspapers and watch the news, so they hear the politicians advising against… no, that is not what they say; they say that they are promoting engineering, right? And then we have a young woman who would rather study Spanish or dramaturgy or something like that… and of course that instills doubt [within her] as to whether she is making the right choice. (Interview with head of studies, February 2018)

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The effects of data on students were thus mediated by the media. In most cases, the data materialized as narratives rather than raw numbers. The doubts as to whether one is making the right choice that the head of studies mentioned were amplified at the time of the interview by another higher education policy called the Education Ceiling (Ministry of Higher Education and Science, 2020). This policy instrument introduced a capped-free participation in higher education programs to one program per student while exempting programs with a significant and unfulfilled demand within the labor market. The Education Ceiling meant that the students’ choice of studies had to be right from the get-go. However, students’ doubts did not evaporate once they had made a choice but followed humanities students throughout their studies, as is apparent in my observations of conversations among students: In a coffee break, I hang out in a sitting area where around ten student representatives from different areas of studies are gathered. They talk about how they enjoy their studies. One female student talks about how she still has doubts about her choice of study program. Even though she finds the area of study very interesting, she does not know if it was the right path to take. She talks about the general anxiety and lack of motivation among her fellow students and herself as a result of the constant talk about unemployment. The other students respond with concern and show that they identify with such doubts. (Observation from a meeting break, May 2017)

The female student at the center of attention in this conversation anxiously observed the future prospects for a graduate with a degree in aesthetics. Her doubts and anxieties were linked to the general narrative on graduate unemployment. A similar concern was expressed among students on many occasions during my fieldwork. Thus, the circulation of data on high unemployment rates among humanities graduates in general had a negative effect on students within this area of study. The capacity of data on graduate unemployment to affect students who are not themselves measured in the data relies on anticipatory modes of engaging with data, as outlined in Chap. 6. Adams et al. emphasize that regimes of anticipation affect subjects individually and collectively (Adams et  al., 2009: 249). Thus, from a theoretical point of view, the forecasts provided by policy initiatives such as the Education Zoom website may not only affect the individual student (and potential student) who actively seeks out such data but may also affect students through discourses circulating in the general public. The negative statistics produce collective anxiety as well as individual fears regarding the risk of unemployment. The collective hopes and anxieties affect individuals and thereby amplify the effect of forecasts. While Education Zoom is an example of a policy instrument that directly utilizes this political potential, the more widespread communication of graduate outcome data and the surrounding political rhetoric stimulate anticipatory states of being among students, as evident in the observation described above.

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7.2.2 Student Enactments of the Human Capital Narrative Being a student with (poor) anticipatory graduate outcome data thus involves an affective state of doubt and anxiety. The future appears as an ‘aggressive and invasive force coming on to the present’ (Nielsen & Sarauw, 2017: 168). The students that I interviewed used words and phrases like ‘the fear of running out of unemployment benefits and getting stuck’, ‘frustration’, ‘panic’, ‘stress’, ‘the nagging feeling in one’s stomach’, and ‘doubts’, and they talked about groups of friends affected by ‘mental disorders, anxiety, and depression’ (Interviews with students, November 2017–March 2018). These affective states were all associated with unemployment rates and the public image of the humanities. Looking back, the choice of a humanities program comes with both a sense of nervous anxiety (Adams et al., 2009: 247) and a sense of guilt toward the collective (Mol, 2008: 79–80). However, many students found different ways of avoiding these negative affectivities by connecting to specific narratives through which they could achieve more optimistic and hopeful affectivities. The hopeful affectivities often involved choices that would limit their future options or require extra effort from the students. The present was thus not merely affected by the anticipated future for these students; their future was also created by present anticipation, as ‘material trajectories of life […] unfold as anticipated by those speculative processes’ (Adams et  al., 2009: 248). The students’ speculative futures were thus enacted through decisions in the present, and students thereby became ‘rational [choosers] in the short run and “makers of [their] own fate” in the long run’ (Guyer, 2007: 413). The narratives that the students connected to often drew on elements in or versions of human capital theory. Several students avoided negative anticipatory affectivities by committing themselves to a future associated with a professional narrative on higher education, where a degree program is more closely linked to a specific occupation than is the case for most graduate employment within the humanities. The specific future that the students I interviewed most often pointed to was employment as a high school teacher. Becoming a high school teacher requires a certain educational profile, thereby limiting the freedom of the student, but is associated with a clearer labor market profile. As one student puts it, ‘When you have a plan of becoming a high school teacher, then you are more capable of taking it easy. You get a sense of calmness by knowing where you are going’ (Interview with student, March 2018). I found the same attitude among musicology students who had chosen the teaching specialization within their program. The commitment to a specific profession connects the students to educational-economic relationships of qualifications and secures employment and thereby counters the humanities narrative and mitigates negative affective states among students. Another way of avoiding the negative affectivities is to connect to an educational-­ economic narrative regarding the benefits of capitalizable skills and positional advantage. Students do this by engaging in part-time work alongside their studies, collaborating with companies on student projects and theses, and choosing particular electives that may make them more attractive within the labor market. With

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inspiration from Sam Sellar (2015: 427), one could argue that they invest in self-­ appreciation, becoming motivated to engage in educational and extra-educational activities that improve their human capital. Sometimes, these motivational effects also involve major shifts of study direction, as in the case of the bachelor’s student in the observation below: Before a meeting, I bump into some students and a teacher from the program that I am following in my fieldwork. The students and the teacher make small talk about different topics. Among the things that come up is the lack of new PhD applicants. The teacher asks one of the students if she would be interested in doing a PhD after her master’s degree. She says that she is not sure if she has the courage to go all in, so she might choose a different area of study for her master’s degree. She is concerned about what doors she might be closing with a master’s degree in an aesthetic area of study. (Observation from an informal conversation before a meeting, May 2017)

In this situation, the female student sees two possible paths—the courageous path where she continues her studies in a purely aesthetic field and might face closed doors later on and the safer path where she chooses a different area of study. It seems like she is anticipating a lack of opportunities if she pursues her studies in the field of aesthetics—a field where she is presumably prospering given that the teacher asks whether she is interested in applying for a PhD. As such, this student is aware of the positional advantage associated with particular choices and appears to be ready to act in response, despite the melancholic affectivities associated with such a radical change of direction. Negative anticipations can also be avoided by connecting to a more dynamic educational-economic narrative than that reflected by the graduate outcome data. Take Elizabeth, a graduate student within culture studies who participated in a group interview: Elizabeth: No, [I have not had doubts] not ever… at all, I mean not at all. I have never felt as sure that I was making the right choice as when I started my first semester here, and I am not at all nervous about whether I get a job when I am done. Alice: (laughing) That was very to the point! Me: But how do you then feel about the way the humanities are talked about? Elizabeth: It is shit. But that is also because so few people actually understand how important the humanities and the things we work with are, because they don’t know what it is. And in a world that becomes more and more digitalized, entirely new problems that we as humanities graduates can take care of will emerge, and that means that we as humanities graduates will continue to be relevant, and that is also the reason why I am not nervous. (Interview with students, February 2018)

As the quote shows, Elizabeth draws on the educational-economic narrative of valuable and relevant skills but relates it to alternative analyses of the value of the humanities than those provided by data on previous graduates. Her analysis resembles ideas embedded in the concept of 21st Century Skills (Partnership for 21st Century Skills, 2002; World Economic Forum, 2016), underlining the importance of the humanities. It connects to the conceptual premise of the graduate outcome data but not the temporal premise of a prolongation of the past into the future. Through this alternative analysis, she questions the negative anticipated futures and instead emerges as an optimistic humanities student.

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Finally, some students actively dissociate themselves from the human capital narrative and the relevance configuration of education altogether, like this young male philosophy student: I make a tremendous effort to free myself from [the statistics]. Because, well, it is the thing you are always confronted with, and that is just fucking frustrating, I feel, because … it almost limits your freedom to make your own decisions as a human being, you know? (Interview with student, March 2018)

This philosophy student questions the appropriateness of circulating the graduate outcome data. He problematizes the morality of softly governing students’ choice of areas of studies through information, stating that this type of governance limits or depreciates the freedom of students to make their own choices in life. With this critique, he refers back to the moral contract embedded in Danish society, which entails that each citizen has both rights and obligations in relation to the state. This contract is the foundation for equal and free access to education based on the student’s personal choice in return for a significant contribution to the collective via high taxes (also see the analyses presented in Chap. 4). With the circulation of graduate outcome data, he as a student faces the hierarchical pressure of the calculative practices, obliging students to make choices in line with state priorities. These hierarchical pressures are in conflict with societal values and expectations of educational freedom as reward for contributing to a highly collective state economy for the rest of one’s life. The student thus points toward a tension between liberal and social democratic values in Danish society and indicates that overly strong governance of educational choices is anti-liberal. This philosophy student also talked about freeing himself from the statistics. This is no easy task. The sense of effort that the student talked about was a common experience among several of the interviewed students. They had decided not to think about unemployment and allow the data to affect them. However, ‘not thinking about it’ is an active stance rather than a passive attitude, since students are continuously and unrelentingly confronted by statistics. While these students enacted a mode of resistance toward the anticipatory human capital regime and thereby a resistance toward the associated negative affective states of being, the result was not relaxed or positive affectivities like students connecting to the profession, positional advantage, or dynamic and progressive human capital narratives but rather a replacement of fear with exertion. Becoming a humanities student with graduate outcome data thus implies a lot of work, whether in the form of living with negative affectivities, making educational choices that narrow one’s future options, doing extra work to obtain a positional advantage, conducting alternative analyses that negotiate the prospects provided by the data, or resisting the data and their embedded narratives. These different ways of being a student are directly influenced by the specific formation of the graduate outcome metrics. They are entangled with the production of differences between areas of studies and the negative stratifications and valuations of the humanities, and with the human capital narratives embedded in the data, which the students address or dissociate themselves from in different ways. They are furthermore entangled

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with the nudging mode of governance that seeks to influence the students’ choices. In the case of Danish humanities students, it thus appears as if a nudging instrument and a broader political nudging rhetoric affect students in many negative and/or demanding ways beyond their educational choices.

7.3 Becoming Teacher with Graduate Outcome Data Almost all of the meetings that I observed during my fieldwork involved university teachers in various roles, be it as staff members, as teacher representatives in various working groups and committees, or as teachers appointed specific leadership or management roles. My observation notes primarily stem from meetings held by boards of studies (as described in relation to students), in specific working groups or project teams to address student employability, between heads of the humanities faculty and heads of particular programs to negotiate new employability initiatives, and with external advisory boards where labor market representatives gave advice on educational development. I also interviewed seven teachers and seven educational leaders, however not including top management, and had informal conversations with one of the interviewed teachers and four of the interviewed leaders, not counting the numerous conversations during meeting breaks. In general, the interviews revolved around the topic of the humanities’ situation and the specific processes of educational development incorporating graduate outcome data in which the informant was involved. Due to the focus on interviewing teachers (and teachers with leader roles) directly involved in the development of degree programs and/or other aspects of education, the interviews primarily examined local quality assurance practices and the general state of the humanities in relation to graduate outcome data, as the other Danish policy instruments analyzed in Chap. 5 do not directly involve teachers in their various roles. However, during the observed meetings, the strategic framework contracts, the performance-based funding system, the Resizing Model, and Education Zoom also came up, either in informal conversations or in relation to specific agenda points. The interviews and observations took place in 2016–2018, after the implementation of the Resizing Model and the launch of the Education Zoom website and during negotiations concerning the performance-based funding model and the new strategic framework contracts, as well as the work of the Committee on Better University Programs (2018). Two of the universities were in the middle of an accreditation process at the time of my fieldwork, while the third had been accredited a few years earlier. The interviewed and observed teachers were all aware of the problematization of the humanities, especially the problematization enacted by the graduate unemployment rate. The teachers had not only encountered these data in the media and in the public discourse on the humanities but also in the organizational context of their university. While the teachers with special representative roles or leadership responsibilities were directly involved in the processing of unemployment indicators as part of the university’s quality assurance work, all

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teachers were continuously informed of their program’s standing, for example, at staff meetings. The graduate unemployment data were thus a presence in the daily lives of teachers within the humanities. The following analysis will show how teachers and educational leaders in the humanities are co-constituted and affected by graduate outcome data that indicate problems with humanities programs. While various affective experiences of data by teachers can be dealt with by focusing elsewhere, educational leaders are required to engage more formally with the data and thus find manageable and tolerable ways of dealing with them. These stances toward data include adapting to codes of conduct on appropriate affectivities and actions but also negotiations of and with data.

7.3.1 Data Ambivalences and Ambiguities The teachers that I encountered in my fieldwork were affected by the graduate unemployment data in a combination of ways. As the graduate unemployment rate is aggregated in different ways for different purposes, it addresses teachers in different ways. Remnants of the analyses provided by the Productivity Commission (2014) and the Committee on Quality and Relevance in Higher Education (2014), as well as the political rhetoric and media coverage in relation to the launch of the Resizing Model and Education Zoom, were still present among the teachers at the time of the fieldwork. The teachers were thus very well aware that the humanities had been identified as problematic in terms of an overproduction of graduates with academic profiles of limited relevance for employers. The aggregations of data according to five broader areas of studies (humanities, social sciences, natural sciences, technical sciences, and health sciences) in these contexts entailed a stratification of areas of studies in which the humanities appeared to represent a significant problem. The problematization of the humanities as an entirety seemed to result in affectivities of resignation among the teachers, as the structural situation of unemployment and an overproduction of graduates seemed out of their hands. For some, this resignation was accompanied by indignation fueled by victimization and oppression—they felt that focusing entirely on outcome measures related to the labor market constituted a political attack on their academic knowledge and values, based on economic values. Furthermore, the teachers had already experienced a number of interventionist university reforms in recent years, including the launch of the Resizing Model in 2014 and the Study Progress Reform from 2013. The combination of the problematization of the humanities and the various political reforms seemed to make the teachers feel vulnerable to further top-down interventions from the Danish Parliament and the Ministry of Higher Education and Science. In this sense, the graduate unemployment data brought about affectivities of fear and uncertainty. Several teachers also expressed considerable concern for their students, who had to navigate what they saw as a devaluation of the humanities. The simplicity and ‘apparent straightforwardness’ (Espeland & Sauder, 2016: 22) of the numbers was thus disputed by a number of widely held views among the humanities

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teachers. These included accusations that national policymakers were responsible for the conditions leading to the situation currently facing the humanities and academic values that they saw as incompatible with the economic values embedded in national higher education policies. Nonetheless, the teachers acknowledged the objectivity of the data and consequently seemed unable to communicate their conflicting viewpoints in a way that could genuinely contest the narratives provided by the data. The aggregations used on the Education Zoom website, where individual programs were rendered comparable, and in quality assurance procedures, where the graduate unemployment rate is aggregated into an indicator comparing individual programs to a threshold value, in turn stratified programs (rather than areas of studies), thereby calling for a sideways glance toward other programs. In my fieldwork, managers, administrators, and particularly eager teachers read these numbers carefully and checked the position of their own program compared to similar degree programs. They wanted to know how they were performing and thus how they were exposed in the public. At one university, comparable data on similar programs— both in-house and at other universities—were explicitly included in the data packages circulated at meetings held by boards of studies. These calculative practices caused affectivities such as envy or pride, depending on how their program scored on a particular indicator, along with a search for explanations as to why the score was different from comparable programs and suggestions of how to address low scores. In this context, the graduate unemployment data were thus considered at least somewhat malleable and furthermore used to motivate teachers to improve their standing compared to other programs. However, teachers were often frustrated by a lack of the resources, and in some instances a lack of a sufficiently well-­ performing student population, that would allow them to take what were considered necessary actions to achieve such improvement. The concurrent competing narratives of a structural problem of the humanities that was out of the teachers’ hands and a performance or quality problem of the specific program that could be partially improved through the actions of teachers seemed to lead to ambivalence and ambiguity among the teachers in relation to the graduate unemployment rate. The combination of resignation, indignation, fear and uncertainty, a sense of responsibility, motivation, and frustration resulted in a state of dismay among the teachers that permeated many of their formal and informal conversations with colleagues. The use of graduate outcome data thus seems to have a negative effect on teachers and their working environment.

7.3.2 Data Codes of Conduct Moving on to the educational leaders that I interviewed, they were all (current or previous) teachers and affected by the same ambivalence and ambiguity concerning the graduate unemployment data as the other teachers. Meanwhile, as leaders with formal roles in the universities’ quality assurance systems, they also had to conduct

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a number of procedural activities revolving around data. Usually, the program heads had the role of writing a report on the status of the program, including comments on the graduate unemployment indicator and other indicators, as well as developing an action plan addressing issues revealed by the indicators. Through the reporting requirements, a sort of code of conduct was established wherein the head of program was morally obliged to adopt a particular stance in response to an unsatisfactory indicator that involved taking responsibility and taking on shameful affectivities on behalf of the group of teachers involved in the program. Furthermore, the mandatory action plan established an entrepreneurial code of conduct of continuously striving to improve the graduate unemployment situation. This code of conduct appears to be institutionalized within the accreditation instrument. In my interview with the chief executive of the Danish Accreditation Institution, he stated that they ‘look at the intentions’ when assessing whether or not unsatisfactory indicators have been adequately addressed. The guide to institutional accreditation similarly states that the assessment will depend on the ‘attempt to solve problems’ demonstrated by the institution (the Danish Accreditation Institution, 2013: 19–20). The role of the head of program when writing the report and the action plan is thus to demonstrate the right intentions. Similarly, leaders at higher institutional levels have formal roles in quality assurance processes. Educational leaders at faculty level are responsible for approving the reports and action plans written by the heads of programs. They are furthermore required to write aggregate reports for approval further up the organizational hierarchy. Ultimately, leaders at both faculty and university executive management levels are also morally obliged to take action in cases of unsatisfactory indicators. As one head of program paraphrased his superior from a meeting where the report and action plans were discussed: ‘We don’t need to talk about anything else; we’ll move directly to the key figures’ [he said]. So that was the main priority, right? And that was of course because it was a mandatory task for him: We need to improve these numbers, right? So all the other things that are also important, like well-being and stuff like that, they have also been acknowledged and approved, or however it was done, but the most important thing was to address the key figures and launch initiatives in relation to them. (Interview with head of program, May 2017)

The quote illustrates that educational leaders at all levels are held responsible for the indicators. This responsibility can also go beyond launching initiatives in response. One head of program told me about such an instance of the university management making responsible decisions based on data during a conversation on our way to a meeting. Apparently, the university’s rector had recommended stopping student enrollment in eight degree programs, including the one for which this head of program was responsible. The program had three ‘red lights’ or unsatisfactory indicators over a prolonged period, including the graduate unemployment indicator. Thus, the problematizations of the program produced by the indicators appeared to require more far-reaching managerial decisions than continuous action plans, to a degree where these decisions threaten the existence of the entire program. The head of program and the faculty management had been working ‘on all cylinders’ to avoid such a drastic decision by producing more recent numbers

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showing an improvement and ‘turning off’ one of the red lights (observation notes, May 2017). As suggested by Miller and Power, heads of programs and other educational leaders are thus subjectivized into a ‘society of auditees’, who are shaped by the ‘need to create trails of evidence of proper performance’ (Miller & Power, 2013: 587), where performance in this case includes intentions as well as actual measured performances. Through these subjectivizing processes, educational leaders become drivers of educational development and other initiatives. Often, I sensed a state of action plan fatigue among the humanities programs that I encountered in my fieldwork. The reason for this fatigue was not merely the need to expend energy on launching new initiatives in a parsimonious environment but also the innate measurement and effect delays of graduate outcome data, as outlined in Chap. 6. Because of this delay, the possible effects of an action planned in a given year on the indicator will not become apparent for a minimum of three years. The delay can potentially be far longer, depending on the type of action and the stage of the study program it targets—for example, an action targeting bachelor’s degree students may not affect graduate unemployment rates for as many as seven or eight years. However, the various quality assurance systems do not take this delay into account, meaning that teachers and educational leaders are obliged to initiate new action plans long before the effect of previous actions is known. Instead, the accreditation instrument establishes a performance management logic of ‘probable’ effects of actions: the panel will consider whether the solution was well-founded in the light of the problem and whether it appears probable that the measure will be effective – although the results may only become evident some years after the measure was implemented. (the Danish Accreditation Institution, 2013: 20)

The result of the combined measurement and effect delay has thus been a constant overlay of several years of action planning initiated to solve bygone problems still reflected in the data. The delay furthermore caused confusion among some teachers when processing the graduate unemployment indicators, as their immediate and locally rooted impression of their program and their graduates did not fit the temporality of measurement. Educational leaders in the humanities are thus going through a period, spanning a number of years, where they are continuously affected by unsatisfactory graduate unemployment indicators despite their huge (and potentially effective) efforts to improve these indicators.

7.3.3 Negotiating with Numbers Meanwhile, the heads of programs did not simply accept the indicators and the code of conduct requiring them to adopt an affectivity of shame. The indicators were constantly negotiated, in the reports produced by program heads and elsewhere, as is also apparent in the above example where a head of program and faculty

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management managed to provide more recent data to counter the threat of stopping the program from enrolling new students. Generally, it was not uncommon for the heads of programs to include additional quantitative information in their reports, thereby contesting the straightforwardness of the dominant graduate unemployment metric. Quite often, they used the graduate unemployment numbers 10 years after graduation, as displayed on the Education Zoom website, to negotiate the relevance of the short-term measure formally used in quality work. The head of one program explained to me how she commented on the graduate unemployment indicator in her annual report: These unemployment numbers, they are another element that we need to… I mean, I have spent some time on that as well in the head of program report, right? Looking at that thing – Education Zoom, you know? Where you can show that after 10 years, well, then they are not unemployed, right? So there is something about how you cut it, you know? And if the cut is after precisely seven quarters, or whatever, then the number is relatively high, and if you then want to look a bit further ahead, then the number would be smaller… And that, I find, can somehow be a good perspective to add to the picture, right? That there is something about that random cut, and I use Education Zoom, for example, to do this. (Interview with head of program, May 2017)

This program head talked about the ‘cut’ of the measure and about how different cuts produce different numbers for the performance of her degree program. She added information on the program’s longer-term utility to the short-term indicator and thereby negotiated the official number and its problematization of her program. While the official graduate unemployment metric provides a cross-sectional comparison across programs, the program head added longitudinal information to her report, enabling a temporal comparison. This was also the case when heads of programs added previous graduate unemployment indicators to their reports to show how a certain graduate unemployment number, which in comparison to other programs was a bad number, was actually an improvement from previous years. Even though the two types of longitudinal comparison are different, as the 10-year measure compares the longitudinal data of the average graduate while the comparison to data from previous years concerns the longitudinal data of the program, they both enable a more optimistic and progressive temporal-affective stance among the actors reporting the numbers. The additional information was thus added to demonstrate that the performance of the program was improving, even if it still fell below the acceptable level of performance as defined by the university’s threshold. Only in one case did I hear someone voice a desire to game the numbers. This happened during a project meeting dominated by expressions of indignation regarding what was experienced as a political attack on the humanities, conducted via the graduate unemployment rate, which, it was assumed, had deliberately been constructed with variables and thresholds that would have a deep negative impact on the humanities. One of the participants at the meeting emphasized the importance of the exact measurements used in the new strategic framework contract. One of the metrics used by the ministry, he stated, apparently measured graduate employment activities on a single day each year, whereby the solution for the university would simply be to employ a large number of graduates on that day or get them accept any

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form of work, such as a job at an ice cream shop. This was followed by a tumultuous exchange of ideas as to how to game the numbers, which faded as the person in charge of the meeting moved on to the next point on the agenda. While the idea of gaming the numbers was raised just this once, the negotiation of graduate outcome data using other data was a lot more common, and the ability to navigate and juggle various data thus emerged as a way of becoming an educational leader.

7.4 Becoming Policymaker with Graduate Outcome Data Finally, I was able to interview a ministry official, who had been involved in developing the Current Unemployment metric and the Resizing Model and Education Zoom policy initiatives based on the recommendations from the Committee on Quality and Relevance in Higher Education (2014). In this process, his role had been both to support the appointment and work of the expert committee and to follow up on their recommendations. He collaborated with the statisticians who developed the calculation models, made sure that that these models met the stipulations of policymakers, and contributed to the design of the policy instruments involving the calculation models. The interview took place after the launch of both the Resizing Model and Education Zoom and during the development of the graduate survey component of Education Zoom, which was added to the website in 2017. With this interview, I sought to understand why these policy initiatives were adopted and how the various policy instruments and calculation models ended up as they did. I furthermore interviewed a lobbyist from the Danish Confederation of Industry (DI), who was involved in their education policy work. This interview focused on the role of a lobby organization in policy development, including the use of data in this work. The following final brief analysis in this chapter is based on these two interviews. Preferably, the empirical material would also have included the Minister of Higher Education and Science at the time, who unfortunately did not accept my invitation for an interview, as well as other key policy actors. Based on the two interviews, the analysis nevertheless shows how policymakers not only wield power over data but are also affected by demands imposed by data.

7.4.1 Crafting Politically Feasible Evidence As mentioned in Chap. 5, the Current Unemployment metric was developed as part of the Resizing Model instrument, which was designed to reduce the production of graduates by programs with ‘systematic and striking excess unemployment’ (Ministry of Higher Education and Science, 2014). The Resizing Model is an algorithmic instrument that relies on both a set of data and a calculative device that can transform these data into a policy decision. Both of these calculative elements were

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developed anew for the policy instrument. While the data set in principle already existed, as graduate unemployment data had been collected by Statistics Denmark for a number of years, the data needed to be groomed into variables developed for the specific purpose of the calculative model. The grooming of data into the Current Unemployment metric involved several methodological decisions, including selecting the categories used to calculate the unemployment rate, the temporal organization of data into comparable time slots, and the number of years included in the data set. Furthermore, the calculative model had to be defined, including the adoption of a list of clusters of programs used to ensure that the resizing of one program would not merely lead to an overproduction of graduates from another similar program, as well as the benchmark that would activate the resizing of a cluster, and the algorithm translating unemployment rates into three resizing rates. These decisions were all entangled with the political ambition of regulating only those programs that contributed to a significant oversupply of graduates and had done so in a systematic and striking way, which was the political definition of the problem that the policy instrument was intended to solve. During the interview, the ministry official explained how technical working groups were assembled to work on the development of the calculative elements. The ministry official called their work an ‘engineering exercise’ (interview with ministry official, March 2017). The technical working groups developed and tested more than a hundred different models, as retuning one or more of the methodological definitions dramatically changed the number of programs exceeding the unemployment threshold and thus encompassed by the resizing calculation. In the end, the final model (or rather the three scenarios presented to the Minister) was selected based on a political ‘gut feeling’ about the right level of impact: Some of the proposals in the process… I had a sinking feeling in the pit of my stomach when I first saw them, thinking: If we present this to the politicians… then it will show them that a lot of degree programs need to reduce enrollments. And they all have to cut the numbers by 30% – not just 5% or 10%. (Interview with ministry official, March 2017)

When the models were tested, their assessment thus partly depended on their political feasibility, balancing between too little and too great an impact. The ‘sinking feeling’ experienced by the ministry official, and the ‘gut feeling’ involved in the selection of the right model that he later spoke about (interview with ministry official, March 2017), are both bodily affective responses that emerge from the anticipatory impressions of the political realities that a given calculative model would bring about, including effects on both the total student population and on the political environment. The crafting of numbers is thus not only political in terms of the political ambition and problem definition behind the policy instrument and development of a calculative model but also in terms of the political feasibility of a particular calculative practice in combination with an algorithmic governance mechanism. However, the ministry official also emphasized the importance of crafting ‘transparent and indisputable numbers’ in order to show that the ministry knows what it is doing (interview with ministry official, March 2017). Transparency is a part of

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what makes numbers objective, and the indisputability of numbers relies on the use of procedures necessary for obtaining objectivity (see also Chap. 3). The emphasis on transparent and indisputable numbers can also be considered political, as the authority and legitimacy of the ministry depend on the objectivity of the numbers it produces and upon which it bases its policy instruments. The lobbyist, who stated that her organization relied strongly on evidence and data analyses in both their policy development and their lobby work, also highlighted this dependence on objective numbers. She said that ‘…it would be a major setback if we published analyses and things that were wrong, so we are quite conscientious about that—that people can trust the things we present’ (interview with lobbyist, January 2018). As the quote shows, the lobby organization (like the ministry) is entangled with epistemological ideas of numbers as either correct, objective, and indisputable or wrong. Both the ministry and the lobby organization are constituted by the crafting and distribution of objective and trustworthy numbers, and the unquestionable status of numbers co-constitutes them as rational, trustworthy, and legitimate bodies basing their policies on facts. The development of quantitative analyses and calculation models thus appears to be a balancing act between different kinds of political interests, including political values and priorities, political realities, and the status of political actors conditioned by the objectivity of the data they provide.

7.4.2 Affected by Data Meanwhile, policymakers are not just actors with a power to craft numbers and calculative devices in line with their political interests. They are also affected by data, just like students, teachers, and educational leaders. In the interview with the ministry official, his response to my question regarding why the calculation model and the policy instruments were needed was that ‘the numbers started speaking for themselves’ (interview with ministry official, March 2017). Those numbers included the general statistics provided to the ministry, including data from the Employment of Graduates metric, as well as the analyses produced by the Productivity Commission (2014). The unemployment data circulating within and around the ministry thus addressed a governance responsibility of the government that set the processes involving national politicians and the ministry in motion (Madsen, 2021). If we examine the quantitative practices of graduate unemployment data, these data might address the ministry and the politicians in several ways. The ministry official explicitly mentioned the ministry interest in graduates securing a job and a high salary, which in turn results in increased tax revenues. Drawing on the analyses in Chap. 4, these interests point toward the economic order of public sector finances, emphasizing the return on state investments, and in particular the economic order of the welfare state, emphasizing a balance between taxpayers and recipients of social benefits. The graduate unemployment data and other data circulating within and around the ministry thus affect the ministry, inciting concerns regarding potential

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risks for the efficiency of the state-as-enterprise and financial balance of the redistributive welfare state. These concerns are closely associated with the theories embedded in and the problematizations articulated by the metrics. However, the data also address the ministry and politicians via a code of conduct associated with data that overlaps the code of conduct that affects educational leaders and directors. In relation to this code of conduct, the ministry and the politicians were called upon to take action, as ‘something was about to go wrong’ (interview with ministry official, March 2017). The mere existence of data that have become high profile through the work of expert commissions responsibilizes the ministry and politicians for any problematizations that may emerge from the data. In order to maintain ministry subjectivity as competent, the ministry has to act in response to data like the graduate unemployment data. Data hence also co-constitute policymaker subjectivities and call upon policymakers to behave in particular ways. This analysis of how policymakers engage with data shows that, while policymakers are attributed with a power to craft numbers and calculative devices in politically suitable ways, the political is not to be understood in terms of a free and unconstrained will. Policymakers are also governed by numbers. They are constrained by political considerations regarding the anticipated impact of calculative practices, as well as in relation to the objective status of the numbers, in their crafting of calculative devices. They can align methodological choices to their political interests, but they need to balance these value-based interests with the risks of crafting disputable or unfeasible calculations. Furthermore, when the metrics are produced and begin to calculate, policymakers are duty-bound to act in accordance with the numbers just as much as any other actor. The problematizations produced by metrics might be used strategically by policymakers to legitimize particular policy initiatives, but they may also require policymakers to take action in particular ways in order to continue to be seen as legitimate and competent.

7.5 The Constitutive Potential of Data Design In conclusion, there are both general and specific subjectivizing effects linked to the use of data in educational governance. The general effects emerge as codes of conduct concerning how to engage with data. These codes not only dictate how actors should make choices and take initiatives in line with the data but also how to adopt appropriate affectivities associated with particular data values. The more formal a role an actor occupies within educational governance and institutionalized data procedures, the stricter these codes of conduct seem to be applied, leaving students and teachers with a certain amount of leeway, as well as allowing educational leaders at lower levels of institutional hierarchies space for negotiation. However, even if these actors can engage with data more freely, this freedom comes at a cost, involving distressing affectivities and efforts to craft alternative narratives and requiring the ability to navigate and juggle various sets of data.

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Meanwhile, the specific subjectivizing effects of graduate outcome data emerge as results of the particular data and policy designs embedded in and surrounding these data. The humanities students in my fieldwork seemed to be primarily affected by the persuasiveness of the overall economic-educational theory of human capital embedded in the data. The data encouraged students to make choices and take actions, or alternatively to construct alternative analyses or resistance narratives, in response to this theory, thereby demanding a variety of complex material-affective efforts of them on top of their studies. While teachers were also affected affectively by the economic-educational theories embedded in the data, educational leaders seemed to be affected by the spatial-temporal materialities of the data, demanding of them meticulous data analysis and endless development of action plans in an environment with few resources and unfit measurement practices. The demands and affective effects on students, teachers, and educational leaders are however rooted in the stratification practices implied and performed by the Danish graduate outcome metrics. This finding invites debate on the human costs of particular data designs, such as the cost of the graduate unemployment rate, which has come to play a dominant role in Danish higher education governance. By contrast, policymakers were not primarily occupied with the specific meaning-­making properties of the designed data but rather with their objectivity and comparability and thus the statistical and political-managerial properties of the data. This difference in ways of engaging with data is important as it may indicate that policymakers are not sufficiently aware of the consequences of particular quantification decisions on human lives when attempting to construct objective and comparable data for use in educational governance. The analyses in this chapter have demonstrated how educational governance and management with numbers have profound effects on the human beings occupying higher education, even in cases where the implementation of quantification practices appears relatively successful in terms of achieving the intended effects of those practices. In the Danish case, gaming effects do not appear dominant, as suggested by other educational governance studies (Baird & Elliott, 2018; Lingard et al., 2016; see also Pollitt, 2013). Instead, the capacity to narrate and negotiate numbers in various ways seems to be important if actors within higher education are to overcome affective-conscious states of being involved in living with graduate outcome data.

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Chapter 8

Educational Development Effects of Graduate Outcome Metrics

Quantification practices affect educational development. The character and content of educational knowledge has changed radically as education has gradually become quantified in both more plentiful and more sophisticated ways, with massive transnational testing regimes as the most impressive examples. While knowledge about education used to be entrenched in the teaching profession, based on aggregations of teachers’ experiences, it is now not only aggregated by statisticians, economists, and sociologists but also, and more predominantly, located in reports, tables, and spreadsheets. The knowledge is no longer based on local, contextualized knowledge of specific students and processes but instead on averages, percentages, and shares that are generalized and stripped away from the specific educational contexts they concern (Piattoeva, 2021). As the knowledge is quantified, it is furthermore confined to narrow and mutually delimitated constructs of educational phenomena like learning, skills, well-being, non-cognitive skills, and quality rather than comprehensive and holistic experiences with students and educational practices. The changes in the character and content of educational knowledge accomplish more than merely providing a different kind of knowledge that is convincing in terms of its objectivity, appropriate for contemporary governance practices, and instrumental for a shift in the power relations among educational actors. Quantified knowledge, like other types of knowledge, is performative beyond measurement. It evokes new realities, with the knowledge produced in quantification practices taken up by not only policymakers when determining standards and regulations for educational design and teaching but also by teachers involved in the development of curricular activities. The ways education is quantified across different types of quantification practices constitute educational ideals and conceptions and thereby leave footprints on educational practices. It is important to study these effects of quantification as key features of specific quantification practices. Meanwhile, educational governance research on quantification still lacks a comprehensive

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_8

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framework for theorizing the performative effects of quantification on educational practices. Over the last decade, educational scholars have shown how specific quantification practices like PISA have affected education policy and reform at both the national (Carvalho, 2014; Grek, 2009, 2017; Sellar & Lingard, 2018) and school levels (Lewis, 2018). While PISA constitutes an important example, educational research has also shown how other transnational surveys (Cort & Larson, 2015; Sorensen & Robertson, 2020) and national quantification practices (Espeland & Sauder, 2016; Hopmann, 2008; Krejsler, 2018; Lingard & Sellar, 2013; Ratner, 2020) affect educational policy decisions and institutional behavior. These studies of the effects of quantification of higher education conclude that the production of comparable numbers, in these examples most often materializing as rankings, motivate national policy communities and institutional leaders to implement changes in pursuit of improving their position (Lingard & Sellar, 2013). At international level, a significant trend toward policy borrowing and lending (Steiner-Khamsi & Waldow, 2012) appears to have emerged as a result of the PISA assessments. At institutional level, perverse effects of gaming the numbers and adapting the management of institutions to improve the numbers appear as an important consequence of rankings and performance measurement (Espeland & Sauder, 2016; Lingard & Sellar, 2013). However, while these studies highlight important effects at the level of policy and governance, there appears to have been less research examining the effects closer to the practice level of education, including both educational design and managerial priorities in the daily operation of education institutions. The educational governance literature does not include many examples of empirical studies of how quantification affects educational (as opposed to governance) practices. One of the few exceptions is a study by Riberi et al. (2021), which shows how a Chilean algorithm on student vulnerability affects pedagogical enactments. At one school, the teachers enacted pedagogies of affection to compensate for the vulnerable background of most of their students, as documented by the algorithm, whereas the other contrasting school in the study avoids the quantification practice of vulnerability because of its status as a private school. Another exception is a study by Spina (2017) showing how a large number of Australian teachers started teaching a specific form of writing due to its role in the NAPLAN (National Assessment Program— Literacy and Numeracy) data. Nevertheless, the transdisciplinary approach of empirically tracing how governance practices affect educational development and educational practices is rare but offers huge potential for future educational governance research. This chapter outlines three examples of educational development found in the Danish context, all constituted directly by the quantification practices analyzed throughout the book. The analysis of the Danish case constitutes an example of the insights such an analysis might provide in terms of how governance and administration affect educational practices. It also attempts to point out a few distinctions that may contribute to a more systematic and comprehensive theorization of how quantification affects educational practices, including not only education policy but also the day-to-day practices and local development processes that play an important

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role in the constitution of education. The chapter thus complements the previous chapter by showing how the performative effects of human capital quantification practices in higher education not only shape educational subjectivities but also educational development.

8.1 Studying Educational Effects of Governance Practices The relation between governance practices and educational practices is complex and thereby difficult to study. I find the concept of enactment useful in understanding this relationship between governance practices, such as quantification, and educational practices, such as educational thinking, curriculum design, or pedagogical practices. The notion of enactment concerns the performative effects of policies, governing practices, and metrics rather than the underlying intentions. With this notion, I seek to generate an understanding of effects that differs from that found in implementation studies, where a policy is expected to have desired effects or outcomes in the specific context where it is implemented and where these effects can then be evaluated (Shore & Wright, 2011: 4–5). By contrast, studying performative effects is not about evaluating a policy but examining how it affects practices in myriad ways beyond the specific scope of the policy. While Stephen Ball and others (Ball, 2015; Ball et al., 2012) refer to policy enactments, thereby highlighting the ways educational practitioners take up policies and enact them as myriad predicted and unpredicted realities, I draw on a broader notion of enactment that also includes enactments of administrative practices and their embedded educational thinking and priorities. Educational development does not only occur as a result of and mediated by educational policy reform but also takes place through day-to-day practices and local development processes. Consequently, enactments do not always refer directly to policy initiatives but may in some cases be affected in more subtle ways by various governance and management practices, including specific quantification practices. Educational designs can, for example, be analyzed as enactments of knowledge provided by graduate outcome metrics; similarly, institutional priorities can be analyzed as enactments of specific mechanisms embedded in a governance instrument. My notion of educational enactments is thus an analytical tool for the study of enacted effects of a quantification practice, which includes policy enactments (Ball et al., 2012), but also encompasses enactments indirectly linked to policies, governance instruments, and administrative practices. I understand enactments as realizations of (some of) the potentials embedded in quantification practices. The specificities of quantification practices, such as the ways they operationalize the measured phenomenon, their productions of difference, the theories embedded in them, the governance mechanisms and effects with which they are entangled, and the materiality of their numbers, enable and sometimes even invite specific enactments. I call the enablement and invitation of specific enactments the performative mechanisms of specific quantification practices. There is a (nondeterministic) causal relationship between the discursive-material

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practices of quantification and the actualized relations in the world (Barad, 2003: 814). Quantification practices constitute fields of possibilities (Barad, 2003: 819) that enable and constrain other practices, such as educational practices. The effects of quantification practices are thus not to be understood as occurrences that are determined by these practices but rather as materializations of a reality that emerges simultaneously with specific quantification practices and becomes part of the world together with them. Enacted effects are realizations that emerge in relations between performative mechanisms of quantification practices and a given educational context. The analyses of educational development as realizations or enactments related to quantification practices draw on the same empirical fieldwork as the analyses in Chap. 7, including observations of various meetings and interviews with teachers, managers, students, and administrative staff. The observations focused on meetings where educational practices were suggested, discussed, decided, and justified and where university governance and administration practices related to teaching would become visible. My entanglements in the practices surrounding metrics did not bring me into the classroom because I was interested in decisions regarding the curriculum rather than in the teaching and learning practices where such decisions were enacted. The subsequent interviews explored the observed governance practices with informants. I furthermore studied a variety of documents that were shared with me or publicly available, ranging from reports to strategy documents to curriculum descriptions to evaluations and more. In addition to this fieldwork, the analyses also draw on interviews with two university school leaders, one head of department, and four teachers, conducted in 2020–2021 as part of the project The Performative Effects of Budgeting in Higher Education.1 These interviews focused specifically on the enacted effects of budgeting and accounting practices at the universities. In my study of enacted effects, I was particularly interested in the effects on educational content and design and on governance and management practices. I was not only attentive to direct effects of quantification practices but also to effects of the governance instruments in which they were embedded and of the distribution and interpretation of their results in press releases, articles by journalists, popular books, and blog posts. The three included examples of enacted educational effects of quantification practices represent the effects that I have been able to observe across this comprehensive empirical material. The examples show how quantification practices that stratify broad areas of studies, with the humanities scoring poorly, have a negative impact on the educational quality of programs in the humanities, including hollowing out the humanities content of these programs to make room for more generic content.

 International postdoc project The Performative effects of budgets in higher education: A culturalstudies account of how ideas and designs of education are built into budgetary numbers, financially supported by Independent Research Fund, Denmark (grant no. 0162-00038B). 1

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8.2 Increased Imbalances of Educational Resources Across Academic Areas of Studies The first example of educational enactments of quantification practices concerns the algorithmic and nudged changes in the student population at a structural level. These changes are grounded in calculative practices established to identify match/ mismatch issues and materialized as a series of policy initiatives and governance practices. Since 2014, the Danish government has implemented two such policy initiatives, the Resizing Model and the website Education Zoom (see Chap. 5), as well as a series of campaigns and other communication activities. The Resizing Model introduced a cap on the graduate production by programs with a graduate unemployment rate above a defined threshold, while the Education Zoom website seeks to nudge potential students to select programs within certain areas of studies via data on various parameters including unemployment rates, average salaries, and student satisfaction. The policy instruments and the campaigns supporting them were introduced to change the composition of graduate population in terms of its distribution across areas of studies. More specifically, the initiatives share the explicit aim of altering the graduate population by reducing the number of students within the humanities and social sciences and instead increasing the graduate production within Science, Technology, Engineering, and Mathematics (STEM). In addition, the Social Democratic government elected in 2019 has sought to attract more students to the professional bachelor’s degree programs preparing for entry to professions like teacher, nurse, early childhood teacher, and social worker. The effects of the promotion of STEM programs, which was already kick-started in 2014 and 2015 with introduction of the Resizing Model and Education Zoom, are beginning to show. Student enrollment in STEM programs increased by 6.6% in the period 2017–2021, while over the same period, enrollment in humanities programs has declined due to the enrollment caps. The policy to change the composition of the graduate population has thus already resulted in changes in the educational landscape. These trends will continue as a result of a 2021 policy initiative called Better educational opportunities in all parts of Denmark [Bedre muligheder for uddannelse i hele Danmark] (the Danish Government, 2021), which enforces a 5.7% reduction of student populations in the four largest Danish cities, with a disproportionally large part of this reduction within the humanities to reflect graduate unemployment numbers. When policy utilizes quantification as a tool for the algorithmic distribution of students (the Resizing Model) and data-based nudging (Education Zoom), it has far-reaching implications for the educational conditions within the affected teaching environments. The changes in the student population have affected downsized educational environments in terms of fewer students and an increased number of small programs. These changes in turn affect the resources of those educational environments as a consequence of the Danish funding model, which (as explained in Chap. 5) includes both a basic grant (approximately 25% of the total funding), an activity-­ based grant (approximately 65% of the total funding), and a performance-based

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grant (approximately 11% of the total funding). As in most other European countries, this model thus combines performance-based funding with the tradition of activity-based funding (Landri et al., 2017: 69). The activity-based grant is calculated by multiplying the number of fulltime equivalent students with a politically defined rate per student, meaning that a large portion of a program’s funding is reduced when the number of students is reduced. The areas where reductions have been made, including most humanities and some social sciences programs, already receive a lower rate per student than other university programs due to historical political processes that are partly based on differences in expected teaching costs in areas with and without requirements for equipment and laboratories. The combination of a lower rate and a reduced student population entails a significant decline in resources within these educational environments. As most costs involved in the provision of education, such as teacher salaries and building costs, vary according to the number of classes rather than the number of students, a decline in resources without a reduction in the number of programs de facto implies fewer resources per student. As a base rule, the funding is allocated directly to the schools2 in charge of producing the student fulltime equivalents. At the universities I have studied, some of the funding of teaching is redistributed across academic fields and faculties, but not to such an extent as to even out funding imbalances. As organizational units, the schools thus end up differing significantly in terms of their resources and thereby their ability to provide a sufficient foundation for high-quality teaching.

8.2.1 Effects on Educational Quality The limited resources not only affect smaller, financially vulnerable programs but also larger, profitable programs in schools with many small programs. The school’s economy is not broken down to the individual program, and the activity-based funding is not distributed directly to individual programs. Instead, initiatives are implemented at the school level. One way of enacting the decline in resources in the affected schools is to provide education based on minimum standards. The Danish higher education system includes a minimum standard for the number of ‘confrontation hours’ per week, meaning the average number of hours students can expect to be in the presence of a teacher, typically in the form of classroom teaching. The minimum standard is defined as 12 h per week for bachelor’s degree students and 8 h for master’s degree students. However, a head of department that I interviewed experienced that the minimum standards were also used as maximum standards by the head of studies at the school in which he worked:

 A school is an organization level between the Faculty level and the Department level. The school organizes one or more degree programs in a particular area, for example, political science or communication and culture. 2

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I also believe, I mean, if I just speak for myself, then I think I have a slightly different view on what it means when the regulations say that a student is entitled to twelve hours of teaching per week, you know? I mean, because [the head of studies] reads it as a minimum but also a maximum requirement, and I read it as a minimum requirement. So [the head of studies] can get upset if the students maybe… let’s say they got seventeen hours per week – that would be horrible, because it costs a lot, whereas I don’t have… let’s say that I don’t share her concern, but I follow her instructions and attempt to reduce that number. (Interview with head of department, October 2020)

In university schools characterized by a decline in resources, offering students more teaching than they are entitled to can be seen as wasteful. The practice of sticking to the minimum requirement is thus a management decision, implementing a quality standard designed to solve the decline in resources but often contradicting the professional standards of the teachers. Conducting education according to minimum standards also includes supervision and assessment. While the number of courses that include individual supervision has been reduced, most schools have also set standards for the number of hours of supervision per student. Similarly, some schools have reduced the use of external assessment and double assessment of student exams, both of which are practices aimed at ensuring students a fair and standardized assessment. These cutbacks undoubtedly affect the quality of teaching and assessment, even though some teachers offer more teaching and supervision than the requirements and compensations justify. Programs affected by this type of educational frugality offer a significantly lower level of educational quality than programs with more resources. Over time, the alignment with minimum standards might affect the ability of these programs to meet the performance targets of the performance-based mechanism in the funding system, thereby amplifying the resource imbalance. The decline in resources is not only enacted as less ambitious teaching standards but also as an increased use of mixed classes, where students from several programs are taught together. This trend most often involves an adjustment of the curriculum, as some of the more discipline-specific content has to be replaced with generic content relevant for several programs. Thereby, it may also reduce educational quality. I will return to the transition of programs toward more generic content below. Finally, the decline in resources has been enacted as the closure of a number of small programs with the aim of protecting other programs from disproportionate cutbacks. For example, several universities have closed some of their language programs (Dalsgaard, 2019). These closures have been criticized for its effects on Danish society and its intercultural capacities. Meanwhile, they have also had consequences for the Danish labor market, as an insufficient number of qualified language teachers are produced, leading to shortages in the rest of the Danish education system. The lack of qualified language teachers became evident when the government in 2017 published a Language Strategy (the Danish Government, 2017), developed to overcome the problems partly caused by the closing of language programs at the universities. As such, the resource imbalances across academic fields have a number of effects on educational quality and production, some of which pose new problems for the government.

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8.2.2 Effects of Quantifying Policy: Changed Knowledge Structures and Resource Allocations The resource imbalances and their consequences for educational quality are an example of how quantification practices involved in the development and implementation of policy affect educational practices. At least two performative mechanisms of quantification are at play in this example. First, the quantitative practice of organizing graduate outcome data according to broader areas of studies (the humanities, social sciences, natural sciences, technical sciences, and health sciences) has established how students are distributed across these areas of studies as a problem. Having established the problem, the need for action seems clear— indeed, a failure to intervene by the government would arguably represent a dereliction of duty. When graduate unemployment statistics and graduate income statistics show that STEM graduates transition more successfully and rapidly into the labor market, especially when compared to humanities graduates, this indicates that the nation needs more of the former and less of the latter. This conclusion is furthermore supported by various forecasts, including both economic forecasts of the public sector’s need for graduates over the coming years (e.g., Committee on Quality and Relevance in Higher Education, 2014) and survey forecasts on the future skill needs of various labor markets and industries (e.g., World Economic Forum, 2016). The spatial-­material characteristics of the quantitative variables on areas of study and the temporal-­material characteristics of the forecasts thus constitute the knowledge that enables problematizations of the composition of the student population. The structure of the knowledge constituting policy represents one performative mechanism of quantification practices, very similar to what previous scholars have found in the case of PISA (Grek, 2009; Sellar & Lingard, 2018). Second, the use of graduate outcome data in the allocation of students, which in this case takes place both algorithmically and through communicative nudging of potential students, has tangible effects on the governed educational environments. The algorithmic governance decisions and the effects of communication on student enrollment patterns are determined by the specific quantification practices involved. In the Resizing Model algorithm, both the temporal operationalization of graduate unemployment as measured in the 4th to 7th quarters after graduation and the defined threshold of excess unemployment directly determine the allocation of students. On the Education Zoom website, the temporal operationalization of graduate unemployment and other graduate outcome indicators similarly determines the hierarchical output and the affective potential of the comparisons conducted by potential students using the website. However, the aesthetic practice of reducing complexity by calculating a single number, as well as the visualizations of data, also play an important role in the production of affectivity and desires among potential students. While the effects of the communicative efforts are questionable or at least largely unknown in the Danish case, similar instruments for comparison may have

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a huge impact on the choices made by students in countries where the choice of education is conceived as high stakes, as shown, for example, by Espeland and Sauder (2016). The allocation of resources, either directly or (as in this case) indirectly in terms of student allocation, thus constitutes another performative mechanism of quantification practices. As the tangible effects of the highlighted quantification practices trickle down the institutional hierarchies and are translated into educational standards and practices, they appear to have a massive impact on the affected educational environments. My point here is not to claim that educational practices within the humanities are generally of poor quality but rather that the imbalances across various areas of studies create different circumstances for producing quality. In my view, educational development and changes in quality standards are not problematic per se, but declining standards and lower quality within humanities programs seem ill-advised at a time where the value of the humanities is questioned by politicians and the general public. Arguably, the problematization of the humanities should instead encourage initiatives to improve quality. However, the changes analyzed in this chapter appear to further reduce the relative value of humanities programs. It might be argued that the effects of the problematization of the humanities and of practices concerning the allocation of students and resources thus far exceed and to some extent run contrary to the intentions of the policy initiatives.

8.3 A Rise in Labor Market Transition Activities The second effect of the Danish human capital quantification practices on educational practices concerns the introduction of and rise in activities aimed at supporting a more smooth transition of students into the labor market following graduation. These activities are typically provided as extracurricular activities for students toward the end of their studies. During my fieldwork, I learned about various activities implemented either by individual programs, by the humanities faculties, or by the universities more broadly. One of these activities was a series of extracurricular Design Your Life courses conducted by an external consultancy company. One university organized bus trips, including visits to a range of local and regional companies, with the aim of establishing connections between companies and students, thereby encouraging collaboration and potentially employment. Another university contemplated hiring a coach that could match graduates with potential employers. One career office organized career days with job search advice and presentations on career paths from alumni. A head of program had developed a concept he called Student Development Dialogues, where he and small groups of students discussed the students’ career reflections and plans. Also, one university offered an elective course module called Career and Labor Market, where students not only studied theories and models of employability and

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corporate organization but also learned to present themselves in the form of a pitch to a potential employer. More significantly, the faculty of humanities at one university comprehensively implemented a Career Management Skills program, colloquially referred to as CMS (Schaumann, 2016). This program was embedded in various elective course modules during the third semester of two-year master’s degree programs, right before students write their master’s thesis and graduate and mandatory for students doing internships. The program supported and prepared students in relation to identifying and communicating relevant competencies, networking, searching and applying for jobs, job interviews, work-life balance, career strategies, and writing a master’s thesis in collaboration with a corporation or organization. The purpose of the program was to provide students with a range of tools they could use to smooth their transition from the university to the labor market. These tools range from media channels that can help them come into contact with the labor market (such as LinkedIn and job applications), to personal relations that can provide access to influential people and possibly a job, to plans and strategies that mobilize the other tools according to a timeline. In addition, the students were each assigned a mentor whom they were required to consult on a regular basis. The CMS program was organized as four sessions and taught by the career-­ counseling unit at the university. It was typical for the transition activities that I came across during my fieldwork that they were organized by career counselors and other administrative staff not involved in ordinary teaching activities at the universities. Career counselors and employability consultants thus play an increasingly important role at Danish universities, at least in the humanities departments that I followed. The employment of professional career counselors within universities is not entirely new, but since the intensification of the political focus on graduate outcomes around 2014, career counseling and employability activities have assumed greater priority. With the increased priority given to such activities, their allocation of resources has also grown, transforming what were a handful of career counselors dotted around the university as part of much broader student-counseling units into entire departments working in the field of careers and employability. Furthermore, their activities have shifted from counseling individual students to coordinating activities and events reaching a far larger number of students. The amount of resources spent on these activities has thus grown considerably over the last decade, particularly within the humanities and other areas that have been problematized in terms of graduate unemployment. The transition activities that I came across represent a host of different approaches to improving graduate transition into the labor market. Meanwhile, the listed activities are all enactments of pressure on universities to improve graduate unemployment rates and, in a broader perspective, the success of their graduates in the labor market.

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8.3.1 Effects of Incentivizing via Quantification: Distorted Educational Priorities The introduction and amplification of transition activities constitutes an example of how soft governance practices based on quantitative indicators affect educational practices. The example shows how quantitative indicators sometimes become a target for improvement, even when they do not directly reflect the politically stated object of improvement. In this example, the quantification practice that evokes the priority of transition activities is the measurement of graduate unemployment, temporally defined as the 4th to 7th quarter after graduation. While the unemployment rates of graduates across areas of studies converge when you look at the data 10 years after graduation, the rates for humanities programs are relatively high in the measurement 4–7 quarters after graduation. This temporality of the measurement has incentivizing effects. As several policy and governance instruments draw on the graduate unemployment indicator, including performance-based funding, accreditation, the Resizing Model, and Education Zoom, the temporal operationalization of graduate unemployment incentivizes universities and their humanities departments to ensure a more rapid transition of their graduates into the labor market. The activities are thus a result of the performative mechanism of an incentive built into the specific temporal quantification of graduate unemployment. Performance measurement only incentivizes as long as the indicators are understood as a direct result of the actions of the measured organizational units (Redden, 2019). Institutional and local attempts to improve the graduate unemployment indicator, which is used across policy instruments, rest on the assumption that quantitative indicators are malleable (see Chap. 6). Meanwhile, when it comes to transition activities, the approaches taken to improve this indicator do not involve altering program curricula, even though doing so would reflect the task explicitly assigned to universities of adapting their programs to the labor market (see, e.g., the Danish Accreditation Institution, 2013). The transition activities are all extracurricular activities, even though participation is in some cases mandatory. They address graduate transition into the labor market rather than the disciplinary or professional profile of the graduates. In other words, these activities facilitate a process but do not affect the educational output of university programs. The approach of prioritizing transitional activities rather than curricular development is supported by an additional incentive to improve the indicator as quickly as possible. The annual data series embedded in quality assurance procedures, performance-­based funding, the Resizing Model, and Education Zoom encourages programs to show improvement from year to year. Meanwhile, in terms of the delay from implementation of an activity to a documented effect on the numbers, which is inherent to graduate outcome measurement, transition activities appear to be an attractive way of improving the numbers. Transition activities may target students at any point in their studies but are often directed toward students in their final year, meaning that improvements of the 4th to 7th quarter data will become evident after only 2–3  years. Two to three years is a relatively short period compared to the

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8–9  years delay when measuring the effects of an initiative aimed at entire programs, such as the examples below. Transition activities are thus more likely to produce an improvement within a manageable time frame. As a result, a significant amount of resources are spent on activities other than teaching, thereby distorting the priorities of educational institutions. In the humanities, which are affected by declining resources and where teaching is reduced to a minimum, the trend of prioritizing transition activities may furthermore amplify negative effects on educational quality. The changed educational practices thus represent borderline perverse effects of performance measurement, as also found in other studies (Espeland & Sauder, 2016; Lingard et al., 2016). The prioritization of transition activities in terms of institutional resources implies a ‘chasing the numbers’ mentality rather than an intention to improve graduate skills to meet the needs of the labor market. However, I argue against simply viewing the implementation of transition activities as a perverse effect of the graduate unemployment indicator. The idea of improving graduate skills to meet the needs of the labor market rests on a traditional theorization of human capital that configures education as a provider of skills that may be capitalized in a workplace as they increase graduate productivity. Meanwhile, seen from the perspective of a welfare state economy focusing on the redistribution of income to provide social security, a lower unemployment rate may be considered valuable in itself, even though the means to achieve it do not increase graduates’ productivity and value for their workplace. The emphasis on instant graduate employment supported by transition activities may thus also reflect a legitimate interpretation of the issue identified in the numbers. The educational effects of performance measurement thus arguably result from higher education policy with multiple and partially contradictory justifications and purposes rather than from perverse reactions to quantification-based incentives.

8.4 Transitions Towards More Generic Study Programs The third educational practice directly affected by the graduate outcome quantification practices that I identify in my material concerns study program regulations and thus the design of programs and the framing of their curricular content. Study program regulations are legal documents that describe the module structure and progression of programs, as well as the learning objectives and examination specifications of each course module or class. The program regulations also include an overall labor market profile and skills profile of the program’s graduates, as well as program-specific enrollment criteria. They are thus key documents when studying the development of educational design and thinking in the Danish context. Study program regulations are revised in cycles of 3 or 5 years, or when otherwise necessary, according to the procedures of each individual institution. During my fieldwork, I closely followed the revision process for one set of program regulations and became familiar with several other revision processes, either during the stages of finalization or during the development of preliminary revision ideas. The

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case examples from my fieldwork all included enactments of graduate outcome data in the form of an upscaling of generic content in the programs at the expense of discipline-specific content. One example was the revision of a bachelor’s degree program in musicology. The revision was a result of the gradual emergence over several decades of new research interests within the research environment in which the program is embedded. These new research interests have been incorporated in the degree program as new modules and as a new specialization directed toward employment in the music and culture industries. The revision that took place during my fieldwork sought to strengthen this new specialization. The other, older specialization was directed toward music teaching, in particular within Danish high schools. While some parts of the program were taught separately according to the specialization, most parts were taught jointly. Even though the new specialization had been offered to students for a number of years at the time of my fieldwork, it was only picked by a few students each year, and some of the teachers believed that one of the reasons for this poor recruitment was the relatively vague profile—and in particular the vague labor market profile—of the specialization (again a sign of the preference among students for regulated labor markets clearly linked to a study program and thus the theorization of education as qualifications). The low number of students was a problem, both because it was the cost-effective specialization and because it best reflected new developments within the research environment. Thus, a working group comprising two teachers and two students was assembled to write the new program regulations over the course of the summer. I was invited to follow their work. The group and I, sometimes in the company of other teachers, met several times over the summer in a small meeting room at the far end of a top-­ floor corridor. The meetings were quite informal and centered on the task of writing the program regulations. This meant that the main activity taking place at the meetings was continuous rewriting of the draft document—at some meetings the entire document and at other meetings a specific section that needed particular care. Through these processes of rewriting, the final course module descriptions, learning outcomes, and examination regulations gradually emerged and were projected onto a large screen in the meeting room. The result was a strengthened specialization aimed at the cultural labor market related to music production and events. At one of the meetings, the generic skills of project management and project design came up: Three teachers and one student representative are gathered to look at the description of one of the course modules that needs revising. One of the teachers explains that the preservation of this particular course module as part of the program is justified by the integration of theoretical and practical elements, and thus the course module requires some sort of practical project, for example producing music or staging an event that includes music in one way or another. At some point in the discussion, one of the teachers introduces project management and project design as relevant content for the course module. ‘These skills are also in demand among employers’, the teacher argues. Project management and design

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skills would thus help strengthen the labor market profile of the music industry and culture specialization. The student representative comments that including these skills would help specify what humanities graduates can offer the labor market, and mentions that they would therefore be attractive to students. The project management skills end up being included in a course module called the Practical Music Project. They materialize as curricular content in the implementation of projects involving composition, performance, or production; in readings of project management literature; and in reflections on the implemented projects based on these readings. Later in the text, they recur as learning outcomes from ‘participation in, development, and implementation of diverse practical-musical project processes…’ and from ‘applying a critical and constructive perspective to one’s own and others’ practical-musical projects’ (Aarhus Universitet, 2018). Other skills, such as cultural analysis, oral and written dissemination, and qualitative methods, are also added to strengthen the position of the music culture specialization within the joint curriculum. Furthermore, within the field of musical production, recorded music, podcasts, beep sounds, and music videos are added to the more traditional genre of live music. (Observation note extracts, May 2017–April 2018) In the observations assembled in the observation notes above, the curricular content of the new specialization emerges as a mixture of project management and design, qualitative methods and cultural analysis, dissemination skills, and experience with emerging music media. Some of these developments are highly discipline specific, such as beep sounds and the staging of music events. However, others can be categorized as introducing generic skills into the curriculum that mirror those offered as response categories in graduate surveys. Oral and written dissemination and, in particular, project management are often highlighted in graduate surveys as skills that are in high demand among employers. The knowledge of what skills employers require is thus enacted in educational development as generic content added to discipline-specific programs.

8.4.1 The Bias of Generic Skills Another empirical example also involved the introduction of generic skills in the development of humanities study programs. This case revolved around an employability meeting, where two heads of culture-related programs and the management team from the Humanities Faculty were gathered to discuss how to improve the employability of graduates from these two study programs. Again, the meeting participants referred to knowledge about the skills in demand among employers, but this time the skills were considered in relation to new electives offered to students rather than as part of the mandatory curriculum. During the meeting, a possible bias of generic skills emerged. Let me share my observation notes from the meeting where a tension was observed:

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We enter the meeting room, where the faculty representatives are seated on one side of the table and the two heads of programs on the other side. I am seated at the end of the table. The negotiations of how to improve the employability of the programs begin. One of the things the participants discuss is how they can provide the graduates with skills that open up new labor markets to them. One head of program mentions culture management as a possible labor market. The Faculty Vice Dean objects that management is more a social science subject than a humanities one. But the head of program maintains that some form of humanist project management could be included in their study program. Later in the meeting, when the Vice Dean states that it is time to compile a list of specific initiatives, project management again comes up as a possible new elective course. The Vice Dean mentions that some graduates may also want to learn some statistical methods. This could be another way of incorporating social science topics within the degree programs, he surprisingly says. Also, a lot of employers demand survey production skills from graduates. He suggests that they may have to let go of the core of the discipline, just a little bit, to make room for the demands from the labor market, and the heads of program do not protest. A few months after the meeting, I receive a report on the results of the series of employability meetings. Here, I read that it was decided to develop an elective course on project and process management, and another one on qualitative and quantitative methods. (Observation notes from employability meeting, May 2017) In the observed conversation, management, project management, statistics, and survey methods are identified as skills that are in demand within the labor market. Project management, quantitative methods, and general business comprehension are all skills included in graduate surveys (e.g., Roskilde Universitet, 2013). These skills are clearly distinct from the culture-related disciplines of the two study programs, while, as the Vice Dean says, programs at the Faculty of Social Sciences may offer these skills as part of their disciplinary content. The negotiations at the meeting point toward a tension in the list of generic skills included in graduate surveys: Do management and statistics belong to a different discipline [faglighed] (within the social sciences), or are they generic skills that can be included in culture-related study programs? A similar example from the humanities could be foreign language skills, which are core skills in language programs, but also included in graduate surveys as skills that may be obtained in all programs. Besides the example of language skills, however, most such examples concern skills that could be considered social science skills, and thus the upscaling of generic skills in humanities programs to meet labor market demands hollows out the disciplinary content of humanities disciplines while expanding social science skills into programs where they did not previously belong. The concept of generic skills thus appears to be biased toward particular forms of disciplinary content.

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8.4.2 A Vocabulary of Skills The graduate survey’s operationalization of educational content as skills promotes the concept of skills as a primary and legitimate typology of educational content. In Denmark, the emphasis on skills was introduced alongside a qualifications framework as a key part of an output-oriented reform of Danish higher education in 2003, rooted in the Bologna Process (Brøgger, 2019; Sarauw, 2012). This reform introduced knowledge, skills (narrow) and competencies (or broader skills) as the three categories used to describe learning outcomes (Sarauw, 2011). The universities were required to outline learning outcomes for each module, as well as for each degree program as a whole, with an emphasis on the broader skills that are defined as relating knowledge and narrow skills to a specific (labor market) context. This output-oriented framework represents a particular approach to curriculum development that has replaced earlier input-oriented approaches rooted in specific academic disciplines (Uljens & Ylimaki, 2017: 6). Through the process of formulating learning outcomes, educational content was configured as skills and thereby also as measureable, matchable, and exchangeable (Karseth & Solbrekke, 2016: 218). Thus, the output-oriented framework and the configuration of education as skills have been extensively enacted as explicitly formulated learning outcomes across the entire Danish university sector. Meanwhile, due to the aforementioned bias, indicating that some areas of studies are more readily associated with the list of generic skills that dominates how skills are perceived in contemporary Danish higher education, some disciplines or areas of studies face difficulties expressing the overall learning outcome of their programs within the framework of skills. One example of these difficulties can be found in a 2013–2014 report for the study program Philosophy and Science Studies at Roskilde University. As part of quality assurance procedures, the head of program was required to comment on the inputs from a recently published graduate survey, including a section on skills match, showing that certain skills (including general business comprehension) were not taught in many programs (including philosophy and science studies) to the extent required by employers. In his comments, he explained that the results had been discussed with alumni from the study program and that they… …generally did not think that course modules in general business comprehension and similar labor-market-oriented elements should be included in the program. However, there was general agreement that the program could do more to 1) help the students put their specific philosophical skills into words, and 2) advantageously could provide more opportunities to apply their philosophical skills to specific cases. (Internal note: head of program report, Philosophy and Science Studies 2013–2014, my translation)

As noted in the report, this educational environment chose not to implement skills alien to their discipline as part of the study program regulations. Meanwhile, the proposals regarding how the program could improve demonstrate a difficulty inherent to the skills framework. The interest in putting philosophical skills into words indicates that the discipline-specific skills obtained from a philosophy

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program are unclear or at least not yet verbalized. The program thus faced difficulties concerning the verbalization of the philosophy program’s learning outcomes in a way that would be recognizable and considered legitimate in the labor market. There does not seem to be a readily available skills vocabulary for graduates or alumni within this discipline. The performative effects of the ways graduate surveys operationalize higher education relevance as a matter of a match between acquired and demanded skills are thus complex. They include not only the transition of educational programs toward greater focus on skills relevant to the labor market, including a bias toward certain forms of disciplinary content at the expense of other content, but also an imbalance in the verbalizability of learning outcomes across programs. Both the generic skills bias and the difficulties of verbalization affect largely the same programs as the imbalances in terms of resources analyzed above, and thereby the transition toward generic content amplifies these imbalances.

8.4.3 Effects of Quantifying Educational Concepts: Constituting Educational Knowledge and Values While the included empirical examples illustrate the difficulties and imbalances implied in the configuration of education as skills, they also constitute an example of how the process of conventionalizing a particular quantitative construct of an educational concept, in this case of skills obtained from higher education, affects the knowledge and values that shape educational practices. The performative mechanism of quantification at play in this example concerns the narrow configuration of educational concepts, which gradually sediments into the general knowledge and educational thinking of teachers and managers at the universities. In contrast to the knowledge constituting policy problematizations highlighted in the first example in this chapter, the knowledge constituting educational thinking seeps into educational designs, curricula, and didactical choices, often without direct reference to the sources of this knowledge. Knowledge from graduate surveys popped up at meetings in comments like ‘we know that our students want more of these skills’ or ‘emphasizing this skill will make our students more attractive to employers’. Even when the reports were not explicitly mentioned, the knowledge they produced had taken root in the minds of the teachers, heads of programs, heads of studies, and career officers at the university. In the case of the transition toward generic program content in the humanities, the specific quantification practice affecting these changes is the graduate survey’s operationalization of skills obtained in education and requested in the labor market. As outlined in Chap. 4, Danish graduate surveys typically include questions on the utility and acquisition of various skills, and the list of skills used as response categories is shared across all educational fields and is furthermore quite similar across the graduate surveys produced by different institutions. The skills are thus considered

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potentially relevant in all educational programs, at least in principle. The graduate surveys provide knowledge on which of these generic skills are requested and required within the labor market based on graduates’ experiences. However, this knowledge is strongly framed by methodological choices involved in developing a particular construct of skills as defined by the response categories, which are evidently informed by classic human capital ideas of skills of relevance for workplaces. When the produced knowledge circulates and sediments in the higher education sector, it gradually becomes part of educational development and thereby defines what constitutes relevant and desired program content, much like national and international test regimes define desirable school knowledge through the test items that they use.

8.5 The Link Between Governance Practices and Educational Practices Conjointly, the three analyzed examples show how quantification practices affect educational practices via various performative mechanisms. First, quantitative practices provide knowledge structured in a way that constitutes not only specific problematizations and policy solutions but also knowledge and values that may affect the educational thinking and educational designs of key actors. Second, quantitative practices define the numerical benchmarks, categorizations, and hierarchies necessary for the allocation of resources and affectivity based on algorithmic and nudging instruments. Third, quantitative practices define the incentives built into governance instruments and thereby the actions that these instruments call for. This list of performative mechanisms may provide a tentative framework for analyzing the effects of quantification practices on education. Meanwhile, the performative effects are also shaped by specific quantification practices involved in each case example, including specific temporal, spatial, and conceptual operationalizations of the educational phenomena and units measured. The analyses provided throughout the book are important prerequisites for a precise analysis of the educational effects. The trends of declining resources (and changes in educational quality as an effect), an increased focus on employability and transition activities, and the implementation of generic skills in curricular content are neither unique to the Danish context nor surprising. However, analyzing these changes in educational practices as enacted effects of quantification practices represents an attempt at developing a new kind of research question for studies of educational governance: How do specific governance and administration practices affect educational thinking and design and thereby core practices in the realm of education? This question is important as it calls for critical discussion of the problem of changing educational practices based on logics and practices located outside the realm of education. While pursuing an ideal of a pure educational realm, unaffected by other realms, is unrealistic and probably unhelpful, the entanglement of educational, administrative,

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managerial, and political practices necessitates that these practices are not only studied from economic and bureaucratic points of view but also from the point of view of educational values and rationales. The above analysis shows a potential for a new research agenda but only follows the educational perspective so far, not including various educational theories and understandings of educational quality, curriculum, and pedagogy. Meanwhile, it does show how particular quantification practices evoke particular educational practices that may be considered problematic from a teacher’s point of view. The reduced educational quality, increased spending on extracurricular activities, and the transition of programs toward more generic content might further threaten the status of the humanities, thereby exacerbating current problems with unemployment and low salaries among graduates. The administrative and calculative practices involved in the quantification of the relevance of higher education may thus end up as an additional factor constraining the outcomes of humanities programs.

8.6 Closing Part IV Wrapping up this part of the book and its focus on the reception of graduate outcome data by individuals and institutions in relation to the presence of human capital theory and neoliberal thinking in Danish higher education, a number of different points stand out. First, it is clear that ideas from human capital theory have gained traction in the population, including among students, to a greater extent than among university teachers. Students align their actions with the assumed wisdom of human capital theory. However, as the humanities arguably (at least according to the graduate outcome metrics) provide a relatively poor level of human capital, some students draw on the idea of a symmetric relation between particular qualifications and particular labor market positions, instead of viewing education as an asset in the open capitalist labor market, as suggested by the original version of human capital theory (Becker, 1993). This move relies on an adaptation of the human capital theory to a more regulated labor market, for example, characterizing public sector positions like the job as a high school teacher. Second, while the attitudes of students, teachers, and educational leaders are clearly affected by the graduate outcome data, I would characterize these attitudes as reflecting soft governance instruments (Lawn, 2011), working through moral economies, rather than a consequence of neoliberal marketization instruments, working through a monetized economy. The teachers seem to fear the shame and embarrassment of public exposure more than actual market consequences, such as falling admission applications, or individual sanctions. The graduate outcome instruments tend to infantilize teachers rather than set them free as market actors. Also, the distribution of graduate outcome numbers limits the free choice of students rather than setting them free in the market as masters of their own fortune. The dominance of soft governance instruments aligns with the managerial version of new public management discussed previously in the book, which works through

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leadership and motivation as more subtle forms of control-and-command governing (Pedersen & Löfgren, 2012; Pollitt & Bouckaert, 2011). The parsimonious alignment of educational practices to minimum standards of teaching and hierarchically established incentives that promote a reduction of the graduate unemployment rate are examples of this managerial version of new public management, founded on the state’s desire to optimize higher education rather than let the market regulate the sector. While soft governance instruments govern by modifying the desires of free educational subjectivities in sophisticated ways, which has by some been considered a neoliberal way of governing (Rose, 1999), they may just as well be considered social democratic in their attempt to steer the population in support of society’s common good. Thus, I suggest that the Danish case can be understood as an example of how new public management techniques can be deployed to address social democratic objectives. Third, the graduate outcome data are largely received as representing the interests of the welfare state by the universities. The focus on labor-market transition activities that address the graduate unemployment rate rather than on the development of skills that address the human capital of graduates shows that a reduced graduate unemployment rate is of value in itself in relation to a healthy welfare state economy. The interests of the welfare state also seem to guide policymakers in their development of policies that address growing unemployment numbers. Meanwhile, part IV has also shown that there is a neoliberal conversation taking place within Danish higher education policy. The funding structure of university teaching promotes discussion of the profitability of programs for both institutions and the state-as-enterprise, identifying the categories of small programs and humanities programs, which in most cases intersect, as unprofitable and thereby problematic. Thus, while neoliberal thinking does not seem to have permeated the modes of governance characterizing most graduate outcome instruments, the Danish higher education sector does face neoliberal policy arguments.

References Aarhus Universitet. (2018). Studieordning for bacheloruddannelsen i Musikvidenskab. Retrieved from https://eddiprod.au.dk/EDDI/webservices/DokOrdningService.cfc?method=visGodkend tOrdning&dokOrdningId=13978&sprog=da Ball, S. J. (2015). What is policy? 21 years later: Reflections on the possibilities of policy research. Discourse: Studies in the Cultural Politics of Education, 36(3), 306–313. https://doi.org/10.108 0/01596306.2015.1015279 Ball, S. J., Maguire, M., & Braun, A. (2012). How schools do policy policy enactments in secondary schools. Routledge. Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs, 28(3), 801–831. https://doi.org/10.1086/345321 Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education (3rd ed.). The University of Chicago Press.

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Ratner, H. (2020). Europeanizing the Danish school through National Testing: Standardized assessment scales and the anticipation of risky populations. Science, Technology, & Human Values, 45(2), 212–234. https://doi.org/10.1177/0162243919835031 Redden, G. (2019). Questioning performance measurement: Metrics, organizations and power. SAGE Publications. Riberi, V., González, E., & Rojas Lasch, C. (2021). An ethnography of vulnerability: A new materialist approach to the apparatus of measurement, the algorithm. Anthropology & Education Quarterly, 52(1), 82–105. https://doi.org/10.1111/aeq.12359 Rose, N. (1999). Powers of freedom: Reframing political thought. Cambridge University Press. Roskilde Universitet. (2013). MASTERsurvey 2012 [KANDIDATundersøgelsen 2012]. Retrieved from http://www.e-­pages.dk/roskildeuniversitet/195/html5/ Sarauw, L. L. (2011). Kompetencebegrebet og andre stileøvelser: Fortællinger om uddannelsesudviklingen på de danske universiteter efter universitetsloven 2003. Retrieved from https://pure. au.dk/portal/files/55994387/LLS_PHD_AFHANDLING_ENDELIG_UPLOAD_august_ grafisk.pdf Sarauw, L. L. (2012). Qualifications frameworks and their conflicting social imaginaries of globalisation. Learning and Teaching: The International Journal of Higher Education in the Social Sciences, 5(3), 22–38. https://doi.org/10.3167/latiss.2012.050302 Schaumann, L. (2016, December 21). Det Gælder Om at Være På Dupperne, Når Karrieren Venter Forude. Fyens Stifttidende. Retrieved from https://www.fyens.dk/indland/ Det-­gaelder-­om-­at-­vaere-­paa-­dupperne-­naar-­karrieren-­venter-­forude/artikel/3108499 Sellar, S., & Lingard, B. (2018). International large-scale assessments, affective worlds and policy impacts in education. International Journal of Qualitative Studies in Education, 31(5), 367–381. https://doi.org/10.1080/09518398.2018.1449982 Shore, C., & Wright, S. (2011). Conceptualising policy: Technologies of Governance and the politics of visibility. In C. Shore, S. Wright, & D. Però (Eds.), Policy worlds: Anthropology and the analysis of contemporary power. Berghahn Books. Sorensen, T. B., & Robertson, S. L. (2020). Ordinalization and the OECD’s governance of teachers. Comparative Education Review, 64(1), 21–45. https://doi.org/10.1086/706758 Spina, N. (2017). Governing by numbers: Local effects on students’ experiences of writing. English in Education, 51(1), 14–26. https://doi.org/10.1111/eie.12109 Steiner-Khamsi, G., & Waldow, F. (2012). Policy borrowing and lending in education. Routledge. the Danish Accreditation Institution. (2013). Guide to institutional accreditation. Retrieved from https://akkr.dk/wp-­content/filer/akkr/Korekturl%C3%A6st-­og-­godkendt_Vejledning-­om-­ institutionsakkreditering-­endelig_godkendt.pdf the Danish Government. (2017). Strategy for the strengthening of foreign languages in the educational system [Strategi for styrkelse af fremmedsprog i uddannelsessystemet]. Online Retrieved from https://ufm.dk/publikationer/2017/filer/strategi-­for-­styrkelse-­af-­fremmedsprog-­i-­ uddannelsessystemet.pdf the Danish Government. (2021). Political agreement on the framework for more and better educational opportunities in all parts of Denmark [Politisk aftale om rammerne for Flere og bedre uddannelsesmuligheder i hele Danmark]. Retrieved from https://ufm.dk/lovstof/politiske-­ aftaler/aftale-­om-­flere-­og-­bedre-­uddannelsesmuligheder-­i-­hele-­danmark/politisk-­aftale-­om-­ rammerne-­for-­flere-­og-­bedre-­uddannelsesmuligheder-­i-­hele-­danmark.pdf Uljens, M., & Ylimaki, R.  M. (2017). Bridging educational leadership, curriculum theory and Didaktik: Non-affirmative theory of education. Springer. World Economic Forum. (2016). The future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution. Retrieved from http://reports.weforum.org/future-­of-­jobs-­2016/ preface/

Part V

Conclusion

Chapter 9

Governance Hybridity and Its Implications for Education and Research on Educational Governance

The book’s previous chapters have shown how details in the quantification practices concerning graduate outcome indicators in Danish higher education affect the stratification of education, educational thinking, the instrumentation of education policy, the governing properties of numbers, subjectivizing processes, and educational development and design in specific ways. In this concluding chapter, I will map the empirical findings across the previous chapters and furthermore discuss them in relation to the contemporary grand narrative of global convergence in educational governance research. An overall trend characterizing educational governance in recent decades is the transnational influence on national education systems, as highlighted by a range of scholars (Andersen & Bager, 2012; Brøgger, 2019; Henry et al., 2001; Karseth & Solbrekke, 2016; Krejsler & Moos, 2021b; Lawn, 2011; Lawn & Grek, 2012; Lingard et al., 2016; Rinne, 2021; Sellar & Lingard, 2018). While this trend is evident in research, the influence of transnational norms and practices may materialize differently in different governance contexts, as they ‘collide and intertwine with “embedded policies” to be found in “local” spaces (national, provincial or local) where global policy agendas come up against existing practices and priorities’ (Rinne, 2021: 154). While transnational norms and practices may promote particular policy decisions, they do not necessarily generate convergence (Carvalho, 2014: 68). The introduction of transnational systems of thinking, often rooted in Anglo-­ American educational thinking (Krejsler & Moos, 2021a), thus most likely materializes differently in a Danish higher education context than in the Anglo-American contexts where they originated. This chapter draws on the conceptualizations of major governance traditions, including neoliberalism, capitalism, new public governance, Cold War grids of thinking, social democracy, and (educational) governance in a Nordic context, as

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Madsen, Governing by Numbers and Human Capital in Education Policy Beyond Neoliberalism, Educational Governance Research 19, https://doi.org/10.1007/978-3-031-09996-0_9

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outlined in Chap. 2, in order to understand why and in what ways higher education governing with numbers in Denmark differs from some of the general understandings of educational governing with numbers. First, the chapter uses findings from the book to discuss how the Danish case exhibits particular modes of governing and thus relates to the various major governance traditions. I also discuss how new instruments of governance, including what I have framed as algorithmic and nudging instruments, relate to this general picture. Second, the chapter discusses how the findings can be understood in relation to dominant theorizations of global convergence, suggesting hybridity as an alternative theorization. Third, the chapter summarizes the implications of these practices for higher education in Denmark. This part of the conclusion also explores how students and teachers respond to the Danish narrative on human capital. Finally, the chapter includes a brief methodological epilogue. The conclusion thus addresses each of the three contributions outlined in the preface to the book, adding reflections on the implications of the findings for Danish higher education.

9.1 Governing with Graduate Outcome Indicators in Danish Higher Education Throughout the book, I have sought to address the question of whether governing by numbers and human capital thinking in education is per definition neoliberal. The overall conclusion is that this is not the case. There are elements of neoliberal thinking within Danish higher education governance using graduate outcome indicators. These elements include the reliance on measuring the preferences of the labor market as indicators of the value of higher education; a configuration of higher education as a resource in the global knowledge economy and a skill provider for workplaces; the publication of indicators with the partial aim of creating a higher education market and subjectivizing potential students as customers as well as instilling competitive pressures among higher education programs; and a neoliberal conversation about the profitability of running small programs. Meanwhile, these elements do not constitute the Danish case as an example of neoliberal higher education governance. The case is instead dominated by a number of specific governance trends, more often bearing traces from a Cold War fascination with planning and optimization and from social democratic (and perhaps Nordic) preferences for equality and collectivity than from neoliberalism. In the following section, I will outline these specific governance trends and show how they align with governance ideals that differ significantly from neoliberalism.

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9.1.1 The Techno-scientific Rationale of Governing with Quantification Quantification in educational governance is often associated with a growing dominance of technical and scientific or cognitive-instrumental rationalities and practices in education (Skedsmo et  al., 2021; Williamson & Piattoeva, 2019). The Danish case includes a range of calculative, communicative, and social practices around educational data that may be termed techno-scientific. The calculative practices of making data objective (Daston & Galison, 2007; Williamson & Piattoeva, 2019) include not only systematic and standardized procedures aligned with both general quantitative-methodological conventions and specific conventions related to the measurement of graduate outcomes but also an ongoing development toward improving the robustness, precision, conceptual strength, and coherence of measurements. The standardized procedures, methodological conventions, and robustness of data, drawing on scientific practices, constitute a mode of governance resembling technocratic ideals of expertise-driven decision-making. These ideals also materialize in the appointment of expert commissions and committees, constituting evidence-for-policy instruments that legitimize education policy (Karseth et al., 2022; Steiner-Khamsi et al., 2020). Decision-making based on educational data presupposes simple and unambiguous numbers. The governance instruments analyzed in this book, in particular the instrument Education Zoom directed at potential students, display a preference toward a data aesthetic of single numbers (Gorur, 2018: 91; Ratner & Ruppert, 2019), often produced through calculations of averages or binaries. These kinds of numbers enable straightforward comparisons and are thus useful both for algorithmic instruments conducting automated decision-making and for nudging instruments producing affectivity and desires in the population. Meanwhile, as well as directly enabling and justifying political and educational decision-making, the simplified data also travel around the higher education sector and subtly inform the knowledge and educational thinking of a wide variety of actors. The techno-­ scientific rationale of governing with educational data thus has implications beyond the realm of governance. As Tröhler and his colleagues point out, the roots of the techno-scientific rationale of governing with data go back to the Presbyterians, who fostered hierarchically structured social governance through standardized examination (Tröhler & Maricic, 2021: 7). The Presbyterian mode of governance, which was disconnected from the government, spread from Anglo-American contexts to other OECD countries during the Cold War (Tröhler & Maricic, 2021: 10), and this mode of governance emerged as a tradition of letting social engineers, rather than politicians, govern society, both in the Nordic context and in the broader OECD area (Buchardt et al., 2013: 19; Bürgi & Tröhler, 2018). The techno-scientific rationale dominating educational governance with data is thus not an (exclusively) neoliberal phenomenon, even though it also dominates neoliberal contexts (e.g., Barber & Ozga, 2014). Rather, it is deeply embedded in continental European and Nordic welfare state

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modes of governance. In the Nordic welfare state, this rationale has become part of the strong state and works in tandem with the egalitarian tradition of the state as a protector rather than an oppressor of the population (Kettunen, 2012). In this context, the idea of a rational and modern mode of governance through objective data is understood as a means for the state to ensure that governance is fair and does not privilege certain groups (e.g., groups of students, academics, or particular institutions) over others. In this sense, the techno-scientific rationale of governing with numbers is perfectly compatible with the Nordic governance model.

9.1.2 A Governance Ideal of Planning The Danish case also displays a strong preference for optimal planning, which is closely related to the ideal of technocratic or instrumental decision-making outlined above. The preference for planning is most clearly expressed in the ways graduate outcome data focus narrowly on the correspondence between educational skills and qualifications and the subsequently obtained job and thereby with the archetype of a straightforward and almost automatic transition from education to work. The focus on correspondence confines the relevance of higher education to the first job or early career of a graduate rather than to the opportunities available to the graduate in a lifelong career. The use of graduate unemployment and job match metrics entails that the correspondence between education and work is important at a system-­wide level rather than at the individual level. The educational contribution of human capital to the workforce is thereby required to work at the system level rather than for the individual graduate. This way of understanding and promoting the human capital of the population differs from neoliberal-capitalist configurations in which the human capital of the population is first and foremost a matter of the prosperity of individuals. This Danish configuration of the relevance of higher education underscores why graduate outcomes are subject to a prominent focus and continuous reform in Danish higher education policy rather than leaving individual students to navigate a higher education market. Another example of this preference for planning is the governance of Danish higher education institutions and programs through resource reallocations. The currently ongoing macro-level conversion of students from the humanities to STEM programs, combined with the dominant principle of activity-based funding in the Danish funding model where funding is allocated according to the number of full-­ time equivalent students, entails that the funding of some areas of studies is reduced, while funding of other areas is increased. For decades, activity-based funding has been used to ensure that the costs of education per student do not increase, and the performance-based funding component in the funding system is used to ensure that universities strive to achieve better outcomes. The state thus seeks to avoid spending public resources on activities that are of low value to society. With the changed distribution of students across areas of studies, resources are also reallocated in line with activity-based funding.

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Locally, reallocations across the sector materialize, for example, in the analyzed trend of using minimum standards as de facto teaching standards, as well as program closures. As university departments have limited possibilities for adapting to the reduced activity level, caused by a high share of fixed costs that do not decrease in line with decreased reduced number of students, as well as by protective labor market policies for academics, the resource reallocations have resulted in  local experiences of austerity. Public sector austerity is often framed as a neoliberal governance trend in higher education and governance literature (Nixon, 2017; Pollitt, 2016: 134), and specifically in relation to performance-based governance instruments like funding models (Landri et al., 2017: 70). However, I argue that the reallocation of resources does not reflect neoliberal ideals of austerity but rather the same Cold War ideals of forecasting, planning, and management that characterize soft governance trends in a Danish context. Parsimonious governing of higher education can thus be viewed as another example of fair, neutral, and optimizing governance practices instituted by the protector state. Danish higher education has certainly witnessed cases of austerity, including a two percent annual budget cut in the years 2016 to 2019 that was implemented across most public sector policy areas, but it is not the main driver of funding reallocations, which more accurately can be defined as an instance of the preference for optimization and planning.

9.1.3 Quantification and Managerial Soft Governance The Danish case also includes several instruments that work through new or soft modes of governance (Lascoumes & Le Gales, 2007; Lawn, 2011). More specifically, Danish higher education governance with quantified graduate outcome data constitutes an example of performance measurement, including strategic framework contracts; a performance-based funding system; an accreditation system encouraging institutions to implement their own performance measurement systems; and the website Education Zoom providing public access to comparable performance data. These instruments do not work by providing direct instructions down the governance hierarchy but rather by enabling and encouraging constant comparisons of oneself (Redden, 2019; Simons, 2014) to either equivalent others or to a future or past version of oneself. Comparisons of oneself to other countries, institutions, areas of studies, or programs govern through competitive pressures, while comparisons of oneself to a future version of oneself function via strategic pressures. Meanwhile, even though the Danish instruments deploy both types of comparisons, and especially cross-sectional comparisons, they most often entail hierarchical pressures, relating the performance of an educational unit to the expectations of the government and the university management. As the empirical examples have shown, the soft governance mechanisms of performance measurement affect not only the institutional actors directly targeted by governance instruments but also the governments rolling out the governance instruments. Soft modes of governance are promoted via specific instrumentation choices

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but at the same time operate beyond the existence of specific instruments, as a dominant code of conduct in our time. The code of conduct of performance measurement implies that professional behavior in relation to data is associated with an active engagement with data and a responsible attitude toward narratives promoted by data (Miller & Power, 2013). If data point out a problematic aspect of the educational activities measured, such as a graduate unemployment rate that is relatively high compared to other equivalent programs, the responsible professional attitude involves adopting appropriate affectivities of guilt and shame, as well as demonstrating a will to improve through adequate action plans. Similarly, data that display problems at the national or institutional level encourage appropriate affectivities of blame and disappointment, as well as the implementation of policies that show a willingness to address the problems defined in the data. The code of conduct of performance measurement is nevertheless promoted and systematized by specific instruments of governance, such as the accreditation instrument, which ensure that it spreads throughout the sector (Brøgger & Madsen, 2021a). In debates on educational governance, performance management and performance measurement are often considered part of the trend of an increased marketization of education (Gorur, 2013; Krejsler & Moos, 2021a; Rinne, 2021) and higher education (Blackmore, 2009; Espeland & Sauder, 2016; Naidoo, 2018; Robertson, 2017), in line with new public management. The argument states that students (and their parents) become consumers of education and are guided by performance data and rankings in their choice of school or university, thereby forcing these institutions into competition with each other (Blackmore, 2009; Rinne, 2021). Meanwhile, in the case of Danish higher education, only one of the governance instruments, Education Zoom, can be characterized as working partly through a form of marketization. Instead, most performance measurement and performance management instruments in the Danish governance context are part of a managerial version of new public management (Le Galès, 2016; Madsen, 2022; Pedersen & Löfgren, 2012; Pollitt & Bouckaert, 2011) that is less influenced by neoliberal ideals of market mechanisms with minimal state intervention. In this version of new public governance, narratives on accountability and audit culture are more relevant than narratives on marketization (Hopmann, 2008; Le Galès, 2016; Power, 1999; Shore & Wright, 2015; Skedsmo et al., 2021). Managerial new public management instruments align with the Nordic model’s ideal of a strong state optimizing the public sector in order to use resources more efficiently and thereby protect the population from inefficiency and a misuse of resources. They also appear as a continuation of the Cold War grid of thinking, focused on forecasting, planning, and management (Bürgi & Tröhler, 2018). Performance measurement in Danish higher education thus emerges as a technology of optimization, enabling the welfare state to deliver as much welfare as possible. In comparison to primary schools, which particularly in Sweden have been affected by marketization via privatization and the publication of rankings (Imsen et  al., 2017; Rinne, 2021: 159; Wennström, 2019), Nordic higher education has largely remained exempt from privatization and marketization (Rinne, 2021: 161). The research narrative arguing that quantification promotes marketization,

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competition, and rankings thus represents a slightly trivial way of describing educational governance when deployed to the case of Denmark. Notions like a competition fetish (Naidoo, 2016, 2018), rivalry (Dale, 2017: 183), and a universalization of social competition (Mau, 2019: 6, 7) are less relevant for a description of governing by numbers in Danish higher education.

9.1.4 New Modes of Governing with Numbers In addition to these trends, the Danish case also shows how quantification-based governance appears in new versions that work through other mechanisms and ambitions of governance closely linked to the same trends. Algorithmic governance instruments constitute an example of a more recently emerging type of instrument based on quantification. The previous Danish funding model, allocating funding based on full-time equivalent students, serves as an early and quite common type of algorithmic governance instrument. However, the current funding model represents a more recent trend of a complex composite funding model, also including performance-­based funding (Landri et al., 2017). In addition, the Resizing Model, which caps graduate production among programs with a relatively high graduate unemployment rate, serves as an example of an even more advanced type of algorithmic governance instrument, even though it remains far from the real-time big-­ data instruments found in other sectors of society (Yeung, 2017). The shared feature of the two algorithmic governance instruments in the Danish case is their reactive character—or in other words, the automated decision-making they produce, which enables policymakers to distance themselves from specific policy decisions. The affectively disinterested orientation of reactive instruments aligns well with the apparently neutral and fair aura of techno-scientific governing. The algorithmic governance instruments thus represent an amplified version of the techno-scientific modes of governance described above, combined with digital technologies. Another more recent kind of governance instrument is public displays of educational data (Decuypere et  al., 2014; Gorur, 2013, 2018), of which the website Education Zoom is an example. With Education Zoom, the Danish government adds the population of potential students to the subjects of higher education governance. The website nudges potential students to make particular educational choices based on particular parameters by displaying indicators considered relevant for those choices. The mechanisms of the website activate the desire to anticipate one’s future and avoid negative patterns displayed in the data. This instrument thus shares features with the other performance measurement instruments in the sense that it opens up a space of action and encourages actors, in this case potential students, to take action based on data (Decuypere et al., 2014). Meanwhile, it differs from other performance measurement instruments in the sense that the actions called for are risk preventive rather than oriented toward accountability and improvement. The role of the data changes from providing facts concerning the current situation to providing probabilities concerning future results of current actions. Whereas traditional

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performance measurement instruments are relatively well established as a type of new or soft governance instruments, the nudging instrument thus represents a new type of soft governance instrument that may become more common in the future, and in which data play a different and more radical role.

9.1.5 Governing for the Benefit of the Collective Finally, the Danish case of graduate outcome governance bears traces of social democratic values of uniformity, equality, and collectivity. Overall, higher education remains a public good that should not be affected by elitism and stratification but rather be provided as a public service of a relatively uniform quality across the sector. Even though the performance measurement mechanisms of soft governance do entail stratification, especially of different areas of studies, that sometimes affects the distribution of resources across the higher education sector in a way that amplifies the differences displayed in the data by further undermining the quality of certain areas of studies, the vertical stratifications of higher education caused by the quantification practices in the Danish case do not reflect a neoliberal ideal of inequality in public service. The construction and distribution of quantitative educational data does not serve as a support mechanism for a market ruled by the law of the survival of the fittest. Rather, quantitative stratifications in Danish higher education appear to be used as a techno-scientific means to identify areas in need of adjustment and sometimes reform, with the aim of achieving uniform quality standards across the higher education sector and thus equal opportunities for all. The inequalities this imposes on the higher education sector, and thus on the students graduating from various areas of studies, seem like an unfortunate side effect, perhaps constituting a blind spot among policymakers rather than a deliberate strategy. Furthermore, the entire idea of higher education as a productivity-enhancing activity is in the Danish case rooted in the social democratic model of a universal welfare state and a unionized and organized approach to how qualifications should be exchanged for labor market positions. The social democratic welfare state idealizes an equal society in which income and risk are redistributed across the population via taxes and an extensive social security system. The welfare state includes a social contract, in which well-educated individuals, who are supported by the welfare state during their studies, in turn are obliged to contribute to the collective by taking a job (Kettunen, 2012: 24). The social democratic model furthermore values worker’s rights in terms of salaries and job content appropriate for a given level of qualifications obtained through education. Looking at the Danish case analyzed throughout this book, the capitalist focus on skills (or kompetencer, which can also be translated to competency or the ability to act) can thus be understood as closely connected to the universal welfare state (Kettunen, 2012) rather than necessarily opposed to it (Imsen et  al., 2017). The Nordic model in education is historically rooted not only in national culture but also in national economy (Rinne, 2021: 154). In other words, important educational

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ideas characterizing the Nordic model of education, such as equality, participation, and democracy, currently materialize in education policy focused on access to useful skills and employment—as they have done historically in other periods dominated by a narrative of global competition, such as during the Cold War (Buchardt, 2020). The relevance agenda in Danish higher education policy reflects more than industrial-economic education policy (Telhaug et al., 2006); it also encompasses the welfare state policies of protecting citizens against ‘useless’ education detached from real life (Buchardt, 2020: 191) and thereby against unemployment leading to inequality and segregation in society. The element that distinguishes current education policy from historical policies, which emphasized the education of the ‘right kind of people’, or in other words of versatile citizens open to life and thus a range of different positions in the labor market (Buchardt, 2020: 198), may simply be the contemporary emphasis on a narrow correspondence between education and labor market. In conclusion, the case of Danish higher education policy that governs with graduate outcome metrics includes neoliberal, new public management, Cold War thinking, and Nordic model modes of governance. However, the governance practices working through quantification only appear to be paired with neoliberal ideologies and ideals to a very limited extent. The quantification practices characterizing the Danish case do not resemble a ‘neoliberal brainchild’ (as also argued by Bürgi & Tröhler, 2018 in relation to the case of PISA). Instead, a managerial version of new public management, permeated by ideals developed during the Cold War and working in tandem with the Nordic welfare state model, seems to dominate the Danish way of governing with numbers. Conjointly, the trends of a techno-scientific rationality, a preference for planning and correspondence, managerial soft governance instruments, and ideals of uniform public services constitute a Danish model of governing with quantification that is defined by a strong state deploying quantification as a tool for the coordination and optimization of society. While neoliberal trends of governing have affected Danish higher education—for example, in the corporatization of the higher education sector that turned universities into autonomous self-owning institutions with an executive board dominated by external members and with an employed rather than elected rector (Brøgger & Madsen, 2021b; Ørberg & Wright, 2019)—these changes in the university sector do not entail that performance measurement in Danish higher education serves neoliberal ideals.

9.2 From Convergence to Hybridity As this conclusion shows, Danish higher education governance with graduate outcome metrics is not a case of neoliberal thinking and practices overtaking traditional national values and governance ideals. It is rather a case of an economization of education in a profoundly social democratic way, obsessed with optimal coordination of society and assisted by techno-scientific legitimacy and managerial soft governance instruments. The few neoliberal practices that do occur in the case can thus

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hardly be taken as proof of global convergence, as suggested in some parts of the literature (e.g., Krejsler & Moos, 2021a; Naidoo, 2018) and by much-used theoretical notions such as isomorphism (DiMaggio & Powell, 1983). In the event that we can speak of a Danish convergence toward neoliberal practices circulating within global society, it remains a convergence at the surface level. Furthermore, the idea of convergence requires that we can clearly define what essentially characterizes ‘Danish’ or ‘Nordic’ educational governance as a coherent or pure mode of governance with clear origins. I think that this idea of an original ‘Danish’ mode of governance, often juxtaposed with ‘foreign’ ideas, is a myth. Empirical studies conducted both in this book and by others (Buchardt, 2020; Bürgi & Tröhler, 2018; Sobe, 2015) rather suggest that the governance tradition embedded in a particular context, for example, the Danish state, has ‘always’ (or at least since the Second World War) been made up of a combination of various lines of thought. In the Danish case, these lines of thought include ideals of a strong state, various ideas of how to govern efficiently, values of equality and autonomy, and policies to ensure a well-educated and employed workforce, albeit constantly and dynamically negotiated and with shifting emphases. The individual lines of thought may at times have been developed with inspiration from other countries, but my conclusions suggest that they have also developed in particular ways within the Danish state.

9.2.1 Danish Governing with Numbers as a Dynamic Hybrid Instead of describing the Danish case through a theorization of convergence, I find it relevant to discuss the potential of a theorization based on the notion of hybridity. This notion stems from postcolonial studies and in particular the works of Bhabha (1994). Bhabha uses hybridity to conceptualize identities (including not only individual, but also national and transnational identities) as fragmented and unstable. With this theory, Bhabha undermines the idea of the coherence of, and thus the possibility of the superiority of, the colonizing power. Instead, he frames cultures as mutually hybridizing and thus as hybrids that may be diverse in their compositions but without essence or pure origins. As pointed out in readings of Bhabha’s work, this implies that ‘cultural values are hybrid, cultures are heterogeneous processes, myths of authenticity and cultural specificities – that generate the traditional concepts of “people” and “nation”—cannot be easily sustained’ and that ‘cultures are constructions and traditions are inventions’ (Souza 2004: 126, here quoted from Andreotti, 2011). While the notion of hybridity was developed within postcolonial studies, and thus specifically used to describe the relationship between the cultures of the colonized and the cultures of the colonial power, Bhabha’s concept of hybridity has been taken up in many other disciplines. It has even been claimed that globalization is hybrid and that the emergence of the nation and the ‘national’ is a result of, or a response to, globalization—a way of establishing demarcations and power relations in an ultimately global world (Pieterse, 1994). From this perspective, it is not

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convergence but claims of national distinctiveness that appear to constitute an act of dominance. Following this argument, I suggest that most cultures, including most ‘governance cultures’ or practices, are hybrid and always have been. In other words, I doubt that a ‘Danish’ or ‘Nordic’ model of education, understood as one single and coherent model, has ever really existed. Instead, we might understand Danish higher education governance as a site of constant negotiation and contestation of various values and governance ideals, producing a mesh of governance practices representing a shifting balance of power and legitimacy in combination with sedimented practices from previous power balances. To further elaborate, and with inspiration from agential realism (Barad, 2007), we can understand the governance practices at any given time as dynamically evolving enactments of a combination of national traditions and global trends. Based on this understanding, the mere ideas of a ‘Danish’ tradition, a ‘Nordic’ model of education, or a global trend of neoliberal thinking emerge through these enactments from the ways they discursively and materially iterate previous practices, as well as boundaries and relations. As Bhabha also unfolds, here cited from Andreotti (2011: 26), hybridity might not always be enacted simply as adherence to foreign ideas but also as a mimicry or copying that entails the mockery (or otherwise ambiguous enactments) of these ideas. It is thus not merely the practices reenacted (and thus iterated and referenced) in educational governance that are important but also in what ways they are reenacted. In that sense, contemporary Danish higher education policy appears to be a reenactment of nationhood, social coherence, and moral obligations of graduates toward society, as well as of a government taking responsibility for the economic sustainability of society and of the risks of unemployment that individual graduates may face. This enactment appears to be able to simultaneously present the Danish government (whether leftwing or rightwing at the time of policymaking) as economically responsible (with reference to global norms and techniques) as well as fundamentally Danish (with reference to national social-democratic governance traditions), or, following Pieterse (1994), as adopting inward-looking and outward-­ looking governance cultures simultaneously. Through this enactment, the government is able to narrate what constitutes Denmark as a nation rather than merely giving in to global narratives. This creolization of bits and pieces from different cultures indicates a less radical process of global interaction than that of convergence. Overall, the analysis has thus shown a story of continuity rather than discontinuity. While transnational ideas of quantification, outcome-based governance and new public management, and neo-classical economics have clearly become part of Danish higher education governance, they have been put to use in line with traditional governance ideals and purposes. The proliferation of transnational ideas can in other words not stand alone in the book’s analysis of Danish quantification practices. In turn, the book’s subtitle may be overconfident, indicating a more unequivocal break from neoliberalism than can be justified based on this discussion. Furthermore, the analysis of Danish higher education governance as currently strongly influenced by social democratic values does not mean that the same applies

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to the governance of primary and secondary education nor does it mean that this was also the case in previous decades. The discussion above nevertheless implies a critical stance toward scholars who make claims of something inherently Danish (and of something inherent to the Anglosphere), which is being threatened by globalization processes. Perhaps we should instead see recently developing practices as co-evolving (Sobe, 2015), with mutual overlaps and similarities across different contexts but also with different flavors and with references to different systems of thought, and inherently incoherent.

9.3 Implications for Danish Higher Education Regardless of the limited influence of neoliberalism on Danish higher education governance, the Danish higher education sector is strongly affected by contemporary modes of governing. Instead of viewing globalization processes and the spread of neoliberal thinking and human capital theory as currently the greatest threat to higher education, this book suggests that it is the economists (and with them the legitimacy of ‘economic culture’) and their role in the state and contemporary governance that we as educational thinkers should be wary of—even when they reenact social democratic, national, and Nordic identities. The implications of economically wired social democratic approaches to higher education policy analyzed in this book may be just as grave as those of neoliberal policies. The emphasis on the limited influence of neoliberalism thus does not entail an absence of policy critique. The economistic policies entail a particular form of tension between students and teachers in higher education and furthermore drive a development in which the humanities become ever more exposed to reforms. As this section shows, both these types of policy effects have implications for educational quality and thus perhaps disrupt the achievement of the stated policy aims.

9.3.1 The Tension in Responses to the Correspondence Approach Students enrolled in programs and academics in charge of programs connect differently to the idea of correspondence between programs and jobs, and this tension leads to contestations of what constitutes educational quality. Disregarding the few students who actively rejected and resisted the human capital narrative of the graduate outcome data, most of the humanities students that I spoke to were striving to improve their future positional advantage in the labor market. Their attitude toward positional advantage does not reflect an individualist, competitive approach of trying to get ahead of their peers. Rather, they acted in accordance with the structural correspondence approach and attempted to imbue their studies with a natural

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correspondence between their academic profile and a future job. The lack of correspondence between the typical humanities program and specific job categories (with the job as high school teacher that some students pursue through program specialization representing a major exception) apparently necessitates students’ engagement in activities like internships, student jobs, voluntary work, collaboration with industry, or specific electives aimed at labor market functions. While these activities can be utilized by students within a competitive and individualist approach to positional advantage, the empirical material regarding students (introduced and analyzed in Chap. 7) suggests that Danish humanities students consider these activities a necessary requirement to enter the labor market rather than a competitive advantage. The desire to live up to the required correspondence to job functions represents what Sam Sellar and Lew Zipin term ‘worries about “staying afloat”’ and attempts to avoid downward mobility (Sellar & Zipin, 2019: 573, 580), in the Danish case defined by unemployment. The Danish students thus generally did not see themselves as in competition with one another for jobs. Only one of the interviewed students expressed a sense of competition against her peers and thereby an understanding of activities outside her program’s curriculum as a way of achieving a positional advantage over these peers. In turn, academic disciplines that traditionally have not been closely associated with a specific kind of job or profession and that thereby seem devalued by the system-level correspondence criterion typically respond with educational development toward more generic educational content. These disciplines thus broaden their relevance rather than deepen their correspondence with the labor market. The responses furthermore include a massive focus on treating the symptoms of the problems indicated by the graduate outcome data: The programs initiate transition activities that do not actually improve the correspondence between education and labor market but merely address the problem by ensuring a smooth transition in spite of the lack of correspondence. These developments of education hardly seem to improve the correspondence of programs to labor market position, but instead risk weakening the correspondence, as resources are spent on generic content and transition activities rather than discipline-specific content and skills. Most of the programs that I followed appeared to avoid developing content that corresponds more closely with the labor market, despite the overall correspondence approach of the Danish policies and the demand for correspondence among students. The few exceptions that I came across in my fieldwork focused on collaborative approaches to labor market correspondence, such as projects involving specific subsectors of the relevant labor market, allowing students to specialize themselves deeply in relation to those subsectors. The respective responses from students and programs suggest a tension in relation to the correspondence approach. As many students prioritize greater correspondence, and furthermore address this priority through activities outside university teaching, they often spend less than full time on their studies. The reduced time spent on studies may weaken quality, adding to the negative effects on quality of minimum-standard teaching, mixed classes, and resources spent on transition activities and generic content rather than disciplinary knowledge. The correspondence

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work of students, partly resulting from a lack of correspondence built into their study programs, thus affects the conditions for teaching. Meanwhile, although the idea of correspondence is not alien to the welfare state, it is arguably relatively new to the university, which has only recently been transformed from an elite institution relatively remote from the labor market into a mainstreamed part of the education system for many young people (Marginson, 2016). The limited alignment of programs with labor market positions and the adaptation of the scaling of various areas of studies to the demands of the labor market perhaps reflects a resistance toward viewing the university as an institution of the welfare state or, in other words, as an institution providing utility and opportunities in the labor market for the many rather than constituting a utility-free space for the few. While it is up for debate whether the university of the twentieth century was ever an institution free from economic interests from the outside (e.g., Buchardt et al., 2013: 19; Kristensen, 2007), it is arguably difficult to maintain that role at a time where the university serves as a primary path to the labor market for many young people.

9.3.2 Humanities Impoverished In continuation of this tension, and as a more general result of the use of graduate unemployment rates and other graduate outcome metrics in the governing of Danish higher education, the higher education sector has faced a devaluation of particular areas of studies for at least a decade. This has particularly affected the humanities but also some disciplines within the social sciences and natural sciences. The devaluation of certain areas of studies results from both the dominant theorization of higher education as valuable by virtue of its close correspondence with particular labor market positions and from quantification practices establishing a higher education landscape comprising areas of studies and programs as the main units of measurement and comparison. These practices entail that higher education is stratified according to programs or areas of studies, condemning the humanities and glorifying the STEM disciplines, rather than according to the prestige of institutions or other principles of differentiation. One understanding of this stratification narrates it as generally favoring programs that have established a strong link to specific occupations over those organized around an academic discipline. On the one hand, this understanding allows critique of universities for providing education without utility that cannot fulfill the promise of a secure future for higher education graduates; on the other hand, it enables critique of policymakers and other powerful actors as influenced by economic thinking rather than appreciating the value of knowledge and cultural preservation as such that the humanities represent. However, viewing the Danish stratification practices from a more historically informed point of view, they can also be understood as favoring programs that maintain a sustainable relationship between education and associated occupations over those that grew rapidly (and beyond the labor market demand) during the previous decades of increased mass education. This

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understanding takes into account the well-established relationship between many humanities programs and the labor market for high school teachers and allows for a critique of the suboptimal rapid growth of some areas of studies in pursuit of activity-­based funding and government goals of increasing participation in higher education. From a humanities perspective, meanwhile, this understanding also allows for critique of policymakers for not giving the humanities sufficient time and resources to conquer new labor markets and promote the value of humanities graduates to other kinds of employers than high schools. As such, there are multiple interpretations of the problem and possible critiques. The stratification of programs and areas of studies has opened a window for policy reform (Sellar & Lingard, 2018), including reductions in the production of humanities graduates and reallocations of resources to other areas of studies. In this respect, higher education policy is imbued with optimism, as the poor numbers are expected to improve as soon as a better balance has been achieved in the graduate production across areas. Meanwhile, this stratification has also led to a range of negative effects for humanities programs, which are now left impoverished. The effects of poor numbers include: an increased administrative burden in relation to quality assurance procedures; a requirement to downscale, which in some places has resulted in lower teaching standards and program closures; distorted priorities toward transition activities rather than disciplinary content; an increased pressure on students in terms of activities outside the studies that improve employability; and a poor reputation among the general public, possibly itself negatively affecting the employability of humanities graduates. These negative effects amplify the risk of reduced educational quality and of increased stress levels and reduced well-being among students and teachers—effects that may in turn affect the presumed poor performance of programs reflected in the numbers, thereby establishing a vicious circle. In this respect, parts of the higher education sector are imbued with pessimism regarding the future as the imbalances seem to gradually increase with no prospect of escaping this vicious circle.

9.4 Methodological Epilogue: Implications for Studies in Educational Governance The main argument in this concluding chapter concerns how local governance traditions and historical contexts of different countries (Pollitt, 2016: 56), including different state and welfare state models (Buchardt et  al., 2013; Esping-Andersen, 1990; Kettunen, 2012), affect the modes and mechanisms characterizing governance via quantification. This argument questions the scholarly narrative of convergence, which has dominated educational governance literature beyond governing with numbers for the last decade. Perhaps transdisciplinary and contextually sensitive approaches, combining historical and educational theory perspectives with transnational studies of educational governance and policy, will open the field for

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studies of national and regional differences that may advance our understanding of contemporary educational governance. The book’s main limitation is its scope as a single-country case study. Even though I have sought to relate the Danish case to international examples and studies throughout the book, my comparisons are predominantly based on studies performed by others and thereby secondhand knowledge. This mode of analysis entails a risk of analyzing differences between the Danish case and other examples in the literature as contextual and empirical, while the differences may in fact be caused by different interpretations and emphases on particular aspects of higher education governance. Most likely, the differences I identify are a little bit of both. Meanwhile, a proliferation of genuine comparative studies of governance with educational data (e.g., Grek, 2009; Hartong & Piattoeva, 2021; Sellar & Lingard, 2018) would allow for a much richer understanding of governing with numbers in various contexts, including similarities and differences across different countries and regions in the world. Genuine comparative studies in qualitative educational governance research is however a lot to ask for, as the barriers of research funding and obstacles for researcher mobility combined with the complexity of educational governance make comparative studies a real challenge, especially with the imperative of comprehensive multi-approach studies promoted in this book. Nevertheless, the contribution of genuinely comparative studies in educational governance would be tremendous and thus worth striving for.

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