Governance in the Developing World: Transnational Insights on Economic Development 9819924928, 9789819924929

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Mohamed Sami Ben Ali Sami Ben Mim   Editors

Governance in the Developing World Transnational Insights on Economic Development

Governance in the Developing World

Mohamed Sami Ben Ali · Sami Ben Mim Editors

Governance in the Developing World Transnational Insights on Economic Development

Editors Mohamed Sami Ben Ali College of Business and Economics Qatar University Doha, Qatar

Sami Ben Mim Institute of Higher Commercial Studies of Sousse University of Sousse Sousse, Tunisia

ISBN 978-981-99-2492-9 ISBN 978-981-99-2493-6 (eBook) https://doi.org/10.1007/978-981-99-2493-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Introduction

According to neoclassical growth theory, the developing countries’ economic lag is mainly attributable to technological gaps and a lack of human capital. The aid programs set up by international donors have therefore focused on these problems, hoping to achieve a real economic take-off and trigger a real catch-up process. But, very quickly, the inability of many developing countries to take full advantage of their economic potential, as well as their recurrent debt crises, has pointed to a clear problem of governance. Institutional economics offered the theoretical underpinnings that established close linkages between economic performance, financial stability, and the quality of institutions in a given country. Governance has therefore become the cornerstone of any funding program, and international donors made their financial support conditional on the ability of countries to make significant advances in terms of public governance. Rating agencies have also exhibited an increasing interest in governance when assigning both sovereign and corporate ratings. As a result, companies have become heavily involved in implementing appropriate governance mechanisms in order to boost both their performances and ratings. More recently, with the challenge of climate change and the growing frequency of social conflicts, environmental and social issues have become important dimensions of the governance process. Decision-makers in developing countries have been forced to readjust their policies in order to address the growing public concern about these issues. Despite this progress, governance is still pointed out as one of the major weaknesses of the developing economies. Poor economic performance in developing economies may be largely attributed to weak governance practices at both the macro and the micro level. This volume assesses the extent to which good governance may contribute to enhancing economic performance and social welfare in developing countries. It also highlights the channels through which governance may act on various economic sectors and key economic development variables. Another objective is to help policymakers and firm managers identify and implement policies and procedures aimed at promoting good governance. The volume provides a transnational perspective, as the contributions consider developing countries in v

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different regions of the world. Moreover, each chapter deals with a case study in a single country setting or in a country’s panel framework. The variety of econometric methods used by the authors has brought more robustness to the results and allowed them to address issues in relation to causality analysis, short-term and longterm relationships, as well as the construction of global indexes, allowing an accurate assessment of multi-dimensional concepts such as governance. The book is organized as follows. The first chapter explores the dynamic role of institutions in sustainable development. The study in this chapter shows how institutional tools such as government stability, the government’s inward investment behavior, distortion of the system through corruption, legal system strength, and democratic accountability can be used to achieve sustainable development goals. It not only investigates the formal institutional channels but also highlights the informal institutions’ importance for sustainable development. The second chapter deals with the determinants of the sovereign ratings assigned by the Moody’s agency for a sample of developing countries. In particular, the chapter investigates whether enhanced governance standards help developing countries to feature among those classed as investment grade. It also assesses the impact of various governance proxies on the probability of downgrading or upgrading a country’s sovereign rating. The third chapter aims to explore the causal nexus between governance indicators and income inequality for 15 developing countries. Income inequality and the quality of the institutions in emerging economies have been the subject of intense debate within the scientific community in recent decades. Nevertheless, the causality issue is still a relatively neglected area. In the fourth chapter, the authors provide new empirical evidence on how egovernment solutions enhance the emergence of inclusive societies, increase institutional quality, and through these channels boost economic development in developing countries. They mainly focus on digital development inequalities and the Human Development Index. The fifth chapter seeks to assess the impact of six governance indicators on FDI inflows toward a sample of MENA and SSA countries. It mainly tries to identify the governance dimensions which matter most for foreign investors. It also verifies if these dimensions differ across MENA and SSA countries and investigates the complementarity between the different governance proxies for both groups of countries. The sixth chapter defines a new global corporate governance index for banks based on 62 criteria and seven internal performance indicators related to the board of directors, internal audit, compensation, risk management, nomination, compliance, ethics, transparency, and disclosure. The new index is tested on seven different banks from both developed and developing countries. The seventh chapter investigates the causality nexus between governance and the performance of the healthcare system in both the short and long run. The study computes a Governance Composite Index based on data relative to a sample of MENA

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countries. The Engle-Granger two-step approach is used to conduct the causality analysis between the aforementioned variables. The eighth chapter assesses the impact of governance on growth for a sample of 20 developing countries from four different regions. The empirical investigation addresses the complementarity between the various dimensions of governance by constructing a global governance index and comparing its effect across different groups of countries. The ninth chapter highlights the impact of board gender diversity on the financial performance of conventional banks from nine North African and Gulf Cooperation Council countries, while discussing the interfering role of corporate governance. It also identifies the transmission channels through which the presence of female directors enhances banking performance and investigates the nonlinearity of the relationship between board gender diversity and financial performance.

Contents

1 Dynamic Institutions as Pillars for Sustainable Development . . . . . . . Muhammad Azam, Sabri Boubaker, and Ahmed Imran Hunjra

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2 Upgrading and Downgrading Developing Countries’ Sovereign Ratings: Does Governance Matter? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sami Ben Mim, Ridha Nouira, and Christophe Rault

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3 Do Governance Indicators Predict Inequality? A Panel Non-causality Framework for the Emerging Countries . . . . . . . . . . . . . Muhammed Sehid Gorus and Mohamed Sami Ben Ali

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4 Enhancing Economic Development Through ICT-Based Governance: Evidence for Developing Countries . . . . . . . . . . . . . . . . . . Ewa Lechman

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5 Which Governance Dimension Matters Most for FDI? A Comparative Analysis Between MENA and SSA Countries . . . . . . . Sami Ben Mim and Dhafer Saïdane

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6 Formulation of a Corporate Governance Index for Banking Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Hani El-Chaarani and Zouhour El-Abiad 7 The Health Systems’ Governance in MENA Countries: A Panel Causality Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Emna Essadik and Chokri Terzi 8 Governance and Growth in Developing Countries: New Insights from a Cross-Regional Empirical Analysis . . . . . . . . . . . . . . . . 169 Montassar Zayati and Mohamed Sami Ben Ali

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9 Governance, Gender Diversity, and Banking Performance: New Evidence from North African and GCC Countries . . . . . . . . . . . . 203 Sana Mohsni, Sami Ben Mim, and Fatma Hajji Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

About the Editors

Mohamed Sami Ben Ali is a professor of Economics at Qatar University. Previously, he was head of the economics department and member of the scientific board at HEC Business School, Tunisia. He holds an H.D.R. degree. Previously, he received a Ph.D. in Economics with high honors from the University of Lille, France, an M.Phil. (D.E.A.) in International Finance and International Trade, and a B.A. in Business Economics. He has been teaching for the past years at graduate and undergraduate levels in Tunisia, Qatar, and France where he was a visiting professor. Dr. Ben Ali is serving as an associate editor for Springer and De Gruyter journals and editor for Palgrave, Springer, and Routledge (Taylor and Francis) books series. He published a couple of books on economic development. He also has published numerous articles in French and in English in international refereed academic journals on different economic development and international monetary macroeconomics issues. He is actively participating and chairing in numerous international conferences. Sami Ben Mim is a full professor of Economics at the Institute of Higher Commercial Studies of Sousse (University of Sousse, Tunisia). He holds an H.D.R. degree and obtained his Ph.D. in Economics from the Paris XII University, France. He has served as a vice-dean and director of graduate studies and member of the scientific board in several universities. Besides his regular teaching activities, he is currently pursuing his research at the Laremfiq Research Center in Tunisia and as an associate member of the ERUDITE Research team in France. He is also a reviewer for various highly indexed scientific journals.

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Chapter 1

Dynamic Institutions as Pillars for Sustainable Development Muhammad Azam, Sabri Boubaker, and Ahmed Imran Hunjra

Abstract In the current era, the development paradigm has shifted from simple economic development to sustainable development. Sustainable development has clear objectives to avoid natural resource exploitation due to increasing economic and environmental threats, such as economic unsustainability, pollution, and natural disasters. To achieve the objective of prosperity along with a protected environment, it is essential to identify the most significant determinant of sustainable development. In this context, institutional economics has gained paramount attention from world scholars. Economists are involved in an in-depth exploration of institutional issues based on politics that confront developing economies. The institutional research provides the scholars to find out enforcement and implementation challenges in the context of Sustainable Development Goals (SDGs). The relevance of institutional variables is essential to assess the rules of law, democratic quality, and bureaucratic structure active engagement along with informal structure of institutions. It assists in devising sustainable development policies for developing nations which is one of the major concerns. Developing economies, like most Asian economies, invoke the existence of authorities that can apply heavy fines, enforce laws, and organize sustainable development activities. Taking contextual importance to achieving the United Nations Millennium Development Goals (MDGs), sustainable development agenda is a serious global challenge, especially in the developing world. The developing countries have to enforce the rules and economic policy actions lined with sustainable development agendas. However, global connectivity in this context remains a huge challenge from a governance point of view. Weak institutional systems are a real fact of the developing world and a major source of human misères which disorder in the global world ranking. The current chapter explores the dynamic role of institutions in sustainable development in developing economies M. Azam Department of Economics, Ghazi University, Dera Ghazi Khan, Pakistan S. Boubaker EM Normandie Business School, Paris, France A. I. Hunjra (B) Rabat Business School, International University of Rabat, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_1

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(Ben Ali and Krammer in Economic development in the Middle East and North Africa. Springer/Palgrave Macmillan, New York, 2016). Sustainable development is a multidimensional ideology having a major focus on social, economic, cultural, and environmental perspectives for intergenerational equity resources. Institutions can be linked with sustainable development through social, economic, environmental, and globalization channels. Efficient institutions can be implemented through a wellstructured institutional framework that can effectively be used to achieve SDGs targets. Our study suggests that institutional tools such as government stability, the government’s inward investment behavior, distortion of the system through corruption, legal system strength, and democratic accountability can be used to achieve sustainable development goals. However, despite formal modes of institutions, the informal modes of institutions issues like ethnic diversity, and religious or cultural diversity are also the most pressing factors which can be used as a bridge toward sustainable development targets. To better grasp the roots of institutions of developing nations with sustainable development is the ultimate target of this study. We explore not only formal institutions’ channels but the informal institution’s channels’ importance for sustainable development, especially in the poor world which can provide useful lessons to learn. The outcomes of this study contribute to the literature by providing insights regarding ideological as well as empirical importance. Our study insights would guide several national and international actors such as the World Bank, and IMF, especially scholars and policymakers in developing countries, to figure out key factors in their geographical areas for optimal implementation and execution of sustainable development goals.

1.1 Introduction The most important issues of the current century are relevant to sustainable development and the negative consequences of unsustainability on human life, environmental quality and biodiversity, and ecosystem services. It is evident that practical actions are required to mitigate the negative impacts of unsustainability and massive funding is required to apply practical sustainable development projects to fight against natural resource depletion. The United Nations (UN) member countries gave a combined census on the 2030 Agenda for Sustainable Development having 17 Sustainable Development Goals (SDGs) along with 169 targets. To achieve sustainable development Agenda, a strong international commitment is required based on governance level, social level, economic level, and environmental quality improvement (United Nations, 2015). Many scholars suggest different considerations regarding sustainable development and its targets achievement and strongly focus on coherent censuses, the public, and private stakeholders’ involvement, and civil society interference (Allen et al., 2017; Kanie et al., 2014; Nilsson et al., 2018; Pradhan et al., 2017; Spaiser et al., 2017). The institutional role is discussed in a normative way (Bowen et al., 2017; Meuleman & Niestroy, 2015). However, the 2030 Agenda’s implementation presents difficulties for various stakeholders at various levels. The literature argues

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that the 17 SDGs’ complexity and interdependence necessitate integrated, holistic, and comprehensive policy-making in which actors from the public and corporate sectors and civil society are involved in making decisions, putting plans into action, and tracking progress (Boas et al., 2016). Bowen et al. (2017) highlight institutional factors’ importance for sustainable development to foster and speed up the environment with combined effort to ensure the execution of the goals. Kanie et al. (2014) broadly referred to institutional governance as the fourth major pillar of sustainable development. Sustainable economic development and environmental sustainability remain prominent debates in the current century, and it has been recognized that institutional governance is also interconnected with sustainable development indicators. Sustainable economic development and environmental sustainability can be significantly threatened by a weak institutional system and vice versa. Sustainable economic development and environmental sustainability are phenomena that provide capacity to economies for sustainable life, but they can also provide a threat, in turn to weak institutional development. There are also multiple levels of interconnection among the institutional and governance economic and social with sustainable economic development and the environmental sustainability dimensions and the UNDESA (2014) report also highlights the significance of these dimensions. In this study, we use an unbalanced panel dataset of 50 developing countries from 1981 to 2020. We take countries based on low income and lower middle income from the World Bank’s world development indicator (WDI) classification. The ICRG dataset follows the rule of law by including information on the country’s government, its attractiveness as an investment destination, its record in combating corruption, the quality of its police force, the responsiveness of its democratic institutions, and the efficiency of its bureaucracy. For informal institutions, we use ethnic conflict data based on the following studies (Esteban & Ray, 1994; Reynal-Querol, 2002). It captures the social conflicts that occurred in society. When different groups in an economy have very distinct traits, social tensions tend to rise. We use war as a barometer of societal disparities in terms of race, class, gender, and other dimensions. Ethnic conflict is quantified on a scale from 0 to 10 (Gören, 2014). When two groups are almost the same size, conflicts between them are likely to rise. The number of people belonging to each ethnic group will decrease if they are all about the same size. It means a lower level of ethnic diversity close to 10 indicates a high level of ethnic conflict while a higher level of ethnic diversity closer to zero indicates a lower level of ethnic conflict. For sustainable economic development, the adjusted net savings per capita (ANS) data is considered. The WDI database provides the adjusted net savings data (Güney, 2019), which is calculated as the difference between the total national savings and the total education expenditure less the total depletion of forests, minerals, and carbon dioxide. GDP per capita is used to analyze the impact of physical capital. Human capital development spending and the health benefits of protecting natural resources are excluded from this analysis of the role of economic activity in sustainable development. Unsustainability may result from the depletion of natural resources in reaction

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to a rise in income (Aidt, 2010). For fiscal capacity evaluation which is important to provide smooth funding for sustainable development projects execution at the national level, this study considers tax revenue as reported in the following studies (Ko, 2020). To evaluate social capital, the Gini index is the most important because it indicates economic inequalities decrease among people. Hoseini (2014) supports this variable to evaluate income inequality. Most of the studies highlight the significance of human capital in explained through public spending on education. Health expenditure is used to access social welfare improvement levels through public interference. Improvement in people is considered a vehicle for SD in developing countries. The main focus of this chapter is to discuss the nexus between institutions and sustainable development. Some fundamental ideas supported by literature have been considered for discussion for institutions and sustainable development nexus. Further, some crucial insights of underlying theories are supported by empirical evidence adopted in previous studies to clarify the relationship. The major theme of this chapter is to contextualize the association in the form of formal and informal institutions dimensions and sustainable development in the economy. It is most important to undertake the debate on institutions and sustainable development in the current era. This debate is based on multiple theories including empirical evidence to better postulate the hypothesis relevant to the impact of institutions on sustainable development (Pigou, 1932; Stern, 2016). In addition, this chapter further provides a comprehensive discussion of various theories and empirical evidence that have recently been given crucial importance in literature to focus on the role of institutions in sustainable development policies. The critical importance of different concepts such as political institutions, regulatory quality issues, and ethnic institutions through policy abatement mechanisms, have emerged in institutional policy models. These have received a lot of attention from UN climate change conferences on sustainable development. The literature discusses the institutional and sustainable development policies, both on theoretical and empirical dimensions to better identify the importance of different policies as well as channels (Azam et al., 2021; Hunjra et al., 2020). The major consideration of institutional-sustainable development policies associated with public policies is now receiving significant attention and its popularity has raised under the current COVID-19 pandemic crises in different countries of the world. The adverse consequences of the lockdown activity have a devastating impact on unemployment, a slowdown in the investment opportunities in different projects, massive financial losses to the business world due to poor institutional quality, and a low level of regulatory enforcement (Wellalage et al., 2021). If special attention is not given, institutional policies and sustainable development in different economies will lead to natural resource crises. The chapter provides potential suggestions regarding a crucial role for a stable institutional policy having a balancing impact on sustainable development to encounter the negative impact of the COVID-19 pandemic crises. The crucial discussion is to highlight how institutional policies affect sustainable development. Different green investment opportunities are having close ties to political institutions and regional institutional bodies supporting sustainable development projects (Hazemba & Halog, 2021). The regulatory systems of different governments

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may find close relevance to this debate specifically important to encounter climate change crises which are expected to have negative consequences on sustainable development mitigation programs due to COVID-19 current pandemic crises.

1.2 The Sustainable Development Over the period, the study of Callendar (1938) validates the greenhouse gas emission with the fact that the temperature of the earth rises 0.05 °C relevant to the previous centuries. Many other factors have also been highlighted like acute hazards due to high heat waves, high temperature, and rapid floods (Bathiany et al., 2018; Hosseini & Kaneko, 2011; Pigato, 2019). The carbon concentrations have raised to 30% in the twentieth-century concentrations (Plass, 1956). Plass (1956) further discusses the temperature level rises by 1.1 °C as compared to previous centuries. Environmental quality degradation creates a substantial issue for development around the globe. Environmental quality degradation is due to human activities and intervention (Weitzman, 2009). The IPCC (2018) report mentions a global discussion on sustainable development for minimizing the environmental effect to improve the earth’s system. Environmental degradation models for physical resource protection also can create socioeconomical and technological inertia issues due to high carbon emissions in different parts of the world (IPPC, 2018). The unsustainability issue has different impacts due to financial capacity and investment hazards, and vulnerabilities due to heavy dependency on more environmental pollutant sectors (Krogstrup & Obstfeld, 2018). It has a greater influence on the socioeconomic system (Hosseini & Kaneko, 2012; Nordhaus, 2007; Svartzman et al., 2021). In the case of high flooding areas, the financial burden has been raised on the government. Many financial institutions are designed to cope with environmental issues. Unsustainability has also negative social outcomes (Espagne, 2018). It has raised the burden on the vulnerable poorest communities. The reason is that the poorer community’s dependency on natural capital is very high to fulfill financial requirements (Pandey et al., 2017). An environmental resource degradation due to an unsustainable development pattern adds economic costs to specific geographic regions. The world is also facing serious challenges to under-preparation of unsustainability. Serious movements around the globe have been focusing on sustainable development adaptation. It is a costly path and a tough choice, especially for the poor economies that require physical capital mobility or human migration. It also requires a judicious mix of coordinated efforts through the active involvement of multiple stakeholders. The concept of sustainable development began in 1980 as a result of an initiative to inform the World Conservation Strategy (WCS) established during the International Union for the Conservation of Nature and Natural Resources (IUCN) conference. Sustainable development is regarded as the primary aim for contributing to the protection of the community’s interests through environmental protection techniques (Khosla, 1987). They highlighted the strategy to restrict living resources, sustaining

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genetic diversity, living habits, and ecological diversity. The sustainable development slogan must adequately protect sensitive issues relevant to the international population and urbanization and economic as well as political order (Khosla, 1987). Sunkel (1987) suggests sustainable patterns of living and development, that require structural changes in demand patterns. The major criticism of WCS is mainly focused only on ecological sustainability instead of sustainable development. The United Nations Environment Program (UNEP) was the main program to discuss the criticism and help in the revision of sustainable development. The sustainable development introduced by UNEPs is considered very poor protection rights through environmental damages, and self-reliant development ideology for natural resource protection. González-Benito (2005) discussed comprehensive factors relevant to sustainable development. These include dynamics conservation and integration with development, the satisfaction of human requirements, social justice equity and cultural diversity, and sustaining ecological integrity. Based on the aforementioned discussion, the World Commission on Environment and Development (WCED, 1987) gives the most popular sustainable development definition. Sustainable development must meet the needs of the present without compromising the ability of future generations for the sake of their needs compensation. The theme of this definition revolves around not compromising future generations’ abilities for sake of their requirement. It is presumed that the requirement of ecological and social sustainability is crucial. The fundamental contents of SD explain reviving and attaining quality growth, environmental importance in economic development sustaining food and energy, jobs, and water sanitation facility for people. To achieve SD, the operational SD concept importance is mandatory due to the significance of the operational goals which are interconnected to each other. Recently, the United Nations (UN) conference held in 2015 combined agreed on the 2030 Sustainable Development Agenda having 169 major targets and 17 Sustainable Development Goals (SDGs). The Agenda declares global commitment to achieve sustainable development and different targets based on its socioeconomic and ecological perspectives (United Nations, 2015). To achieve SDGs goals, a broader commitment to the global community is required. Recently, the sustainable development fundamental concept highlighted 232 indicators covering measurement, sustainability, and integration dimensions. The new SDGs goals can be devised based on understanding global emerging problems and responses having objectives to involve global interconnection. However, the SDGs have taken tremendous attention from scholars around the globe who are working on different perspectives considering the synergies of interrelation and the preferences in SDGs goals. A list of scholars has worked on SDGs goals and the development of multiple indicators for empirical assessment include (Hák et al., 2016; Le Blanc, 2015; Pradhan et al., 2020; Reyers et al., 2017; Spaiser et al., 2017).

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1.3 The Institutions The concept of institutions has different meanings for different scholars. For instance, some scholars consider political anticipation based on the structure of rules and others consider interventions and deliberate efforts for institutional adaptation. Institutional theories consider political actions through institutional participatory approaches for rational action (Cyert & March, 1963). Additionally, institutional theories consider institutional participation through anticipations of rules and regulations (North, 1981). Taylor and Charles (1985) identified self-interest as a distortionary factor of most of the world’s legal systems that can be socially legitimized through legal regulatory restrictions. Institutional theories are also based on behavioral and social preferences and rational choices and identify those preferences are dynamic and inconsistent having endogeneity exist in political institutions. The institutional system also encourages self-interest which can be countered through well cooperative socialization process of institutional factors (Wildavsky, 1987). Institutional theories also revolve around influential factors considering the perception of reality for actionable steps (Campiglio et al., 2018). However, institutional concepts regarding action differ in the rational choices framework. Searing (1991) highlighted that people mostly follow political institutions’ rules depending upon their choices. In institutional theories, actions are acceptable behaviors and norms designed through codes of rights and practical duties of the institutions but with legal rules and regulations. Institutionalized rules are contractual agreements to interpret norms that are mutually acceptable as discussed in the following studies by Shepsle (1989) and Lefort (1988). There is significant importance of institutional rules and rights. It is commonly argued that rules should have salient features that inform of natural and right consideration instead of unnatural and wrong consideration. Cyert and March (1963) define rules as standard operating procedures for routine life. The institutional concept was also considered in a broader framework considering the political power redistribution comprehensively in the context of the welfare perspective (Lefort, 1988). Appropriate rules of action are crucial and derived from the natural reasoning of the self. The ultimate source of rules and regulations are People’s actions and selfconceptions regarding societal matters. The cognitive process is also important for rules interpretation (March & Olsen, 1983). Later on, human actions are given considerable importance (Quarantelli & Dynes, 1977). The institutional theories relevant to politics also identified the human involvement for rights essential for common life spending. The common societal opportunities include community values instead of individual self-interest and individual rights protections (Chapman & Galston, 1993; Mansbridge, 1990). The institutional approaches also highlight that informal factors inform behavior importance (Steinmo et al., 1992). Biddle (1986) identified the importance of regulatory processes and behavioral factors for a constructive explanation of institutional rules and regulations for resource allocations. The informal institutions like culture and ethnic diversity, immigration, religious movements, and power transformations

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have also subsequent impacts on society (Flora et al., 1983). There are irreconcilable differences that exist among cultures creating marginalization and inassimilable for immigrants. These institutional factors have a devastating impact on the welfare of people in that society, for instance, the processes that used to turn foreigners into Frenchmen are faltering (Brubaker, 2009; Sandel, 1982). Indeed, the civic basis of identities is often intrinsic to the concept of a person, citizen, or public official. Self-interest protection is the discretionary element for the community, and it is also the type of corruption with community rights (Sandel, 1982). Modern institutional theories discussions also emphasize political democracy. The initial discussion on political democracy was based on the requirement of community. It also creates a sense of community instead of own individual interest’s protection (Olsen, 1990). The democratic dimension of institutions can also be designed based on civic rules. The community obligations of citizenship rights protection must be free from self-interest in democracy. An institutional democracy concept emphasizes the acceptable preferences of rules for the community and the importance of socializing individuals (Elster, 1989). Institutional theories also identified that group-based identities inform religious diversity and ethnic diversity. A communitarian level effort is required for common goods. Historically, institutional theories are more endogenous and less determinate. They generally assume conditions for political development that can be derived quickly for a specific outcome. The institutional system and institutional environment become homogenous for unique survival advantage based on restrictions (Baum & Singh, 1994). The informal institutional development reflects functional imperatives dynamics. The institution’s development either informal or political informal has multiple possibilities. Moreover, the institution’s development paths are also due to internal dynamics which are closely linked with surrounding environment dynamics. The exogenous environment contains path dependencies and an interconnected diffusion network. In addition, environmental adaptation and institutions are mutually dependent. Institutional development is complicated to predict in advance. The effect of wars and occupation change the institutional structure in different economies (Farza et al., 2021). Institutions can be established to oblige specific interests of a group but have negative outcomes in the long run. There are no unique connections between institutional options and institutional outcomes. North (1981) highlights that institutional development should not be based on the existing environment and institutional conditions but also institutional history should be given preferences. A specific feedback and development mechanism is mandatory to ensure institutional efficiency (Levitt & March, 1988). The institutional rules and regulations adaptation does not only depend upon the external environment, but the internal dynamics of institutions modify rules themselves endogenously. Dynamic institutions have also crucial importance which usually leads to vulnerability. Institutional changes have salient features either discontinuous, problematic, or contested (Orren & Skowronek, 2018). These changes can be punctuated by the equilibrium and also indicate critical junctions (Krasner, 1988). According to March and Olsen (1986), institutional changes can also be linked with performance crises

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which can stimulate not to follow established rules and practices in routine life. Most scholars also identify institutional changes can be mobilized and interchangeable (Krasner, 1988). However, institutional dynamism depends upon these factors which include debate on reasoning, education factors, and adaptation. Institutions are important for the creation of temporal and imperfect rules for sake of historical regulatory practices to be continued. Thus institutional dynamism provides such rules and regulations which are easily communicable and can be repeated persistently. The dynamic nature of institutions provides adaptability, flexibility, and creativity to rules and regulations. There is an extensive requirement for the stabilization of institutional norms and essential for durability and consistency (Hannan & Freeman, 1989). To secure stability for the institution’s rules and regulations; firstly, institutional stability is crucial that provides comparative advantage and it can easily provide vulnerability. There are hindrances in the dynamics of institutions due to competence and reliability factors (Levitt & March, 1988). Secondly, the institutional rules and regulations interpretations matter a lot. Institutional change normally occurs through community members learning from the culture or failing to adopt from the culture. Thus, outcomes from such factors can bring institutional change and may lead to alternative institutional patterns.

1.4 The Nexus Between Institutions and Sustainable Development The nexus between institutions and sustainable development has received greater attention in modern economics. Sustainability is a comprehensive ideology. There are different dimensions of the concept of sustainability considering dynamics of sustainable development, sustainable growth, sustainable finance, etc. Sustainability focuses on intergeneration connections between present and future generations. Sustainability requires a balancing act between social and economic as well as environmental issues to secure a quality of life for upcoming generations. Piketty (2015) identifies particular attention should be given to social problems, financial capitalism, and environmental challenges. Societal problems are crucial and have negative effects on sustainable development and sustainable growth. These issues have attained significant attention and they also affect the quality of life and basic rights of people. In this situation, sustainable development is in high demand. One significant part of sustainable development is institution-based politics. Currently, institutional economics has received tremendous attention as a composite ideology with attention given to sustainable development. The role of institutions in the governance perspective has mainly been addressed in the context of SDGs literature (Abhayawansa et al., 2021; Baer et al., 2021). The achievement of SDGs requires integration among different national development plans and policies (UN DESA, 2014). However, the 2030 agenda is complex due to uncertain challenges set for different actors. Achieving the 17 SDGs requires the

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integration of coherent policy as well as it also involves actors inform of public and private stakeholders and it also requires civil society’s extensive cooperation (Kanie et al., 2019). According to Newig et al. (2007) to achieve sustainability targets there is greater complexity and uncertainty involved. Institutional reinforcement in form of a better governance system is a crucial requirement to achieve SDGs (Bowen et al., 2017). To implement and enables SDGs goals, a collective effort is critical and actors’ accountability is important to deal with complex trade-off targets. Kanie et al. (2014) considered institutional governance as the fourth pillar of achieving sustainable development. The literature supports that institutional enforcement is an efficient way that can be utilized to deal with exclusion issues in an economy. The institution’s inclusion process results in abatement activity and also helps in natural resource conservation essential for sustainable development. Importantly, Kestel and Godmer (2003) defines institutional inclusion means a process that guarantees rules and regulations availability and adaptability for all members of society. In his regard, Bird (2013) showed that a high level of institutional quality significantly reduces intergenerational economic resources inequality. Furthermore, Ward (2008) identified authoritarian and democratic systems as the crucial factors to design sustainable development policies to maximize the welfare of society. Sustainable development through environmental efficiency improvement is considered a public good. However, environmental regulations often have quite different effects on different industries within a country. The institutional economist Aidt (2010) measures sustainable development with genuine investment and analyzes the relationship between corruption and sustainable governance after analysis of the data of 110 countries, emphasizing the critical significance of corruption for sustainable development and highlighting how corruption actively impacts sustainable growth through the legislative process, especially in investments. Only when the institution’s system is sufficiently mature and advanced an extensive number of sustainability projects can be undertaken (Güney, 2017). It highlights that there is a greater dependency on natural resources and institutions on sustainable resources (Apergis & Ben Ali, 2020; Ben Ali et al., 2016). Institutional development depends upon an effective legislative and judicial independence system. The study identifies institutional quality roles through good governance and sustainable development. Sustainable development models discuss the importance of abatement policies for natural resource savings in new and sustainable projects which is important for sustainable development (Somanathan et al., 2014). In recent times, many theoretical and empirical studies have shown the effectiveness of institutional systems which is critical for sustainable development. The institution’s quality is a critical subject including fragility in concept and empirical evidence, with a negative impact on sustainable development (Koziuk, 2020). Regarding whether institutions affect sustainable development, there is enlightening debate in the literature. Most economists looked closely at the idea that institutions might account for disparities in sustainable development between nations. Stoever (2012) finds positive the institutions on sustainable development using adjusted net savings (ANS) as a measure of SD. For institution quality, the study

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considers government effectiveness, control of corruption and rules of law, and political factors. Barbier (2010) identifies corruption in sustainable development in Africa and Asian countries. The overall result finds a negative association with sustainable development in Africa. The magnitude of corruption and sustainable development is comparatively less in African countries. Abou-Ali and Abdelfattah (2013) also evaluate sustainable development using the Millennium Development Goals (MDG) in 62 countries from 1990 to 2007. The Resource Curse Hypothesis model keeps important consideration for institutional quality. Carbonnier and Wagner (2015) evaluate institutions’ armed violence and excessive resource extraction negatively impact sustainable development. The empirical work of ethnic conflict on sustainable development is a limited specific set of countries. However, in the present study, we discuss the importance of informal institutions in form of ethnic conflict. The literature on the role of informal institutions in the form of ethnic conflicts for the provision of public goods like better sustainability is ambiguous. Ethnic conflict, according to some research, weakens the political and legal system, which in turn has a detrimental effect on public policies (Draper & Selway, 2019). Even though ethnic conflict is frequently at the forefront of societal separation, ethnic leaders have a robust capacity to harness the political-legal system to their advantage at the expense of other parties (Campiglio et al., 2018). One of the most discussed theories in the field of sociopolitical scientific knowledge is that ethnicity and other forms of informal institutionalization undermine the ability of politicians to argue for and find agreement on any agenda, ultimately leading to ineffective policies that threaten long-term sustainability. It is found that such informal institutions lead to less policy consensus as the number of participants in negotiation and debate increases. In the same manner, the study of Belay et al. (2013) finds a positive linkage between ethnic fractionalization and government instability, having a high level of political instability directly related to ethnic fractionalization. Jike (2004) supports those ethnic conflicts have insignificant effects on public policies relevant to public provisions sustainability. In cases where there are small geographic concentrated groups and such informal institutions in ethnic fragmentation, it may make little sense to devote energies to political activities ignoring their local problems. Thus, public policies in diversified economies are little influenced by such kinds of groups (Collier, 2000).

1.5 Empirical Results The study evaluates the impact of both forms of institutions (formal and informal) on sustainable development. Empirically specification of the model is specified as follows: SDi,t = α0 + α1 FINSi,t + α2 INFINSi,t + α3 PCi,t

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+ α4 FCi,t + α5 SCi,t + α6 HCi,t + α7 HEi,t + u i

(1.1)

The above equation SDit depicts sustainable development which is measured by adjusted net savings (ANS) per capita growth and carbon emission (CO2 ). FINS is formal and INFINS informal institutions considering regulatory perspective and informal perspectives like ethnic or cultural diversity. The regulatory perspective is based on the dynamics of institutions’ administrative dimensions including government stability, corruption, law & order, democratic, and bureaucratic. Similarly, informal institutions cover ethnic or cultural diversity. A set of the explanatory variables are indicated by a composite proxy covering physical Capital (PC), Fiscal Capacity (FC), Social Capital (SC), Human Capital (HC), and Health Expenditure (HE), see the variables’ description in Table 1.1. We use Random Effects Method or Fixed Effects Methods models specifically designed for cross-section-specific data sets. The basic assumptions of the random effect are (i) the random intercept having a mean value that is random and (ii) the error is a composite of time series and cross-section-specific error component. To select whether the fixed-effect method is appropriate for analysis or the random effect is suitable, the Hausman test is applied. The null hypothesis pertains to the random effect, however, the alternate hypothesis pertains to the fixed-effect preferences. We also use the dynamic model by using the Generalized Moments Method (GMM) to analyze the results. This approach offers the solutions to the problems which are common in panel data analysis: the individual behavior for heterogeneity in the study, the endogenous (among the repressors, the presence of lagged endogenous variables) and simultaneity, and the issue of bidirectional causality between the variables) see the results of endogeneity in Appendix. In Table 1.2, we show the descriptive statistics of each variable. The mean values of adjusted net savings and carbon emission are 09.04 and 17.84, respectively. High mean values suggest a higher level of sustainable development is an achievement in countries. The mean value of formal institutional structure based on political institutions is greater relative to informal institutions that inform ethnic diversity. The mean value of PC is 12.64, suggesting a high level of economic growth in these economies. High mean values of physical capital in these economies reflect a higher-level role in economic stabilizing. The mean value of FC indicates a fiscal capacity level which is used as a measure of fiscal capacity potential to deal with the sustainable development policies in developing countries. The mean value of SC capital is 4.30, suggesting a macroeconomic policy in the context of economic disparity level. Arguably, this is the result of a low level of economic disparity issues in the developing world. The mean value of HC openness is high at 13.13, indicating the human capital development position in developing countries. It means that the human capital channel has greater potential to significantly impact sustainable development in the developing world. As most developing economies are focusing on the human capital development factor. The mean value of HE is 12.61 suggesting that health expenditure also matters in developing countries.

SC HC HE

Gini index

Public spending on education total (% of GDP)

Health expenditure per capita (current US$)

Social capital

Human capital

Health expenditure

(PC) FC

GDP per capita

Tax revenue Share In GDP

Physical capital

(INFINS)

Fiscal capacity

Ethnic and cultural Heterogeneity

Informal institutions

CE (FINS)

Carbon Emission

Government stability, Investment profile, Control over corruption, law, and order, Democratic accountability, Bureaucratic quality (BQ)

Formal institutions

ANS

Adjusted net savings per capita

Sustainable development

Symbol

Description of variable

Variable name

Table 1.1 Variables description

Abou-Ali and Abdelfattah (2013) and Güney (2019)

Abou-Ali and Abdelfattah (2013) and Güney (2019)

Abou-Ali and Abdelfattah (2013) and Güney (2019)

Ko (2020)

Abou-Ali and Abdelfattah (2013) and Güney (2019)

Reynal-Querol (2002) and Esteban and Ray (1994)

Carbonnier and Wagner (2015)

Güney (2017)

Stoever (2012), Abou-Ali and Abdelfattah (2013) and Güney (2019)

References

1 Dynamic Institutions as Pillars for Sustainable Development 13

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Table 1.2 Descriptive statistics Variable

Obs

Mean

Std. Dev

Min

Max

ANS

580

09.044

20.212

1.224

16.190

CO2

530

17.848

13.531

05.217

38.790

FINS

522

14.829

24.850

02.701

45.000

INFINS

525

10.059

10.056

04.000

03.266

PC

517

12.649

08.450

03.000

16.501

FC

528

19.175

12.624

01.000

29.450

SC

532

04.302

09.910

05.477

18.208

HC

510

13.130

04.083

01.000

20.812

HE

518

12.610

03.201

02.000

17.130

Note ANS is sustainable development inform of Adjusted net savings, CO2 sustainable development inform of Carbon Emission, FINS is Formal institutions based on the political structure, INFINS is Informal institutions role inform of ethnic heterogeneity, PC is used for physical capital assessment inform of GDP, FC is used to access the fiscal capacity inform of taxable revenue income, SC is used to access social capital through income inequality, HC is used to access the human capital through public education spending, HE is for health expenditure

Table 1.3 Matrix of correlations Variables (1)

(2)

SD1

1.000

SD2

0.342** 1.000

(3)

(4)

FINS

0.225*

0.130

1.000

INFINS

0.114

0.010

0.017

PC

0.340** 0.161* 0.268*

0.014 0.148*

0.108

0.245*

(5)

(6)

(7)

(8)

(9)

1.000 1.000

FC

0.259*

SC

0.44*** 0.170* 0.047*** 0.208*

HC

0.182*

0.197* 0.360**

0.338** 0.140*

HE

0.114

0.124

0.401** 0.440*** 0.37** 0.018 0.147* 1.000

0.150*

0.340**

1.000

0.016

0.037

1.000

0.155* 0.051 1.000

Note ANS is adjusted net savings, CO2 is Carbon Emission, FINS is Formal institutions, INFINS is Informal institutions, PC is used for physical capital, FC is fiscal capacity, SC is social capital, HC is human capital, and HE is health expenditure

The Pearson pairwise coefficient matrix for the variables is displayed in Table 1.3. Since the correlations between the explanatory variables are only moderately strong, we do not have to be concerned about multicollinearity in our panel regression study. In Table 1.4, we report the results estimated through the fixed-effect and GMM techniques. Overall results support that formal institutions are significantly contributing to sustainable development. This outcome is important to understand that rules and regulation enforcement policy can be crucial for sustainable development. Therefore, it is also reasonable to assume that in developing economies, low

1 Dynamic Institutions as Pillars for Sustainable Development

15

quality institutional rules and regulations increase the cost of economic decisionmaking in sustainable economic development activity. To manage sustainable development issues, all types of regulatory, governance, and institutional quality issues are critical to be solved. Similarly, in the case of environmental sustainability, formal institutions help in the reduction of CO2 emissions in developing economies. It is reasonable to assume that any improvement in formal institutions’ quality contributes to abatement activity which affects economic decisions in developing economies. To manage environmental sustainability issues, it is critical to focus on mitigation and adaptation of environmental policies. The findings are discussed that informal institutions inform ethnic heterogeneity on sustainable development, and it reduces the consensus in the economy due to ethnic heterogeneity. The results imply that ethnic conflicts negatively impact sustainable development. However, much economic and political justification for these arguments is reported in the literature. It is argued that in the case of minimal democracies, ethnic diversity and ethnic conflicts have a significant impact on public goods provision. The authoritarian regime in these countries builds the pressure of ethnic political conflict on public goods provisions like sustainability issues. In some ways, we observe that most of the developing countries selected in our sample like India, Pakistan, etc., remained under the pressure of authoritarian regimes even in democracies. In some ways, developing countries like China and India represent a high level of ethnic diversity. These high levels of ethnic fragmentation in developing countries transfer the focus of ethnic groups from local-level problems to global-level problems. So the attention of ethnic fragmentation in developing economies has a significant impact on sustainable development and environmental quality in developing economies. The following studies support the informal institutions’ theory (Collier, 2000; Olson, 1993). The finding can be relevant to fiscal capacity in form of taxation revenue have an impact on sustainable economic development and environmental sustainability also. It means that fiscal capacity in selected developing economies matters a lot. The results find a significant relationship between sustainable economic development and fiscal capacity. The results also imply that fiscal capacity positively impacts environmental sustainability through a reduction in carbon emissions. Summerhill (2008) stresses fiscal capacity and the judicial system, Acemoglu et al. (2005) identifies fiscal capacity rules and regulation system as important for public goods, and Gordon (2010) describes tax structure as important to the economy. The study of Torgler (2007) is also important to discuss the significance of tax and tax systems in theoretical and empirical analysis, and also identifies the complication problems for the economy in achieving public goods.

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Table 1.4 Model testing Variable

Fixed effect

GMM

ANS

CO2

ANS

CO2

FINS

0.08* (0.04)

0.05 (0.04)

0.190* (0.10)

−0.312** (0.13)

INFINS

0.90*** (0.26)

−1.35*** (0.17)

0.97*** (0.07)

−1.26*** (0.08)

PC

0.009 (0.03)

0.07*** (0.03)

0.05* (0.05)

0.04* (0.10)

FC

0.116** (0.051)

−0.125** (0.061)

0.007* (0.06)

−0.17* (0.11)

SC

3.376*** (0.705)

3.356*** (0.605)

0.04* (0.07)

−0.02 (0.01)

HC

0.42** (0.15)

−0.22*** (0.06)

0.07** (0.02)

−0.04* (0.02)

HE

0.44** (0.15)

−0.21*** (0.04)

1.507*** (0.536)

−1.407*** (0.436)

Constant

−12.09*** (2.93)

−7.65*** (2.30)

−3.23*** (0.92)

−3.78** (1.95)

Country FE

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

R2

0.51

0.52

–

–

F-values

130.94

110.0

–

–

Hausman test

120.21 (0.001)

21.80 (0.95)

–

–

Wald Chi2 value

–

–

225.30

17.90

AR1 Test

–

–

0.008

0.035

AR2 Test

–

–

0.621

0.910

Observations

550

410

550

410

No of countries

50

45

50

45

Note ANS is adjusted net savings, CO2 is Carbon Emission, FINS is Formal institutions, INFINS is Informal institutions, PC is physical capital, FC is fiscal capacity, SC is social capital, HC is human capital, and HE is health expenditure. Robust standard errors are in parentheses. *p < 0.1, **p < 0.05 and ***p < 0.01

1.6 Concluding Remarks The role of institutions in promoting sustainable development becomes an important topic of discussion in recent times. Many scholars are contributing in their ways to evaluate the impact of institutions and different dynamics of institutions, social and economic factors, and institutions to ensure sustainable economic development. The main focus of this chapter is to discuss the institutions as the fourth pillar to achieving

1 Dynamic Institutions as Pillars for Sustainable Development

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sustainable development. This chapter contributes to the existing literature by investigating the formal and informal institutions of sustainable development. The results confirm that the institutional theory is either relevant to the regulatory process or relevant to informal institutions’ characteristics of the economy in form of ethnic and cultural characteristics. The analysis also captures economic, social, human capital, and health aspects of the economy to evaluate their significance in sustainable development. This chapter is useful to policymakers, especially from developing countries by providing directions where regulatory institutional policies are not working alone to deal with sustainable development issues. This chapter will help out regulatory bodies in multiple ways to utilize different forms of institutions as policy instruments in achieving sustainable development goals in developing economies.

Appendix: Test of Endogeneity (Durbin-Wu-Hausman)

Null hypothesis (H0: Variable is exogenous)

p-value

FC is correlated with the error term

0.0005

Formal Institutions correlated with the error term

0.0003

Physical capital is correlated with the error term

0.00021

Social capital is correlated with the error term

0.001

Human capital is correlated with the error term

0.000

Social capital is correlated with the error term

0.0007

Health expenditure is correlated with the error term

0.008

Informal institutions are uncorrelated with the error term

0.2212

Note The residual of each variable is firstly predicted and then tested to evaluate the significance level after regression analysis on exogenous variables

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Chapter 2

Upgrading and Downgrading Developing Countries’ Sovereign Ratings: Does Governance Matter? Sami Ben Mim, Ridha Nouira, and Christophe Rault

Abstract This paper investigates the determinants of the sovereign ratings assigned by the Moody’s agency for a sample of 29 developing countries over the 2000–2021 period. In particular we investigate whether enhanced governance standards help developing countries to figure among investment grade. We also assess the impact of various governance proxies on the probability of downgrading or upgrading a country’s sovereign rating. Estimation results reveal that the debt ratio, banking credits, and the investment rate are the economic fundamentals that influence most sovereign ratings. Results also offer strong evidence for a positive impact of an improved institutional framework on sovereign ratings, except for the voice and accountability index. The political stability index is the one producing the most consistent effect on the probability of figuring among investment grade. Another important finding is that governance does not affect in the same way upgrading and downgrading decisions. Political stability contributes to reduce the probability of downgrading sovereign rating, while an increase in voice and accountability amplifies this probability. On the other hand, governance indicators don’t produce any significant positive effect on the probability of upgrading sovereign rating. Some interesting policy recommendations can be drawn from these conclusions. Keywords Sovereign rating · Investment grade · Governance · Political Stability

S. B. Mim (B) LaREMFiQ, IHEC Sousse, University of Sousse, Sousse, Tunisia e-mail: [email protected] R. Nouira LAMIDED and ISFF, University of Sousse, Sousse, Tunisia C. Rault LEO, University of Orléans, Orléans, France CESifo, and IZA, Munich, Germany © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_2

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2.1 Introduction Rating agencies provide investors with valuable information about the creditworthiness of countries and companies (Elkhoury, 2007). Developing countries attach crucial importance to their sovereign ratings, as they largely determine their financing conditions on international capital markets. Improving the sovereign rating, or preserving it, is therefore a natural goal of any decision-maker. Given the discretionary nature of the rating process, the first step in this direction is to identify the main determinants of the sovereign rating. The early literature highlighted the importance given by the rating agencies to the economic fundamentals. The seminal paper of Cantor and Packer (1996) showed that per capita income, external debt, inflation, default history and economic development explain more than 90% of the sovereign ratings. Since the macroeconomic variables produce different effects on sovereign ratings, it is important to investigate how each of them weigh on the rating decision. In this respect, Borenszstein and Panizza (2006) showed that GDP per capita alone explains about 80% of the variation in ratings. Based on a sample of 200 countries, Yamanari and Souza (2022) came to a totally different conclusion and showed that 12 control variables are necessary to explain about 90% of the sovereign ratings variance. However, several studies highlighted that the sovereign ratings’ determinants may differ substantially between developed and developing countries (Wüste, 2022). Some of these differences may be attributed to the specificities of developing countries, while others stem from the rating process set up by rating agencies. First, the many episodes of default in developing countries can lead rating agencies to a more severe valuation of their sovereign risks. Gaillard (2012a) emphasized that rating agencies attach particular importance to previous default events when assessing sovereign risk, while Afonso et al. (2011) found that default history produces a long-run effect on the sovereign rating. Moreover, the financial soundness of these countries is particularly sensitive to their foreign reserves, which are tightly related to their exporting activities (Fitch Ratings, 2013). According to Amstad and Packer (2015), “a 10 percentage point increase in the ratio of foreign exchange reserves to GDP strengthens the rating by 0.4 notches”. Workers remittances represent also an important source of foreign exchange reserves for many developing countries (Gaillard, 2012b) and can therefore affect their ratings. Wüste (2022) highlighted more differences between developed and developing countries. A major difference between these two groups is that trade openness is assessed positively for developing countries, while it is negatively perceived by rating agencies for developed countries. Moreover, his empirical results revealed that ratings in developing countries are highly dependent on the debt ratio and the age structure of the population, while those of developed countries are more sensitive to the unemployment rate and to fiscal rules. In a recent study on South Africa, Takawira and Muteba Mwamba (2022) showed that the household indebtedness ratio is a powerful predictor for sovereign rating. They argued that a decline in this ratio would significantly reduce sovereign risk in developing countries. In addition to economic fundamentals, Haque

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et al. (1996, 1997) argued that developing countries’ ratings are particularly sensitive to increases in international interest rates and to the structure of a country’s exports and its concentration. Several studies have also emphasized the subjectivity of the rating process and showed that high ratings are more likely to be attributed to developed countries (Gultekin-Karaka¸s et al., 2011). According to Wigglesworth (2012), policymakers in developing countries have openly complained about an adverse assessment by the largest US agencies. Fuchs and Gehring (2013) confirmed that “western agencies” assign higher ratings to their home countries and to countries showing high cultural similarities and strong financial connections with their home countries. New agencies implemented in non-western countries tried to overcome this bias by providing rankings which are more profitable to developing countries. Chinese rating agencies for example are assigning higher ratings to China and BRIC countries compared to American agencies (Fuchs & Gehring, 2013). The growing financial instability and the proliferation of financial crisis have also put a strain on the credibility of sovereign ratings. The inability of the rating agencies to predict the onset of these crises has called into question the reliability and soundness of their assessment processes. In this respect, Baumann et al. (2022) showed that while ratings are producing a significant effect on equity and bonds markets before the global financial crisis, they no longer produce any significant effect on these international financial flows during the post-crisis period. Such results suggest that, for reputational reasons, investors are no longer relying on ratings during the decisionmaking process. To remedy this problem, Amstad and Packer (2015) pointed out the evolving methodologies for the measurement of sovereign credit risk. Following the subprime crisis, the rating agencies opted for new methodologies relying on more quantitative inputs. In this respect, the authors asserted that more emphasis has been put on the implications for sovereign risk of monetary policy regimes, currency internationalization, event risk, and economic growth. Amstad and Packer (2015) also noticed that the recent financial crisis affected differently the sovereign ratings of advanced and emerging economies. Considering the subprime crisis and the euro area debt crisis, they noticed a significant deterioration of the sovereign risk profile of many advanced economies, while the sovereign ratings relative to emerging economies remained relatively stable. Their empirical investigations didn’t provide any evidence supporting the hypothesis of a bias against the emerging economies. The significant differences between the sovereign ratings of developed and developing countries can also be attributed to the important governance gaps between the two groups of countries. Many studies confirmed that the poor economic performance of developing countries is largely due to inappropriate governance (North, 1990). In this way, enhancing the governance standards should boost the economic performance (Kauffman et al., 1999) and lead to an improvement of the sovereign rating. Examining the link between governance and sovereign rating is therefore a critical issue for developing countries. This paper tries to investigate this relationship by providing new empirical evidence about the impact of governance on sovereign ratings for developing countries. To that end, we assess the impact of various economic fundamentals on sovereign credit ratings for a sample of 29

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emerging economies over the 2000–2020 period. Firstly, we try to identify the determinants of the probability of a sovereign debt being classified in investment grade. We mainly focus on the impact of the six governance indicators developed by Kaufmann et al. (2010) which cover the various dimensions of the institutional framework. In a second set of estimations we assess the determinants of the upgrading and downgrading probabilities. Particularly, we investigate which among the governance dimensions may lead to an upgrade or a downgrade of the sovereign rating. This should provide decision-makers with valuable insights into the dimensions of the institutional system that need to be further developed in order to successfully improve the sovereign rating. To our knowledge this is the first study to assess the impact of various institutional and governance features on the downgrading and upgrading probability. This paper is structured as follows. Section 2.2 presents a brief literature review about the impact of governance on sovereign ratings. Section 2.3 presents the model and discusses the econometric methodology. Section 2.4 summarizes and discusses the main empirical results. Finally, we conclude and provide some policy recommendations.

2.2 Does Governance Contribute to Enhance Sovereign Rating? Good governance is likely to improve sovereign ratings through various channels. Fighting corruption, for example, boosts growth by promoting public and private investments (Shleifer & Vishny, 1997; Tanzi & Davoodi, 1997) and by improving the efficiency of the financial intermediation process (Beck et al., 2005). Similarly, an effective legal system which protects the investors’ rights should help to promote investment and growth (Levine, 1998; Levine et al., 2004). Political stability is also an attractiveness factor for both domestic and foreign investors. Government Effectiveness favors sound economic policies and reduces the need for external debt. According to Afonso et al. (2007), improved government effectiveness should enhance countries capacity to meet their liabilities. Moreover, democracy allows a smooth transition of power and reduces the economic costs associated with brutal regime changes. Democratic regimes also ensure a greater control over policy makers, leading to a more efficient use of public resources. For all these reasons, improving the institutional framework represents an important prerequisite for a sound financial and economic situation, and should therefore be associated with a lower sovereign risk. Empirical studies relative to the impact of governance on sovereign ratings led to controversial results. A first stream of the literature argues that an improved institutional framework has a positive impact on sovereign rating. In this respect, Bissoondoyal-Bheenick (2005) found that the economic and financial indicators alone do not fully explain the sovereign rating. Their results reveal that sovereign

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ratings are particularly sensitive to the quality of the political framework. Mellios and Paget-Blanc (2006) showed that an increase in perceived corruption negatively affects sovereign ratings. A similar result was detected by Pretorius and Botha (2017) for a sample of African countries. Considering data relative to the United States, Sychowiec et al. (2021) found that the impact of corruption on sovereign rating is contingent on fiscal dependence and federal transfers. Overes and van der Wel (2022) examined the ratings assigned by Moody’s to 62 developed and developing countries over the period 2001–2019 using five different empirical methodologies. According to their results regulatory quality is one of the most influential variables on sovereign rating. In another recent study, Sychowiec (2021) confirmed that even in advanced democratic countries, sovereign rating is highly dependent on the rating agencies’ perception of political stability. A positive long-run relationship between government effectiveness and sovereign rating was also highlighted by Afonso et al. (2011). Butler and Fauver (2006) examined the impact of the six institutional indicators developed by Kaufmann et al. (2010) on sovereign ratings for a sample of 86 countries. Their results showed that a country’s political and legal frameworks are major determinants of its sovereign rating. In particular, a one standard deviation increase in the legal index produces 0.466 standard deviations increase in the sovereign rating. In a more recent paper, Amstad and Packer (2015) confirmed that institutional strength produces a consistent positive effect on sovereign rating. This effect remained of similar importance before and after financial turmoil episodes. However, a second strand of the literature argues that the impact of institutional variables on sovereign rating is non-significant (Archer et al., 2007). For Haque et al. (1996) and Cantor and Packer (1996), including political variables slightly improves the predictive capacity of the rating models, but their inputs are marginal compared to economic fundamentals. Their exclusion does not bias the parameter estimates. Other studies have pointed out the qualitative nature of governance, which accentuates the subjectivity of the rating process and may even lead to biased sovereign ratings (Fuchs & Gehring, 2013). This subjectivity often benefits developed countries, while emerging countries classified within speculative grade are the main victims (De Moor et al., 2018). To ensure a higher degree of transparency and objectivity it would be better to base the rating process on quantitative criteria that are easy to identify and to measure. It is also important to note that some studies have questioned the positive effects of governance on economic performance. First, the potential positive effects of an improved institutional framework are only visible in the long term, while their economic costs are immediately incurred. Moreover, in countries with poor regulatory frameworks, corruption allows investors to circumvent bureaucratic obstacles and produce therefore a positive impact on investment and growth (Chen et al., 2013; Laeven & Levine, 2009). On the other hand, in some developing countries, freedom of expression can be a source of political and social instability and thus contribute to deteriorate economic performance. In democratic regimes, the frequent turnover of decision-makers may lead to changes in economic policies and thus compromise the long-term economic performance. Similarly, economic freedom generates financial instability and contributes strongly to the emergence of financial crises which

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cause huge economic damages (Lipscy, 2018). These various arguments suggest that the positive relationship between governance and economic performance is far from being obvious. This naturally calls into question the positive impact that governance may produce on sovereign rating. This paper tries to shed light on the governance-rating nexus. We contribute to the empirical debate by assessing which governance dimensions contribute the most to explain the sovereign rating. We mainly examine if the governance indicators produce a significant effect on the probability of sovereign debt being classified within investment grade. We also try to identify which governance proxies contribute to explain the upgrading and downgrading decisions taken by the Moody’s rating agency.

2.3 Model and Methodology The main objective of this paper is to assess the impact of various governance indicators on developing countries’ sovereign ratings. In particular we try to determine their impact on the probability to figure among investment grade. To that end, we decompose Moody’s rating grid into two zones: investment grade (green zone) and speculative grade (red zone). This decomposition is summarized in Table 2.1. We first consider the following model: SRit = μi + α1 Inf it + α2 FDIit + α3 Growthit + α4 Invit + α5 Unempit + α6 Debt it + α7 Credit it + α8 Stabit + α9 Voiceit + α10 Ruleit + α11 Corr it + α12 Govit + α 13 Reguit + εit

(2.1)

where μi represent the country fixed effects and εit the error term. The dependent variable (Sovereign Rating, SR) is a dummy variable taking the value of 1 if the sovereign rating is classified within investment grade. Following the relevant literature, a set of frequently employed sovereign rating determinants has been selected, including the inflation rate, foreign direct investment, economic growth, the investment rate, the unemployment rate, external debt, and domestic credits. We also include the six Table 2.1 Moody’s investment and speculative grades Investment grade Aaa, Aa1, Aa2, Aa3, A1, A2, A3, Baa1, Baa2, baa3 (Green zone) Speculative grade Ba1, Ba2, Ba3, B1, B2, B3, Caa1, Caa2, Caa3, Ca, C, WR (Red zone)

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governance indicators developed by Kaufmann et al. (2010): Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The macroeconomic aggregates and the governance indicators are respectively extracted from the World Development Indicators (WDI) and the Worldwide Governance Indicators (WGI) World Bank’s databases. The definitions of the variables are provided in Table 2.2. Our sample is composed of 29 developing countries1 and covers the 2000 to 2020 period. We included in the sample all developing countries for which Moody’s assigned regularly a sovereign rating during the full sample period. Among the three big agencies, Moody’s is the one offering the largest coverage for developing countries. Descriptive statistics and the correlation matrix are respectively reported in Tables 2.3 and 2.4. The correlation matrix shows that governance indicators are highly correlated which may generate a multicollinearity problem. Multicollinearity leads to unreliable and unstable estimates. To identify which governance indicators represent potential sources of multicollinearity, we compute the Variance Inflation Factors (VIF) relative to all the independent variables in Model (1). According to O’Brien (2007) a VIF of 4 (which corresponds to a tolerance level of 0.25) is commonly used as a threshold to indicate excessive multicollinearity. Following this conservative rule of thumb, results reported in Table 2.4 reveal that Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption are showing high VIF values and represent serious sources of multicollinearity. These variables will be dropped and then introduced alternately in the model. Consequently, five different specifications will be estimated by applying a logit regression. Specification (1): SRit = μi + α1 Inflationit + α2 FDIit + α3 Growthit + α4 Investment it + α5 Unempit + α6 Debt it + α7 Creditsit + α8 PolStabit + α9 VAit + εit Specification (2): SRit = μi + α1 Inflationit + α2 FDIit + α3 Growthit + α4 Investment it + α5 Unempit + α6 Debt it + α7 Creditsit + α8 PolStabit + α9 VAit + α10 RLawit + εit Specification (3): SRit = μi + α1 Inflationit + α2 FDIit + α3 Growthit + α4 Investment it + α5 Unempit + α6 Debt it 1

The following countries are included in the sample: Argentina, Bahamas, Bolivia, Botswana, Brazil, Chile, China, Colombia, Costa Rica, Ecuador, Egypt, El Salvador, Guatemala, Honduras, India, Indonesia, Kenya, Malaysia, Mexico, Morocco, Pakistan, Paraguay, Peru, Philippines, South Africa, Thailand, Tunisia, Turkey, Uruguay.

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+ α7 Creditsit + α8 PolStabit + α9 VAit + α10 CCorr it + εit Specification (4): SRit = μi + α1 Inflationit + α2 FDIit + α3 Growthit + α4 Investment it + α5 Unempit + α6 Debt it + α7 Creditsit + α8 PolStabit + α9 VAit + α10 GovEff it + εit Specification (5): SRit = μi + α1 Inflationit + α2 FDIit + α3 Growthit + α4 Investment it + α5 Unempit + α6 Debt it + α7 Creditsit + α8 PolStabit + α9 VAit + α10 RQit + εit where μi represents the country fixed effects and εit the error term. In logistic regressions, a logit transformation is applied to the odds—that is, the probability of success divided by the probability of failure. The coefficients are estimated using the maximum likelihood method (MLE). The logistic regression coefficient β associated with a control variable X represents the expected change in log odds of having the outcome per unit change in X. Thus, increasing X by 1 unit multiplies the odds of having the outcome by eβ . As mentioned previously, another major objective of this study is to determine the impact of the governance indicators on the probability of downgrading or upgrading sovereign ratings. To that end, two dummy variables were created: • ΔS R it− :indicates a deterioration of sovereign rating. It takes the value of 1 if the rating was downgraded and 0 otherwise. • ΔS R it+ :indicates an improvement of sovereign ratings. It takes the value of 1 if there is an improvement of ratings and 0 otherwise. The five specifications described previously will be re-estimated while considering + ΔS R − it and ΔS R it as dependent variables.

2.4 Results and Discussion The estimation results for model (1) are reported in Table 2.4. To avoid multicollinearity, only the political stability and voice and accountability indexes were included in all the specifications. The remaining governance indicators are introduced alternately among the set of independent variables in columns 2 to 5.

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Results relative to the control variables are consistent with those highlighted by the empirical literature. The debt ratio, the percentage of domestic credits and the investment rate are the main determinants of the probability to figure among investment grade. The debt ratio negatively affects the probability of a country’s sovereign bonds to be classified among investment grade. This is the only variable whose coefficient is significant in all five equations, which suggests that the debt ratio is one of the variables that weighs most on the rating agencies’ decision. A similar result was highlighted by Wüste (2022) who proved that, unlike ratings assigned to developed countries, developing countries’ ratings are highly dependent on the debt stock. Results also reveal that higher domestic credits provided by the banking sector significantly increase the likelihood to belong to investment grade. The coefficient associated with this variable is relatively stable (around 0.2) and is significant in four out of five equations. Such a positive effect may be explained by the fact that higher credits are often associated with higher investment rates and contribute in that way to boost economic growth. Results relative to the investment rate are in line with this idea. In three equations out of five an increase in the investment rate significantly enhances the probability to belong to investment grade. The coefficients associated with the investment rate are all positive and fairly stable (around 0.3), which confirms the robustness of its effect on sovereign rating. The growth rate produces a positive effect on the dependent variable. Nevertheless, this effect is significant only in one out of the five estimated equations. As expected, the inflation rate significantly increases the probability of belonging to speculative grade, and this effect proved to be significant in two equations out of five. It is largely admitted that higher inflation undermines macroeconomic stability, which may partly explain its negative effect on the ratings assigned by Moody’s. For the governance indicators, political stability positively influences the likelihood of being classified in the green zone. This positive effect is significant in four out of the five estimated equations, which reflects the robustness of this result. Similarly, Sychowiec (2021) emphasized the importance of political stability in the rating process. His empirical results showed that even in well-established democracies political stability is positively valued by the rating agencies. Oppositely, we notice that voice and accountability have no significant effect on the likelihood of belonging to the green zone. The results reported in columns 2 to 5 show that control of corruption, rule of law, government efficiency, and the regulatory quality all produce positive and significant effects on the likelihood of being in investment grade. Except for voice and accountability, all other dimensions of governance appear to be positively valued by Moody’s during the rating process. Such results are in line with those of Butler and Fauver (2006), Mellios and Paget-Blanc (2006), and Afonso et al. (2011) which, respectively, highlighted the impact of the legal system, corruption, and government effectiveness on sovereign rating. A robust positive effect produced by regulatory quality on ratings was also detected by Overes and van der Wel (2022). In a second set of estimations, we tried to identify the variables which contribute the most to explaining sovereign rating’s upgrades and downgrades, with a special focus on governance indicators. As regards the likelihood of a deterioration in sovereign rating, the results in Table 2.5 show that an increase in the debt ratio

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contributes to accentuate this probability. An increase in banking credits leads to a similar outcome, although this effect is only significant in three equations out of five. The results also suggest that the growth rate is a major determinant of the deterioration of sovereign rating. In all five equations, a decrease in the growth rate significantly accentuates the probability. An increase in the unemployment rate also produces a similar effect. This effect is nevertheless significant only in three out of the five estimated equations. As for governance indicators, only two variables produce a significant effect on the probability of a sovereign rating’s downgrade. A deterioration in political stability exacerbates sovereign risk and therefore increases the likelihood of a sovereign rating being downgraded. Surprisingly, an increase in the voice and accountability index increases the downgrading probability. This result can be attributed to the fact that, in many developing countries, freedom of expression and organization often leads to strikes, protests, and social and political conflicts, which produces a negative impact on macroeconomic performance. In this vein, Block and Vaaler (2004) found that developing countries are more likely to be downgraded during election years. They concluded that both rating agencies and investors are viewing elections as a source of instability, which increases the financing costs for developing democracies. Results in Table 2.7 show that an improvement of sovereign rating is mainly due to an increase in the growth rate. A decrease in the debt ratio or in banking credits may also contribute to a rating upgrade (the coefficients associated with these variables are not significant in all the estimated equations). The estimation results also reveal that none of the governance indexes contribute to improve sovereign rating. The rule of law index even contributes to reduce the probability of a rating upgrade. Combining the results from Tables 2.6 and 2.7, it can be concluded that some governance indicators help to explain the deterioration of sovereign rating, but don’t contribute to explain its improvement. Such results suggest that the governance indicators weigh on the downgrading decision rather than on the upgrading decision. This can be explained by the fact the benefits of an improved governance will appear only in the long term. Consequently, we should also expect a long-term effect on sovereign rating. A similar conclusion was asserted by Afonso et al. (2011) who found that government effectiveness produces a long-term effect on sovereign ratings.

2.5 Conclusions and Recommendations The objective of this paper was to identify the impact that governance indicators may produce on the probability of ranking a sovereign debt among investment grade. We also tried to verify which governance dimensions contribute to reduce the likelihood of a deterioration in sovereign rating or lead to an increase in the probability of an upgrade. These issues are of crucial importance for developing countries. Among the economic fundamentals, we found that the debt ratio, banking credits, and the investment rate are the main determinants of the probability to be classified among investment grade. Estimation results also offered strong evidence for a positive

2 Upgrading and Downgrading Developing Countries’ Sovereign …

33

impact of governance on sovereign rating. Except for the voice and accountability index, all the governance indicators contribute to enhance the probability of belonging to investment grade. Among these indicators, political stability is the one producing the most consistent effect on this probability. Another important finding is that governance doesn’t affect the same way the upgrading and downgrading decisions. We mainly notice that political stability contributes to reduce the probability of downgrading sovereign rating, while an increase in voice and accountability amplifies this probability. Such a result is probably due to the fact that freedom of expression often fuels social and political conflicts in developing countries, leading to a deterioration in the economic and financial environment. Elections may also be viewed as an important source of uncertainty by rating agencies and investors. On the other hand, results reveal that governance indicators don’t produce any significant positive effect on the probability of an upgrade of sovereign rating. This is probably because governance enhances economic performance in the long run and don’t produce therefore any short-term effect on sovereign rating. These findings suggest that improved governance standards would help developing countries improve their sovereign ratings. However, among the various dimensions of governance, emphasis should be put on political stability which should be considered as the cornerstone of the institutional framework. On the other hand, empirical investigations should focus on the interaction between the short- and longterm effects to accurately assess the impact of an improved governance on the rating agencies’ decisions.

Appendix See Tables 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, and 2.8.

34

S. B. Mim et al.

Table 2.2 Independent variables definitions Variable

Definition

Unemployment rate (Unemp)

Total Unemployment as a percentage of total labor force

Investment rate (Investment)

Gross fixed capital formation (% of GDP)

Economic growth (Growth)

GDP per capita growth rate (annual %)

Foreign Direct Investment (FDI)

Net Foreign Direct Investment inflows (% of GDP)

Inflation rate (Inflation)

Variation rate of consumer prices (annual %)

External Debt (Debt)

Total external debt stocks to gross national income (% of GNI)

Domestic credits (Credits)

Domestic credit to private sector by banks (% of GDP)

Control of corruption (CCorr)

The extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. Estimate ranging from −2.5 to 2.5

Rule of Law (RLaw)

The extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Estimate ranging from approximately −2.5 to 2.5

Government effectiveness (GovEff ) The quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Estimate ranging from −2.5 to 2.5 Voice and accountability (VA)

The extent to which a country’s citizens can participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Estimate ranging from approximately −2.5 to 2.5

Political stability (PolStab)

The likelihood of political instability and/or politically motivated violence, including terrorism. Estimate ranging from −2.5 to 2.5

Regulatory Quality (RQ)

The ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Estimate ranging from approximately −2.5 to 2.5

2 Upgrading and Downgrading Developing Countries’ Sovereign …

35

Table 2.3 Descriptive statistics Variables

Mean

Government effectiveness

−0.004

Std.dev 0.53

Min −1.15

Max 1.3

Control of corruption

−0.12

0.72

−1.39

1.59

Rule of law

−0.22

0.62

−1.25

1.45

Political stability

−0.37

0.81

−2.81

1.28

Regulatory quality

−0.01

0.59

−1.29

1.53

Voice and accountability

0.056

0.74

−1.74

1.31

Inflation

6.47

7.22

−1.09

54.4

FDI

2.76

2.26

−6.18

12.47

Growth

2.04

3.86

−15.33

16.99

Invesment

22.13

6.62

10.35

44.66

Unemployment

8.14

6.13

0.4

46.19

External debt

39.69

20.84

3.87

159.89

Domestic credits

44.2

27.31

9.5

182.43

0.65

−0.11

0.41

−0.11

0.2

0.87

0.6

0.8

0.43

−0.16

0.34

0.017

0.23

0.32

−0.18

0.47

Rlaw

PolStab

RQ

VA

Infl

Fdi

Growth

Invest

Unemp

Debt

Credits

0.33

−0.18

0.4

0.17

−0.01

−0.04

0.06

0.39

−0.11

0.54

0.79

0.63

1

Rlaw

0.42

0.73

0.72

0.87

0.8

1

1

GovEff

CCorr

CCorr

GovEff

Table 2.4 Correlation matrix

0.19

−0.01

0.35

0.08

−0.1

0.35

−0.09

0.56

0.52

1

PolStab

0.29

−0.21

0.26

0.04

−0.02

0.43

−0.23

0.52

1

RQ

0.14 −0.37

0.01 −0.25

0.077

−0.18

0.32

−0.1

−0.22

−0.07

1

Infl

−0.19

0.25

0.05

1

VA

0.14

−0.14

0.01

0.11

0.17

1

Fdi

0.09

0.43

−0.35

−0.04 −0.22

1 −0.18

Invest

0.36

1

Growth

0.04

0.009

1

Unemp

−0.12

1

Debt

1

Credits

36 S. B. Mim et al.

2 Upgrading and Downgrading Developing Countries’ Sovereign … Table 2.5 Variance inflation factors

Government effectiveness

37 6.98

Control of corruption

6.37

Rule of law

7.2

Political stability

2.37

Regulatory quality

4.05

Voice and accountability

2.87

Inflation

1.29

FDI

1.45

Growth

1.29

Investment

1.68

Unemployment

1.37

Debt

1.26

Domestic credits

2.39

Mean VIF

3.12

Table 2.6 Determinants of the probability of the sovereign rating being classified in investment grade Specification 1

Specification 2

Specification 3

Specification 4

Specification 5

Inflation

−0.17 (0.11)

−0.2** (0.11)

−0.16 (0.12)

−0.18 (0.1)

−0.2* (0.1)

FDI

−0.01 (0.18)

−0.18 (0.18)

−0.13 (0.19)

−0.02 (0.18)

0.19 (0.16)

Growth

0.12 (0.11)

0.1 (0.11)

0.12 (0.11)

0.12 (0.11)

0.18* (0.1)

Invesment

0.24 (0.16)

0.3* (0.16)

0.28 (0.18)

0.28* (0.16)

0.33** (0.14)

Unemp

−0.4 (0.29)

−0.4** (0.22)

−0.48* (0.29)

−0.41* (0.22)

−0.19 (0.15)

Debt

−0.1*** (0.04)

−0.2*** (0.04)

−0.19*** (0.05)

−0.16*** (0.03)

−0.16*** (0.03)

Credits

0.2*** (0.05)

0.19*** (0.05)

0.22*** (0.06)

0.2*** (0.05)

0.19*** (0.04)

PolStab

4.5*** (1.38)

3.27** (1.35)

4.02*** (1.54)

3.03** (1.49)

1.51 (1.3)

VA

2.03 (1.44)

0.32 (1.53)

1.47 (1.67)

2.15 (1.63)

0.55 (1.27)

RLaw

–

5.68*** (2.36)

–

–

– (continued)

38

S. B. Mim et al.

Table 2.6 (continued) Specification 1

Specification 2

Specification 3

Specification 4

Specification 5

CCorr

–

–

3.14* (1.86)

–

–

GovEff

–

–

–

4.01* (2.11)

–

RQ

–

–

–

–

6.4*** (1.68)

Constant

2.61 (4.04)

4.5 (3.9)

3.4 (4.32)

1.52 (0.39)

−1.06 (3.24)

Nb. of obs

399

399

399

399

399

Nb. of Groups

20

20

20

20

20

Prob. Chi2

0.0004

0.0006

0.006

0.0005

0.0001

Robust standard deviations in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively

Table 2.7 Determinants of the probability to downgrade sovereign rating Specification 1

Specification 2

Specification 3

Specification 4

Specification 5

Inflation

−0.003 (0.02)

0.002 (0.02)

−0.003 (0.02)

0.001 (0.02)

−0.006 (0.023)

FDI

−0.07 (0.09)

−0.05 (0.09)

−0.07 (0.1)

−0.06 (0.09)

−0.03 (0.1)

Growth

−0.2*** (0.04)

−0.2*** (0.04)

−0.2*** (0.04)

−0.17*** (0.04)

−0.2*** (0.04)

Invesment

−0.03 (0.03)

−0.02 (0.03)

−0.03 (0.03)

−0.02 (0.03)

−0.04 (0.03)

Unemp

0.04 (0.02)

0.05* (0.03)

0.04 (0.02)

0.04* (0.02)

0.04 (0.02)

Debt

0.02** (0.008)

0.01** (0.008)

0.02** (0.008)

0.02** (0.008)

0.01** (0.008)

Credits

0.013 (0.008)

0.02** (0.009)

0.01 (0.008)

0.02** (0.009)

0.01** (0.008)

PolStab

−0.61** (0.3)

−0.56* (0.31)

−0.62** (0.31)

−0.53* (0.3)

−0.56* (0.3) (continued)

2 Upgrading and Downgrading Developing Countries’ Sovereign …

39

Table 2.7 (continued) Specification 1

Specification 2

Specification 3

Specification 4

Specification 5

VA

0.51 (0.41)

0.82* (0.5)

0.48 (0.45)

0.76* (0.46)

0.72* (0.43)

RLaw

–

−0.74 (0.52)

–

–

–

CCorr

–

–

0.08 (0.43)

–

–

GovEff

–

–

–

−0.75 (0.53)

−

RQ

–

–

–

–

−0.63

Constant

−3.3*** (1.04)

−4.1*** (1.21)

−3.2*** (1.08)

−3.9*** (1.1)

−3.4*** (1.02)

Nb. of obs

579

579

579

579

579

Nb. of Groups

29

29

29

29

29

Prob. Chi2

0.000

0.000

0.000

0.000

0.00

Robust standard deviations in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively Table 2.8 Determinants of the probability of an improvement in sovereign rating Specification 1

Specification 2

Specification 3

Specification 4

Specification 5

Inflation

−0.05 (0.03)

−0.04 (0.03)

−0.05 (0.03)

−0.05 (0.03)

−0.06* (0.034)

FDI

0.02 (0.07)

0.05 (0.07)

0.03 (0.07)

0.02 (0.07)

0.06 (0.07)

Growth

0.2*** (0.05)

0.21*** (0.05)

0.2*** (0.05)

0.2*** (0.05)

0.2*** (0.05)

Invesment

−0.02 (0.027)

−0.02 (0.02)

−0.02 (0.02)

−0.03 (0.03)

−0.03 (0.026)

Unemp

−0.01 (0.01)

0.007 (0.02)

−0.007 (0.029)

−0.01 (0.03)

0.001 (0.027)

Debt

−0.01 (0.007)

−0.01* (0.008)

−0.01 (0.009)

−0.01 (0.009)

−0.01* (0.008)

Credits

−0.01** (0.007)

−0.01 (0.007)

−0.01** (0.007)

−0.02** (0.008)

−0.01* (0.007)

PolStab

−0.2 (0.24)

−0.13 (0.24)

−0.18 (0.25)

−0.2 (0.24)

−0.19 (0.23) (continued)

40

S. B. Mim et al.

Table 2.8 (continued) Specification 1

Specification 2

Specification 3

Specification 4

Specification 5

VA

0.25 (0.31)

0.57 (0.35)

0.29 (0.35)

0.24 0.34)

0.49 (0.33)

RLaw

–

−0.69* (0.39)

–

–

–

CCorr

–

–

−0.08 (0.38)

–

–

GovEff

–

–

–

0.01 (0.44)

–

RQ

–

–

–

–

−0.6 (0.38)

Constant

−0.32 (0.92)

−0.96 (0.96)

−0.36 (0.93)

−0.3 (0.97)

−0.45 (0.88)

Nb. of obs

579

579

579

579

579

Nb. of Groups

29

29

29

29

29

Prob. Chi2

0.0006

0.0004

0.001

0.001

0.0005

Robust standard deviations in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively

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Chapter 3

Do Governance Indicators Predict Inequality? A Panel Non-causality Framework for the Emerging Countries Muhammed Sehid Gorus and Mohamed Sami Ben Ali

Abstract This investigation aims to explore the causal nexus between governance indicators and income inequality for 15 emerging countries for the period 2002– 2019. This study focuses on developing countries because income inequality and the quality of the institutions of emerging economies have been the subjects of intense debate within the scientific community for recent decades. For this purpose, this paper conducts the panel non-causality test which is introduced by (Dumitrescu and Hurlin, Economic Modelling 29:1450–1460, 2012). The empirical results of the paper exhibit that, in the panel setting, income inequality can better be predicted using the past and current values of the voice and accountability, the regulatory quality, the rule of law, and the control of corruption. On the contrary, the government effectiveness and the political stability cannot be used to predict future values of the income inequality in these emerging countries. Therefore, one can state that governance quality can be a good predictor of income inequality in emerging economies. The empirical results of this study may provide some implications to the policymakers of developing countries. Keywords Causality · Emerging countries · Governance indicators · Income inequality · Institutional quality

3.1 Introduction For centuries, governments and economists have been interested in plain economic growth, which means a rise in gross domestic product per capita. However, economic development has been at the center of much attention for several decades instead of plain economic growth (Shrabani & Ben Ali, 2017; Ben Ali & Saha, 2016). M. S. Gorus (B) Ankara Yıldırım Beyazıt University, Ankara, Turkey e-mail: [email protected] M. S. Ben Ali College of Business and Economics, Qatar University, Doha, Qatar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_3

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According to Nafziger (2012), economic development refers to a change in output distribution and the economy’s structure in addition to a rise in income level. It also includes the well-being of the citizens, such as a reduction in poverty, a decrease in income inequality, a rise in education level, and an increase in life expectancy. This chapter focuses on the income inequality issue in developing countries since it can be regarded as one of the fundamental problems in emerging economies in today’s world. Income inequality generally is measured by the GINI index, which ranges from 0.00 to 100.00 (or 0.00 to 1.00). The lowest index value, 0.00, denotes perfect equality, while the highest index value, 100.00, shows perfect inequality across the population. Income inequality can be affected by various factors such as fertility rate, inflation, population, unemployment rate, education level, income level, and institutional quality (Apergis et al., 2010; Blancheton & Chhorn, 2021; Kunawotor et al., 2020; Policardo & Carrera, 2018). In the empirical literature, there are lots of studies that investigate the impact of institutional or governance quality on income inequality such as Shafique and Haq (2006) for four South Asian Association for Regional Cooperation countries, Dincer and Gunalp (2012) for 48 American states, Huang and Ho (2018) for 10 Asian countries, Kunawotor et al. (2020) for 40 African countries, and Adams and Akobeng (2021) for 46 African countries. However, only several studies directly explore the causal link between institutional quality and income inequality. Governance should be clearly distinguished from corruption (Ben Ali & Sassi, 2016). Governance is more broadly defined than corruption in the sense that bad governance encourages corruption and corruption undermines good governance (Blackburn & Forgues-Puccio, 2009). Mainly, most studies focus on the control of corruption as a governance indicator because of data unavailability (Ben Ali & Gasmi, 2017; Sassi & Ben Ali, 2017). Among the preliminary studies, Chong and Gradstein (2007) tested the causal relationship between income inequality and a set of institutional quality indicators for 121 countries for the period 1960–2000. They found that there is two-way causality between the variables examined. Apergis et al. (2010) investigated the causal relationship between corruption and income inequality in the United States during the period 1980–2004. For this purpose, they utilized the annual dataset of 50 U.S. states. The empirical findings based on the panel vector error correction model (VECM) stated that these variables Granger cause each other both in the short-run and in the long-run. Besides, Huang (2013) studied the causal nexus between corruption and income inequality for 10 Asian countries covering 1995–2010. The author employed the bootstrap panel Granger causality test of Kónya (2006). It was found that the corruption level Granger causes the GINI index only in China and the Philippines, while the reverse causation was confirmed in Indonesia, Japan, South Korea, and Thailand. Sulemana and Kpienbaareh (2018) found that there is a one-way causality from the control of corruption to income inequality for 48 Sub-Saharan countries for the period between 1996 and 2016. Besides, Policardo and Carrera (2018) conducted the Dumitrescu–Hurlin panel non-causality test to determine whether income inequality

3 Do Governance Indicators Predict Inequality? A Panel Non-causality …

45

Granger causes the corruption perception index or not for 50 countries covering 1995–2015. The paper’s empirical results demonstrated that these variables are mutually affected; in other words, there was a bidirectional causality between the variables mentioned above. Recently, Blancheton and Chhorn (2021) investigated the relationship between income inequality and institutional quality for eight countries in Asia and the Pacific during 1988–2014. The authors found that the neutrality hypothesis is valid between these variables according to the panel causality analysis. Lastly, Khan’s (2021) empirical findings on the panel Granger causality showed that there is a bidirectional relationship between control of corruption and income inequality (considering a 10% significance level) for 23 developing countries during 1996–2017. Overall, in the literature, the majority of the studies that examined the causal relationship between institutional quality and income inequality only consider the corruption level. However, there are also other dimensions of the institutional quality in addition to control of corruption such as voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, and rule of law. What remains unknown is whether there is a causal relationship between types of governance indicators and income inequality or not. This study fills a gap in the empirical literature by investigating the causal nexus between the dimensions of institutional quality and income inequality using novel panel data techniques for 15 developing countries during the period 2002–2019. In this study, we focus on the governance indicators, a proxy for institutional quality, as a predictor of income inequality. Primarily, this study investigates whether governance indicators can be used to predict future values of income inequality or not for 15 emerging countries covering 2002–2019. Most of the papers examined the causal link between corruption level and income inequality in the empirical literature. However, in this study, we consider the other dimensions of governance (see Table 3.1).

3.2 Governance Indicators: A First Look Table 3.1 exhibits the values of governance indicators across 15 developing countries in 2019. It is seen that especially index value of political stability, the rule of law, and control of corruption are very low in these countries. Their average values are −0.21, −0.33, and −0.27, respectively, while the average values for voice and accountability and regulatory quality are positive. At the country-specific level, voice and accountability is very low in Belarus (−1.41) and Turkey (−0.83). Besides, there is not a politically stable environment in Ukraine (−1.42) and Turkey (−1.37), while government effectiveness is worse in the Kyrgyz Republic (−0.68) and Honduras (−0.61) than in the remaining countries. Also, the table shows that regulatory quality is miserable in Belarus and Honduras, while the rule of law index is close to −1 in Honduras (−1.01) and the Kyrgyz Republic (−0.89) in 2019. Lastly, it is seen that there is a low level of control of corruption in almost all countries in our sample. The index value is very low in the

46

M. S. Gorus and M. S. Ben Ali

Table 3.1 The value of the governance indicators in 2019 Countries

Voice and Accountability

Political Stability

Government Effectiveness

Regulatory Quality

Rule of Law

Control of Corruption

0.06

−0.41

−0.07

0.25

−0.13

−0.18

−1.41

0.33

−0.18

−0.54

−0.79

−0.04

Costa Rica

1.10

0.45

0.42

0.50

0.54

0.70

Dominican Republic

0.18

0.01

−0.36

−0.05

−0.35

−0.77

El Salvador

0.11

−0.11

−0.47

0.02

−0.76

−0.56

Georgia

0.17

−0.49

0.83

1.12

0.31

0.70

−0.57

−0.58

−0.61

−0.49

−1.01

−0.81

Indonesia

0.13

−0.49

0.18

−0.09

−0.34

−0.42

Kyrgyz Republic

−0.45

−0.24

−0.68

−0.35

−0.89

−0.95

Armenia Belarus

Honduras

Panama

0.60

0.30

0.06

0.36

−0.12

−0.58

Paraguay

0.06

−0.01

−0.53

−0.20

−0.56

−0.83

Peru

0.26

−0.15

−0.07

0.56

−0.49

−0.45

−0.83

−1.37

0.05

−0.01

−0.28

−0.31

Ukraine

0.02

−1.42

−0.30

−0.26

−0.70

−0.76

Uruguay

1.23

1.04

0.69

0.51

0.62

1.25

Turkey

Source World Governance Indicators (2021)

Kyrgyz Republic (−0.95), Paraguay (−0.83), and Honduras (−0.81). It is significant to note that all of the index values of governance indicators are positive in Costa Rica and Uruguay in 2019. One can conclude that these countries have higher institutional quality than the other developing countries in the sample. Figure 3.1 displays the value of the income inequality index (GINI) for 15 emerging countries in 2019. According to the figure, it is seen that Belarus, Ukraine, the Kyrgyz Republic, and Armenia have a lower GINI index compared to the other countries. All of their GINI index value is below 30.00. On the other side, Panama has the highest income inequality level with a value of 49.80. Besides, Costa Rica (48.20) and Paraguay (45.70) place second and third, respectively. In addition, it is essential to state that all of these countries decreased the GINI index (improvement in income inequality) for the period 2002–2019 except for Indonesia and Turkey. In this period, El Salvador, Peru, and Paraguay showed a remarkable performance on income equality, while it deteriorated significantly in Indonesia. This study conducts a bunch of novel panel data techniques, including the crosssection dependence test for series, the second-generation panel unit root test, and the non-causality test for heterogenous panel data models. One of the fundamental advantages of this test is that the panel statistics cause a significant increase in the power of the Granger non-causality tests even for samples with very small T

3 Do Governance Indicators Predict Inequality? A Panel Non-causality …

47

Fig. 3.1 The value of the GINI index in 2019. Source World Development Indicators (2021)

and N dimensions (Dumitrescu & Hurlin, 2012). In addition to the methodological advantages of this study, it also considers lots of dimensions of governance quality. Therefore, policymakers can design and implement more rational policies to decrease income inequality. The rest of the chapter includes the following sections: Sect. 3.2 reviews the empirical literature on the causal relationship between income inequality and governance indicators. Section 3.3 introduces the panel data methodologies examined in this study, while Sect. 3.4 presents the dataset and empirical results of the investigation. Lastly, Sect. 3.5 concludes this chapter and provides policy implications.

3.3 Data, Model, and Methodology This study utilizes the annual dataset for 15 developing countries—Armenia, Belarus, Costa Rica, Dominican Republic, El Salvador, Georgia, Honduras, Indonesia, the Kyrgyz Republic, Panama, Paraguay, Peru, Turkey, Ukraine, and Uruguay—during the period from 2002 to 2019. These countries are selected by data availability and their development level. We use the GINI index as a measure of income inequality. This series is gathered from the World Bank database, and it ranges from 0.00 (perfect income equality) to 100.00 (perfect income inequality). Besides, this paper employs six basic governance indicator indexes, namely, Voice and Accountability (VA), Political Stability and Absence of Violence/Terrorism (PS), Government Effectiveness (GE), Regulatory Quality (RQ), Rule of Law (RL), and Control of Corruption (CC). These indexes are collected from World Governance Indicators (2021) and

48

M. S. Gorus and M. S. Ben Ali

range from −2.5 to + 2.5. In detail, −2.5 denotes those institutions are weak, while + 2.5 shows that institutions are strong. This study conducts various panel data techniques to determine the causal relationships between the governance indicators and the income inequality in 15 developing countries. Methodologically, this paper employs the cross-sectional dependence (CD) test developed by Baltagi et al. (2012), namely, the bias-corrected scaled LM test. The presence of cross-sectional dependence may damage the empirical results; they can be biased and inconsistent. Therefore, in the first step, we conduct this analysis for each series. Then, this study performs a panel unit root test introduced by Pesaran (2007) that considers CD in the series—the CADF (Cross-Sectionally Augmented Dickey–Fuller) test. After examining the unit root properties of each series, this paper utilizes a panel non-causality test of Dumitrescu and Hurlin (2012). This test is a modified and extended version of Granger’s (1969) analysis for the time-series data. The Dumitrescu–Hurlin non-causality test is based on the following regression:

Yi,t = αi +

K ∑ k=1

γik Yi,t−k +

K ∑

βik X i,t−k + εi,t

(3.1)

k=1

X i,t and Yi,t are two stationary variables and it is allowed that the coefficients differ across units. K represents the lag number and it is same for all units. The null and the alternative hypotheses of this test are provided below: H0 = βi1 = · · · = βi K = 0, ∀i = 1, . . . , N { H1 =

βi1 = · · · = βi K = 0, ∀i = 1, . . . , N1 βi1 /= 0or . . . orβi K /= 0, ∀i = N1 + 1, . . . , N

}

The null hypothesis states that there is no causality for all units in the panel, while the alternative asserts that at least one causal relationship exists in the cross-section units (Lopez & Weber, 2017). The methodological steps of our analysis can be sorted as follows: • Determining whether cross-sectional dependence for series is present or not, • Conducting proper types of panel unit root tests (first-generation or secondgeneration) based on the CD test, • Taking first-difference of nonstationary series, • Employing the Dumitrescu–Hurlin panel non-causality test for bivariate causal analyses

3 Do Governance Indicators Predict Inequality? A Panel Non-causality …

49

3.4 Results and Discussion Table 3.2 exhibits the cross-sectional dependence test results for each series. According to the bias-corrected scaled LM test statistics, one can reject the null hypothesis of “no cross-section dependence” for all the series. Therefore, it is needed to conduct the second-generation panel unit root tests instead of the first-generation panel unit root tests. The unit root properties of the series in the panel setting are presented in Table 3.3. This study conducts the second-generation panel unit root test, namely, the CADF test. The empirical results show that the income inequality, the government effectiveness, and the regulatory quality are stationary at their level value. At the same time, the remaining variables—the voice and accountability, the political stability, and the rule of law—are stationary at their first differences. Therefore, we will take the first-difference of the nonstationary variables for further analyses in this study. This paper carries out the Dumitrescu–Hurlin panel non-causality test to determine the causal linkage between the variables mentioned above. One of the main advantages of this test is that the panel statistics cause a significant increase in the power of the Granger non-causality tests even for samples with very small T and Table 3.2 Cross-sectional dependence test results

Variables

Bias-corrected scaled LM test statistics

GINI

40.587***

VA

28.308***

PS

28.271***

GE

14.539***

RQ

21.068***

RL

22.690***

CC

22.311***

Note ***shows the statistical significance at 1%

Table 3.3 Panel unit root test results

Variables

Level

First difference

Result

GINI

−2.892**

–

I(0)

VA

−2.286

−2.959**

I(1)

PS

−2.314

−3.251***

I(1)

GE

−2.949**

–

I(0)

RQ

−3.135***

–

I(0)

RL

−2.509

−3.543***

I(1)

CC

−2.076

−4.555

I(1)

Note ***, **, and * show the statistical significance at 1%, 5%, and 10%, respectively

50

M. S. Gorus and M. S. Ben Ali

N dimensions (Dumitrescu & Hurlin, 2012). Therefore, we choose this test in our empirical analysis for emerging countries. Firstly, this study examines the causal relationship between VA and GINI for 15 developing countries covering the period 2002–2019 (see Table 3.4). According to the empirical results, the voice and accountability Granger causes the income inequality in Honduras, Panama, Peru, and Turkey, while the reverse causation is valid only in Georgia. Besides, the panel results exhibit a one-way Granger causality running from the voice and accountability to the income inequality. Intuitively, an increase in voice and accountability in a country, meaning that more freedom of expression, more freedom of association, more free media, and an increase in the ability of citizens to participate in electing a government, can reduce income inequality within a country. People can demand more equal distribution of income from the government through these types of benefits. Table 3.5 presents the causal link between the political stability and the income inequality for the countries examined. The country-specific empirical findings reveal that there is unidirectional causality from PS to GINI in El Salvador, while reverse causation is valid in Indonesia. Besides, bidirectional causation is found between these variables for Peru. However, it is found that there is no causal nexus between them in the panel setting. Therefore, the empirical findings of this study show that it Table 3.4 The panel non-causality test results (VA, GINI)

H0 : ΔVA . GINI

H0 : GINI . ΔVA

Wald statistics

Wald statistics

Armenia

5.107

0.050

Belarus

3.697

0.011

Costa Rica

0.371

0.698

Dominican Republic

3.656

0.045

El Salvador

1.631

0.001

Georgia

2.517

8.829**

Honduras

12.974*

0.012

Indonesia

2.264

0.818

Kyrgyz Republic

2.643

0.938

Panama

11.229*

0.002

Paraguay

3.321

0.162

Peru

29.657***

0.524

Turkey

12.810*

2.005

Ukraine

6.171

1.556

Uruguay

4.265

0.026

Z-bar statistics

Z-bar statistics

6.041***

0.123

Countries

Panel

Note ***, **, and * show the statistical significance at 1%, 5%, and 10%, respectively. Δ is the first-difference operator

3 Do Governance Indicators Predict Inequality? A Panel Non-causality … Table 3.5 The panel non-causality test results (PS, GINI)

Countries

51

H0 : ΔPS . GINI

H0 : GINI .ΔPS

Wald statistics

Wald Statistics

Armenia

0.006

3.873

Belarus

1.493

0.179

Costa Rica

0.018

0.114

Dominican Republic

0.013

2.178

El Salvador

4.344*

0.544

Georgia

0.439

2.424

Honduras

1.896

0.022

Indonesia

0.000

8.591**

Kyrgyz Republic

0.941

0.380

Panama

0.962

0.581

Paraguay

1.543

2.972

Peru

4.441*

10.043**

Turkey

0.139

3.896

Ukraine

0.611

1.836

Uruguay

0.038

2.082

Z-bar statistics

Z-bar statistics

0.343

1.254

Panel

Note *** and **show the statistical significance at 1% and 5%, respectively. Δ is the first-difference operator

cannot be predicted the further values of income inequality by using the current and past values of the political stability index. Table 3.6 displays the panel non-causality test results for the government effectiveness and the income inequality for 15 emerging countries. It is seen that the government effectiveness is not a good predictor for the income inequality for the majority of the countries, except for Belarus. However, this study finds that GINI is an essential predictor for GE for Dominican Republic, El Salvador, Peru, and Turkey. Also, the panel results support these findings. That is a more equal income distribution across people forces governments to provide a better quality of public and civil services. The Dumitrescu–Hurlin panel non-causality test results for the regulatory quality and the income inequality are shown in Table 3.7. On the one hand, the panel test results indicate that these series are mutually affected. That means that if policymakers change policies related to income inequality or regulatory quality, another one can be affected significantly. On the other hand, the country-specific results exhibit that RQ Granger causes GINI in Belarus, the Kyrgyz Republic, Paraguay, and Peru. Contrarily, we find that GINI Granger causes RQ in Costa Rica, Indonesia, and Uruguay.

52 Table 3.6 The panel non-causality test results (GE, GINI)

M. S. Gorus and M. S. Ben Ali Countries

H0 : GE . GINI Wald statistics

Wald statistics

Armenia

0.101

0.165

Belarus

11.437***

2.121

Costa Rica

0.178

0.288

Dominican Republic

0.076

10.504***

El Salvador

0.100

6.991**

Georgia

0.298

0.000

Honduras

0.274

0.179

Indonesia

0.009

1.709

Kyrgyz Republic

0.005

1.219

Panama

0.185

0.158

Paraguay

1.425

0.648

Peru

0.755

5.402**

Turkey

0.048

12.402***

Ukraine

0.984

0.821

Uruguay

1.995

0.412

Z-bar statistics

Z-bar statistics

0.523

5.115***

Panel

H0 : GINI . GE

Note ** and *show the statistical significance at 5% and 10%, respectively

The causal nexus between the rule of law and the income inequality is reported in Table 3.8. In the panel setting, this study finds that there is a unidirectional causality running from RL to GINI. According to Sonora (2019), an improvement in the legal system including the protection of property rights help to reach a better income equality level. The empirical findings also demonstrate that there is one-way causality from RL to GINI in Belarus, Costa Rica, Honduras, and Paraguay. At the same time, there is one-way causality from GINI to RL only in Turkey. Lastly, Table 3.9 exhibits the Dumitrescu–Hurlin panel non-causality test results for the control of corruption and the income inequality for the countries examined. The country-specific results show that CC can be regarded as a good predictor of GINI for the Dominican Republic, Honduras, Turkey, Ukraine, and Uruguay. Besides, it is found that GINI Granger causes CC in Belarus, Dominican Republic, El Salvador, and Uruguay. Moreover, the panel results confirm the bidirectional causality between these variables. Thus, one can assert that corruption can affect income inequality through various channels, such as a decrease in economic growth and a decrease in government spending on education, health, and social services. Also, the biased tax system and

3 Do Governance Indicators Predict Inequality? A Panel Non-causality … Table 3.7 The panel non-causality test results (RQ, GINI)

Countries Armenia

53

H0 : RQ . GINI

H0 : GINI . RQ

Wald statistics

Wald Statistics

0.068

2.059

Belarus

11.300***

1.009

Costa Rica

0.032

3.135*

Dominican Republic

2.171

2.975

El Salvador

0.160

0.162

Georgia

0.192

0.164

Honduras

0.001

0.425

Indonesia

0.090

4.013*

Kyrgyz Republic

8.113**

1.982

Panama

0.777

1.548

Paraguay

4.812**

2.668

Peru

5.500**

0.831

Turkey

0.323

2.202

Ukraine

0.584

0.060

Uruguay

0.081

21.262***

Z-bar statistics

Z-bar statistics

3.506***

5.385***

Panel

Note ***, **, and *show the statistical significance at 1%, 5%, and 10%, respectively

high levels of tax evasion can deteriorate the income equality in these countries (Kunawotor et al., 2020). An overview of the causalities running from the governance indicators to the income inequality is displayed in Fig. 3.2. The figure exhibits that the income inequality can better be predicted using the past and current values of the voice and accountability, the regulatory quality, the rule of law, and the control of corruption. However, the remaining variables—the political stability and the government effectiveness—cannot be used to predict future values of the income inequality in these 15 emerging countries. As seen, governance indicators have a significant impact on shaping income inequality/equality. Therefore, an increase in government performance regarding these issues can provide a better distribution of income across citizens. Similarly, one can infer weak government policies may deteriorate the income distribution. The empirical results of this study seem to be consistent with other research which found bidirectional causality between governance and income inequality, especially considering the corruption level, e.g., Apergis et al. (2010), Policardo and Carrera (2018), and Khan (2021). Also, even though Chong and Gradstein’s (2007) governance indicators are not the same, our results partially support their findings. In

54 Table 3.8 The panel non-causality test results (RL, GINI)

M. S. Gorus and M. S. Ben Ali Countries

H0 : ΔRL . GINI

H0 : GINI .ΔRL

Wald statistics

Wald Statistics

Armenia

5.497

0.284

Belarus

10.666*

0.419

Costa Rica

10.642*

0.159

Dominican Republic

0.762

0.662

El Salvador

3.329

0.338

Georgia

2.538

0.088

Honduras

29.113***

0.268

Indonesia

0.780

0.027

Kyrgyz Republic

2.357

1.296

Panama

0.510

0.081

Paraguay

11.898*

0.235

Peru

6.340

0.739

Turkey

4.378

4.291*

Ukraine

1.161

0.188

Uruguay

5.954

0.018

Z-bar statistics

Z-bar statistics

5.368***

−1.078

Panel

Note *shows the statistical significance at 10%. Δ is the firstdifference operator

the empirical literature, to our knowledge, there is not any study except these that examine the causal relationships between governance quality and income inequality, directly.

3.5 Conclusion and Recommendations The main goal of the current study was to determine the causal links between governance quality and income inequality across 15 emerging countries for the period 2002–2019. The sample included the following countries: Armenia, Belarus, Costa Rica, Dominican Republic, El Salvador, Georgia, Honduras, Indonesia, the Kyrgyz Republic, Panama, Paraguay, Peru, Turkey, Ukraine, and Uruguay. For this purpose, this study employed the Dumitrescu–Hurlin panel non-causality test in addition to the auxiliary panel data techniques. The empirical findings of the study revealed that governance indicators were a good predictor for income inequality except for the political stability and the government effectiveness. Besides, it was found the GINI index can be used to predict

3 Do Governance Indicators Predict Inequality? A Panel Non-causality … Table 3.9 The panel non-causality test results (CC, GINI)

Countries Armenia

55

H0 : ΔCC . GINI

H0 : GINI .ΔCC

Wald statistics

Wald statistics

0.822

0.114

Belarus

0.336

4.346*

Costa Rica

2.365

0.061

Dominican Republic

3.385*

3.319*

El Salvador

2.684

8.497**

Georgia

0.669

0.796

Honduras

3.337*

0.000

Indonesia

1.032

0.006

Kyrgyz Republic

1.608

1.533

Panama

0.760

1.718

Paraguay

0.184

0.050

Peru

0.821

0.074

Turkey

6.898**

0.036

Ukraine

10.346***

0.004

Uruguay

3.888**

3.819*

Z-bar statistics

Z-bar statistics

4.406***

1.711*

Panel

Note ** shows the statistical significance at 5%. Δ is the firstdifference operator

Government Effectiveness

Regulatory Quality

Political Stability

Voice and Accountability

Rule of Law

Income Inequality

Control of Corruption

Fig. 3.2 Overview of the causal links from governance indicators to income inequality. Source Author’s own calculation. Note The green arrows show that the governance indicators Granger cause income inequality, while the red arrows denote the non-causality.

56

M. S. Gorus and M. S. Ben Ali

future values of the government effectiveness, the regulatory quality, and the control of corruption indexes for the countries examined. Institutions in general can have numerous potential impacts on economic development weak institutions that hinders long-run economic activity through different channels by shrinking public spending on health and education (Swaleheen et al., 2019) increasing inflation (Ben Ali, 2016), creating more banking instability (Ben Ali, 2022; Ben Ali et al., 2020), creating more inflation (Ben Ali & Sassi, 2016), inhibiting international trade (Ben Ali & Mdhillat, 2015), creating more financial constraints for enterprises (Ben Ali & Siddy Diallo, 2022) and more broadly deters economic development (Saha & Ben Ali, 2017). Overall, this study strengthens the idea that the quality of institutions can affect the economic development processes of the countries. In this investigation, we find that they have a significant impact on income inequality. These empirical results can be considered by policymakers carefully. They can design and implement required public policies to enhance accountability, regulatory quality, rule of law, and control of corruption for achieving a more equal level of the income distribution. Further modeling work will have to be conducted in order to determine the causal differences across developed and developing economies.

3 Do Governance Indicators Predict Inequality? A Panel Non-causality …

Appendix See Table 3.10. Table 3.10 Definition and source of dataset Variables

Range

Definition

Source

Income inequality

0.00 (perfect equality) to 100.00 (perfect inequality)

The GINI index measures the income inequality across the population

World Development Indicators

Voice and accountability −2.5 (weak institutions) to +2.5 (strong institutions)

It measures freedom of World Governance expression, freedom of Indicators association, and free media. Also, it considers whether citizens can participate in electing their government, or not

Political stability and absence of violence/terrorism

−2.5 (weak institutions) to +2.5 (strong institutions)

It measures perceptions World Governance of the possibility that the Indicators government will be destabilized or overthrown by unconstitutional or violent means

Government effectiveness

−2.5 (weak institutions) to +2.5 (strong institutions)

It mainly measures the World Governance quality of public Indicators services, the quality of the civil services, and their independence level

Regulatory quality

−2.5 (weak institutions) to +2.5 (strong institutions)

It measures whether the government policies assist private sector development, or not

World Governance Indicators

Rule of law

−2.5 (weak institutions) to +2.5 (strong institutions)

It mainly measures the quality of contract enforcement, property rights, the police, and the courts

World Governance Indicators

Control of corruption

−2.5 (weak institutions) to +2.5 (strong institutions)

It measures perceptions of the extent to which public power is exercised for private gain

World Governance Indicators

Source World Governance Indicators (2021) and World Development Indicators (2021)

57

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References Adams, S., & Akobeng, E. (2021). ICT, governance and inequality in Africa. Telecommunications Policy, 45(10), Article 102198. Apergis, N., Dincer, O. C., & Payne, J. E. (2010). The relationship between corruption and income inequality in US states: Evidence from a panel cointegration and error correction model. Public Choice, 145(1), 125–135. Baltagi, B. H., Feng, Q., & Kao, C. (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1), 164–177. Ben Ali, M. S. (2022). Credit bureaus, corruption and banking stability. Economic Systems. Ben Ali, M. S., Fhima, F., & Nouira, R. (2020). How does corruption undermine banking stability? A threshold nonlinear framework. Journal of Behavioral and Experimental Finance, 27. Ben Ali, M. S., & Gasmi, A. (2017). Does ICT diffusion matter for corruption? An economic development perspective. Telematics and Informatics, 34(8), 1445–1453. Ben Ali, M. S., & Mdhillat, M. (2015). Does corruption impede international trade? New evidence from the EU and the MENA countries. Journal of Economic Cooperation and Development, 36(4), 107–120. Ben Ali, M. S., & Saha, S. (2016). Corruption and economic development. In M. S. Ben Ali (Ed.), Economic development in the Middle East and North Africa (pp. 133–154). Palgrave Macmillan. Ben Ali, M. S., & Sassi, S. (2016). The corruption-inflation nexus: Evidence from developed and developing countries. The BE Journal of Macroeconomics, 16(1), 125–144. Ben Ali, M., & Siddy Diallo, B. (2022). Credit bureaus and financial constraints do corruption matter? Middle East Development Journal. Blackburn, K., & Forgues-Puccio, F. (2009). Why is corruption less harmful in some countries than in others? Journal of Economic Behavior and Organization, 72(3), 797–810. Blancheton, B., & Chhorn, D. (2021). Government intervention, institutional quality, and income inequality: Evidence from Asia and the Pacific, 1988–2014. Asian Development Review, 38(1), 176–206. Chong, A., & Gradstein, M. (2007). Inequality and institutions. The Review of Economics and Statistics, 89(3), 454–465. Dincer, O. C., & Gunalp, B. (2012). Corruption and income inequality in the United States. Contemporary Economic Policy, 30(2), 283–292. Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. Huang, C. J. (2013). Corruption and income inequality in Asian countries: Bootstrap panel Granger causality test. Romanian Journal of Economic Forecasting, 16(4), 161–170. Huang, C. J., & Ho, Y. H. (2018). The impact of governance on income inequality in ten Asian countries. Journal of Reviews on Global Economics, 7, 217–224. Khan, S. (2021). Investigating the effect of income inequality on corruption: New evidence from 23 emerging countries. Journal of the Knowledge Economy, 1–27,. https://doi.org/10.1007/s13 132-021-00761-6 Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23, 978–992. Kunawotor, M. E., Bokpin, G. A., & Barnor, C. (2020). Drivers of income inequality in Africa: Does institutional quality matter? African Development Review, 32(4), 718–729. Lopez, L., & Weber, S. (2017). Testing for Granger causality in panel data. The Stata Journal, 17(4), 972–984. Nafziger, E. W. (2012). Economic development (5th ed.). Cambridge University Press. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.

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Policardo, L., & Carrera, E. J. S. (2018). Corruption causes inequality, or is it the other way around? An empirical investigation for a panel of countries. Economic Analysis and Policy, 59, 92–102. Saha, S., & Ben Ali, M. S. (2017). Corruption and economic development: New evidence from the Middle Eastern and North African Countries. Economic Analysis and Policy, 54, 83–95. Sassi, S., & Ben Ali, M. S. (2017). Corruption in Africa: What role does ICT diffusion play. Telecommunications Policy, 41(7–8), 662–669. Shafique, S., & Haq, R. (2006). Governance and income inequality. The Pakistan Development Review, 45(4), 751–760. Shrabani, S., & Ben Ali, M. S. (2017). Corruption and economic development: New evidence from the Middle Eastern and North African countries. Economic Analysis and Policy, 54, 83–95. Sonora, R. (2019). Income inequality, poverty, and the rule of law: Latin America vs the Rest of the World (MPRA Working Paper, No. 91512). https://mpra.ub.uni-muenchen.de/id/eprint/91512 Sulemana, I., & Kpienbaareh, D. (2018). An empirical examination of the relationship between income inequality and corruption in Africa. Economic Analysis and Policy, 60, 27–42. Swaleheen, M., Ben Ali, M. S., & Temimi, A. (2019). Corruption and public spending on education and Health. Applied Economics Letters, 26(4), 321–325. World Development Indicators. (2021). GINI Index. https://data.worldbank.org/indicator/SI.POV. GINI World Governance Indicators (2021). World Governance Indicators. https://info.worldbank.org/gov ernance/wgi/Home/FAQ. Accessed on 17 November 2021.

Chapter 4

Enhancing Economic Development Through ICT-Based Governance: Evidence for Developing Countries Ewa Lechman

Abstract This shows novel empirical evidence on how e-government solutions enhance the emergence of inclusive societies, increase institutional quality, and through that channels dynamize economic development in developing countries. With this aim we examine digital development inequalities adopting 2 core ICT indicators: mobile cellular telephony and Internet users; and gross per capita income and Human Development Index to show the level of economic development, and these indicators are then with e-governments developments. Adopted empirical strategy involves graphical identification of changes in digital and economic development as well as it aims to identify changes in e-governance development, using kernel densities functions, time trends analysis, and panel regression approach. Our empirical sample combines 40 developing economies, and the time span for the analysis is 1990–2019. Statistical data are entirely extracted from World Telecommunication/ICT Indicators Database 2020 and World Band Development Indicators 2021. Our major conclusions unveil that e-government solutions are positively associated with economic growth and development. We claim that these results are poorly robust and massive cross-country disparities exist in regard to the state of deployment of electronic government mainly due to uneven diffusion of digital technologies in developing economies. Keywords Economic development · Digital technologies · e-government · Developing countries

4.1 Introduction Developing economies are suffering not only from poor economic performance, extensive poverty, financial and infrastructural shortages, but these economies often face poor quality of governance, weak institutions that hinders long-run economic E. Lechman (B) Faculty of Management and Economics, Gdansk University of Technology, Gda´nsk, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_4

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activity through different channels by shrinking public spending on health and education (Swaleheen et al., 2019) increasing inflation, creating more banking instability, inhibiting international trade, creating more financial constraints for enterprises and more broadly deters economic development (Saha & Ben Ali, 2017). Digital technologies that are broadly deployed also in economically worse off countries, bring potential to enhance better, more effective, and less-corrupted governance. ICT’s positive impact on better governance shall finally allow for more dynamic and stable economic development. Improved governance is usually treated as the key to ensure economic growth and development, while digital technologies offering, for instance, e-governance solutions, through more inclusive actions are more broadly used in developing societies. This research provides novel empirical evidence on how e-government solutions are contributing to more inclusive societies, better institutional quality, and hence dynamize economic development in developing countries. To test changes in digital development inequalities we use 2 core ICT indicators: mobile cellular telephony and Internet users; while regarding economic performance we consider gross per capita income and Human Development Index that are confronted with e-governments developments. Our statistical data are entirely extracted from World Telecommunication/ICT Indicators Database 2020 and World Band Development Indicators 2021. Our empirical strategy combines visual identification of shifts in digital and economic development as well as it aims to identify changes in e-governance development, using kernel densities functions and time trends analysis. To enrich the analysis we also use panel regression approach. Our empirical sample combines 40 developing economies, and the time span for the analysis is 1990–2019. This work encompasses 5 sections. Section second, followed by the introduction, discusses contextual background of this study and selected recent studies examining the relationships between digital technologies, e-government solutions, and economic development. Next, section third explains data rational and empirical underpinnings. Section fourth presents and discusses results of our empirical analysis, and finally section fifth draws major conclusions.

4.2 Contextualization and Recent Evidence Developing economies permanently suffer from various deficiencies, shortages, exclusions, and shocks which heavily impedes and violates entering stable development and economic growth pattern. Still, what is reported by international statistics, even in materially worse off countries for the last 2 decades massive shifts in deployment of digital technologies are reported. According to ITU (2021) estimates in 2021 the world average for mobile telephony subscription is at about 110 per 100 inhab., while 62% of world population is using Internet network. If we consider developing countries as a group and least developed countries (LDCs) the above cited statistics are accordingly 105 and 75 per 100 inhab. for mobile telephony, and 57 and 35% for Internet users. At the same time in 2021 (ITU, 2021) barely 40% of population

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residing in LDCs enjoyed access to mobile-broadband networks. These elementary statistics reporting on access to and use of digital technologies suggest that even in materially poor and mostly illiterate societies ICT-based solutions are massively accessed and used by general public. Growing accessibility of ICT services is to a large extent enhanced by dropping prices and increasing affordability. In 2021 in 84 countries out of 195 classified by ITU, the annual cost of accessing ICT was less than 2% of gross national income. Luckily tendencies for dropping prices of ICT access to and use are also observable in developing economies, and these where this cost is still excessively high which allows including growing share of population in the digital world. Along with massive and dynamic changes in digital technologies diffusion in developing economies, we observe in those countries a new phenomenon which includes implementation of ICT-based solution for governing the country. The broadly defined e-government solutions, enhanced by increasing access to ICT, bring new perspectives to societies offering new, more effective, more inclusive, more engaging solutions for all society members. Developing a civic, empowered, and engaged society, feeling its individual influence on decisions made by country’s authorities constitutes one (of many) fundaments for enhancing economic growth and development. Many claim that especially for low and lower-middle income countries, implemented e-government solutions can play a unique role of equalizers of opportunities and access to basic government information and services. For people living in remote and geographically isolated regions online government services can be the only way to access information and empower. Surely financial resources and infrastructural shortages, lack of adequate and sufficient capabilities as well as other deep contextual factors still constitute heavily impediments in economically and institutionally poor countries. Despite the fact that during last years much progress in this area is visible in least developed countries, as group they still significantly lag behind. According to data and estimates of E-Government Development Index provided in United Nations report (2020), the averaged value for least developed economies is at 0.34, compare world average slightly exceeding 0.6. Not surprisingly this is mostly Africa and Asia which are relatively worse off in this respect compared to the rest of the world. As noted, in these two regions, infrastructural gaps and low development of human capital prevent African and Asian countries from advancing in egovernment solutions implementation. Needless to claim that political will, societies attitudes toward broad and novel use of digital technologies, state authorities propensity to create empowered and engaged societies, along with financial and infrastructural fundaments, are essential for e-government developments. Clearly, if deployed adequately, such solutions—in the longer time horizon, shall convert into human and economic development. The key element in effective and stable implementation of e-government strategies is the e-participation, which enhances the participatory process and to a large extent it perfectly reflects the advancement in e-government solutions, mostly detected through the quality and content of government websites and portals, provision and accessibility to information, and/or consultation between state authorities and citizens through digital solutions. Higher engagement, better visibility, and transparency are just few elements to benefit. Still, especially if we

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refer to developing countries, “it is not always clear that (…) electronic platform has translated in broader and deeper participation. In many cases, the take-up of e-participation remains low” (United Nations, 2020, p. 33). Although some recent studies on the subject (Ben Ali, 2020, 2022a, 2022b, 2022c; Ben Ali & Sassi, 2017, 2020; Ali et al. 2021; Ben Ali et al. 2020; Kondrateva & Ben Ali, 2021), the empirical evidence tracing the links between digital technologies deployment, economic growth and development, and e-government solutions is not that abundant as I might initially be thought (for more see also works of: Ben Ali and Mdhillat, 2015; Ben Ali and Sassi, 2016; Ben Ali and Siddy Diallo, 2022). Dhaoui (2021) examined the relationship between electronic government and economic development in MENA economies between 2003 and 2018, he finds generally positive impact of e-government on sustainable development, and he emphasizes crucial role of digital technologies in controlling corruption. More related evidence on egovernance in MENA is traced in Mira and Hammadache (2017). Similar conclusions regarding the positive association between electronic government, development and achieving sustainable development goals are traced in works of Estevez and Janowski (2013) and Estevez et al. (2013), however their raise arguments emphasizing the need of coherent approaches to implement effective solutions. In Glass and Newig (2019) we find study for 41 high-income and upper-middle-income economies for 2015, which tests the impact of—inter alia, sound governance for sustainable development. They found positive relationships between these two, and they have also underlined the seminal role of education, economic power, or geographic location. Adam (2020a) in his study, using structural modeling approach, he tackles these issues more broadly and considers these relationships in the context of country’s digitalization. His results show positive and strong relationships between ICT deployment, electronic government implementation, and socio-economies development in 76 world countries he examined for the year 2015. In recent studies we also trace some well-established evidence regarding the impact of e-government solutions on different spheres of economy, society, and institutional life. For example, the study of Mouna et al. (2020) uses a sample of 146 world countries between 2012–2016 shows how and why ICT and digital governments can help to control corruption activities, mainly through enhancing transparency. In Cichocki and Naga´nska (2021) we trace evidence for 129 countries for the period 2008–2019 highlighting again the effectiveness of e-government solution to prevent corruption, however they underline the seminal role educational level and backbone infrastructure, which if poorly developed may hinder the positive effects of e-government on fighting corruption. Adam (2020b) in his study for African countries also confirms the linkages between ICT deployment, institutional quality, e-government solutions, and anti-corruption effects a result previously reported in the study of Sassi and Ben Ali (2017). In another strand of empirical literature we detect the evidence linking digital technologies, e-government strategies with other elements of economy. For instance, from the work of Elbahnasawy (2021), where Granger causality tests are run, we learn on positive effects of e-government solution on reduction of informal economy, although the author claims that these effects are rather unveiled in the long time horizon, while in the short-run may be hard to trace.

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Uyar et al. (2021) in the global study for 2006–2017 report how e-government solutions may help to reduce tax evasion. More evidence linking these elements we find, e.g. in Veiga and Rohman (2017), Berdiev et al. (2018), or Nguyen (2021). Surely the empirical evidence in this field is not limited to the elements raised above. There is extensive literature explaining the relationships between e-government and, inter alia, education (Ratnawati et al., 2020), eliminating digital divides and social trust creating (Botri´c & Boži´c, 2020; Pérez-Morote et al., 2020), enhancing quality of institutions (Ben Ali, 2020; Ben Ali & Gasmi, 2017; Bharosa et al., 2020; McQuiston & Manoharan, 2020), but also we trace several country-studies, like Sulistyowati et al. (2020), Thompson et al. (2020), Khan et al. (2021), Mensah et al., (2021), or Sharma et al. (2021).

4.3 Data and Empirical Settings In this research we consider 40 developing countries1 that are analyzed in the period 1990 and 2019.2 For analytical purposed we have selected several indicators that allow approximating, first—level of economic development and tertiary education, second—ICT deployment and usage, and third—state of development of e-government-related elements and also 3 selected indicators reporting on general governance quality in a country. As the major objective of this research is to trace hypothetical relationships between economic development and e-government solution implementation driven by ICT, to control for the level of economic development we have selected 2 basic indicators—gross domestic per capita income (GDPpci,y )3 and Human Development Index (HDIi,y ). As for GDPpci,y we extract our data from World Development Indicators database (2021 edition), and for all 40 countries we have well-balanced time series between 1990 and 2019. Values of Human Development Index we have derived from consecutive Human Development Reports for the period 1990–2019, however significant breaks in time series exist in this case. In our second perspective—ICT development—we use 2 macro-ICT indicators, namely Internet users (IUi,y ) and Mobile cellular telephony subscriptions (MCSi,y ) that report on the depth of digital technologies penetrations in given society and economy. These 2 macro-ICT indicators tell to what extent digital technologies have diffused and are used by society members and hence constitute a prerequisite for broad implementation of e-government-type solutions. In this research we treat both (IUi,y ) and (MCSi,y ) variables are these factors that precondition development, implementation, and usage of e-government-related solutions in examined countries. The higher ICT deployment the potentially broader e-government solutions implementation, which enables economic development in longer time horizon. Regarding ICT indicators 1

See Table 4.3 in appendix for sample composition. In case of some variables the period of analysis is shorter due data unavailability in precedent periods. 3 In PPP, constant 2017 international $. 2

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we have derived all data from World Telecommunication/ICT Indicators Database 2021, and for both variables time series track back to 1990 and are available until 2019. To examine ICT-based governance solutions we have chosen E-Government Development Index (including its 3 sub-indexes: Online Services Index, Human Capital Index, and Telecommunication Infrastructure Index), E-Participation Index, and −3 indicators included in The Worldwide Governance Indicators. E-Government and related indicators, developed and calculated by United Nations, they report on the state of e-government development and patterns across counties embodying the effectiveness of e-government state strategies and practices. The E-Participation Index, also developed by United Nations, derived as supplementary measure to EGovernment Development Index, it shows the extent to which societies are engaged and contribute to public debates and policies, if and how they are empowered in terms of public policies designing and access to public information. Data on E-Government and E-Participation indices are entirely derived from E-Government Surveys4 for the time period ranging from 2003 to 2019. Finally, we consider three indicators proposed by Worldwide Governance Indicators, namely: Political Stability and Absence of Violence/Terrorism (Poli_Stabi,y ), Government Effectiveness (Gover_Effecti,y ), and Regulatory Quality (Reg_Quali,y ). The Worldwide Governance Indicators, developed by World Bank, are extracted from Worldwide Governance Indicators databank.5 Lastly, we have decided to control for educational achievements in countries in scope, and we add gross enrollment at tertiary level of education (Tert_Educi,y ). These data are derived from World Development Indicators database for the period spanning from 1990 to 2019. Our analytical approach combines several techniques that facilitate uncovering how variables in scope behave over time. First, for visual examination of crossvariables statistical associations we use locally weighted polynomial smoother, a robust and non-parametric technique that allows data exploration (Cleveland, 1979; Cleveland & Devlin, 1988). Next, to verify how changes in distribution of used variables we adopt a non-parametric density estimator—kernel density estimator, that allows verifying the variables distribution changes over time. The use of kernel density estimator also allows checking in increasing or decreasing cross-country heterogeneity regarding selected aspects. Finally, to trace the relationships between economic development, ICT deployment, and e-government solutions, we apply random panel regression. We estimate the regression holding a general form: 

yi,y = β0 + X i,y β + α y + u i,y

(4.1)

  and it is assumed that αi ∼ iid(0, σα2 ) and u i,y ∼ iid 0, σu2 . The (αi + u i,y ) is an error consisting of α y that stands for an individual specific component not varying across time, and uncorrelated part u i,y . The αi and u i,y components are mutually inde pendent, and they are also independent of all X i,y a vector of explanatory variables. 4 5

See: https://publicadministration.un.org/egovkb/en-us/Reports/UN-E-Government-Survey-2020. https://databank.worldbank.org/source/worldwide-governance-indicators.

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The value of αi is specific for each individual y, , and these individuals are independent, it is time invariant and homoscedastic across individuals (in our research countries). The β0 parameter captures the overall mean of the specification. The random model can be estimated using GLS estimator (Buse, 1973; Grubb & Magee, 1988) that usually shows   higher efficiency than the standard OLS estimator. As long as E xi,y αi + u i,y = 0 is satisfied and αi and u i,y remain uncorrelated the explanatory variables demonstrate exogeneity and the estimates are consistent.

4.4 Empirical Results Here below we provide numerical evidence on the state of economic development, digital technologies which are confronted with the progress in field of implementation of various e-government-related elements. We additionally on political stability, government effectiveness, and regulatory quality as these elements are considered of high impact of economic development in general but they also may be treated as prerequisites of e-government effective implementation. This empirical evidence is considered for developing 40 countries6 in which first—we usually observe dynamic economic development reflected both in dynamic shifts of gross domestic per capita income and improvements regarding other basic fields of human existence (these are captured in our study in Human Development Index), and second—for the last two decades elementary statistics report on fast adoption and growing usage of digital technologies (in here reflected through two basic macro-ICT indicators, namely Internet users and mobile telephony subscriptions per 100 inhab. in each case). Developing economies are also characterized by dynamically changing governance sector. Despite the fact that some of them are violated by political instability, in general rather positive tendencies are observed. Growing “quality” of governance is additionally accompanied by increasing implementation of e-government solutions, which is highly facilitated and empowered by massive digital technologies societywide adoption. Hence our intention is to trace statistical links potentially existing between economic development and growth, digital technologies deployment, egovernance solutions, and governance quality. The time span of this evidence is set for 1990–2019, although in case of some indicators considered the time series are shorter. We start our discussion with brief examination of economic development and digital technologies deployment trends across 40 developing countries, between 1990 and 2019. Figures 4.3 and 4.4 (see Appendix) visualize time trends regarding gross per capita income, Human Development Index, Internet users, and mobile telephony subscribers for each individual country. More detailed observation of gross domestic per capita income raises 2 fundamental conclusions regarding this group of countries. First, the selected sample of 40 developing economies is highly heterogeneous in this respect (compare Fig. 4.3). For instance in this group, we trace 6

Full sample composition—see Table 4.3 in the Appendix.

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extremely materially poor countries, like e.g. Burundi, Malawi, or Niger where in 2019 GDPpci,y is reported at 751 int. U$, 1514 int. U$, and 1224 int. U$ accordingly. On the other hand, some countries seem to materially much better off—see, for instance—Tunisia with 10.755 of GDPpci,y , Sri Lanka—13.070 GDPpci,y , or Morocco—7537 GDPpci,y . These bring to mind the thought about enormous material inequalities across developing economies, which suggest that also in other fields these divided shall be unveiled. The second seminal conclusion derived from the visual and numerical analysis of economic growth is that two groups of countries can be easily distinguished; the first group where elementary time trend analysis shows rapid advanced in GDPpci,y between 1990 and 2019 (in here we find, e.g. Bhutan— shift from 2888 to 11.832 int. U$ per capita; or Moldova, Mongolia, Sri Lanka, or Vietnam) and the second group where economic growth was not that dynamic or even a kind of stagnation is observed (see countries like, e.g. Burkina Faso, Burundi, Tanzania, or Uganda). The identification of the second group of countries brings to mind that fact that these countries are potentially locked in the low-income trap being unable to take-off along the economic growth path. Time trends in gross per capita shifts are well-reflected in changes of Human Development Index. Not only due to the fact that economic growth constitutes 1/3 of this index value, but also because its 2 remaining fields—health and education, are preconditioned by material wellbeing and financing opportunities. Again, visual inspection of country-wise trends regarding HDI report on country’s progress in economic development, and similarly to what was observed regarding GDPpci,y significant cross-country differences are observed in this respect. To cite one example, in 2019 in Burkina Faso the value of HDI7 was 0.452, while in Moldova—0.75, which signals massive gap in terms of human developments. Still more detailed observation of HDI trends and elementary numerical analysis of raw values, speak in support of the hypothesis that all 40 examined countries are progressing more in terms of social developments (education and health care fields) than it would be reflected in economic growth. The latter is probably a positive effect of multiple UN-financed projects directed to improve education and health care systems functioning. Moving to the evidence displayed in Fig. 4.3 we observe relative homogeneity when digital technologies diffusion patterns are considered across developing countries, and what is promising dynamic growths in ICT penetration in a huge majority of cases. In only very few countries, in 2019, the state of digital technologies deployment remained low especially if share of population using Internet network is considered (IUi,y )—see countries like Burkina Faso (16%), Burundi (2.6%), Madagascar (9.8%). In a huge majority of countries the values of IUi,y in 2019 reached more than 30%, and in e.g. Moldova we got 76%, Mongolia 51%, Morocco 74%. If we consider mobile cellular telephony subscriptions the results are even more promising. MCS diffusion trajectories (see Fig. 4.2) show dynamic and stable shifts in mobile telephony deployment across examined economies. Still, as in previous case, some laggard countries are traced, but in most of them the MCS penetration rates are high even exceeding 100 7

The Human Development Index values range from 0.00 to 1.00; higher values report higher economic/human development of a country.

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per 100 inhab. (the case of Mongolia, Morocco, Mali, Mauritania, Philippines (154 per 100 inhab.!!), or Kenya just to cite few examples). In many, even materially worse off economies, the MCS deployment is high which explains that mobile telephony solutions are available and affordable even for low-income societies. Calculated correlation coefficient for GDPpci,y and MCSi,y is 0.50, suggesting that in only 50% of cases higher gross per capita income corresponds to higher mobile telephony penetration rates, while in remaining 50% we do not observe such statistical relationships. Analogous statistical relationship applied for GDPpci,y and IUi,y where r −2 is 0.57 for considered countries. Massive, rapid, and undisrupted diffusion of digital technologies constitute a perfect fundament for implementation of e-government solutions. As seen from the evidence on ICT changing penetration, even developing countries seem to have necessary prerequisites to start e-government projects enabling society members for more active participation in social and political life, empowering disadvantaged and socially excluded groups. It seems that especially for developing countries broad implementation of empowering solutions can contribute to general economic development. Before we test formally the statistical dependencies between e-government development, governance effectiveness, and political stability versus economic development we take a closer look at several more specific indicators reporting on the latter’s developments economies. Figure 4.1, using box plot visualization, summarizes reported values for 5 indicators: E-Government Development Index, Online Services Index, Human Capital Index and Telecommunication Infrastructure Index, E-Participation Index reflecting combined effect of improved ICT access, and e-government-related elements, that can effectively enhance or hinder development. Next, Fig. 4.2, also using box plot visualization, explains values of Political Stability and Absence of Violence/Terrorism (Poli_Stabi,y ), Government Effectiveness (Gover_Effecti,y ), and Regulatory Quality (Reg_Quali,y ). In case of these indicators time trends trace back to 1996, and its values range from (−2.5) to (+2.5), while (−2.5) stands for the poorest performance in selected field, and the (+2.5)—the best. Graphical evidence displayed in Figs. 4.1 and 4.2 is additionally enriched by density curves displayed in Figs. 4.5 and 4.6 in the appendix. Let us first refer to Fig. 4.1 explaining values of egovernment-related indicators. The consecutive box plots show ranges8 of values of respective indicators between 1996 and 2019, with several outlying observations marked. Apparently the higher cross-country inequality is observed in terms of Online Services Index, although numerical evidence shows significant improvements in this regard between 2003 and 2019, which is obviously is a direct positive effect of increasing ICT penetration. Similar conclusions apply to E-Participation Index. When examining changes over time, we observe massive shifts from close to zero in 2003 to 0.6 or 0.7 in 2019 in some better off countries. That shows that digital technologies demonstrate huge power in bringing voice to societies and offer them improved participation opportunities in social and political life. Still the most seminal for our study seems to be the E-Government Index encapsulating different 8

The indicators values range from 0.00 to 1.00.

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1

.8

.6

.4

.2

0 E-Government Development Index Online Service Index

E-Participation Index Telecommunication Infrastructure Index

Human Capital Index

Fig. 4.1 E-Government-related indices values. Period 2013–2019. Source Author’s elaboration

perspectives of governing the country with the “help” of digital technologies. Also in this area significant improvements are reported showing that countries are doing much better in this respect in 2019 than in 2003. Surely, by definition, in 2003 the ICT penetration rates were negligible in examined countries, still, we observe parallel advances in digital technologies deployment and e-government solutions. Lastly, we consider change in 3 selected governance-related indicators, reflecting changes in overall institutional quality and political stability. Collected for the time period spanning from 1996 to 2019, values of Political Stability and Absence of Violence/Terrorism (Poli_Stabi,y ), Government Effectiveness (Gover_Effecti,y ), and Regulatory Quality (Reg_Quali,y ) are visualized in Fig. 4.2 again using box plots approach. As long as regarding political stability quite significant differences among countries are reported (see respective box in Fig. 4.2 and for confirmation density curve in Fig. 4.6 in appendix), in another 3 indicators government effectiveness and regulatory quality cross-country diversity is relatively low and no significant changes are reported (see also Fig. 4.6 in appendix). Taking a closer look specific cases and values of Gover_Effecti,y and Reg_Quali,y indicators, in majority of cases we observe only marginal changes and—what is even worse, drops in indicators values are traced in our country sample. This is the case of, for instance, Bangladesh, Bhutan, Egypt, Kyrgyz Republic, or Malawi. That evidence shows, that despite massive advances in digital technologies implementation and advances in human development, developing countries are permanently facing political instability and suffer from poor

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1

0

-1

-2

-3 Political stability

Government effectiveness

Regulatory quality

Fig. 4.2 Governance Indicators (Political Stability and Absence of Violence/Terrorism; Government Effectiveness; Regulatory Quality). Period 1996–2016. Source Author’s elaboration

quality of state institutions and weak authorities. The latter may significantly hinder, e.g. economic growth process that is perfectly visible in our case. The last step of our research is examining the relationships between gross per capita income and Human Development Index versus other included indicators using panel regression approach. Our basic intention is to verify whether e-government solutions, digital technologies deployment, political stability, and quality of institutions expressed through Government Effectiveness and Regulatory Quality impact economic growth and human progress. We additionally control for gross school enrolments at tertiary level of education. Tables 4.1 and 4.2 summarize our estimation results. Table 4.1 reports panel regression examining factors influencing changes in gross domestic per capita income. We have estimated 10 different regression specifications to check for the stability of the results. Referring to digital technologies, our estimates suggest the existence of positive and statistically significant relationships between GDPpci,y and two ICT indicators. As for the MCSi,y returned parameters in 3 regressions are (0.02) suggesting rather fairly strong impact on gross per capita income shifts. Meanwhile, the estimated coefficients standing by IUi,y , although statistically significant and positive are trivial in their value. Still, both in case of MCSi,y and IUi,y the coefficients are rather stable across estimated specifications. Relatively higher coefficients are reported for E-Participation Index, and they vary from 0.08 to even 0.23 depending on the equation estimated. Significantly

72

E. Lechman

Table 4.1 Panel regression estimates. GDP determinants. Period 1990–2019 GDPpci,y

RE_1 RE_2 RE_3 RE_4 RE_5

IUi,y

0.003 [0.00]

MCSi,y E_Govi,y

RE_6

RE_7

RE_8 RE_9

RE_10

0.004 0.002 0.009 0.008 0.006 [0.00] [0.00] [0.00] [0.00] [0.00] 0.02 0.02 [0.00] [0.00]

0.02 [0.00]

0.53 0.47 [0.13] [0.13]

E_Partici,y

0.65 [0.22] 0.11 0.08 [0.04] [0.05]

0.23 [0.05]

0.21 [0.05]

0.11 [0.04]

Online_Serv

0.28 [0.05]

Telecom_Infrai,y

0.19 [0.11]

Human_Capi,y

−0.15 −0.02 −0.13 0.04 −0.11 0.00 [0.10] [0.11] [0.12] [0.11] [0.11] [0.10]

Polit_Stabi,y

0.05 0.05 0.04 0.038 [0.02] [0.02] [0.01] [0.02]

0.20 [0.10] 0.46 1.4 [0.16] [0.10]

0.03 0.05 0.03 0.03 0.02 [0.02] [0.02] [0.02] [0.02] [0.02]

Gover_Effecti,y

0.08 0.03 0.05 0.07 [0.00] [0.05] [0.06] [0.05]

Reg_Quali,y Tert_Educi,y

0.13 [0.05]

0.13 0.16 0.06 [0.05] [0.06] [0.05]

0.02 0.02 0.02 0.02 0.02 [0.00] [0.00] [0.00] [0.00] [0.00]

0.02 [0.00]

#obs

236

236

244

244

284

318

310

318

318

244

R2 (overall)

0.60

0.61

0.58

0.57

0.59

0.38

0.48

0.46

0.41

0.63

Rho

0.95

0.95

0.96

0.95

0.94

0.94

0.94

0.93

0.91

0.95

Wald χ 2

501.5 543.5 550.4 474.1 629.2 372.1 437.7 376.5 269.2 567.2 [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]

(Prob > χ 2 )

Notes panel strongly balanced. GLS regression applied. Hausman results tests—available on request; below coefficients—standard errors reported; in bolds—results statistically significant at 5% of level of statistical significance. Source Author’s estimates

higher results are for E-Government Index and Telecommunication Infrastructure Index, and in all cases the coefficients are positive and significant which potentially might suggest relatively strong significance of e-government-related elements for dynamizing economic growth. Analogous conclusions apply for political stability and regulatory quality bringing up the statement that—in general—quality of institutions is a significant element allowing economies to grow and develop. Lastly, we have controlled for tertiary education enrolments and—as expected, it unveils its positive impact on GDPpci,y . Finally, Table 4.2 examines analogous relationships versus Human Development Index. Qualitative conclusions that can be derived from estimates summarized in Table 4.2, to a large extent, coincide with those in Table 4.1, with several violations. Both for Internet users and mobile cellular subscriptions

0.008 [0.00]

0.92

Rho

0.93

0.81

60

60

0.81

#obs

0.003 [0.00]

0.003 [0.00]

R2 (overall)

Tert_Educi,y

Reg_Quali,y

Gover_Effecti,y

0.97

0.70

62

0.004 [0.00]

0.007 [0.00]

0.96

0.71

62

0.004 [0.00]

0.009 [0.00]

0.92

0.76

101

0.003 [0.00]

0.03 [0.02]

Human_Capi,y

Polit_Stabi,y

0.02 [0.02]

−0.006 [0.02]

RE_5 0.0007 [0.00]

Telecom_Infrai,y

0.008 [0.00]

RE_4 0.001 [0.00]

0.04 [0.01]

−0.003 [0.01]

0.0004 [0.00]

RE_3

Online_Serv

E_Partici,y

0.18 [0.07]

E_Govi,y

0.22 [0.07]

0.0003 [0.00]

RE_2

MCSi,y

RE_1

0.0007 [0.00]

HDIi,y

IUi,y

Table 4.2 Panel regression estimates. HDI determinants. Period 1990–2019 RE_6

0.95

0.66

78

0.004 [0.01]

0.01 [0.00]

0.04 [0.02]

0.03 [0.02]

0.002 [0.00]

RE_7

0.93

0.79

76

0.04 [0.02]

0.003 [0.00]

−0.05 [0.06]

−0.08 [0.08]

0.49 [0.22]

0.001 [0.00]

RE_8

0.91

0.81

78

0.001 [0.00]

0.04 [0.02]

0.001 [0.01]

0.12 [0.03]

0.21 [0.07]

0.04 [0.02]

0.0007 [0.00]

0.92

0.79

78

0.008 [0.02]

(continued)

0.95

0.77

62

0.003 [0.00]

0.003 [0.00]

0.04 [0.02]

0.001 [0.00]

−0.002 [0.01] 0.05 [0.02]

0.04 [0.03]

0.01 [0.01]

0.0005 [0.00]

RE_10

0.11 [0.03]

0.34 [0.04]

RE_9

4 Enhancing Economic Development Through ICT-Based Governance … 73

158.6 [0.00]

RE_2 142.6 [0.00]

RE_3 125.2 [0.00]

RE_4 243.4 [0.00]

RE_5 62.7 [0.00]

RE_6 95.9 [0.00]

RE_7 89.1 [0.00]

RE_8 79.1 [0.00]

RE_9 168.2 [0.00]

RE_10

Notes panel strongly balanced. GLS regression applied. Hausman results tests—available on request; below coefficients—standard errors reported; in bolds— results statistically significant at 5% of level of statistical significance. Source Author’s estimates

(Prob >

χ 2)

RE_1

154.1 [0.00]

HDIi,y

Wald χ 2

Table 4.2 (continued)

74 E. Lechman

4 Enhancing Economic Development Through ICT-Based Governance …

75

returned coefficient in fact zero. Still, for E_Govi,y and Telecom_Infrai,y the coefficients are similar, while for E-Participation Index in none of estimated regressions it resulted to be significant. For Gover_Effecti,y the results are stable, positive, and significant, while these for regulatory quality are insignificant. Bearing in mind our results reported both in Tables 4.1 and 4.2 it is hard to draw valid conclusions on whether digital technologies deployment, e-government, or quality of institutions and political stability strongly affect economic growth and human development. Results are poorly robust and no rigid conclusions shall be drawn on that basis.

4.5 Conclusions This work contributes to the state of knowledge two-directionally. First it shows, in descriptive terms, the state of digital technologies deployment in 40 developing economies using 2 macro-ICT indicators—Internet users and mobile telephony subscription rates. We have demonstrated massive advances in this respect that took place between 1990 and 2019, but despite these we have also traced significant digital inequalities in this group of countries. Similar conclusions apply to the second examined aspect—changes in per capita income and Human Development Index. We have found significant cross-country disparities in material wealth and general level of socio-economic development. These findings support the view on extreme internal heterogeneity of the group of developing countries. Within the sample group we can easily trace countries which are relatively better of both in terms of digital technologies adoption and use as well in term of income per capita, while on the other hand we see countries locked in low-level trap being unable to take-off. Second, these massive inequalities in ICT deployment and economic development are then reflected in diversified state of implementation of e-government. Examined e-government-related indicators also indicate that considered 40 countries are hugely differentiated in this field, but also in terms of institutional quality expressed through political stability, government effectiveness, and regulatory quality. Obvious shifts and improvements are traced but still, in 2019, we observe countries significantly lagging behind where being unable to advance neither in e-government solution nor in institutional quality. Finally, we have tested, using regression analysis, the relationships between economic development, digital technologies penetration, and electronic government. Our results only partially confirm the existence of strong, positive, and stable relationships among these elements. We have managed to confirm the positive impact of e-participation, ICT, political stability, and government effectiveness. Still, these results are poorly robust and we claim that no rigid conclusions can be derived from our numerical analysis. Despite multiple efforts, the issue of broad and effective implementation of egovernment in a significant number of developing countries remains an open question. Not only infrastructural and financial constraints impede this process. Existing great economic and social disparities, and digital divides are other obstacles to

76

E. Lechman

combat in the near future. Weak institutions, poor governance, political instability, and corruption engrave the problem. As claimed by many sole creation of government websites is a prerequisite but not sufficient to tell that e-government solutions have been established and work in a country. The issue is much more complex. Egovernment systems need to be shaped and designed to suit best not only the needs of state authorities, but above all, they must be adequate for society members addressing their basic needs but also their capabilities and skills. Only for well-designed egovernment systems citizens shall put willingness and ability to use. Luckily, implementation of e-government constitutes of the strategic priority of many countries, and if we regard developing economies, international agencies and organizations often play a seminal role in achieving this goal. Valuable initiatives and plans governed by United Nations, International Telecommunication Union, or Inter-American Development Bank just to cite few, strongly support e-government development plans, and try to help to make it deliverable especially for vulnerable and marginalized populations, which shall ensure growing integration and interoperability, combined will accessibility and affordability for all citizens. Acknowledgements This work was supported by the National Science Centre in Poland under Grant no. 2015/19/B/HS4/03220.

Appendix See Fig. 4.3, 4.4, 4.5, and 4.6 and Tables 4.3 and 4.4.

50 100 150

0

50 100 150

0

50 100 150

0

50 100 150

2000

MCS

37

36

2000

30

29

1990

23

22

2020

16

15

2010

9

8

2010

2020

1990

2000

38

31

24

17

10

3

2020

IU

2010

1990

2000

39

32

25

18

11

4

2010

2020

1990

2000

40

33

26

19

12

5

2010

2020

0 20 40 60 80

1990

2000

35

34

2000

28

27

1990

21

20

2020

14

13

2010

7

6

2010

2020

Fig. 4.3 Country-wise ICT diffusion trajectories. Internet users and mobile telephony subscriptions. Period 1990–2019. Note Long dash line stands for Internet users (IU) per 100 inhab.; solid line stands for mobile telephony subscriptions (MCS) per 100 inhab.; MCS values—left-hand axis;’ IU values—right-hand axis; numbers refer to specific countries— see Table 4.3. Source Author’s elaboration)

1990

2 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80

0

50 100 150

0

50 100 150

0

1

4 Enhancing Economic Development Through ICT-Based Governance … 77

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

2010

2020

1990

2000

.2

Fig. 4.4 Country-wise development trajectories. GDP per capita and Human Development Index. Period 1990–2019. Note On left-hand axis—GDP per capita (in PPP) (constant 2017 international $); on right-hand axis—Human Development Index; numbers refer to specific countries—see Table 4.3. Source Author’s elaboration

HDI

.4

0

GDP

.6

5000

40 .8

39

10000

38

15000

37

.2

36

.4

0

35 .6

34

.8

33

5000

32

10000

31

15000

30

.2

29

.4

0

28 .6

27

.8

26

5000

25

10000

24

15000

23

.2

22

.4

0

21 .6

20

5000

19 .8

18

10000

17

15000

16

.2

15

.4

14 .6

13

0

12

5000

11 .8

10

10000

9

15000

8

.2

7

.4

6 .6

5

0

4 .8

3

5000

2

10000

1

15000

78 E. Lechman

4 Enhancing Economic Development Through ICT-Based Governance …

79

5

4

3

2

1

0 0

.2

.4

.6

E_Gov

E_Partic

Telecom_Infra

Human_Cap

.8

1

Online_Serv

Fig. 4.5 E-government Development Index and its components. Period 2003–2019. Note Nonparametric approximation applied. Source Author’s elaboration

80

E. Lechman

1.5

1

.5

0 -3

-2 Polit_Stab

-1

0 Gover_Effect

1 Reg_Qual

Fig. 4.6 The Worldwide Governance Indicators. Period 1996–2019. Note Non-parametric approximation applied. Source Author’s elaboration

4 Enhancing Economic Development Through ICT-Based Governance … Table 4.3 Sample composition

No

Country

81

1

Bangladesh

21

Mauritania

2

Bhutan

22

Moldova

3

Bolivia

23

Mongolia

4

Burkina Faso

24

Morocco

5

Burundi

25

Mozambique

6

Cameroon

26

Nicaragua

7

Egypt

27

Niger

8

El Salvador

28

Nigeria

9

Ethiopia

29

Pakistan

10

Gambia

30

Philippines

11

Ghana

31

Rwanda

12

Guinea

32

Senegal

13

Honduras

33

Sri Lanka

14

India

34

Tajikistan

15

Kenya

35

Tanzania

16

Kyrgyz Rep

36

Tunisia

17

Lao

37

Uganda

18

Madagascar

38

Ukraine

19

Malawi

39

Vietnam

20

Mali

40

Zambia

Source Author’s compilation

0.74 [399]

0.86 [154]

0.59 [399]

0.78 [399]

0.11 [399]

0.008 0.10 [819] [310]

0.85 [399]

0.17 [399]

0.62 0.53 [980] [1035]

0.62 [283]

0.65 [399]

0.69 [278]

0.01 [825]

0.23 [825]

0.16 [825]

Online_Servi,y

Telecom_Infrai,y

Human_Capi,y

GDPpci,y

HDIi,y

Polit_Stabi,y

Gover_Effecti,y

Reg_Quali,y

0.14 [819]

0.14 [819]

0.62 [389]

0.64 [399]

0.72 [399]

E_Partici,y

0.42 [310]

0.41 [310]

0.75 [389]

0.84 [389]

0.75 [389]

0.63 [389]

0.71 [389]

E_Govi,y

1.00 [389]

0.79 1.00 [959] [1035]

E_Govi,y

MCSi,y

MCSi,y

1.00 [981]

IUi,y

IUi,y

0.27 [318]

0.28 [318]

0.03 [318]

0.57 [158]

0.53 [399]

0.11 [399]

0.72 [399]

0.85 [399]

1.00 [399]

E_Partici,y

Table 4.4 Pairwise correlations. Period 1990–2019

0.37 [318]

0.21 [318]

0.20 [318]

0.01 [318]

−0.02 [318] 0.42 [318]

0.67 [158]

0.68 [399]

0.10 [399]

1.00 [399]

Telecom_Infrai,y

0.57 [158]

0.52 [399]

0.22 [399]

0.62 [399]

1.00 [399]

Online_Servi,y

0.22 [318]

0.18 [318]

0.15 [318]

0.73 [158]

0.41 [399]

1.00 [399]

Human_Capi,y

HDIi,y

Polit_Stabi,y

0.31 0.32 0.35 [839] [158] [839]

0.39 0.41 0.41 [839] [158] [839]

0.15 0.17 1.00 [839] [158] [839]

0.84 1.00 [312] [313]

1.00 [1195]

GDPpci,y

0.61 [839]

1.00 [839]

Gover_Effecti,y

1.00 [839]

Reg_Quali,y

(continued)

Tert_Educi,y

82 E. Lechman

0.47 [667]

IUi,y

0.48 [694]

MCSi,y

0.71 [276]

E_Govi,y

0.48 [284]

E_Partici,y 0.44 [284]

Online_Servi,y 0.53 [284]

Telecom_Infrai,y

Note Number of observations reported below coefficients. Source Author’s elaboration

Tert_Educi,y

Table 4.4 (continued) 0.58 [284]

Human_Capi,y

HDIi,y

Polit_Stabi,y

0.77 0.80 0.11 [799] [205] [589]

GDPpci,y 0.16 [589]

Gover_Effecti,y 0.25 [589]

Reg_Quali,y 1.00 [801]

Tert_Educi,y

4 Enhancing Economic Development Through ICT-Based Governance … 83

84

E. Lechman

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Swaleheen, M., Ben Ali, M. S., & Temimi, A. (2019). Corruption and public spending on education and Health. Applied Economics Letters, 26(4), 321–325. Thompson, N., Mullins, A., & Chongsutakawewong, T. (2020). Does high e-government adoption assure stronger security? Results from a cross-country analysis of Australia and Thailand. Government Information Quarterly, 37(1), 101408. United Nations. (2020). E-government survey in 2020. Digital government in the decade of action for sustainable development. Uyar, A., Nimer, K., Kuzey, C., Shahbaz, M., & Schneider, F. (2021). Can e-government initiatives alleviate tax evasion? The moderation effect of ICT. Technological Forecasting and Social Change, 166, 120597. Veiga, L., & Rohman, I. K. (2017, September). e-Government and the shadow economy: Evidence from across the globe. In International conference on electronic government (pp. 105–116). Springer.

Chapter 5

Which Governance Dimension Matters Most for FDI? A Comparative Analysis Between MENA and SSA Countries Sami Ben Mim and Dhafer Saïdane

Abstract This paper assesses the impact of governance on foreign investments for a sample of 18 MENA countries and 49 Sub-Saharan African countries. We intend to identify the main determinants of FDI inflows and to investigate the complementarity between the different governance proxies. Results relative to the control variables show that living standards are boosting FDI in MENA countries, while foreign investors are attracted by Sub-Saharan countries characterized by weak domestic industry, poor infrastructure, and low living standards. The SGMM results also reveal that government effectiveness is the indicator producing the most important effect on FDI. Political stability and the quality of the legal framework are also significantly enhancing foreign investments. Finally, estimation results suggest that political stability contributes to strengthen the impact of the other governance indicators on FDI for both subsamples, while regulatory quality represents a major weakness for SSA countries. Keywords FDI · Governance · System GMM · MENA · Sub-Saharan Africa

5.1 Introduction Developing countries are competing to attract FDI inflows. Besides their direct impact on growth and employment, FDI could benefit the host economy through various channels (Ben Mim et al., 2022). Technology transfer is one of the main mechanisms through which FDI can produce positive externalities in the developing host economy. In this regard, McIntyre et al. (1996) showed that the search for quality in the host country is a main cause of technology transfers. To be able to export, a foreign subsidiary must meet the quality standards of the world markets. Thus, multinational S. B. Mim (B) LaREMFiQ, IHEC Sousse, University of Sousse, Sousse, Tunisia e-mail: [email protected] D. Saïdane SKEMA Business School, Université Côte d’Azur, Nice, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_5

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companies have a knock-on effect on the level of training in the host country since they are led to train local suppliers, often SMEs. Technology transfers, or the “diffusion effect,” generally take place through the following relationships: • Vertical relations with suppliers or buyers in the host countries. The existence of positive spillovers translates into technical assistance, training, and various information to improve the quality of the local suppliers’ products. Multinational firms also help domestic suppliers to buy raw materials and modernize their production units. • Horizontal relations with companies in the same branch. These spillovers, when they exist, generally have long-term “demonstration effects.” The relatively few studies of horizontal relationships have yielded mixed results because foreign firms may seek to avoid the transfer of their know-how to their competitors. This is why it seems that horizontal spillovers are greater between firms operating in unrelated industries. FDI can also play a key role in promoting human capital, mainly through the “demonstration effect.” Indeed, the skilled labor needs of foreign companies allow the public authorities of the host country to quickly identify which qualifications are sought. Then there is the role played by multinational companies. The human capital of the subsidiaries of multinational companies is improved through training and on-the-job learning. These subsidiaries can also have a positive influence on human capital in the other companies of the group with which they establish links, including their suppliers. Foreign companies can also replace expatriates with local executives who have been upgraded. As pointed out by Gershenberg (1987), while foreign firms resort more to expatriates during the settlement phase, they subsequently tend to offer training to technical experts and executives of local companies. Foreign firms are therefore seeking to replace expatriates with less expensive local employees, but they must be given adequate training in the meantime. The impact of FDI on human capital and the stock of knowledge in developing countries also seems to result from the measures taken by local governments to attract FDI. When a knowledge gap persists between foreign investors and the rest of the host economy, FDI is unlikely to have a significant spillover. In addition, this gap is supposed to discourage the multinational firms which don’t find a conductive environment in which they can easily operate by applying the standards of their home countries. FDI may also contribute to enhance financial development in the host country. According to Desai et al. (2006), foreign companies can enhance financial intermediation by seeking funding from domestic banks. The presence of foreign investors can also lead banks from their home countries to establish subsidiaries in the host countries to offer financial support to their multinational companies. This leads to an increased competition in the banking system. On the other hand, Soumaré and Tchana (2015) detected causality running from foreign investments to financial markets. Foreign investment can be made by holding significant shares in the capital of domestic companies, which contributes to boost the host countries’ stock markets.

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FDI can also translate into sustainable development benefits. These are the environmental and social benefits to host economies through the dissemination of good practices and technologies used by multinational companies and their subsequent spillovers to local businesses. According to Schmidheiny (1992), in general, the technologies that are transferred to host countries via FDI are generally more modern and environmentally friendly than those that exist locally. Some negative effects may also be associated with FDI, particularly in developing countries. In a small country, the entry of multinational companies can reinforce the market concentration. Such a situation can hamper competition and degrade the business environment. Various studies showed that the effect of FDI on the concentration of the host country markets is stronger in developing countries. The empirical investigation conducted by Caves (1996) on developing economies revealed that foreign presence in the form of FDI is associated with an increasing concentration of the domestic markets. Similarly, Lall (1979) studied 46 Malaysian industries and found that FDI tends to reduce competition. Concentration effects occur through barriers to entry erected by multinational firms and through modern capital-intensive production processes. Blomstrom and Kokko (1996) came to the same conclusions in the case of Mexico: FDI has increased market concentration because of modern capital-intensive production processes. A large body of literature emphasized that the conditions in the host country determine to a large extent its capacity to take advantage from the positive externalities generated by FDI. The conditionality issue has been extensively explored in the work of Houde and Lee (2000). The main factors that encourage FDI and its positive externalities on the host country can be summarized as follows: • the expected profitability of the projects, • the ease with which the operations of the subsidiaries can be integrated into the overall strategies of the parent firm, • the quality of the conditions offered by the host country: regulations, institutions, transparency, geography, natural resources, infrastructure, technology, human skills, taxation, macroeconomic and political stability, and financial system. In this respect, various studies have pointed out the importance of building up an appropriate infrastructure to attract foreign investments (Fung et al., 2005; Nor et al., 2012). Moreover, foreign investors may opt for countries endowed with a minimum level of industrial development which enables them to establish forward and backward connections with domestic firms (Markusen & Venables, 1999; Vito et al., 2013). Foreign investors are also sensitive to the quality of the financial system in host countries. The level of financial development contributes not only to attract foreign investors, but also to shape the impact of FDI on the domestic economy (Alfaro et al., 2004). On the other hand, empirical evidence suggests that FDI flows are more likely to be oriented toward countries exhibiting high levels of trade openness (Buthe & Milner, 2008; Liargovas & Skandalis, 2012). This result may be explained by the fact that such countries simplify export procedures and provide an easier access to imported raw materials and equipments.

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On the other hand, various studies suggested that human capital is one of the key factors in attracting FDI (Kheng et al., 2017; Koukpo, 2005; etc.). Investment in education is therefore essential for a country to provide supportive conditions for FDI. The idea is that it is imperative that the population has a minimum level of education for the country to be a candidate for FDI. Human capital increases the attractiveness of a given country, as it endows foreign firms with qualified workers able to master the production process (Borensztein et al., 1998). The fact that foreign investors are seeking a low-cost labor force, does not prevent them from requiring a minimum level of qualification among domestic workers. The experience of some initially underdeveloped countries shows that even if their level of human capital is initially low, they can nevertheless catch up with the level of the world average in the space of a generation. For a government, investing in human capital is “a very profitable operation” even in the short term. Nunnenkamp (2002) showed the importance of education as a determining factor in attracting FDI. Following cross-sectional studies, they found that for efficiency-oriented SMEs, human capital becomes an important determinant of FDI attraction, but for resource-oriented SMEs, human capital is not a priority. Proof of that is the case of African countries, which have benefited from large flows of FDI seeking natural resources but have not stimulated investment in human capital. More recently, the empirical literature focused on the relationship between FDI and the host country’s institutional quality. According to Daude and Stein (2007), a poor institutional framework hampers FDI by increasing uncertainty and by acting as an additional tax on foreign firms. Foreign investors are also particularly sensitive to the quality of the legal system. Accordingly, Aizenman and Spiegel (2006) confirmed that enforcing property rights contributes to spur FDI. Transparency and more generally the level of sustainable development improve the business climate. These are increasingly essential conditions put forward by foreign investors in order to comply with the standards of their home countries. In this respect, Wei (2000) highlighted the important deterrence effect produced by corruption on FDI. Regarding political factors, Jensen (2003) found that democratic regimes attract 70% more FDI than their autocratic counterparts. However, despite the large body of literature dealing with the institutional qualityFDI nexus, little empirical work focused on comparison of the impact produced by the various institutional dimensions on FDI. In this respect, Daude and Stein (2007) emphasized the importance of exploring the various dimensions of the institutional framework, because some institutional aspects matter more than others for FDI. They argued that the unpredictability of laws, regulations, policies, excessive regulatory burden, government instability, and lack of commitment are particularly harmful to FDI. Moreover, few studies have attempted to investigate these relationships for different groups of countries. It is particularly important to investigate how the influence of institutional quality varies across contexts. This study aims to contribute to the empirical literature by highlighting the impact of the institutional framework on FDI for the MENA and Sub-Saharan African countries. We try to identify which among the various dimensions of governance matters the most for foreign investors. We also try to verify if the impact of the institutional quality differs from one group

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of countries to another, by comparing the results relative to the MENA zone and SubSaharan Africa. Another important contribution of this study is that it assesses the institutional determinants of FDI while investigating the complementarity between the different governance proxies. The rest of this paper is organized as follows. Section 5.2 discusses the empirical model and the econometric methodology. Section 5.3 highlights the main statistical features of our sample. Section 5.4 presents and discusses the empirical results. Finally, we conclude and formulate some policy recommendations.

5.2 Model and Methodology We estimate the following model which includes the main determinants of FDI inflows: FDIit = u i + vt + α1 · hcapitalit + α2 · marketsizeit + α3 · financialDevit + α4 · tradeit + α5 · inf ltionit + α6 · inf rastructureit + α7 · industryit + α8 · institutionalDevit + εit

(5.1)

where ui and vt represent, respectively, individual and period effects and εit the error term. The definitions and sources of the independent variables are summarized in Table 5.1. Human capital increases the attractiveness of a given country, as it makes skilled workers available for foreign firms (Borensztein et al., 1998). Foreign firms may sell a part of their production to the local customers and are therefore sensitive to the host country’s market size. Financial development is an additional attractiveness factor for foreign investors (Alfaro et al., 2004). Trade openness is also expected to boost FDI inflows (Buthe & Milner, 2008; Liargovas & Skandalis, 2012). Foreign firms are likely to prefer countries endowed with an appropriate infrastructure (Nor et al., 2012). Moreover, the existence of a sufficiently developed domestic industry enables foreign firms to establish forward and backward connections with domestic firms (Markusen & Venables, 1999). Foreign investors also prefer to establish their activities in countries characterized by a stable macroeconomic environment. Macroeconomic stability is proxied by the inflation rate (Casey, 2014). Finally, we control for the quality of the institutional and regulatory framework of the host country by including the six governance indicators developed by Kaufmann et al. (2010). These indicators are covering various dimensions of the institutional framework and are therefore reflecting the different aspects of governance (Ben Ali & Krammer, 2016). To estimate model (1), we rely on the System GMM method (SGMM) which controls for endogeneity. In fact, FDI may contribute to promote the host countries’ domestic industries, to enhance human capital through know-how transfers, and to boost trade by accelerating imports and exports. Such reverse causalities represent potential sources of endogeneity which may lead to biased estimates. To control

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for endogeneity and overcome the weak instrument problem, the SGMM method estimates a system composed of the differenced and level equations using the second and higher lags of the endogenous variables as instruments (Arellano & Bover, 1995): ⎧ ΔFDIit = α1 Δhcapitalit + α2 Δmarketsizeit + α3 ΔfinancialDevit ⎪ ⎪ ⎪ ⎪ + α4 Δtradeit + α5 Δ inf lationit + α6 Δ inf rastructureit ⎪ ⎪ ⎨ + α7 Δindustryit + α8 ΔinstitutionalDevit + Δεit FDI = u i + α1 hcapitalit + α2 marketsizeit + α3 financialDevit ⎪ it ⎪ ⎪ ⎪ + α ⎪ 4 tradeit + α5 inf lationit + α6 inf rastructureit ⎪ ⎩ + α7 industryit + α8 institutionalDevit + εit The lagged levels are used as instruments for the differenced equation, while the lagged differences are retained as instruments for the level equation. The validity of the instruments is checked via the Sargan–Hansen Test. The Arellano and Bond (1991) tests are also executed to verify the absence of second-order autocorrelation within the estimated residuals.

5.3 Sample and Descriptive Statistics Our sample covers the period from 1996 to 2020 and includes 68 developing countries belonging to the Middle East and North Africa (MENA) region and to SubSaharan Africa (SSA). The list of the countries is reported in Table 5.2. The descriptive statistics for the whole sample and for the two subsamples are summarized in Table 5.3. We note that the macroeconomic indicators relative to the MENA countries outperform those of the SSA countries. On average, MENA countries are recording better schooling rates, higher access to credit, more advanced infrastructure, and higher shares of industrial output compared to SSA countries. The MENA region is also characterized by lower inflation rates, a higher degree of trade openness, and better living standards proxied by per capita GDP. However, despite exhibiting deteriorated economic conditions, SSA countries are enjoying a higher mean share of FDI in GDP (2,72% for the MENA zone versus 4,52% for SSA). This result may be imputed to the fact that foreign investments in SSA are mostly oriented toward natural resources and extracting activities and are therefore less sensitive to the economic conditions in the host countries (Gui-Diby & Renard, 2015). Another explanation for such a counterintuitive result is that an important fraction of FDI in MENA countries takes the form of portfolio investments and was therefore highly affected by the recent episodes of financial turmoil. Figure 5.1 confirms that FDI flows toward the MENA region showed a highly increasing trend till 2007, before registering a sharp reversal after the subprime crisis. On the other hand, FDI continued to exhibit an increasing trend for the SSA countries until 2012. Descriptive statistics also reveal that both groups of countries are exhibiting negative mean values for all the institutional development proxies, which indicates that most of the countries belonging to the MENA region and SSA are endowed with poor

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8.000 7.000 6.000 5.000 Full Sample

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Fig. 5.1 FDI mean values, 1996–2020

institutional frameworks. Once again, the MENA countries are performing better than their SSA counterparts, except for political stability and voice, and accountability. Figures 5.2, 5.3, and 5.4 provide further insights into the dynamics of the institutional frameworks for the full sample and for both groups of countries over the sample period.1 Regarding the MENA countries, four out of the six governance indicators (control of corruption, rule of law, government effectiveness, and regulatory quality) are following a downward trend which indicates that the institutional frameworks in these countries are deteriorating rather than improving (Saha & Ben Ali, 2017, Ben Ali & Saha, 2016). We also note that these countries are performing very poorly in terms of voice and accountability, while political stability dropped dramatically during the sample period. For the SSA countries, the political stability and voice and accountability indicators exhibit the highest mean values over the sample period, while the lowest values are associated with the control of corruption index, which confirms that African countries are riddled by corruption. All six indicators seem to follow a steady path, which precludes any significant improvement of the institutional framework within these countries in the near future. The correlation coefficients reported in Table 5.4 show that political stability and voice and accountability contribute to spur foreign investments, while no significant correlation is detected between the four remaining governance proxies and FDI. We also note a positive and significant correlation between trade openness and FDI. Correlation coefficients relative to the two groups of countries provide different results.2 For the MENA countries, all the governance indicators are significantly and positively correlated with FDI. FDI is also boosted by trade openness and domestic credits. For Sub-Saharan countries, trade openness, per capita GDP, and political

1

A pairwise comparison of the six indicators for both regions is provided in Fig. 5.5 in the Appendix. Correlation coefficients are provided in Tables 5.4 and 5.5 in the Appendix, respectively, for MENA countries and SSA countries.

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0

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stability are positively and significantly correlated with FDI. Oppositely, a negative and significant correlation is detected between the regulatory quality index and FDI. Finally, we note that correlation is particularly high among the governance indicators for both groups of countries, except for the political stability and voice and accountability indexes which are weakly and non-significantly correlated in the MENA countries. This is probably due to the Arab Spring which led to a democratic momentum in some Arab countries but also contributed to fuel political and social instability.

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5.4 Results and Discussion The SGMM results for the whole sample are reported in Table 5.6. We first notice that the coefficients associated with lagged FDI are positive and significant, which suggests that foreign investment inflows toward MENA and SSA countries are persistent. This persistence may partly be explained by the fact that foreign firms frequently reinvest in the host countries to extend their activities. It is also common for these firms to establish investment plans over several years. Results also reveal that countries with higher degrees of trade openness are enjoying higher percentages of FDI. Similar results were highlighted by Liargovas and Skandalis (2012). Oppositely, higher credits provided by the domestic banking systems contribute significantly to deter foreign investments. According to Desbordes and Wei (2014) developed financial systems lead to higher levels of domestic investment, which intensifies competition and adversely affects FDI. It seems that foreign investments substitute for domestic investments in countries characterized by shallow financial systems. The negative and significant impact produced by the share of manufacturing output on FDI offers strong support for such a conclusion. Such a result indicates that foreign investors are attracted by countries with weak domestic industries, which suggests that foreign investments are taking advantage of the lack of domestic investments in the manufacturing sector in MENA and SSA countries. As expected, inflation produces a negative and significant impact on FDI, which implies that foreign investors are attracted by countries characterized by a stable macroeconomic context. Finally, per capita GDP, infrastructure, and human capital proxies do not produce consistent significant effects on FDI. A major finding of this study is that government effectiveness is the governance proxy producing the largest positive effect on FDI. Such a result implies that when considering institutional quality, foreign investors are mainly focusing on

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the adequateness and the effectiveness of economic policies and the quality of public services in the host countries. Results also reveal that political stability and rule of law contribute significantly to spur FDI inflows toward MENA and SSA countries. Political and social instability may deteriorate the overall economic performance and lead therefore to dramatic changes in economic policies. Foreign firms may also incur serious damages to their assets in case of violent social disturbances. On the other hand, it has been widely proved that investors devote a particular attention to the quality of the legal framework (Levine, 1998). An effective legal system stimulates investment mainly by ensuring the protection of the investors’ rights. Our results are in agreement with most previous studies. Sachs and Sievers (1998) argued that political stability is one of the most important determinants of FDI in Africa. Busse and Hefeker (2007) and Anyanwu and Yameogo (2015) also found that there is a negative and statistically significant relationship between FDI and political instability. This indicates that multinationals are interested in political stability and the quality of regulation when they relocate their activities to African and MENA countries. The descriptive analysis conducted in the previous paragraph revealed that MENA and SSA countries are characterized by different dynamics of their governance proxies. It is therefore interesting to compare the effects of these proxies on FDI within each group of countries. Estimation results relative to MENA and SSA countries are, respectively, provided in Tables 5.5 and 5.6. We first notice that the coefficients associated with the lagged FDI terms are higher in MENA countries compared to SSA countries, which suggests that FDI inflows are much more persistent in the MENA region. In line with the full sample results, trade openness produces a positive effect on FDI in most of the specifications for both groups of countries. Results also show that per capita GDP contributes to accelerate FDI in MENA countries, while it produces the opposite effect in Sub-Saharan countries. It seems that foreign firms are interested by the purchasing power of the domestic MENA markets, while they give little attention to domestic markets in SSA countries and are focusing mainly on exporting activities. Another important finding is that the schooling rate is significantly deterring foreign investments in MENA countries. Such a result suggests that human capital is not meeting the needs of foreign firms and that governments in MENA countries should reform their training and education policies to meet the expectations of the labor market. On the other hand, results reveal that domestic industry contributes to reduce FDI inflows in SSA countries. Such a result confirms that foreign investments are compensating for the weak industrial fabric in African countries. Infrastructure impacts negatively on FDI inflows to SSA countries. These results show that some foreign companies find it particularly advantageous to invest in countries with weak infrastructure. This was the case of incumbent diamond mining companies operating in Angola and the Democratic Republic of Congo and the case of Shell in Nigeria who were able to take advantage of the country’s situation and weak infrastructure by accessing raw materials at low prices. Such examples support the contention that the effect of infrastructure on FDI largely depends on the type of FDI and varies significantly across sectors (Burger et al., 2016). Regarding governance indicators, only government effectiveness and rule of law are producing positive effects on FDI in MENA countries. As for the whole sample,

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government effectiveness is still the indicator producing the strongest effect on FDI. Results relative to SSA countries show that that four out of the six governance indicators are contributing to enhance their attractiveness to foreign investors. Voice and accountability are the one producing the strongest effect on FDI followed by government effectiveness. It is important to note that voice and accountability is the only governance dimension where SSA countries outperform MENA countries over the sample period. It seems that the extremely low level of voice and accountability in MENA countries prevents this governance indicator to produce any positive effect on FDI. Having identified the governance proxies that most affect FDI for both groups of countries, we turn to investigate the complementarity between these indicators. To that end, we introduced interaction terms between the governance proxies contributing to spur FDI and each of the remaining governance variables. For example, a positive and significant coefficient associated with the interaction term “GE × RQ,” indicates that government effectiveness (GE) contributes to promote FDI more intensively in countries endowed with higher levels of regulatory quality (RQ). These variables may be considered as complementary, as regulatory quality contributes to amplify the effect of governance effectiveness on FDI. Results relative to the full sample are reported in Table 5.7. We notice that all the interaction terms including political stability (PS) are producing positive and significant effects on FDI. Such a result means that political stability contributes to intensify the positive effects that other governance proxies may produce on FDI. It seems therefore that political stability is an important prerequisite for the effectiveness of the other governance indicators. A similar conclusion can be drawn from the results relative to MENA countries reported in Table 5.8. Political stability is an important catalyst of the positive effects generated by government effectiveness (GE) and the legal framework (LAW) on FDI. Results also indicate that voice and accountability and government effectiveness are acting in a complementary way. Such a result suggests that accountability contributes to enhance the effectiveness of economic policies and the quality of public services in the MENA region. Finally, results relative to SSA countries show that government effectiveness, rule of law, control of corruption, and political stability act in a complementary way, as their interaction terms produce positive and significant effects on FDI. Regulatory quality is the only governance proxy which does not interact significantly with the remaining governance indicators. This result highlights the existence of major regulatory impediments to the development of the private sector in SSA countries. International trade and private investments may be seriously hindered by inappropriate regulations. Barriers limiting the access to the financial system are also leading to low levels of financial inclusion in Sub-Saharan countries (Demirgüç-Kunt & Klapper, 2012).

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5.5 Conclusions and Recommendations This paper seeks to assess the impact of six governance indicators on FDI inflows toward a sample of MENA and SSA countries over the 1996–2020 period. We mainly try to identify the governance dimensions which matter the most for foreign investors. We also verify if these dimensions differ across MENA and SSA countries and investigate the complementarity between the different governance proxies for both groups of countries. The empirical investigation leads to some interesting conclusions. We first notice that FDI determinants differ considerably across MENA and SSA countries. The full sample results show that trade openness contributes to boost FDI, while financial development and the share of industrial output impede foreign investment inflows. Such results suggest that foreign investors are taking advantage from low levels of domestic investment in the host countries. The inefficiency of the domestic financial systems is a potential explanation for the low investment rates. Results relative to MENA countries reveal that living standards represent a major attractiveness factor for foreign investors, while the stock of human capital does not meet the requirements of foreign firms. In Sub-Saharan African countries, infrastructure, domestic industry, and living standards are producing negative effects on FDI inflows, which suggests that foreign firms are focusing on exporting activities and are profiting from the lack of domestic investments in the industrial sector. Secondly, although the sensitivity of FDI to the governance indicators differs widely across MENA and SSA countries, government efficiency is invariably a major factor in attracting foreign investors. Political stability and the quality of the legal system are also significant attractiveness factors for the full sample. For MENA countries, governance effectiveness and rule of law are the only relevant governance indicator for foreign investors. Government effectiveness is the indicator producing the most important effect on FDI. Indeed, during the events of the Arab Spring, several countries in the MENA region embarked on a democratic transition, with, however, political instability along the way, with also fears of insecurity. Investors, thus, need to be reassured about the absence of violence (terrorist attacks, civil violence, and war). In SSA countries four out of the six governance indicators contribute to boost foreign investments. Voice and accountability are having the largest impact on FDI, while rule of law and the regulatory quality are the only governance proxies which do not produce any significant effect on FDI. The poor regulatory quality seems to be a major hindrance to the emergence of a strong private sector in Sub-Saharan African countries. However, some foreign companies in the natural resource sectors find it particularly advantageous to invest in countries with poor laws and regulations quality. Finally, another important finding of this study is that political stability amplifies the positive impact produced by the remaining governance indicators on FDI. This conclusion holds for the full sample as well as for MENA and SSA countries. Political stability is therefore an important prerequisite for the effectiveness of all the other

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governance indicators. Even when they are interested in other governance dimensions, foreign investors always keep an eye on political stability, which explains the fact that the remaining governance proxies produce stronger effects on FDI when they are associated with higher levels of political stability. Four major recommendations can be drawn from these results. First, MENA countries should review their human capital development strategies to meet the needs of foreign investors and attract a higher percentage of FDI. Upgrading human capital should also allow domestic firms to profit from technological and know-how transfers from foreign firms. On the other hand, SSA countries should no longer focus on foreign investors exclusively interested in natural resources. They should rather favor FDI which contributes to promote domestic industries and generate positive externalities in the host economies. To that end, they should upgrade their infrastructures, their financial systems, and their domestic industries to become sufficiently attractive for this class of foreign investors. Secondly, enhancing government effectiveness should increase MENA and SSA countries’ attractiveness to FDI. Thirdly, both groups of countries should promote political stability in order to amplify the effect of the other governance indicators on FDI. Finally, SSA countries should urgently reform their regulations to give real impetus to their private sectors, attract more foreign investors, and benefit from the positive externalities provided by FDI.

Appendix See Fig. 5.5 and Tables 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 5.10, 5.11, and 5.12.

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Table 5.1 Variables definitions Variable

Definition

Source

Foreign direct investment (FDI)

Net total foreign direct investment inflows as a percentage of GDP

World Bank Development Indicators

Human capital (School)

Gross secondary school World Bank Development enrollment rate (total enrollment Indicators to the population of the age group that officially corresponds to the level of education)

Size of the domestic market (pcGDP)

GDP per capita (domestic product in constant U.S. dollars divided by midyear population)

World Bank Development indicators

Financial Development (Credit)

Domestic credit provided by banking sector (% of GDP)

World Bank Development Indicators;

Trade openness (Openness)

Imports and Exports of goods and services as a percentage of GDP

World Bank Development Indicators;

Inflation

Growth rate of GDP deflator (annual %)

World Bank Development Indicators

Infrastructure

Number of telephone lines per 100 people

World Bank Development Indicators

Industrialization (Industry)

Value added of the manufacturing sector as a percentage of GDP

World Bank Development Indicators;

Rule of Law (Law)

The extent to which agents have World Bank Governance confidence in and abide by the Indicators rules of society, and in particular the quality of contract enforcement, property rights, police, and courts, as well as the likelihood of crime and violence

Control of Corruption (Corruption)

The extent to which public World Bank Governance power is exercised for private Indicators gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests (continued)

102

S. B. Mim and D. Saïdane

Table 5.1 (continued) Variable

Definition

Source

Government Effectiveness (GE)

The quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies

World Bank Governance Indicators

Regulatory Quality (RQ)

The ability of the government to World Bank Governance formulate and implement sound Indicators policies and regulations that permit and promote private sector development

Voice and Accountability (VAA)

The extent to which a country’s World Bank Governance citizens are able to participate in Indicators selecting their government, as well as freedom of expression, freedom of association, and a free media

Political Stability (PS)

The likelihood of political instability and/or politically motivated violence, including terrorism

World Bank Governance Indicators

Table 5.2 List of countries MENA countries

SSA countries

Algeria Bahrain Egypt Iran Israel Jordan Kuwait Libya Mauritania Morocco Oman Palestine Qatar Saudi Arabia Sudan Tunisia United Arab Emirates Yemen

Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Congo Cote d’Ivoire Democratic Republic of the Congo Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau

Kenya Lesotho Liberia Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Senegal Seychelles Sierra Leone South Africa Swaziland Tanzania

Tchad Togo Uganda Zambia Zimbabwe

2,183

1271,583

15,926

77,231 42,086

12,234,120 43,178

4491,642

23,788

9,443

1098

Std. Dev

NB. Obs

637

5,283

−8,703

Minimum

12,728

17,659

440

1148

2900,499

102,598 1102

16,651

0,403

114,381 22,942,610 106,260

1884,149

769,756

42,411

38,784

481

15,867,490 25,114

304

23,789

3,980

465

Std. Dev

1,266

NB. Obs

Minimum

374,011

116,462 85,075,990 136,564

83,135

1542

−11,625 15,467

Maximum 33,566

2,719

1,591

Mean

Median

1629

10,122,530 22,598

941

28,822

8,247

1563

Std. Dev

NB. Obs

0,403

102,598

Minimum

1035

21,000

4,978

173,998

30,158

36,244

450

15,233

0,013

95,936

41,276

42,274

1485

19,625

0,013

173,998

34,048

38,071

Infr 1,841

6,480 10,104

1426

6,696

0,233

0,652

0,885

2,900

475

9,478

10,633

426

10,795 7,705

0,430

50,044 50,037

11,995 11,730

14,621 12,919

1555

9,336

0,001

1126

167,447

−31,566

1080

5,686

0,001

1000

6,094

0,233

4800,532 36,128 40,064

5,644

18,258

473

12,256

−30,200

91,499

4,607

6,433

1599

140,758

−31,566

VA

GE

1,282

1,317

1,007

1,510

1,279

1,224

1,317

0,787

1,510

997

0,640

−1,869

1,217

1,282

1,127

1,007

1,057

996

0,674

996

0,943

996

0,641

996

0,762

995

0,643

−2,606 −3,315 −2,645 −2,226 −2,484

1,077

−0,722 −0,555 −0,718 −0,610 −0,782

0,784 420 −0,732 −0,379 −0,680 −0,624 −0,798

0,579 420 −0,640

0,828 420 −0,724

1,036 420

420

0,760

−2,090 −3,181 −2,347 −2,050 −2,279 420

0,744

−1,689

1,567

−0,250 −0,662 −0,296 −0,933 −0,229

0,732 1415

−0,164 −0,606 −0,168 −0,944 −0,196

0,728 1416

−0,255

0,728 1416

−0,266

0,972 1416

1416

0,733

−2,606 −3,315 −2,645 −2,226 −2,484

1,279

1417

0,695

−1,869

1,567

−0,623 −0,461 −0,579 −0,796 −0,662

RQ

−0,632

PS

−0,582 −0,587 −0,593 −0,706 −0,618

−0,526

Industry Corruption Law 11,316

4800,532 50,044 50,037

5,452

14,760

CREDITBK Openness Inflation 24,941

116,462 85,075,990 136,564

46,913

GDPPC

4940,218

School

53,660

−11,625 5,283

Maximum 161,824

Median

FDI

3,982

Mean

4,518 Sub-Saharan Mean African Median 2,433 Countries Maximum 161,824

MENA Countries

Full Sample

Table 5.3 Descriptive statistics

5 Which Governance Dimension Matters Most for FDI? A Comparative … 103

0,622***

0,647***

0,000

−0,025

Law

0,484***

0,650***

0,544***

0,219*** −0,004

0,538***

0,320***

0,505***

0,504***

0,036

0,602***

0,704***

0,308***

0,626***

0,277***

0,653***

0,603***

0,116***

0,664***

−0,063**

0,264***

1,000 1,000

Inflation

1,000

−0,066**

0,228*** −0,052*

0,048* 0,631***

0,654*** 0,705*** 1,000

PS

RQ

VA

GE

0,155*** 0,882*** 0,918*** 0,616*** 0,897*** 0,556*** 1,000

0,571*** 0,620*** 0,511*** 0,597*** 1,000

0,104*** 0,809*** 0,891*** 0,580*** 1,000

0,183*** −0,043

0,185*** −0,083*** 0,471***

0,126*** 0,905*** 1,000

Law

0,038

0,095*** 1,000

Corr.

0,233*** −0,085*** 0,558***

0,537***

0,281*** −0,065**

Industry

0,283*** −0,085*** 0,208***

0,150***

1,000

Infra

−0,047*

−0,035

0,304*** −0,041

−0,044*

1,000

CREDITBK Openness

***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

0,049*

−0,029

VA

−0,038

GE

RQ

PS

0,285***

0,242***

−0,134***

Industry

0,084***

0,742***

Corruption

0,277***

0,471***

1,000

GDPPC

−0,132*** −0,030

−0,026

0,000

Inflation

0,347***

Infra

0,298***

0,730***

CREDITBK −0,024

Openness

1,000

0,616***

−0,040

−0,042

School

School

1,000

FDI

GDPPC

FDI

Table 5.4 Correlation matrix, full sample

104 S. B. Mim and D. Saïdane

0,627***

0,526***

0,597***

0,608***

0,130***

0,158***

Law

0,214***

0,560***

0,170***

0,193***

VA

GE

0,632***

0,167***

0,574***

0,671***

0,644***

0,462***

0,590***

0,285***

0,618***

0,530***

1,000 −0,022

1,000

Inflation

0,006

−0,160*** 0,336*** 0,113**

0,503*** 0,517*** 0,102**

VA

0,559*** 1.000

0,865*** 0,894*** 0,563*** 1,000

RQ

GE

0,309*** −0,186*** 0,540*** 0,136*** 0,911*** 0,909*** 0,660*** 0,898*** 0.535*** 1.000

−0,021

0,369*** −0,210*** 0,371*** 0,019

0,122**

0,678*** 0,697*** 1,000

PS

0,271*** −0,086*

Law

1,000

Corr.

0,931*** 1,000

1,000

Industry

0,305*** −0,207*** 0,443*** 0,114**

0,035

1,000

Infra

0,323*** −0,157*** 0,456*** 0,096*

−0,186*** −0,045

0,050

−0,088*

***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

0,166***

0,470***

0,093*

0,258***

PS

RQ

0,399***

−0,310***

0,303***

1,000

CREDITBK Openness

−0,129*** −0,072

Corruption

0,141**

0,013

0,483***

−0,106**

0,322***

Industry

0,605***

1,000

0,338***

0,035

−0,119**

0,217***

0,522***

GDPPC

Infra

−0,032

0,299***

Openness

Inflation

0,277***

CREDITBK

1,000

0,589***

−0,037

−0,031

School

School

1,000

FDI

GDPPC

FDI

Table 5.5 Correlation matrix, MENA Zone

5 Which Governance Dimension Matters Most for FDI? A Comparative … 105

1,000

0,593*** 0,680***

0,180*** 0,107***

0,321*** 0,596**

0,424*** 0,393***

0,717***

−0,168*** 0,148***

0,617***

0,602***

0,047

0,009

−0,025

Infra

Industry

Corruption

Law

1,000

0.149***

0.104***

0,069**

0,297***

0,164***

0,232***

−0,005

0,434***

−0,039

***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

0.344*** 0.646***

0.165*** 0.542***

0.526***

0.618***

0.010

−0.035

VA

GE

0,259*** 0,576***

0,496***

0,505***

0,084***

−0,078**

PS

RQ

0,270*** 0,580***

−0,060**

0,431*** 0,211***

0,412***

−0,121*** −0,035

0,319***

0,386*** 1,000

−0,004

CREDITBK −0,029

−0.049

−0.081**

0,753*** 0,878*** 0,650*** 1,000

RQ

VA

0.616*** 0.085**

GE

0.853*** 0.912*** 0.685*** 0.886*** 0.760*** 1.000

0.484*** −0.060* 0.730*** 0.817*** 0.666*** 0.772*** 1.000

−0,094*** 0,462*** 0,088**

0,697*** 0,783*** 1,000

PS

−0,105*** 0,481*** 0,070**

1,000

Law

0,881*** 1,000

0,571*** 0,028

Corr.

−0,091*** 0,587*** 0,061*

−0,067**

Industry

0,121*** 1,000

1,000

Infra

−0,052*

−0,035

1,000

CREDITBK Openness Inflation

Openness

0,708***

0,053*

GDPPC

GDPPC

Inflation

1,000

0,694***

1,000

0,051

FDI

School

School

FDI

Table 5.6 Correlation matrix, Sub-Saharan Africa

106 S. B. Mim and D. Saïdane

5 Which Governance Dimension Matters Most for FDI? A Comparative …

107

Table 5.7 FDI and institutions, full sample Corruption

Law

RQ

GE

PS

VA

0.147***

0.167***

0.122***

0.207***

0.176***

0.163***

(0.0439)

(0.0432)

(0.0409)

(0.00958)

(0.0304)

(0.0434)

−0.0606

−0.121

−0.0784

−0.316***

−0.0932

−0.106

(0.0848)

(0.0942)

(0.104)

(0.0290)

(0.0740)

(0.110)

−0.00320

−0.0252

−0.0312

−0.0250*

−0.0785*

−0.0110

(0.0340)

(0.0387)

(0.0459)

(0.0131)

(0.0447)

(0.0424)

−0.0648*** -0.0559*** -0.0387*

-0.0504***

-0.0351

-0.0562***

(0.0187)

(0.0161)

(0.0219)

(0.00638)

(0.0335)

(0.0204)

Openness

0.335***

0.353***

0.356***

0.405***

0.270***

0.416***

(0.0624)

(0.0667)

(0.0699)

(0.0194)

(0.0641)

(0.0550)

Inflation

-0.0380***

-0.0401*** -0.0335*** −0.0416*** −0.0270*** −0.0464***

(0.00782)

(0.00739)

Industry

−0.200**

−0.248*** −0.217**

L.FDI pcGDP School Credit

(0.00866)

(0.00196)

(0.00746)

(0.00937)

−0.461***

−0.290**

−0.227**

(0.130)

(0.0922)

(0.0921)

(0.104)

(0.0469)

Infrastructure −0.0948

−0.0964

−0.0683

−0.0694*** 0.0332

−0.0668

(0.0947)

(0.0869)

(0.0925)

(0.0196)

(0.0897)

Corruption

(0.118)

(0.115)

0.862 (0.846)

Law

2.239*** (0.813) −0.210

RQ

(1.412) GE

4.450*** (0.404) 2.591**

PS

(1.255) −1.270

VA

(1.866) Constant

−3.352

−1.928

−3.421

−0.00839

3.143

−6.838*

(3.460)

(3.838)

(3.930)

(1.020)

(4.004)

(4.058)

Prob. AR(2)

0.174

0.180

0.186

0.132

0.135

0.223

Prob. Sargan

0.565

0.615

0.720

0.383

0.427

0.541

Nb of obs

615

615

615

615

615

615

Nb of countries

57

57

57

57

57

57

Robust Standard errors are in parentheses. ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

108

S. B. Mim and D. Saïdane

Table 5.8 FDI and institutions, MENA countries Variables

Corruption

Law

RQ

GE

PS

VA

L.FDI

0.627***

0.540***

0.587***

0.565***

0.345***

0.508***

(0.113)

(0.118)

(0.116)

(0.124)

(0.112)

(0.153)

pcGDP

0.0699**

0.00214

0.115***

0.0441

0.111

0.114***

(0.0356)

(0.0352)

(0.0368)

(0.0283)

(0.0710)

(0.0334)

−0.0755**

−0.0523

−0.138***

−0.105**

−0.0883

−0.0589*

(0.0358)

(0.0335)

(0.0307)

(0.0426)

(0.0677)

(0.0328)

Credit

−0.0391

−0.0505

0.00396

−0.0439

−0.0598

−0.0511

(0.0293)

(0.0310)

(0.0343)

(0.0389)

(0.0380)

(0.0394)

Openness

0.0609

0.0784

0.0552

0.0763*

0.0854*

0.119**

(0.0504)

(0.0536)

(0.0542)

(0.0456)

(0.0481)

(0.0566)

School

0.0274

−0.00473

0.119***

0.0524

−0.0107

−0.000836

(0.0372)

(0.0386)

(0.0458)

(0.0393)

(0.0450)

(0.0363)

Industry

−0.147

−0.165

−0.0423

−0.177

−0.00615

−0.104

(0.0920)

(0.101)

(0.0709)

(0.115)

(0.0616)

(0.111)

Infrastructure

0.00833

0.0616

−0.0512

−0.0306

0.0349

−0.0577

(0.0493)

(0.0627)

(0.0570)

(0.0694)

(0.0650)

(0.128)

Inflation

Corruption

0.763 (0.777)

LAW

1.853** (0.944) 0.431

RQ

(0.789) 2.983**

GE

(1.192) PS

1.182 (0.751) 3.434

VA

(3.431) Constant Prob. AR(2)

8.077***

6.772*

9.604***

11.98***

7.816

8.168

(3.085)

(3.557)

(3.321)

(4.202)

(6.150)

(6.616)

0.639

0.807

0.622

0.650

0.805

0.851

Prob. Sargan

0.909

0.738

0.720

0.927

0.979

0.940

Nb. Obs

202

202

202

202

202

202

Nb. Countries

18

18

18

18

18

18

Robust Standard errors are in parentheses. ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

5 Which Governance Dimension Matters Most for FDI? A Comparative …

109

Table 5.9 FDI and institutions, SSA countries Variables

Corruption

Law

RQ

GE

PS

L.FDI

0.0950***

0.300***

0.340***

0.0762***

0.0530***

0.0363

(0.0117)

(0.108)

(0.0905)

(0.0258)

(0.0123)

(0.0250)

pcGDP School

VA

−1.514***

−1.249*** −1.116*** −1.031***

−1.284***

−1.193***

(0.145)

(0.390)

(0.158)

(0.145)

(0.356)

(0.360)

0.0285

0.0582

0.0729

0.00588

0.0270**

−0.0865*

(0.0263)

(0.0651)

(0.0460)

(0.0490)

(0.0132)

(0.0460)

−0.00965

-0.0374

-0.101**

-0.0284

-0.0390*

0.0314

(0.0171)

(0.0587)

(0.0413)

(0.0381)

(0.0203)

(0.0465)

Openness

0.400***

0.100

0.0286

0.412***

0.373***

0.359***

(0.0205)

(0.0749)

(0.101)

(0.0651)

(0.0287)

(0.0328)

Inflation

−0.0464*** −0.0350** −0.0267 (0.00375)

(0.0137)

(0.0194)

(0.00932)

(0.00739)

(0.00482)

Industry

−0.960***

−0.125

−0.104

−0.934***

−0.893***

−1.061***

Credit

(0.0976) Infrastructure −0.155*** (0.0582) Corruption

−0.0430*** −0.0285*** −0.0374***

(0.234)

(0.296)

(0.170)

(0.139)

(0.109)

0.490***

0.629***

−0.299**

−0.385***

−0.186**

(0.160)

(0.186)

(0.138)

(0.0783)

(0.0789)

3.014*** (0.530)

Law

0.921 (3.454) 1.073

RQ

(2.430) GE

4.076** (1.910) 3.952***

PS

(1.256) VA

7.882*** (1.751)

Constant

3.355*

0.533

2.558

5.502

5.232***

11.99***

(1.906)

(5.693)

(3.600)

(4.062)

(1.668)

(2.380)

Prob. AR(2)

0.161

0.147

0.139

0.191

0.153

0.257

Prob. Sargan

0.329

0.441

0.433

0.308

0.574

0.416

Nb. Obs

413

413

413

413

413

413

Nb. of Countries

39

39

39

39

39

39

Robust Standard errors are in parentheses. ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

(0.0407)

GE × PS

GE × RQ

GE × CORR

GE × LAW

(0.0308)

(0.0717)

0.381***

GE_PS

GE_VA

(0.0314)

−0.0317

(0.0431)

−0.132***

(0.0164)

0.163***

(0.0501)

0.291***

(0.0201) (0.0304)

0.403***

(0.0165)

−0.0615*** −0.0343**

(0.0288)

−0.0482*

(0.0412)

−0.146***

(0.0167)

0.181***

LAW_PS

LAW_RQ

(0.0443)

−0.00192

(0.0928)

−0.195**

(0.0428)

0.125***

(0.0486)

0.267***

(0.0158) (0.0600)

0.421***

(0.0249)

−0.0465*** −0.0481*

(0.0306)

−0.0507*

(0.0386)

−0.150***

(0.0181)

0.184*** (0.0179)

(0.0422)

0.384***

(0.0117)

−0.0390***

(0.0298)

−0.0233

(0.0438)

−0.120***

(0.0605)

(0.227)

0.428*

(0.180)

−0.236

(0.0431)

(0.472)

−0.429

(0.105)

0.0165

(0.114)

0.0376

(0.0603)

(0.0538)

(0.00934)

−0.191*

(0.00710)

−0.195***

(0.00649)

−0.281***

(0.00663)

(0.241)

1.067***

(0.0810)

0.0244

(0.0647)

−0.360***

(0.00708)

(0.0676)

0.0288

(0.0485)

−0.231***

(0.00616)

(0.112)

−0.00685

(0.0799)

−0.283***

(0.00895)

(0.101)

−0.0362

(0.103)

−0.233**

(0.00724)

(0.0394)

−0.0171

(0.0526)

−0.209***

VA_PS

(0.0358)

(0.0500)

RQ_PS

CORR_PS

(0.0432)

−0.0911**

(0.0165)

0.190***

(0.0422)

0.301***

(0.0175)

−0.0208

(0.0253)

(0.0480)

0.298***

(0.0118)

−0.0714***

(0.0264)

−0.0976*** −0.0368

(0.0364)

−0.124***

(0.0132)

0.211***

(0.0880)

−0.0220

(0.112)

−0.135

(0.00872)

(0.0677)

−0.0909

(0.0783)

−0.332***

(0.00572)

(0.0591)

0.0955

(0.0487)

−0.319***

(0.00473)

(continued)

(0.0834)

−0.00626

(0.0692)

−0.234***

(0.00465)

−0.0359*** −0.0361*** −0.0390*** −0.0340***

0.408***

(0.0235)

−0.0458*

(0.0395)

0.00841

(0.0350)

−0.235***

(0.0187)

0.190***

0.433***

(0.0205)

−0.0503**

(0.0397)

0.00292

(0.0818)

−0.139*

(0.0414)

0.149***

LAW_CORR VA_LAW 0.162***

−0.0304*** −0.0282*** −0.0352*** −0.0295*** −0.0354*** −0.0276*** −0.0355*** −0.0328***

(0.0415)

(0.0146)

0.356***

(0.0174)

0.373***

−0.0306

(0.0453)

(0.0322)

−0.0158

(0.0319)

(0.0777)

−0.00773

(0.0451)

−0.0533*

(0.0431)

−0.0353

−0.0325*

(0.0439)

−0.203***

(0.0168)

−0.0892**

(0.0147)

−0.189***

Infrastructure −0.0262

Industry

Inflation

Openness

Credit

School

pcGDP

GE_RQ

0.130***

GE_CORR

0.157***

GE_LAW

0.162***

Variables

L.FDI

Table 5.10 Complementarity between governance proxies, full sample

110 S. B. Mim and D. Saïdane

−2.778

615

57

0.144

0.516

615

57

0.141

0.435

Observations

Nb of conuries

Prob. AR(2)

Prob. Sargan

0.652

0.215

57

615

(4.313)

−4.485

GE_RQ

0.484

0.106

57

615

(3.138)

−2.719

GE_PS

GE_VA

0.408

0.191

57

615

(2.707)

−4.287

(0.278)

−0.389

0.563

0.119

57

615

(3.580)

−4.452

(0.277)

1.332***

LAW_PS

0.648

0.288

57

615

(3.761)

−6.830*

(0.308)

−0.0389

LAW_RQ

0.439

0.165

57

615

(2.508)

−5.088**

(0.117)

−0.0504

0.618

0.302

57

615

(3.612)

−6.656*

(0.396)

−0.597

LAW_CORR VA_LAW

Robust Standard errors are in parentheses. ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

Constant

(2.622)

GE_CORR

−5.225*

GE_LAW

(2.727)

CORR × PS

RQ × PS

VA × PS

VA × LAW

LAW × CORR

LAW × RQ

LAW × PS

GE × VA

Variables

Table 5.10 (continued)

0.294

0.167

57

615

(3.405)

−11.37***

(0.343)

1.280***

VA_PS

0.441

0.114

57

615

(2.603)

−0.815

(0.197)

0.549***

RQ_PS

0.643

0.144

57

615

(3.180)

−5.385*

(0.231)

1.068***

CORR_PS

5 Which Governance Dimension Matters Most for FDI? A Comparative … 111

GE × PS

GE × CORR

GE × LAW

Infrastructure

Industry

Inflation

Openness

Credit

−0.109

(0.104)

0.0227

(0.0505)

−0.152

(0.0991)

0.0287

(0.0508)

(0.282)

(0.280)

0.229

(0.0464)

(0.0331)

0.217

0.0647

0.0263

(0.0612)

(0.0295)

(0.0259)

(0.0561)

−0.0146

−0.0244

0.0658

(0.0437)

(0.0252)

0.0765

(0.0644)

−0.101**

(0.0446)

(0.0944)

School

−0.0817***

0.0817

(0.181)

0.0292

(0.120)

0.0685

pcGDP

GE_PS

0.474**

(0.0731)

0.00232

(0.0747)

−0.0574

(0.0546)

0.00902

(0.0537)

0.0922*

(0.0468)

−0.0522

(0.0686)

−0.109

(0.113)

0.369***

GE_CORR

0.638***

GE_LAW

0.517***

Variables

L.FDI

(0.108)

(0.169)

−0.337**

−0.0383

0.0543

(0.0379)

(0.140)

−0.0203

(0.0662)

0.0760

(0.0339)

(0.0340) 0.00546

0.152***

(0.0425)

−0.0196

(0.0479)

−0.0464

(0.0814)

(0.124)

(0.0568)

(0.0537) 0.121

−0.0287

0.224***

(0.0444)

−0.0726

(0.0410)

−0.0761*

(0.0795)

0.209***

(0.185)

LAW_CORR 0.180***

GE_VA −0.0645

(0.0971)

−0.143

(0.0457)

0.0795*

(0.0500)

0.0646

(0.0362)

−0.0255

(0.0349)

−0.113***

(0.0323)

0.0945***

(0.114)

0.583***

GE_RQ

Table 5.11 Complementarity between governance proxies, MENA countries LAW_PS

(0.0675)

0.00522

(0.0734)

−0.0510

(0.0522)

0.00409

(0.0546)

0.0900*

(0.0403)

−0.0513

(0.0715)

−0.0995

(0.0889)

0.0776

(0.119)

0.381***

LAW_RQ

(0.0623)

−0.00341

(0.0951)

−0.151

(0.0435)

0.0656

(0.0604)

0.0424

(0.0306)

−0.0254

(0.0305)

−0.0834***

(0.0345)

0.0682**

(0.119)

0.610***

VA_LAW

(continued)

(0.0711)

0.0243

(0.115)

−0.152

(0.0452)

0.0327

(0.0744)

0.0339

(0.0239)

−0.0259

(0.0334)

−0.0518

(0.0535)

0.00975

(0.124)

0.539***

112 S. B. Mim and D. Saïdane

0.596

0.815

Prob. Sargan

0.990

0.925

18

202

(6.008)

7.597

(0.236)

GE_PS

0.921

0.644

18

202

(3.261)

8.750***

(0.264)

0.279

GE_RQ

1.000

0.122

18

202

(3.910)

0.554

(0.751)

1.262*

GE_VA

0.972

0.406

18

202

(4.749)

0.461

(0.204)

−0.285

LAW_CORR

0.987

0.902

18

202

(6.287)

7.259

(0.209)

0.361*

LAW_PS

Robust Standard errors are in parentheses. ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

0.596

0.786

Prob. AR(2)

202

18

202

18

(3.610)

(2.820)

Observations

6.449*

GE_CORR

5.857**

GE_LAW

Nb of countries

Constant

VA × LAW

LAW × RQ

LAW × PS

LAW × CORR

GE × VA

GE × RQ

Variables

Table 5.11 (continued)

0.735 0.764

0.917

18

202

(3.073)

5.822*

(0.243)

0.264

VA_LAW

0.632

18

202

(2.931)

7.232**

(0.185)

0.221

LAW_RQ

5 Which Governance Dimension Matters Most for FDI? A Comparative … 113

114

S. B. Mim and D. Saïdane

Table 5.12 Complementarity between governance proxies, SSA countries VARIABLES

GE_LAW

GE_CORR

GE_PS

GE_RQ

GE_VA

VA_LAW

VA_CORR

L.FDI

0.0887***

0.0864***

0.0798***

0.0900***

0.0735***

0.0753***

0.0956***

(0.0133)

(0.00848)

(0.0106)

(0.0258)

(0.0169)

(0.0121)

(0.0132)

pcGDP

−1.141***

−1.677***

−1.702***

−1.139***

−1.002***

−0.673***

−1.487***

(0.200)

(0.189)

(0.186)

(0.365)

(0.208)

(0.167)

(0.142)

School

−0.0141

0.0548**

0.0760***

−0.0102

−0.0601**

−0.153***

−0.0200

(0.0254)

(0.0233)

(0.0221)

(0.0509)

(0.0249)

(0.0417)

(0.0279)

Credit

0.0125

−0.0246

−0.0459**

0.00342

−0.0101

0.0410

−0.00764

(0.0400)

(0.0164)

(0.0232)

(0.0486)

(0.0567)

(0.0548)

(0.0152)

Openness

0.332***

0.423***

0.413***

0.431***

0.355***

0.334***

0.425***

(0.0281)

(0.0245)

(0.0284)

(0.0498)

(0.0377)

(0.0320)

(0.0221)

Inflation

−0.0347***

−0.0456***

−0.0376***

−0.0440***

−0.0382***

−0.0441***

−0.0479***

(0.00446)

(0.00402)

(0.00598)

(0.0103)

(0.00497)

(0.00384)

(0.00277)

Industry

−0.910***

−1.009***

−1.075***

−1.025***

−0.909***

−1.014***

−0.961***

(0.118)

(0.0722)

(0.0934)

(0.199)

(0.111)

(0.102)

(0.0738)

Infrastructure

−0.530***

−0.237***

−0.424***

−0.221**

−0.628***

−0.643***

−0.233***

(0.0729)

(0.0587)

(0.0908)

(0.104)

(0.0824)

(0.0862)

(0.0480)

GE × LAW

2.224*** (0.417)

GE × CORR

0.958*** (0.178)

GE × PS

1.449*** (0.335)

GE × RQ

0.751 (0.550)

GE × VA

3.094*** (0.530)

VA × LAW

3.177*** (0.452)

VA × CORR

1.172*** (0.241)

VA × PS VA × RQ CORR × RQ CORR × PS LAW × CORR RQ × PS LAW × PS Constant

−2.995*

−2.499

−3.280*

0.744

−5.968***

−1.708

−1.648

(1.672)

(1.705)

(1.770)

(3.501)

(1.493)

(2.168)

(1.567)

Observations

413

413

413

413

413

413

413

Nb countries

39

39

39

39

39

39

39

Prob. AR(2)

0.239

0.209

0.207

0.144

0.686

0.525

0.156 (continued)

5 Which Governance Dimension Matters Most for FDI? A Comparative …

115

Table 5.12 (continued) VARIABLES

GE_LAW

GE_CORR

GE_PS

GE_RQ

GE_VA

VA_LAW

VA_CORR

Prob. Sargan

0.576

0.340

0.199

0.156

0.618

0.578

0.418

VARIABLES

VA_PS

VA_RQ

CORR_RQ

CORR_PS

LAW_CORR

RQ_PS

LAW_PS

L.FDI

0.0532***

0.0801***

0.0845***

0.0795***

0.0904***

0.0858***

0.0648***

(0.00962)

(0.0307)

(0.0105)

(0.0102)

(0.00917)

(0.0229)

(0.0107)

pcGDP School Credit Openness Inflation Industry Infrastructure

−1.148***

−0.867**

−1.452***

−1.702***

−1.471***

−0.859**

−1.323***

(0.167)

(0.341)

(0.174)

(0.171)

(0.165)

(0.378)

(0.190)

−0.0296

−0.0501

0.0243

0.0468**

0.0416

0.0139

0.0190

(0.0346)

(0.0576)

(0.0173)

(0.0202)

(0.0269)

(0.0564)

(0.0289)

0.0303

0.0361

−0.0192

−0.0256

−0.0177

0.0373

0.00611

(0.0462)

(0.0396)

(0.0156)

(0.0191)

(0.0157)

(0.0383)

(0.0393)

0.383***

0.469***

0.396***

0.408***

0.392***

0.378***

0.344***

(0.0377)

(0.0521)

(0.0238)

(0.0256)

(0.0241)

(0.0533)

(0.0322)

−0.0407***

−0.0453***

−0.0456***

−0.0386***

−0.0453***

−0.0394***

−0.0293***

(0.00695)

(0.00947)

(0.00418)

(0.00498)

(0.00326)

(0.00908)

(0.00686)

−0.960***

−0.893***

−1.022***

−0.983***

−0.972***

−0.919***

−1.002***

(0.146)

(0.207)

(0.0735)

(0.0941)

(0.0817)

(0.226)

(0.134)

−0.517***

−0.337***

−0.162**

−0.302***

−0.222***

−0.141

−0.574***

(0.0832)

(0.101)

(0.0631)

(0.0666)

(0.0463)

(0.135)

(0.114)

GE × LAW GE × CORR GE × PS GE × RQ GE × VA VA × LAW VA × CORR VA × PS

1.652*** (0.401)

VA × RQ

0.771 (0.685)

CORR × RQ

0.802*** (0.236)

CORR × PS

1.191*** (0.259)

LAW × CORR

0.827*** (0.218) −0.0976

RQ × PS

(0.610) LAW × PS

1.906*** (0.384)

Constant

−2.529

−1.072

−0.440

−3.065*

−1.756

2.273

−2.664

(1.615)

(3.608)

(1.828)

(1.659)

(1.790)

(3.920)

(2.026)

Observations

413

413

413

413

413

413

413

Nb countries

39

39

39

39

39

39

39

Prob. AR(2)

0.426

0.417

0.137

0.161

0.150

0.101

0.195

Prob. Sargan

0.691

0.298

0.314

0.309

0.317

0.288

0.234

Robust Standard errors are in parentheses. ***, **, and * stand for significance at the 1%, 5%, and 10% levels, respectively

116

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Chapter 6

Formulation of a Corporate Governance Index for Banking Sector Hani El-Chaarani and Zouhour El-Abiad

Abstract The objective of this paper is to define a novel international corporate governance index for banks (GIBX62) based on sixty-two criteria and seven internal performance indicators related to board of directors, internal audit, compensation, risk management, nomination, compliance, ethics, transparency, and disclosure. The new index model was tested on seven different banks from the US, France, Spain, Italy, Lebanon, Egypt, and Jordan in 2021. The GIBX62 can be generalized and applied by international banks to measure their corporate governance efficiency. In addition, the GIBX62 could be employed by shareholders, depositors, and regulators to monitor the process of governance practices in the banking firms. Keywords Corporate governance · Bank · Performance · Transparency · Board of directors · Internal control

6.1 Introduction The financial crisis of 2008–2009 revealed the deficiencies of the banking sector and international financial system across the globe. Despite the financial support and help offered by the US and European governments, several international and well-known institutions like Washington Mutual Bank and Lehman Brothers announced their bankruptcy. In the US, over 465 banks and financial institutions were closed by the FDIC (Federal Deposit Insurance Corporation) between 2008 and 2012. At the end of 2013, the total number of bankrupted financial institutions declined to 24 showing the end of the international financial crisis. For many researchers, financial regulators, and international analysts, the deficiencies and weaknesses of corporate governance tools were the principal reason H. El-Chaarani (B) Beirut Arab University, Beirut, Lebanon e-mail: [email protected] Z. El-Abiad Lebanese University University, Beirut, Lebanon e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_6

119

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for bank failures and bankruptcy of large financial institutions in the US, Europe, and Asia. OECD (2009), the BASEL Committee (2017), and International Monetary Funds (2010) stated that the corporate governance deficiencies were the cause of the international banking system crush. For the EBA-European banking authority (2011), the international banking and financial crisis of 2008 was caused by both the weakness of legal protection and the failure of governance system. In this context, several international financial committees, international banking boards, and financial regulators stated that the corporate governance system in the banking firms must be reviewed and reinforced to protect the rights of savers and the interests of the other stakeholders. For example, the OECD (2009) and European commission boards (2010) published several reports in which they recommended banks to enhance the design and structure of governance structures. They claimed banks to improve the financial and non-financial control and independence of their board of directors. BASEL committee (2017) also recommended banks to enhance their risk and operational controlling process through the development of transparency, independence, and disclosures. For BASEL committee (2017), banks must improve their risk management function and enhance their board composition and qualifications. In addition, bankers must develop the risk communication level and reinforce the roles of the internal audit and compensation committee. Many scholars explored the correlation between governance structure and banks’ performance before, during, and after the international financial crisis of 2008. They showed that corporate governance tools like the board of directors structure, internal audit process, and compensation policy have an impact on the financial efficiency of the banking industry in different countries. In addition, they indicated that an efficient corporate governance system could lead to minimize the negative impact of crisis periods. In the US context, Lloyd (2009) stated that the deficiencies of risk management functions and governance tools caused the international banking crisis in 2008. Erkens et al. (2012) indicated that a weak level of public legal protection increased the risk of governance mechanisms deficiencies on banks’ valuation. EL-Chaarani (2015) confirmed the role of an efficient governance structure in the efficiency of Lebanese banking sector. The author reported that an efficient board of directors and a high level of ownership concentration led to enhance financial and non-financial performance in the banking sector. Several other studies (Fernandes & Fitch, 2013; Hunjra et al., 2020; VicenteRamos et al., 2020; Zulfikar et al., 2020) showed that the existence of high bank transparency, efficient risk management function, and independent audit committee leads to increase the performance of the banking sector. They revealed that a welldesigned governance structure is also able to decrease the financial risk and protect the relevant stakeholders in the banking industry (De Haan and Vlahu, 2015). Fernández et al. (2018) analyzed the structure of governance structures and systems before and after the international crisis of 2008. Based on the largest 45 banks across the globe, the authors revealed that after the financial crisis, the corporate governance structures in some large European banking were marginally enhanced.

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Therefore, more improvement and development of governance mechanisms are needed in the banks. As a result, several scholars like Song and Li (2012), Zagorchev and Gao (2015), and Gulati et al. (2020) created a specific governance index to measure the effectiveness of corporate governance structures in banks and guide bankers and financial regulators to evaluate and improve the process of control. These indexes were tailored to be employed in specific countries and can not be generalized. In addition, no index has considered all the dimensions of governance mechanisms in banks. This research aims to formulate an international governance index that can be employed and practiced by bankers. The importance of this new governance measurement tool is to deliver a new index that could be practiced by bankers and regulators to assess the effectiveness of their governance mechanisms. The main advantage of the proposed index is extracted from several research papers and international reports recommended by financial regulators like BASEL committee. The rest of the research is divided into four sections. The first section identifies the proposed component of internal governance indicators. The second section defines the governance index for the banking firms. The last two sections of this chapter discuss the utility of the new governance index and conclude the research paper.

6.2 Definition of Internal Governance Indicators For the agency theory (Jensen & Meckling, 1976), the divergences between owner and manager arise when they do not have the same objectives and interests and when the manager is willing to maximize his private benefits instead of increasing the performance of the firm. Shleifer and Vishny (1989) confirmed that managers could entrench themselves and increase their privacy by abusing their positions and employing specific managerial behaviors. Based on the entrenchment theory (Shleifer & Vishny, 1989), the information asymmetric between shareholders and managers increase conflicts and decrease financial and non-financial efficiency. In the banking sector, the situation is much more complex since conflicts and oppositions could exist between depositors, owners, managers, and other debtholders. Rose (1992) and Saunders et al. (1990) stated that bank managers could select risky and idiosyncratic investments to entrench themselves and block their replacement possibility. As a result, owners of banks must enhance the control of managers and limit any expropriation behavior. The classical agency and entrenchment theories proposed to control the manager through the employment of external and internal governance tools that should eliminate the opportunism of managers within any organization. The external corporate governance mechanisms include different tools like takeover bids and legal protection. These external mechanisms can not be controlled by owners because they must be imposed by regulators and external investors.

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On the other hand, shareholders can control the behavior of managers by tailoring several internal corporate governance mechanisms. They could use the ownership structure, board of directors’ composition, and payment plan to align the interests of executives with that of owners. In the case of the banking sector, the internal tools proposed by the classical theories must be developed and improved to consider the specificity of the operational management of banks. Thus, several international professional boards recommended banks to enhance their internal governance devices through the employment of specific functions and tools like risk management function and disclosure unit. Based on the classical financial theories and the recommendations of professional banking boards like BASEL (2017), several internal governance tools must be used by the banking industry to enhance the process of control and defend the interests of owners, depositors, and relevant stakeholders (Fig. 6.1).

Risk-management committee

Nomination committee

Board of directors

Internal corporate governance tools in banks Disclosure and transparency level

Internal audit committee

Compliance and ethics committee

Fig. 6.1 Internal corporate governance tools in banks

Remuneration committee

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First, every bank has to hire an independent and professional board of directors to control the strategic and operational decisions. Second, a professional risk management committee should be present in each bank to monitor the financial and non-financial risk levels. Third, an internal independent audit committee should exist in the banking sector to supervise the internal operating system. Fourth, a compensation committee must be present in every bank to define the incentive payments and eliminate any expropriation behavior. Fifth, executives should monitor the level of disclosure and transparency to control the financial behavior and block any financial and operational deficiencies within banks. Sixth, a nomination committee must be present in every bank to monitor the nomination of professional and independent executives. Finally, banks must hire an efficient compliance and ethics committee to verify the consistency with financial and non-financial regulations.

6.3 Formulation of Novel Governance Index Efficient governance mechanisms in the banking firms could lead to reduce the problems of moral hazards and conflicts between shareholders and debtholders (Becht et al., 2011; Mülbert, 2010). Liang et al. (2013), El-Chaarani (2015), De Haan and Vlahu (2015), Zulfikar et al. (2020), and many others showed that an efficient governance system structure could enhance the financial performance and mitigate all types of risk. On the other hand, Andries, et al. (2018) and Love and Rachinsky (2015) proposed innovative indexes based on the linear unweighted measurement and principal component analysis, respectively. In their indexes, they employed many components and attributes related to internal control and risk management. In the same line, Elmagrhi et al. (2020) created a new qualitative index that could reveal the efficiency of governance structure. They stated that the qualitative approach is more accurate since it reflects the performance of different corporate governance attributes. A deep analysis of the different indices defined by scholars reveals that there is no consistency of the employed measurement. In addition, the majority of proposed indices were created based on the regulations of each country. Thus, there is no general index that employs all the internal attributes of corporate governance mechanisms without considering the different codes and regulations of each country. As a result, in this section, we are proposing for bankers a new global index that includes the following internal corporate governance tools:

6.3.1 Indicator of the Board of Directors (BDI.13) The main mission board of directors is to control the managers and protect the rights of shareholders, depositors, and debtholders. The board outlines the policy of the bank and designs its governance system. For professionals (BASEL, 2017;

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OECD, 2009) and researchers (Andres & Vallelado, 2008; Fernandes & Ftch, 2013; Grove et al., 2011; Jensen & Meckling, 1976; Mishra & Nielsen, 2000), an efficient board of directors must include professional and independent members. In addition, they stated that the size of the board has to be medium to ease the communication between members. They also argued that the CEO duality must be absent to avoid Table 6.1 Indicator of the board of directors (BDI) Symbol

Criterion

Value of each criterion

BDI(1)

Size of board

This criterion equals to: 5 if: 9 < board size < 12, 4 if: 8 < board size < 13, 3 if: 7 < board size < 14, 2 if: 6 < board size < 15, 1 if: 5 < board size < 16, 0 otherwise

BDI(2)

Number of Independent

This criterion equals to: 5 if the level of independent members: > 50% 4 if: 40% < % independent members < 50%, 3 if: 30% < % independent members < 40%, 2 if: 20% < % independent members < 30%, 1 if: 10% < % independent members < 20%, 0 otherwise

BDI(3)

Duality between the chairman of board and CEO

This criterion equals to 5 in case of separation between chairman and CEO 0 otherwise

BDI(4)

Meetings number

This criterion equals to: 5 if the meeting number is 12 and above 4 if: meetings number = 10 or 11, 3 if: meetings number = 8 or 9, 2 if: meetings number = 6 or 7, 1 if: meetings number = 4 or 5, 0 otherwise

BDI(5)

Presence of women

This criterion equals to: 5 if the presence of women is: > 50% 4 if: 40% < % presence of women < 50%, 3 if: 30% < % presence of women < 40%, 2 if: 20% < % presence of women < 30%, 1 if: 10% < % presence of women < 20%, 0 otherwise (continued)

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Table 6.1 (continued) Symbol

Criterion

Value of each criterion

BDI(6)

Experience average of members

This criterion equals to: 5 if the average experience of board member is greater than 10 years 4 if: 9 < experience average of board member < 10 3 if: 8 < experience average of board member < 9 2 if: 7 < experience average of board member < 8 1 if: 6 < experience average of board member < 7 0 otherwise

BDI(7)

Experience majors of board members (Management skills, corporate governance, strategic planning, compensation, risk management, finance, IT, regulation)

This criterion equals to: 5 if the experience of all board members exists in eight majors, 4 if: 4 majors < the experience of all board members < 7 majors 3 if: 3 < the experience of all board members < 6 2 if: 2 < the experience of all board members < 5 1 if: 1 < the experience of all board members < 4 0 otherwise

BDI(8)

Training hours in the banking industry received

This criterion equals to: 5 if the members of board followed training related to the banking sector 0 otherwise

BDI(9)

Chairman independence

This criterion equals to 5 if the chairman is not an executive director 0 otherwise

BDI(10)

Number of positions board members

This criterion equals to 5 if all board members do not hold another position within the same bank 0 otherwise

BDI(11)

Information related to corporate governance

This criterion equals to 5 if the bank published information related to corporate governance 0 otherwise

BDI(12)

Information related to corporate governance code

This criterion equals to 5 if the bank defined its own codes related to corporate governance 0 otherwise (continued)

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Table 6.1 (continued) Symbol

Criterion

Value of each criterion

BDI(13)

Number of committees (Nomination, Compliance, Internal Audit, Risk Management, Remuneration committees)

This criterion equals to: 5 if there are 5 efficient committees within the bank 4 if there are 4 efficient committees within the bank 3 if there are 3 efficient committees within the bank 2 if there are 2 efficient committees within the bank 1 if there is 1 efficient committee within the bank 0 otherwise / 13 ∑ ( B D I.i) 13

Total Index Value

i=1

any entrenchment and lobbying possibility. Table 6.1 shows the board of directors index. This index is founded on the following 13 attributes: (BDI.1) Size of the board, (BDI.2) Independent members, (BDI.3) Duality, (BDI.4) Meetings, (BDI.5) Diversity, (BDI.6) Experience level, (BDI.7) Experience major, (BDI.8) Knowledge level, (BDI.9) Chairman of board, (BDI.10) directorship/Chairmanship, (BDI.11) governance report, (BDI.12) governance code, and (BDI.13) Board committees. All the details of each attribute are shown in Table 6.1.

6.3.2 Indicator of the Risk Management Committee (RMCI.9) In the banking firms, the risk management commission has an important mission since it analyzes and controls the different types of risks, namely operational, financial, portfolio, and liquidity risks. BASEL committee (2017) recommended banks to employ an efficient risk management policy to monitor and mitigate the impact of different types of risk within banks. Several scholars like Subramaniam et al. (2013); Oino and Itan, (2018), and Zulfikar et al., (2020) indicated that the risk management committee must be led by Chief Risk Officer (CRO) who should control the liquidity and any new financial product offered by the bank. In addition, they indicated that the members of this committee have to be independent and professional. The risk management efficiency is measured based on the 9 following criteria (Table 6.2): (RMCI.1) Existing of CRO position, (RMCI.2) Split between executive director and CRO position, (RMCI.3) Number of reunions, (RMCI.4) Reporting processes of the committee, (RMCI.5) independent members, (RMCI.6) experience level, (RMCI.7) committee size, (RMCI.8) Presence of a professional guide and sophisticated models, and (RMCI.9) diversification.

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Table 6.2 Indicator of risk management committee (RMCI.9) Symbol

Criterion

Value of each criterion

RMCI(1)

Presence of CRO

This criterion equals to 5 in case of the existence of an independent CRO 0 otherwise

RMCI(2)

Separation between CRO and other positions

This criterion equals to 5 if the CRO position does not hold another position 0 otherwise

RMCI(3)

Meetings of committee

This criterion equals to: 5 if the meeting number is 12 and above per year 4 if: meetings number = 10 or 11 per year, 3 if: meetings number = 8 or 9 per year, 2 if: meetings number = 6 or 7 per year, 1 if: meetings number = 4 or 5 per year, 0 otherwise

RMCI(4)

Direct reporting to the board

This criterion equals to 5 if the committee reports to the board members and CEO 0 otherwise

RMCI(5)

Independent members in the committee This criterion equals to 5 if the % of independent is above 66% 0 otherwise

RMCI(6)

Experience of risk committee

This criterion equals to: 5 if the average experience of board member is greater than 10 years 4 if: 9 < experience average of committee members < 10 3 if: 8 < experience average of committee members < 9 2 if: 7 < experience average of committee members < 8 1 if: 6 < experience average of committee members < 7 0 otherwise

RMCI(7)

Size of risk committee

This criterion equals to 5 if the number of members of risk committee is greater than 3 0 otherwise

RMCI(8)

Policies, procedures, and quantitative models of risk management

This criterion equals to 5 if the bank has risk management plan, quantitative models, and specific procedures 0 otherwise

RMCI(9)

Diversification

This criterion equals to 5 if the bank has no lending exposure to just one client 0 otherwise / 9 ∑ ( R MC I.i ) 9

Total Index Value

i=1

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6.3.3 Indicator of Internal Audit Committee (IACI.8) The mission of audit committee is to monitor the internal operating system and verify that all the decisions taken by executives and managers are in line with the plan predefined by the board of directors and shareholders. For BASEL committee (2017) and numerous researchers like Khanifah et al. (2020) and Zulfikar et al. (2020), the audit committee members have to be transparent and professional and thus, lead to enhance the control and performance process within banks. This committee must be independent of the CEO and must report to the board of directors. The efficiency of this commission is measured through the following 8 criteria: (ICAI.1) Existing of the independent chair; (ICAI.2) Split between Executive director and committee chair, (ICAI.3) Reporting processes of the committee, (ICAI.4) Percentage of Independent, (ICAI.5) Number of reunions, (ICAI.6) Experience level of the committee members, (ICAI.7) Internal audit committee size, and (ICAI.8) Direct reporting to the committee (Table 6.3). Table 6.3 Indicator of internal audit committee (IACI.8) Symbol

Criterion

IACI(1)

Presence of a professional chief for the This criterion equals to 5 in case of the committee existence of independent chief for the committee 0 otherwise

IACI(2)

Separation between audit committee and other positions

This criterion equals to 5 if the committee members do not hold another position 0 otherwise

IACI(3)

Meetings of committee

This criterion equals to: 5 if the meeting number is 12 and above per year 4 if: meetings number = 10 or 11 per year, 3 if: meetings number = 8 or 9 per year, 2 if: meetings number = 6 or 7 per year, 1 if: meetings number = 4 or 5 per year, 0 otherwise

IACI(4)

Direct reporting to the board

This criterion equals to 5 if the committee reports to the board members without passing by the CEO 0 otherwise

IACI(5)

Independent members of internal audit This criterion equals to 5 if the % of committee independent is 100% 0 otherwise

Value of each criterion

(continued)

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Table 6.3 (continued) Symbol

Criterion

IACI(6)

Experience of internal audit committee This criterion equals to: 5 if the average experience of board member is greater than 10 years 4 if: 9 < experience average of committee members < 10 3 if: 8 < experience average of committee members < 9 2 if: 7 < experience average of committee members < 8 1 if: 6 < experience average of committee members < 7 0 otherwise

IACI(7)

Size of internal audit committee

This criterion equals to 5 if the number of members of internal audit committee is greater than 3 0 otherwise

IACI(8)

Internal members of committee direct report to the committee

This criterion equals to 5 if all auditors report directly to the committee 0 otherwise / 8 ∑ ( I AC I.i) 8

Total Index Value

Value of each criterion

i=1

6.3.4 Indicator of Remuneration Committee (RCI.8) The remuneration commission has a significant role in banks since it defines the compensation plans and monitors the performance-based payment strategy defined by the board of directors. Furthermore, this committee must define with board members of the remuneration strategy that must be paid for executives in the banking sector. The members of this committee have to be professional, aware of the banking sector, and independent to avoid any manipulation of payment plans to develop the private benefits and expropriate the relevant stakeholders (El-Chaarani & El-Abiad, 2021; Erkens et al., 2012; Guo et al., 2014). The efficiency of the remuneration committee in banks is based on the following 8 criteria (Table 6.4). (RCI.1) Existing of an independent chair, (RCI.2) Split between executive director and committee chair, (RCI.3) number of reunions, (RCI.4) reporting processes of the committee, (RCI.5) number of independent members, (RCI.6) experience level of the committee, (RCI.7) committee size, and (RCI.8) presence of compensation strategy.

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Table 6.4 Indicator of remuneration committee (RCI.8) Symbol

Criterion

Value of each criterion

RCI(1)

Presence of professional chief for the committee

This criterion equals to 5 in case of the existence of independent chief for the committee 0 otherwise

RCI(2)

Separation between remuneration committee and other positions

This criterion equals to 5 if the committee members do not hold another position 0 otherwise

RCI(3)

Meetings of committee

This criterion equals to: 5 if the meeting number is 12 and above per year 4 if: meetings number = 10 or 11 per year, 3 if: meetings number = 8 or 9 per year, 2 if: meetings number = 6 or 7 per year, 1 if: meetings number = 4 or 5 per year, 0 otherwise

RCI(4)

Direct report to the board

This criterion equals to 5 if the committee reports to the board members without passing by CEO 0 otherwise

RCI(5)

Independent members of compensation committee

This criterion equals to 5 if the % of independent is 66% 0 otherwise

RCI(6)

Experience of remuneration committee

This criterion equals to: 5 if the average experience of board member is greater than 10 years 4 if: 9 < experience average of committee members < 10 3 if: 8 < experience average of committee members < 9 2 if: 7 < experience average of committee members < 8 1 if: 6 < experience average of committee members < 7 0 otherwise

RCI(7)

Size of remuneration committee

This criterion equals to 5 if the number of members of remuneration committee is greater than 3 0 otherwise

RCI(8)

Compensation strategy

This criterion equals to 5 if the committee defined a pay-performance structure 0 otherwise / 8 ∑ ( RC I.i) 8

Total Index Value

i=1

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6.4 Indicator of Ethics and Compliance Committee (CECI.8) The ethics and compliance committee has the objective to verify if the bank is respecting the national and international financial codes and laws defined by financial authorities and regulators (BASEL committee, 2015; Oino & Itan, 2018). BASEL (2017) recommended banks to define a code of ethics and standards to be used by employees and executives. Like the other committees, this committee must include professional and independent members and its members should report to the bank’s board of directors without passing by executives. The efficacy of ethics and compliance committee is based on the following 8 criteria (Table 6.5). (CECI.1) existing of an independent chair, (CECI.2) Split between executive director and committee chair, (CECI.3) number of reunions, (CECI.4) reporting processes of the committee, (CECI.5) number of independent members, (CECI.6) years of experience of compliance and ethics committee, (CECI.7) ethics and compliance committee size, and (CECI.8) existing of specific ethics and compliance codes. Table 6.5 Indicator of compliance and ethics committee (CECI.8) Symbol

Criterion

Value of each criterion

CECI(1)

Presence of professional chief for the committee

This criterion equals to 5 in case of the existence of independent chief for the committee 0 otherwise

CECI(2)

Separation between compliance committee and other functions

This criterion equals to 5 if the committee members do not hold another position 0 otherwise

CECI(3)

Meetings of committee

This criterion equals to: 5 if the meeting number is 12 and above per year 4 if: meetings number = 10 or 11 per year, 3 if: meetings number = 8 or 9 per year, 2 if: meetings number = 6 or 7 per year, 1 if: meetings number = 4 or 5 per year, 0 otherwise

CECI(4)

Direct report to the board

This criterion equals to 5 if the committee reports to the board members without passing by CEO 0 otherwise

CECI(5)

Independent members of compliance and ethics committee

This criterion equals to 5 if the % of independent is 66% 0 otherwise (continued)

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Table 6.5 (continued) Symbol

Criterion

Value of each criterion

CECI(6)

Experience of compliance and ethics committee

This criterion equals to: 5 if the average experience of board member is greater than 10 years 4 if: 9 < experience average of committee members < 10 3 if: 8 < experience average of committee members < 9 2 if: 7 < experience average of committee members < 8 1 if: 6 < experience average of committee members < 7 0 otherwise

CECI(7)

Size of compliance and ethics committee

This criterion equals to 5 if the number of members of compliance and ethics committee is greater than 3 0 otherwise

CECI(8)

Compliance and ethics codes

This criterion equals to 5 if there are well-defined ethics and compliance codes 0 otherwise / 8 ∑ ( C EC I.i) 8

Total Index Value

i=1

6.4.1 Indicator of the Nomination Committee (NCI) The nomination commission has a significant role in banks since it identifies and nominates suitable candidates for executive positions. It is responsible to prepare a succession plan for top and key positions in the banking industry. This committee must analyze and identify the needed requirements to fill each position within banks. In addition, the nomination committee should verify if the key positions within banks are led by independent and professional members. Thus, this committee has to execute a periodic evaluation of top positions. BASEL (2017) and Andries, et al. (2018) recommended banks to hire a professional nomination committee that must report directly to the board of directors’ members without passing through the general manager or other executives. The performance of the nomination committee is evaluated based on the following 8 criteria (Table 6.6). (NCI.1) existing of an independent chair, (NCI.2) split between executive director and committee chair, (NCI.3) number of reunions, (NCI.4) reporting processes of the committee, (NCI.5) number of independent members, (NCI.6) experience level of the committee, (NCI.7) nomination committee size, and (NCI.8) existing of a professional evaluation system and succession strategy.

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Table 6.6 Indicator of the nomination committee (NCI.8) Symbol

Criterion

Value of each criterion

NCI(1)

Presence of professional chief for the committee

This criterion equals to 5 in case of the existence of independent chief for the committee 0 otherwise

NCI(2)

Separation between nomination committee and other functions

This criterion equals to 5 if the committee members do not hold another position in banking sector 0 otherwise

NCI(3)

Meetings of committee

This criterion equals to: 5 if the meeting number is 12 and above per year 4 if: meetings number = 10 or 11 per year, 3 if: meetings number = 8 or 9 per year, 2 if: meetings number = 6 or 7 per year, 1 if: meetings number = 4 or 5 per year, 0 otherwise

NCI(4)

Direct report to board members

This criterion equals to 5 if the committee reports to the board members 0 otherwise

NCI(5)

Independent members of nomination committee

This criterion equals to 5 if the % of independent is 66% 0 otherwise

NCI(6)

Experience level of nomination committee

This criterion equals to: 5 if the average experience of board member is greater than 10 years 4 if: 9 < experience average of committee members < 10 3 if: 8 < experience average of committee members < 9 2 if: 7 < experience average of committee members < 8 1 if: 6 < experience average of committee members < 7 0 otherwise

NCI(7)

Size of nomination committee

This criterion equals to 5 if the number of members of nomination committee is greater than 3 0 otherwise

NCI(8)

Committee succession plan and defined system

This criterion equals to 5 if the committee have a succession plan and evaluation system for HR 0 otherwise / 8 ∑ ( N C I.i) 8

Total Index Value

i=1

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6.4.2 Indicator of Disclosure and Transparency (DTLI.16) The last corporate governance mechanism considered in the proposed index includes both disclosure and transparency levels. Elamer et al. (2020) revealed that the quality of transparency and disclosure reflects the efficiency of corporate governance performance and leads to control the efficiency of executives. OECD (2009) and BASEL (2017) recommended banks to disclose all their information to the relevant stakeholders through their websites and annual reports. The disclosed information must be accurate and includes all the data related to risk management, board of directors composition, financial data, and proprietorship composition. The efficiency of disclosure must be evaluated based on the existence of published data related to the following components: (DLTI.1) compensation of executives, (DLTI.2) financial situation and performance, (DLTI.3) ownership structure, (DLTI.4) risk management strategy, (DLTI.5) the compliance and penalties, (DLTI.6) board of directors’ characteristics, (DLTI.7) committees composition and management, (DLTI.8) financial and accounting policies, (DLTI.9) objectives, missions, and strategies, (DLTI.10) dividend policy, (DLTI.11) stocks data, (DLTI.12) related party transaction, (DLTI.13) shares owned by executives, (DLTI.14) information related to executives, (DLTI.15) managerial processes and strategies, and (DLTI.16) whistleblower policy (Table 6.7). Table 6.7 Indicator of disclosure and transparency (DTLI.16) Symbol

Criterion

Value of each criterion

DTL(1)

Published information related to compensation

This criterion equals to 5 if all the information related to the compensation of executives are published 0 otherwise

DTLI(2)

Published information related to financial data and performance

This criterion equals to 5 if all the information related to financial situation are published 0 otherwise

DTLI(3)

Published information related to ownership

This criterion equals to 5 if all the information related to the ownership concentration and ultimate ownership identity are published 0 otherwise (continued)

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Table 6.7 (continued) Symbol

Criterion

Value of each criterion

DTLI(4)

Published information related to risk management

This criterion equals to 5 if all the information related to the risk management policy and risk exposure are published 0 otherwise

DTLI(5)

Published information related to non-compliance

This criterion equals to 5 if all the information related to the compliance and penalties are published 0 otherwise

DTLI(6)

Published information related to the board of directors

This criterion equals to 5 if all the information related to the board of directors’ recruitment, composition, and meetings are published 0 otherwise

DTLI(7)

Published information related to committees

This criterion equals to 5 if all the information related to the different committees’ recruitment, composition, and meetings are published 0 otherwise

DTLI(8)

Published information related to accounting and financial system

This criterion equals to 5 if all the information related to the accounting and financial policies are published 0 otherwise

DTLI(9)

Published information related to objectives, missions, and strategies

This criterion equals to 5 if all the information related to the objectives, missions, and strategies are published 0 otherwise

DTLI(1)0

Published information related to dividend

This criterion equals to 5 if all the information related to the dividend policy are published 0 otherwise

DTLI(11)

Published information related to stock market price and stock transaction (repurchasing, transfer…)

This criterion equals to 5 if all the information related to the stocks market price and stocks transaction are published 0 otherwise

DTLI(12)

Published information related to third party transaction

This criterion equals to 5 if all the information related to third-party transaction are published 0 otherwise

DTLI(13)

Published information related to shares This criterion equals to 5 if all the own by executives information related to the shares owned by executives are published 0 otherwise (continued)

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Table 6.7 (continued) Symbol

Criterion

Value of each criterion

DTLI(14)

Published information related to CEO, CFO, CRO and other executives

This criterion equals to 5 if all the information related to the new CEO, CFO, CRO, and other executives, and data in case of cessation and resignation are published 0 otherwise

DTLI(15)

Published information related to managerial strategies and practices

This criterion equals to 5 if all the information related to the managerial strategies and practices are published 0 otherwise

DTLI(16)

Published information related to existing of whistleblower policy and communicated

This criterion equals to 5 if all the information related to the whistleblower policy are published 0 otherwise / 16 ∑ ( DT L I.i ) 16

Total Index Value

i=1

6.4.3 Formulation of the Novel Global Governance Index Based on the information presented above 7 governance components could be employed in the banking sector. Every component is split into several subcomponents. In total, a weighted index is formed by considering 7 components and 62 sub-components (Fig. 6.2). After presenting the different important components of efficient governance in the banking firms. This section presents the GIBX62 (Governance Index of the Banking sector), formulated based on 62 internal corporate governance indicators. The GIBX62 can be generalized and applied to all international banks. This index can be used as a tool to monitor the governance performance of banking firms by depositors, investors, regulators, and other shareholders. This index can provide two sets of indicators: the first set is the general index of the governance devices in the banking firms and the second set is the measurement indices of the different internal governance mechanisms (compensation, internal audit, board of directors, disclosure, transparency, nomination, compliance, ethics, and risk management). In total there are sixty-two criteria and seven indicators of the internal mechanisms, that are used to calculate the following indicators: • Indicator of board of director efficiency: BDI.13 =

• Indicator of

i=1

B D I.i)

∑8

(

∑9 13

R M C I .i ) 9 ( I AC I.i) internal audit efficiency: IACI.8 = i=1 8 ∑ ( 8 RC I.i) compensation committee efficiency: RCI.8 = i=1 8

• Indicator of risk management efficiency: RMCI.9 = • Indicator of

∑13

(

i=1

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Internal audit committee index (IACI)

8 criteria

Board of directors index (BDI)

13 criteria

Risk management committee index (RMCI)

8 criteria

Remuneration committee (RCI)

9 criteria

Compliance and ethics committee index (CECI)

8 criteria

Nomination committee indicator (NCI)

8 criteria

Disclosure and transparency level index (DTLI)

16 criteria

Governance index of banks GIB.X62 62 criteria

Fig. 6.2 The global GIB.X index

• Indicator of compliance and ethics performance efficiency: CECI.8 = ∑ (

8 i=1

C EC I.i) 8

• Indicator of nomination committee efficiency: NCI.8 = • Indicator of disclosure and transparency efficiency:

∑8

(

N C I.i) 8 ∑ ( 16 DT L I.i ) DTLIx16 = i=1 16 i=1

Based on the seven indicators presented above, the formula of corporate governance indicators (GIB.X62) is defined as follows:

(GIB.X62) =

BDIx13 + RMCIx9 + NCIx8+ IACIx8 + RCIx8 + DTLIx16 + CECIx8 13 ∑

7

(B D I.i )

i=1

+

13 8 ∑

=> (GIB.X62) =

+

9 ∑ i=1

8

+

9 8 ∑

(C EC I.i)

i=1

+

8 ∑

(R MC I.i )

i=1

8

+ 7

8 16 ∑

(N C I.i )

i=1

8 ∑

(I AC I.i ) +

(RC I.i )

i=1

8

(DT L I.i )

i=1

16

The margin value of GIBX62 varies between 0 and 5. The performance of internal governance devices is very weak if it is the value of GIBX62 is between 0 and 1, weak if its value between 1 and 2, average if its value is between 2 and 3, efficient if its value between 3 and 4, and finally very efficient if its value is between 4 and 5.

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6.5 Discussion and application of GIBX62 The GIBX62 and its 7 components can be used by bankers and other relevant stakeholders like shareholders and financial supervisors to evaluate the effectiveness of internal governance tools in banks. This new tool could be employed by all banks in all countries since its attributes are general and do not consider the regulations and codes of each country. The GIBX62 also could match the practices of governance structures and mechanisms between different banks in different nations. The new index also could give investors a general observation of the operational control and governance mechanisms within each bank. The GIBX62 index can show the efficiency of the 7 important functions and lines of safeguard that could be employed by shareholders and executives within the bank. To verify the different advantages of GIBX62 in the banking sector, we tried to test it on different banks from different countries. Thus, an email has been sent to 38 banks from 15 countries during the first quarter of 2021. Only 7 banks from 7 countries accepted to complete the questionnaire in which each bank has to fill all the information related to the GIBX62 index. The corporate governance data received from the 7 banks were verified and completed by other information extracted from the website and published annual reports of each bank. The sample of the study is presented in Table 6.8. We kept the names of banks anonymous based on their requests. The results in Table 6.9 present the GIBX62 and the 7 internal governance indicators. The results show the indicator differences between the 7 banks. It is very clear from the results in Table 6.9 that the indexing value of governance structure in the three selected banks in the MENA region is on average (between 2.11 and 2.51). Thus, reinforcement of governance mechanisms is required in the three banks in the MENA region. The indexing value is greater in Europe and the US (Fig. 6.3). The results in Fig. 6.4 present the different internal performance indicators of the governance tools in the banking industry. All internal indicators show that the US banks and the European banks except Italy have strong indicators (3 and above). In Table 6.8 Sample of the case study

Region

Country

Number of the banks

US

US

1

Europe

France

1

Spain

1

Italy

1

Egypt

1

Lebanon

1

MENA region

Jordan Total →

1 7

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Table 6.9 Governance indicators values of 7 different banks Name of the indicator Board of director performance indicator Risk management performance indicator Internal audit performance indicator Compensation performance indicator Compliance and ethics performance indicator Nomination performance indicator Disclosure and transparency performance indicator Banking Global Index

Region Country Symbol

US US Mean

France Mean

Europe Italy Mean

Spain Mean

Lebanon Mean

MENA Jordan Mean

Egypt Mean

BDI.x13

4.1

4

3.6

3.3

2.6

2.3

2.1

RMCI.x9

4.1

3.2

3.5

2.6

3.3

2.3

2.7

IACI.x8

3.8

3.2

3

3.2

2.5

2.2

3.1

RCI.x8

4.2

4.4

2.5

3.6

2

2.1

2.5

CECI.x8

3

3.3

2.7

3.4

2

1.5

2.3

NCI.x8

3.3

3.7

2.4

2.4

2.4

2.1

2.4

DTLI.x16

4.2

4.1

3.2

3.3

2.1

2.4

2.2

(GIB.X62) y1

3.81 Strong

3.7 Strong

2.98 Middle

3.11 Strong

2.41 Middle

2.11 Middle

2.51 Middle

Results

Jordan bank

0

Very Weak

1

Weak

2

3

Middle

Lebanon bank

US

Spain bank

Egypt bank

Italy bank

bank

4

Strong

Very Strong

5

France bank

Fig. 6.3 Classification of banks based on GIBX62 index

addition, the results reveal that the internal performance indicators are weak in the MENA region, mainly in the three selected banks from Lebanon, Jordan, and Egypt.

6.6 Conclusion The main objective of this research is to define a new index and measure the efficiency of governance structure in the banking industry. The proposed index is built based on 7 internal governance indicators associated with: (1) board of directors, (2) internal audit, (3) compensation control, (4) risk management control, (5) ethics and compliance, (6) nomination of executives, and (7) disclosure and transparency. Based on the recommendations of experts and regulators such as BASEL and OCED, 62 criteria were considered to formulate the governance index of the banking sector (GIB.X62). This index can be used by bankers to monitor the weakness of their internal governance structure. The GIBX62 could also be employed by governments and

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Fig. 6.4 Internal performance indicators and GIBX62 index

regulators to protect the rights of debtholders and depositors and ensure the stability of national financial system. The GIBX62 index has been applied to 7 different banks from 7 countries. The results reveal that the application of this new index is easy to be practiced by bankers across the globe to monitor the performance of their internal governance structure. In addition, the results of the GIBX62 index show that the internal governance practices are more efficient in European and US banks than their counterparts in the MENA region. This result could be explained through the existence of low level of public legal protection and control in MENA region that could lead bankers to entrench themselves in banking firms. Finally, this chapter has some limitations that have to be considered in further research. First, the GIBX62 index was tailored based on the recommendation of scholars and international professional boards and commissions without considering the vision of bankers. Thus, this index must be discussed with bankers and financial regulators to be improved. Second, in this research, the GIBX62 was applied to a very small sample based on an online questionnaire. Therefore, it is recommended

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in further research to test the new proposed index on a larger sample based on the qualitative method. The new index considers the internal corporate governance index and thus must lead the researcher to extend this index by employing external corporate governance mechanisms like public legal protection.

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Chapter 7

The Health Systems’ Governance in MENA Countries: A Panel Causality Framework Emna Essadik and Chokri Terzi

Abstract This study investigates the causality nexus between governance and the health system performance in the short and long run. In the first step, we compute a Governance Composite Index (GCI) based on data relative to 14 MENA countries and covering the period 1996–2019. A panel data model is then used to identify the main determinants of the health system performance and to assess the impact of governance on the health system outputs. The Engle-Granger two-step approach is used to conduct the causality analysis. Estimation results show that governance contributes to promote the health system’s outcomes in the long run. We also find that an increase of 4% in the governance standards should trigger the short-term effect. Results also reveal that allocating a bigger budget contributes to enhance the health system’s outputs. Keywords Governance · Health system performance · Granger causality test · MENA countries

7.1 Introduction The recent decades have been marked by an increasing importance devoted to governance, which became a government priority particularly in developing countries. This growing interest may be imputed to the fact that governance affects a broad

E. Essadik (B) · C. Terzi Faculty of Economic Sciences and Management of Sousse, University of Sousse, Sousse, Tunisia e-mail: [email protected] C. Terzi e-mail: [email protected] C. Terzi Polytechnic School of Tunis, Carthage University, Tunis, Tunisia

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_7

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range of economic sectors, i.e., the public sector (Jordan, 2014), the financial sector (Handley Schaller et al., 2007). Governance can be defined as the most efficient management of a country’s economic and social resources to achieve economic development. It is not only the government’s responsibility, but also the community’s overall responsibility at the macro level (Jafari, 2018). Decision-makers are constantly seeking to strengthen governance in various sectors, including the legal system, education and learning, culture, media, and sports. To achieve this objective, governments must engage in several actions, including the implementation of laws and regulations, the formulation of policies and strategies, and reforms. An efficient health system is crucial for developing human capital and promoting economic growth. However, this system suffers from a significant lack of governance in developing countries, which often leads to a deteriorated health status of the population. Given the economic and social importance of the health system, improving its performance should figure among the priorities of any government. An effective way to achieve this goal is to promote good governance. Nevertheless, this task can be extremely difficult to meet since decision-making in the health sector depends on various structural constraints in developing countries. As asserted by Carmen (2021), improving the health system’s governance leads countries to deal with various challenges. Indeed, the health service output is dependent on the socioeconomic development, education, nutrition, and living conditions of the population (World Health Organization [WHO], 2008). Despite its importance, the relationship between governance and the health system has received little attention. This chapter tries to bridge this gap by investigating the government’s impact on the health system performance in MENA countries. We will also carry out a review of the health system performance in order to identify its main determinants. On the empirical side, we will mainly focus on the causal relationship between health system outputs and governance. Our empirical framework is based on a panel data model which includes 14 MENA countries and covers the period from 1996 to 2019. We use the Granger causality and Engle-Granger two-step approaches to identify the short- and long-term relationships. This study is useful as it provides policymakers with valuable recommendations relative to health system management, particularly for the MENA region where countries are facing important difficulties in the health sector. This chapter is organized as follows. Section 7.2 summarizes the theoretical background in relation to the health sector governance. Section 7.3 provides a synopsis of prior empirical literature. Section 7.4 presents our empirical methodology including the Governance Composite Index’s calculation method. Section 7.5 summarizes and discusses the main empirical results. The conclusions and policy recommendations are formulated in Sect. 7.6.

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7.2 Health Sector Governance This section covers the theoretical aspects of governance, with a focus on the health sector.

7.2.1 The General Concept The governance concept represents the country’s level of political and administrative maturity. The World Bank Report (2020) recognizes inclusiveness and accountability as governance pillars. It is worth mentioning that, while the functions of “governance” and “management” are different, they may be considered as synonymous in the health sector (WHO, 2008). The primary function of health system management is the establishment of health policies and strategies to achieve the targeted goals. The expected objectives of the “management” function at the national level are: equity, improved health, responsiveness, protection against social and financial hazards, and efficiency improvements. The state, the public sector, the households, and the private sector all contribute to the financing scheme.1 Policymakers can strengthen the sector and profit from universal health coverage by enhancing the governance of the health system (Carmen, 2021; Fryatt et al., 2017). Barbazza et al. (2014) distinguished three health governance dimensions: fundamental values, sub-functions, and outcomes. Differently, Kickbush and Szabo (2014)2 clustered global health governance into three different concepts: Global health governance, Global governance for health, and Governance for global health. Hong and Kuan (2017) decomposed health governance into four dimensions: the corporate governance, the good governance, the clinical governance, and the global governance. Inspired by the different European health system policies, we can distinguish various health governance mechanisms, such as the insurance market, the public–private partnership in the health system and the pharmaceutical sector, the intergovernmental governance for health, the hospital governance.

7.2.2 The Main Governance Actors According to Bigdeli et al. (2020), the governance relationship between the actors in the health system can be represented by a triangle. In fact, a good health governance involves an interaction among three main actors: citizens/customers, service 1

Public: social health insurance funds, private: private voluntary health insurance; household: outof-pocket. 2 De Pas et al. (2016).

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providers, and government actors. On the other hand, the World Bank report (2020)3 emphasized the role of the Ministry of Health in the health system’s governance. Fryatt et al. (2017) considered that the governing entities in the health sector can operate at four levels: global, national, subnational, and local. The global health governance is defined through four functions: stewardship, the production of global public goods, the mobilization of global solidarity, and the management of the externalities. Institutions in charge of implementation might be formal, involving the public and non-state sectors, or informal, involving communities, workplaces, and special interest groups. Developing countries are facing major health governance challenges which include lack of financial resources, lack of political will, inadequate legal framework, basic defects in policy specification, governability, and conflicts. The need for government involvement in the health sector is crucial to support good governance in order to preserve and promote human health. This is due to the health services specificities, which can be summarized as follows: • It is a complex sector, characterized by its interdependence with other sectors. • The right to health: each individual must have access to health services4 (Alma Ata Declaration 1978; White Paper “Together for Health: A Strategic Approach for the EU 2008-2013”). • Health is considered as a public good and a global public good as well.5 This is supported by De Pas et al. (2016). • Promoting health coverage and improving health services are among the millennium goals (De Pas et al., 2016). Besides these challenges, most countries are aiming to achieve three main health objectives: • Decreasing or removing health inequalities. • Improving health system performance. • Achieving Universal Health Coverage (UHC). The UHC objectives, during the period 2017–2030, are mentioned in the UNDP (2019) report.

7.3 Literature Review The recent decade has been marked by a multitude of studies dealing with governance in the health sector. Following the objectives of the empirical studies, the existing literature may be clustered into three main groups:

3

Sriram et al. (2020). Article 25 of the Universal Declaration of Human Rights. 5 Smith (2003). 4

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• Studies dealing with the whole health system: Faranak et al. (2019), Hartigan-Go et al. (2013), Meessen (2020) and Siddigi et al. (2009) (focused on developing countries), Masefield et al. (2020). • Studies dedicated to the public health system: De Regge et al. (2020), MikkelsenLopez (2014) and Afriyie et al. (2019) (emphasized the impact of corporate governance on hospital performance). • Studies focusing on the private health system: Mutale et al. (2013), Greenfield et al. (2011) and Balabanova et al. (2008). Some studies focused on some particular aspects of the relationship between governance and the health system. Filmer and Pritchett (1999) investigated the different dimensions of public health expenditure. Riayati et al. (2016) analyzed the relationship between public health expenditures, governance, and health outcomes in Malaysia. Elola et al. (1995) found that government expenditure on health and GDP per capita has a positive effect on the country’s health condition when infant mortality rate and life expectancy at birth by are used as health indicators. Jafari et al. (2019) found that the main challenges facing the Iranian health system are reducing health inequalities and implementing a Universal Health Coverage. Hastings et al. (2014) studied the relationship between health system governance and the workforce outcomes. This theme has also been widely discussed by different international organizations (European Commission, World Bank, UNDP, USAID, WHO…). Labonté (2010) raised the issue of equal access to health by addressing the role of governance and emphasizing the importance of transparency and participation. An equitable health system has a dual aspect: a social aspect, which consists to reduce discrimination, and a political aspect since health systems equity does not concern only access to health services, but it also deals with all health determinants. It is argued that healthcare quality is higher in high-income countries. Individuals with high income have access to and consume all of the commodities and services that contribute directly or indirectly to preserving and/or improving their health. In lowand middle-income nations, the key difficulty is how to enhance governance when the political environment is not highly supportive. In this respect, some major reforms of the health system and its standards are required. Among the urgent reforms in this area are decentralization, reduction of corruption, accreditation in human resources, and improving health services quality. The WHO emphasized the vital role of government in ensuring that a country’s health system meets the required standard. Good governance is exercising authority in a way that respects integrity, rights, and the needs of all citizens. It contributes simultaneously to economic and social development (Kaufmann and Kraay 2015). Achieving good governance requires a government which is perceived and accepted as legitimate and committed to improve the public welfare, responds to the needs of its citizens, is eligible to ensure law and order, delivers public services, creates an enabling policy environment for productive activities and which is equitable in its conduct. The governance quality affects the health system’s ability to be sustainable, universal, and of high quality. We can assess governance impact on the health sector through the market’s ability, networks, and organizations contributing to healthy societies. The health sector can

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experience governance consequences either directly or indirectly, whether the governance measures are specific to the health sector itself or implemented in one of the related sectors. There are only a few empirical studies assessing the impact of governance on health. Riayati et al. (2016) and Klomp et al. (2008) were interested in investigating the link between governance and individual health. Their results suggest that poor governance has a negative impact on health. Klomp et al. (2008) tested both the direct relationship between governance and the individuals’ health, and the indirect effect via the impact of governance on the healthcare sector and on income. Assessing the relationship between governance and the health system requires the existence of reliable and appropriate governance indicators. In this respect, various studies used global governance indicators such as the six indicators developed by Kaufmann et al. (2010) and provided by the Worldwide Governance Indicators database, or the four indicators resulting from the Country Policy and Institutional Assessment. Another stream of empirical literature focused on the development of indicators specific to the health system. The World Health Organization fixed some conditions to be fulfilled in order to implement a good health governance and recommended using determinants, outcome performance, and the common “government results” to facilitate comparability. In order to have a strong health system, USAID (2015) proposed monitoring which covers three dimensions of the health system: health financing, human resources, and leadership and governance. The outcomes-based indicators cover various fields such as human resources, health financing, health service delivery, pharmaceutical regulation, voice, and accountability. This issue was addressed by various studies. Houngbo et al. (2017) attempted to develop a good governance model in the healthcare public sector in Benin based on a variety of indicators. Jafari et al. (2018) proposed measures of good governance in the health sector based on 10 functions and 42 sub-systems. Faranak et al. (2019) identified two categories of good governance indicators in the health system. The first corresponds to equity, responsiveness, efficiency, effectiveness, and the health orientation, while the second corresponds to accountability. Another body of studies pointed out health financing as an important governance mechanism. Financing is also tightly linked to the improvement of health system outputs. If we approach good governance in financing the health system, every country must apply the optimization principle. To ensure good governance in health services financing Peerenboom et al. (2014) proposed a reform based on the function separation principle, management autonomy, and community participation. A better financial resource allocation allows an equitable access to health services for all the citizens (especially the poorest) and protects them from the depletion and health stock deterioration. Measuring good governance in financing is done through the performance criteria. Another indicator relative to health financing refers to the proportion of government funds which reach the district-level facilities.

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7.4 The Health System Governance in MENA Countries: Causality, Short- and Long-Run Dynamics 7.4.1 The General Scope in the MENA Countries Our study focuses on MENA countries. Before presenting the model, we will start by briefly describing the macroeconomic characteristics related to the problem addressed in this chapter. The macroeconomic environment in MENA countries is characterized by macroeconomic stability, low fiscal pressure and current account deficits, moderate inflation rates, and an improved infrastructure. The government’s main challenges concern unemployment and human development (education, health …). The health systems challenges in the MENA countries are: the financial burden, the disease profiles, and the access to an equitable quality care. In fact, by referring to Dehbi (2017), the challenges for Algeria, Morocco, and Tunisia are the health services quality and overcoming the growing increase in the health expenditures. In order to enhance governance in the MENA region, policymakers have been focusing on inclusiveness and accountability. As for the health system financing scheme, the MENA region countries are experiencing huge private expenditures on health services. These expenses are nonrefunded and amplify poverty and lead to severe inequalities. This is due to the low state funding, the lack of health insurance coverage for the poorest, and the weak role of public insurance. The concern of each country is to improve the population’s health. As discussed previously, governance is an important tool to enhance the performance of the healthcare system. The performance of the health system is judged by its output, which corresponds to the health status of the population. However, the scarcity of empirical research dealing with governance in the health sector on one hand, and the absence of data associated with governance indicators in the health system on the other, will limit our ability to assess the impact of governance on health system performance. Our sample is composed of 14 MENA countries over the period 1996–2019. These countries are: Yemen, United Arab Emirates, Tunisia, Saudi Arabia, Oman, Morocco, Malta, Lebanon, Kuwait, Jordan, Iran, Bahrain, Algeria, and Egypt. The study period is limited by data availability since the governance metrics were measured only in 1996. The annual data were collected from the World Bank database. There has been a little empirical study on health governance for this group of countries. The model developed in this chapter is inspired by the earlier work of Riayati et al. (2016) and Rajkumar (2008). In order to measure the health services’ governance, the WHO opted for two main indicators: the country’s level of Universal Health Coverage (UHC) and the external resources on health as a percentage of the total health expenditures. As for the first indicator, we notice that half of the observed sample has reached the UHC threshold, while the others are still below. These countries are: Algeria, Bahrain, Jordan, Kuwait, Oman, Tunisia, and the United Arab Emirates. Regarding the second

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indicator, it cannot be interpreted since data are totally missing for some countries. All the statistics are provided in Appendix B. The health system’s inputs are: the Domestic General Government Health Expenditure (DGGHE, % of general government expenditure) and Per Capita Gross Domestic Product (PCGDP). Mobile Cellular Subscriptions (per 100 people) (MCS), Age Dependency Ratio (ADR), and the inflation rate (INF) (consumer prices annual variation rate %) reflect the country-specific characteristic and are introduced as control variables. In order to evaluate the health system’s output, we use the health status indicators. The WHO provided various indicators reflecting the health status. In this research, we have chosen the two most commonly used indicators: the Infant Mortality Rate (IMR) and the Life Expectancy at Birth (LEB) which are considered as dependent variables. It is argued that the quality of health is better in high-income countries. With a high income, the individual can have access to and consume all the goods and services that contribute directly or indirectly to maintain and/or improve his or her health (Klomp, 2008). After classifying countries according to the income level (Appendix A), we have represented graphically two health status indicators for each group. An examination of the six graphs, provided in the appendix, shows that: • Firstly, all countries, regardless of income level, have experienced an improvement in the population’s health status over time (increase in LEB and decrease in IMR). A priori, we can confirm that our sample is characterized by a fairly good health system governance. • Secondly, high-income countries are characterized by a better health status than the other two categories. This conclusion is valid for both indicators the LEB and the IMR. Theoretically, any increase in the country’s wealth will have a positive impact on the health status of its population. In other words, if the GDP per capita increases then the IMR will decrease and the LEB will increase, which reflects an improved health status. Many studies have shown a positive correlation between the health status and wealth effect of a country. This finding is related to governance and health system management (WHO, 2008). In our sample, the countries suffering most from corruption in 2019 are Algeria, Egypt, Kuwait, Morocco, Tunisia, Iran, Lebanon, and Yemen. The last three countries are the most affected by this phenomenon. All these countries need to improve governance in the health sector to promote the quality of their health services (WHO, 2008). The European Union studies of corruption (2014) found that the healthcare sector was the most likely to suffer from corruption problems.

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7.4.2 Methodology We first describe the construction of the Governance Composite Index (GCI). It should be mentioned that the indicator’s construction is complex as it is a function of six sub-indicators. To solve such a problem, we are going to opt for the methodology of the Human Development Index constructed by the Union Nations Development Program (2010), and according to which several indicators are aggregated in a single composite index, i.e., the Governance Composite Index (GCI). We rely on the six governance indicators provided by the Worldwide Governance Indicators database, namely control of corruption, political stability, regulatory quality, government effectiveness, rule of law, and voice and accountability. Let X kit (k = 1,…6) be a governance indicator for country i (i = 1,…,14) at year t (t = 1996,…0.2019). The GCI for country i at year t is measured as described below: 1 ∑ X kit − X minki × 100 6 k=1 X maxki − X minki 6

GCIit =

(7.1)

where X minki and X maxki are, respectively, the maximum and minimum values of the variable k for the country i observed in year t. The new variables obtained are, thus, normalized and vary between 0 and 100 whatever the measurement unit. This method is based on the hypothesis that all the variables have the same direction of importance level, i.e., the higher is the variable value, the better is the situation (X max indicates a better situation than X min ). This condition is met in our case, as any increase in one of the governance proxies corresponds to an improved governance in the concerned field. In order to study the causal relationship between governance and health systems outputs, we estimate a Vector Error Correction Model (VECM) following the EngleGranger two-step approach. The main objective is to test the existence of shortand long-run relationships between the model variables. However, the first step is to perform the stationarity and cointegration tests. Since we are working with macroeconomic variables, we need to transform them into natural logarithms. To test the stationarity of the variables retained in the model, we use: – The (Levin et al., 2002) test which assumes that there is a common unit root process. – The (Im et al., 2003) test which allows the individual unit root processes. For the two tests the null hypothesis corresponds to the existence of a unit root. Therefore, the rejection of this hypothesis implies that the variable is stationary. Regarding the cointegration test, we apply Pedroni’s Cointegration (1999, 2004) test. Once this step is achieved, we move to the second step where we implement the Granger (1969) causality test. Finally, we specify the VECM to be estimated. To

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carry out these various steps, all the variables should be integrated in the same order I(1); if not, we should opt for an Autoregressive Distributed Lag (ARDL). The Granger causality test is useful for determining whether a time series is useful for forecasting another, considering that they are stationary, i.e., given two stationary time series X and Y, Y is Granger-caused by X if X contributes significantly to the prediction of Y. In this study the granger test is used to test the causal relationship between governance and health system’s outputs by estimating the following equation: X it = δ0 +

K ∑

δk X i (t−k) +

k=1

J ∑

α j GCIi(t− j) + εit ; i = 1, ...., 14; t = 1996, ...., 2019

j=1

(7.2) where X represents IMR and LEB, GCI is the governance composite Index, δk and α j (k = 1, ....K and j = 1, ...., J ) are the model unknown parameters, considering K and J as the maximum lags number of the selected variables (we rely on the Akaike Information Criterion (AIC) to determine optimal lags). εit are the error terms. It is important to note that we consider only the causality running from governance to health system’s outputs, i.e., the other sense of causality has no significant interpretation. Given the hypothesis of a unidirectional causal relationship, under the null hypothesis we assume that there is no causal link between the two variables, versus the alternative hypothesis which suggests the existence of long-run causality running from the governance to health. {

H0 : α1 = α2 = . . . α J = 0 H1 : At least : α j /= 0; j : 1 . . . .J

(7.3)

It should be mentioned that the Granger test only tests for short-term causality between the variables. Engle and Granger (1987) started from the previous method and developed a two-step technique, allowing to test at the same time the short and long-term association between the variables. This technique is relying on a dynamic system marked as follows: the current state of the long-term relationship may help to explain the short-run dynamics. The first step corresponds to the Granger causality (1969) test to confirm the causality direction. The second step consists to estimate a VECM for each variable as follows:

7 The Health Systems’ Governance in MENA Countries: A Panel …

ΔX it = δ0 +

K ∑

δk ΔX i(t−k) +

J ∑

k:1

+

L ∑

Q ∑

S ∑

P ∑

ϕ p PCGDPi (t− p)

p=1

γq MCSi (t−q) +

R ∑

ωr ADRi (t−r )

r =1

q=1

+

α j ΔGCIi (t− j )

j:1

θl DGGHEi (t−l) +

l=1

+

155

λs INFi (t−s) + ECi(t−1) + μit

(7.4)

s=1

where Δ is the first difference operator, δk , α j , θl , ϕ p , γq , ωr and λs are the unknown model parameters, with lag orders K , J, L , P, Q, R, and S, respectively. EC is the lagged error-correction term obtained from Model (2) related to the cointegration equation which control for the long-run relationship. μit are error terms. It should be noted that in this study we are limited to one causality direction considering the health system’s outputs as the dependent variable. The short-term causality test, from the governance to the health system’s outputs, is formulated as follows: H0 : α j = 0 for j = 1, ...., J H1 : α j /= 0 for at least one j

(7.5)

By referring to Engle and Granger (1987), the long-term causality test is performed on the basis of the EC term significance in model (4).

7.5 Results and Discussion Table 7.1 summarizes all the descriptive statistics of the model’s variables. The descriptive statistics by country are provided in Appendix C. The stationarity test results, with trend and constant, are summarized in Table 7.2. The number of lags chosen is determined by the AIC criterion. All variables are characterized by the existence of a unit root in level. However, passing to the first differences, they all become stationary. All the variables are therefore I(1) rather than I(0). So, we meet the required condition for the causality analysis and the VECM. The results of the cointegration test are summarized in Table 7.3. The null hypothesis rejection, which implies the existence of at least one long-term causal relationship between the variables at the 5% significance level. To test the causal relationship from governance to the health system’s outputs, as mentioned above, the Granger causality test was applied. The objective is to check the existence of a long-term relationship based on Model (2). The results are reported

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Table 7.1 The descriptive statistics of all the variables Variables

Mean

Control of corruption

−0.1

Std. Dev 0.63

Min

Max

−1.68

1.28

Government effectiveness

−0.004

0.62

−2.28

1.51

Political stability and absence of violence/terrorism

−0.33

0.96

−2.99

1.60

Regulatory quality

−0.06

−1.72

1.43

0.72

Rule of law

−0.023

Voice and accountability

−0.76

Governance Composite Index

51.05

19.31

0.70 0.68

−1.79

1.63

−1.91

1.37

2.36

100

Infant Mortality Rate

19.31

13.84

5.69

78.1

Life Expectancy at Birth

73.62

4.04

59.096

82.6

Domestic General Government Health Expenditure

9.32

3.76

2.01

22.94

Per Capita Gross Domestic Product

14,160.53

14,859.41

631.49

64,864.72

Mobile cellular subscription

72.61

Age Dependency Ratio

7.82

Inflation

5.003

57.76 5.24 6.23

0.012 0.796 −3.75

212.39 32.098 39.91

Source Author‘s estimates

in Table 7.4 and suggest the rejection of the null hypothesis at the 5% level, which implies the existence of a long-term causality link running from good governance to the health system’s outputs. Such results highlight the governance’s importance in promoting the health system’s outputs, which provides an incentive to implement more policies relative to the health system’s governance. The next step is to check the short-run causal relation between governance, health system’s inputs, and health system’s outputs. For that, a VECM is estimated according to the Model (4). The findings are summarized in Table 7.5. It is also clear that the EC terms are EC negative and significant (at the 5% level), which is a basic condition to reach the long-run equilibrium. The short-term impact is identified according to the significance of coefficients associated with the first difference-lagged variables. We notice that the P-value related to ΔIMR and ΔLEB are, respectively, 0.5981 and 0.4574. They largely exceed the 5%, which indicates that in the short term, governance doesn’t have a significant impact on the development of the health system’s outputs. The results show also that for the first health status indicator (IMR) there are only 2 parameters which are statistically significant at the 5% level: Δ PCGDP and Δ

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Table 7.2 Unit root test results Variables

Level

First difference

LLC Infant Mortality Rate −1.23 (0.11)

IPS

LLC

−0.67 (0.25)

−4.6a

IPS (0.0)

−3.27a (0.0005)

Life Expectancy at Birth

3.84 (0.99)

−1.35 (0.089)

−13.42a

Governance Composite Index

0.55 (0.71)

−0.36 (0.36)

−7.29a (0.0)

−10.71a (0.0)

Domestic General Government Health Expenditure

0.037 (0.51)

−0.84 (0.20)

−15.82a (0.0)

−14.31a (0.0)

Per Capita Gross Domestic Product

0.005 (0.50)

1.99 (0.98)

−3.12a (0.0)

−3.96a (0.0)

Mobile Cellular Subscription

1.75 (0.96)

1.36 (0.91)

−4.43a (0.0)

−2.73a (0.003)

Age Dependency Ratio

3.42 (0.99)

−0.74 (0.23)

−1.96a (0.03)

−5.99a (0.0)

Inflation

1.59 (0.94)

−0.94 (0.18)

−4.006a (0.0)

−4.89a (0.0)

The P-values are in brackets.

a Denotes

(0.0)

−14.13a (0.0)

significance at the 1% level. Source Author’s estimates

Table 7.3 Pedroni’s cointegration test results (IMR and LEB as the dependent variable) IMR

LEB

Statistic test

Panel

P-Value

Panel

P-value

V

34.86

0.0000

−2.05

0.02

ADF

−1.93

0.0266

−3.57

0.0002

Source Author’s estimates

Table 7.4 The Granger causality test results: From GCI to IMR and from GCI to LEB

Statistic

Z-bar (IMR)

Z-bar (LEB)

3.8334

5.8213

Source Author’s estimates

DGGHE, which are, respectively, equal to 0.254 and 0.312. The positive signs associated with these coefficients lead to counterintuitive results, according to which an increase in the considered independent variables leads in the short term, to an increase in the Infant Mortality Rate. Concerning the second health status indicator (LEB), only 3 independent variables are producing statistically significant effects at the 10% level: ΔPCGDP, ΔDGGHE, and ΔINF. The associated coefficients are, respectively, equal to 0.135, 0.266, and −0.159. In the short term, these results are consistent with the theoretical expectations. An increase in the country’s wealth (PCGDP) has a positive impact on life expectancy. At the same time, the increase in the state budget devoted to health (ΔDGGHE) is generally accompanied by an improvement in the

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Table 7.5 The vector error correction model ΔIMR

ΔLEB

Coefficient

P-value

Coefficient

P-value

Explicative variables −0.136

0.0268

−0.163

0.0315

ΔGCI(−1)

0.095

0.5981

0.142

0.4574

ΔDGGHE(−1)

0.312

0.0369

0.266

0.0852

ΔPCGDP(−1)

0.254

0.0456

0.135

0.0765

ΔMCS(−1)

0.053

0.1245

0.092

0.1186

EC

ΔADR(−1) ΔINF(−1)

0.154

0.0952

0.079

0.1058

−0.068

0.0856

−0.159

0.0943

5.126

0.0037

4.256

0.0021

Constant Source Author‘s estimates

health status. For the rate of inflation, a high rate increases the deterioration of the health system’s outputs. In fact, inflation is having a negative impact on purchasing power which may explain its negative impact on life expectancy. The other control variables related to the countries’ characteristics don’t contribute significantly to explain the health system’s outputs in the MENA countries. According to the obtained results, governance contributes to improve the health system’s outputs on the long term, i.e., the governance growth rate in these countries is not sufficient to contribute to enhance the health system’s outputs in the short run. To overcome this problem, it is recommended to make additional efforts to raise awareness about the importance of governance and the conscience degree of the policymakers in order to trigger a short-run effect. An important question emerges from these results: what is the required governance extra growth to produce a significant short-run effect on the health system’s outputs? To that end, we apply a simulation technique by generating a 1–10% additional GCI annual growth rate, while maintaining all the other variables constant. The newly obtained variable GCIk , including the supplementary annual growth rate k (k = 1,…,10) for country i (i = 1,….,14) at year t (t = 1996,…,2019), is determined as follows: GCIitk = (1 + r + k)GCIi (t−1)

(7.6)

Note that r denotes the observed GCI growth rate: r = (GCIit −GCIi(t−1) )/GCIi (t−1) . For the initial year t = 1996, the GCI remains the same whatever the value of k, k = GCIi1996 ∀ k. i.e., GCIi1996 Once the new series GCIk are calculated for each k value, we will substitute it in Eq. (7.4), and then estimate it.

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The interesting estimates are the coefficient and the P-value associated with EC and the ΔGCI(−1) terms. The first one indicates the adjustment speed to the longterm equilibrium. So, it is possible to evaluate the impact of the additional simulated annual growth rate on the velocity of the adjustment to the long-run target, i.e., a EC high value means that the adjustment toward the long-run equilibrium is faster. The second is used to analyze the impact of the simulated additional growth of the concerned variable on the short-run causal relationship’s significance from governance to the health system’s outputs. Therefore, it is possible to determine the additional required governance’s growth rate to produce short-term significant effects on the health system’s outputs. Table 7.6 summarizes the simulation results. The P-Value analysis reveals the need for an additional 4–5% governance growth rate in order to significantly enhance the health system’s outputs in the short term (at the 5% significance level). These findings should prompt MENA countries to upgrade their governance frameworks and to give more importance to the health system governance. By focusing on the EC coefficients, we notice that the speed of the adjustment toward the long-run equilibrium varies between 11 and 13%, i.e., the supplementary governance impact on the long-run equilibrium remains similar. However, on the short term, the results are quite different. This is confirmed by Fig. 7.1, which refers to the p-value evolution based on the simulated additional mean growth rate in the observed governance. It is clear that the curve slope is decreasing since P-values are tending toward zero with additional governance. However, the convergence of the Pvalues to zero takes place exclusively for the first four additional annual growth rates (k = 1,2,3,4), which indicates that the impact of the four first additional governance annual growth rates on the health system’s outputs is the most important in the short run. Table 7.6 The simulation analysis results (dependent variable ΔIMR and ΔLEB) Simulate rate

EC

ΔGCI

EC

P-Value

ΔGCI P-Value

0%

−0.136

0.5981

−0.163

0.4574

1%

−0.116

0.3957

−0.137

0.3745

2%

−0.114

0.2768

−0.132

0.2259

3%

−0.111

0.1531

−0.129

0.1486

4%

−0.113

0.0952

−0.133

0.0478

5%

−0.114

0.0496

−0.135

0.0415

6%

−0.115

0.0398

−0.136

0.0357

7%

−0.112

0.0258

−0.139

0.0268

8%

−0.113

0.0118

−0.141

0.0219

9%

−0.113

0.0195

−0.138

0.0159

10%

−0.114

0.0133

−0.137

0.0112

Source Author’s estimates

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Fig. 7.1 The P-values based on the simulated additional mean growth rate k in the governance. Source Author’s estimates

In light of the empirical results, the most important findings of this study may be summarized as follows: • According to the Granger causality test, there is a significant long-term causal relationship running from governance to health system outputs.

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• The population’s health status in the MENA countries has improved thanks to good governance. This statement highlights the governance’s importance and the countries’ awareness level about the health system’s development. • For the short term, as revealed by the VECM estimates, governance did not contribute significantly to the development of the health system’s outputs. • By referring to the simulation analysis, it can be deduced that there is a need for an additional 4% or 5% in governance (at the 5% level of significance) to benefit from its short-run impact on the health system’s outputs and reach an optimal impact for both the short and long run.

7.6 Conclusions and Recommendations The main objective of this study is to assess the impact of governance on the health system’s outputs. Given the health capital importance at the micro and macroeconomic levels, each country must develop a health system characterized by good governance. In this way, it can benefit from the expected positive externalities. Good governance incites decision-makers to ensure transparency and accountability throughout the system and to define precisely the role of each actor. Governance in the health sector can concern the financing system, the healthcare systems, and the healthcare performance (Chanturidze & Obermann, 2016). While the relationship between governance and health has been extensively tested for other regions of the world, studies are scarce for the MENA region. This study tries to bridge this gap by addressing the causality issue between governance and the health system’s outputs. To that end, we compute a composite governance index based on the six governance proxies developed by Kaufmann et al. (2010) and assess its short and long-term effects on the Infant Mortality Rate and on Life Expectancy at Birth. The VECM results reveal that governance significantly affects the health system’s performance only in the long run. The current governance standards in MENA countries are not sufficiently developed to significantly affect the health system’s outcomes in the short run. In the second step, we have tried to compute the additional annual growth rate of governance needed for its short-term effect on the health system’s outcomes to be significant. We have simulated an additional annual growth rate of 1–10% and showed that an annual growth rate of 4% or 5% of the governance indicator is needed to produce a significant short-term effect. The highest impact should be achieved with the first 4% additional annual governance growth rate. The obtained results have important implications. Indeed, if governance is seen to contribute significantly to the development of the health system’s outcomes in the long term, decision-makers will have additional tools to boost the health system in the future and plan effective sustainability strategies. In the short term, there is a need for further policies aiming to modernize governance and to raise awareness of the health system in countries, which our findings suggest will have a significant impact.

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Decision-makers should also put emphasis on the financial dimension of the health system. Our results suggest that providing higher budgets contributes significantly to enhance the health system’s outputs. Finally, we stress that the health status is influenced by a variety of social variables which were not considered in this study. The same remark applies to the variables reflecting the specific characteristics of the countries. Finally, the governance proxies used in this study are not specific to the health sector. Future studies taking into account these remarks can lead to an improved empirical framework.

Appendix A: MENA Countries Classification by Income Level 2020–2021 Levels

Low income

Lower-middle income

Upper-middle income

High income

Country

Yemen

Algeria, Egypt, Iran, Morocco, Tunisia

Jordan, Lebanon

United Arab Emirate, Bahrain, Kuwait, Malta, Oman, Saudi Arabia

Source World Bank

Appendix B: Health Services Governance Indicators

Indicators

Countries that have passed legislation (UHC)

External resources on health as % of total health expenditure 2013

2014

Yes

0.015

0.032

Countries Algeria Bahrain

Yes

–

–

Egypt

No

0.14

1.26

Iran

No

0.04

0.031

Jordan

Yes

6.92

5.99

Kuwait

Yes

–

–

Lebanon

No

1.1

1.02

Malta

No

0

–

Morocco

No

1.03

1.61 (continued)

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163

(continued) Indicators

Countries that have passed legislation (UHC)

External resources on health as % of total health expenditure 2013

Countries

2014

Oman

Yes

–

–

Saudi Arabia

No

–

–

Tunisia

Yes

3.22

0.3

United Arab Emirates

Yes

–

–

Yemen

No

4.66

6.38

Source WHO

See Figs. B.1, B.2, B.3, B.4, B.5 and B.6 60

2018

2019

2018

2019

2015

2016

2013

2014

2012

Maroc

2017

Iran

2011

2009

2010

2008

2006

2007

2005

Egypt

2017

Algeria

2004

2003

2002

2001

2000

1999

1997

1998

1996

10

Tunisie

Fig. B.1 IMR in lower-middle-income countries

55

Liban

2015

2016

2013

2014

2011

2012

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1996

1997

5

Jorda

Fig. B.2 IMR in upper-middle-income countries

25 20 15 10 5

UAE

Bahrein

Fig. B.3 IMR high-income countries

Koweit

Malte

Oman

Saoudi,Ara

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E. Essadik and C. Terzi

86.00 76.00 66.00

Algeria

Egypt

Iran

Maroc

Tunisie

Fig. B.4 LEB in lower-middle-income countries

80.00 78.00 76.00 74.00 72.00 70.00

Jorda

Liban

Fig. B.5 LEB in upper-middle-income countries 88.00 83.00 78.00 73.00 68.00

UAE

Bahrein

Koweit

Malte

Oman

Saoudi,Ara

Fig. B.6 LEB in EVN high-income countries

Appendix C: Descriptive Statistics of Governance Composite Index and Health System Outputs by Countries

Countries Algeria

Bahrain

Egypt

Variables

Mean

Std. Dev

Min

Max

GCI

55.5

19.6

20.4

93.4

IMR

27.1

5.6

19.9

36.1

LEB

73.6

2.6

68.9

76.9

GCI

51.6

18.8

14.8

88.5

IMR

8.7

2.3

5.9

14.2

LEB

75.6

1.1

73.6

77.3

GCI

54.4

23.9

17.7

86.4 (continued)

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165

(continued) Countries

Iran

Jordan

Kuwait

Libanon

Malte

Morocco

Oman

Saudi Arabia

Tunisia

United Arab Emirates

Yemen

Variables

Mean

IMR

28.5

Std. Dev 8.7

Min 17.3

Max 46.8

LEB

69.9

1.4

67.3

72.0

GCI

46.1

19.6

11.0

70.6

IMR

20.5

7.0

12.0

34.2

LEB

72.9

2.6

68.8

76.7

GCI

46.8

14.8

16.3

69.3

IMR

19.4

3.8

13.4

25.6

LEB

72.9

1.1

71.1

74.5

GCI

48.5

24.0

6.5

78.1

IMR

9.5

1.6

6.8

12.2

LEB

74.1

0.9

72.9

75.5

GCI

52.0

21.0

12.3

80.4

IMR

11.7

4.8

6.2

20.9

LEB

76.9

2.1

72.1

72.6

GCI

49.2

17.2

3.5

73.6

IMR

6.4

0.6

5.9

7.9

LEB

80.0

1.8

77.3

GCI

44.7

22.9

10.8

IMR

31.6

9.5

18.3

82.6 100 49.2

LEB

72.5

3.2

67.2

76.7

GCI

52.8

17.7

27.6

88.3

IMR

11.8

2.8

9.5

19.1

LEB

74.6

2.3

70.3

77.9

GCI

45.5

10.7

24.2

67.7

IMR

12.9

5.5

5.69

23.4

LEB

73.6

1.1

71.5

75.1

GCI

53.7

10.5

36.2

71.4

IMR

19.9

6.1

14.5

33.8

LEB

74.5

1.4

71.9

76.7

GCI

54.5

10.0

32.8

75.2

IMR

8.1

1.4

6.4

10.9

LEB

75.8

1.4

73.4

78.0

GCI

59.5

27.8

2.4

90.2

IMR

54.1

12.4

43.2

78.1

LEB

63.7

2.5

59.1

66.1

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Chapter 8

Governance and Growth in Developing Countries: New Insights from a Cross-Regional Empirical Analysis Montassar Zayati and Mohamed Sami Ben Ali Abstract This paper investigates the impact of governance on growth for a sample of 20 developing countries. We address the complementarity between the various dimensions of governance by constructing a global governance index and comparing its effect across different groups of countries. The PCA’s results indicate that variables relative to the economic and administrative dimensions weigh more on the global index than those reflecting the political dimension. The SGMM results reveal that the different dimensions of governance contribute significantly to promote growth. In particular, the administrative governance index is producing the most important effect on growth in all groups of countries, except in Sub-Saharan Africa where the political governance index outperforms the administrative one. Results also show that the higher the governance indicator, the greatest is the impact on growth, which suggests that the impact of governance on growth increases as the quality of the institutional framework improves. Another important finding of this study is that the global governance index does not necessarily produce the expected positive impact on growth, particularly when the variables composing this indicator are following divergent trends. According to these results, developing countries must be involved in the three dimensions of governance on an equal basis. Giving priority to one dimension at the expense of others can compromise the economic outcomes of the whole governance process. Keywords Governance · Economic growth · System GMM · Principal Component Analysis

M. Zayati (B) ISFF of Sousse and LaREMFiQ, University of Sousse, Sousse, Tunisia e-mail: [email protected] M. S. Ben Ali College of Business and Economics, Qatar University, Doha, Qatar © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_8

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8.1 Introduction Since the 1990s, the international institutions such as the IMF and the World Bank have emphasized governance as a key driver of both economic growth and development in developing countries (Ben Ali & Saha, 2016; Ben Ali & Krammer, 2016). The World Bank monitoring report (2005) asserted clearly that governance and economic growth go hand in hand. Developing countries have therefore been urged to improve their public governance in order to accelerate their convergence process and improve both their social and economic performances. For some scholars the emphasis put on governance is a roundabout way to encourage developing countries to implement institutional and political reforms (Bräutigam, 2000), as the governance standards of a given country are highly dependent on the efficiency of its institutional framework. Various empirical studies confirmed this hypothesis, suggesting that the economic performance is explained to a large extent by the quality of the public institutions (Knack & Keefer, 1995). Accordingly, the issue of governance is closely tied to that of institutions, and assessing the impact of governance necessarily involves studying the question of the capacity of institutions to foster growth. A large body of theoretical and empirical literature has already emphasized the importance of institutions in determining the long-term economic performance. In this respect, Kauffman et al. (1999) found a strong positive relationship between three governance indicators (control of corruption, rule of law and voice and accountability) and real per capita GDP adjusted to purchasing power parity in a number of Latin American countries. Similarly, MacFarlan and Edison (2003) showed that developing countries exhibiting the highest institutional quality are those enjoying the highest levels of per capita income. They also asserted that institutions play a crucial intermediary role between the geographic characteristics of a given country and its level of economic development. Several studies emphasized the impact produced by a particular dimension of governance on key economic indicators. For instance, Kormendi and Meguire (1985), Scully (1988), Grier and Tullock (1989), Barro (1996), Helliwell (1994) and Isham et al. (1997) all detected a positive correlation between civil liberties indicators and economic growth for different samples. In the same vein, Acemoglu et al. (2004) have shown that the growth gap between rich and poor countries is largely due to the difference in securing property rights. In the same vein, Rodrik et al. (2002) confirmed that securing property rights accelerates growth. In a totally different setting, Barro (1991) and Londregan and Poole (1992) showed that instability and political violence lead to low growth rates, while Alesina and Perotti (1996) and Svensson (1998) highlighted a negative effect of political instability on investment. Furthermore, several studies have shown that putting in place democratic institutions may enhance significantly economic growth. Having conducted a meta-analysis including 188 studies, Colagrossi et al. (2020) concluded that democracy contributes to spur economic growth despite the regional disparities. Similarly, for a sample of 140 countries, Mathonnat and Minea (2019) found that democracy contributes to reduce growth volatility.

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The literature on governance has focused mainly on the institutional framework, considering that good governance depends mainly on the quality of institutions. In particular, this literature has shown that governance is a multidimensional concept reflecting simultaneously the quality of the legal system, the effectiveness of public administrations, individual freedoms, the efficiency of anti-corruption policies and the accountability of decision-makers. These various dimensions of governance are likely to affect economic performance through multiple channels. For instance, good institutions may contribute to foster growth by enhancing the efficiency of the financial system and by ensuring financial stability (Ben Ali et al., 2016; Sassi & Ben Ali, 2014). In this respect, Levine (1998) suggested that the development of the legal framework promotes financial development, improves resource allocation and boosts economic growth, while Fernández and González (2016) proved that banking crises are more likely to occur in countries characterized by weak institutions. The legal system also contributes to spur investment by protecting the rights of entrepreneurs. Djankov et al. (2007) have shown that an efficient legal system enhances banks’ soundness by reducing non-performing loans. On the other hand, Goel and Hasan (2011) found that corruption leads to higher percentages of non-performing loans. Networks of corrupted bankers and investors provide financing to poor quality projects, while access to credit is denied to clean investors. Consequently, combating corruption should improve the quality of investment and produce a positive impact on growth. Similarly, the accountability of policymakers should lead to a better use of public resources and to the implementation of policies that meet the expectations and the needs of the population. Human capital is another channel through which the institutional framework may contribute to boost growth. Dahlum and Knutse (2017) have shown that countries endowed with efficient institutions provide better access to education, while Wigley and Akkoyunlu-Wigley (2017) highlighted that good institutions contribute to reduce the infant mortality rate. An effective Institutional framework may also act indirectly on growth by fostering innovation. Accordingly, Ober (2008) found that democratic regimes contribute to promote innovation, while Miettinen (2013) contented that fostering innovation requires implementing appropriate economic policies. Tihanyi and Roath (2002) argued that the institutional characteristics are an important determinant of cross-country technology transfers. Despite this large body of empirical evidence, the literature dealing with the impact of governance on economic performance suffers from three main drawbacks. As mentioned previously, various studies focused on political institutions and individual liberties and gave little attention to other important dimensions of governance, such as economic and administrative governance. In their seminal paper, Acemoglu and Robinson (2004) emphasized that economic institutions explain to a large extent the poverty gaps between countries. Moreover, they asserted that political institutions represent a major hurdle for implementing the necessary reforms of economic institutions. In a related issue, Goel and Nelson (2005) have shown that economic freedom matters more than political freedom in tackling corruption. Secondly, most of the empirical studies focused on a single dimension of the institutional framework and tried to assess its impact on major economic indicators. Such restrictive analyses don’t take into consideration the interaction and the complementarity between the

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various institutional features and thus cannot capture the overall effect that institutional development may produce on economic performance. Thirdly, the few studies which took into account the multiple dimensions of the institutional framework have simply taken the mean value of various governance indicators as a global measure of governance. Such an approach assumes that the different dimensions of governance are of equal importance from an economic point of view, which is a highly controversial hypothesis. This paper tries to bridge some of these gaps by constructing a global governance index taking into account the various dimensions of governance. In a first step, we compute three sub-indexes reflecting respectively the quality of the economic, administrative and political governance. In a second step we use the Principal Component Analysis (PCA) to build an aggregate governance indicator reflecting simultaneously the three different dimensions of governance. We contribute to the empirical literature by highlighting the impact of the three governance dimensions on economic growth for a panel of 20 developing countries belonging to four different regions. We also investigate if the global governance index outperforms the sub-indexes reflecting specific dimensions of governance. The remainder of this paper is structured as follows. Paragraph 2 defines the variables retained for each governance dimension and presents the construction of the sub-indexes as well as the global governance index. We also comment the obtained results for the different countries over the sample period. In paragraph 3, we estimate the impact produced by the different governance indicators on economic growth and discuss the main results. Paragraph 4 concludes and formulates some policy recommendations.

8.2 The Aggregate Governance Indexes The empirical literature suggests several indicators of governance, such as those provided by Freedom House or those developed by Kaufmann et al. (2010) and annually updated by the World Bank’s WGI database. The main drawback of these indicators is that they focus on a single dimension, or even a sub-dimension, of governance. A major objective of this study is to consolidate some of the existing indicators in order to build up new aggregate indicators reflecting broader dimensions of the governance process. In a first step, we focus on administrative, political and economic governance by developing an aggregate indicator for each of the three dimensions. These indicators will be particularly useful to assess which dimension contributes most to economic growth. They will also allow to check whether the countries in our sample are putting equal emphasis on all dimensions, or they are focusing on a particular dimension of governance. The second step consists to develop a global governance index reflecting simultaneously the three dimensions of governance. Based on this global indicator, we will verify if the countries endowed with the most efficient institutional frameworks are those drawing the maximum economic benefits from an improved governance. Moreover, we will check if this global index is more

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relevant for growth than the three indicators reflecting separately the administrative, political and economic dimensions of governance. Firstly, we describe the composition of the political, administrative and economic indexes respectively. The Political Governance Index (PGI) is based on the political rights index and the civil liberties index provided by Freedom House. The political rights index reflects the capacity of people to freely participate in the political process, while civil liberties refer to their ability to express their opinions, to create institutions and to act independently of the state. For both indexes Freedom House assigns a score on a scale of one to seven, with one for the highest degree of freedom and seven for the lowest. We adjust the scores provided by Freedom House by reversing the initial evaluation grid and assigning 1 to the least free country and 7 to the most free one. The PGI is defined as the average of these two adjusted indicators weighted by their standard deviations: PG I =

D Pol × σ Pol + DCi vil × σCivil 2

The Administrative Governance Index (AGI) is a weighted average of four governance indicators developed by Kaufmann et al. (2010) and extracted from the World Governance Indicators database provided by the World Bank. The control of corruption index (Corr) reflects the extent to which public power is exercised for private gain, including all forms of corruption. The rule of law index (LAW) captures the quality of contract enforcement, property rights, as well as the likelihood of crime and violence. The government effectiveness index (Gov) reflects the quality of public and civil services and their independence from political pressure, while the regulatory quality index (REG) gauges the capacity of the government to implement appropriate policies and regulations, which contribute to promote the private sector. Higher values of these indexes reflect an improved administrative governance. The Administrative Governance Index is defined as follows: PG I =

Corr × σcorr + Law × σ Law + Gov × σGov + R E G × σ R E G 4

Economic governance covers public policies which affect the macroeconomic performance and life quality within a given country. This dimension of governance is assessed through four macroeconomic aggregates. We first consider the inflation rate and the tax rate. Both indicators reflect the willingness of public authorities to improve the business environment and thereby create a framework conducive to sustainable growth. High inflation increases business uncertainty. Similarly, strong fiscal pressure discourages investment and hinders economic growth. We also include the unemployment rate and government expenditure as a share of GDP. Persistent high unemployment rates are the result of inappropriate economic policies. Unemployment is also often used as a proxy for poverty. High government expenditure is usually associated with higher public debt ratio which translates into higher future tax burdens. The Economic Governance Index (EGI) is computed in the following way:

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I n f lation × σinf + T axes × σtax + Gov E x penditur e ×σexp + U nemployment × σunemp EGI = 4 It is important to note that the EGI should be interpreted differently: lower values of this index correspond to an improved economic governance, while higher values are associated with poor governance. We compute the different governance indexes for a sample covering the period 2002–2020. The sample includes 20 developing countries belonging to five different regions: Middle East and North Africa (MENA): Algeria, Bahrain, Egypt, Tunisia; Central and Eastern Europe (CEE): Bulgaria, Romania, Poland, Hungary; Latin America: Brazil, Argentina, Mexico, Peru; Asia: China, India, Singapore, Philippines and Sub-Saharan Africa (SSA): Angola, Gabon, Rwanda, South Africa. Figures 8.1–8.3 provide the mean values of the three governance indexes for the different countries in the sample, while Figures 8.4, 8.5, and 8.6 summarize their dynamics for the different regions over the sample period. Regarding political governance, the CEEC and Latin American countries are those exhibiting the highest mean values over the sample period. For Asian countries, India and Philippines are showing the highest scores, while Singapore and China are performing poorly as far as political governance is concerned. MENA and Sub-Saharan African countries are those showing the lowest PGI indicators, except for South Africa which is the only African country performing well in this governance dimension. As regards administrative governance, Fig. 8.2 reveals that Singapore is by far the most effective in this field, followed by the CEEC, particularly Poland and Hungary. 6

PGI

5 4 3 2 1 0

Fig. 8.1 Political Governance Index (PGI) mean values by country, 2002–2020

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AGI

2 1.5 1 0.5 Poland

Hungary

Poland

Hungary

Bulgaria

Romania

Singapore

India

China

Peru

Philippines

Mexico

Brazil

Argentina

Gabon

South Africa

-1

Rwanda

Angola

Egypt

Bahrain

Tunisia

-0.5

Algeria

0

-1.5 Fig. 8.2 Administrative Governance Index (AGI) mean values by country, 2002–2020

EGI

40 35 30 25 20 15 10 5

Bulgaria

Romania

Singapore

India

China

Philippines

Peru

Mexico

Brazil

Argentina

South Africa

Gabon

Rwanda

Angola

Bahrain

Egypt

Tunisia

Algeria

0

Fig. 8.3 Economic Governance Index (EGI) mean values by country, 2002–2020

The remaining countries are showing negative AGI values, except for Bahrain and South Africa. According to Fig. 8.4, the quality of administrative governance is exhibiting a slight upward trend in the Asian and Sub-Saharan African countries. Oppositely, a downward trend is detected for the MENA countries, which suggests that administrative governance is regressing within this region. Finally, Latin American countries are showing the highest economic governance scores over the sample period, while the lowest average scores are those of the Asian

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Political Governance Index 6 5 Amerique Latine

4

Asie

3

MENA 2

PECO

1

Afrique Subsaharienne 2020

2018

2016

2014

2012

2010

2008

2006

2004

2002

0

Fig. 8.4 Political Governance Index (PGI) by region, 2002–2020

0.6

Administrative Governance Index

0.4 Amerique Latine 0.2 Asie 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

-0.2

MENA PECO Afrique Subsaharienne

-0.4 -0.6

Fig. 8.5 Administrative Governance Index (AGI) by region, 2002–2020

countries. We also note that the average score of the CEEC is declining steadily, revealing that these countries have experienced a constant deterioration of their economic governance over the sample period. The second step consists to build an aggregate governance indicator which includes the ten variables used to construct the political, administrative and economic indexes. A Principal Component Analysis (PCA) is conducted in order to determine the optimal weight which should be associated with each variable. The results reported in Table 8.1 show that our sample meets the factoring conditions required for a Principal Component Analysis. The Kaiser–Meyer–Olkin (KMO) index, which measures the sampling quality, is 0.608 which is considered as a good value by Kaiser (1974). The Bartlett Sphericity Test is also significant at the 1% level.

8 Governance and Growth in Developing Countries: New Insights …

177

EGI

30 28 26 24

Latin America

22

Asia

20 18

MENA

16

CEEC

14

Sub-Saharan Africa

12 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

10

Fig. 8.6 Economic Governance Index (EGI) by region, 2002–2020

Table 8.1 KMO index and Bartlett test

Kaiser–Meyer–Olkin index for measuring sampling quality

.608

Bartlett’s sphericity test

Khi-square approx

177.074

dof

45

Signification

0,000

The PCA results are reported in Table 8.2. Three different methods are commonly used to select the appropriate components. First, the Kaiser (1960) rule suggests that only factors with eigenvalues greater than 1 should be retained. Results in Table 8.2 indicate that the first and the second components meet this condition. The second rule consists to select the components which describe at least 80% of variance. Results reveal that the first two principal components explain 81.282% of the total variance. The variables forming the first component capture 56.777% of the variance, while the second component accounts for 24.505% of the total variance. Finally, the Scree test criterion is an alternative method to select the best components. It consists to examine the eigenvalues plot and to retain the values which lie to the left of the elbow point. Figure 8.7 confirms that the two first components are sufficient to describe the data. Table 8.3 summarizes the quality of representation of the different variables. Results suggest that six out of the ten variables have a good representation on the principal components as their saturation coefficients exceed 0.70. The tax rate and government expenditure have a moderate quality of representation, while the inflation rate and the unemployment rate have weak cos2 values which indicate that they are not perfectly represented by the retained components. The variables correlation plot (Fig. 8.8) and the coefficients matrix (Table 8.4) show that the first component reflects two set of variables: government effectiveness, regulatory quality, rule of law and control of corruption on one side, and the inflation

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Table 8.2 Eigenvalues and percentage of explained variance Component

Eigenvalue

Percentage of variance

Percentage of cumulative variance

1

4.478

56.777

56.777

2

2.250

24.505

81.282

3

.984

8.398

89.680

4

.782

5.824

95.504

5

.618

2.382

97.886

6

.549

1.393

99.279

7

.078

.478

99.757

8

.051

.115

99.870

9

.040

.097

99.969

10

.013

.031

100.000

Fig. 8.7 The eigenvalues Plot

rate and the tax rate on the other side. The variables forming the first group are well represented on the factor map as they are far from the origin and quite close to the first principal component. Variables relative to the administrative governance are therefore those contributing the most to the first component. The unemployment rate, government expenditure, political rights and civil liberties are the variables represented by the second principal component.

8 Governance and Growth in Developing Countries: New Insights … Table 8.3 Representation quality

179

Variable

Initials

Extraction

GovExp

1,000

.439

Unemp

1,000

.256

Inf

1,000

.351

taxRate

1,000

.521

GE

1,000

.719

RQ

1,000

.956

RL

1,000

.892

CC

1,000

.856

PLI

1,000

.831

CLI

1,000

.906

Fig. 8.8 Variables Correlation Plot

Figure 8.9 provides a projection of the 20 countries on the plane formed by the two principal components, which captures 81.282% of the information contained in the set of variables. Countries displayed to the right of the factorial plane, like Brazil, Argentina and South Africa are characterized by strong levels of government effectiveness, regulatory quality, rule of law and control of corruption. On the other hand, countries positioned on the left side of the first axis are characterized by high tax rates and inflation rates. This is particularly the case of Bahrain, Rwanda and China. Countries on the top of the graph and relatively close to second factorial axis,

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Table 8.4 Coefficients Matrix

Component 1

2

GovExp

.242

.617

Unemp

−.050

.503

Inf

−.554

.212

taxRate

−.524

.497

GE

.848

−.018

RQ

.969

−.131

RL

.926

−.186

CC

.886

−.265

PLI

.518

.750

CLI

.517

.800

like Poland and Hungary, are those exhibiting high unemployment and government expenditure while enjoying high political rights and civil liberties. The opposite is true for countries like Angola, Algeria, Gabon and Egypt which are figuring on the bottom of the plot and close to the second principal component. Based on the percentage of variance explained by each two principal components (Table 8.2) on one hand, and on the coefficients matrix (Table 8.4) on the other hand, we can compute the weight associated with each of the ten variables reflecting the various dimensions of governance. The Global Governance Index is defined as follows: GGI = −0.2901 × Inflation−0.7237 × TaxRate + 0.4441 × Gov + 0.5074 × Reg + 0.4849 × Law + 0.464 × Corr + 0.1525 × GovExpenditure

.

+ 0.1243 × Unemployment + 0.1854 × PLI + 0.1977 × CLI Figure 8.10 shows that CEEC, together with Singapore, South Africa and Brazil, are showing high GGI values, while the remaining countries are exhibiting weak or negative values, which reflects their poor quality of governance. Figure 8.11 confirms that this conclusion applies for all the sample periods. It also reveals that Sub-Saharan African countries noticed a remarkable progress of their global governance index from 2000 to 2015.

8 Governance and Growth in Developing Countries: New Insights …

Fig. 8.9 Countries projection on the two principal components

1

GGI

0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 Fig. 8.10 Global Governance Index mean values, by country

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12 10 8 MENA

6

Sub-Saharan Africa

4

Latin America 2

Asia

0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

-2

CEEC

-4

GGI

-6

Fig. 8.11 Global Governance Index mean values, by region

5

Fig. 8.12 Political Governance and GDP

4.5

Log_GDP

4 3.5

y = 0.0588x + 3.5627 R² = 0.0601

3 2.5 2 0

2

4

6

PGI

Figures 8.12, 8.13, 8.14, and 8.15 investigate the relationship between the various governance indicators developed in this paragraph and GDP per capita. All four graphs1 suggest that the governance indexes contribute to enhance GDP per capita.

1

Lower values of the EGI correspond to higher governance quality. The decreasing relationship highlighted by Fig. 8.14 indicates that enhanced economic governance boosts GDP per capita.

8 Governance and Growth in Developing Countries: New Insights …

183

5

Fig. 8.13 Administrative Governance and GDP

4.5

y = 0.4036x + 3.7755 R² = 0.3844

Log_GDP

4 3.5 3 2.5 2 -2

-1

0

1

2

3

AGI

5

Fig. 8.14 Economic Governance and GDP

4.5

Log_GDP

4 3.5

y = -0,006x + 3,918 R² = 0,122

3 2.5 2 0

10

20

30

40

EGI

8.3 Governance and Growth: A Dynamic Panel Data Approach 8.3.1 Model and Methodology When studying economic growth, two main strands of literatures can be highlighted. The first strand focuses on the sources of economic growth. the second one focuses on the determinants of economic growth (Acikgoz et al., 2016; Ben Ali & Acikgoz, 2019; Ben Mim & Ben Ali, 2021). In a final step we try to assess the impact of governance on

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5

Fig. 8.15 Global Governance and GDP

4.5

Log_GDP

4

y = 0,039x + 3,576 R² = 0,340

3.5 3 2.5 2

-1.5

-1

-0.5

0

0.5

1

1.5

GGI

economic growth for our sample composed of 20 developing countries over the period 2002–2019. We intend to investigate which among the three governance dimensions is producing the strongest effect on growth. We also verify if the global governance index outperforms the sub-indexes reflecting specific dimensions of the governance process. In particular, we seek to ascertain whether the impact of each governance dimension on growth needs to be identified separately, or whether it is preferable to measure their joint effect through a global indicator. Finally, we check if the impact of governance on growth differs from one region to another. Particularly, we try to evaluate if the economic, political and administrative dimensions of governance are producing different effects on growth across different groups of countries. To achieve these objectives we estimate the following model: G D P pcit = α0 + αi + αt + γ G D P pcit−1 + β1 I nvestmentit + β2 Laborit + β3 Cr editit + β4 Schoolit + β5 T radeit + β6 Gover nanceit + εit where α i and α t are individual and period effects and εit the error term. GDPcp denotes the logarithm of GDP per capita. Equation (1) can be easily transformed into a model assessing the impact of the independent variables on the growth rate of GDP per capita: G D P pcit − G D P pcit−1 = α0 + αi + αt + (γ − 1)G D P pcit−1 + β1 I nvestmentit + β2 Laborit + β3 Cr editit + β4 Schoolit + β5 T radeit + β6 Gover nanceit + εit where (γ -1) stands for the convergence coefficient which is expected to be negative (Solow, 1956). According to the neoclassical growth theory investment should promote GDP per capita growth, while an increase in the labor force (Labor) should

8 Governance and Growth in Developing Countries: New Insights …

185

produce the opposite effect. Financial development, proxied by the credits provided by the banking sector (Credit), is expected to spur economic growth (Levine, 1998). Similarly, various studies have also shown that trade openness (Trade) contributes to accelerate growth (Yanikkaya, 2003). According to the endogenous growth theory, human capital (School) should boost economic growth (Barro, 2001). Governance is proxied by the four indexes developed in the previous paragraph, namely the PGI, AGI, EGI and GGI. The definitions of the variables are provided in Table 8.5 in the appendix. We rely on the System GMM estimator developed by Arellano and Bover (1995) which overcomes the endogeneity problem arising from reverse causality between some independent variables and per capita growth. In particular, higher growth rates should spur investment, international trade and credits provided by banks. High growth may also provide governments with higher tax revenues which facilitates the implementation of costly institutional reforms. We execute the Arellano and Bond (1991) autocorrelation test and the Sargan-Hansen over-identification test to check for the validity of the instruments and confirm the robustness of the results. The descriptive statistics and the correlation matrix are provided respectively, in Tables 8.6 and 8.7 in the appendix. We notice that per capita GDP is positively correlated to the administrative and political governance indexes as well as to the global governance index, while it is negatively correlated to the economic governance index (higher values of this index indicate poor economic governance). Such results suggest that higher governance standards are associated with higher GDP per capita.

8.3.2 Results and Discussion Estimation results for the full sample are provided in Table 8.8. We notice that the investment rate, credits provided by the banking sector and trade openness are significantly boosting economic growth. On the other hand, the labor force and the secondary school enrollment rate don’t produce any significant effect on growth. Such results suggest that human capital doesn’t meet the needs of the labor market in the considered sample. As expected, all the governance indicators significantly contribute to promote per capita GDP growth, which confirms that institutional development is one of the main drivers of economic growth. The administrative governance index is the one producing the most important effect on growth. Such a result suggests that the effectiveness of the legal framework, the willingness to fight corruption and the quality of regulations and public services are the backbone of an effective institutional framework. In order to test the sensitivity of these results to the regional contexts, we divided our sample into five different groups of countries which correspond to the following regions: MENA, CEEC, Latin America, Asia and Sub-Saharan Africa. The estimation results for each group of countries are summarized respectively in Tables 8.9–8.13. Among the control variables, we notice that “Trade” is the only one to produce a positive and significant effect on growth in all the specifications for the five subsamples.

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Various studies have already pointed out that trade openness is a major determinant of growth in developing countries (Chang et al., 2009; Jouini, 2015; Zahonogo, 2016). Investment is also significantly contributing to promote growth in 15 out of the 20 estimated specifications. Oppositely, we notice that the credits provided by the banking sector contribute to spur growth only in CEEC and two specifications out of four in MENA countries. For the remaining three regions, banking credits don’t contribute to boost growth, which highlights the inefficiency of the financial intermediation process in these countries. Similarly, the schooling rate only contributes to stimulate growth in one specification among the 20 estimated models. Moreover, it produces a negative impact on growth in Sub-Saharan African countries (the negative effect is detected in two equations out of four for this group of countries). A similar result was highlighted by Okunade et al. (2022), who claimed that beyond a threshold of 45.07% human capital contributes to deter productivity growth in African countries. These results confirm that developing countries should upgrade their education systems to meet the requirements of the labor market and take advantage from the expected positive externalities that human capital may produce on growth. The regional framework highlights some interesting conclusions regarding the impact of the governance indicators on growth. Firstly, the three sub-indexes relative to the administrative, political and economic governance contribute significantly to spur growth in the five groups of countries, while their respective impacts on growth differ from one sub-sample to another. For instance, the effect associated with the administrative governance index is by far the largest in the CEEC compared to the other regions. This result confirms the superiority of CEEC in terms of administrative governance. Among the four governance indexes, we notice that the AGI is the one producing the most important effect on growth in all groups of countries, except in Sub-Saharan Africa where it is outperformed by the political governance index. SubSaharan African countries still lag far behind in terms of administrative governance, despite the clear progress made during the sampling period and illustrated by Fig. 8.5. In this respect, Ben Mim et al. (2022) pointed out that domestic industries in SubSaharan countries are seriously hampered by the poor regulatory quality. Results also show that the economic governance index is producing the highest effect on growth in MENA countries, followed closely by CEEC. Figure 8.6 has already shown that both groups of countries are exhibiting the highest EGI values over the sample period. Finally, results relative to the global governance index show that it contributes to boost growth, except for Asian countries where an increase in the GGI significantly deters growth. Such a puzzling result may be explained in two different ways. First, we note the existence of significant disparities between the three dimensions of governance within this group of countries. While they are exhibiting high quality of administrative governance (Fig. 8.5), Asian countries are relegated to the last place in terms of economic governance (Fig. 8.6). The PCA results discussed in the previous paragraph showed that the global governance index assigns the most significant weight to the tax rate which reflects the quality of the economic governance, followed by four variables reflecting the effectiveness of the administrative governance. Variables relative to the economic and administrative governance are therefore producing contradictory effects on the global governance index, which prevents this

8 Governance and Growth in Developing Countries: New Insights …

187

indicator from having a clear trend. Moreover, the four Asian countries included in our sample show significant disparities within each dimension of governance. For example, while Singapore has a fairly good level of administrative governance, the performance of China and India is rather poor in this field (Fig. 8.2). In conclusion, the negative effect produced by the GGI on growth is likely to be attributed to the important disparities from one dimension of governance to another and within each dimension. Such findings imply that decision-makers should act simultaneously in all three dimensions of governance. Focusing on a single dimension should not lead to the expected economic outcomes. In other words, the governance strategy must be global and coherent to produce the expected positive externalities on growth.

8.4 Conclusions and Recommendations This paper aims to provide new insights into the governance-growth nexus in developing countries. Based on sample of 20 countries belonging to five different regions, we first define three indexes reflecting respectively the quality of the administrative, political and economic governance. In a second step, we develop a global governance index reflecting simultaneously the three dimensions of governance. The PCA’s results indicate that the tax rate, regulatory quality, control of corruption and rule of law are the variables weighting most on the global governance index. Such results suggest that the economic and administrative dimensions of governance weigh more than the political dimension. The second empirical investigation consists to assess the impact of the various governance indexes on growth. In particular, we intend to identify the governance dimension producing the strongest effect on growth for each group of countries, and to verify if the global governance index outperforms the sub-indexes reflecting a single aspect of the governance process. The estimation results reveal that the different dimensions of governance contribute to promote growth in a significant way. However, the intensity of these effects differs from one group of countries to another. In particular, the results show that countries endowed with the highest governance indicators are those enjoying the greatest impact on growth. Such findings suggest that the impact of governance on growth increases as the quality of governance improves. An important policy recommendation can be drawn from this conclusion: policymakers should engage in serious and deep reforms of the governance system in order to reap the expected economic benefits. Oppositely, mild governance reforms should not generate any significant positive impact on growth. Another important finding of this study is that the global governance index does not necessarily produce the expected positive impact on growth, despite the fact that the three governance dimensions considered separately are contributing to boost growth. Such a counterintuitive result is mainly noticed when the different variables forming the global indicator are following divergent trends. This is particularly the case for the Asian countries where the scores relative to the three dimensions of governance are exhibiting large gaps. A central recommendation emerges from these results.

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Table 8.5 Variables definitions Variables

Definitions

GDP per capita (GDPpc)

Gross domestic product divided by midyear population

Investment rate (Investment)

Gross fixed capital formation (% of GDP)

Total labor force (Labor)

People ages 15 and older who supply labor for the production of goods and services

Secondary School enrollment rate (School) Ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to secondary education Inflation rate (Inflation)

Variation rate of consumer prices (annual %)

Trade openness (Trade)

The sum of exports and imports of goods and services measured as a share of gross domestic product

Domestic credits to private sector (Credit)

Financial resources provided to the private sector by financial corporations (% of GDP)

Control of corruption (Corr)

The extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. Estimate ranging from −2.5 to 2.5 (continued)

Developing countries must be involved in the three dimensions of governance on an equal basis. Giving priority to one dimension of governance at the expense of others can undermine the whole governance process, thereby compromising its impact on growth.

Appendix See Tables 8.5, 8.6, 8.7, 8.8, 8.9, 8.10, 8.11, 8.12, and 8.13.

8 Governance and Growth in Developing Countries: New Insights …

189

Table 8.5 (continued) Variables

Definitions

Rule of Law (Law)

The extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police and the courts, as well as the likelihood of crime and violence. Estimate ranging from approximately −2.5 to 2.5

Government effectiveness (Gov)

The quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation and the credibility of the government’s commitment to such policies. Estimate ranging from −2.5 to 2.5

Voice and accountability (VA)

The extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association and a free media. Estimate ranging from approximately −2.5 to 2.5

Political stability (Stability)

The likelihood of political instability and/or politically motivated violence, including terrorism. Estimate ranging from −2.5 to 2.5

Regulatory Quality (REG)

The ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Estimate ranging from approximately −2.5 to 2.5

2.368

0.451

380

Minimum

Std. Dev

Observations

4.824

Maximum −0.922

−0.454

380

0.781 380

380

0.264

1.344

2.641

−0.351

2.903 0.118

1.847 1.811

1.120

−0.642

Trade

Investment −0.652

1.134

3.732

3.798

Mean

Median

Labor

GDPpc

Table 8.6 Descriptive statistics

380

0.219

1.098

2.049

1.945

1.861

School

380

0.322

0.914

2.261

1.577

1.572

Credit

380

6.363

−26.891

13.552

0.045

0.497

GGI

380

7.121

8.820

47.932

22.199

22.150

EGI

380

0.645182

−1.136

2.143

−0.125

−0.032

AGI

380

1.780

0.5

6

4

3.399

PGI

190 M. Zayati and M. S. Ben Ali

0.299 0.162

−0.187 −0.071 −0.413

0.255

−0.222

0.136

−0.151

0.580

0.219

EGI

AGI

PGI

0.138

−0.139 0.552

0.280

0.296

0.235

−0.433 0.317

0.074 −0.115

0.015

−0.302

0.051

0.426

Credit

GGI

1

1 0.001

−0.164

0.335

0.313

School

1

School

0.020

Trade

0.155

Investment

−0.156

0.047

0.123

Investment

1

Labor

Trade

1

−0.327

GDPpc

Labor

GDPpc

Table 8.7 Correlation matrix

0.059

−0.002

0.099

0.022

1

Credit

0.435

0.705

−0.556

1

GGI

0.204

−0.437

1

EGI

0.330

1

AGI

1

PGI

8 Governance and Growth in Developing Countries: New Insights … 191

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M. Zayati and M. S. Ben Ali

Table 8.8 GMM-system estimation, full sample System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations = 360 Number of groups = 20 Time periods = 18 Dependent variable: GDPpc Model 1

Model 2

Model 3

Model 4

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Constant

0.166 (0.2891)

0.247 (0.3191)

0.3142** (0.1288)

0.3071 (0.3288)

GDPpct−1

0.757*** (0.0523)

0.7858*** (0.0442)

0.7918*** (0.0407)

0.7439*** (0.0579)

Investment

0.164*** (0.0485)

0.119* (0.0623)

0.1273* (0.0703)

0.1986*** (0.0396)

Labor

0.014 (0.1075)

−0.0195 (0.0382)

−0.0151 (0.0448)

0.0379 (0.0886)

Credit

0.133** (0.0618)

0.1000* (0.0513)

0.0769 (0.0666)

0.2032*** (0.0781)

School

0.005 (0.1575)

−0.0729 (0.0755)

−0.0323 (0.0858)

−0.0663 (0.1550)

Trade

0.342*** (0.1156)

0.2341** (0.0988)

0.2824*** (0.0755)

0.2723** (0.1233)

GGI

0.0006*** (0.00019)

Independent variables

−0.0017*** (0.00068)

EGI AGI

0.0296** (0.0142)

PGI

0.0171*** (0.0063)

Sargan test Chi2 (57) Prob > chi2

17.8911 [1.0000]

18.2589 [1.0000]

17.1385 [1.0000]

17.43648 [1.0000]

Arellano-Bond tests for autocorrelation AR(1)

−2.7826 [0.0054]

−3.0943 [0.0020]

−3.0795 [0.0021]

−2.9098 [0.0036]

AR(2)

−0.804 [0.1027]

−0.2629 [0.1109]

−0.3268 [0.5218]

−0.9706 [0.0879]

*significant at level 10%, **significant at level 5% and ***significant at level 1% (.) Robust standard deviation; [.] Probability test

8 Governance and Growth in Developing Countries: New Insights …

193

Table 8.9 System GMM estimation for MENA Countries System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations =72 Number of groups=4 Time periods =18 Dependent variable: GDPpc Independent Variables

Constant GDPpct−1 Investment Labor Credit School Trade GGI

Model 1

Model 2

Model 3

Model 4

Coef.

Coef.

Coef.

Coef.

(z-stat)

(z-stat)

(z-stat)

(z-stat)

−0.0673

−0.0217

0.3142**

0.3071

(0.2916)

(0.5049)

(0.1288)

(0.3288)

0.8518***

0.8072***

0.7918***

0.7439***

(0.0388)

(0.0524)

(0.0407)

(0.0579)

−0.1468*

−0.1738

0.1273*

0.1986***

(0.0774)

(0.152)

(0.0703)

(0.0396)

0.1377***

0.0944**

−0.0151

0.0379

(0.0422)

(0.0399)

(0.0448)

(0.0886)

0.0473

0.1014**

0.0769

0.2032***

(0.0376)

(0.0391)

(0.0666)

(0.0781)

−0.1366

0.0526

−0.0323

−0.0663

(0.0997)

(0.1283)

(0.0858)

(0.155)

0.3329***

0.3118***

0.2824***

0.2723**

(0.0805)

(0.0561)

(0.0755)

(0.1233)

0.0051** (0.0021) −0.0106***

EGI

(0.0035) AGI

0.0296** (0.0142) 0.0171***

PGI

(0.0063) Sargan test Chi2 (49) Prob >

chi2

21.30433

15.0879

17.1385

17.43648

[0.4204]

[0.9095]

[0.8379]

[0.8907]

−3.5836

−2.4906

−3.0795

−2.9098

[0.0233]

[0.0361]

[0.0221]

[0.0296]

−1.5051

−1.2729

−0.3268

−0.9706

Arellano-Bond Tests for Autocorrelation AR(1) AR(2)

(continued)

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Table 8.9 (continued) System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations =72 Number of groups=4 Time periods =18 Dependent variable: GDPpc [0.1323]

[0.2030]

[0.5218]

[0.3079]

*significant at level 10%, **significant at level 5% and ***significant at level 1% (.) Robust standard deviation; [.] Probability test Table 8.10 System GMM estimation for CEE countries System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations = 72 Number of groups = 4 Time periods = 18 Dependent variable: GDPpc Model 1

Model 2

Model 3

Model 4

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Constant

0.8802 (0.5709)

0.8193 (0.5452)

0.0720 (0.5335)

0.2849 (0.5518)

GDPpct−1

0.6179*** (0.0392)

0.5581*** (0.0447)

0.5883*** (0.0390)

0.6065*** (0.0371)

Investment

0.2555*** (0.0684)

0.3539*** (0.0742)

0.2194*** (0.0711)

0.3921*** (0.0790)

Labor

0.2798*** (0.0903)

0.2774*** (0.1407)

0.3567** (0.1441)

0.3455** (0.1462)

Credit

0.2577*** (0.07022)

0.1662** (0.0748)

0.3239*** (0.0903)

0.1585** (0.0771)

School

0.0168 (0.0938)

0.1114 (0.2139)

0.0332 (0.2182)

0.0848 (0.2221)

Trade

0.5406*** (0.0750)

0.3450*** (0.0844)

0.4715*** (0.0873)

0.3619*** (0.0872)

GGI

0.0048** (0.0021)

Independent variables

EGI AGI

−0.0098*** (0.0021) 0.2496*** (0.0506) (continued)

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Table 8.10 (continued) System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations = 72 Number of groups = 4 Time periods = 18 Dependent variable: GDPpc Independent variables

Model 1

Model 2

Model 3

Model 4 0.0646*** (0.0130)

PGI Sargan test Chi2 (49) Prob > chi2

19.8484 [0.9024]

22.8504 [0.8854]

14.3566 [0.9518]

10.3247 [0.9977]

Arellano-Bond tests for autocorrelation AR(1)

−4.6374 [0.0231]

−3.2229 [0.0358]

−3.9851 [0.0226]

−3.7655 [0.0313]

AR(2)

−2.0017 [0.1278]

−1.6929 [0.1960]

−0.4346 [0.5040]

−1.2908 [0.2042]

*significant at level 10%, **significant at level 5% and ***significant at level 1% (.) Robust standard deviation; [.] Probability test

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Table 8.11 System GMM estimation for Asian countries System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations = 72 Number of groups = 4 Time periods = 18 Dependent variable: GDPpc Model 1

Model 2

Model 3

Model 4

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Constant

−0.2075 (0.3635)

−0.3542 (0.5108)

−0.1704 (0.3934)

−0.4497 (0.3473)

GDPpct−1

0.9461*** (0.0482)

0.8808*** (0.0442)

0.9026*** (0.0509)

0.8021*** (0.0465)

Investment

0.2103 (0.1349)

0.3651*** (0.1265)

0.4000*** (0.1241)

0.3606*** (0.1163)

Labor

−0.0812*** (0.0226)

−0.1019*** (0.0281)

−0.0935** (0.0403)

−0.0993*** (0.0254)

Credit

−0.0832 (0.0563)

−0.0092 (0.0544)

−0.0754 (0.0565)

0.0185 (0.0644)

School

0.1027 (0.2136)

0.2121 (0.1318)

0.2422 (0.2386)

0.4929*** (0.1878)

Trade

0.3452*** (0.0766)

0.2211*** (0.0476)

0.3385*** (0.0894)

0.3805*** (0.0776)

GGI

−0.0092*** (0.0030)

Independent variables

−0.0041* (0.0021)

EGI AGI

0.0453* (0.0233)

PGI

0.0262*** (0.0093)

Sargan test Chi2 (33) Prob > chi2

47.4023 [0.1500]

52.3242 [0.1176]

54.8553 [0.1098]

54.1151 [0.1117]

Arellano-Bond tests for autocorrelation AR(1)

−4.7661 [0.0225]

−3.3124 [0.0348]

-4.0957 [0.0220]

−3.8700 [0.0304]

AR(2)

−2.0017 [0.1278]

−1.6929 [0.1960]

−0.4346 [0.5040]

−1.2908 [0.2042]

*significant at level 10%, **significant at level 5% and ***significant at level 1% (.) Robust standard deviation; [.] Probability test

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Table 8.12 System GMM estimation for Latin American countries System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations = 72 Number of groups = 4 Time periods = 18 Dependent variable: GDPpc Model 1

Model 2

Model 3

Model 4

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Coef (z-stat)

Constant

1.2567*** (0.4743)

1.2810*** (0.4752)

1.2573*** (0.4699)

0.8502 (0.6735)

GDPpct−1

0.7405*** (0.0529)

0.7472*** (0.0534)

0.7848*** (0.0485)

0.7853*** (0.0478)

Investment

0.7547 (0.1891)

0.7469*** (0.2008)

0.5523*** (0.1559)

0.5576*** (0.1604)

Labor

0.0520 (0.0762)

0.0147 (0.0708)

−0.0427 (0.0611)

−0.0283 (0.0672)

Credit

−0.0090 (0.0901)

−0.0017 (0.0926)

−0.0639 (0.0861)

−0.0132 (0.1008)

School

−0.0384 (0.2879)

−0.0767 (0.2867)

−0.1112 (0.2843)

−0.0131 (0.3127)

Trade

0.1612** (0.0780)

0.1546* (0.0808)

0.1916** (0.0763)

0.1742** (0.0796)

GGI

0.0058** (0.0026)

Independent variables

−0.0035* (0.0019)

EGI AGI

0.0510* (0.0293)

PGI

0.0343* (0.0180)

Sargan test Chi2 (33) Prob > chi2

16.9197 [0.9004]

18.3547 [0.9003]

25.8971 [0.8241]

24.1181 [0.8808]

Arellano-Bond tests for autocorrelation AR(1)

−4.5122 [0.0238]

−3.1359 [0.0369]

−3.8775 [0.0233]

−3.6638 [0.0322]

AR(2)

−1.8950 [0.1363]

−1.6027 [0.2091]

−0.4114 [0.5378]

−1.2220 [0.2178]

*significant at level 10%, **significant at level 5% and ***significant at level 1% (.) Robust standard deviation; [.] Probability test

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Table 8.13 System GMM estimation for Sub-Saharan African countries System Dynamic Panel Data Program: Arellano-Bover / Blundell-Bond estimation Method: Panel Generalized Method of Moment (GMM) two-step Number of observations =72 Number of groups=4 Time periods =18 Dependent Variable: GDPpc Model 1

Model 2

Model 3

Model 4

Coef. (z-stat)

Coef. (z-stat)

Coef. (z-stat)

Coef. (z-stat)

Constant

−0.1047 (0.3709)

−0.3020 (0.3196)

0.1723 (0.6164)

0.4117 (0.2912)

GDPpct−1

1.0895*** (0.0782)

1.0890*** (0.0761)

1.0854*** (0.0987)

0.9981*** (0.0778)

Investment

0.2856** (0.1329)

0.2529* (0.1343)

0.2863*** (0.0716)

0.0874 (0.1394)

Labor

−0.0219 (0.0577)

−0.0180 (0.0588)

−0.0261 (0.0332)

−0.0496 (0.0588)

Credit

−0.2290 (0.1691)

−0.1997 (0.1728)

−0.2236 (0.2808)

−0.1001 (0.1736)

School

−0.1281 (0.1045)

−0.1491 (0.1061)

−0.1932*** (0.0702)

−0.2246** (0.1074)

Trade

0.1578* (0.0912)

0.1969** (0.0872)

0.0722 (0.1547)

0.0181 (0.0832)

GGI

0.0016** (0.0006)

Independent Variables

−0.0063** (0.0029)

EGI AGI

0.0397** (0.0159)

PGI

0.0486*** (0.0116)

Sargan Test Chi2 (33) Prob>chi2

45.5606 [0.2000]

38.7100 [0.4415]

51.3497 [0.1241]

55.9036 [0.1008]

Arellano-Bond tests for autocorrelation AR(1)

−4.3903 [0.0245]

−3.0512 [0.0380]

−3.7728 [0.0240]

−3.5649 [0.0332]

AR(2)

−1.8439 [0.1408]

−1.5594 [0.2160]

−0.4003 [0.5555]

−1.1890 [0.2250]

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References Acemoglu, D., Johnson, S. & Robinson, J. (2004). Institutions as the fundamental cause of long-run growth (NBER Working Paper 10481). Acemoglu, D., & Robinson, J. (2008). The role of institutions in growth and development (Commission on Growth and Development Working Paper No. 10). World Bank. Acikgoz, S., Ben Ali, M. S., & Mert, M. (2016). Sources of economic growth in mena countries: Technological progress, physical or human capital accumulations? Economic development in the Middle East and North Africa (pp. 27–69). Springer/Palgrave Macmillan. Aldahdah, E. (2005). Gouvernance et Développement au Maghreb. Note présentée à l’occasion de la table ronde organisée à Tunis par la Banque Mondiale les 24 et 25 Mai 2005. Alesina, A., & Perotti, R. (1996). Political instability and economic growth. Journal of Economic Growth, 1(2), 189–211. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297. Arellano, M., & Bover, O. (1995). Another look at the instrumental-variable estimation of error components models. Journal of Econometrics, 68, 29–51. Barro, R. (1991). Economic growth in a cross section of countries. The Quarterly Journal of Economics, 106, 407–443. https://doi.org/10.2307/2937943 Barro, R. (1996). Democracy and growth. Journal of Economic Growth, 1(1), 1–27. Barro, R. J. (2001). Human capital and growth. The American Economic Review, 91(2), 12–17. Ben Ali, M. S., & Acikgoz, S. (2019). Where does economic growth in the Middle Eastern and North African countries come from? The Quarterly Review of Economics and Finance, 73, 172–183. Ben Ali, M. S., Krammer, S. M. (2016). The role of institutions in economic development. Economic development in the Middle East and North Africa (pp. 1–25). Springer/Palgrave Macmillan. Ben Ali, M. S., Samargandi, N., & Sohag, K. (2016). The finance-growth Nexus: Which factors can interfere? Economic development in the Middle East and North Africa (pp. 155–171). Springer/ Palgrave Macmillan. Ben Ali, M. S., Saha, S. (2016). Corruption and economic development. Economic development in the Middle East and North Africa (pp. 133–154). Springer/Palgrave Macmillan. Ben Mim, S., Ben Ali, M. S. (2021). Short- and long-run causality between remittances and economic growth in MENA countries: A panel ARDL approach. In M. S. Ben Ali (Ed.), Economic development in the MENA region : New perspectives (pp. 29–44). Springer. Ben Mim, S., Hedi, A., & Ben Ali, M. S. (2022). Industrialization, FDI and absorptive capacities: Evidence from African Countries. Economic Change and Restructuring, Springer, 55(3), 1739– 1766. Bräutigam, D. (2000). Foreign aid and the politics of participation in economic policy reform. Public Admin. Dev., 20, 253–264. Chang, R., Kaltani, L., & Loayza, N. V. (2009). Openness can be good for growth: The role of policy complementaries. Journal of Development Economics, 90(1), 33–49. Colagrossi, M., Rossignoli, D., & Maggioni, M. A. (2020). Does democracy cause growth? A meta-analysis (of 2000 regressions). European Journal of Political Economy, 61. Dahlum, S., & Knutse, C. H. (2017). Do democracies provide better education? Revisiting the Democracy-Human Capital Link, World Development, 94, 186–199. Djankov, S., McLiesh, C., & Shleifer, A. (2007). Private credit in 129 countries. Journal of Financial Economics, 84(2), 299–329. Fernández, A. I., González, F., & Suárez, N. (2016). Banking stability, competition and economic volatility. Journal of Financial Stability, 22, 101–120. Goel, R. K., & Hasan, I. (2011). Economy-wide corruption and bad loans in banking: International evidence. Applied Financial Economics, 21(7), 455–461. Goel, R. K., & Nelson, M. A. (2005). Economic freedom versus political freedom: Cross-country influences on corruption. Australian Economic Papers, 44, 121–133.

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Chapter 9

Governance, Gender Diversity, and Banking Performance: New Evidence from North African and GCC Countries Sana Mohsni, Sami Ben Mim, and Fatma Hajji Abstract This study assesses the impact of board gender diversity on financial performance of conventional banks from nine North African and Gulf Cooperation Council countries. Using a sample of 61 banks covering the period 2009–2021, the system GMM results show a positive and significant impact of gender diversity on banks’ financial performance as measured by the return on assets and return on equity. We also identify transmission channels through which the presence of female directors enhances banking performance and investigate the nonlinearity of the relationship between board gender diversity and financial performance. The results suggest that female directors contribute to enhancing cost monitoring and are associated to a reduction in the risk exposure of banking firms. Our estimates also show that the gender diversity–performance relationship is nonlinear and that the positive effect of gender diversity intensifies as the number of female directors increases to reach a threshold at a female board composition of 42.1%. Keywords Gender diversity · Financial performance · Transmission channels · Nonlinearity

9.1 Introduction The financial crisis of 2008 was a bitter reminder of the failure and limitations of banks’ governance mechanisms. The crisis raised concerns regarding the composition of banks boardrooms and the need for more gender diversity as gender diversity has been linked to improved firm performance, conservative risk-taking behavior, better S. Mohsni (B) Sprott School of Business, Carleton University, Ottawa, ON, Canada e-mail: [email protected] S. B. Mim IHEC, LaREMFiQ, University of Sousse, Sousse, Tunisia F. Hajji IHEC, University of Carthage, Tunis, Tunisia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6_9

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boards, and improved corporate decisions (see for instance, Adams & Ferreira, 2009; Carter et al., 2003; Bernile et al., 2018). Over the past two decades, the role of gender diversity in corporate boards has gained interest from policymakers, researchers, and academics. Several studies emphasize the benefits of the presence of women at corporate decision-making levels and their positive effect on firm performance and bank performance (Cardillo et al., 2021; Del Prete & Maria Lucia, 2021; Owen & Temesvary, 2018). In addition, research shows that female directors exhibit better board attendance and are better monitors than their male counterparts (see for instance, Adams & Ferreira, 2009); are more conservative in debt appetite (see for instance, Garcia & Herrero, 2021); and tend to reduce firm risk (see for instance, Bernile et al., 2018; Li et al., 2022). Although a large body of literature finds that female presence in corporate boards improves board decisions and firm performance, the results remain mixed with some papers arguing that board gender diversity may have a negative or an insignificant impact on the firm and link female board members to increased decision-making costs, poor interaction between board members, and self-categorization based on gender differences (Adams & Mehran, 2012; Berger et al., 2014; García-Meca et al., 2015; Khan et al., 2020). Although the effect of board gender diversity has been extensively examined in developed economies in Europe, Asia, and North America, only a handful of studies explore the effects of board gender diversity in North African and GCC countries (AlYahyaee et al., 2017; Issa et al., 2021; Sherif & Anwar, 2015). Given their cultural, social, and organizational characteristics, these economies represent a unique field of investigation. For instance, they are characterized by strong masculine and patriarchal cultures which limit women access to labor market and management positions. In addition, women in these regions favor public employment to the exigencies of the private sector (Young, 2016), and although, the situation is slightly better in North Africa where women enjoy more rights and freedoms, traditions in the highly conservative GCC countries tend to lead women to home confinement and to restrict their role to family life. This is exacerbated by oil resource dependency and overreliance on foreign workers which reduces the need for women in the GCC countries to participate in the labor market (Young, 2016). As a consequence, women are largely underrepresented in upper level corporate positions and board of directors. The most recent report of Hawkama1 discloses that among 5597 board seats examined, only 2.5% are occupied by women. OECD (2019) reported that the average representation of women on boards of the largest 142 public companies in MENA remains modest, at 4.8% of total voting board seats (60 out of 1258 seats). Overall, the participation of women in the labor force in the GCC region is about half (26.9% against 51.7%) of the world average (Hendy, 2019). Push for gender diversity is however gaining momentum in the region and new corporate governance reforms are treating gender balance as a business priority rather than an ethical imperative (Hendy, 2019). The cultural and social specificities of the North African and GCC region, in addition, 1

Hawkamah Gender Report 2016, website link: www.hawkamah.org/uploads/reports/Hawkamah% 20-%20Gender%20Report.pdf.

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to the current imbalance of gender representation in the related labor markets, add to the importance of examining the relationship between board gender diversity and the financial performance of firms in the region. This study aims to assess the relationship between board gender diversity and financial performance of banks in the North African and GCC region. We choose the banking sector because of its importance and dynamic role in the economy. In addition, consistent with other regions in the world, this is the sector where higher levels of diversity are observed and therefore can provide an adequate setup to examine the current research question. The study has three goals. First, we investigate the impact of the board’s characteristics, including gender diversity, on the return on assets and return on equity of banks. Second, by focusing on the capacity of female directors to enhance cost monitoring and to mitigate banks’ risk exposure, we examine the main transmission channels through which the presence of female directors may affect banks’ financial performance. Third, we test the possible nonlinearity of the relationship between bank performance and gender diversity. Only a few studies examine the relationship between board gender diversity and firm performance in the MENA, North African, and GCC regions (Al-Yahyaee et al., 2017; Issa et al., 2021; Sherif & Anwar, 2015), and most of the findings indicate a negative effect or no association between board gender diversity and firm performance. Given the poor levels of gender diversity in these regions, the mixed results can be explained by the critical mass theory that argues that until a certain threshold of any specific group is reached, the different abilities and skills that such group brings may be undermined and untapped into (Kanter, 1977). This study will shed more light on the effect of board gender diversity on the performance of banks in the North African and GCC region, while examining the critical mass theory effect and the possible nonlinearity of the relationship between gender diversity and bank performance. Our results offer evidence of a positive impact of board gender diversity on banks’ financial performance. In addition, consistent with the board gender diversity literature (see for instance, Bernile et al., 2018), we find evidence suggesting that gender diversity is associated with higher CAR ratios and therefore is likely to reduce banks’ risk, and that it is also associated with lower cost ratios, indicating that female directors contribute to the effectiveness of banks’ spending. These results emphasize the role of gender diversity in promoting sustainability in the banking sector. Finally, we document the existence of a nonlinear relationship between gender diversity and financial performance. Specifically, the estimation results suggest that an increase in the percentage of female directors beyond a certain threshold may contribute to a decrease in banking performance. However, this critical threshold is far from being reached in any of the North African or the GCC countries. Overall, consistent with a major strand in the gender diversity literature, our results indicate a positive impact of board gender diversity and associate gender diversity with better bank performance, higher cost efficiency, and better capitalization. These results should convince banks in the North African and GCC region to further diversify the composition of their boards.

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This study contributes to the existing literature on board gender diversity in several ways. First, we contribute to the burgeoning literature on board gender diversity in emerging markets, specifically, the North African and GCC countries. We shed light on the effect of board gender diversity on firm performance, while considering the critical mass theory and the possible nonlinearity of such relationship. Our findings of a positive relationship between board gender diversity and firm performance coupled with the nonlinearity of such relationship can help explain some of the mixed findings in the current literature. Second, this study offers new evidence on the role played by women in boardrooms of banks in emerging economies. To the best of our knowledge, this study is the first to emphasize the leading role of gender diversity on bank performance in the North African and GCC region. Third, we bring a closer understanding of the transmission channels through which women on board can enhance banking performance. Finally, consistent with findings on developed countries, we document nonlinearity in the relationship between gender diversity and bank performance. Overall, our findings reinforce the positive effect of gender diversity documented in developed economies and suggest that some of the effects of board gender diversity seem to be universal. The remainder of the chapter is organized as follows. Section 9.2 is a review of the main theoretical and empirical studies on board gender diversity. In Sect. 9.3, we present the sample and describe the methodological framework. In Sect. 9.4, we discuss and summarize the main results. Finally, Sect. 9.5 contains the conclusion and some policy recommendations.

9.2 Theoretical Background and Empirical Evidence Several theories, related to sociology, psychology, and cognitive processes, establish the relationship between gender diversity and board performance. These include the agency theory, the social identity theory, and the critical mass theory. The agency theory (Jensen & Meckling, 1976) highlights the monitoring role of the board of directors, specifically, in a world of asymmetric information. Carter et al. (2003) suggest that a more diverse board improves board effectiveness, as including more women on boards enhances board independence and power. Huse and Grethe Solberg (2006) assert that women are severe controllers and are more committed to their activities. Therefore, female presence on boards is expected to improve board performance. In addition, a diversified board generates stronger independence and thus allows better monitoring (Adams & Ferreira, 2009; Ain et al., 2020; Carter et al., 2003). By strengthening the monitoring functions of the board, women directors contribute to reducing agency costs in terms of time spent on control and quality of oversight (Adams & Ferreira, 2009; Campbell & Mínguez-Vera, 2008). The resource dependency theory argues that organizational behavior is affected by external resources and among these resources, that link the corporation to the external world, are the board of directors. Corporate boards bring in legitimacy, access to information and advice to the firm (Pfeffer & Salancik, 2003), and given the

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multiplicity of skills, resources, backgrounds, and networks of a more diverse board, it represents more valuable resources which should lead to better firm performance (Hillman et al., 2000). The critical mass theory (Kanter, 1977) suggests that until a certain threshold of any specific group is reached, the different abilities and skills that such group brings and can contribute may be undermined and untapped into. According to this theory, there is a critical mass or threshold that should be reached for women to effectively influence the dynamics and the decision-making of the board of directors (Birindelli et al., 2020). If the number of female directors is below this threshold, women are perceived as tokens and remain unable to play an important role within the board. Joecks et al. (2013), show that at least three female directors are necessary to significantly contribute to enhancing firm performance. Although most theories suggest that gender diversity is beneficial to the board and the firm, some do not. The social identity theory (Turner et al., 1979), for instance, argues that women’s effectiveness on the board can be limited or even negative. The social identity theory posits that as a group becomes more heterogeneous communication is affected and the group becomes more difficult to manage and less efficient in reaching consensus and decisions (Smith et al., 2006). Heterogenous groups tend to be less cooperative and experience more conflicts (Williams & O’Reilly, 1998), and diversity may lead to self-categorization and to in-group versus out-group stereotyping and behavior which diminishes the efficiency of board decision-making. Women, as they are categorized as the out-group, will face barriers to communication and collaboration inside the board, therefore, limiting the effectiveness of sound decision-making. Several empirical findings support this theory mainly in countries where cultural, organizational, and religious challenges limit gender diversity in the boardroom. Although a robust body of literature examines the effects of board gender diversity in Europe, Asia, and North America, the literature on North African and GCC countries is scarce. Exceptions include Al-Yahyaee et al. (2017) who, using a sample of 596 firms over the period 2007–2011, examine the impact of gender diversity on risk disclosure of financial firms in the GCC region. Their findings suggest that the presence of female directors in financial institutions boards negates the positive association between corporate governance and market risk disclosure. They link these findings to the conservative culture of GCC societies, which is transmitted to the GCC business environment. Using 153 publicly traded banks from 17 MENA countries, Sherif and Anwar (2015) find a negative relationship between gender diversity and performance over the period 2000–2013. These results are explained by “tokenism”, and the fact that banks are hiring directors only to satisfy regulatory requirement, or by the scarcity of high performing female directors. More recently, Issa et al. (2021) examine the diversity-performance nexus using a sample of listed banks belonging to 11 MENA countries over the period 2011–2018. Their findings suggest that board gender diversity does not lead to significant changes on bank performance. We argue that some of these inconclusive results can be explained by the critical mass theory. Given the low levels of female directors in corporate boards in the North African and GCC countries, there is not enough of a critical mass of female

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directors to make significant changes within boards behavior and decisions. In line with the critical mass theory, several empirical studies test the existence of a nonlinear relationship between the number of women on board and firm performance. A special interest was given to the banking sector. Birindelli et al. (2019) study 96 listed banks in the EMEA (Europe, Middle East, and Africa) region from 2011 to 2016. Their results reveal a nonlinear relationship between board gender diversity and bank environmental performance. They conclude that the critical mass required in female directors composition is of around 30%.

9.3 Data and Methodology 9.3.1 Data and Sample Our sample is comprised of 61 banks from nine countries belonging to the North African and GCC region, over the period 2009–2021. Data relative to board composition was collected from banks’ annual reports over the period 2009–2021. Industry and country level data were collected from World Integrated Trade Solution (WITS) and the World Bank, respectively, and financial statements data were collected from Refinitiv Eikon database. The average number of banks per country is 6.77 and it varies from 3 in Egypt to 12 in the United Arab Emirates. Tables 9.1 and 9.2 show the number of banks per country and the variables definitions and data sources, respectively. Table 9.1 Sample description

Country

Number of banks

Bahrain

6

Egypt

3

Saudi Arabia

6

Kuwait

6

Morocco

6

Oman

6

Qatar

5

Tunisia

11

United Arab Emirates

12

Average

6.77

Full sample

61

9 Governance, Gender Diversity, and Banking Performance: New …

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Table 9.2 Variables definitions Variable

Definition

Source

ROA

Return on assets measured as the ratio of net income to total assets of the bank

Refinitiv Eikon database

ROE

Return on equity measured as the Refinitiv Eikon database ratio of net income to the bank’s equity

ASSETS

Bank’s total assets (USD)

Refinitiv Eikon database

COST

Cost to income measured as a ratio of total operating expenses to total operating income

Refinitiv Eikon database and Banks’ annual reports

CAR

Total capital ratio measured as the Refinitiv Eikon database and ratio of (Tier 1 + Tier 2 capital) Banks’ annual reports to risk weighted assets

NLOANS_ASSETS

Measured as the ratio of total net loans to total assets

NONINTINCOME

Non-Interest Income measured as Refinitiv Eikon database the ratio of Non-Interest Income to the bank’s total revenue

GROWTH

The annual percentage change of real GDP

World Bank world development indicator (WDI)

Inflation

Inflation rates measured by the annual percentage Change of the consumer price index

World Bank -world development indicator (WDI)

HHI

The Hirschman Herfindahl Index: World Integrated Trade is the sum of the squares of the Solution (WITS) market shares of all banks competing in the market

BOARD_SIZE

The number of directors on the board

Banks’ annual reports

WOMEN (%)

The ratio of the number of women to the total number of directors on the board

Banks’ annual reports

Nb_WOMEN

The total number of women on the board

Banks’ annual reports

INDEPENDENT (%)

The ratio of the number Banks’ annual reports of outsiders to the total number of directors on the board

Refinitiv Eikon database

(continued)

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Table 9.2 (continued) Variable

Definition

Source

Nb_COMMITTEES

Total number of board committees

Banks’ annual reports

Nb_BOARD_MEETINGS

The number of board meetings during the year

Banks’ annual reports

ATTENDANCE (%)

The average overall attendance of Banks’ annual reports board meetings, in percentage form

Nb-AUDITMEETINGS

The number of audit committee meetings

Banks’ annual reports

9.3.2 Methodology This study seeks to examine the impact of internal corporate governance mechanisms, specifically board gender diversity, on banking profitability. To answer this research question, we use the following empirical model: ROAit = α0 + αi + β X + γ Govit + εit

(9.1)

where financial performance, as measured by the Return on Assets ratio (ROA), is explained by seven governance indicators (Gov), which reflect the composition and the activity of the board of directors, in addition to a set of control variables (X), which reflect bank, industry, and macro-economic effects. α i represents individual fixed effects and εit the error term. Given the possible endogeneity and reverse causality between board gender diversity and firm performance, the model is estimated using the System GMM (SGMM) estimator developed by Arellano and Bover (1995).2 The list of control variables reflects bank characteristics, industry characteristics, and macro-economic variables. Bank characteristics include the following: bank size (Assets), capital adequacy ratio (CAR), cost to income ratio (Cost), net loans to assets ratio (Loans), and non-interest income (NIIncome). In addition to bankspecific variables, we also include industry characteristics and macro-variables, such as the Herfindahl-Hirschman Index (HHI), which measures the concentration of the banking system, GDP growth rate (growth), and the inflation rate (Inflation) which control respectively for the business cycle and macro-economic stability. To measure the relationship between board gender diversity and bank performance, seven governance proxies are introduced in model (1): The size of the board of directors (Board_Size), the percentage of independent directors (Independent), the percentage of female directors (Women), the number of committees within the 2

To avoid the instrument proliferation problem, in most specifications, the number of instruments is limited to the second and third lags. The validity of the instruments is tested via the SarganHansen over identification test, while the Arellano bond test confirms the absence of second order correlation.

9 Governance, Gender Diversity, and Banking Performance: New …

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board (Nb_Committees), the number of the board’s meetings (Nb_Board_Meetings), the attendance to the board’s meetings (Attendance), and the number of the audit committee’s meetings (Nb_AuditMeetings). Improved governance mechanisms are expected to produce positive effects on financial performance. For instance, optimal board size (Adams & Mehran, 2008), focused committees, diversified board composition, higher board independence (Fama and Jensen, 1983; Anderson et al., 2003), and regular board meetings (Grove et al., 2011) are expected to enhance the effectiveness of the board and to improve the monitoring process, which in turn should boost the bank’s performance. Inconsistent with such expectations, several studies argue that banks incur higher costs as the number of committees, meetings, and board directors increase (Eluyela et al., 2018). Moreover, diversifying the board’s composition can be a source of conflicts by reducing the board’s ability to carry out effectively its monitoring role (Hermalin & Weisbach, 2003; Pathan & Faff, 2013; Yermack, 1996). Therefore, the effects of some of the governance mechanisms represent an empirical question. According to the empirical literature, most of the control variables may produce ambiguous effects on Return on Assets (ROA). The size (Assets) of a bank is a major determinant of banks financial performance. On one hand, banks are likely to achieve economies of scale and to reduce their funding costs as they get bigger, which should boost their performance (Dijkstra, 2013). On the other hand, very big banks may incur diseconomies of scale as they are much more difficult to monitor (Eichengreen & Gibson, 2001). In addition, the too big to fail hypothesis suggests that big banks often engage in risky strategies which may negatively affect their profitability (Davies & Tracey, 2014). The relationship between bank size and its financial performance is therefore ambiguous. The capital adequacy ratio (CAR) of a bank is associated to its performance because a higher CAR is expected to offer a security buffer, which promotes stability and enhances long term performance. Similarly, higher cost efficiency (lower cost to income ratio) should promote financial performance. However, the remuneration of bank employees is often partly indexed to annual profits. This implies that periods of high profitability are associated with higher payrolls and hence higher cost ratios. Given that they are one of the main sources of income from financial intermediation, loan ratios (Loans), are linked to bank performance. Higher loans generate higher interest income which is expected to lead to higher return and better performance. However, due to the adverse selection problem in the financial intermediation industry, a poor loan selection process can lead to higher non-performing loans which would negatively impact financial performance of the bank. The percentage of noninterest income (NIIncome) reflects the capacity of banks to diversify their sources of revenue, and therefore become less dependent on income generated by financial intermediation. Income diversity is expected to produce a positive impact on banking performance, except when non-interest income is solely derived from highly speculative investments. Such diversification strategies contribute to deteriorate the banks’ assets quality and to increase their risk exposure.

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Financial performance of banks can also be affected by the economic conjuncture and industry competitiveness. Various studies have shown that high industry concentration (HHI) leads to higher profits and banking performance. Market power enables banks to impose higher prices for their services and products, while competition encourages moral hazard (Jimenez et al., 2007). At the same time, a second stream of literature argues that, competition enhances banking efficiency and leads to higher profits, whereas market concentration leads to higher risk taking (Boyd & De Nicolo, 2005; Schaeck et al., 2009). Given that banks provide higher volumes of credits during periods of high economic growth, they are expected to enjoy higher interest revenues during high growth periods. Inflation may increase or lower banking performance according to the capacity of banks to adjust their interest rates to an increase in the rate of inflation (Tan & Floros, 2012).

9.4 Empirical Results 9.4.1 Descriptive Statistics Table 9.3 summarizes the descriptive statistics of the variables used in this study. The performance of the North African and GCC banks composing our sample is quite volatile, since the ROA (ROE) has a mean of 0.014 (0.107) with a standard deviation of 0.012 (0.100). Firm size (ASSETS), as measured by the value of total assets, ranges from $0.403 billions to $11,610 billions with a mean of $275 billions, indicating that size varies significantly across banks. The percentage of women on board (WOMEN) and number of females directors (Nb_WOMEN) have a mean (standard deviation) of 7.83% (12.5) and 0.809 (1.226), respectively, indicating low female presence and high variability in female board members across banks. The median value of the percentage of women and the number of women on boards of directors are zero, reflecting the fact that in a large fraction of banks women are excluded from decision-making processes. At the same time, the presence of independent directors in the North African and GCC banks is quite large with an average percentage of 37.99%. Figure 9.1 represents the board characteristics by country and indicates that board independence is consistently higher than board gender diversity for our sample over the whole sample period. This suggests that banks in the region favor a diversification strategy based on independence rather than gender. This strategy is pursued by all countries over time as indicated in Fig. 9.1. Figures 9.2 and 9.3 represent the evolution of the board independence and female directors over time and by region (GCC vs. North African countries) and show that the percentage of female directors is higher in North Africa (with an increasing trend), while the presence of independent directors is more important in banks of the GCC region. Table 9.4 reports the correlation matrix showing correlation coefficients between all variables included in the model. The correlation between the percentage of women

5.000

7.204

84.570

5.803

Nb_BOARD_MEETINGS

ATTENDANCE (%)

Nb-AUDITMEETINGS

85.000

3.958

Nb_COMMITTEES 6.000

4.000

0.000 36.363

0.809

37.998

0.000

10.000

2.699

0.283

0.552

INDEPENDENT (%)

Nb_WOMEN

0.100

HHI

07.833

2.671

INFLATION

WOMEN (%)

0.087

2.414

GROWTH

9.850

0.302

NONINTINCOME

BOARD_SIZE

2.236

0.490

NLOANS_ASSETS

0.152

0.159

CAR

0.409

0.447

COST

10.7E+10

2.75E+11

ASSETS

0.014 0.113

0.014

0.107

Median

Mean

ROE

ROA

Table 9.3 Descriptive statistics

14.000

100.000

31.000

8.000

90.909

7.000

77.778

15.000

0.246

29.507

19.592

1.237

1.097

0.427

3.257

1.16E+13

0.418

0.094

Maximum 0.012

0.057

−0.062

3.242

−2.540

1.000

44.000

1.000

2.000

0.000

0.000

0.000

5.000

2.194

10.588

3.444

1.151

19.440

1.226

12.501

1.752

0.047

3.871

−8.855 0.032

0.121

−0.097

0.229

0.414

−2.702 0.108

1.47E+12

4.03E+08

0.100

−0.095 −0.896

Std. Dev

Minimum

391

159

437

530

509

542

544

587

587

671

671

627

525

560

583

648

598

588

Observations

9 Governance, Gender Diversity, and Banking Performance: New … 213

214

S. Mohsni et al.

Board Structure 12 10 8 Board Size 6

Independent Directors

4

Female Directors

2 0 BAH

EGY

KSA KWT MAR OMN QTR TUN UAE

Fig. 9.1 Mean board structure by country

Percentage of Women in Board, Mean values 16 14 12 10

GCC

8

North Africa

6

Full Sample

4 2 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Fig. 9.2 Percentage of female directors by region

on board and ROA and ROE is weak and insignificant (correlation coefficients are −0.025 and 0.042, respectively). The board’s size and the number of commissions are negatively and significantly correlated to return on assets, while a positive and significant correlation is detected between the percentage of independent directors and ROA. In addition, the correlation between the percentage of women on board and bank size, as measured by total assets is negative (−0.091), which shows that big banks opt less for gender diversity. We also notice a negative and significant correlation between the percentage of women on board and the percentage of independent members

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215

Percentage of Independent Directors, Mean values 60 50 40 GCC 30

North Africa Full Sample

20 10 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Fig. 9.3 Percentage of independent directors by region

(−0.192), which confirms that banks are opting for independent directors rather than gender diversity. Finally, higher percentages of female directors are associated with larger boards of directors. Overall, the correlation results indicate no evidence of a potential multicollinearity problem.

9.4.2 Board Gender Diversity and Banking Performance In this section, we examine the determinants of banks performance, in particular those reflecting the composition and the activity of the Board of Directors. Table 9.5 shows the regression results using ROA as the measure of performance. We estimate different model specifications, where we include firm and country characteristics (Model 1), board size (Model 2), board independence (Model 3), board gender diversity (Model 4), number of committees (Model 5), number of board meetings (Model 6), meeting attendance (Model 7), and number of audit meetings (Model 8). The results show that board gender diversity and board independence are positively and significantly related to bank performance as measured by ROA. However, the number of committees and the number of audit meetings are negatively and significantly (at the 1% level) associated with bank performance. Our findings are consistent with a strand of the literature that finds board gender diversity to have a positive effect on firm performance (e.g., Ahmadi et al., 2018; Campbell & Mínguez-Vera, 2008; Kim & Starks, 2016; and Martin-Ugedo & Minguez-Vera, 2014). The positive relationship between performance and board independence is consistent with the agency theory and Fama and Jensen (1983) who argue that independent boards are likely to provide additional expertise in order to maintain their reputation. The negative and significant relationship between ROA and the number of audit meetings could indicate that frequent meetings may result in a waste of managerial time and a possible

Infl

HHI

−0.080

−0.120

B_Size

−0.019

-0.119

0.182

NII

Loans

CAR

Cost

Assets

ROE

ROA

0.048

A_meetings

0.063

−0.001

−0.052

0.050

−0.115

B_Meetings

Attendance

Nb_Com

0.042

0.043

−0.025

0.063

Indep

0.018

−0.144 Women

0.018

−0.149

Nb_Com

0.025

0.073

0.128

−0.039

0.160

−0.138

−0.075

0.078

−0.290

−0.289

−0.157

−0.020

−0.308

−0.160

1.000

CAR

−0.253

0.268

0.196

0.087

−0.091

0.072

−0.132

0.033

−0.062

Women

0.160

Infl

−0.062

−0.002

Indep

0.253

0.208

Growth

0.211

0.252

−0.199

1.000

Cost

−0.086

0.026

0.154

−0.152

NII

−0.278

−0.055

0.010

−0.117

Loans

0.011

−0.051

−0.033

0.310

CAR

1.000 −0.049

−0.195

0.117

−0.007

Cost

HHI

0.017

0.073

Assets

B_Size

1.000

0.076

0.746

ROE

ROE

1.000

ROA

Assets

ROA

Table 9.4 Correlation matrix

B_Meetings

−0.281

−0.029

−0.295

−0.211

−0.255

0.394

0.173

0.304

0.162

−0.004

0.284

1.000

Loans

Attendence

−0.022

0.154

−0.013

−0.175

−0.364

−0.039

0.212

0.241

0.169

−0.043

1.000

NII

(continued)

A_Meetings

−0.155

−0.003

−0.105

−0.155

0.150

−0.005

−0.061

−0.059

0.099

1.000

Growth

216 S. Mohsni et al.

0.042

0.085

−0.113

0.060

−0.243

0.051

0.036

−0.333

−0.105

−0.021

−0.149

−0.070

Women

Indep

Nb_Com

B_Meetings

Attendance

A_meetings

Correlations in bold are significant at the 5% level

1.000

0.016

0.120

B_Size

1.000

HHI

−0.110

Infl

Infl

HHI

Growth

Table 9.4 (continued)

0.017

−0.076

−0.131

−0.035

−0.315

0.140

1.000

B_Size

−0.050

−0.112

−0.129

−0.044

−0.192

1.000

Women

−0.023

0.051

0.002

0.074

1.000

Indep

0.087

−0.063

0.192

1.000

Nb_Com

0.423

−0.228

1.000

B_Meetings

−0.139

1.000

Attendence

1.000

A_Meetings

9 Governance, Gender Diversity, and Banking Performance: New … 217

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S. Mohsni et al.

misuse of firms’ resources due to travel expenses and sitting allowances (Eluyela et al., 2018); may increase the probability of conflicts (Jensen, 2013); and may indicate a period of poor financial performance (Andres & Vallelado, 2008). The results also show that both capital adequacy (CAR) and economic growth have a positive and significant impact on firm performance in all specifications. We also run the same regressions using ROE as a measure of bank performance (Table 6). All other variables are kept the same. Overall, the results are consistent with Table 9.4 and show a positive and significant relationship between board gender diversity and firm performance. The results are also positive and significant for board independence. Overall, the results provided in Table 9.5 confirm the robustness of the positive link between gender diversity and financial performance.

9.4.3 Board Gender Diversity and Firm Performance: The Transmission Channels Our results indicate a positive relationship between board gender diversity and bank performance. In this section, we explore the possible channels through which this positive relationship occurs. Given the components of ROA, the improvement in performance can emanate from a reduction in costs or an increase in income. A reduction in the operational costs of a bank (Costs) can occur through optimal managerial decisions and an efficient use of the banks’ resources. It can also occur if a bank is perceived to be better capitalized and less risky, therefore, through an improvement in the bank’s capital adequacy ratio (CAR). An increase in income can occur through an improvement in interest-based or non-interest-based income. It can also occur through more profitable loan allocations. To examine how board gender diversity affects banks profitability, we interact our measure of board gender diversity (Women) with Cost, Loans, and CAR. The regression results are presented in Table 9.7. In Model 1, the coefficient of the interaction term “Women×Cost” is negative and significant, while the coefficients of Women and Cost are both positive and significant, indicating that having more women on boards is associated with lower Costs. In Model 2, the coefficient of the interaction term “Women×CAR” as well as the coefficients of Women and CAR are positive and significant indicating that having more women on board is associated with higher capital adequacy ratios. In Model 3, the coefficient of the interaction term “Women×Loans” is negative and significant, while the coefficients on Women and Loans are positive and negative, respectively, indicating that having more women on board is associated with lower loan ratios. Such results offer further support to the agency theory by confirming that female directors enhance monitoring effectiveness, as their presence in the board is associated with lower operational costs and a limited exposure to the insolvency and credit risks (higher capital adequacy reduces the insolvency risk, while lower credit ratios limit the exposure to credit risk). The results are also consistent with Garcia

Independent

Board_size

Inflation

Growth

HHI

NLoans_assets

NonIntIncome

COST

CAR

LnAssets (0.00109)

0.0131*** (0.00261)

−0.00638

(0.000272)

7.93e-05

(0.000152)

(0.000167)

0.00385***

(7.13e-05)

(6.89e-05) 0.000214***

(0.000128)

(0.000194)

0.000364***

−2.01e-05

0.000336***

0.000570***

(0.00447)

0.0188***

(0.00435)

−0.0231***

0.00263***

0.000645***

0.0210*

(0.0113)

0.0304

(0.00779)

(0.00778)

(0.0188)

(0.00628)

−0.0145*

(0.0112)

−0.0155**

0.00492

(0.000850)

−4.00e-05

(0.00131)

−0.00527*

(0.00301)

(0.0119)

(0.0231)

0.101***

(0.0243)

(0.00188)

0.117***

(0.00286)

−0.00341***

0.105***

(0.0552)

−0.00540***

(0.0513)

−0.00111

(0.0248)

Model 3 0.371***

Model 2

0.543***

Model 1

0.508***

Variables

L.ROA

Table 9.5 ROA determinants Model 4

(0.000105)

0.000157

(0.000134)

0.000307**

(0.00933)

0.0418***

(0.00681)

−0.0265***

(0.00600)

−0.0112*

(0.00112)

0.00343***

(0.0149)

0.137***

(0.00131)

−0.00672***

(0.0414)

0.513***

Model 5

Model 6

−0.00455

(0.000111)

8.73e-05

(0.000114)

0.000323***

(8.68e-05)

0.000113

(4.09e-05)

0.000461***

(0.00687)

−0.00576 (0.00637)

(0.00302)

−0.00544*

(0.00402)

0.00381

(0.00112)

−0.000115

(0.0174)

0.118***

(0.000752)

−0.00254***

(0.0307)

0.155***

(0.00343)

0.00150

(0.00142)

0.00260*

(0.00127)

−0.00294**

(0.00846)

0.0385***

(0.000430)

0.00118***

(0.0262)

0.488***

(0.000975)

0.00279***

(0.000347)

0.000991***

(0.0595)

0.141**

(0.0256)

−0.0393

(0.0137)

0.0149

(0.0305)

0.0471

(0.125)

0.381***

(0.00611)

−0.00504

(0.0992)

(continued)

(6.84e-05)

0.000795***

(8.01e-05)

0.000394***

(0.00337)

0.0103***

(0.00200)

−0.000321

(0.00160)

0.00468***

(0.00104)

0.000964

(0.00897)

0.0571***

(0.000523)

0.00245***

(0.0219)

Model 8 0.402***

Model 7 −0.210**

9 Governance, Gender Diversity, and Banking Performance: New … 219

0.405

0.544

0.986

Sargan Prob

AR(2) Prob

0.367

0.187

52

294

(0.0264)

0.0748***

(0.00142)

Model 3

0.838

0.197

54

325

(0.0328)

0.152***

(0.00496)

0.0121**

Model 4

0.282

0.320

53

307

(0.0108)

−0.0244**

(0.000182)

−0.000699***

Model 5

0.477

0.454

47

266

(0.0169)

0.0557***

(4.81e-05)

−6.99e-05

Model 6

Robust standard errors in parenthesis. *, **, and *** denote respectively significance at the 10, 5, and 1% level

0.956

55

56

Number of Banks

(0.0470)

338

(0.0704)

355

0.120**

Model 2

0.0187

Model 1

Observations

Constant

Audit_meetings

Attendance

Board_meetings

Nb_committees

Women

Variables

Table 9.5 (continued)

0.251

0.999

21

101

(0.127)

0.0456

(0.00950)

−0.00714

Model 7

0.274

0.445

45

248

(0.0132)

−0.0628***

(5.37e-05)

−0.000281***

Model 8

220 S. Mohsni et al.

9 Governance, Gender Diversity, and Banking Performance: New …

221

and Herrero (2021) and Jia (2019) who find gender diversity to reduce the risk of financial distress.

9.4.4 Board Gender Diversity and Firm Performance: The Nonlinearity Effect In the following paragraph, we test the critical mass theory and the possible nonlinearity of the relationship between board gender diversity and firm performance. The critical mass theory suggests that until a certain threshold of any specific group is reached, the focus of those group members will not be on the different abilities and skills that the group can contribute. Joecks et al. (2013) argue that board gender diversity can at first negatively affect firm performance and only when a critical mass of about 30% is reached would the effect of women on performance become positive and significant. To test the critical mass theory, we rerun our regressions using different levels of board gender diversity. The results of the regressions are reported in Table 9.8. In Model 1, we use the number of female board members as our measure of board gender diversity. The results indicate a positive and significant effect of board gender diversity on bank performance. In Model 2, we rerun our regressions using only firms that have at least one female member on their boards. Consistent with previous findings, the results indicate a positive relationship between board gender diversity and bank performance, with a significant coefficient of 0.00305. In Model 3, we restrict our sample to firms with at least 2 female board members. The results indicate a positive and an even stronger effect of board gender diversity on bank performance with a significant coefficient of 0.00350. Inconsistent with the critical mass theory, these results indicate that even at low levels of diversity, with the appointment of one female board member, the effect of board diversity on bank performance is positive and significant. The results also indicate that the effect of diversity on firm performance becomes stronger as more women join the board. To further examine the nonlinearity of the relationship between board gender diversity and bank performance, we use a quadratic specification as shown below: ROAit = α0 + αi + β X + γ Govit + δ1 Women + δ2 Women2 + εit

(9.2)

where Gov represents all governance variables except the percentage of women in the board, Women. All other variables are as previously defined. The results are shown in Model 4 in Table 9.8. The coefficient on the gender diversity variable, Women, is positive and significant, and the coefficient on the quadratic variable, Women2 , is negative and significant, indicating that the relationship between board gender diversity and performance is nonlinear with an inverted u-shape, and that the effect of board gender diversity on bank performance is positive and significant up to a specific threshold after which the effect becomes negative. The critical threshold can

222 Table 9.7 Transmission channels

S. Mohsni et al. Variables

Model 1

Model 2

Model 3

L.ROA

0.492***

0.545***

0.573***

(0.0316)

(0.0364)

(0.0392)

LnAssets CAR COST NonIntIncome

−0.00688*** −0.00939*** −0.00695*** (0.00106)

(0.00136)

(0.00115)

0.153***

0.130***

0.136***

(0.0122)

(0.0129)

(0.0143)

0.0124***

0.00576***

0.00347***

(0.00195)

(0.00119)

(0.00119)

−0.00820

−0.00719

−0.0130**

(0.00675)

(0.00652)

(0.00612)

−0.0298***

−0.0140**

(0.00600)

(0.00624)

(0.00605)

0.0444***

0.0345***

0.0407***

(0.00850)

(0.0107)

(0.0111)

0.000317**

0.000129

0.000155

(0.000143)

(0.000144)

(0.000137)

3.22e-05

−2.22e-05

−0.000207

(0.000127)

(0.000128)

(0.000156)

0.0273***

−0.0567***

0.0369**

(0.00490)

(0.0156)

(0.0154)

NLoans_assets −0.0283*** HHI Growth Inflation Women

Women * Cost −0.0404*** (0.0109) Women * CAR

0.374*** (0.0729) -0.0468*

Women * Loans

(0.0273) Constant

0.150***

0.217***

0.153***

(0.0264)

(0.0341)

(0.0292)

Observations

325

325

325

Number of Banks

54

54

54

Sargan Prob

0.236

0.326

0.214

AR(2) Prob

0.752

0.758

0.792

Robust standard errors in parenthesis. *, **, and *** denote respectively significance at the 10, 5, and 1% level

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Table 9.8 Critical mass theory and nonlinearity Variables

Model 1: Nb_Female

Model 2: Nb_Female > 0

Model 3: Nb_Female > 1

Model 4: Quadratic

L.ROA

0.520***

0.547***

−0.0771

0.357***

(0.0391)

(0.0971)

(0.465)

(0.0278)

−0.00667***

−0.00546***

−0.00869*

0.00126**

(0.00141)

(0.00196)

(0.00485)

(0.000495)

0.143***

0.0949*

−0.0504

0.139***

(0.0108)

(0.0566)

(0.0491)

(0.0115)

0.00534***

0.0119***

−0.00680

0.0103***

(0.00109)

(0.00361)

(0.00485)

(0.000654)

−0.0121**

0.0176***

−0.0451

0.0184***

(0.00542)

(0.00650)

(0.0396)

(0.00397)

−0.0262***

−0.0435***

−0.0283***

−0.0237***

(0.00714)

(0.00746)

(0.0109)

(0.00405)

0.0364***

0.0162

0.0389

0.0509***

(0.0107)

(0.0272)

(0.0295)

(0.00759)

0.000334**

0.000968***

−8.92e-05

0.000270***

(0.000143)

(0.000336)

(0.000304)

(5.32e-05)

8.04e-05

0.000197

−1.74e-05

0.000947***

(0.000108)

(0.000443)

(0.000953)

(8.88e-05)

0.00103*

0.00305***

0.00350**

–

(0.000563)

(0.00100)

(0.00169)

Women

–

–

–

Women2

–

–

–

Constant

0.149***

0.119**

0.248*

−0.0514***

(0.0354)

(0.0502)

(0.127)

(0.0140)

Observations

324

134

51

325

Number of Banks

54

30

17

54

Sargan Prob

0.215

0.845

0.999

0.938

AR(2) Prob

0.785

0.630

0.400

0.906

LnAssets CAR COST NonIntIncome NLoans_assets HHI Growth Inflation Nb_women

0.0226*** (0.00562) −0.0275*** (0.00996)

Robust standard errors in parenthesis. *, **, and *** denote respectively significance at the 10, 5, and 1% level

224

S. Mohsni et al.

be derived by applying the first order condition to Eq. (9.2): ROA = 0.0226 × Women − 0.0275 × Women2 ∂ROA = 0.0226 − 0.055 × Women = 0 ⇒ ∂Women 0.0226 = 0.4109(41.09%) ⇒ Women∗ = 0.055 According to these results gender diversity continues to boost performance as long as the percentage of women on board doesn’t exceed 41.09%.

9.5 Conclusions and Recommendations Women are still largely underrepresented in corporate boards of North African and GCC listed companies. The lack of gender diversity in these countries is often attributed to cultural and social factors. In this chapter, we examine the relationship between board gender diversity and performance of banks in the North African and GCC region over the period 2010–2021, the channels through which female directors’ presence affects bank performance, the critical mass theory, and the possible nonlinearity of the relationship between board gender diversity and firm performance. Our results indicate that the percentage of female directors and independent directors are positively and significantly related to banks ROA. Similar results are obtained when ROE is used to measure performance, confirming the robustness of our results. Given the positive effect of female directors’ presence on bank performance, we decided to examine the transmission channels supporting this positive effect. Our results indicate that higher percentages of female directors are associated with lower costs to income, higher capital adequacy ratios, and lower credit ratios. Such results offer support to the agency theory which emphasizes the monitoring role ensured by female directors. Greater involvement of women in decision-making appears to reduce costs and to limit bank exposure to solvency risk (by increasing the capital adequacy ratio) and credit risk (by limiting the volume of granted credits). Our last set of estimates examines the critical mass theory, by testing the nonlinearity of the relationship between gender diversity and banking performance. Our results confirm that higher female presence in the board boosts firm performance. Moreover, we find that this positive relationship holds up to a critical threshold of 42.1% of women on board. This study adds to the current literature on board gender diversity in North Africa and the GCC region and can help explain some of the mixed results currently observed in the empirical literature by showing that the positive effect of female presence in the board is an increasing function of the number of female board members up

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to an optimal level, and that modest levels of diversity may not produce the same effects as higher ones. It also leads to two important recommendations for banks operating in North African and GCC countries. First, the lack of women on boards seems unjustified and counterproductive, therefore, promoting gender diversity in the board of directors is expected to lead to more effective monitoring and higher financial performance. Secondly, inconsistent with the critical mass theory, even a modest presence of women on boards can produce an effect on bank performance. To tap into female directors potential, their presence in corporate boards should be promoted and encouraged, beyond tokenism and cultural stereotypes.

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Conclusions and Recommendations

A major objective of this book is to provide an overview of governance in developing countries. The nine chapters dealt with different aspects of governance and tried to highlight both the benefits to developing countries and their main governance gaps. A variety of econometric methodologies have been employed and led to some interesting results. It is important for both scholars and policymakers to be given a brief summary of the main empirical findings and to draw the main policy conclusions. Azam et al. (Chapter 1) obtained an interesting set of results supporting the view that formal institutions significantly contribute to sustainable development. Similar results hold for environmental sustainability as proxied by the reduction of CO2 emissions in developing economies. Another important finding of this study is the confirmation of the importance of informal institutions. Indeed, empirical results revealed that ethnic conflicts impact negatively sustainable development and environmental quality in developing economies. Accordingly, in countries characterized by high levels of ethnic fragmentation, informal institutions prove to be a major determinant of sustainable development. Ben Mim et al. (Chapter 2) highlighted the emphasis put by the rating agencies on the developing countries’ governance standards. The logistic estimation results offered strong evidence for a positive impact of governance on sovereign rating. Except for the voice and accountability index, all the governance indicators provided by the WGI database contribute to enhancing the probability of being placed in the investment grade category. Among these indicators, political stability is the one producing the most consistent effect on this probability. Another important finding of this study is that governance does not affect in the same way the upgrading and downgrading decisions. We mainly notice that political stability contributes to reducing the probability of downgrading sovereign rating, while an increase in voice and accountability amplifies this probability. It seems that freedom of expression fuels social and political conflicts in developing countries, leading to a deterioration in the economic and financial environment. Elections may also be viewed as an important source of uncertainty by rating agencies and investors. On the other hand,

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. S. Ben Ali and S. Ben Mim (eds.), Governance in the Developing World, https://doi.org/10.1007/978-981-99-2493-6

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230

Conclusions and Recommendations

governance indicators do not contribute to upgrading the sovereign rating. This is probably because governance enhances economic performance in the long run and does not therefore produce any short-term effect on sovereign rating. Using a panel dataset composed of 15 emerging countries over the period 2002– 2019, Gorus and Ben Ali (Chapter 3) highlighted the impact of governance on income inequality. Their empirical findings confirmed that governance indicators are useful predictors of income inequality. Moreover, the Dumitrescu-Hurlin panel noncausality test reveals the existence of a bidirectional relationship between income inequality and the governance indicators. In particular, it was found that the Gini index can be used to predict future values of the government effectiveness, regulatory quality, and control of corruption indexes for the examined countries. Information and communications technologies are playing a growing role in the governance process. Lechman (Chapter 4) provides new insights into the relationship between e-governance and growth. For a sample of 40 developing countries covering a 30 year period, she shows that, despite multiple efforts, the implementation of broad and effective e-government in a significant number of developing countries remains below expectations. It is not only infrastructural and financial constraints that impede this process. The great economic and social disparities and digital divides that exist are significant obstacles to the development of e-governance. Weak institutions, poor governance, political instability, and corruption are also major hurdles in this direction. Ben Mim and Saidane (Chapter 5) offer some interesting conclusions in relation to developing countries’ attractiveness to FDI. First, trade openness boosts FDI, while financial development and the share of industrial output impede foreign investment inflows. Such results suggest that foreign investors are taking advantage of the inefficiency of the domestic financial systems in the host countries, which leads to low levels of domestic investment. In MENA countries, living standards represent a major attractiveness factor for foreign investors, while the stock of human capital does not meet the requirements of foreign firms. In Sub-Saharan African countries, infrastructure, domestic industry, and living standards are producing negative effects on FDI inflows, which suggests that foreign firms are focusing on exporting activities and are profiting from the lack of domestic investment in the industrial sector. Secondly, government effectiveness plays a major role in attracting foreign investors. Political stability and the quality of the legal system are also significant attractiveness factors. However, results differ across groups of countries. For MENA countries, government effectiveness and rule of law are the only relevant governance indicators for foreign investors. Government effectiveness is the indicator producing the most important effect on FDI. In SSA countries, four out of the six governance indicators contribute to boosting foreign investments. Voice and accountability have the largest positive impact on FDI, while rule of law and regulatory quality do not produce any significant effect. The poor regulatory quality seems to be a major hindrance to the emergence of a strong private sector in Sub-Saharan African countries. Some foreign companies in the natural resource sector find it particularly advantageous to invest in countries with a poor standard of laws and regulations. Finally, another important finding of this study is that political stability amplifies the positive impact produced

Conclusions and Recommendations

231

by the remaining governance indicators on FDI. Political stability is therefore an important prerequisite for the effectiveness of all the other governance indicators. Developing countries are highly reliant on bank financing. The governance of the banking sector is therefore a crucial issue for these economies. The contribution of this book in this respect is twofold. To assess the quality of governance within banking institutions, Al-Chaarani et al. (Chapter 6) constructed a banking sector governance index (GIB.X62) based on 62 internal governance indicators. Applying this index to a large sample of banks, they found that the internal corporate governance mechanisms are more efficient in European and US banks than in their counterparts in the MENA region. These results may be imputed to the low level of legal protection and control in the MENA region, which helps bankers to embed themselves in banking firms. However, the GIB.X62 index can help the board of directors and the relevant executives to detect and improve the weaknesses of the internal governance mechanisms in the banking sector. Using a VECM, Essadik and Terzi (Chapter 7) detected a long-run relationship between governance and the healthcare system’s performance in developing countries. Nevertheless, the actual governance standards in the sample countries are not high enough to generate a short-term effect on the healthcare system’s outcomes. Simulations performed by the authors showed that an annual growth rate of 4% to 5% of the governance indicator is needed to produce a significant short-term effect. Zayati and Ben Ali (Chapter 8) found that the economic, administrative, and political dimensions of governance contribute significantly to promoting growth. However, the intensity of these effects differs from one group of countries to another. In particular, the empirical results show that countries endowed with the highest governance indicators are those enjoying the greatest impact on growth. Such findings suggest that the impact of governance on growth is non-linear and increases as the quality of governance improves. They also found that the global governance index does not produce the expected positive effect on growth when the administrative, economic, and political governance indexes are following divergent trends. Such results suggest that the three governance dimensions may prove to be complementary. Further results relative to banking governance in MENA countries were highlighted by Ben Mim et al. (Chapter 9). The empirical results suggest that the percentages of female directors and independent directors are positively and significantly related to banks’ financial performance. Greater involvement of women in decisionmaking appears to reduce costs and to limit bank exposure to solvency risk (by increasing the capital adequacy ratio) and credit risk (by limiting the volume of granted credits). A last set of estimations shows that this positive relationship holds up to a critical threshold of 42.1% of women on the board. The conclusions discussed below can provide valuable insights for decisionmakers and help them to implement policies which enable developing countries to take full advantage of governance. According to Azzam et al. (Chapter 1) the enforcement of rules and regulations is crucial for sustainable development. Poor institutional frameworks increase the cost of economic decision-making. To achieve sustainable development goals, the solution of all types of regulatory, governance, and institutional quality issues is

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Conclusions and Recommendations

therefore critical. Public authorities must also deal with informal institutions as a major lever for sustainable development. Dampening ethnic conflicts is an important step in this direction. Ben Mim et al. (Chapter 2) argue that improved governance standards are associated with higher sovereign ratings. Particular attention should be given to political stability, which seems to be regarded by the rating agencies as the cornerstone of the institutional framework. The empirical results obtained by Gorus and Ben Ali (Chapter 3) suggest that policymakers can design and implement public policies to enhance accountability, regulatory quality, rule of law, and control of corruption, to achieve a more equal income distribution. Similarly, Essadik and Terzi (Chapter 7) argued that enhancing governance standards is necessary to promote the healthcare system’s output in the short run. Important recommendations related to the crucial role of IT in governance were also formulated by Lechman (Chapter 4). E-government systems need to be shaped and designed not only to suit the needs of state authorities; above all, they must be adequate for society members, addressing their basic needs, capabilities, and skills. It is only well-designed e-government systems that citizens show willingness and ability to use. Developing economies should also take advantage of plans put forward by international agencies and organizations, which play a seminal role in achieving this goal. Valuable initiatives and plans governed by the United Nations, International Telecommunication Union, or Inter-American Development Bank strongly support e-government development plans and try to help to make it deliverable, especially for vulnerable and marginalized populations. Ben Mim and Saidane (Chapter 5) provide recommendations which would help developing countries to take better advantage of FDI inflows. MENA countries should review their human capital development strategies to meet the needs of foreign investors and attract a higher percentage of FDI, while SSA countries should no longer focus on foreign investors exclusively interested in natural resources. SubSaharan countries should rather favor FDI which contributes to promoting domestic industries and generates positive externalities in the host economies. To that end, they should upgrade their infrastructures, their financial systems, and their domestic industries, in order to be attractive to this class of foreign investors. On the other hand, government effectiveness represents a major attractiveness factor for both groups of countries. Promoting political stability should also amplify the effect of the other governance indicators on FDI. Finally, SSA countries should reform their regulatory frameworks to give real impetus to the private sector, accelerate FDI inflows, and benefit from the positive externalities provided by foreign investments. Zayati and Ben Ali (Chapter 8) claim that policymakers should engage in serious and deep reforms of the governance system in order to reap the expected economic benefits. In contrast, mild governance reforms are not expected to generate any significant positive impact on growth. Moreover, developing countries must be involved in the three dimensions of governance (economic, administrative, and political) on an equal basis. Giving priority to one dimension of governance at the expense of others

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can undermine the whole governance process, thereby compromising its impact on growth. Finally, results obtained by Ben Mim et al. (Chapter 9) lead to two important recommendations for banks operating in North African and GCC countries. First, the lack of women on boards seems unjustified and counterproductive. Therefore, promoting gender diversity on the board of directors is expected to lead to more effective monitoring and higher financial performance. Secondly, consistent with the critical-mass theory, a modest presence of women on boards can only produce a small, or no, effect on bank performance. To tap into female directors’ potential, their presence on corporate boards should be promoted and encouraged, beyond tokenism and cultural stereotypes.