Incentives for Collaboration and Competition: Experimental Evidence on Innovation, Behavior and Performance (Innovation und Entrepreneurship) 365829230X, 9783658292300

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Innovation und Entrepreneurship

Jonas Heite

Incentives for Collaboration and Competition Experimental Evidence on Innovation, Behavior and Performance

Innovation und Entrepreneurship Series Editors Nikolaus Franke, Wien, Austria Dietmar Harhoff, München, Germany Joachim Henkel, München, Germany Carolin Häussler, Passau, Germany

Innovative Konzepte und unternehmerische Leistungen sind für Wohlstand und Fortschritt von entscheidender Bedeutung. Diese Schriftenreihe vereint wissenschaftliche Arbeiten zu diesem Themenbereich. Sie beschreiben substanzielle Erkenntnisse auf hohem methodischen Niveau. Innovative concepts and entrepreneurial performance are crucial for prosperity and progress. This publication series brings together scientific contributions on these topics. They describe substantial findings at a high methodological level.

More information about this series at http://www.springer.com/series/12264

Jonas Heite

Incentives for Collaboration and Competition Experimental Evidence on Innovation, Behavior and Performance With a Foreword by Prof. Dietmar Harhoff, Ph.D.

Jonas Heite Munich, Germany Dissertation LMU Munich and Max Planck Institute for Innovation and Competition, 2019

ISSN 2627-1168 ISSN 2627-1184  (electronic) Innovation und Entrepreneurship ISBN 978-3-658-29230-0 ISBN 978-3-658-29231-7  (eBook) https://doi.org/10.1007/978-3-658-29231-7 © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved 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 Gabler imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Foreword Economic research examines the interactions among individuals and firms with the aim of understanding and ultimately improving their performance. Two types of interactions are of particular relevance in this context: collaboration and competition, but it is largely unknown how to provide optimal incentives for them. In his dissertation, Jonas Heite combines sophisticated econometric methods with novel experimental approaches to shed light on these questions. In particular, he conducts three distinct large-scale experiments which allow him to derive causal inference results. First, a randomized controlled trial investigates the effectiveness of a publicly funded innovation voucher scheme that stimulates the use of external knowledge by small and medium-sized enterprises. The subsidy program provides financial support for engaging the services of external experts in the context of innovation-related projects. By analyzing responses from two survey rounds, Jonas Heite and his co-authors provide evidence that the innovation voucher accelerates the execution of innovation projects, with positive short-term effects on innovation outcomes such as the number of patent applications and the number of new products. The findings should allow policymakers to refine the design and scope of innovation programs seeking to enhance R&D collaboration. In the second chapter Jonas Heite investigates effort, stress and performance of individuals in competitive environments characterized by varying degrees of heterogeneity in terms of contestants’ abilities. In contrast to other economic studies, stress is not the residual explanation of performance differentials in contests, but is actually measured via multiple physiological stress diagnostics. The results show that individuals react differently to heterogeneous contests, depending on their ability. For low- and medium-ability individuals increases in stress levels and a deterioration of performance can be observed, while high-ability individuals show no discernible reaction. These findings support the theory of choking under pressure for individuals with low and medium ability and present new causal evidence regarding individual behavior in contests. The third chapter restricts the analysis to individuals with medium ability levels competing either in a low- or high-ability group. Field and laboratory experiments are combined to exploit the benefits of both methods. In a first step, Jonas Heite analyses a natural experiment on the Topcoder contest platform. The data are suitable for a regression discontinuity design, where individuals with a medium ability level are competing against stronger or weaker contestants. The second step involves a laboratory experiment that enriches the analysis by measuring levels of effort and of self-confidence which cannot easily be assessed in the field. Results show that individuals competing against higher-ability contestants perform significantly lower than equally capable individuals competing against lower-ability contestants. This effect is partly driven by a higher risktaking propensity of individuals competing in the high-ability group. The laboratory experiment reveals that individuals with a high (low) self-confidence perform better (worse)

VI

Foreword

in high-ability groups than in low-ability groups. The level of effort does not explain the detected performance differential. Taken together, all three essays contribute to the discussion regarding the determinants and incentives of collaboration and competition. The dissertation opens new research avenues in innovation and entrepreneurship, economics, and behavioral neuroeconomics. I strongly recommend this volume to scholars, practitioners and policymakers alike.

Prof. Dietmar Harhoff, Ph.D.

Acknowledgements Considering that part of this thesis examines the performance effects of collaboration, I am particularly aware of the importance of co-authors, colleagues and mentors. First and foremost, I want to thank Dietmar Harhoff for his continuous support and valuable advice throughout the past three years. He has been a knowledgeable and reliable supervisor on my path to write this thesis. By providing freedom, financial support and trust, he made working at the Max Planck Institute for Innovation and Competition a productive and rewarding endeavor. I would like to extend my thanks to my second advisor, Tobias Kretschmer, for his consistent support and constructive feedback during the MBR program and beyond. I feel fortunate to have been able to learn from and collaborate with my co-authors Karin Hoisl, Laura Rosendahl Huber and Marco Kleine. The projects in this volume have benefitted tremendously from their experience and our regular discussions. Special thanks to Karin for her outstanding mentorship, team spirit and ongoing support. Furthermore, I am grateful to Karim Lakhani for hosting me at the Laboratory for Innovation Science at Harvard and for his motivating guidance, insightful cooperation and for sharing profound Basketball knowledge at TD Garden. In addition, the support of my colleagues at the MPI has been of particular value to me. I am especially grateful to Stefano Baruffaldi, Laura Bechthold, Dennis Byrski, Marina Chugunova, Nadine Chochoiek, Fabian Gaessler, Stefan Nothelfer, Timm Opitz, Felix Poege, Zhaoxin Pu, Myriam Rion, Matthias Schmitt, Stefan Sorg, Christian Steinle, Gisela Stingl, Magdalena Streicher and Rainer Widmann. I also want to thank my research assistants Barbara Höing, Maximilian Schrader, Arne Seeliger and Nataliia Shvets for supporting me in writing this thesis. During my stay at Harvard, I enjoyed working together with my esteemed colleagues, most notably Philip Brookins, Michael Menietti, Misha Teplitskiy and Stephanie Ureña. I thank the Harvard Decision Science Laboratory, especially Dana Carney, Alki Iliopoulou, Gabe Mansur and the team of research assistants, for hosting my experiment and the Laboratory for Innovation Science at Harvard for considerable support and resources towards carrying out the experiment. I further thank my friends Matthias Batz, Jan-Christian Hansen, Rouven Kanitz, Robert Eirich, Jürgen Depner, Alexander Gabriel, Maximilian Wolfinger, Raphael Zeller, Philipp Simon, Alexander Kersting, Felix Griese, Lisa Schopp, Maximilian Schietzel and Simon Hochstein for both being a source of affirmation and distraction. Lastly, I want to thank my family for their encouragement and faith. I feel fortunate to have you in my life, Hermann, Julia, Benedikt, Peter and Kristina as well as Angi, Peter and Oliver. To my sister Lea – thank you for your love and unconditional support that means so much to me. My heartfelt gratitude goes to my dad and grandmother, who would most certainly read these lines with a proud smile on their faces.

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Acknowledgements

With special appreciation I thank my girlfriend Claudia for her love, care and encouragement. You enrich my life every single day. In closing, I want to express my deepest gratitude to my mother for all her faith and support throughout my life. Your strength and sense of responsibility, especially in difficult times, impresses and encourages me on my own path through life. This dissertation is dedicated to you: Danke.

Jonas Heite

Contents Preface ...........................................................................................................1 1 Subsidized R&D Collaboration: The Effect of Innovation Vouchers on Innovation Activity and Performance................9 1.1

Introduction ...........................................................................................................9

1.2

Context and Experimental Design ....................................................................... 13

1.3

1.4

1.5

1.2.1

Context and Program ............................................................................... 13

1.2.2

Design of the Field Experiment ................................................................ 13

Data and Methodology ........................................................................................ 14 1.3.1

Data and Sample ...................................................................................... 14

1.3.2

Variables ................................................................................................... 17

1.3.3

Randomization Check and Response Bias ................................................ 24

1.3.4

Empirical Strategy .................................................................................... 27

Results ................................................................................................................. 29 1.4.1

Comparison of Means ............................................................................... 29

1.4.2

Main Results on Project-level Outcomes .................................................. 30

1.4.3

Main Results on Firm-level Outcomes ...................................................... 34

1.4.4

Robustness Tests ...................................................................................... 37

Discussion and Conclusion ................................................................................... 40

2 Choking Under Pressure: The Effect of Asymmetric Contests on Effort, Stress, and Performance ..................... 45 2.1

Introduction ......................................................................................................... 45

2.2

Literature Review and Mechanisms ..................................................................... 48

2.3

2.2.1

Contest Theory ......................................................................................... 48

2.2.2

Choking Under Pressure ........................................................................... 50

Design of Laboratory Experiment ........................................................................ 54 2.3.1

Work Task ................................................................................................ 55

2.3.2

Design and Procedure ............................................................................... 56

2.3.3

Implementation ......................................................................................... 59

2.3.4

Stress-related Arousal Measures ............................................................... 62

2.4

Sample and Descriptives ...................................................................................... 65

2.5

Results ................................................................................................................. 71

2.6

Discussion ............................................................................................................ 81

2.7

Conclusion............................................................................................................ 86

2.5.1

Robustness Tests ...................................................................................... 80

X

Contents

3 Performance in Contests: The Role of Risk and Confidence ................................................................................ 91 3.1

Introduction ......................................................................................................... 91

3.2

Literature Review and Mechanisms ..................................................................... 94

3.3

Regression Discontinuity Design .......................................................................... 96

3.4

3.5

3.3.1

Context ..................................................................................................... 96

3.3.2

Data and Descriptive Statistics ................................................................ 99

3.3.3

Empirical Strategy .................................................................................. 101

3.3.4

Results .................................................................................................... 107

3.3.5

Robustness Tests .................................................................................... 111

Laboratory Experiment...................................................................................... 113 3.4.1

Work Task .............................................................................................. 114

3.4.2

Design ..................................................................................................... 114

3.4.3

Results .................................................................................................... 118

Discussion and Conclusion ................................................................................. 121

Appendix .................................................................................................... 125 Appendix to Chapter 1 ............................................................................................... 125 A.1 Supplementary Information ....................................................................... 125 A.1 Supplementary Figures............................................................................... 135 A.1 Supplementary Tables ................................................................................ 136 Appendix to Chapter 2 ............................................................................................... 155 A.2 Supplementary Information ....................................................................... 155 A.2 Supplementary Figures............................................................................... 161 A.2 Supplementary Tables ................................................................................ 163 Appendix to Chapter 3 ............................................................................................... 181 A.3 Supplementary Information ....................................................................... 181 A.3 Supplementary Figures............................................................................... 183 A.3 Supplementary Tables ................................................................................ 193 Bibliography ............................................................................................... 207

List of Figures Figure 1.1:

Timeline of the field experiment ............................................................. 15

Figure 2.1:

Experimental steps ................................................................................. 57

Figure 2.2:

Performance in different compensation schemes .................................... 67

Figure 2.3:

Arousal levels in different compensation schemes and ability types ...... 69

Figure 2.4:

Composite of stress by compensation scheme and ability type .............. 71

Figure 2.5:

Regression results ................................................................................... 78

Figure 2.6:

Choking under pressure for M-types ...................................................... 79

Figure 3.1:

Composition of SRMs ............................................................................. 97

Figure 3.2:

Performance of contestants (global view) ............................................. 103

Figure 3.3:

Performance of contestants (global view with quartic function) .......... 104

Figure 3.4:

Performance of contestants (local view) ............................................... 104

Figure 3.5:

Main procedure of lab experiment ........................................................ 115

Figure 3.6:

Regression results for performance, clustered by confidence (lab) ....... 121

Figure A.1.1:

Innovation voucher’s logic chain .......................................................... 135

Figure A.2.1:

Highly simplified illustration of stress reaction in human body ........... 161

Figure A.2.2:

Output distribution for submissions and errors .................................... 161

Figure A.2.3:

Performance by compensation scheme and ability type ....................... 162

Figure A.2.4:

Kernel density estimate of self-reported wake-up time......................... 162

Figure A.3.1a:

Placebo tests for artificial cutoffs (panel 1) .......................................... 183

Figure A.3.1b:

Placebo tests for artificial cutoffs (panel 2) .......................................... 184

Figure A.3.2a:

Smoothness of covariates: Number of SRMs participated .................... 185

Figure A.3.2b:

Smoothness of covariates: Number of contests participated ................. 185

Figure A.3.2c:

Smoothness of covariates: Experience in months ................................. 185

Figure A.3.2d:

Smoothness of covariates: Experience in high-ability group ................. 186

Figure A.3.2e:

Smoothness of covariates: Experience in low-ability group .................. 186

Figure A.3.2f:

Smoothness of covariates: Competed in high-ability group .................. 186

Figure A.3.2g:

Smoothness of covariates: Number of switches .................................... 187

Figure A.3.2h:

Smoothness of covariates: Student (0-1) .............................................. 187

Figure A.3.2i:

Smoothness of covariates: Located in USA (0-1) ................................. 187

Figure A.3.2j:

Smoothness of covariates: Male (0-1) ................................................... 188

Figure A.3.2k:

Smoothness of covariates: Primary interest cash (0-1) ......................... 188

Figure A.3.3:

Histogram of the skill rating threshold ................................................. 188

Figure A.3.4:

Histogram (a) and Kernel density plot (b) of the rating variable ........ 189

Figure A.3.5:

McCrary density test ............................................................................ 189

Figure A.3.6:

RD manipulation test using local polynomial density estimation ........ 189

Figure A.3.7:

Performance of contestants incl. challenge points ................................ 190

Figure A.3.8:

Performance of competing in a low- versus high-ability group............. 190

XII Figure A.3.9:

List of Figures Margins-plot of performance: Interaction with confidence ................... 191

Figure A.3.10:

Margins-plot of risk: Interaction with confidence ................................. 191

Figure A.3.11:

Histogram of expected rank after piece rate (Task 2) .......................... 192

List of Tables Table 1.1:

Sample composition .............................................................................. 16

Table 1.2:

Summary statistics of survey sample .................................................... 16

Table 1.3:

Supplier types ...................................................................................... 21

Table 1.4:

Descriptive statistics ............................................................................ 23

Table 1.5:

Randomization checks .......................................................................... 25

Table 1.6:

Randomization checks for specific supplier types ................................... 25

Table 1.7:

Test of response bias ............................................................................ 26

Table 1.8:

Descriptive statistics by treatment and control group ........................... 29

Table 1.9:

Treatment effects on product and service outcomes by supplier type ..... 32

Table 1.10:

Treatment effects on IP outcomes by IP advisor ................................... 33

Table 1.11:

Treatment effects on collaboration outcomes ........................................ 34

Table 1.12:

Treatment effects on joint ventures by supplier types ........................... 35

Table 1.13:

Treatment effects on innovation activity outcomes ............................... 36

Table 1.14:

Treatment effects on business outcomes ................................................ 37

Table 2.1:

Number of observations per treatment cell .............................................. 65

Table 2.2:

Descriptive statistics .................................................................................66

Table 2.3:

Regression results .....................................................................................74

Table 3.1:

Descriptive statistics: Topcoder setting ................................................ 100

Table 3.2:

Regression results for performance (RDD: local strategy) .................... 107

Table 3.3:

Regression results for problem 1 (RDD: local strategy) ....................... 109

Table 3.4:

Regression results for problem 2 (RDD: local strategy) ....................... 109

Table 3.5:

Regression results for problem 3 (RDD: local strategy) ....................... 110

Table 3.6:

Descriptive statistics (lab) .................................................................. 117

Table 3.7:

Regression results for performance (lab) ............................................. 119

Table 3.8:

Regression results for interaction with confidence (lab) ....................... 119

Table 3.9:

Regression results for performance, clustered by confidence (lab) ........ 120

Table A.1.1:

Comparison between compliers and non-compliers................................. 136

Table A.1.2:

Observations per supplier type by treatment and control group............ 136

Table A.1.3:

Randomization checks ............................................................................ 137

Table A.1.4:

Randomization checks for supplier types ................................................ 138

Table A.1.5:

Response bias: Survey to population ...................................................... 139

Table A.1.6:

Check for response bias........................................................................... 140

Table A.1.7:

Treatment effects on product and service outcomes (subsamples) ......... 141

Table A.1.8:

Treatment effects on product and service outcomes (dummies)............. 142

Table A.1.9:

Treatment effects on product and service outcomes (broad category) ... 143

Table A.1.10: Treatment effects on product and service outcomes (no controls) ......... 144

XIV

List of Tables

Table A.1.11: Treatment effects on IP outcomes (subsample: IP advisor) ................... 145 Table A.1.12: Treatment effects on IP outcomes (dummies) ........................................ 146 Table A.1.13: Treatment effects on IP outcomes (broad category) .............................. 147 Table A.1.14: Treatment effects on patent outcomes (no controls) .............................. 148 Table A.1.15: Treatment effects on collaboration outcomes (broad category).............. 149 Table A.1.16: Treatment effects on collaboration outcomes (no controls) .................... 149 Table A.1.17: Treatment effects on joint venture outcome (subsamples) ..................... 150 Table A.1.18: Treatment effects on the initialization of joint ventures (dummies) ...... 151 Table A.1.19: Treatment effects on joint venture outcome (broad category) ............... 152 Table A.1.20: Treatment effects on joint venture outcome (no controls) ..................... 152 Table A.1.21: Treatment effects on innovation activity outcomes (broad category) .... 153 Table A.1.22: Treatment effects on innovation activity outcomes (no controls) .......... 153 Table A.1.23: Treatment effects on business outcomes (broad category) ..................... 154 Table A.1.24: Treatment effects on business outcomes (no controls) ........................... 154 Table A.2.1:

Procedure and timing of experiment ...................................................... 163

Table A.2.2:

Descriptive statistics of subject pool (5,433 subjects, August 2018) ....... 164

Table A.2.3:

Number of observations per treatment cell for physiological measures .. 166

Table A.2.4:

Separate regression results: L-types in asymmetric contest .................... 167

Table A.2.5:

Separate regression results: M-types in asymmetric contest ................... 168

Table A.2.6:

Separate regression results: H-types in asymmetric contest ................... 169

Table A.2.7:

Separate regression results: M-types in {MHH} contest......................... 170

Table A.2.8:

Separate regression results: M-types in {LLM} contest ......................... 171

Table A.2.9:

Regression results incl. MHR, HRV, and cortisol ................................... 172

Table A.2.10: Regression results for effort, submissions, MHR, and stress composite .. 173 Table A.2.11: Regression results without demographic control variables ..................... 174 Table A.2.12: Regression results without smokers ........................................................ 175 Table A.2.13: Regression results without subjects taking any kind of medication........ 176 Table A.2.14: Regression results without subjects having exercised ............................. 177 Table A.2.15: Regression results for alternative composite measures ........................... 178 Table A.2.16: Regression results for skin conductance .................................................. 179 Table A.3.1:

Descriptive statistics: Problem levels ...................................................... 193

Table A.3.2:

Placebo tests of artificial cutoffs ............................................................. 194

Table A.3.3:

Covariate balance ................................................................................... 195

Table A.3.4:

Sequential analysis of covariates ............................................................. 196

Table A.3.5:

Robustness check of covariate balance ................................................... 197

Table A.3.6:

Robustness checks of local strategy ........................................................ 198

Table A.3.7:

Robustness check of local strategy excl. wrongly assigned contestants .. 199

Table A.3.8:

Regression results without contestants that did not submit a solution .. 200

Table A.3.9:

Regression results for performance (RDD: global strategy).................... 201

List of Tables

XV

Table A.3.10: Robustness checks of global strategy ...................................................... 201 Table A.3.11: Regression results for performance incl. challenge points ....................... 202 Table A.3.12: Regression results if contestants are competing in D1 for first time ...... 203 Table A.3.13: Regression results: Sample split by contestant’s type ............................ 204 Table A.3.14: Regression results: Sample split by contestant’s age and gender ........... 205 Table A.3.15: Regression results for performance incl. session controls ........................ 206 Table A.3.16: Regression results for interaction with confidence incl. session controls. 206

List of Supplementary Information Appendix A.1.1: Innovation Voucher’s survey .............................................................. 125 Appendix A.2.1: Experimental instructions .................................................................. 155 Appendix A.2.2: Detailed description of the experimental instructions ........................ 155 Appendix A.2.3: Pictures of the experimental room and physiological equipment ....... 156 Appendix A.2.4: Overview on different systems responding to stress ........................... 157 Appendix A.2.5: Detailed information on cortisol, HRV, HR, and EDA ...................... 157 Appendix A.2.6: Challenges of stress measurement ...................................................... 159 Appendix A.3.1: Calculation of the skill rating ............................................................. 181

List of Abbreviations ACTH ANS CATI CAWI CITI COMS CT D1 D2 DNA ECG EDA EMG ER-SCR EUR FE GBP GPA HDSL HF HPA HR HRV IBI IP IRB LF MHR MSE MSC MVP N NASCAR NI NGSES NS-SCR

Adrenocorticotropic hormone Autonomic nervous system Computer-assisted telephone interview Computer-assisted web interview Collaborative Institutional Training Initiative Committee On Microbiological Safety Contest incentivized task in lab experiment Division 1: High-ability group in SRMs Division 2: Low-ability group in SRMs Deoxyribonucleic acid Electrocardiogram Electrodermal activity Electromyography Event-related skin conductance response Euro Fixed effects Pound sterling Grade Point Average Harvard Decision Science Laboratory High frequency Hypothalamic-pituitary-adrenal axis Heart rate Heart rate variability Inter-beat-interval Intellectual property Institutional review board Low frequency Mean heart rate Mean squared error Mean skin conductance Minimum viable product Number of observations National Association for Stock Car Auto Racing Not incentivized task in lab experiment New general self-efficacy scale Non-specific skin conductance response

XX Obs. OLS P1 P2 P3 PI PP PPG PR PSNS RCT RD RDD RMS RMSSD RPI RS RSE R&D SAM SC SCR SE SF SISE SME SNS SP S.r. SRM St.D. TSST TSST-G UK US/USA USD

List of Abbreviations Observations Ordinary least squares Problem level 1: Easy problem in SRMs Problem level 2: Medium problem in SRMs Problem level 3: Difficult problem in SRMs Principal investigator Peak-to-peak Photoplethysmography Piece rate incentivized task in lab experiment Parasympathetic nervous system Randomized controlled trial Regression discontinuity Regression discontinuity design Root mean square Root mean square of successive differences Relative performance information Resting period in lab experiment Rosenberg self-esteem scale Research and development Sympathoadrenal medullary axis Skin conductance Skin conductance response Standard errors Sampling frequency Single-item self-esteem scale Small and medium enterprise Sympathetic nervous system Skin potential Self-reported Single round match (Topcoder contest type) Standard deviation Trier social stress test Trier social stress test for groups United Kingdom United States of America United States dollar

Preface One of the fundamental issues in management and economics is to understand the behavior of individuals and firms in making decisions and how their decision making behavior ultimately affects productivity and performance. Crucial determinants for performance are, specifically, interactions among individuals and firms (Granovetter 1985, Contractor and Lorange 1988, Barringer and Harrison 2000). These interactions may vary on a continuum between collaboration and competition (Contractor and Lorange 1988, Uzzi 1997).1 Collaboration is a process in which individuals or firms work together to achieve a specific outcome (Hagedoorn 1990, Smith et al. 1995). In competitions, players use scarce resources (i.e., effort, money, or time) in order to exceed their opponents to achieve a certain objective (Lazear 1981, Rosen 1988, Konrad 2009, Dechenaux et al. 2015). Even if the two approaches follow opposing principles, both are expected to impact positively on productivity and performance (Lazear and Rosen 1981, Contractor and Lorange 1988, Kogut and Zander 1992 Gulati et al. 2000, Belderbos et al. 2004b). However, constraints, market failures, and externalities may influence the behavior of individuals and firms so that the theorized benefits of collaboration and competition diminish or even vanish (Teece 1992, Blumberg 2001, Konrad 2009). As a consequence, economists, practitioners, and policymakers promote incentives for collaboration and competition as a means to improve performance. Collaboration expands firms’ organizational and spatial boundaries. This is of particular importance in innovation activities due to the increasing complexity and uncertainty of innovation processes (Love and Roper 2001, Contractor and Lorange 2002, Hagedoorn 2002, Cassiman and Veugelers 2002, 2006, Hall and Lerner 2009).2 Firms complement internal innovation efforts by acquiring missing knowledge and supplementary resources through external networking (Grant 1996, Cassiman and Veugelers 2006, Chesbrough et al. 2006, Tether and Tajar 2008, Teirlinck and Spithoven 2013). Yet, constraints, incentive problems, and market failures complicate or inhibit collaborations – especially for small and medium enterprises (SMEs).3 SMEs often suffer from financial constraints and a lack of external funding opportunities for innovation-related projects (Hall 2002, 2010, Czarnitzki and Hottenrott 2011). Another constraint for SMEs is limited access to innovation-relevant knowledge. Due to the size of the firms, this knowledge might not be available internally (Van de Vrande et al. 2009). Then again, external partnerships might be harder to establish for SMEs due to transaction costs, information asymmetries, and a lack of absorptive capacity within the firm (Holmstrom 1989, Van de 1

Note that there are also hybrid forms of collaboration and competition such as “coopetition” (e.g., Brandenburger and Nalebuff 1996, Tsai 2002, Veugelers and Cassiman 2005). 2 Innovation is seen as a main driver of economic growth (Schumpeter 1942, Romer 1990, Aghion and Howitt 2008). 3 SMEs contribute substantially to economic growth (Scherer 1986, Audretsch et al. 2006, Haltiwanger et al. 2013, Akcigit and Kerr 2018) and have been shown to pursue both more basic as well as more radical innovation projects (Henderson and Clark 1990, Schneider and Veugelers 2010, Haltiwanger et al. 2013).

2

Preface

Vrande et al. 2009, Hall 2010). From a societal perspective, technological spillovers from innovation to other firms improve overall productivity so that the social benefits outweigh the private benefits that lead companies to underinvest compared to a macroeconomic optimum (Nelson 1959, Arrow 1962, Bloom et al. 2013). This has led to a call for targeted policy interventions to alleviate these constraints and market failures. Hence, policymakers seek to stimulate collaborations between SMEs and research and development (R&D) partners to enhance firms’ innovation capabilities and thus their competitiveness (Clark and Guy 1998, Hottenrott and Lopes-Bento 2014b). Competition is ubiquitous with mechanisms at play whenever countries, organizations, or individuals compete over scarce resources (Konrad 2009, Dechenaux et al. 2015). Competition has intensified in recent years and has spread to all sectors of society where growing needs meet scare resources (Rosen 1988, Porter 2008). A particular and wellknown type of competition is referred to as contests or tournaments (Konrad 2009).4 The theory on contests was originally formulated by Lazear and Rosen (1981) to design optimal labor contracts based on differences in individual productivity. Since then, contests have often been used in the economy to promote production and innovation (Boudreau et al. 2016). In essence, the underlying principle of contests is relative performance evaluation, where players exert effort while competing for a prize (Nalebuff and Stiglitz 1983). Contests have been shown to improve performance if all players believe to have a chance to win (Lazear and Rosen 1981). One crucial factor that determines players’ winning probability refers to the abilities of the competitors, which impose a strong externality on players’ behavior (Konrad 2009, Ederer 2010). Therefore, economists investigate contest designs with different degrees of heterogeneity among players’ ability levels to assess their effectiveness in shaping optimal competitive incentives. The importance of collaboration and competition for performance in combination with the outlined constraints and externalities raises a central question: How to effectively incentivize and design collaboration and competition schemes in order to enhance performance? This dissertation experimentally investigates the very question by assessing (a) efforts to encourage R&D collaboration and (b) properties and underlying mechanisms of ability configurations in contests. Thereby, this thesis seeks to expand the knowledge about incentives and mechanisms in striving to improve the performance of individuals and firms. The dissertation takes an experimental economics perspective by conducting field, lab, and natural experiments. The experimental approach allows for causal inferences from observed behavior due to (quasi-)random assignment of treatments. Lab experiments naturally emphasize internal over external validity due to a controlled variation of key circumstances and an exclusion of confounding factors. Contrary, field and

4 In this dissertation, I use the terms tournaments and contests interchangeably. Following Lazear and Rosen (1981), these terms denote rank-ordered incentive schemes that rely on the premise of relative performance evaluation.

Preface

3

natural experiments benefit from higher generalizability at the expense of reduced control over potential confounding factors. The thesis consists of three essays, respectively, chapters. First, a field experiment evaluates the effectiveness of a publicly funded innovation voucher scheme that incentivizes R&D collaboration between SMEs and external experts. Second, a real-effort laboratory experiment investigates the effect of different ability configurations in contests on individuals’ effort, physiological stress, and performance. Third, evidence from both lab and natural experiments further deepens our understanding of competing against contestants with higher or lower abilities by examining the impact of risk and self-confidence on performance. Together, all three essays discuss how the potential of collaboration and competition can be better exploited in order to increase individual and firm output. As a result, this dissertation presents evidence that even a small financial intervention that stimulates firms to pursue an innovation-related project with an external partner can result in improved innovation output. Furthermore, substantial parts of this thesis take a behavioral economics perspective and provide evidence that ability configurations of competitions have a significant effect on stress, risk-taking, and performance with heterogeneous effects of individual characteristics such as selfconfidence. Chapter 1: Subsidized R&D Collaboration: The Effect of Innovation Vouchers on Innovation Activity and Performance The first chapter is joint work with Laura Rosendahl Huber and Marco Kleine. It is based on a large-scale randomized controlled trial (RCT) assessing the effectiveness of innovation vouchers on innovation outcomes of SMEs. In general, the causal empirical evidence on the effectiveness of R&D subsidies is still scarce (David et al. 2000, Howell 2017). The existing literature has mainly four shortcomings. First, the identification of causal effects is problematic because R&D subsidies are mainly allocated according to certain selection criteria instead of random draws (Dechezleprêtre et al. 2016). Second, most studies investigate the impact of R&D subsidies on innovation inputs instead of outputs (Belderbos et al. 2004b, Bronzini and Piselli 2016). Third, a major fraction of studies investigating the effectiveness of R&D subsidies does not consider SMEs due to limited data on innovation in- and output of SMEs, which are often not bound to report R&D to governmental entities (Dechezleprêtre et al. 2016). Finally, while there is a large strand of literature focusing on R&D grants that seek to alleviate financial constraints, much less is known about the impact of policy mechanisms that aim to foster innovation activities through increased R&D collaboration with external knowledge providers.5 This study aims to fill this gap by testing the effectiveness of a randomly allocated innovation subsidy, targeted at increasing external 5 On a more general level, R&D collaborations are known to trigger additional R&D spending and increase innovation productivity (Cassiman and Veugelers 2002, Hagedoorn 2002, Belderbos et al. 2004b, Veugelers and Cassiman 2005, Hottenrott and Lopes-Bento 2014b).

4

Preface

knowledge collaboration, on innovation activities and outputs of SMEs. In particular, the study evaluates a policy tool known as innovation vouchers that has become popular in recent years. The subsidy provides SMEs with financial incentives to collaborate with R&D partners across the public or private sectors. So far, the literature on innovation vouchers is largely of conceptual and explorative nature with limited causal evidence about the effectiveness of such programs on SMEs’ innovation output (Cornet et al. 2006, Sala et al. 2016, Chapman and Hewitt-Dundas 2018).6 Hence, it is still an open question, whether innovation vouchers may support SMEs to collaborate with external partners on innovation projects and whether this collaboration translates subsequently into innovation output. This essay addresses this research gap. The study is a large-scale RCT evaluating the causal effect of subsidized R&D collaboration on innovation performance. We analyze the effect of an innovation voucher scheme in the United Kingdom (UK) that grants SMEs from all sectors financial support of up to 5,000 GBP for engaging the services of experts when pursuing an innovationrelated project within the firm. Firms could choose to collaborate with universities, design advisors, research and technology organizations, non-technical consultants, or IP advisors. The study is based on a unique data set comprised of 744 firms that applied for three application rounds in 2015. We collected data by means of two surveys that were conducted by an independent research agency one and two years after the award of the innovation voucher. We analyze project-level outcomes conditional of the chosen partner type, since the determinants of R&D collaborations strongly depend on the objective of the innovation project and the selected partner type (Belderbos et al. 2004a, 2004b). Our findings provide evidence that the innovation voucher program successfully accelerates the execution of R&D projects with short-term effects on innovation outcomes. We find that being awarded a voucher has a positive short-term impact on product development for firms that collaborated with a university. In addition, we find a positive effect on the number of patent applications for firms indicating to be in need for specialist IP knowledge. We do not find evidence for an effect on firm-level outcomes such as collaboration, innovation activities, or business outcomes. However, we present evidence that subsidized university-industry collaborations result in an increase of joint ventures two years after the voucher has been awarded. The findings contribute to the literature on the effectiveness of governmental instruments to foster innovation. Existing empirical research on the evaluation of R&D collaboration schemes is primarily restricted by concerns about a convincing causal design with an adequate control group. This issue is resolved due to an RCT with a lottery determining the treatment and control group. At a policy level, this study serves as a first large-scale assessment of a national innovation voucher program to evaluate whether the endeavor to initiate and amplify collaboration with R&D partners has been success6 To the best of our knowledge, the only paper that looks at innovation outcomes of a similar governmental scheme is the study by Bakhshi et al. (2015). They find that innovation vouchers have positive impacts on products, services, and processes. However, these effects disappear after a short period of time.

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5

ful. The results allow policymakers to further refine the design and scope of programs seeking to enhance R&D collaborations. Hence, this study contributes to a more informed political debate on appropriate policy instruments to promote innovation. Chapter 2: Choking under Pressure: The Effect of Asymmetric Contests on Effort, Stress, and Performance The second chapter focuses on effort, stress, and performance of individuals in competitive environments with different degrees of heterogeneity among players by conducting a laboratory experiment measuring physiological stress levels. The heterogeneity of players’ ability levels has an essential impact on productivity in contests (Konrad 2009, Ederer 2010). Prior economic research argues that competing against contestants with similar ability levels (i.e., symmetric contests) is supposed to provide incentives to maximize one’s effort to win a prize (Rosen 1986, Baik 1994, Prendergast 1999, Stein 2002). Conversely, if individuals compete against players with divergent ability levels (i.e., asymmetric contests), performance is expected to deteriorate (Lazear and Rosen 1981). In this regard, two mechanisms might be particularly important to explain performance differentials. On the one hand, economists argue on the basis of effort reduction due to incentive problems that are caused by asymmetric contests (Lazear and Rosen 1981). Weaker players reduce their effort because they are discouraged when facing superior competitors (Beviá and Corchón 2013). At the same time, stronger players are presumed to anticipate the behavior of weaker players and reduce their efforts as well (Szymanski and Valletti 2005, Gürtler and Kräkel 2010). Psychologists, on the other hand, consider arousal levels to explain detrimental effects on performance (Yerkes and Dodson 1908). In particular, situations of increased pressure (e.g., as caused by asymmetric contests) might lead to stress-related arousal passing an optimal threshold with adverse effects on individual performance – a phenomenon known as “choking under pressure” (Baumeister 1984). All in all, the factors that might be decisive of the performance decline in asymmetric contests have not yet been sufficiently investigated. As economists, why should we care about the mechanisms that drive the performance decline if the outcome is the same in both lines of reasoning? Most competitive environments include some degree of heterogeneity among players. They often determine crucial facets for future prospects and career success such as hiring or promotion. Even if contests are designed to be symmetric, once intermediate performance feedback is revealed and players know the ability of their opponents the competition becomes asymmetric (O’Keeffe et al. 1984). Combining the prevalence of asymmetric competitive environments with the expected performance decrements, the economic potential for improvement becomes obvious. As a consequence, the dominant mechanism causing the performance decline has to be identified in order to design procedures and incentives that alleviate the main driver of impaired performance. Hence, the second chapter investigates

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the two pre-eminent mechanisms of effort reduction and higher stress levels that are expected to lead to performance differentials in asymmetric contests. In order to test the hypotheses provided by the two competing streams of literature, a real-effort laboratory experiment with a rich variety of physiological stress measurements is conducted. The analysis further relies on self-reported effort. 346 participants took part in the experiment. Subjects are classified into three ability types (low, medium, or high ability) depending on their performance in a piece rate incentivized mathematical problem-solving task. Conditional on the assigned ability type, subjects are matched in groups of three that differ by the ability composition of players. Finally, subjects compete against the two other participants in the respective contest group by solving math problems, whereupon only the participant with the highest number of points in the group receives a prize bonus. The results show that taking part in asymmetric contests triggers heterogeneous responses among individuals with different skill levels. While for low- and medium-ability contestants higher stress levels and lower performance are documented, no changes at all are observed for high-ability participants. Medium-ability contestants show a similar stress and performance response when facing stronger as well as weaker contestants. Selfreported effort does not explain the output decrements. Taken together, the results support the theory of choking under pressure for individuals with low and medium abilities. The results have important implications for both theory and practice. For economists, it provides novel insights into behavioral factors affecting performance in contests. In addition, the findings add to psychological research examining sources of choking under pressure. The article further contributes to the literature on behavioral economics by comparing the mechanisms of effort exertion and stress responses. From a practitioner’s point of view, introducing competitive incentive schemes as a means of improving employee productivity may have the opposite effect if heterogeneity among competitors increases with adverse effects on employees’ performance and stress levels – especially for those who are dominated by a stronger opponent. Chapter 3: Performance in Contests: The Role of Risk and Confidence In Chapter 3, joint work with Karin Hoisl, we use a mixed-method approach to dive deeper into the behavior and performance response of individuals with intermediate ability competing in asymmetric contests; either in a high- or low-ability group. We combine field and lab experiments for two reasons. First, we extend our analysis of the mechanisms affecting performance in contests by assessing individuals’ effort and self-confidence. Second, we are able to test the validity of our findings from the field in a controlled laboratory environment. We focus on medium-ability contestants since they have been shown to react strongly to increased competitive pressure (Boudreau et al.

Preface

7

2016)7, which reveals a high potential for performance improvements once the mechanisms are uncovered. Furthermore, medium-ability contestants allow investigating both situations of competing against stronger as well as weaker players given their intermediate position between low- and high-ability contestants. Existing literature shows that individuals competing in the two above mentioned settings will not exert their maximum effort (Lazear and Rosen 1981). Yet, it remains an open question whether individuals perform higher or lower when facing stronger instead of weaker opponents. Further, the mechanisms causing potential performance differentials need closer examination. Closing these research gaps is important: First, relative performance evaluation is widely used to incentivize individuals to exert effort; second, environments where all contestants have the same abilities are rare. Once the mechanisms causing performance differentials are known, they may be controlled. Literature from economics and psychology has discussed a variety of mechanisms that potentially affect performance in contests. Two mechanisms that might be particularly important in a setting where contestants are characterized by diverging abilities are risk and selfconfidence. Economists have shown that individuals facing contestants with higher ability take higher risks (Konrad and Lommerud 1993, Buser 2016). Psychologists consider psychological factors like self-confidence to explain behavior and performance in contests (Baumeister 1984, Woodman et al. 2010). While the performance of people with low selfconfidence may be impaired when they compete against better individuals, individuals with a high level of self-confidence should be spurred by competing against better individuals. To answer our research questions, we employ a two-step empirical approach. In a first step, we use field data (1,677 unique coders competing in 38 software algorithm competitions) to investigate the performance of individuals competing against contestants with higher or lower abilities. The field data is gathered from a natural experiment in crowdsourcing contests hosted on the Topcoder platform and qualifies for a regression discontinuity (RD) design. The RD design allows to compare the behavior and performance of two contestants characterized by the same intermediate ability: One of the contestants competes amongst the top performers of a low-ability group and the other competes amongst the bottom performers of a high-ability group. Our results show that individuals characterized by a medium ability competing against higher-ability contestants perform significantly lower than individuals characterized by a medium ability competing against lower-ability contestants. Furthermore, we find that the bottom performers of a high-ability group tend to pursue riskier strategies, possibly because they are aware of the fact that they can only win if they increase their risk propensity. In a second step, we use data from the laboratory experiment (69 participants) described in Chapter 2. We assess effort, risk, self-confidence, and performance of individuals with intermediate ability levels competing in two different contest groups that closely mirror our field setting:

7

See also Chapter 2.

8

Preface

One subject competes against two higher-ability contestants, whereas the other subject faces two individuals with lower abilities. In the laboratory experiment we obtain a significant effect that goes in the opposite direction, i.e., contestants in high-ability groups outperform contestants in low-ability groups. We do not find evidence that the effort put in the task or the willingness to take risks can fully explain our results. Once we split the sample by the level of self-confidence of the individuals, we find that medium-ability contestants with high self-confidence perform better in high-ability groups than in low-ability groups. Medium-ability contestants with low self-confidence, on the contrary, perform worse in high-ability groups than in low-ability groups, which is in line with our first step finding. This study contributes to the contest literature by providing new and causal evidence of the mechanisms causing performance differentials in tournaments. Furthermore, our results provide evidence that risk-taking can be an explanation for the observed performance differentials, since it typically leads to a higher variance in outcomes. Finally, this essay adds to the literature on psychological factors explaining performance differentials under pressure. Concluding Remarks All in all, the dissertation builds on three distinct and comprehensive data sets that were collected in three large-scale experiments spanning field, lab, and natural experiments. The analyses rely on data from surveys, interviews, governmental databases, crawled websites, physiological measures, and individual behavior in lab and natural experiments. The thesis methodologically and empirically contributes to the literature on innovation and entrepreneurship, economics, psychology, and behavioral (neuro-)economics. Since collaboration and competition schemes are essential for shaping interactions between individuals and firms it is important to understand how to promote and design them efficiently in order to optimize individual and firm performance. This dissertation inspires new research avenues and contributes to a more informed discussion on the incentives and determinants of collaboration and competition.

1 1 Subsidized R&D Collaboration The Effect of Innovation Vouchers on Innovation Activity and Performance 1.1

Introduction

There is ample evidence that innovation and research and development (R&D) by new ventures are important for economic growth and employment creation (e.g., Haltiwanger et al. 2013, Howell 2017). Moreover, research shows that small and medium-sized enterprises (SMEs) are particularly effective in developing radical innovations which, in turn, have been associated with value creation and productivity growth (Scherer 1986, Audretsch et al. 2006, Hottenrott and Lopes-Bento 2014a). Unfortunately, SMEs are also more likely than large firms to be constrained in their resources (Cornet et al. 2006, Canton et al. 2013). Research in this area indicates that the most dominant factors that hamper innovation activities by SMEs are financial constraints, human capital constraints, and a lack of opportunity for risk diversification in their innovation portfolios (Acs and Audretsch 1990, Van de Vrande et al. 2009). This has led to a call for targeted policy interventions to alleviate these constraints for this specific group (MoncadaPaternò-Castello et al. 2010). In terms of financial constraints, there is a large strand of literature studying the effects of R&D subsidies on innovation activities by SMEs. The results from these studies typically show a positive impact of these subsidies on innovation outcomes such as patents and new product development (e.g., Lerner 2000, Czarnitzki and Hottenrott 2011, Czarnitzki and Delanote 2015, Howell 2017). Another constraint that SMEs face is limited access to the relevant knowledge. Due to the size of the firm, knowledge might not be available internally and external partnerships might be harder to develop for SMEs due to information asymmetries, transaction costs, and the lack of absorptive capacity within the firm (Van de Vrande et al. 2009). While there are many studies focusing on policy interventions that aim to relax financial constraints, much less is known about the effectiveness of policy interventions that foster innovation activities through increased collaborations between SMEs and external knowledge providers. This paper aims to fill that gap by testing the effectiveness of a small innovation subsidy, targeted at increasing external knowledge collaborations, on innovation activities and outcomes of SMEs. One policy tool that aims at stimulating the use of external knowledge among SMEs and that has become popular in many European countries in recent years is the Innova-

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 J. Heite, Incentives for Collaboration and Competition, Innovation und Entrepreneurship, https://doi.org/10.1007/978-3-658-29231-7_1

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tion Voucher (Schade and Grigore 2009, Bakhshi et al. 2015, Sala et al. 2016).8 This is a small subsidy of typically 5,000 to 10,000 EUR. SMEs can apply for the voucher to acquire knowledge that is not available within their organization to develop or accelerate a particular innovation activity or project. The rationale behind these subsidies is that successful innovation depends on accessing and integrating new knowledge, be it internally or through open innovation. While firms traditionally mostly relied on internal R&D, in recent years the new paradigm for innovation management has shifted increasingly towards the use of external sources in R&D and innovation processes (Chesbrough et al. 2006, Van de Vrande et al. 2009). The key assumption underlying these practices and the related academic literature is that due to labor mobility, widely dispersed knowledge, and increased technological complexity of products, enterprises no longer have all the required resources in-house to conduct innovation activities successfully. Indeed, research has shown that R&D collaborations are an effective tool to enhance R&D outcomes and firm performance (Cassiman and Veugelers 2002, Belderbos et al. 2004b). Although a lot of the research on open innovation has focused on large firms, Van de Vrande et al. (2009) show that this type of innovation is also highly relevant for SMEs. That is, in today’s increasingly complex and knowledge-intensive environment, SMEs need to leverage their networks to acquire missing knowledge and find complementary resources to innovate and grow their businesses (Bakhshi et al. 2015). Open innovation can take many different forms and can be related to both knowledge inflows and outflows. In line with the objective of the innovation voucher, in this paper we focus on one particular type of knowledge inflow: external networking. External networking is defined as all activities aimed at establishing and maintaining links with external sources, including formal collaborative projects as well as more general and informal networking (Van de Vrande et al. 2009). The overall purpose of external networks is that it allows SMEs to quickly fulfill knowledge needs without having to develop them internally or acquire them through vertical integration.9 We are not the first to study the impact of innovation vouchers as a policy tool. Some recent studies have looked at how innovation vouchers influence behavioral outcomes in terms of attitudes towards external knowledge providers and the number of projects conducted with external partners (Cornet et al. 2006, Chapman and Hewitt-Dundas 2018). The results from these studies indicate that innovation voucher recipients indeed have more positive attitudes towards external knowledge providers (Chapman and

8 Innovation voucher programs are widely spread throughout Europe, Australia, Canada, and the US with schemes on the national and regional level (Schade and Grigore 2009). The respective scope varies from consultancy services, intellectual property protection, technical development, to design advice and ranges from amounts of 500 EUR to 25,000 EUR (Schade and Grigore 2009). Most recently, innovation voucher programs have also been introduced by universities, such as the University of Surrey, the University of Essex, or the University of Chester. 9 Of course, the innovation voucher program could also have an impact on the knowledge providers. The knowledge base commonly tends to focus on larger companies as customers, neglecting SMEs as potential collaboration partners (Bruhn and McKenzie 2017). Working with SMEs as a result of the voucher could also change their attitudes and behavior. Unfortunately, we do not have any further information on the knowledge providers and are thus unable to address this question in this paper.

1.1 Introduction

11

Hewitt-Dundas 2018) and are more likely to collaborate with external partners in their innovation activities one year after having received the voucher (Cornet et al. 2006). However, six months later these network effects seem to have disappeared (Cornet et al. 2007). These studies show that being awarded an innovation voucher leads to an increase in external collaborations for innovation activities among SMEs (at least in the short term). The crucial follow-up question is to test if it also leads to improved innovation outcomes. That is the aim of our study. To the best of our knowledge, the only other paper that looks at innovation outcomes of a similar policy intervention is the study by Bakhshi et al. (2015). They study the effectiveness of the Creative Credits program in a randomized controlled trial (RCT), where applicants are randomly assigned to the treatment (N=150) and the control group (N=522). The program they study provides firms in the treatment group with a small (4,000 GBP) subsidy intended to promote cooperation between SMEs and creative service providers in the Manchester City region (United Kingdom). They find that companies that received the subsidy are more likely to have undertaken the innovation project they applied for than those in the control group. In terms of innovation output, they find that firms in the treatment group are more likely to have product, service, or process innovations, or new to market innovations one year after they have been awarded the voucher. However, this effect has disappeared six months later. This is partly due to the fact that the firms in the control group catch up (especially for product or service and new to market innovations), and partly because treatment firms fall back (process innovation). They do not find any effect on behavioral or network related outcomes one year or 18 months after the program. Our study builds on the study by Bakhshi et al. (2015) by conducting a large-scale RCT among SMEs in the United Kingdom (UK) that applied for the innovation voucher program in 2015. The innovation voucher is similar to the Creative credits program in the amount of money awarded, i.e., 5,000 GBP. But the scope of the program is much larger because it is available for all SMEs in the UK and firms are free to choose the type of supplier they want to work with. Applicants were randomly assigned to the treatment group and the control group and were checked for eligibility (cf. section 1.2.2 for eligibility criteria). The population of all eligible firms in 2015 consisted of 1,463 firms (1,107 in the treatment and 356 in the control group). We collected data by means of two surveys. The surveys were administered by an independent research agency under our supervision one and two years after the award of the voucher. Our sample covers 760 observations (from 570 unique firms) that had applied for the voucher in 2015 and replied to one or both of our surveys. To evaluate the effectiveness of the innovation voucher, we collected various outcome measures related to innovation outcomes and activities. Due to the random assignment of companies to the innovation voucher, we can make use of an experimental setup that allows us to draw causal inferences on the program’s effectiveness. From a theoretical perspective, being rewarded an innovation voucher can reduce several constraints faced by SMEs. First and foremost, the goal of the intervention is to

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reduce knowledge constraints. As described above, one of the main premises of open innovation is that the targeted inflow of knowledge can be used to accelerate internal innovation processes (Chesbrough et al. 2006). Thus, in the context of our experiment, we expect that gaining access to the relevant knowledge by collaborating with an external knowledge provider will have a positive effect on innovation outcomes and activities. More in particular, given the scope of the voucher and in line with the argumentation by Belderbos et al. (2004a, 2004b), we expect the innovation outcomes to vary by the aim of the project and the type of external knowledge acquired. That is, if the applicant at the time of application for example indicates to plan to work with an advisor on intellectual property (IP), we also expect the innovation outcome to be in the IP space (e.g., new patent or trademark application). If, on the other hand, the applicant indicates to have knowledge deficiencies in the product development space and aims to work with a university or research and technology organization, then we also expect the outcomes to be of this kind (e.g., new minimum viable product (MVP), new product, or new service). A secondary result of the program might be that the innovation voucher reduces financial constraints. The amount of the voucher is probably too small to make substantial R&D investments, but it may help SMEs to push forward or speed up the development of one particular (already planned) innovation activity or project. To test this we compare the innovation outcomes of the treatment group and the control group between the different survey rounds, i.e., one year and two years after the award of the innovation voucher. Finally, although it is not the core of our study, we check if we can replicate the findings by Cornet et al. (2006) in terms of increased collaborations and test if the voucher has a general impact on business outcomes of SMEs, such as turnover, profit, and number of employees. We find that being awarded a voucher has a positive effect on product development for those working together with a university. For this subgroup, we find positive effects on new products and services in the first year after the award of the voucher. In terms of MVPs, we find that receiving the voucher merely seems to speed up the development. That is, we find a positive significant effect on the number of new MVPs one year after the award, but a non-significant negative effect one year later. The innovation voucher also has a positive impact on the number of patent applications for those firms that indicated to be in need of external knowledge in this regard at the time of application. We cannot replicate the findings by Cornet et al. (2006) on collaboration outcomes. Although we find a positive coefficient for the proportion of innovation activities conducted with an external partner in the first survey, it is not statistically significant. However, for the firms intending to work together with a specific university at the time of application, we find that those who received the voucher are significantly more likely to have formed a new joint venture at the time of our second survey. This indicates that the innovation voucher might provide an important push for SMEs towards building long term collaborations. Finally, as perhaps can be expected from such a small subsidy, we do not find any impact of the voucher on overall business outcomes of SMEs.

1.2 Context and Experimental Design

1.2

13

Context and Experimental Design

1.2.1 Context and Program The innovation subsidy that is analyzed in this study is called “Innovation Vouchers Programme”. It was established by InnovateUK10 in 2012 with an annual budget of 4 million GBP. The aim of the program is to improve the collaboration between SMEs and external knowledge providers, such as universities, research and technology organization, and IP advisors, in the UK. In the 13 rounds that were conducted before our study, over 6,600 firms applied for a voucher with the result of over 3,100 subsidies being awarded. Of those, nearly 2,000 vouchers were redeemed. The program has three main objectives: First, it aims to stimulate SMEs to work with external knowledge providers by incentivizing a first contact. Second, collaboration with external experts is presumed to result in enhanced innovation output and capabilities of SMEs. Finally, the goal of the voucher is to stimulate ongoing collaborations with the knowledge base even after the voucher has expired.11 To this end, the governmental initiative grants SMEs from all sectors financial support of up to 5,000 GBP for engaging the services of experts from academia, research and technology organizations, or the private sector when pursuing an innovation-related project within the firm. Given the relatively small amount of support, the scheme is mainly targeted to small-scale projects, for example leading to IP applications and product, service, or process development, rather than breakthrough innovations.

1.2.2 Design of the Field Experiment To analyze the effectiveness of the program we use the randomized allocation of the voucher for three application rounds in 2015. The vouchers in these rounds were awarded in April, July, and October of 2015, respectively. We focus on the year 2015 because the application rounds before and after this time period were targeted at specific themes such as energy, water, or cyber-security. There are four main stages for participation in the innovation voucher program: (1) application, (2) lottery and eligibility checks, (3) voucher claim, and (4) final payment. In the initial application stage, firms indicated the specific innovation project that they wanted to pursue with external help. The applicants further proposed a certain supplier type that they anticipated working with and assessed the potential impact of the innovation project on their business. In addition, applicants provided data on their prior experiences with external partners and their innovation activities and capabilities. They also indicated future prospects with respect to applying for IP, working with external 10 InnovateUK (also referred to as the Technology Strategy Board) is the UK government’s national innovation agency and part of the UK Research and Innovation organization. Its aim is to improve productivity and economic growth by supporting firms to develop and realize the potential of ideas and innovative projects. 11 Figure A.1.1 in the Appendix depicts the innovation voucher’s logic chain (developed by InnovateUK).

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partners, or introducing new products, services, or processes. Additionally, data on business measures such as the number of employees, the firm’s turnover, and the specific industry classification were submitted with the application. In the second step, a lottery was run. The randomization was conducted within the financial restrictions of the overall budget of the innovation voucher program. As such, the lottery could produce as many offers as were needed to ultimately meet the budget. The selected firms were then reviewed by three independent reviewers who checked for certain eligibility criteria. The eligibility criteria for the program required an applicant to be located in the UK and to be a start-up, micro ( 50 employees Total

Survey 1 27 386 34 12 459

Survey 2 18 249 29 5 301

Total 45 635 63 17 760

% 5.9% 83.6% 8.3% 2.2% 100%

Unique firms 34 475 48 13 570

Note: One firm changed the industry classification from “professional, scientific and technical services” in survey 1 to “information and communication services” in survey 2. In the column “unique firms”, this firm is assigned to the latter category since this is the more recent firm activity.

1.3 Data and Methodology

17

Panel B shows the firm size distribution at the time of application. The majority of our survey respondents reported to have 1-10 employees (84%). 6% of the observations refer to firms not having any employees, whereas 2% indicated that they had more than 50 employees.

1.3.2 Variables Outcome variables All outcome measures rely on survey data and capture information on the 12 months before the respective survey round. Based on the objective of the innovation voucher program, we consider two groups of outcomes. First, in line with the aim of the voucher program to support beneficiaries to conduct an innovation-related project, we measure innovation outputs at the project level. In particular, we analyze the number of MVPs, the number of new products and services, and the number of new patent, design right, and trademark applications, as well as the sum of all three types of IP applications. On a broader scale, we also measure the potential impact at the company level. First, we try to replicate the findings from previous studies that have found positive (short-term) effects of similar subsidies on external collaborations (e.g., Cornet et al. 2006). External collaborations are measured by the number of newly formed joint ventures and technology alliances, the proportion of innovation activities conducted with the help of external partners, the total number of external partners within innovation activities, and the diversity of different types of support (e.g., IP advice, laboratory/technical testing, or design advice including initial prototyping) that the firm received within its innovation activities. Second, we look at innovation activity, which is measured by the total amount spent on innovation and the proportion of employees working on innovation activities. Finally, we study the effect of the voucher on overall business outcomes captured by turnover, profit, and the number of employees. Since the majority of our firms are small companies, we rely on dummies indicating whether a firm is generating turnover or is making any profit. It is important to note that the distributions of most of the above outcome measures show large variance and are highly skewed (cf. Table 1.4). Therefore, unless indicated otherwise, we use a logarithmic transformation (log) for all outcome measures to account for outliers, to satisfy the normality assumption of dependent variables, and to simplify interpretation (variables that have been log-transformed are marked by (log) in the regression tables). Explanatory variables The most important explanatory variable in this study is of course the indication whether or not a firm was awarded an innovation voucher. This is captured by a binary variable (treatment effect) that is equal to one if the firm was offered the subsidy and zero otherwise. As in many randomized controlled trials, participation is voluntary among

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1 Subsidized R&D Collaboration

those randomly assigned to the treatment group. In our case, 40% (442 of 1,107) of firms that were offered a voucher did not redeem it. The reasons for this non-compliance are diverse and range from missing the deadline and a lack of time, to having found other funding for the project or missing a suitable external partner.13 Since we do not know, which of the firms in the control group would have complied with the treatment if they had been offered the subsidy, we cannot restrict the treatment group to those that eventually redeemed the voucher. As a consequence, we base our evaluation on the initial treatment assignment and not on the treatment actually received, thus applying an intention-to-treat analysis. Hence, the treatment effect we estimate in this paper is the effect of being offered a voucher. Since governments can also only offer certain programs but will not be able to force people to actually take-up and use them, we feel that this effect is also the most interesting from a policy perspective. In addition, we include several control variables in our analyses. The firm’s age (log) controls for the fact that older firms might be less innovative and less likely to apply for a governmental funding that supports innovation activities. In a similar vein, older firms might be less financially constrained compared to younger firms. Furthermore, we include a binary variable indicating whether a firm is active in the service industry to account for industry effects. Service firms have been shown to be more likely to engage the services of external partners (especially consultants) compared to manufacturers (Tether and Tajar 2008). The firm size is clustered in four groups according to the number of employees at the time of application (cf. Panel B in Table 1.2) and takes possible size effects into account (e.g., larger firms should have more relationships, all else equal). All regressions further include round fixed effects to control for the selection into one of the three rounds that are being analyzed in this study. Moreover, we use outcome specific covariates that are expected to have a significant influence on the focal outcome measure. For the product and service outcomes, we include dummy variables to control for the intention of the firm to introduce new or significantly improved products, services, or processes in the next 12 months after applying for the subsidy. Considering the IP outcomes, we add control variables that capture the firm’s anticipation to apply for new patents, design rights, or trademarks in the future. Applicants could rate their intention on a scale from 1 “don’t know” to 6 “strongly agree” (2 “strongly disagree”, 3 “disagree”, 4 “neutral”, 5 “agree”). We capture the firm’s IP intention with a binary variable that is equal to one for companies that agreed or strongly agreed with the statement above. Furthermore, one of the innovation voucher’s objectives is to raise awareness and recognition within businesses about the services of external

13

Table A.1.1 in the Appendix presents further evidence on baseline application data between compliers and non-compliers. Firms that had a parent company when applying for the voucher scheme are less likely to comply with the treatment. Additionally, businesses that had a strong intention to introduce new services or processes in the next year after submitting the application form were also significantly less likely to redeem the offered voucher. These findings are suggestive evidence that (a) firms having sufficient resources from a parent company might not dependent on the 5,000 GBP and (b) firms having a particularly strong incentive to introduce new services and processes might be faster in executing the project before being able to redeem the voucher.

1.3 Data and Methodology

19

knowledge providers, thus aiming at firms that have no extensive prior experience with external partners. Moreover, using a wide range of external partners has been shown to positively affect innovation performance (e.g., Zeng et al. 2010). Therefore, when analyzing collaboration outcomes, we control for the firm’s collaboration activity. This is defined by the number of different types (diversity) of external partners that the firm had collaborated with prior to the application for the voucher by means of two variables. One control variable captures the diversity with external partners within the last years before the application for the voucher. Another control variable takes the diversity of external partners in the last three years (excl. last year) into consideration. Finally, we control for the selected supplier type when assessing the potential impact of the program on collaboration, innovation activities, and business outcomes. Interaction variables As previously discussed, the innovation voucher program has a broad scope and is rather vague with respect to how the funding of the 5,000 GBP should be used. The regulations lay out that the program shall aim to assist SMEs to collaborate with knowledge-based institutions in the public or private sector. However, it can be assumed that the projectlevel outcomes are strongly interrelated with the chosen supplier type and the specific objective of the innovation project. In line with this argumentation, Belderbos et al. (2004a, 2004b) indicate that the goals and thus the determinants of R&D collaborations vary conditional on the type of innovation project and the chosen partner. Therefore, we will analyze specific project-level outcomes conditional on the proposed supplier type. When applying for the voucher, firms were asked to indicate the supplier type they want to work with. Applicants could choose from four categories covering universities, design advisors, IP advisors, and research and technology organizations. Yet, multiple supplier types could be chosen to characterize the supplier, which makes it difficult to analyze project specific innovation outcomes conditional on the proposed supplier type. Therefore, we manually classified all applications in order to assign one specific supplier type that best characterized the supplier for each firm in our sample. To this end, we relied on the data provided in the application form. The following information was used for our classification: the initially chosen supplier types (multiple choices possible), the description of the proposed innovation project, the explanation of the external partner’s contribution to the project, and the potential impact of the idea on the applicant’s future success (as explained by the applicant in the application form). The classifier was unaware of the treatment status of the firms. The supplier categories were defined exclusively, i.e., each application was clearly assigned to one supplier type. The classification builds on the four supplier types that could be chosen in the application form and adds a fifth type, based on the extensive analysis of the application data. This fifth supplier type covers consultants that were not chosen for any technological activity. As a result, the following categorization was developed.

20

1 Subsidized R&D Collaboration

Non-technical consultants were chosen for projects that requested broad support for a range of activities. As a consequence, these projects were on average in a very early stage, where applicants could not explain what kind of help was necessary to realize the specific idea and with no clear description of the actual impact. Ideas in this category neither required technical help nor needed design advice. An example of such an application is: “We require an external expert to help us perform a feasibility study of our concept, including confirmation of the validity of our concept, assessment of the market opportunity, reviewing the novelty of our concept and competing solutions, and identifying technical/commercial risks and develop appropriate mitigations.” Ideas that were already in an early development stage but still had issues concerning the technical transformation of the idea into a tangible product were assigned to either research and technology organizations or universities. The distinction between these two groups was made as follows: Research and technology organizations were chosen as a supplier type in case the applicant indicated the need for technical assistance but the provider of the help is not further specified. An example is: “The company needs some assistance with the development of its first demonstration model. The supplier will assist in the development of the application’s databases, the application programming interface and a simple front end.” Universities were selected, if the applicant not only had a precise knowledge of what kind of help they needed but also specifically addressed the type of university or academic institution they planned to work with, and/or explicitly named a particular university as the desired partner. The following ideas are examples for this supplier type: “ Working with a university research team will provide the necessary expertise in these fields and allow us to fast-track the new product development process” or “We would like to use the Centre for Industrial Ultrasonics based at Warwick University to help us with the feasibility stage of our product development.” Design advisors were selected when the ideas were already beyond the technical development stage and the focus was on design matters only. This includes issues concerning the visible appearance and design of an already developed product or brand. Whereas the three supplier types above focused on the development of a product in general, design advisors were chosen if a product was close to market readiness. An example for this supplier type is: “We are at a point where interest has been generated from potential clients; however, they have requested some design changes before they can approve them for a trial order. We require a design consultant to assist in the changes and to advise on the most suitable production methods and materials.” Lastly, IP advisors included external expert help for any IP related issues, e.g., advice on how to file a patent, whether an idea is patentable or not, how to commercialize an existing patent, or on financial and legal issues in the patenting process. The following statement exemplifies which projects fall under this category: “The Innovation Voucher

1.3 Data and Methodology

21

will be used to further develop our IP strategy and engage with a patent attorney to investigate patent search and application.” Overall, we argue that the project-related objectives pursued with the chosen supplier type refer to different stages of the innovation process (cf. Cornet et al. 2007 for a similar approach). Cornet et al. (2007) distinguish four stages. Stage one covers fundamental knowledge acquisition and development, which is mostly covered by projects with (nontechnical) consultants and research and technology organizations. Stage two involves the design process of a MVP, product, process, or service. Firms applying for specialized help from a research and technology organization, university, or design advisor most likely address this stage. However, projects with research and technology organizations refer to an earlier stage of product development, whereas collaborations with universities and design advisors are more targeted towards the production of a first product version. Stage three covers the production and stage four the commercialization phase. As indicated by the explanation of the supplier types above, design advisors also refer to stage three and maybe even to stage four, because their product is close to market readiness and production. Table 1.3 shows the distribution of the five supplier types that firms in our sample anticipated working with. Most of the companies (57% of our observations) were planning to collaborate with research and technology organizations. Roughly 14% of the firms were interested in collaborating with a university or academic institution. 18% of the firms were applying for IP advice and 8% for design advice. Only a small fraction (4%) was interested in working with non-technical consultants. Table 1.3: Supplier types Supplier type University Research/Technology org. IP advice Design advice Consultant (non-tech) Total

Survey 1 59 273 80 32 15 459

Survey 2 46 162 55 25 13 301

Total 105 435 135 57 28 760

% 13.8% 57.2% 17.8% 7.5% 3.7% 100%

Unique firms 78 329 103 41 19 570

Based on the project’s objective and the chosen supplier type, diverse outcomes are to be expected from the innovation voucher program. In particular, projects with universities, research and technology organizations, and design advisors were mainly targeted to create MVPs, new products, and new services. Hence, we will analyze the voucher’s effect on these outcomes separately for these chosen supplier types. Unfortunately, we have only very few observations in the category of design advisors (cf. Table A.1.2 in the Appendix for an overview of observations for each supplier type, separated into treatment and control group). Therefore, we will refrain from analyzing the effect of the innovation voucher on this subgroup separately. Businesses that were planning to collaborate with IP advisors are expected to apply for new patents, trademarks, or design rights. Thus, we will analyze IP-related outcomes for this specific type of supplier separately. Finally, it

22

1 Subsidized R&D Collaboration

can be assumed that the likelihood to establish long term collaborations is higher for companies that collaborated with universities or research and technology organizations than for companies working with consultants, IP advisors, or design advisors. Therefore, we will analyze the effect of the voucher on joint ventures for these two supplier types separately. Descriptive statistics Table 1.4 depicts the descriptive statistics for the variables employed in our analysis. The application data shows that most firms applying for the innovation voucher were, on average, planning to introduce new products (89%), services (70%), or processes (70%) within the next year. Furthermore, 50% of the firms were planning to apply for new patents in the future, 46% for new design rights, and 51% for new trademarks. The number of different supplier types, as an indicator for experience with diverse R&D collaboration, provides evidence that firms had already worked with external partners in the past (on average, firms worked with 3.5 different supplier types in the last year and 2.5 different types in the last three years, excluding the year right before the application). This suggests that firms had already engaged the services of external experts at the time of the application with an increase from the last three years to the last year. Next, we focus on the survey data from the first and second year after the subsidy was awarded. Firms that responded to the first survey round were on average 6 years old and mostly active in the service industry (72%). They were further characterized by an innovation output that on average amounted to two new MVPs and two new products and services within 12 months after the subsidy was awarded. The number of applications for new patents, design rights, and trademarks varied from 0.2 for design right applications to 0.5 for patent and trademark applications within the year following the voucher’s award. Furthermore, our data indicates that firms had on average 0.7 newly established joint ventures, conducted 40% of innovation activities with the help of external partners and received support for three different types of innovation activities (e.g, IP advice or laboratory/technical testing). The average total amount spent on innovation activities was 85,000 GBP, 32% of the firms’ employees were working more than 50% of their time on innovation activities, most companies generated turnover (66%), and had an average of 8 employees. 41% of the respondents to the first survey round indicated that they generated profit in the year right after the innovation voucher was awarded. The firms that replied to the second survey round are comparable to the respondents of the first round in terms of age (mean: 7 years), industry classification (68% were active in the service industry), and measures such as new products, IP, collaboration, innovation activities, and business turnover. The number of different types of support received increased slightly from 2.7 in the first to 3.5 in the second survey. Another average increase from year 1 to year 2 can be observed when looking at the total amount spent on innovation (year 1: 85,000 GBP; year 2: 133,000 GBP).

1.3 Data and Methodology

23

Overall, it is important to note that most of the variables are characterized by a high variance, which is an indication for the heterogeneity of the firms that applied for the innovation voucher program. Table 1.4: Descriptive statistics Panel A: Application data Intention: New products next year Intention: New services next year Intention: New processes next year Intention: New patent appl. in future Intention: New design right appl. in future Intention: New trademark appl. in future Diversity of supplier types last year Diversity of supplier types last 3 years (excl. year -1) Panel B: Survey data Year 1 Age Service industry Number of new MVPs Number of new products and services Number of new patent applications Number of new design right applications Number of new trademark applications Number of new IP applications Number of newly formed joint ventures Proportion inno. activities with partner Total number of partners Diversity of type of support received Total amount spent on innovation (in thousands) Proportion employees working on inno. activities Turnover Profit Number of employees Year 2 Age Service industry Number of new MVPs Number of new products and services Number of new patent applications Number of new design right applications Number of new trademark applications Number of new IP applications Number of newly formed joint ventures Proportion inno. activities with partner Total number of partners Diversity of type of support received Total amount spent on innovation (in thousands) Proportion employees working on inno. activities Turnover Profit Number of employees

Type

Mean

0-1 0-1 0-1 0-1 0-1 0-1 Count Count

0.89 0.70 0.70 0.50 0.46 0.51 3.54 2.54

Cont. 0-1 Count Count Count Count Count Count Count Count Count Count Cont. Cont. 0-1 0-1 Count

6.40 0.72 2.02 2.01 0.51 0.21 0.46 1.18 0.65 40.43 56.60 2.70 84.99 32.08 0.66 0.41 7.96

Cont. 0-1 Count Count Count Count Count Count Count Count Count Count Cont. Cont. 0-1 0-1 Count

7.44 0.68 2.40 1.91 0.47 0.27 0.34 1.08 0.51 35.10 9.70 3.51 133.19 34.35 0.73 0.46 10.75

St.D.

Median

2.81 2.90

3 1

11.38

2

4.41 4.60 1.82 1.50 1.74 4.58 2.63 35.01 941.41 2.34 192.59 39.98

1 1 0 0 0 0 0 30 6 2 24 3.3

26.76

2

10.84

4

7.36 3.58 1.15 1.23 0.97 2.43 1.10 36.48 14.29 2.57 326.25 40.54

1 1 0 0 0 0 0 22.5 6 3 20 9.1

42.97

2

Obs. 558 555 554 570 570 570 565 560

459 459 442 442 442 442 442 442 442 451 451 459 435 454 440 395 458 298 297 272 272 272 272 272 272 272 272 279 205 263 282 261 241 292

24

1 Subsidized R&D Collaboration

1.3.3 Randomization Check and Response Bias Randomization check In this section, we will test whether firms have been randomly assigned to the treatment and the control group based on baseline firm characteristics from the application form. Table 1.5 exhibits four randomization checks on different levels. The comparison of the entire population is shown in Columns (1) to (4). Columns (5) to (8) take into account the firms’ eligibility for the innovation voucher program by testing the randomization results after firms were excluded that did not pass the eligibility checks. The last eight columns report the results of the randomization tests for those firms that answered the survey. The randomized treatment group of survey respondents (Column (9)) was compared to the corresponding control group (Column (10)) while Columns (13) to (16) restrict the sample to those firms that responded to the survey and passed the eligibility checks. We find significant differences between the treatment and the control group for the total sample (diversity of supplier types last year and last three years (excluding last year)) and for the sample after the eligibility checks (diversity of supplier types last three years (excl. last year)). However, we do not observe any significant differences in baseline firm characteristics between the treatment and the control group in our sample of survey respondents. Furthermore, the Chi2-test for joint orthogonality (McKenzie 2015) is not significant for any of the four sample specifications (cf. Table A.1.3 in the Appendix). Next, we also compare the assignment to treatment and control groups categorized by supplier types that we will use for the subsample analyses. Table 1.6 reports pretreatment differences between treatment and control groups per supplier type. Columns (1) to (4) focus on universities as a supplier type, Columns (5) to (8) restrict the sample to research and technology organizations, and the last four Columns (9) to (12) depict treatment and control differences for firms that chose to collaborate with IP advisors. There is only one significant difference between the groups. With universities as suppliers, firms in the treatment group used R&D tax credits more often than firms in the control group. Yet, the standard deviation is quite high and the difference is only marginally significant on the 10% level. We further test for joint orthogonality with insignificant results for all four sample specifications (cf. Table A.1.4 in the Appendix). Overall, we conclude that the random allocation to the treatment and the control group has been successful and valid. Response bias In Table 1.7 we test for a potential response bias between respondents (R), i.e., firms that participated in at least one of our two survey rounds, and non-respondents (NR). Columns (1) to (4) compare firms that responded to the first survey with the overall population of firms that did not. Respondents are characterized by a significantly higher diversity of supplier types in the last year and a significantly lower intention to introduce new services in the next year following the application.

(T-C) (3) 1.20 0.00 0.02 0.00 0.02 0.01 0.02 0.02 0.03 0.03 0.36** 0.38**

S.E. (4) 1.17 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.14 0.15

Eligibility sample Control Treatment (5) (6) 6.96 7.79 0.04 0.04 0.19 0.20 0.22 0.22 0.48 0.48 0.52 0.50 0.46 0.45 0.93 0.90 0.70 0.74 0.70 0.74 3.29 3.55 2.29 2.68 356 1107 (T-C) (7) 0.83 0.00 0.01 0.00 0.00 -0.02 -0.02 -0.03 0.04 0.04 0.26 0.39**

S.E. (8) 1.34 0.01 0.02 0.03 0.03 0.03 0.03 0.02 0.03 0.03 0.18 0.19

Survey sample (after lottery) Control Treatment (9) (10) 5.59 8.40 0.03 0.04 0.15 0.19 0.21 0.22 0.44 0.45 0.48 0.50 0.41 0.45 0.89 0.86 0.67 0.73 0.69 0.73 3.38 3.52 2.23 2.56 176 568 (T-C) (11) 2.81 0.01 0.04 0.01 0.01 0.02 0.03 -0.03 0.05 0.04 0.14 0.33

S.E. (12) 2.60 0.02 0.03 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.24 0.25

Survey sample (after lottery and eligibility check) Control Treatment (T-C) S.E. (13) (14) (15) (16) 6.45 7.54 1.09 2.46 0.03 0.04 0.01 0.02 0.15 0.20 0.04 0.04 0.21 0.23 0.01 0.04 0.50 0.50 0.00 0.05 0.50 0.51 0.01 0.05 0.46 0.46 0.01 0.05 0.93 0.88 -0.05 0.03 0.65 0.71 0.06 0.05 0.66 0.71 0.05 0.05 3.58 3.53 -0.05 0.29 2.22 2.63 0.41 0.30 123 447

(T-C) (3) 8.68 -0.05 0.20* 0.03 0.21 0.06 0.08 -0.08 -0.01 -0.08 0.67 0.16

S.E. (4) 9.30 0.07 0.11 0.13 0.14 0.14 0.14 0.07 0.14 0.12 0.74 0.79

Supplier type: research/techn. org. Control Treatment (T-C) (5) (6) (7) 8.29 7.54 -0.75 0.01 0.04 0.03 0.17 0.17 0.00 0.16 0.21 0.05 0.44 0.45 0.01 0.44 0.48 0.04 0.41 0.43 0.02 0.93 0.88 -0.05 0.67 0.75 0.07 0.65 0.71 0.06 4.08 3.54 -0.54 2.42 2.62 0.20 75 254

S.E. (8) 3.33 0.02 0.05 0.05 0.07 0.07 0.07 0.04 0.06 0.06 0.38 0.39

Supplier type: IP advisor Control Treatment (9) (10) 2.42 3.23 0.00 0.00 0.21 0.25 0.37 0.19 0.84 0.73 0.74 0.68 0.63 0.58 0.89 0.87 0.63 0.70 0.63 0.73 2.53 3.24 1.68 2.51 19 84

(T-C) (11) 0.81 0.00 0.04 -0.18 -0.12 -0.06 -0.05 -0.03 0.07 0.10 0.71 0.82

S.E. (12) 0.91 0.00 0.11 0.11 0.11 0.12 0.13 0.09 0.12 0.12 0.65 0.71

Note: S.E. stands for standard errors. The number of observations varies between different variables and sample cuts. Only the highest number of observations for the control and treatment groups is depicted in the last row. */**/*** indicates significance at the 10%/5%/1%-level.

Number of employees Parent company (0-1) R&D tax credits (0-1) Exporting (0-1) Intention: New patent appl. in future (0-1) Intention: New trademark appl. in future (0-1) Intention: New design right appl. in future (0-1) Intention: New products next year (0-1) Intention: New services next year (0-1) Intention: New processes next year (0-1) Diversity of supplier types last year Diversity of supplier types last 3 years (excl. year -1) Observations

Supplier type: university Control Treatment (1) (2) 4.94 13.62 0.12 0.07 0.06 0.27 0.29 0.32 0.29 0.51 0.41 0.48 0.41 0.49 1.00 0.92 0.63 0.61 0.81 0.73 3.00 3.67 2.41 2.58 17 61

Table 1.6: Randomization checks for specific supplier types

Note: S.E. stands for standard errors. The number of observations varies between different variables and sample cuts. Only the highest number of observations for the control and treatment groups is depicted in the last row. */**/*** indicates significance at the 10%/5%/1%-level.

Number of employees Parent company (0-1) R&D tax credits (0-1) Exporting (0-1) Intention: New patent appl. in future (0-1) Intention: New trademark appl. in future (0-1) Intention: New design right appl. in future (0-1) Intention: New products next year (0-1) Intention: New services next year (0-1) Intention: New processes next year (0-1) Diversity of supplier types last year Diversity of supplier types last 3 years (excl. year -1) Observations

Total sample Control Treatment (1) (2) 6.64 7.83 0.04 0.04 0.16 0.18 0.21 0.21 0.42 0.44 0.48 0.49 0.42 0.44 0.86 0.88 0.74 0.76 0.72 0.75 3.07 3.43 2.16 2.54 560 1589

Table 1.5: Randomization checks

1.3 Data and Methodology

25

S.E. (8) 1.34 0.01 0.02 0.02 0.03 0.03 0.03 0.02 0.02 0.02 0.16 0.17

Response bias: control group (narrow) NR R (R-NR) (9) (10) (11) 7.23 6.45 -0.78 0.04 0.03 -0.01 0.21 0.15 -0.05 0.22 0.21 -0.01 0.47 0.50 0.02 0.53 0.50 -0.02 0.47 0.46 -0.01 0.93 0.93 0.01 0.73 0.65 -0.08 0.72 0.66 -0.06 3.13 3.58 0.45 2.32 2.22 -0.10 233 123 S.E. (12) 2.23 0.02 0.04 0.05 0.06 0.06 0.06 0.03 0.05 0.05 0.33 0.34

Response bias: treatment group NR R (13) (14) 7.96 7.54 0.05 0.04 0.20 0.20 0.22 0.23 0.47 0.50 0.50 0.51 0.43 0.46 0.92 0.88 0.76 0.71 0.75 0.71 3.56 3.53 2.71 2.63 660 447

(narrow) (R-NR) (15) -0.41 -0.01 0.00 0.01 0.03 0.01 0.03 -0.03* -0.04 -0.04 -0.03 -0.08

S.E. (16) 1.38 0.01 0.02 0.03 0.03 0.03 0.03 0.02 0.03 0.03 0.18 0.19

Note: S.E. stands for standard errors. NR refers to non-respondents and R stands for respondents. The number of observations varies between different variables and sample cuts. Only the highest number of observations for non-respondents and respondents is depicted in the last row. */**/*** indicates significance at the 10%/5%/1%-level.

Number of employees Parent company (0-1) R&D tax credits (0-1) Exporting (0-1) Intention: New patent appl. in future (0-1) Intention: New trademark appl. in future (0-1) Intention: New design right appl. in future (0-1) Intention: New products next year (0-1) Intention: New services next year (0-1) Intention: New processes next year (0-1) Diversity of supplier types last year Diversity of supplier types last 3 years (excl. year -1) Observations

S.E. (4) 1.15 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.14 0.15

Response bias: survey 2 to population NR R (R-NR) (5) (6) (7) 7.67 6.84 -0.83 0.05 0.03 -0.01 0.17 0.19 0.03 0.21 0.23 0.02 0.43 0.47 0.04 0.49 0.48 -0.01 0.43 0.45 0.03 0.87 0.86 -0.01 0.77 0.67 -0.10*** 0.75 0.71 -0.04 3.32 3.41 0.09 2.45 2.41 -0.04 1766 383

Table 1.7: Test of response bias Response bias: survey 1 to population NR R (R-NR) (1) (2) (3) 7.43 7.77 0.35 0.04 0.04 0.00 0.17 0.17 -0.01 0.20 0.23 0.03 0.42 0.46 0.04 0.48 0.51 0.03 0.42 0.45 0.02 0.87 0.87 0.00 0.77 0.72 -0.05** 0.76 0.72 -0.04 3.26 3.55 0.30** 2.42 2.49 0.06 1558 591

26 1 Subsidized R&D Collaboration

1.3 Data and Methodology

27

Respondents to the second survey are also characterized by a significantly lower intention to introduce new services in the next year (cf. Columns (5) to (8)).14 Columns (9) to (12) test a potential response bias for the control group of firms that passed the eligibility test, but failed the lottery. There are no significant differences between respondents and non-respondents. Finally, Columns (13) to (16) compare responding firms that passed the eligibility and lottery stage with those firms that also passed both stages but did not respond to any survey round. Here, only the intention to introduce new products in the next years is significantly different on a 10% level between both groups. We control for this variable when analyzing the effect of the subsidy on product outcomes. Table A.1.6 in the Appendix displays the Chi2-test for joint orthogonality for the five potential response biases. The results show that the joint test for orthogonality is significant when comparing respondents to the first or second survey round with the nonrespondents of the overall population (cf. Columns (2) and (3)). These differences are primarily driven by differences in the diversity of the supplier types and the intentions to introduce services in the next year. For the former, even though it is statistically significant, the difference between the two groups is small, i.e., 3.3 vs. 3.6 different supplier types for the non-respondents and the respondents. Regarding the intention to introduce new services, our estimations show that these intentions are positively correlated with the actual outcome. The respondents have on average lower intentions to introduce new services than the non-respondents ex-ante, and difference seems to be strongest in the control group. This suggests that our estimates of the treatment effect for this outcome could be an upper bound. We will keep this in mind when discussing our results.

1.3.4 Empirical Strategy We analyze the effect of innovation vouchers on the outcome variables in two stages. In the first stage, we examine whether the subsidy results in measurable outcomes from the proposed innovation project for the different supplier types (e.g., number of patent applications when applying for IP advisors). In the second stage, we focus on firm-level outcome variables that might be affected by spillovers from the initial project that was (partly) funded by the innovation voucher program (e.g., proportion of employees working on innovation activities). Given the broad range of different supplier types that a firm could collaborate with, we expect diverging project outcomes. In order to unbundle the average effect of the innovation voucher, we explore differential effects per supplier type. Our main specification considers the full sample regression that differentiates the treatment effect for a specific supplier type from that of the other suppliers. Hence, we 14 Table A.1.5 in the Appendix compares respondents to any of the two survey rounds to the overall population of firms that did not respond to any survey. Firms that responded to at least one survey round are significantly different to non-respondents with respect to the intention to introduce new services and processes next year. Furthermore, the diversity of supplier types within the last year before the application is significantly higher for those firms that responded to our surveys. We control for all variables in the specific regressions that focus on product and service outcomes as well as on collaboration.

28

1 Subsidized R&D Collaboration

analyze equation (1) using ordinary least squares (OLS) with interaction terms, where the outcome variable for firm is regressed on the binary treatment variable (being offered an innovation voucher) for the supplier type of interest ̇ .15 (1)

= +

1

= ̇ +

1

≠ ̇ +

̇ +

+

+

represents the treatment effect for those companies that planned to collaborate with the supplier type of interest ̇ , i.e., it captures the difference in the outcome variable between the treatment and the control group that anticipated to work with the same supplier type. More specifically, for product outcomes, we focus on the sub-samples that have universities or research and technology organizations as their preferred supplier type. For IP outcomes, we examine firms that planned to work with IP advisors. The coefficient of the second interaction term reveals the treatment effect on all other supplier types \ ̇ . We further include control variables and round dummies . refers to

the random error. The control variables that are included in all models are: firm size (measured by the number of employees), the age of the firm (log), and whether the firm is active in the service sector (dummy). As a robustness check, we also run split sample regressions per supplier type of interest. Our second stream of analysis focuses on firm-level outcomes. These outcomes refer to potential effects of the innovation voucher in general (e.g., collaboration, innovation activities, and business outcomes). Since it can be assumed that specific supplier types do not have any particular treatment effect on these firm-level outcomes in any specific manner, we do not expect any supplier-type differences on firm-level outcomes. To estimate the effect of the innovation voucher on firm-level outcomes we use the OLS regression as depicted in equation (2), where the outcome variable is regressed on the treatment .16 (2)

= +

+

+

+

The other variables refer to the same specification as above. However, the vector of control variables now also includes dummy variables for the different supplier types. As an exception, we do disentangle the overall effect of the innovation voucher on the number of newly formed joint venture and technology alliances. As described above, we argue that collaborating with consultants, design advisors, or IP advisors is not intended to result in long term collaborations. However, collaborating with universities or research and technology organizations might lead to the initiation of joint ventures and technology alliances. Hence, we apply equation (1) also to the outcome variable of the number of joint ventures, conditional on having chosen a university or research and technology organization. 15 For a similar estimation model, see Galasso and Schankerman‘s (2018) instrumental variables regressions, which also consider differential effects. 16 OLS is also applied for the dummy outcomes since OLS does as well as logit in estimating marginal effects (Angrist 2001).

1.4 Results

1.4

29

Results

1.4.1 Comparison of Means Table 1.8 presents treatment and control comparisons of the sample means for our outcome variables of interest. The results show that although there are some positive differences between the treatment and the control group, none of these differences is statistically significant. Only the number of design right applications is significantly different between the treatment group and the control group (-0.39) two years after the voucher was awarded. Table 1.8: Descriptive statistics by treatment and control group Survey data Panel A: Year 1 Age Service industry (0-1) Number of new MVPs Number of new products and services Number of new patent applications Number of new design right applications Number of new trademark applications Number of new IP applications Number of newly formed joint ventures Proportion inno. activities with partner Total number of partners Diversity of type of support received Total amount spent on innovation (in 1000) Proportion employees working on inno. activities Turnover (0-1) Profit (0-1) Number of employees Panel B: Year 2 Age Service industry Number of new MVPs Number of new products and services Number of new patent applications Number of new design right applications Number of new trademark applications Number of new IP applications Number of newly formed joint ventures Proportion inno. activities with partner Total number of partners Diversity of type of support received Total amount spent on innovation (in 1000) Proportion employees working on inno. activities Turnover (0-1) Profit (0-1) Number of employees */**/*** indicates significance at the 10%/5%/1%-level.

Treatment (T) Mean St.D. Obs.

Control (C) Mean St.D. Obs.

T=C diff(T-C)

6.35 0.73 2.04 2.12 0.49 0.23 0.46 1.18 0.69 41.04 68.27 2.76 79.88 33.53 0.65 0.39 8.00

10.89 0.44 4.63 5.07 1.80 1.68 1.87 5.02 2.93 34.52 1058 2.27 162.53 40.73 0.48 0.49 27.25

364 364 349 349 349 349 349 349 349 358 357 364 343 360 349 315 363

6.56 0.67 1.97 1.61 0.59 0.14 0.45 1.18 0.47 38.06 12.29 2.49 104.05 26.52 0.70 0.45 7.80

13.14 0.47 3.51 2.09 1.91 0.41 1.16 2.35 0.84 36.94 20.72 2.60 277.76 36.65 0.46 0.50 24.97

95 95 93 93 93 93 93 93 93 93 94 95 92 94 91 80 95

-0.20 0.06 0.07 0.50 -0.10 0.09 0.01 0.00 0.22 2.98 55.98 0.26 -24.17 7.02 -0.06 -0.06 0.20

7.34 0.68 2.63 1.93 0.44 0.19 0.34 0.96 0.51 34.38 9.74 3.46 136.23 33.63 0.74 0.46 9.48

10.63 0.47 8.11 3.62 1.03 0.90 0.91 2.10 1.14 36.60 15.04 2.58 332.55 39.71 0.44 0.50 39.13

237 236 215 215 215 215 215 215 215 217 223 162 211 226 209 195 233

7.82 0.69 1.56 1.81 0.58 0.58 0.35 1.51 0.51 37.93 9.57 3.70 120.86 37.25 0.67 0.50 15.78

11.70 0.47 3.08 3.45 1.52 2.02 1.17 3.41 0.95 36.18 10.94 2.54 302.05 44.03 0.47 0.51 55.75

61 61 57 57 57 57 57 57 57 55 56 43 52 56 52 46 59

-0.48 -0.01 1.07 0.13 -0.14 -0.39** -0.01 -0.55 0.00 -3.55 0.16 -0.23 15.37 -3.62 0.07 -0.04 -6.30

30

1 Subsidized R&D Collaboration

Thus, at a first view, these results suggest that the innovation voucher did not have a significant short- or medium-term effect on the treatment group with respect to any of the outcome variables. However, as indicated above, we unbundle the average effect of innovation vouchers and explore different dimensions of heterogeneity by considering the different supplier types with divergent project outcomes.

1.4.2 Main Results on Project-level Outcomes In this section, we examine the effect of the innovation voucher program on projectspecific outcomes by differentiating between the chosen supplier type and other suppliers. Table 1.9 summarizes the effects of the subsidy for firms that were planning to collaborate with universities (Models (1) to (4)) or research and technology organizations (Models (5) to (8)) with the aim to develop a new MVP, or to introduce new or significantly improved products and services. In these models we also control for the intention at the time of application to introduce new products, services, or processes in the next year. Each outcome variable is shown for the first (i.e., one year after the voucher’s award) and second survey round (i.e., two years after the voucher’s award). Hence, short- to medium-term effects of the program are being evaluated. Models (1) and (2) show a positive and significant treatment effect in the first year after the voucher’s award on the number of MVPs and the number of products and services for firms that proposed to work with universities. The effect size for both outcomes is large since firms that were offered a voucher are estimated to have 49% more MVPs than firms that also anticipated to work with universities but were not offered a voucher. Furthermore, being offered the voucher is estimated to increase the number of products and services by 39%. Models (3) and (4) report the effects of the voucher on the number of MVPs and products and services two years after the voucher was awarded. Interestingly, the positive treatment effect on the number of MVPs from the first year disappears in the second year and even becomes negative (though not significant on conventional levels). Apparently, the innovation voucher accelerates the R&D project by speeding up the development of MVPs. Yet, this positive effect does not continue in the next year, suggesting that the firms in the control group were also able to introduce MVPs, but with a time lag of one year. For the number of products and services we do not see the same pattern. Here the coefficient remains positive in the second year, albeit not significant. Firms that were applying for collaboration with research and technology organizations do not show any treatment effect from the offered voucher (cf. Models (5) to (8)). In order to evaluate the effect of the innovation voucher on IP outcomes we focus on firms that applied for IP advice. In these analyses, we control for the applicant’s intention to apply for patents, trademarks, or design rights in the future (at the time of application). Table 1.10 shows a significant treatment effect of the innovation voucher on the number of new patent applications in the first year after the voucher was awarded (cf.

1.4 Results

31

Model (1)). Firms that applied for IP advice and were offered an innovation voucher, are estimated to have 28% more patent applications in the first year than firms that were planning to work with an IP advisor, but were not offered a voucher. We do not find a significant short-term treatment effect for the number of trademark or design right applications in the first year after the voucher’s award (Models (2) and (3)). The total number of IP applications, as the sum of patent, trademark, and design right applications, is estimated to be 33% higher for beneficiaries than for the control group. However, this result is primarily driven by the number of new patent applications. Models (5) to (8) report no treatment effects of the innovation voucher on the number of patent, trademark, or design right applications, as well as on the overall number of IP applications two years after the award of the voucher. Hence, the positive effect of the voucher on IP applications in the first year does not continue in the second year. Overall, our findings indicate that the relatively small treatment of the innovation voucher successfully supports SMEs in carrying out their plan to apply for IP protection in the short run.

-0.098 (0.128) -0.013 (0.192) 0.243 (0.305) 0.026 (0.036) -0.039 (0.083) 0.409*** (0.098) 0.019 (0.090) -0.077 (0.085) 0.546*** (0.192) Y 427 0.054

-0.045 (0.117) 0.353* (0.180) 0.223 (0.258) 0.129*** (0.037) 0.137* (0.082) 0.362*** (0.099) 0.068 (0.085) 0.095 (0.076) -0.018 (0.170) Y 427 0.123

Robust standard errors in parentheses. *** p