Wealth Inequality, Asset Redistribution and Risk-Sharing Islamic Finance 9783110583731, 9783110586664, 9783110583885

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Tarik Akin, Abbas Mirakhor Wealth Inequality, Asset Redistribution and Risk-Sharing Islamic Finance

De Gruyter Studies in Islamic Economics, Finance and Business

Edited by Abbas Mirakhor and Idris Samawi Hamid

Volume 1

Tarik Akin, Abbas Mirakhor

Wealth Inequality, Asset Redistribution and Risk-Sharing Islamic Finance

ISBN 978-3-11-058373-1 e-ISBN (PDF) 978-3-11-058666-4 e-ISBN (EPUB) 978-3-11-058388-5 ISSN 2567-2533 Library of Congress Control Number: 2018963422 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2019 Walter de Gruyter GmbH, Berlin/Boston Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck Cover image: nnnnae / iStock / Getty Images Plus www.degruyter.com

Contents List of Figures

IX

List of Tables

X

Abbreviations

XI

Preface

XIII

1 1.1 1.2 1.3 1.4

Basics of Wealth Inequality 1 Introduction 1 Composition of Wealth 2 Distribution of Wealth: Concepts, Patterns, Trends Income vs. Wealth 15

2 2.1 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.3 2.4 2.5 2.6 2.6.1 2.6.2

Evolution of the Mindset in Inequality 18 Introduction 18 Mainstream Analytical Frameworks on Economics of Inequality Rationality and Self-Interest 19 Equity and Efficiency Trade-off 21 Complete Markets and Contracts 23 Institutions and Inequality 24 Change in Perceptions in the Policy Arena 25 Piketty’s Contribution and Subsequent Controversies 27 The Second Inequality Crisis in the Post-Piketty Era 34 Wealth Inequality from the Lens of Islam 38 A Digression on Rule-compliance 39 From Rule-noncompliance to Inequality 42

3 3.1 3.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.4 3.4.1

Wealth Residual and Wealth Inequality 44 Introduction 44 Role of Savings on Wealth Inequality: Bewley Models 45 The Puzzles of New Stylized Facts on Inequality 49 Capital-Labor Ratio is Increasing 50 The Rate of Return to Capital Should Diminish 52 Increasing Capital-Labor Ratio Should Lead to Higher Wages Elasticity of Substitution Between Capital and Labor 54 Divergence Between Wealth-Income Ratios and Savings 55 The Concept of Wealth Residual 55 Increase in the Value of Land 58

8

18

53

VI

3.4.2 3.4.3 3.4.4 3.5 3.6 3.6.1 3.6.2

Contents

4 4.1 4.2 4.3 4.4

Risk-Sharing Asset-Based Redistribution 79 Introduction 79 The Concept of Risk and Risk-Sharing 79 Risk-Sharing Asset-Based Redistribution 84 Redistribution in Islam: An Overview 90

5 5.1 5.2 5.3 5.3.1 5.3.2

Stock-Flow Consistent Modeling: A Primer 94 Introduction 94 Stock-Flow Consistent Modeling in Historical Perspective Setting up A Generic SFC Model 100 Accounting Identities 100 Behavioral Equations 104

6 6.1 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.4 6.4.1

6.5

The Model 107 Introduction 107 Structure of the Model 107 Model Equations 112 Households 112 Firms 139 Government 143 Banks 144 The Benchmark Model 145 Initialization of the Benchmark Model – Initial Values and Parameters 146 The Base Scenario – Evolution of the Inequalities under Financialization 148 Simulations Under Alternative Redistribution Policies 156

7

Summing-up and Final Thoughts

6.4.2

58

Increase in the Value of Other Inelastically Supplied Factors Increase in the Value of Intellectual Property 59 Increase in the Exploitative Rents 59 The Nature of Capital, Interest Rate, and Rate of Return to Capital 61 Interest-Based Debt Contracts and Wealth Residual 68 Volume Channel 70 Price Channel 73

163

95

Contents

Appendix 1: Household Portfolio Choice

166

Appendix 2: Endogenous and Pre-Determined Variables Appendix 3: Parameter Values References Index

193

179

176

167

VII

List of Figures Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Figure 12: Figure 13: Figure 14: Figure 15: Figure 16: Figure 17: Figure 18: Figure 19: Figure 20: Figure 21: Figure 22: Figure 23: Figure 24: Figure 25: Figure 26: Figure 27: Figure 28: Figure 29:

Per capita wealth (USD) 6 Non-Financial Assets to Gross Wealth Ratio (%) 6 Financial Assets to Gross Wealth Ratio (%) 7 Gross Debt to Gross Wealth Ratio (%) 8 Graphical Representation of Gini Coefficient 10 Mean to Median Ratio 12 Trends in Global Wealth Inequality (1980-2050) 14 Top 1% Wealth Share in Selected Countries 14 Global Income and Wealth Growth by Percentile 15 Correlation Between Wealth and Income Inequality 16 Wealth to Income Ratios 28 Rate of Return and Rate of GDP Growth at the World Level 30 Realized and Required Saving Rates 58 Components of Wealth Inequality 61 Global Real Rates of Return 76 Gini Index in the Base Scenario 152 Atkinson Index in the Base Scenario 152 Coefficient of Square Index in the Base Scenario 153 Relative Wealth Shares of the Household Groups in the Base Scenario 153 Components of GDP in the Base Scenario 154 Household Saving Rate in the Base Scenario (% of GDP) 155 Government Net Lending in the Base Scenario (% of GDP) 155 Change in Relative Real Estate Prices (%) 156 Inequality Effects of 50% Increase in Government Transfers (Deviation from the BaseScenario) 158 Inequality Effects of Increase in Income Taxes (Deviation from the Base Scenario) 159 Inequality Effects of Switching to GDP-linked Bonds (Deviation from the Base Scenario) 159 Inequality Effects of Tax on Wealth (Zakah) (Deviation from the Base Scenario) 160 Inequality Effects of Benchmarking Rate of Return (Deviation from the Base Scenario) 161 Inequality Effects of Imposing All of the Redistribution Policies at All (Deviation from theBase Scenario) 162

https://doi.org/10.1515/9783110586664-201

List of Tables Table 1: Table 2: Table 3: Table 4: Table 5: Table 6: Table 7: Table 8: Table 9: Table 10: Table 11: Table 12:

Components of Wealth 4 Summary of Piketty’s Arguments and His Critics 33 Bewley Models: Model Outputs vs. Realizations 49 Rate of Return to Wealth and GDP Growth in Selected Countries A Comparison of the SFC Approach with the DSGE Approach Balance Sheet Matrix 102 Transaction Matrix 103 Flow of Funds Matrix 105 Balance Sheet of the Model 110 Transaction Matrix of the Model 111 Balance Sheet of the Benchmark Model 147 Transaction Matrix of the Benchmark Model 148

https://doi.org/10.1515/9783110586664-202

69 99

Abbreviations AHM DSGE GDP GFC IMF NIE NIPA OECD OIC PBUH R&D REH SFC SFCA SNA SWT TurkStat WBG WID WWI WWII

Arrow-Hahn-McKenzie competitive general equilibrium model Dynamic Stochastic General Equilibrium Gross domestic product Global Financial Crisis of / International Monetary Fund New Institutional Economics National income and product accounts Organisation for Economic Co-operation and Development The Organization of Islamic Cooperation Peace Be Upon Him (= sallallahu alayhi wa salaam (S.A.W)) Research and development Rational Expectations Hypothesis Stock-flow consistent Stock-flow consistent accounting The System of National Accounts May He be glorified and exalted (= Subhanahu wa ta’ala) Turkish Statistical Institute The World Bank Group World Income Lab The World War I The World War II

https://doi.org/10.1515/9783110586664-203

Preface Between zealous proponents of perfect credit markets, for whom the unequal distribution of capital raises no problem of efficiency, and radical critics of capitalism, for whom the problem can be solved only by the abolition of private property, a climate of civil war has long since reigned, and this has discouraged the accumulation of knowledge in this nevertheless important area. (Piketty 2015e, 64)

A recurring area of controversy in economics is the undeniably complex relationship between growth and equality. Precedence of growth vis-à-vis equality, equity-efficiency trade-off and the causal relationship between growth and (re)distribution are arguably the fundamental lines of division among several schools of economic thought in the last two centuries. Indeed, alternating dominancy of different economic mindsets is also a reflection of the growth and equity controversy. Classical Economics of the late19th century believed in supply-side economics, which was embodied in Say’s Law, with their economic growth-oriented precedence in policy-making. Marginalist revolution of the Neoclassical Economics integrated the theory of distribution into the theory of production by leaving no room for a separate treatment of distribution. The Great Depression of the 1930s put an end to the dominancy of the Classical Economics and paved the way for the Keynesian aggregate demand policies that sought for balanced growth with distribution. Oil and unemployment shocks of the 1970s gave rise to the emergence of the New Classical Macroeconomics, for which the notion of the distribution was of secondary importance with the contribution of the financialization process, the Washington Consensus and repression on wages. The idea of the New Classical Macroeconomics was that economic growth through trickle-down policies would automatically bring higher living standards to all segments of society with the motto “a rising tide lifts all boats” (Stiglitz 2015a, 1). Arguably, all the boats but the wealthiest ones have sunk following the Global Financial Crisis of 2007/2008. The Post-Crisis Era has again spurred interest in equitable and inclusive growth. Economics, as a social science, is in search of stylized facts – empirical regularities – on which the economists can develop theories and build models. Nicholas Kaldor (1961, 178–79) states six such stylized facts to summarize the patterns of economic growth and distribution of capital in the 20th century, as well as, to frame the research agenda going forward. Jones and Romer (2010, 225) summarize these stylized facts as follows: – Sustained growth of labor productivity, – Sustained growth of capital per worker, – Stable interest rates and return to capital, – Stable capital to output ratio, – Stable shares of capital and labor income in national accounts, – Appreciable variation in the rate of growth in the order of 2–5% among the fastgrowing economies. https://doi.org/10.1515/9783110586664-204

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On the contrary, empirical evidence of the last half century indicates that Kaldorian stylized facts are no more valid. Instead, growing inequalities, surge in return to wealth, declining share of labor in GDP and secularly increasing wealth to income ratios have been the “new normal” for the last three decades (Jones and Romer 2010, 226; Stiglitz 2015c, 136). The mainstream economic theory has seemingly lagged behind these “new normal” of the stylized facts. Economics of inequality and redistribution are two indispensable and important areas of research in economics. Economists have spent much time in understanding positive, as well as, normative aspects of economics of inequality. Although economics of inequality encompasses not only income but also wealth, quality of life, health, and access to opportunities, the focus of the economics profession was exclusively on income inequality rather than other aspects of inequality, specifically wealth. One possible explanation for disproportionate interest in income inequality is that it is directly linked to the components of the national accounts (national income, consumption, income, etc.) and to the occurrence and size of cyclical movements (Decancq, Fleurbaey, and Schokkaert 2015, 68). Therefore, “income (in)equality is instrumental to reach other social objectives” (Decancq, Fleurbaey, and Schokkaert 2015, 68). It is worth mentioning here that the main determinants of income inequality, such as role of education, technology and globalization, are already well-studied in the literature and the determinants are, to some extent, justifiable in moral and economic sense. Another explanation is that the concept of wealth has been considered as a direct function of income in the mainstream economics. Thus, the general view is that focusing directly on income would also help us understand the drivers and the dynamics of wealth inequality. However, recent literature which we discuss throughout the book, has seriously challenged this view. Wealth inequality, which is a stock variable, runs far more pronounced than income inequality, which is a flow variable. Nevertheless, the mainstream literature of economics still hasn’t provided compelling explanation on why wealth inequality has dissociated so much from income inequality.1 In the pre-GFC period, the economics of inequality literature mostly focused on social, public finance, and short-run growth effects of high and persistent inequality such as social conflicts and unproductive public spending. The literature has indeed ample evidence that high inequality has negative social, public finance, and shortrun growth effects. On top of that, new evidence indicates that high and persistent inequality can also give rise to two additional detrimental outcomes, which are arguably two major menace to the working of the market economy and the idea of democracy:

1 Throughout the book, we use mainstream economics and neoclassical/marginalist economics interchangeably.

Preface

–

–

XV

Inequality was one of the underlying causes of the Global Financial Crisis and widening inequality is a driver of global imbalances and financial crises (Goda, Onaran, and Stockhammer 2016, 2; de Haan and Sturm 2016, 4; Kumhof, Rancière, and Winant 2015, 1218–19; Rajan 2010, 14; Turner 2016, chap. 7); Inequality not only undermines long-term economic growth prospects but also gives rise to further unequal distribution of income and wealth by leading to a vicious-cycle (Bagchi and Svejnar 2015, 3; Cynamon and Fazzari 2016, 375; IMF 2017, 2–5; Islam and McGillivray 2015, 20; OECD 2015; Ostry, Berg, and Tsangarides 2014, 25–26; World Economic Forum 2017, vii).

These two findings have played considerable role in changing the direction of interest from growth to distribution since it has been well understood by the economics profession that the economic growth per se is not strong and sustainable without tackling inequality seriously, as well as, implementing redistribution policies effectively. In addition, the human history has shown us that heightened inequalities are usually followed by warfare, revolutions, collapse of the states and catastrophic events (Scheidel 2017, 11). In turn, both economic theory and policy-making now take the inequality problem very seriously and try to understand the drivers of inequality as a top agenda item. Given that income and wealth inequality are high and keep widening, addressing the inequality problem requires understanding what mainly causes such a high level of income and wealth inequality. As accentuated by Albert Einstein (1946), “We can’t solve problems by using the same kind of thinking we used when we created them.” Thus, a new mindset stemming from out of the box thinking is needed to cope with the inequality problem. We assert in this book that such an out of the box thinking should include prospective role of interest-based debt contracts on formation of the inequality problem. Although many scholars, especially in the post-GFC Era, have shown that there is a linkage between debt and inequality problem (Bogliacino and Maestri 2016, 61; Lebarz 2015, 4; Mian and Sufi 2014, 19–21; Stockhammer and Wildauer 2016, 1611–12; Turner 2016, chap. 7), the mainstream economics still seem to be reluctant to focus on the prospective key role that the interest-based debt contracts might play on economic inequalities rather than other well-investigated drivers. Wealth inequality was of secondary importance compared to income inequality in the economics of inequality literature but it has been an important area of research in the whole economics literature since publication of Thomas Piketty’s Capital in the Twenty-First Century in 2014, which then quickly became a bestseller in the world.2

2 A quick search at ideas.repec.org website reveals that the total number of studies on wealth (income) inequality was 2,121 (12,386) between 1970–2013 and rose to 1,163 (5,191) between 2014– 2017. In other words, average number of studies on wealth inequality in the former period was only 64; it increased to 388 in the latter period.

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Thanks to his unique collection of data, Piketty sketches out the long-run dynamics in income and wealth inequality in twenty countries going as far back as the eighteenth century. He shows that wealth accumulation has concentrated in the hands of a small group of super-rich and such a concentration is unsustainable over the longrun. By the help of his dataset which shows that the rate of return on wealth is higher than the growth rate of the economy, Piketty’s book underlines that increasing wealth inequality is an inevitable by-product of capitalism.3 It is this stark inevitability supported by historical evidence that has had Piketty’s work gain much popularity. History tells us that unequal distribution of wealth is as old as the human history with numerous social, political, institutional and economic causes (Scheidel 2017, para. I). The main source of wealth inequality was exploitation in the form of slavery and colonization for centuries. Exploitation of labor by the “capitalists” was considered to be another source of wealth inequality with the arrival of the marginal revolution in the 19th century. The frictions, such as the institutional structures, which diverge the factors of production from their optimal allocation were considered to be another source of wealth inequality in the 20th century. While improvements in the institutional scaffolding in many countries have the role of historical factors attenuate, empirical evidence indicates that wealth inequality is still increasing. Hence, the question becomes which other forces feed ever increasing wealth inequality? The current literature already provides many important determinants of the inequality problem ranging from top income shares to heterogeneity in saving rates. In general, these determinants are related to meritocracy and saving behavior. However, current models based on meritocracy and saving behavior still lack in satisfactorily explaining the deep determinants of income and wealth inequality. Some recent studies indicate that bulk of the wealth inequality can’t be explained by the capital accumulation models, as extension of the saving behavior, because most of the wealth inequality stems from rents which should not exist in a competitive economic system. This book argues that interest-based debt contracts are one of the deep determinants of wealth inequality. Providing compelling evidence that the interest-based debt contracts play important role in emergence of high wealth inequality is just one part of the whole story. If interest-based debt contracts are one of the deep determinants of the inequality problem, the subsequent question is then how to redesign the economic policies to break the link between interest-based debt contracts and wealth inequality. In other words, what redistribution policies should be implemented to tame the increasing

3 The question of why the rate of return to wealth is higher than the rate of economic growth leads to inequality is discussed in Chapter 3 in detail. It is sufficient to say here that the rate of return to wealth is associated with the growth of the rich’s assets and economic growth rate is associated with the growth of the average person’s assets in an economy. In effect, the divergence between rate of return to wealth and growth rate of the economy lead to the increasing inequality.

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wealth inequality problem? One such alternative proposal to the wealth inequality problem comes from the idea of risk-sharing, as one of the pillars of Islamic finance. Following an in-depth investigation on the link between interest-based debt contracts and wealth inequality, we discuss how risk-sharing asset-based redistribution policies can give effective response to the increasing wealth inequality problem. The book, which aims at making a humble contribution to the body of research on wealth inequality, consists of two main parts. The first part gives a comprehensive literature review on causes and consequences of wealth inequality, as well as, potential role that risk-sharing can play in mitigating the inequality problem. Second part introduces a large SFC macroeconomic model to shed light on how wealth inequality occurs in a debt-based economy and how risk-sharing based redistribution proposal compared to other redistribution proposals can play a significant role in mitigating the wealth inequality. Wealth inequality is an active area of research in the mainstream, as well as, heterodox economics especially in the post-GFC Era. On the contrary, there are still a few studies in Islamic economics and finance literature focusing on wealth inequality and the role that Islamic economics and finance can play to solve the inequality problem. The authors believe that the wealth inequality should be a priority research agenda item for the researchers in Muslim majority countries because not only wealth is more unevenly distributed in the OIC countries compared to the rest of the world (Credit Suisse 2017, sec. 3) but also Islamic finance already has effective redistribution tools, such as the notion of risk-sharing, to fight against the inequality problem. The concept of risk-sharing is a viable and sustainable alternative to the debt-based system. However, potential of risk-sharing in finance and development has not fully harnessed by the conventional and Islamic economics literature yet. This book explores how risk-sharing policies can have a prospective role in mitigating wealth inequality, and contributes to the economics of inequality literature by theoretically and computationally showing that it outperforms many of the conventional redistribution proposals in coping with the wealth inequality problem. In this regard, this book is the first application of stock-flow consistent (SFC) modeling of asset-based redistribution and risk-sharing in the literature. Employing SFC approach to the distributional issues in economics can open a new strand of literature fully compliant with the methodology of Islamic economics and finance. As becoming the first of its kind in Islamic economics and finance, the benchmark model in this book is open for further improvements and extensions. This book consists of seven chapters. Chapter 1 gives the basics of the wealth inequality and introduces the key concepts. The chapter starts with introducing the concept of wealth, its composition and its main features. The chapter also gives the stylized facts about wealth inequality with a specific focus on the period starting from the 1980s when the financialization process has geared up. Finally, we clarify that

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capital and wealth are different concepts since confusing these two concepts have significant consequences in understanding the drivers of inequality. Chapter 2 gives a comprehensive review of the literature on wealth inequality since the mid-1950s. Because the research on wealth inequality as a distinct topic within the economics of inequality literature has gained momentum following the publication of Capital in the Twenty-First Century, we bifurcate the research on wealth inequality as before and after Piketty’s book. In this regard, this chapter reviews the literature on wealth inequality under three consecutive periods, namely, economics of inequality until 2014 (Period 1), contribution of Piketty’s book to the literature (Period 2), and studies on wealth inequality after the publication of the Piketty’s book (Period 3). The first section delves into the mindset in mainstream economics which more or less depicts the perception of inequality in the literature in Period 1. The next section expounds the change in the perception towards economics of inequality and its consequences on the economics of inequality literature. Subsequently, contribution of Piketty’s book to the economics of inequality literature and controversies based on Piketty’s arguments are discussed. Then we explain why the inequality crisis has commenced and how wealth inequality has become a core area of research in economics in the post-Piketty era. Final section overviews the wealth inequality and its causes from the lens of Islamic economics. Chapter 3 introduces and explains the concept of wealth residual. The chapter starts with a review on the main determinants of wealth inequality in the mainstream models within the context of Bewley Models. Then we discuss the concept of wealth residual. In the literature, a typical defense for income and wealth inequality is the argument of meritocracy, which can be defined as rewarding people commensurate with their effort, talent, and risk-taking (Jacobs 2015, 6). Merited inequality can be regarded as fair in the sense that people get what they deserve. Indeed, compelling evidence shows that people usually don’t bother inequality itself but are troubled by unfair causes of inequality (Frankfurt 2015, pt. I). For instance, several indicators imply that inequality in labor income is, to a certain extent, related to merits of the people. Inequality in capital income, to a lesser extent, is related to talent, effort and risk-taking. On the contrary, the current level of wealth inequality and its dissociation from income inequality indicate that distribution of wealth is, to a large extent, driven by factors other than meritocracy. The bulk of the wealth inequality that can’t be explained by the capital accumulation models is called wealth residual, which is the wealth increase without concomitant increase in capital. Subsequently, the chapter delves into the nature of capital and reviews the controversy on the nature of interest and profits. This is necessary to understand how interest-based debt contracts are linked to the inequality problem. Then the chapter explains how and why the interest-based debt contracts are intimately linked to the notion of wealth residual. This section specifically focuses on two main areas of the debt contracts which give rise to wealth inequality, real estate and credit markets.

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Chapter 4 introduces the risk-sharing asset-based redistribution as a potentially effective mechanism to deal with the wealth inequality problem. The chapter starts with introducing the concepts of risk and risk-sharing. Then it gives an overview of the redistribution policies as a response to the second inequality crisis. Subsequently, it explains why and how risk-sharing asset-based redistribution can be an effective solution to the wealth inequality problem. The final section gives an overview of the concept of redistribution in Islam. Instead of reviewing the whole redistribution policy literature, the specific focus is given to the redistribution based on the notion of risk-sharing. Chapter 5 introduces the Stock-Flow Consistent Approach (SFCA) in a succinct way with the aim of preparing the reader for the next chapter in which a stock-flow consistent (SFC) model is constructed step by step to simulate how risk-sharing based redistribution tools perform better compared to the conventional redistribution policies. Chapter 6 constructs a large SFC model with several household classes and other sectors within a prototype economy. The model structure allows for evolution of the economy from low degree of inequality to high level one due to the financialization process. In the subsequent sections the model is simulated in order to gauge comparative effectiveness of the redistribution policies, including the risk-sharing assetbased redistribution. Finally, Chapter 7 concludes and makes concrete policy recommendations on the basis of the risk-sharing asset-based redistribution framework.

1 Basics of Wealth Inequality 1.1 Introduction In common parlance, wealth usually refers to the stock of valuable possessions yet the specific meaning of wealth changes contextually. In economics, the wealth means net marketable value (worth), which consists of the sum of all real and
 financial assets less debt of the economic entities. Net worth Wij of the ith individual can be expressed as follows (Cowell and Van Kerm 2015, 673): Wij =

n X

πj Aij − Di

(1)

j=1

where Aij , πj , Di are type of the jth asset, price of the jth asset and level of debt owned by the ith individual, respectively.4 The expression in eq. (1) underlines three key features that any analysis of wealth should take into consideration:
 – Which assets to be included in the expression Aij ,
 – Valuation criteria for the assets πij , – Implications of debt and negative wealth ðDi Þ. Although which assets to be included in the definition of the wealth might be seen as a naïve question, the reality is that it has nontrivial implications on measuring the distribution of wealth. The confusion and controversy about which financial assets to include in the definition of wealth are relatively small. On the contrary, when it comes to non-financial assets there is pronounced divergence not only between theoretical and policy-related outcomes but also among the wealth definitions of different institutions, agencies and other domestic or international organizations. One example is the conflation of land and productive capital and resulting conflation between return to capital and return to wealth. Having consistent valuation criteria for the assets is also important since sharp changes in asset prices can significantly alter the level of the wealth inequality (Wolff 2013, 337). For instance, boom in asset prices (real estate and stock market) were responsible for 40% of the rise in private wealth to income ratios in recent decades in the world (World Inequality Lab 2017, 165). Besides, existence of debt in calculation of the wealth, as with eq. (1), has

4 In defining the wealth and its distribution, identification of the wealth holding unit of the entity is significantly important so as to interpret the results correctly. It is a common practice to work at the household level if the data source is tax records and surveys. There are also studies based on individual level data sources such inheritance records. Choice of wealth holding unit, which then usually depend on available data and beyond the discretion of the researcher, can have significant effects on the measurement of wealth inequality. It should also be noted that individual-based data, such as tax records, tend to overestimate the wealth inequality compared to the household level data (Roine and Waldenström 2015, 489). https://doi.org/10.1515/9783110586664-001

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1 Basics of Wealth Inequality

important repercussions. Finally, negative net worth requires different parametric models from those employed for income distribution analysis (Cowell and Van Kerm 2015, 675). Understanding why the wealth has been so unevenly distributed and how much distribution of wealth can further be unequal require, above all, having a basic knowledge-base in composition and distribution of wealth, as well as, how wealth inequality has evolved over time and what the underlying determinants of wealth inequality are. Chapter 1 aims at giving such a basic understanding of the wealth distribution. We start by introducing a detailed exposition of the composition of the wealth since different definitions of the wealth ends up with different levels of wealth inequality. Another motivation regarding the composition of wealth is to highlight the fact that wealth and capital are different concepts, yet they are usually conflated both in theory and practice. Such confusion has indeed significant effects in understanding what determines the wealth inequality. Dissociating wealth from capital also helps the reader understand the concept of wealth residual, which is the “measure of the mainstream economics’ ignorance” in measuring the wealth inequality.5 Finally, we discuss why income and wealth distributions have diverged in the last several decades. We highlight that assuming wealth as a culmination of the savings and income flows give rise to overlooking the substantial role played by valuation effects and other factors. The bottom-line is that interest-based debt contracts and distortions in the financial system have played consequential role in formation of the divergence between income and wealth inequalities.

1.2 Composition of Wealth The composition of the production function and elasticity of substitution among the factors of production are important determinants of income inequality because these two components determine how value-added is shared between (and within) capital owners and labor (Atkinson and Bourguignon 2015, xiix). By the same logic, composition of wealth directly affects how wealth is distributed among and within economic actors. As opposed to ample data in composition and distribution of income for many countries over a long horizon of time, the economics literature has still lacked in detailed historical data covering many countries to understand how composition of wealth has evolved across and within countries, yet data sources in wealth distribution, which aim at providing data in a systematic and 5 In growth theory, Solow residual is considered as “measure of our ignorance” which cannot be explained by the conventional variables of the production function. Similarly, bulk of the wealth inequality is unexplained by the variables of the conventional inequality models. This issue is further discussed in Chapter 3.

1.2 Composition of Wealth

3

transparent manner, have been augmented. There are now several studies with their datasets which allow us to understand how composition and distribution of wealth have evolved for numerous countries in the short-run, such as Allianz’s Global Wealth Reports and Credit Suisse’s Global Wealth Reports, and for several countries in the long-run, such as the World Inequality Lab’s World Inequality Report. In the macro field, the System of National Accounts (SNA) provides a framework for wealth and its components for the whole economy.6 Furthermore, there are recent attempts to develop standardized definitions and classifications for microstatistics on household wealth such as the OECD’s Guidelines for Micro Statistics on Household Wealth (2013). Although the definitions and classifications overlap, to a large extent, between these two guidelines, there are certain and important differences between micro (household level) and macro (national accounts) fields. These differences are worth explaining in detail since the inconsistency of definitions of the wealth components between the theory and the datasets give rise to incomplete, even biased, results in economics of inequality. A trace of such conflation, to give an example, can even be found in Piketty’s Capital in the Twenty-First Century. Total capital is assumed to equal to the total wealth in his notion of “fundamental inequality” in the book (Piketty 2014, 25). The corollaries derived from this seemingly innocuous assumption is one of the reasons for the controversy in the literature stemmed from Piketty’s findings. Below, we shortly expound the components of the household wealth based on the OECD (2013) Guidelines. Subsequently, we focus on the definition and components of wealth on the basis of the SNA in more detail. This is because the SNA methodology expands on productive and non-productive capital distinction, which is typically missing in mainstream theory in explaining the drivers of the wealth inequality. Table 1 shows the main components of household wealth based on the OECD (2013) Guidelines. Net wealth of the households consists of three main elements, namely, non-financial assets, financial assets and liabilities. Real estate is the main component of non-financial assets, followed by the durable goods such as vehicles. Financial assets of the households mainly consist of money, deposits, debt securities, listed and unlisted shares, insurance and pension funds. The liabilities are composed of different types of debt

6 As stated in the original document (EC&IMF&OECD&UN&WB 2009, 1), “[t]he System of National Accounts (SNA) is the internationally agreed standard set of recommendations on how to compile measures of economic activity in accordance with strict accounting conventions based on economic principles. The recommendations are expressed in terms of a set of concepts, definitions, classifications and accounting rules that comprise the internationally agreed standard for measuring such items as gross domestic product (GDP) [. . .]” Here, we summarize the general framework on wealth given in the SNA guidebook and overlook many details. Interested reader can resort to the document for very detailed explanations on wealth and its components.

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1 Basics of Wealth Inequality

Table 1: Components of Wealth. Type of Asset/Liability

Explanation

Non-Financial Assets Principal residence Other real estate Vehicles Valuables Other

Residence in which household live Second homes, investment real estate, farm land Vehicles owned by household and used for private purposes Works of art, antiques, fine jewelry, etc. Other consumer durables, intellectual property, etc.

Financial Assets (excluding pension assets related to employment) Currency and deposits Currency held, bank accounts, other deposits, etc. Debt securities Bonds, commercial paper, treasury bills, etc. Investment funds Mutual funds, hedge funds, unit trusts, income trusts, etc. Equity in corporations Share of the net equity in unincorporated enterprise Stocks Shares in publicly listed corporation Unlisted shares/other equity Value of ownership in incorporated businesses not publicly traded, net equity in partnership Other Non-pension financial assets such as managed accounts, money owed to household, etc. Insurance and pension funds Voluntary assets in life insurance and pension plans Total Liabilities Principal residence loans Other residence loans Other loans

Loans taken for the principal residence of household Loans for the other dwellings, buildings and land Car loans, education loans, credit card debt, overdrafts, etc.

Source: OECD Guidelines for Micro Statistics on Household Wealth (OECD 2013, 67).

instruments. As with the OECD Guidelines for Micro Statistics on Household Wealth, the SNA decomposes wealth into financial assets, non-financial assets, and liabilities for the whole economy. Non-financial assets are composed of produced tangible capital, non-produced tangible capital and intangible capital. Produced tangible capital (henceforward called as productive capital) is the ‘capital’ which is the subject matter of the theory of growth and distribution. Machinery and equipment, dwellings, buildings and inventories are all classified under this rubric. Non-produced tangible assets encompass natural resources including land, subsoil land and other natural assets. Movements in the real estate prices are recorded under the land in the balance sheets. Intangible capital includes research and development (R&D), other intellectual property, licenses and brand names. Financial assets are composed of monetary gold, currency, deposits, debt securities, equity, insurance, financial derivatives and account receivables. These instruments can again be classified as debt-based and equity-based instruments. The former stands for genuine monetary and financial

1.2 Composition of Wealth

5

assets whereas the latter, at least in theory, are linked to the productive capital as being another form of ownership in the capital. The productive capital, land and financial assets form bulk of the assets in national accounts so that we entirely focus on these three types of components of wealth in the rest of the book. Besides, we keep human capital outside of the definition of the wealth.7 Financial wealth and land, as well as real estate, is almost absent in the mainstream models. Conceiving real estate and financial assets within the definition of wealth, instead of conflating capital with wealth, has significant implications in understanding the source and repercussions of the wealth inequality. To give an example, Piketty and Zucman (2014a, 1255) show that wealth to income ratios have been increasing in the countries included in their dataset since the World War II whereas the investment rates, as a proxy for productive capital, haven’t followed the same pace. There are two main repercussions of overlooking the semantic difference between the wealth and the capital. Firstly, stock of wealth is exposed to capital gains because wealth is measured on the basis of current market values. On the other hand, capital is calculated through what is known as perpetual inventory method in which past investment or saving flows are aggregated with adjustments of depreciation and price changes. Since investment, saving and depreciation are all flow variables, change in their market value is trivial compared to the wealth, which is a stock variable. Secondly, it is the rate of return to capital from which marginal product of capital is derived. In the age of financialization and debt-based growth, the linkages between the real sector and financial returns have weakened. If we conflate rate of return to capital with rate of return to wealth, then one can come up with confusing results in wealth inequality. Although data availability on the composition of wealth is still scant, recent studies provide important insights for patterns of wealth holdings within countries. Among them, Credit Suisse’s annual Global Wealth Report is quite comprehensive as it provides household wealth estimates for over 200 countries covering the last two decades. As given by Figure 1, per capita wealth and its components have been perpetually increasing for the last two decades, except the slump in the aftermath of the GFC. Figure 2 gives the share of non-financial assets to gross wealth held by the households based on the Credit Suisse Global Wealth Report 2017. Non-financial assets account for slightly less than half of the total household wealth all over the

7 Data appendix of Piketty and Zuckman (2014b, 5) gives a compelling answer to this issue as follows: “Because ownership rights cannot be enforced on human beings, this definition excludes human capital. Including human capital would raise major conceptual difficulties, and we believe its exclusion is justified. In particular, treating human capital as an asset would call for treating education and health services as investment. But these services are largely viewed as having a consumption value per se, independently of the accumulation of any asset, so that the most basic distinction upon which national accounts are built – consumption vs. investment – would collapse.”

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1 Basics of Wealth Inequality

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1.2 Composition of Wealth

world with significant variation among the regions. On average, there is a slight decline in the share of the non-financial assets since the GFC in the world. On the other hand, share of non-financial assets has significantly increased in the Latin America. Furthermore, share of the non-financial assets is quite low in the North America compared to the other regions indicating relative importance of capital markets as an important alternative means of asset accumulation. In Europe, it is above the world average due to very high level of home ownership. According to Figure 3, financial assets account for the bulk of the total household wealth all over the world. As with the trends in non-financial assets, there is a significant variation among the regions. North America countries have a quite large share of financial assets than other regions. Among the regions, only Latin America has decreased its share of financial assets over the last decade, all other country groups have increased their financial asset shares compared to the non-financial assets.

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Figure 3: Financial Assets to Gross Wealth Ratio (%). Source: Own graph based on Credit Suisse (2017, 33–115).

Debt is the other facet of the wealth accumulation as given in eq. (1). A true evaluation of the trends in wealth accumulation requires evaluation of the debt accumulation. Figure 4 indicates a large degree of variation among the regions in debt accumulation. Against this backdrop, there is a pronounced deleveraging, especially in the

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1 Basics of Wealth Inequality

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Figure 4: Gross Debt to Gross Wealth Ratio (%). Source: Own graph based on Credit Suisse (2017, 33–115).

developed countries while share of debt in gross wealth has been showing a quick increase in Latin America and Africa. A rough comparison of Figure 2 and Figure 4 indicate that increase in the share of non-financial assets (mainly composed of real estate) might be associated with the surge in debt.

1.3 Distribution of Wealth: Concepts, Patterns, Trends Per capita wealth figures of Chapter 1.2 tell much about the level and composition of the wealth. That the world is getting richer is just one side of the whole story. How this total wealth is distributed among the members of the societies is much more important and has much more economic, social and moral implications than just the aggregate total figures of the wealth. Distribution of wealth is skewed to the right with thick upper tails alike the distribution of income and gauging the distribution of wealth is a much more challenging task. Presence of a substantial fraction of negative net worth in survey data on wealth, and the skewness and fat tails stemming from outliers require design of different parametric and non-parametric models for the distribution of wealth (Cowell and Van Kerm 2015, sec. 3). Typically, distribution of wealth is calculated either directly from the data (surveys, administrative data, etc.) or indirectly through statistical models, which are functional forms to characterize all or part of the

1.3 Distribution of Wealth: Concepts, Patterns, Trends

9

distribution. There are three types of approaches in measuring wealth inequality (Cowell and Van Kerm 2015, sec. 4)8: Non-parametric approach: Inequality comparisons are made directly by employing the observations from the data. Semi-parametric approach: Typically, the upper tail of the distribution is modelled by using a functional form and the rest of the distribution is directly formed by using the observations. To give an example, survey data for the top 1% of the population is usually sparse so the general approach is to employ Pareto-type distributions as a proxy for the upper 1% tail of the distribution. Parametric approach: A full model is employed for all the distribution of wealth, which then requires specification of a functional form that fit to the overall distribution. Some commonly used distributions are log-normal and gamma distributions, the Singh-Maddala, the Dagum Type I, and the more flexible Generalized Beta Distribution of the Second Kind. Here, we also shortly explain three important wealth inequality indices which belong to the family of non-parametric approach. We will employ all of these three indices (Gini index, Atkinson index and Coefficient of Variation) in Chapter 6 to model the wealth inequality through the SFCA. It should also be noted here that using all of these indices to measure wealth inequality instead of solely focusing any of them gives us a much better picture of the inequality since each of these indices differ in their sensitivity to inequalities in different tail points of the distribution: The Gini index is more sensitive to the inequality in the middle of the distribution, Coefficient of Variation is more sensitive to the inequality at the top of the distribution and the Atkinson is more sensitive to the inequality of wealth at the bottom (Dafermos and Papatheodorou 2015, 800). In case all three indices give the same results, it should be taken as a self-consistency and selfrobustness check in the model. Gini Coefficient: As the most common measure of inequality, Gini index takes value between 0 (indicating absolute equality) and 1 (indicating absolute inequality where one individual takes all). The index is calculated as the ratio of the area between the Lorenz curve of the distribution and the uniform distribution line to the area under the uniform distribution line. Figure 5 gives graphical representation of the Lorenz curve and the Gini coefficient. The Lorenz curve depicts the cumulative share of wealth corresponding to the cumulative population share. In a uniform, completely equal, distribution the corresponding Lorenz curve is a 45 degree line, known as the line of equality (Niño-Zarazúa, Roope, and Tarp 2016, 8). The Gini

8 For interested readers, Cowell and Van Kerm (2015) and Cowell and Flachaire (2015) provide a very detailed literature review of these approaches.

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1 Basics of Wealth Inequality

0.8

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Figure 5: Graphical Representation of Gini Coefficient. Source: Own graph based on Atkinson and Bourguignon (2015, xxiii).

coefficient is the area which lies between the line of equality and the actual Lorenz curve. Given in Figure 5, the Gini coefficient is defined as the ratio of A to (A+B). The popularity of the Gini index is largely due to its intuitive geometric interpretation (Niño-Zarazúa, Roope, and Tarp 2016, 8). Atkinson Index: The Gini coefficient measures the distribution of wealth in an objective way while there are also other forms of indices that measure inequality normatively by linking changes in inequality to changes in social welfare. The most prominent of such normative indices is Atkinson’s (1970, 257) index. Welfare-based measures of inequality, which are used when the ordering through different possibilities of distributional dominance cannot provide a definite ranking, provide for a complete ordering by reducing the inequality observations into a single index number (Bellù and Liberati 2006, sec. 3). The Atkinson index shows the percentage of total wealth that a given society would have to tradeoff in order to have more equal shares of wealth among the units of measurement. This index depends on the degree of aversion to inequality (a theoretical parameter decided by the researcher), where a higher value indicates more willingness by the units of measurement (individuals or households) to accept smaller share of wealth in return for a more equal distribution (UN/DESA 2015, 1). The Atkinson index can be decomposed into within- and between-group inequality (Bellù and Liberati 2006, sec. 3). Similar to the Gini index, the

1.3 Distribution of Wealth: Concepts, Patterns, Trends

11

Atkinson index lies between 0 and 1, where higher index number means more unequal sample. To give an example, an Atkinson index value of 0.25 indicates that the population is ready to give up 25% of their total wealth in order to have equally distributed level of wealth. Coefficient of Squared Variation: The coefficient of squared variation belongs to the class of general entropy models, which range from zero to infinity. Higher the index number means higher the level of inequality. A key feature of the general entropy models is that they can be decomposed into population groups, sources of wealth and other dimensions (UN/DESA 2015, 2). A key feature of such models is that the different choices of the parameter value (α) assigns a weight to distances between level of wealth in different parts of the distribution. Lower value of the parameter make the index more sensitive to changes in the lower tail of the distribution whereas the bigger values make the index more sensitive to changes in the upper tail (Bendel et al. 1989, 394). The most common values for the parameter are 0, 1, and 2. The index is called “Theil’s L” and “Theil’s T” index when the parameter value is 0 and 1, respectively. When the parameter value equals to 2, it is called “coefficient of squared variation” (UN/DESA 2015, 2). Davies and Shorrocks (2000, 607) juxtapose important “stylized facts” about the distribution of wealth which are useful to highlight at the outset: – Wealth is distributed much more unevenly than income and consumption. – Financial assets are less equally distributed than non-financial assets. However, the reverse may be true in countries where land value is important. – The distribution of inherited wealth is much more unequal than that of wealth in general. – In many countries, majority of the population have low financial assets at all ages. – Wealth inequality has a U-shaped trend since the beginning of the 20th century. It trended downward until the 1970s and has increased sharply since then. These stylized facts, or empirical regularities, are supported by compelling evidence. Figure 6 summarizes (i) level of the within-country wealth inequality in 2000 and 2016 and (ii) how it changed between 2000 and 2016. Since we don’t have longitudinal indicators of within-country wealth inequality for a large number of countries, we calculated a proxy for wealth inequality from the Credit Suisse Global Wealth Reports published since mid-2000s. The indicator is average to median per capita household wealth, which should be around the index number of one for equitable distributed wealth and goes towards large numbers as the level of inequality increases. Practically, “mean to median ratio” above the index number seven indicates very high level of inequality so we dropped countries with the index number above seven from the sample. As can be clearly seen in Figure 6, the mean to median ratio for most of the countries in

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our sample (each dot in the figure indicates a country) is much above one both in 2000 (x-axis) and 2016 (y-axis). Figure 6 also depicts evolution of withincountry income wealth inequality in the last two decades. A non-increasing wealth inequality should wander around the 45-degree line in the figure but the data underlines that there has been a stark increase in the wealth inequality between 2000 and 2016 as bulk of the points in the figure lies well above the ideal 45-degree line. A recent Oxfam report highlights pronounced and dismal facts about wealth inequality (Alejo Vázquez Pimentel, Macías Aymar, and Lawson 2018, 8): Last year saw the biggest increase in the number of billionaires in history, with one more billionaire every two days. There are now 2,043 dollar billionaires worldwide. Nine out of 10 are men. Billionaires also saw a huge increase in their wealth. This increase was enough to end extreme poverty seven times over. 82% of all of the growth in global wealth in the last year went to the top 1%, whereas the bottom 50% saw no increase at all. . . .Between 1990 and 2010, the number of people living in extreme poverty (i.e. on less than $1.90 a day) halved, and has continued to decline since then. This tremendous achievement is something of which the world should be proud. Yet had inequality within countries not grown during that period, an extra 200 million people would have escaped poverty. This number could have risen to 700 million had poor people benefited more from economic growth than their rich fellow citizens. Looking to the future, the World Bank has been clear that unless we close the gap between rich and poor, we will miss the goal of

1.3 Distribution of Wealth: Concepts, Patterns, Trends

13

eliminating extreme poverty by a wide margin. Even if the target of reducing poverty to 3% is achieved, around 200 million would still be living on $1.90 a day in 2030.

Besides looking at the whole distribution and the measures of central tendency for the distribution, such as mean and median, patterns and trends of the distribution of wealth for specific groups/segments in the population, such as top 1%, should also be examined to get a complete picture of the within- and between-country inequalities. The World Wealth and Income Database (WID. world), published by World Income Lab (WIL), attempts to present the inequality statistics for specific groups by harmonizing different sources of data (national accounts, tax accounts, household surveys, etc.) for around 30 countries with a long time-series data. Such a harmonization allow us to intimately analyze wealth dynamics of the top 1% – even smaller groups such as the top 0.01% –, which is usually absent in survey results. The compiled data and the pertinent results are then annually published in the World Inequality Report. Available data in the report indicates that global wealth inequality is extremely high and still rising. The report also highlights that (World Inequality Lab 2017, 200): At the global level (represented by China, Europe, and the United States), wealth is substantially more concentrated than income: the top 10% owns more than 70% of the total wealth. The top 1% wealthiest individuals alone own 33% of total wealth in 2017. This figure is up from 28% in 1980. The bottom 50% of the population, on the other hand, owns almost no wealth over the entire period (less than 2%) focusing on a somewhat larger group, we see that the bottom 75% saw its share oscillate around 10%. Wealth concentration levels should probably be even higher if Latin America, Africa, and the rest of Asia were included in the analysis, as most people in these regions should be in the poorer parts of the distribution.

Figure 7 confirms the main highlights of the World Inequality Report 2018. Assuming the world is represented by China, Europe and the US, the global wealth share of the top 1%, top 0.1%, and the top 0.01% has been permanently increasing since 1980. Under the base scenario, the global share of these groups will increase in the next three decades, until 2050. This will happen at the expense of the global middle class, which is represented by the middle 40%. On the other hand, there are some contrasting views with respect to the level of wealth inequality in historical context. As highlighted by several recent works, the wealth inequality has not reached at its heights which was observed in the early 20th century, though it is in increasing trend since 1980s (Alvaredo et al. 2013, 2017, fig. 3a, Piketty 2014, 2, 2015a, 42–43). Especially, available long-run data for the US, UK and France confirm that wealth inequality levels, represented by the wealth share of the top 1%, is still below the levels seen at the dawn of the WWI (see Figure 8).

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Figure 7: Trends in Global Wealth Inequality (1980–2050). Source: Own graph based on WID.world (2017). See wir2018.wid.world for data series.

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1.4 Income vs. Wealth

1.4 Income vs. Wealth

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As indicated in Chapter 1.2, income and wealth are different concepts. Moreover, the traditional link between income and wealth has weakened for the last few decades by giving rise to declining role of income inequality in explaining wealth inequality. Before delineating the income and wealth relationship, let’s start with Figure 9, which shows cumulative global income and wealth growth by percentiles.9 One stark pattern in Figure 9 is that growth of wealth resembles the “elephant curve” of the global income growth, which means wealth has more or less evolved similar to income over the last several

Total income growth by percentile in China, India, US-Canada, and Western Europe, 1980–2017 Total income growth by percentile across all world regions, 1980–2016 Global wealth growth by percentile, 1987–2017: China, Europe and the US Figure 9: Global Income and Wealth Growth by Percentile. Note: On the horizontal axis, the world population is divided into a hundred groups of equal population size and sorted in ascending order from left to right, according to each group’s income and wealth levels. Source: Own graph based on WID.world (2017). See wir2018.wid.world for data series and notes.

9 Due to availability of data, country and time coverage slightly differ among the data series employed in the figure. Wealth data covers three countries (the US, UK and China) between 1987 and 2017 since there is still no detailed wealth inequality data available covering more than several countries and decades. The first income growth series covers almost all the countries for which data is available between 1980 and 2016. The second income growth series encompasses only China, India, US, Canada and Western Europe between 1980 and 2017.

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decades.10 On the other hand, cumulative growth rates of wealth on the tails of the percentiles differ significantly from that of income. To give an example, growth rate of the wealth (156%) is almost twice of the growth rate of income (88.8%) for the top 1% in the last three decades. That is, income inequality explains part of the wealth inequality since 1980, though the economic theory says that income and savings should explain the most of the distribution of wealth. In studying the wealth and its distribution, semantic difference between the wealth and income, as well as, the consequences of this semantic difference should be considered. While confused both in theory and policy-making, there are clear lines of distinction between these two concepts and the distinction has been more pronounced in the last few decades. To give an example, rank correlation between income and wealth distribution for the OECD countries is around 0.38 with high heterogeneity across countries (see Figure 10). The low correlation coefficient 0.7 0.6

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Figure 10: Correlation Between Wealth and Income Inequality. Note: The figure gives rank correlation between the distribution of net wealth and disposable income. For the full dataset see: http://dx.doi.org/10.1787/888933208576 Source: Own graph adapted from OECD (2015).

10 Plotting growth rate of income at each percentile gives a good visualization of the patterns of global income inequality dynamics. A famous visualization by Milanovic (2016, fig. 1) shows that growth rate of income at the bottom and upper-middle segments of the distribution are low, and relatively high in the middle percentiles. The growth rate is very high above the 90th percentile. Such a graph resembles an elephant with a long trunk (Milanovic 2016, fig. 1).

1.4 Income vs. Wealth

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underlines the fact that income and wealth are two distinct concepts that should be studied separately. A pronounced example is Germany in which wealth inequality is higher than any other EU country whilst income inequality is lower compared to the EU average (Deutsche Bundesbank 2016). Until recently, economic research on inequality, which encompasses both income and wealth inequalities, has mainly focused on inequalities derived from labor market and income. However, income inequality is just one aspect, sometimes even a small part, of the general picture of economic inequalities. The central role of the wealth has been neglected or taken as an abstract extension of income inequality but wealth stands for a more comprehensive measure of economic well-being as compared to income (Skopek 2015, 23). Income as a flow variable is restricted to certain point in time and related to labor market activity. Reversely, wealth is a stock variable which is accumulated over a longer time span and is “the capacity to maintain a particular standard of living” (Spilerman 2000, 497). Another important characteristics of wealth which differs, to a large extent, from income is that wealth allows access to capital, as the internal source of fund, independent of past investments (including human capital) and personal abilities (Elmelech 2008, 182). Besides, wealth has intergenerational facet through bequest, which allows wealth to increase instantaneously. From a system dynamics perspective, wealth, as a stock variable represents the state of the system and stands for the basis for decision making. As opposed to assuming consumption or investment as the decision variables in most of the economic models, such as intertemporal optimal saving models, it is the stock variable (wealth here) that determines direction and magnitude of economic decisions. For instance, whatever the current level of income of an entrepreneur, amount of his collateral as a reflection of his wealth decides on his credit usage conditions.

2 Evolution of the Mindset in Inequality 2.1 Introduction For decades, academic studies on wealth inequality were relatively sparse as the literature on economic of inequality almost totally focused on inequality of income. The mainstream economics’ attention to wealth inequality has only recently soared. The popularity of research by Thomas Piketty, his colleagues, and subsequent studies have put wealth inequality at the core of the research in economics of inequality. This chapter gives a comprehensive review of the literature on wealth inequality since the mid-1950s. Since research on wealth inequality as a distinct topic within the economics of inequality literature has soared up following the Capital in the Twenty-First Century, we bifurcate the research on wealth inequality as before and after Piketty’s book. In this regard, this chapter reviews the literature on wealth inequality under three consecutive phases, namely, economics of inequality until 2014 (Period 1), contribution of Piketty’s book to the literature (Period 2), and studies on wealth inequality after the publication of the Piketty’s book (Period 3). Chapter 2.2 delves into the mindset in mainstream economics which more or less depicts the perception of inequality in the literature in Period 1. Chapter 2.3 expounds the change in the perception towards economics of inequality and its consequences on the literature of wealth inequality. Contribution of Piketty’s book to the literature of wealth inequality and controversies to the Piketty’s arguments are discussed in Chapter 2.4. We explain why the inequality crisis has commenced and how wealth inequality has become a core area of research in economics in the post-Piketty era in Chapter 2.5. We also give an overview of the wealth inequality and its causes from the lens of Islamic economics in Chapter 2.6.

2.2 Mainstream Analytical Frameworks on Economics of Inequality Distribution and growth are the two core fields in the research agenda of economics. The former field relatively lost its importance in economic research till the GFC. Understanding why economics of distribution took a secondary place compared to economics of growth, which overlaps with the ascent of the mainstream neoclassical economics, help us understand the role of the mindset in economics towards increasing inequalities. This is important because it is the mindset that also has led to the financialization and debt-based growth over decades. In this regard, this section reviews the main analytical frameworks which form the pillars of the mainstream economic mindset. Such a review gives a picture of the economics of inequality from early 1950s, when Kuznets (1955) published his pioneering work, to 1990s, when the https://doi.org/10.1515/9783110586664-002

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change in the perceptions towards distribution and inequality in the policy arena started. There are four important analytical frameworks in the mainstream economics that are intimately related to the overlook of the inequality in the literature. These frameworks are, (i) rational and self-interested economic agent assumption, which is the main pillar of the mainstream economics; (ii) equity and efficiency trade-off, which is the key idea in prioritizing growth above distribution in the last 50 years; (iii) complete markets and contracts and their relationship with the financialization process; (iv) role of institutions in formation of the inequalities.

2.2.1 Rationality and Self-Interest Rationality and self-interest postulates are arguably the most fundamental analytical frameworks in the Neoclassical Economics. These two postulates describe a representative economic agent as an acquisitive, self-interest-motivated, non-cooperative, nonsympathetic individual with perfect foresight and full information, as well as perfect cognitive ability to choose among alternatives the one that serve her/his self-interest the best. This agent is also the sole “source of ontology and epistemology. All the needed knowledge is there for consumers to maximize utility, producers their profits, and society its welfare” (Askari, Iqbal, and Mirakhor 2009, 222). The knowledge blended with complete markets, where there exists a market for every conceivable risk, and complete contracts, which encompass all contingencies, form the market economy as envisioned by the Classical and the Neoclassical Economics. It was considered as the conception of Adam Smith’s vision of the market system guided by the “invisible hand” that coordinates “autonomous individual choices in an interdependent world” (Evensky 1993, 197). Such a market economy, with no state intervention, has a social optimum and it is the job of economics to find that equilibrium. Elegant mathematical models constructed by Arrow and Debreu (1954), McKenzie (1954) and Arrow and Hahn (1971) defined conditions for social optimum to prove that a decentralized economy motivated by self-interest “could be regarded, in a well-defined sense, as superior to a large class of possible alternative dispositions” (Arrow and Hahn 1971, vi). These models are then named as Arrow-HahnMcKenzie (AHM) “competitive general equilibrium” models. Importantly, competitive general equilibrium is believed to be the proof of the validity of the Classical Economics. Although the assumptions of complete markets and complete contracts are grossly unrealistic, it was believed in the profession that both of these assumptions could have been first best approximations if there were contingent securities in the market with state-dependent payoffs, and if risks were allocated to all market participants based on their ability to bear risk (Iqbal and Mirakhor 2013, 29). One direct corollary of the AHM models is the fundamental theorems of welfare economics with the implication that the distribution should not take precedence over growth. The First Welfare Theorem states that perfectly competitive markets generate

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Pareto optimal outcomes in which an economic agent cannot be made better-off without making another agent worse-off. This equilibrium holds totally independent of the initial distribution of wealth. The Second Welfare Theorem states that Pareto optimal redistribution can only be attained through lump-sum transfers or taxes. Mainstream economics infer from these two theorems that redistribution policies lead to efficiency loses in an ideal system defined by the AHM competitive general equilibrium models. Another corollary of rationality postulate is the median voter theorem, which is the workhorse model in political economy to understand preferences for redistribution. The model assumes that economic agents maximize their utility based on their consumption with their different level of productivity (Meltzer and Richard 1981, 917– 19). In this static model, lump-sum transfers and taxes play redistributive role and, in effect, each voter seeks for utility maximizing level of redistribution. The theory states that preference of the median voter matters in majority-rule systems. As level of income of the median voter is less than the mean voter, due to right-skewed income/wealth distribution, the median voter prefers and votes for redistribution. One important implication of the model is that the rich does not have preference for redistribution (Bowles 2012, 132). The rationality and self-interest postulates (AHM framework, welfare theorems and median-voter theorem) render the (re)distribution literature as a research area of secondary importance in economics for many years. The first assault in early 1950s came from Herbert A. Simon (1947, 1957). Simon questioned the assumptions of complete information, perfect foresight and unlimited economic man to choose the best among alternatives. He argued that the cognitive capacity of humans was limited, information was costly, and future was unknown therefore uncertain. Under the assumption of the framework, rational decision-making meant that the “economic man” had to know all the alternatives as well as each of their consequences and their efficiencies. Herbert A. Simon underlined that these were impossible conditions for ordinary humans to meet; their rationality was limited by the available information, the cognitive limitations of their mind and the available time. These constraints impose a limit on rationality. Instead of perfect rationality, humans make their decisions under “bounded rationality”. Simon explained that humans choose a heuristic strategy to find the alternative that meets this threshold. Once they are satisfied with this choice, they stop searching. Hence instead of maximizing, humans search for alternatives that “satisfice.” This was the beginning of two new fields in economics, behavioral and cognitive economics. Recent developments in economics, especially in behavioral, cognitive and experimental economics, have shown clearly that both rationality and self-interest assumptions are indeed at odds with the reality. For instance, experimental economics shows that not only humans are cooperative and “inequality averse”, but also they are sympathetic, other-regarding and engage in “strong reciprocity” (Bowles 2012, 131; Bowles and Gintis 2013, chap. 3; Cameron 1999, 48; Fehr and Gächter 2000,

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159–60).11 It should be noted here that strong reciprocity has significant implications on the redistribution policies because of the fact that humans have much more support for the redistribution policies if the source of the inequality is inheritance, luck, differentiated rates of return from portfolio and rents from the financial system.

2.2.2 Equity and Efficiency Trade-off The second analytical framework that is responsible for neglect of the distributional question from the mainstream economics is the “equity and efficiency trade-off” thesis. While the term first explicitly came from a book by Arthur Okun (1975, chap. 1), his hypothesis was a culmination of three influential growth economists (Simon Kuznets, Nickolas Kaldor, and Robert Solow). Indeed, the Kaldor-Kuznets-Solow consensus on equity-growth theory not only made consideration of distributional question almost irrelevant to the mainstream economics but also gave rise to the theoretical and policy mindset in the mainstream economics that shaped Okun’s equity and efficiency tradeoff (Fisher and Erickson 2007). As argued by Fisher and Erickson (2007, 54), “the powerful influence of this consensus has created something analogous to a threelegged stool for neoclassical growth theory and its application”. The first pillar was Kuznets’ hypothesis that focused on evolution of within country inequality, the second pillar was Solow’s complementary theory of growth convergence among nations and the third pillar was Kaldor’s theory on saving based on different propensities among different classes. The post-WWII early literature on relationship between economic development and inequality was sparked by Simon Kuznets. By using administrative tax data of the US economy, Kuznets (1955, 1–6) postulated that there was an inverse-U shaped relationship between inequality and growth: at the early stages of economic development inequality increases, it slows down as the development process advances, and finally it starts to decline after a tipping point of economic development. Being antithetical to the apocalyptical expectations of Marx and Ricardo, Kuznets’ claim was that “inequality would automatically decrease in advanced phases of capitalist development, regardless of economic policy choices or other differences between countries, until eventually it stabilized at an acceptable level” (Piketty 2014, 11). Despite Kuznets’ dataset was based on the US data between 1913 and 1948, legacy of his findings has lingered among the mainstream economists in the form of the Theory

11 Bowles (2012, 131) defines strong reciprocity as “a propensity to co-operate and share with others similarly disposed, even at personal cost, and a willingness to punish those who violate cooperative and other social norms, even when punishing is personally costly and cannot be expected to result in net personal gains in the future. Strong reciprocity goes beyond self-interested forms of co-operation which include acting tit-for-tat and what biologists call reciprocal altruism, which is really just selfinterest with a long-time horizon.”

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of the Kuznets Curve, which states that inverse-U shaped inequality curve is universally true. This theory also implies that policies of redistribution are not effective. Instead, countries should follow growth-friendly policies and be patient until reaching at the post-industrialization stage of development. Robustness of the Kuznets Curve received a major boost a decade after publication by Montek Ahluwalia, who employed a different methodology and cross-sectional data and came up with empirical support for the inverse-U shaped inequality curve (Ahluwalia 1976, 338). Two years after Kuznets published his work, Nickolas Kaldor formalized that savings was an increasing function of income and argued that contribution of the capitalist and high income earners to the national investment was higher because of their higher propensity to save out of income (Kaldor 1957, 623). A natural implication of his work is that, as a generalization of the Kuznets Curve, it is inequality that spurs economic growth. As the third pillar, Robert Solow’s (1956) work mainly focused on capital accumulation and cross-country growth nexus. Given decreasing returns to scale for each of the factors of productions, Solow argued that poor nations would eventually converge to the rich ones (Solow 1956, 93). Solow’s convergence hypothesis was a complementary to the Kuznets’ hypothesis because the former studied inequality in between-country context whereas the latter studied inequality in within-country context. As a complementary to the pillars mentioned above, Arthur Okun formalized his “leaky bucket” hypothesis which stated that redistributive policies, as a tool to mobilize resources from the rich to the poor, generated deadweight losses for the society due to fundamental trade-off between efficiency and equality (Okun 1975, 100). His formulation of the leaky bucket hypothesis stemmed from disincentive effects of (i) decreasing investment by means of taxation of capital accumulation owned by the rich and (ii) diminishing work efforts through high marginal tax rate for the high-skilled individuals. The result was substantial deadweight losses through rewarding low productivity (Okun 1975, chap. 4). While there was little empirical support for the hypothesis, it was a popular formalization among the opponents of the egalitarian welfare state. The equity and efficiency trade-off from Kuznets to Okun went unchallenged for many decades in the mainstream economics. Especially in the 1990s, “the trade-off turned out to be more like a unicorn than hard science” (Bowles 2012, 20). The first serious attack to this view came from endogenous growth theory in the mid-1990s. In the succeeding decades, culminated in the works of Piketty, Kuznets’ inverse U curve was severely challenged – for a survey of this literature see Fields (2001). Subsequently, consensus has emerged that no empirical support can be found for the inverse relationship between development and equality (Kanbur 2012, 6). Piketty’s dataset shows that there is indeed a U-shaped curve, not an inverse U-shaped one,

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between economic development and inequality (Piketty, 2014).12 Even earlier empirical studies showed that inequality of income and wealth had a negative impact on economic performance (Alesina and Rodrik 1994; Banerjee and Duflo 2003; Bowles, Gordon, and Weisskopf 1990; Perotti 1996; Persson and Tabellini 1994), while egalitarian income distributions and high levels of social spending were found to be positively correlated with GDP per capita (Kenworthy 1995). Although there are some studies showing the positive association between inequality and economic growth, such as Forbes (2000), and Li and Zou (1998), the empirical evidence for a positive causality from inequality to growth is mixed at best (Barro 2000, 2008).

2.2.3 Complete Markets and Contracts Another analytic framework is the idea of complete markets, complete contracts and that all contracts are costless to enforce, which already found unrealistic in the economics literature in the 1970s. However, it was not until the late 1980s that economic theory of contracts was recognized as a field of inquiry (see Stiglitz, 1987). The theory suggests that contracts are incomplete because they cannot cover all the information that the participants need in an exchange to ensure that the interests of both parties are served by the contract. The result is coordination failures which lead to sub-optimal results. For example, a labor contract usually doesn’t cover some factors (i.e. level of effort, honesty, trust) that directly influence the actions of the participants of the contract. This is also the case in debt contracts, which can’t include provisions of truth telling, gambling, speculative risk-taking, malfeasance and other actions that affect the borrower’s promise to repay. For this reason, a debt contract is not enforceable without coercive power of governments, which provide creditors free insurance. A major contention of contract theory is that principal-agent problems stem from the incentive structure of contracts, which are not efficient to elicit the kind of behavior that can serve the interest of both parties. This idea gave birth to the field of incentive economics. Incentive economics searches for contracts where both parties have sufficient incentive to achieve efficient outcomes to improve the gains from exchange for both as compared to contracts without such incentives (Laffont and Martimort 2001, 2). These are referred to as incentive-compatible contracts. Examples of such contracts are those in the labor and credit market where the agents

12 Recently, the debate has been revived by Brueckner and Lederman, who conclude that: “Overall, our empirical results provide support for the hypothesis that income inequality is beneficial to economic growth in poor countries, but it is detrimental to economic growth in advanced countries.” (Brueckner and Lederman 2018, 22)

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become residual claimants, in effect they become property rights owners (have “skinin-the-game”) and thus become principals themselves. An example of the first type is an incentive contract that allows labor to share in the profit of the firm (Weitzman 1984, 87–88). Another example of incentive-compatible contracts in the credit market is risk-sharing contracts where the risk and reward of the project subject of the contract are shared between the two sides of the contract (Askari et al. 2012, 84). The major advantage of these types of incentive-compatible contracts is enhancing productivity because agents are residual claimants. There is an incentive structure in place to elicit truth-telling, trust, cooperation, hard work, and efficiency in resource management; factors that could not be written into contracts and enforced. Hence these contracts attenuate coordination problem and improve the efficiency of outcomes. Without this incentive structure, there are considerable transaction, monitoring and enforcement costs involved in designing and implementing contracts.

2.2.4 Institutions and Inequality The earlier institutionalist literature until 1980s was mainly shaped by the elements of the neoclassical mindset and focused mostly on institutions-growth nexus such as income catch-up based on correcting institutional deficiencies (Gerschenkron 1962), big push theory (Rosenstein-Rodan 1943), and role of institutions in economies of scale (Hirschman 1958). The consideration of inequality in the institutional setting was another reflection of the Kuznets hypothesis in the sense that inequality was conceived as a transitory outcome toward the equilibrium growth. The birth of new institutional economics in the 1980s and 1990s has started to change the previous mindset by contending that economic performance of societies depends much on their governance structures, composed of the rules governing economic behavior, that determine the institutional “scaffolding” usually contained in the society’s social contract (North 1990, chap. 1). Economic inequality has featured centrally in the institutional literature since Sokoloff and Engerman who connected today’s economic inequality to the age of colonization through long-term effects of institutions on factor endowments (Sokoloff and Engerman 2000, 223). Similarly, Acemoglu et al. (2001, 1369) argued that colonization and historical juncture have implications for inequality through institutions. The literature then mainly focused on democratization, rule of law and jurisprudence aspects of institutions regarding economic inequalities. Among others, Barro (2000, 20) reported that higher equality is robustly associated with higher levels of the rule of law. Acemoglu et al. (2015, 16) argued that different from what implied by median-voter theorem there is no uniform relationship between democratization and redistribution, the relationship varies as per economic structures, initial level of wealth inequality and the gap between the middle class and the poor. Another strand of the literature looks into implications of institutional structures on

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income tax and financial deregulation as determinants of inequality (Bivens and Mishel 2013; Philippon and Reshef 2013; Piketty, Saez, and Stantcheva 2014). It is worth noting that recently Stiglitz (2015f, 11) has argued that impaired social contract was the reason for the massive inequality in the United States and highlighted that the problem of inequality can’t be solved without a new social contract that rewrites major rules (about 40 rules) governing economic behavior. Generally, poor governance structures are self-reinforcing because certain groups who benefit from the current governance structures impede reforms, which would provide better sharing of the fruits of economic growth performance. On the contrary, these groups have incentive to spend resources to maintain an inefficient governance structure. Due to this inertia of status quo, there is a feedback loop between increasingly poor governance and higher concentration of income and wealth.

2.3 Change in Perceptions in the Policy Arena Regarding the mindset on economic inequality, there has been gradual change concurrent with the change in the mindset in economic theory. In the few decades following the WWII, a common belief was that progress of humanity was almost equal to economic development, which then equaled to economic growth. It was believed that the problem of “underdevelopment” meant lack of capital. Hence, the emphasis was placed on “getting-capital accumulation-right” as the answer to “economic backwardness.” In the 1980s and 1990s, the emphasis shifted to “getting-therelative-prices-right.” The idea was that reducing public sector intervention in the economy, increasing openness, and improving macroeconomic stability would result in efficient allocation of capital by allowing the price of capital to converge to its opportunity cost, rate of return to capital. This rationale for this idea was simple: The general competitive exchange model in which no government intervention take place would guarantee social optimum and equality of all relative prices. However, evidence mounted on the weakness of this model in terms of market failure. It appeared that the invisible hand did not exist. An important example of market failure was attributed to a phenomenon called “financial repression” by the neoclassical economists. McKinnon (1973, 69) and Shaw (1973, 15) asserted that developing countries misallocated their financial resources through directed lending and ceilings on interest rate. Presumably, there was an equilibrium “market” rate of interest that cleared the market for financial resources. Deviation from this rate meant distortion and inefficiency. When this happened, financial markets were “repressed.” Hence developing countries were urged to liberalize their financial system (Villanueva and Mirakhor 1990, 509–10). Soon the recommendation converged with the “getting-the-prices-right” prescription culminating in a set of free market policy recommendation that came to be known as the “Washington Consensus” supported by major financial institutions, the USA and Europe (Williamson 2008, 14).

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Conceptually, financial repression is the deviation of actual interest rate from its “market equilibrium” rate. Presumably, the latter is the opportunity cost, or the true scarcity price, of financial resources measured by the productivity of these resources in their next best alternative use. Throughout the phase of financial liberalization push, the benchmark for “market interest rate,” which were mostly policy determined, influenced by the market power of banks in industrial countries. It cannot be claimed that such rates are determined by either the true opportunity cost of financial resources or by any reasonable measure of the marginal productivity of these resources, a requirement of free-market general equilibrium theory that lay at the foundation of liberalization policy recommendations of the “Washington Consensus”. All theories of interest determination are essentially equilibrium theories of price determination as the result of interaction of forces of supply and demand reflecting the true opportunity cost (or the scarcity price) of resources. These theories cannot explain existence of fixed, pre-determined rate of interest in an economy in which market forces were freely operating to equate prices of factors and products to their opportunity costs. At no time in contemporary economic history has the absurdity of equating the notion of opportunity cost of financial resources with pre-determined policy rate of interest been as obvious as at the present when the policydetermined “market rate” of interest is zero or even negative. Concurrently, the opportunity cost of financial resources, as approximated by the rate of return to the real sector of the economy, is a large multiple of the policy-determined rate (Bacha and Mirakhor 2015, 2). This deviation of the market rate of interest from its true opportunity cost represents a significant financial repression, a significant market failure. With the advancement of research and development in new institutional economics in the 1990s and early 2000, policy prescription shifted from “getting-theprices-right” to “getting-the-institutions-right.” Policy recommendations focused on prescriptions of how to reform the “rules of the game” including property rights, contract enforcement, and overall governance structure (Acemoglu and Johnson 2005, 950; Rodrik 2000, 4). The shock of the GFC has led to search for its underlying causes. Aside from technical reasons, attention was also focused on general moral failure on the part of major financial institutions. The focus then became “gettingthe-values-right.” The argument was that these institutions, in their pursuit of greed, betrayed their fiduciary responsibilities and committed “economic crimes against humanity” (Zuboff 2009, col. 1). The main policy prescription was to give more emphasis on meta-economic values by focusing on development of social and moral capital of societies. Concurrently, the publication of the painstaking empirical research of Piketty and his colleagues, which showed that inequality of income and wealth was increasing and that the top income earners were receiving the lion’s portion of income, has heightened concerns over inequality and has shifted policy attention to “getting-the-distribution-right” (Alvaredo et al. 2013; Atkinson, Piketty, and Saez 2011; Piketty and Saez 2006, 2014).

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In this context, the World Bank Group (WBG) has taken the lead in advocating policies to combat poverty and inequality with a program of “shared prosperity” aiming at the reduction of inequality gap between the rich and the poor by boosting the income levels of the bottom 40% of the population in member countries through more equal sharing of the fruits of economic growth. It is worth noting that the “shared prosperity” program of WBG excludes redistribution of income and wealth. In the words of the World Bank’s Vice President for Poverty Reduction and Economic Management, Jaime Saavedva-Chanduvi (2013, para. 6): “We need to focus first on growing, as fast as possible, the welfare of the less well off. But we’re not suggesting that countries redistribute an economic pie of a certain size, or to take from the rich and give to the poor. Rather, we’re saying that if a country can grow the size of its pie, while at the same time share it in ways that boost the income of the bottom 40 percent of its population, then it is moving toward shared prosperity. So the goal combines the notions of rising prosperity and equity.” This implies that current income and wealth inequality is taken as given, with the idea that additions to growth and prosperity be shared more equally, especially for the bottom 40% of the population. One wonders how this would be different from the “trickle-down” policies of the past. WBG’s answer is that emphasis should be on ensuring that the growth is so broad-based that it would generate investments focused on job creation and broadened economic opportunities, and that societies would need to commit to a new social contract that improves and equalizes opportunities for all citizens. Given the earlier discussion that inequality of income and wealth stems from governance structures that favor the wealthy and that the wealthy has an inertia against change, it is a bit unrealistic to expect that the wealthy will agree to a new social contract that gives more of the fruits of growth to the poor. Moreover, wealth and income inequality are related (Saez and Zucman 2016, 521) and income inequality leads to more wealth inequality which in turn leads to more income inequality (Piketty 2015b, 50). Thus, there is slim chance of reaching at a more equal distribution of income and wealth just by sharing of the fruits of economic growth without implementing redistribution policies.

2.4 Piketty’s Contribution and Subsequent Controversies As we discuss in Chapter 2.3, the change in the mindset towards understanding the roots of the economics of inequality (specifically, wealth inequality) was quite slow and stubborn until the advent of Thomas Piketty’s book Capital in the Twenty-First Century, which has prompted a new debate on the prospects and underlying drivers of wealth inequality, in 2014. The book sketches out the evolution of the income and wealth inequality for several countries since the Industrial Revolution by using tax data and other fiscal sources, as well as, derives a “grand theory” of capital and inequality. In a nutshell, the book argues that capitalism has a natural tendency

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8

(Wealth-to-income ratio)

7 6 5 4 3

1

1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

2

US

France

Germany

China

UK

Figure 11: Wealth to Income Ratios. Source: Own graph based on WID.world (2017). See wir2018.wid.world for data series and notes.

towards inequality and wealth owned by the rich increases much faster than economic growth in the long-run. Maybe the most important reason for success of the book in the market is that it has revived interest in economics of inequality based on long-run dataset by revealing very stark facts on the evolution of inequality. Although the coverage of the book is immense ranging from historical cases on inequality to predictions on the future of capital, the gist of the book “consists of one definitional relationship, two fundamental economic laws of capitalism (as they are called by Piketty), and one inequality relationship” (Milanovic 2014, 521). The fundamental laws aim at building fundamental relationships among the factors of production to simply capture key features of capitalism by following the tradition of David Ricardo, Thomas Malthus, and Karl Marx (Milanovic 2014, 521). Piketty starts his analysis of the fundamental laws by defining wealth to income ratio ðβÞ.13 From historical tax records for a number of advanced countries, he shows that wealth to income ratios followed a U-pattern in the very long-run by refuting the Kuznets hypothesis (see Figure 11). He adds that wealth to income ratio reached its peak in Europe just before the First World War and did its bottom after the Second World War followed by a secular increase. He argues that destruction of capital in the two World Wars, nationalization in the PostWWII era, high taxation of income and inheritance, labor-friendly policies and high inflation rates kept wealth to income ratio at moderate levels until the end of

13 Piketty uses wealth and capital interchangeably throughout his book by classifying any asset that generates a return, including housing, under the rubric of capital.

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capitalism’s Golden Age (1945–1975) in Europe. Piketty also emphasizes that the Golden Age was an extraordinary bout of prosperity in capitalism’s history and the capitalism has returned to its normal pace since early 1980s. Despite wealth to income ratio per se shows historical evolution of the wealth inequality, the full significance of the ratio comes out clearly when it is combined with the fundamental laws and the fundamental inequality (Milanovic 2014, 521–22). The first fundamental law of capitalism, which is indeed an identity, then states that the capital income share in total income ðαÞ, equals to (Piketty 2014, 52): α=r  β

(2)

where real rate of return to capital is ðrÞ and wealth to income ratio is ðβÞ. The second fundamental law of capitalism is derived from Solow and Kaldor growth theories and shows that wealth to income ratios, in the long run, equals to the savings rate ðsÞ divided by the rate of growth of the economy ð gÞ (Piketty 2014, 166). The formula is given below as follows: β=

s g

(3)

The second fundamental law of capitalism indicates that saving without growth directly drives wealth to income ratios up. Unlike the first fundamental law, which is an identity, the second fundamental law of capitalism is an equilibrium condition which then holds only in steady-state. As given in Atkinson and Bourgoignon (2015, I), if base wealth to income ratio is 3.3 then it takes almost 30 years to reach at 4 and more than a century to reach to 4.8. Sato (1966, 263) accentuates that the adjustment to the steady state in neoclassical model can be very long. In this regard, using steady-state formulation have very little practical policy content. These two fundamental laws then are followed by the fundamental inequality, namely, ðr > gÞ. Piketty (2014, 25) shows that: [T]he return of high capital/income ratios over the past few decades can be explained in large part by the re-turn to a regime of relatively slow growth. In slowly growing economies, past wealth naturally takes on disproportionate importance, because it takes only a small flow of new savings to increase the stock of wealth steadily and substantially. [. . .] If, moreover, the rate of return on capital remains significantly above the growth rate for an extended period of time (which is more likely when the growth rate is low, though not automatic), then the risk of divergence in the distribution of wealth is very high.

This inequality plays a fundamental role in understanding the nature of capitalism and the sources of inequality. Piketty (2014, 26) also adds that: [T]he rate of return on capital significantly exceeds the growth rate of the economy (as it did through much of history until the nineteenth century and as is likely to be the case again in the twenty-first century), then it logically follows that inherited wealth grows faster than output and income. People with inherited wealth need save only a portion of their income from capital to see

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that capital grow more quickly than the economy as a whole. Under such conditions, it is almost inevitable that inherited wealth will dominate wealth amassed from a lifetime’s labor by a wide margin, and the concentration of capital will attain extremely high levels – levels potentially incompatible with the meritocratic values and principles of social justice fundamental to modern democratic societies. What is more, this basic force for divergence can be reinforced by other mechanisms. For instance, the savings rate may increase sharply with wealth. Or, even more important, the average effective rate of return on capital may be higher when the individual’s initial capital endowment is higher (as appears to be increasingly common). The fact that the return on capital is unpredictable and arbitrary, so that wealth can be enhanced in a variety of ways, also poses a challenge to the meritocratic model. Finally, all of these factors can be aggravated by the Ricardian scarcity principle: the high price of real estate or petroleum may contribute to structural divergence.

To sum up, the fundamental inequality ðr > gÞ ensures that increase in the capitalization of the existing wealth amplifies initial heterogeneity in the wealth distribution whereas the less the level of ðr > gÞ inequality the more the rate of accumulation of new wealth to the system through saving out of labor earnings (De Nardi, Fella, and Yang 2015, 8). The fundamental inequality takes Piketty to a pessimistic conclusion that the current state of capitalism will ultimately converge into the patrimonial (inheritance-based) capitalism, which resembles a new form of aristocracy stemmed from inheritance and “a mockery of equal opportunity and meritocracy” (Milanovic 2014, 525). This dismal outcome of capitalism described by Piketty crucially depends on how far ðrÞ outpaces ð gÞ (see Figure 12). If ðrÞ equals to ð gÞ, then wealth to income 6

5

(%)

4

3

2

1

0 0–1000

1000– 1500

1500– 1700

1700– 1820

1820– 1913

r (before taxes)

1913– 1950

1950– 2012

2012– 2050

2050– 2100

g

Figure 12: Rate of Return and Rate of GDP Growth at the World Level. Source: Own graph based on Table S10.3 in piketty.pse.ens.fr/files/capital21c/en/pdf/supp/TS10.3.pdf.

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ratio becomes stable and refutes Piketty’s pessimistic claim. For this reason, Piketty collected empirical evidence from different countries and showed that returns to wealth holders were indeed in increasing trend. It should be noted here that low growth rates are a new normal for most of the countries in the post-crisis era. In Piketty’s view, it is not the growth rate of the economies, which are then determined by technology and population dynamics, as the culprit in widening inequality gap but extraordinarily high rate of returns due to inherited wealth and high rate of return from accumulated capital. Piketty’s fundamental inequality, which states that rate of return of capital outgrows rate of real growth if saving rate of the capitalists out of their wealth goes to one in an economy as a natural repercussion of the capitalist system, has attracted much attention, as well as, criticism. (Piketty 2014, 571) notes that: [T]he inequality r > g implies that wealth accumulated in the past grows more rapidly than output and wages. This inequality expresses a fundamental logical contradiction. The entrepreneur inevitably tends to become a rentier, more and more dominant over those who own nothing but their labor. Once constituted, capital reproduces itself faster than output increases. The past devours the future.

As underlined by Stirati (2016, 6) “provided that the wealthy tend to have higher saving propensity, the importance of r and g in determining the trends of the wealth to national income ratio holds true even under less simplified assumptions”. If Piketty’s fundamental inequality is true, it has significant implications on our understanding of the concept of inequality, as well as, policy-making. The literature, to a large extent, agrees on the data provided by Piketty and his colleagues but there is a big controversy and criticism regarding the fundamental inequality, whether ðr > gÞ is an important driver of the wealth inequality and inevitable fate of capitalism.14 The critics of the theoretical accuracy and empirical relevance of the fundamental inequality provide compelling counter-evidence. Acemoglu and Robinson (2015, 3) criticize Piketty’s generalized laws of capitalism on the ground that he does not take role of politics and institutions in the evolution of the distribution of resources. Mankiw (2015, 43–44) notes that the fundamental inequality derives as a steady-state condition in Solow growth model but the difference between ðrÞ and ð gÞ must be much higher to render the difference a wealth inequality generator. Weil (2015, 35) considers the definition of capital and measurement problems associated with the market value of tradeable assets used as proxy of the quantity of physical capital in Piketty (2014). Krusell and Smith (2014, 1) states that the second law of capitalism and the fundamental inequality rest on a theory of

14 There are also claims that the dataset provided by Piketty and his colleagues contains serious errors. For such claims see Giles (2014, 2) and Sutch (2017, 587).

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saving that is hard to justify. Jones (2015, 45) argues that ðrÞ also changes in response to ð gÞ and net saving rate is not constant over time, which then invalidate Piketty’s assumptions. He also adds that “if one wishes to fit Piketty’s long-run data to macroeconomic growth models – to say something about the shape of production functions – then it becomes crucial to distinguish between land and physical capital” (Jones 2015, 43). Rognlie (2014, 3) underlines that if capital has grown to levels shown by Piketty we should expect low rates of return but the reverse has happened. Milanovic (2014, 527) states that ðrÞ may not be sticky, it does not need to surpass ð gÞ at every point in time. Basu and Stiglitz (2016, secs. 1–3) calculate that in a standard Solow model with constant returns to scale not only is ðr < gÞ but ðsr < gÞ (s is saving rate here) by making the fundamental inequality even more difficult to achieve. The list of the critics on the theoretical ground goes on. Many empirical papers on the validity of Piketty’s fundamental inequality has ended up with no support for the fundamental inequality (Acemoglu and Robinson 2015; Góes 2016) or with partial support (Fuest, Peichl, and Waldenström 2015; Madsen, Minniti, and Venturini 2015).15 Table 2 summarizes Piketty’s main claims and critics’ responses to his claims. To reiterate what Piketty’s book underlines: – The literature agrees that Piketty’s data, which revolves around the concept of wealth that encompasses the productive capital in strict sense, real estate, land and other forms of financial wealth, is correct, – Moreover, ðr > gÞ argument is logically correct. Beyond all theoretical arguments, if rate of return of a variable (wealth) which is held by a specific segment of the population (the wealthy) outgrows the rate of return of another variable (national income) which is produced by all segments of the society (the economy), it is a natural conclusion that the share of the former increases above which of the latter. – There is controversy about his analytical derivation of the fundamental inequality. – The empirical findings usually do not support the fundamental inequality. The inconsistency between historical evidence and the theory, and the empirical findings mostly stem from the fact that both of Piketty and most of his critics seemingly conflate the notion of capital with the notion of wealth. As a corollary of this confusion, ðrÞ represents only the rate of return of the productive capital in the theory of the critics, though, it must cover much more than that. Assumption of ðrÞ as the rate of return of the productive capital and focusing on the theoretical models on the basis of ðrÞ render the whole discussion into a distributional analysis of

15 It should also be noted that many of the empirical papers still attempt to find an association between the fundamental inequality and income inequality. As underlined in this study, wealth and income inequality are different concepts. That is why one of the reasons that most of the empirical studies end up with insignificant results in their regressions.

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Table 2: Summary of Piketty’s Arguments and His Critics. Piketty’s Claims

Critics’ Response

Wealth-to income ratios and wealth inequality were much higher in Europe around s than they are today.

Yes. Piketty’s new data are a good window into the economic structure of the past.

A simple model can account for the way that wealth and income co-evolved over  years.

No. The model treats home prices incorrectly, requires an unrealistic substitutability of capital and labor, and misses the major mid-th The model predicts that capital-to-income ratios century swings in income. Academic economists will rise in the st century, and capital’s share of have thoroughly rejected it as a predictive model. income will rise. The richest earns the highest returns and save Piketty cannot find evidence that the richest earn most of their earnings, so rising capital will also higher returns. Historical data show a high increase wealth inequality. degree of churning among the top fortunes and top companies. Capitalism has a fundamental tendency toward high and centralized wealth.

No. Most economic models show that economic growth can be balanced. History shows that labor’s share of income has changed only a little with ups and downs in  years of capitalism. Much more importantly, capitalism has a fundamental tendency toward raising all incomes.

High wealth inequality breaks down democracy.

Democratic government expanded during the prosperous late th century and collapsed in some countries during the low-wealth decades between the world wars.

Source: Own table based on http://www.heritage.org/research/reports/2014/09/understandingthomas-piketty-and-his-critics#_ftn5.

national income accounting, which is just a subset of distribution of wealth. So, where is the problem? Let’s start with a counterfactual argument. Assume rate of return of the wealth is just a (ex-post) function of the productive capital. In such a case, well-known rules of the economy, such as diminishing marginal product of capital, applies. A natural consequence would be that the rate of return is a function of growth of the national income. That is, ðr = f ð gÞÞ. In such an economy, as correctly stated by Maghrebi and Mirakhor (2015, 106), “since the payoffs are contingent on the realization of a particular state of nature, the realized return on real investment is known only on ex-post basis. The growth rate can be positive or negative depending on the realization of favorable or unfavorable states of nature. This implies that capital is not allowed to increase irrespective of growth rates, and that it is bound to decrease with negative growth”. In such a risk-sharing economy, the main source of wealth

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inequality would be the difference in saving behavior among the economic agents rather than the wealth residual. Because there is divergence between growth rate of wealth and rate of return to productive capital mostly due to the decoupling in the real and financial sector, the factual evidence based on the observed wealth clashes with the theory of productive capital, as accentuated several times in this book. Piketty’s policy recommendations and his proposal for wealth tax are deferred to the Chapter 4, which delves into the redistribution policies and their relevance to the risk-sharing framework.

2.5 The Second Inequality Crisis in the Post-Piketty Era Piketty’s Capital in the Twenty-First Century, given hefty criticism over his predictions and theory, has been a milestone in research on the economics of inequality. The book has also led to arouse heightened interest on a nascent but fertile literature on wealth inequality. It wasn’t arguably a coincidence that the success of the book concurred with the heydays of the second inequality crisis in the immediate aftermath of the GFC.16 Indeed, it is this inequality crisis which then paved the way for heightened interest in wealth inequality as a distinct field of research in the economics of inequality. Even in the pre-GFC period, there was a vast literature about repercussions of high income and wealth inequality such as eroding trust (Leigh 2006, 268; Rothstein and Uslaner 2005, 41), low social mobility for the present and future generations (Andrews and Leigh 2009, 1489; Jencks and Tach 2005, 3), social resentment (Barbalet 1992, 152–53), and undermining effective governance (Marjit, Mukherjee, and Kolmar 2006, 325). On top of that, new evidence indicates that high and persistent inequality can also give rise to two detrimental outcomes, which are arguably the two considerable menace to the working of the market economy and the idea of democracy: – Inequality was one of the underlying causes of the Global Financial Crisis and widening inequality is a driver of global imbalances and financial crises (Goda, Onaran, and Stockhammer 2016, 2; de Haan and Sturm 2016, 4; Kumhof, Rancière, and Winant 2015, 1218–19; Rajan 2010, 14; Turner 2016, chap. 7); – Inequality not only undermines long-term growth prospects but also gives rise to further unequal distribution of income and wealth by leading to a vicious-cycle (Bagchi and Svejnar 2015, 3; Cynamon and Fazzari 2016, 375; IMF 2017, 2–5; Islam and McGillivray 2015, 20; OECD 2015; Ostry, Berg, and Tsangarides 2014, 25–26; World Economic Forum 2017, vii).

16 The first inequality crisis span the period between the Industrial Revolution and the WWI.

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These two findings have played role in changing the direction of economic research from growth to distribution, as it has been well-understood that the economic growth per se is not strong and sustainable without tackling inequality seriously, as well as, implementing redistribution policies effectively. The heightened inequality in the postGFC era can be confidently called the second inequality crisis and this second crisis “threatens to undermine the very basis of society” (Rosanvallon 2013, 5). Indeed, the human history has shown us that heightened inequalities are usually followed by warfare, revolutions, collapse of the states and catastrophic events (Scheidel 2017, 11). For instance, Rosanvallon (2013, 4) reminds in his influential book that the rampant inequality became the “mother idea” of the French Revolution. He also discusses that the pathologies of the present inequality crisis and the first inequality crisis that was initiated by the Industrial Revolution have stark similarities. The response to the first inequality crisis manifested itself in form of nationalism, protectionism and xenophobia. Recently, the OECD Secretary-General expressed in his speech that the world in a same manifestation alike the first inequality crisis (Gurría 2017, sec. Global Risks): Inequalities have reached alarming levels in the OECD, where the average income of the richest 10% is nearly 10 times greater than that of the poorest 10%. A 2016 report from Credit Suisse estimates that the wealthiest 10% have 89% of the world’s wealth, while the top 1% has 50% of global wealth. [. . .] We should not be surprised, then, that calls to populism, protectionism or exclusive nationalism, to paraphrase Paul Collier, are finding political and electoral resonance in the majority of our countries.

The nexus between inequality (especially the wealth inequality) and financial crises has many aspects and is becoming an active area of research. Kumhof et al. (2015, 1217) built a theoretical model where higher leverage and crises are stemmed from inequality and argue that empirical evidence from periods 1920–1929 and 1983–2008 illustrate importance of inequality on debt, leverage and financial crises. Rajan (2010, col. 1) also discusses that rising inequality and extension of the mortgage credits led to the financial crisis in the US in 2007. Similarly, Treeck (2014, 421) indicates that easy credit conditions and rising inequality have contributed to the credit bubble which eventually triggered the U.S. financial crisis. Galbraith (2012, 3) also underlines that the link between inequality and financial crisis runs exactly through private debt. Adair Turner highlights in his book “Between Debt and the Devil: Money, Credit, and Fixing Global Finance” that growth rate of private credit outpaced growth rate of nominal GDP in many countries for several decades before the GFC (Turner 2016, 7). This paved the way for excessive leverage and private debt. But the economies really need such a rapid credit growth? The answer is no for two reasons. First, because much of the credit growth played no necessary role in stimulating nominal demand growth, but it still contributed to excessive leverage and debt overhang. And second, because if necessary we can stimulate nominal demand without relying on private credit creation (Turner 2016, 109).

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If the answer is no, then why the economies have relied upon such a high private credit growth for achieving economic growth? In his view, there are three main drivers of unnecessary private credit growth: real estate, global current account imbalances and rising inequality (Turner 2016, 8). However, a detailed examination of these drivers reveals that the former two are also pertinent to the rising inequality. It can then be argued that inequality might be the relatively exogenous to the other drivers and the root cause of the GFC. If so, increasing inequality in the post-GFC era might give rise to another and more catastrophic financial crisis. Role of real estate boom in formation of the financial crises is well-documented in the literature. For instance, Mian and Sufi (2014, 9) underline that the household debt is the root cause of financial crises. The lending boom fuels surge in real estate prices in a vicious cycle because the surge requires more borrowing especially for the poor and the middle-class. Subsequent fall in household consumption is then the catalyst for the initial big fall in GDP, then the crisis resumes.17 Inequality not only plays an important role during the real estate boom but also it amplifies the repercussions after the boom bursts. Firstly, the home-owners, who benefit from the surge in real estate prices, are usually at the higher level of the wealth scale as opposed to the borrowers, who are usually the poor and the middle-class. In effect, the latter two classes are the ones who drive the credit boom as they do not have enough internal funds to buy homes prices of which incessantly go up. Secondly, as underlined by Askari and Mirakhor (2015, 31) the financial crises hurt the poor more adversely because borrowers with debt contracts end up losing some, or all, of their initial down payment. “Foreclosures are a result of debt and lead to housing prices going down even further. Lenders (ultimately the wealthy who own financial assets, including bank shares) have contracts that impose all initial losses (the down payment equity) on the borrower” (Askari and Mirakhor 2015, 31). Thirdly, it is usually the lenders who benefit from the bail-outs following a financial crisis. As a result, financial crises exacerbate wealth inequality by exposing borrowers, who are the poor and the middle-class, and protecting lenders, who mostly consist of the rich (Askari and Mirakhor 2015, 31). The neoclassical literature predicts that countries with higher investment requirement attract more foreign capital (Gourinchas and Jeanne 2013, 1485). Thus, the standard theory considers current account imbalances not dangerous from the lens of countries’ long term growth prospects. However, most of the capital inflows do not finance investment but consumption in recipient countries. Even the inflows go to the investment, it is not genuine but real estate investment. As the current account imbalances reflected in excessive capital flows do not feed investment,

17 As a note, the role of real estate prices in formation of the boom, heightened investment in construction, fall in household consumption and differentiated effect of financialization on the classes overlap, to a large extent, with the dynamics and outputs of the model developed in Chapter 6.

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productivity and real sector activities, the result is excessive debt that contributed to the GFC (Turner 2016, 124–25). This second driver of the unnecessary credit growth is again closely linked to the inequality (especially wealth inequality) because the need for capital inflows stem from real estate, consumption and credit usage. Ceteris paribus, direct effect of the inequality on formation of the financial crises works mainly through differentiated average propensities to save. Richer people have higher propensity to save with respect to their income and wealth. Rising inequality then increases the desired saving rate above the required investment rate in an economy. On the other side of the coin, the credit mechanism allows more and more indebtedness of the lower segment of the people in the wealth scale through over-borrowing as the people need to maintain their consumption in an environment of stagnant income prospects and rising real estate prices. “The combination of rising inequality and the self-reinforcing dynamics of credit and asset price cycles can therefore result in unsustainable credit growth and rising leverage” (Turner 2016, 121). The second prospectively disastrous effect of the wealth inequality that has been much discussed in the post-GFC period is the linkage between widening inequalities and the prospects for long-run growth. As highlighted by the OECD (2017, 7), inequalities are reaching a tipping point and the ultimate objective of economic growth should be improving people’s well-being. Rethinking economic growth in such a way that all socioeconomic groups can contribute to it and derive fair benefits from their participation means making educational systems, labour markets and institutions more inclusive and more fair, [. . .] More recently, the focus shifted to the consideration of the factors that are driving inequalities at the heart of economic functioning, in particular through productivity growth dynamics.

However, the effect of inequality on economic performance is not monotonic. Robles and Grigoli (2017, 20) conclude that the relationship between inequality and growth is positive at lower levels inequality whereas after a threshold the relationship becomes negative. Given the importance of inequality on long-term growth prospects, OECD included inclusive growth as one of the prior policy targets for the first time in its history in 2017. Inequality has negative consequences on the long term-growth through capital formation, labor force and technological progress, which are the three main pillars of long-term economic growth. The most important effect of inequality on long-term growth is through impediments in human capital formation. Here, financial markets play an important role. In case there are financial market imperfections, the poor may not invest in improving human capital such as secondary education. Instead they may choose to enter the labor market with their much-below-the-potential skills. In turn, under-investment by the poor give rise to lower aggregate output (OECD 2015, 16). There are also skill mismatch and health consequences of inequality, which directly affects labor productivity (OECD 2017, 37).

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2.6 Wealth Inequality from the Lens of Islam This section succinctly overviews the notion of wealth inequality from the lens of Islam. We focus on Islam’s understanding of wealth inequality and whether extreme wealth inequality can exist in an ideal rule-compliant society. The redistribution mechanisms in Islam, the notion of distributive justice and other issues related to the redistribution are deferred to Chapter 4.4, in which we exclusively focus on the redistribution. Islam doesn’t oppose accumulation of wealth but exhorts legitimate acquisition of wealth. However, the priority is given to the elimination of extreme inequalities and securing equitable growth. It is very clearly underlined in Holy Qur’an that “wealth does not circulate only among your rich” [59:7]. This verse simply implies that acquisition of wealth is just a means not an end in itself. Askari et al. (2014, 22) explain further as follows: The Messenger (sawa) emphasized that it is always the rich, powerful, and the opulent who are the exploiters of other humans, who, in order to amass wealth, are the source of the persecution and suffering of the prophets and their followers. The Prophet is constantly reminded in the Qur’an that the crucial aspect of his own mission, and that of the prophets before him, was to establish justice. In practical terms, the Qur’an is clear that this means creating a balanced society that avoids extremes of wealth and poverty; a society in which all understand that wealth is a blessing afforded by the Creator for the sole purpose of supporting the lives of all members of society.

The message above is quite clear: Islam exhorts hard work and approves legitimate acquisition of wealth earned from the hard work. It should be noted here, indeed, Islam recognizes two ways for the Muslims to acquire their wealth, (i) their own labor, and (ii) transfers from others, such as inheritance, who have accumulated their wealth through their own labor (Çizakça 2011, 14). However, Islam opposes concentration of wealth, as well as, circulation of the wealth only among the rich. These prohibitions aim at securing justice, which is the core concept in Islam, in society. According to the Holy Qur’an, scarcity per se is not an economic problem as opposed to the mainstream economics, which considers the concept of scarcity as a central theme in relation to efficiency and choice. On the other hand, Islam considers scarcity neither in Nature, nor in production, but in human behavior (Reda 2018, 82). Allah (swt) makes the bounties of the world subservient to the humanity (Reda 2018, 83). It is, however, “selfishness, the misuse of resources, and human greed that cause scarcity, poverty, misery, and destitution are the real problems” (Askari, Iqbal, and Mirakhor 2014, 22). If societies don’t comply with what the ordains of the Divine Law in their institutional setup and power relations, scarcity in accessing to the resources and resulting inequalities come out (Askari, Iqbal, and Mirakhor 2014, 22). The bottomline of the message of the Holy Qur’an is that scarcity is, in essence, an

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important driver of excessive inequalities and the scarcity is a natural outcome of the institutional structure and power relations that diverge from the ideal society envisioned in Islam. Askari et al. (2014, 22) explains further and link the inequality to the deviation from rule-compliance as follows: Self-development is necessary to transcend selfish- ness. The Qur’an clearly states the need for a revolution in the orientation of the “self” [11:13]. The revolution, as defined comprehensively throughout the Qur’an, is a change toward compliance with the rules of just conduct for the individual. The “ethos of justice” is created in society by a critical mass of those whose behavior fully complies with the prescribed rules.

Since the concept of inequality is pertinent to the notion of the rule-compliance, understanding the concept of wealth inequality from the lens of Islam requires understanding the notion of rule-compliance and how transgression of the rules prescribed by Islam leads to inequality.

2.6.1 A Digression on Rule-compliance Islam is rule-based in that human behavior is governed by a set of rules prescribed in the Holy Qur’an and these rules were explained, explicated and operationalized by the Messenger (sawa).18 These rules are comprehensive and cover all aspects of human life including economic and social dimensions. Questions regarding distribution revolve around the rules governing property relations. Before proceeding to a brief discussion of these rules, it is useful to consider the role of rules in society and the economy in general and in Islamic society and economy in particular. As defined by Mirakhor and Askari (2017, 179), “rule-compliant individuals are the ones who have become Allah-conscious, and have responded to the call by Allah (swt) and His Beloved Messenger”. Indeed, in the Holy Qur’an, Allah (swt) asks the believers to “respond affirmatively to Allah and to His Messenger as he calls you to what makes you alive” [24:8]. These believers choose to comply with the rules and prescriptions ordained in Islam with their free will, so they are the rule-compliant believers. This is a surrendered consciousness (Mirakhor and Askari 2017, 179). In this regard, Mirakhor and Askari (2017, 180) explain the surrendered consciousness and its implications as follows:

18 The term “sawa” is an abbreviation of the sentence: Peace and Blessings of Allah be Upon the Messenger Mohammad and his family.” This sentence itself is a shortened version of honorific invocation whenever the name of the Messenger is mentioned. This saying of the Messenger is reported in major books of Prophetic Tradition, such as Bukhari and Muslim, and is invoked by Muslims in their daily prayers. As well, the term “swt” appearing after the name of Allah is an abbreviation of the sentence: “Praised and Exalted.”

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A surrendered consciousness is one that has entered deeply and effectively the safety and security of Allah’s (swt) Sanctuary. This consciousness has achieved a state of experiential knowledge, propelled by repeated testing through trials and tribulations, that the created does not have intrinsic, independent power to help, hurt, give or take. It is at that stage that, exercising its free choice, surrenders its will to the Will of Allah (swt), knowing that neither, it nor anyone or anything else, can do any better for itself than its Creator. The point of surrender represents a negative and a positive current. In the former, the person gives up his own free will. In the positive sense, the person places full reliance on Allah (swt) but rejects all other dependency. He relies fully on Allah (swt) for everything, and is in a state of serenity with whatever the Creator provides. The person is now alive fully as she/he is in a state of Tawakkol, full dependency on Allah (swt) [. . .] Such a person’s state is no longer what was before. She/he is now endowed with special insight (Baseerah) that cognates the reality of things.

Importance of rules in social and economic order in contemporary thought owes its legacy to the works of researchers such as Douglass North and others who originated a field of research known as the New Institutional Economics (NIE) in the second half of the 1980s. The central contribution of this field of research is its definition of institutions as: “Rules of the Game.” Unlike the traditional mainstream economics which takes resource endowments, education and technology as the most crucial elements determining economic performance of economies, NIE argues that the “belief system” of a society, consisting of institutions and culture of that society, shapes its economic performance. Rules, once formed and endowed with enforcement characteristics, define the institutions of economies. Culture, according to NIE, is collective body of information (a collective database) composed of conventions, traditions, norms, customs and habits that are transmitted from one generation to the next in a society.19 But why are rules so important? To answer this question, we need to discuss the role that rules play in making economic, social, and political life manageable. Institutions matter because they determine how efficiently and effectively a society performs. Institutions of a society, collectively, present a coherent system of shared enforced rules of behavior for the members of the society. One of the most important challenges facing human agents is decision making under conditions of risk and uncertainty. Without rules, this challenge becomes extremely difficult because the agents not only have to worry about the consequences of their own decision-actions, they have also to consider the reaction of other agents to these

19 It is worth noting that often there is confusion in the literature about “rules” and “norms” (see for example reference in the published works of Professor Timur Kuran to Islamic rules as “norms”). The NIE literature, however, makes clear that there is a sharp distinction between “rules” and “norms” (see, for example, the works of Elinor Ostrom). Accordingly, the difference between the two is that formal sanctions are attached to violations of “rules” (as is the case with rules in the Qur’an where rule-violation have, at times, severe sanctions) while norms have only informal sanctions such as peer pressure or social exclusion. Some also make the distinction that “rules” are formal and norms are informal.

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decision-actions. Lack of sufficient information on these issues converts decisions under uncertainty to one under ambiguity while, concurrently, creating unstable expectations about the outcome of decision-actions. These conditions place a significant demand on the cognitive capacity of agents at the point of decision. Collective demands of decision making under conditions of uncertainty, ambiguity and lack of sufficient information about how other agents may respond could potentially lead to paralysis of the decision-making process and large transaction costs. Rules reduce the cognitive burden in the sense that shared rules reduce the magnitude of uncertainty about how others react to an agent’s decisions if the latter is rule compliant. Rules promote coordination of decision-actions of agents by assimilating their individual cognitive maps, i.e., their beliefs, into an overarching societal cognitive framework. As a result, individuals will have stable expectations about others’ decision-actions since all are expected to be rule compliant. Moreover, all agents know that the rules tell them what they can and cannot do and what to expect as consequence in either case. This is because, generally, rules are always accompanied by incentive structures with positive and negative elements. It is the rules matrix, with its enforcement characteristics and its incentive structure, that constitutes the institutional scaffolding of a society to guide decision making, stabilize expectations and reduce transaction costs. Most importantly, efficient institutional structure promotes social order. Costs of transactions determine the efficiency of the institutional structures. All social orders or disorders have their roots in the institutional structure of the society. Disorders are endemic as they appear in all societies at one time or another. However, in some societies disorders are short-lived while in others they last longer. The length and the severity of disorder depend on the stability of the institutional structure of the society. Since social disorders create uncertainties, they are costly to the economy as, for example, investment decisions are either cancelled or postponed leading to increased unemployment and reduced income. A society has a stable social order if it has an institutional scaffolding with a set of comprehensive rules that not only tells agents what they can and cannot do, but also establishes a set of rights, privileges, responsibilities and incentives associated with compliance or otherwise. Among these rules are those that create, govern and protect a stable structure of exchange relationships in economic and political markets. Such a society has an efficient state with legislative and coercive power to enforce rule compliance. In a society in which a stable social order is lacking, disorder becomes extremely costly as it disrupts exchange relationships, the workings of economic and political markets, and weakens rule compliance and the institutional structure. The scaffolding that creates a stable social order and good economic performance, according to NIE, has clear and unambiguous rules that govern property relations, promote high level of trust in the society as well as strengthen contracts and contract enforcement. Additionally, such institutional structure has an independent, efficient, impartial and effective legal system. Some NIE scholars, such as Acemoglu

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(2005, 462), go as far as to suggest that an institutional structure with clear and unambiguous rules governing property relations and an efficient contract enforcement would ensure good economic performance. 2.6.2 From Rule-noncompliance to Inequality There are four concepts that are directly related to the rule-compliance behavior of the believers in Islam. The first one is called walayahh, which can be defined as “the unconditional, dynamic, active, ever-present Love of the Supreme Creator for His Creation manifested through the act of creation and the provision of sufficient resources to sustain life and flourish during their temporary existence on this earth” (Mirakhor and Askari 2010, 57). A direct reflection of walayahh is to extend the love to other creation. The second concept is called karamah, which is the dignity of human beings, who are given intelligence by their Creator to be cognizant of their Creator and to understand why they have been created. The third concept is called meethaq, which is “the primordial covenant in which all humans are called before their Supreme Creator and asked to testify that they recognize in Him the One and Only Creator and Sustainer of the entire Creation and all other implications flowing from this testimony” (Mirakhor and Askari 2010, 57). The final concept is called khilafah, which can be defined as agency-trusteeship. Mirakhor and Askari (2010, 70) link there four concepts to the need for harmonious existence of the human beings as follows: The ultimate walayahh toward Allah – úbudiyyah or adoration of the Creator through service to His Creation – is to be intended for and returned only to Allah. That is, no one or nothing should be associated with the ultimate walayahh to Allah. The world is built upon a multitude of walayahhrelationships. Since the unity of creation is a corollary of the Unity of the Creator, any act or thought that creates disunity or discord in the creation – for example, the acceptance of factors, such as race, color, creed that compartmentalizes humans for discriminatory treatment – is a reflection of shirk (associating partners with Allah). For this reason, the Qur’ān condemns any basis for differentiation (doling out different treatment) between humans. Walayahh of the Supreme Creator provides the basis for human dignity, which, in turn, empowers humans with the ability to utilize all material resources. Three other non-material faculties allow humans to dynamically respond to walayahh: (i) áql, empowering reflective reasoning in humans; (ii) a primordial nature (fitrah), serving as an ultimate compass imprinted on the essence of humans; and (iii) freedom of choice. These provide support for humans to be fully conscious and aware of the dignity of their human state. Once humanity made the correct choice by entering into a covenant of cognition of the Unity of the Creator and His Walayahh and of returning the Walayahh of the Creator through the exercise of the gift of freedom of choice, humanity was then appointed as agent-trustee on earth. This, according to the Qur’ān, was a momentous decision that even the angels questioned [172:7; 30:2]. The autonomy provided by the freedom of choice is exercised through compliance, or non-compliance, with rules (institutions) specified by the Creator that are necessary for a harmonious existence.

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The harmonious existence of the human beings then depends on “removing barriers on the path of other humans to empower them to perform their own function of úbudiyyah” (Mirakhor and Askari 2010, 79).20 Extreme, even high, inequalities in a society is one of the most important barriers for the harmonious existence of the human beings. This is arguably the main reason why Islam opposes unacceptable level of inequalities in a society. Indeed, “removing these barriers from the path of the poor is an act of íbādah, a demonstration of the walayahh, the active-dynamic love for one’s own kind in adoration of the Creator and in return for His Active- Dynamic Love for His Creation” (Mirakhor and Askari 2010, 79). To sum up, as we underline in the beginning of Chapter 2.6, existence of extreme inequality in society is regarded as a transgression of the rules prescribed by Islam. The extreme inequality is a significant obstacle to achieve an ideal society in which mutual relations, such as solidarity and cooperation, pervade.

20 Úbudiyyah can be defined as “the return and reciprocation of the love of the Creator in the form of His Adoration and service to His Creation”.

3 Wealth Residual and Wealth Inequality 3.1 Introduction A typical defense for the existence of high income and wealth inequalities is that the inequality is a byproduct of meritocracy and savings, which are two important elements of a well-functioning market economy. The first element, meritocracy, is defined as rewarding people commensurate with their effort, talent, and risk-taking (Jacobs 2015, 5). Meritocracy-based inequality is, more or less, acceptable and fair for many people since people, more or less, get what they deserve in a meritocracy-based world. Indeed, compelling evidence indicates that people usually don’t bother inequality per se but are concerned for unfair causes of inequality (Frankfurt 2015, pt. 1). In case the main underlying source of inequality is meritocracy, there is a general consensus in the literature that policies targeting inequality problem should seek the effective ways to improve the state of the least well-off. In such a case, education, health and social policy should be the key policy variables to reduce inequality. Indeed, securing equality of opportunity by investing in people through education, health and labor markets is one of the core policy recommendations by the international organizations in addressing the inequality problem. The second element, savings, is considered to be the main economic source of wealth inequality in the mainstream models, such as the Bewley-type models. The mainstream models underline that it is mostly the heterogeneity in saving rates among individuals that gives rise to different level of wealth in a dynamic model setup. Many determinants, such as meritocracy, transmission of bequests, degree of aversion to risk, and type of the risks play role in heterogeneity in saving rates among the individuals. However, the wealth distribution observed in the real world and calculated from this type of models usually diverge from each other, especially at the tails of the wealth distribution. A subsequent question is that why the reality and the models diverge? It should be noted at the outset that meritocracy and savings are good determinants of income inequality in the literature since labor income (one driver of income inequality) is a function of, but not limited to, meritocracy and capital income (another driver of income inequality) is a function of savings, effort and risk-taking. To the contrary, the current level of wealth inequality and its dissociation from income inequality is an indication that other factors which are beside of meritocracy and savings should be at work on the formation of the wealth inequality. But what are these factors? This chapter looks into the factors beyond meritocracy and savings and argues that the interest-based debt contracts is one of the underlying drivers of wealth inequality. Some of the recent studies on inequality asserts that existence of the rents, which should not exist in an ideal market system, are responsible for the dissociation between income and wealth inequality. Unexplained part of the observed wealth inequality by the mainstream models of wealth inequality is called “wealth residual”, https://doi.org/10.1515/9783110586664-003

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which also means the wealth increase without concomitant increase in capital. Section 3.2 reviews the main determinants of wealth inequality in the mainstream models within the context of the Bewley models. Section 3.3 discusses the new puzzles on the distribution of wealth that the mainstream standard economic theory hasn’t given satisfactory answers. Section 3.4 introduces and discusses the notion of wealth residual. Section 3.5 delves into the nature of capital and then reviews the controversy on the nature of interest and profits. This section helps understand how interest-based debt contracts are linked to the inequality problem. Section 3.6 explains how and why the interest-based debt contracts are intimately linked to the notion of wealth residual. This section specifically focuses on two main areas of the debt contracts, real estate and credit markets, which play significant role in formation of the wealth inequality.

3.2 Role of Savings on Wealth Inequality: Bewley Models As underlined in Chapter 3.1, wealth inequality is an indirect function of the meritocracy and savings through income inequality in the mainstream models. Since the meritocracy ultimately affects the level of the savings, it is the saving behavior that is assumed to determine wealth inequality in an ideal market system. Thus, why people save is the primary question to understand the wealth distribution in the mainstream economic models (De Nardi and Fella 2017, 280). This section, Chapter 3.2, overviews the role of savings on formation of wealth inequality from the lens of the mainstream economics. We focus on Bewley models, which are the workhorse framework that connect saving behavior to wealth inequality.21 Against this backdrop, this section points out that the Bewley models don’t capture the whole story of the current state of the wealth distribution, especially at the right tail of the wealth distribution. This is because not only the Bewley models are based on flow variable (savings) to explain accumulation of the stock variable (wealth) but also the underlying driver of wealth inequality is rents, which should not exist theoretically, in a well-functioning market economy. The missing part in the Bewley models, wealth residual, will be then explained in Chapter 3.3. In the mainstream models, the notion of savings is composed of two main parts, bequests as the intergenerational transmission of savings and the life-cycle savings. The original Bewley (1977) model assumes that incomplete-markets in which households are exposed to the same idiosyncratic random shocks give rise to differentiated saving behavior among the economic agents. The random shocks are typically in the

21 In this book, we don’t delve into the details of the Bewley models. For interested reader, Ljungqvist and Sargent (2012) give a very good overview of the Bewley models. See also Aiyagari (1994) and Hansen and Imrohoroglu (1992) for general equilibrium versions of these models.

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form of exogenously specified earnings process, which is usually estimated from survey or micro-level data (De Nardi 2015, 6). The Bewley models seek to understand “which mechanisms generate saving behavior that leads to a distribution of asset holdings consistent with the data” (De Nardi 2015, 4). The canonical Bewley model subject to a multi-period budget constraint is given as follows (De Nardi and Fella 2017, 283): n Y t−1 X c1 − σ E sl  βðt − hÞ  t max T 1−σ fct , at + 1 gt = h i = 0 l = h

(4)

subject to the budget constraint, at + 1 = ð1 + rÞ  at + yt − ct , at + 1 ≥ a

(5)

where ct , at , yt and r denote consumption, risk-free asset, income and rate of return on this asset, respectively. As the agents maximize their expected utility t − 1  Q sl represents the probability that the household over the remaining of their life, l=h

survives to the period t, where sl is the survival probability between two age points. The borrowing limit in the budget constraint is denoted as a. The utility function is in the form of constant relative risk-aversion. In addition, labor earnings follow a firstorder Markov process in this canonical model.22 Life-cyclicality of the savings stems from gradual accumulation of wealth from entering the labor market to retirement. The basic model is solved and evaluated at the steady-state condition in which there is no aggregate uncertainty in the economy with constant distribution of the rich and the poor. Towards the equilibrium in steady-state, endogenous dynamics of the model generates asset holdings (wealth) stemming from the differentiated saving motives of the economic agents exposed to random shocks (De Nardi and Fella 2017, 281). In the overlapping-generations version of the basic model, heterogeneity in precautionary savings is the driving force of wealth inequality (De Nardi, Fella, and Yang 2015, 14). Furthermore, there is now two more savings motive stemming from age-dependent earnings process: consumption smoothing during retirement and self-insurance against longevity risk. While introducing these two saving motives has the capacity to generate more wealth inequality, saving to self-insure naturally implies negative saving rate above a threshold level of wealth. Therefore, saving rate out of income for the rich should be negative according to these models but this finding doesn’t go in tandem with the empirical regularities (De Nardi and Fella 2017,

22 The model given here as the canonical form is not the one studied in the original Bewley (1977) model, which assumes infinitely-lived households. Instead, the canonical model given here can be considered as the overlapping-generations version of the basic model since the first generation infinitely-lived version captures almost nothing in matching the model with the real data.

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281). Although imposing overlapping-generations features into the basic model have the model outputs fit better to the real world data, this is accomplished at the expense of overestimating the fraction of people with little or no savings (De Nardi and Fella 2017, 280). Since the basic Bewley models are not satisfactory in matching the model outputs with the data, the subsequent literature has added new dimensions and features to the existing models. The rest of Chapter 3.2 overviews improvements in the basic model. However, it should be noted here that even the new variants of the Bewley models partly explain the determinants of the distribution of wealth, especially at the top tail of the wealth distribution. Below, we juxtapose the important variants of the canonical Bewley models: Bequests and Transmission of Human Capital: Life-cycle saving motive forms the foundation of the basic Bewley models. Studies show that intergenerational transfers (in the form of human capital through better education opportunities and voluntary bequests in monetary terms) might be responsible for the bulk of the total wealth accumulation (Gale and Scholz 1994, 147; Kotlikoff and Summers 1981, 706–7). Apart from self-insurance and life-span risks of the previous models, saving for bequest motive is also introduced. The first such model by Becker and Tomes (1986) is extended and enriched by De Nardi (2004), Aiyagari et al. (2002), Brown et al. (2009), Scholz and Seshadri (2007), Bernheim et al. (2001), De Nardi and Yang (2014), Gokhale and Kotlikoff (2002), and Nishiyama (2002) under different utility functional forms and type of bequests. Although there is an improvement in model outputs in explaining the wealth inequality, there is still a large gap between the model estimations and the real world data. Moreover, the calibration results give too many poor people compared to the real world observations in data (De Nardi and Fella 2017, 288). Heterogeneity in Preferences: These models impose heterogeneity in time preferences (i.e. patience) and risk-aversion among the members of the same generation to the canonical model. In effect, discount factor and risk-aversion coefficient in the canonical Bewley model now evolve stochastically (De Nardi and Fella 2017, 288). For different variants of these extensions see Lawrance (1991), Cagetti (2003), Krusell and Smith (2006), Hendricks (2007), Heer (2001), Laitner (2001). As further extensions, Dı́az et al. (2003, sec. 4.3) introduce habit formation and Carroll (1998, 26) considers wealth as a luxury good. Adding heterogeneity in preferences doesn’t significantly increase the match between the data and the model outputs. De Nardi and Fella (2017, 281) concludes that the heterogeneous preferences are more to do with amplifying other mechanism than being a crucial driving mechanism in wealth inequality. Earning Risk: The earlier models assume that labor earnings follow an ageindependent linear process with homoscedastic Gaussian innovations while subsequent studies show that age and previous earning level are related to the distribution of wealth (Blundell, Graber, and Mogstad 2015, 59). This type of models assumes that there is a large productivity difference between the high-earning

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households, who are exposed to much higher earnings volatility, and the rest of the population. In such a high risk environment, the high-earning households save disproportionally high so as to smooth their consumption between their working age and retirement (Castañeda, Díaz‐Giménez, and Ríos‐Rull 2003, 822). Much higher saving motive by the top tail of the income distribution due to earnings and business risks are also supported by other empirical studies (DeBacker et al. 2012, 3; Guvenen et al. 2015, 3; De Nardi, Fella, and Pardo 2016, 5; Parker and Vissing-Jørgensen 2009, 404). Heterogeneity in life expectancy: Medical expenses during retirement and heterogeneous longevity of the households give rise to large income risk which compel the households to save more (De Nardi and Fella 2017, 281). Heterogeneity in rates of return: Although the canonical Bewley model assumes that there is single risk-free rate of return in the economy, the literature provides ample evidence that heterogeneity in rates of returns for the assets held by different segments of the households can also give rise to wealth inequality (Bach, Calvet, and Sodini 2017, 2; Fagereng et al. 2016, 651; Moskowitz and Vissing-Jørgensen 2002; De Nardi and Fella 2017, 281). The main role for the heterogeneity in rates of return is that stochastic returns shift the technology through factor returns by generating a long Pareto tail as an indicator of wealth inequality (De Nardi and Fella 2017, 293). Heterogeneous rates of return is endogenously determined by choices, entrepreneurial decisions and information acquisition (Kacperczyk, Nosal, and Stevens 2014, 29; Lusardi, Michaud, and Mitchell 2017, 432). Entrepreneurship: Several studies find that entrepreneurship is a key force driving wealth inequality (Buera, Kaboski, and Shin 2015, 416; De Nardi and Fella 2017, 294; Quadrini 1999, 1). The relationship between the entrepreneurship and wealth distribution is that entrepreneurial ability requires having wealth above a threshold level so as to increase the firm size and to borrow more but having more wealth also requires saving more (Cagetti and De Nardi 2006, 839). Each of these modifications to the basic model provides a better match to the data, there is still considerable unexplained part especially for the rich households though. To illustrate how much each of these modifications improve the model outputs, Table 3 compares the wealth inequality data with estimations from each of the modifications. The outputs in Table 3 show that even the best performing model can partly explain the wealth distribution for the richest 1%, 5% and 10%. Net worth estimates are also biased. In Model 1, the comparison of the data with the basic infinitely-lived model reveals that this version of the model comes nowhere near to the 1989 US Survey of Consumer Finances (SCF) data. To give an example, the richest 1% holds only 4% of the total wealth as per the model predictions while the realization in the survey data is 29%. Overlapping-generations version of the basic model is given in Model 2 has better fit in wealth Gini from 0.41 to 0.67, while the distribution among the percentiles fits poorly to the realization. Model 3 introduces

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Table 3: Bewley Models: Model Outputs vs. Realizations. Model

US data ( SCF) Model * Model ** Model *** Model **** Model *****

Wealth Gini

Percentage wealth in the top %

%

%

.





. . . . .

    

    

Share of negative/ zero wealth (%)

%

%









    

N/A    

N/A    N/A

N/A    N/A

Notes: * Basic infinitely-lived: (1994) with higher variability. ** Basic overlapping generations: Hugget (1996) and De Nardi (2004). *** Bequest motive and productivity inheritance: De Nardi (2004) **** Earning risk: De Nardi et al. (2016) ***** Entrepreneurship: Cagetti and De Nardi (2006). Source: Adapted from De Nardi and Fella (2017, 283, 284, 286, 289, 295).

voluntary bequests and transmission of ability with human capital link. There is a further improvement in both the wealth Gini coefficient and distribution of wealth, the top 1% is underestimated though. Model 4 gives the estimation outputs based on earning process. While fraction of people with negative or zero wealth in the model matches the data well, wealth Gini and the overall distribution of wealth doesn’t fit well ot he realization. Finally, Model 5 gives the model outputs of adding entrepreneurship to the canonical model. The modified model is successful in generating high degree of wealth inequality at the top tail of the distribution whereas the model does not capture large heterogeneity among the entrepreneurs.

3.3 The Puzzles of New Stylized Facts on Inequality The economics profession has been in search of the stylized facts because “in any assessment of progress, as in any analysis of macroeconomic variables, a long-run perspective helps us look past the short-run fluctuations and see the underlying trend” (Jones and Romer 2010, 225). Arguably, the most important stylized facts in defining empirical regularities of economic growth and distribution in the advanced economies in the Golden Age of capitalism, which roughly covered the period between the WWII and the 1980s, were stated by Nicholas Kaldor (1961, 178– 79). The Kaldorian Stylized Facts can be summarized as follows: (i) sustained

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growth of labor productivity, (ii) sustained growth of capital per worker, (iii) stable interest rates and return to capital, (iv) stable capital to output ratio, (v) stable shares of capital and labor income in national accounts, and (vi) appreciable variation in the rate of growth of the order of 2–5% among the fast-growing economies (Jones and Romer 2010, 225). One can argue that the mindset which is reflected in the Bewley models is also closely associated with the Kaldorian stylized facts. In essence, the Bewley modeling seeks to connect skewness in the wealth distribution to skewness in the income distribution (mostly distribution of earnings) and skewed income distribution to the variables related to well-functioning market mechanism, such as luck and meritocracy (see Benhabib and Bisin (2016, sec. 1.1) for a detailed discussion on skewed wealth distributions). In such a world, the analysis is based on stationary distribution of wealth, constant returns to scale, or linearized consumption, which all imply stationarity and stability stemmed from the market mechanism. There is no room for other determinants of the wealth inequality which are extraneous to the well-functioning market mechanism, such as economic rents. However, stability and sustainability of the variables indicated in the Kaldorian Stylized Facts have been seriously challenged by the empirical facts over the last 30 years. The challenge is more pronounced in the distributional aspect of the economic growth. In other words, the mindset, which is incarnated in Bewley models that are based on accumulation of productive capital, has been challenged by the new stylized facts over the last several decades. The new stylized facts not only challenge the Kaldorian Stylized Facts but also give rise to puzzles that don’t comply with the assumptions of the mainstream standard economic theory. In the rest of this section, we discuss the puzzles of the new stylized facts of inequality à la Stiglitz (2015b, pt. 2). Understanding the puzzles then takes us to the point that standard economic theory is not successful in fully explaining high wealth inequality in the post-GFC era and the source of the wealth inequality should be sought in the wealth residual, which we delineate in Chapter 3.4. The five main puzzles of the new stylized facts of inequality are as follows:

3.3.1 Capital-Labor Ratio is Increasing The Kaldorian Stylized Facts assume that the capital-labor ratio is eventually constant. The constancy of the capital-labor ratio, according to Keynes (1939, 48), is one of the best-established regularities in all of economic science. However, there is a significant increase in the capital-labor ratio in the last several decades. The increase is more pronounced after the financialization and deregulation era starting from the 1990s. Shift from stable capital-labor split to increasing share of capital in national income means a pronounced change in functional income distribution between capital and labor. As discussed by Piketty (2014, 244), capital income is more

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unequally distributed than labor income. Thus, increasing capital-income ratio implies ever increasing income inequality. The standard Walrasian economics, which is the basis for the mainstream general equilibrium theory, takes economic growth, personal income distribution (how income is distributed among the economic agents), and functional income distribution (how income is distributed among the factors of production) separately. This is mostly because of the underlying assumption that the economic agents enter transactions with the already given endowments of factors of production. Therefore, constant capital-labor split is a natural extension of the Walrasian assumptions. The overemphasis on personal income distribution and separation of economic growth, personal and functional distribution of income has been changing and there is an upsurge in interest toward studying the functional income distribution separately, especially the notion of the capital-labor split (Milanovic 2017, 2). There is one direct repercussion of increasing capital-labor split. Since capital is usually owned by the rich, higher capital-income ratio leads to increasing income inequality. This is a significant divergence from the mindset reflected in the Bewley modeling because inequality is mostly a function of savings in the mainstream world. However, the notion of the capital-labor split asserts that there is an extra inequality generated due to ownership of a specific factor of production. The ownership implications of the functional distribution of income are explained by Milanovic (2017, 4) as follows:23 It may be useful, even before we embark on the study of the relationship between α and Gini, to indicate why this is important. The increase in α is not, by itself, a “problem” if it does not lead to an increase in inequality between individuals. In effect, when the underlying distribution of capital is egalitarian, an increase in α may cause a decrease in inter-personal inequality or leave it unchanged. Hence, even for the proponents of strong egalitarianism, the increase in capital share cannot be a problem as such. It becomes a “problem” only because in most of real-world situations the underlying distribution of capital assets is extremely skewed. The realization of this fact leads me, in the prescriptive part, to argue in favor of equalization of ownership of assets amongst individuals. This provides a realistic agenda for fighting inequality and is especially relevant for the rich societies where rising wealth/income ratio implies that, unless the return on capital decreases sufficiently, a greater share of national net product will be received by asset-holders. Thus we have a choice between acquiescing in the rising interpersonal inequality, trying to reduce it through taxation, or working on the deconcentration of asset ownership.

But there is still one more puzzle regarding the increasing capital-labor ratio. Even if there is an increase in income inequality stemming from ownership of productive capital, it still can’t explain high wealth inequality. That is, additional income inequality due to the ownership of the productive assets (in addition to the already

23 The quotation is also intimately related to the necessity and viability of the asset-based redistribution, which is the topic of the next chapter.

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generated inequality coming from the savings) is still take a small share in the income-wealth inequality split. Hence, we need to dig more to understand what else might cause dissociation of wealth inequality from income inequality.

3.3.2 The Rate of Return to Capital Should Diminish One of the core ideas of the economics is the law of diminishing returns, which states that using more and more of an input in production process, holding other inputs constant, will ultimately leads rate of return to that factor to decrease. In addition, marginal product of capital equals to the rate of return to capital in a competitive economy. Therefore, rate of return to capital is an endogenous variable that depends on use of capital. Combining law of diminishing returns, equality between rate of return to capital and marginal product of capital, and increasing capital-labor ratio imply either capital stock to output ratio (β coefficient that we explained in Chapter 2) or rate of return to capital must decrease. The first possibility of decrease in capital stock to output ratio should increase the rate of return to capital because less of capital increases marginal product of capital. Thus, rate of return to capital and wage rate move in tandem with the increasing capital-labor ratio. The second possibility of increasing capital stock to output ratio implies downward shift in the rate of return to capital due to, again, law of diminishing returns. We can eliminate the first option from the outset because the empirical evidence indicates that there is upward trend in capital stock to output ratio. It means that rate of return to capital should diminish but long-run data revealed by Piketty (2014, 207–9) shows that the reverse has been happening for the last several decades. This is another puzzle that one needs to address in studying the dynamics of wealth inequality. As we discussed in Chapter 2, Piketty’s first fundamental law states that the share of capital incomes in national income (α) equals to the rate of return to capital (r) multiplied by capital stock to output ratio (β). From this identity, one can infer that the rate of return to capital (r) should diminish (remember this is due to diminishing returns). But Piketty’s model depends on the assumption of fundamental inequality (r>g). Piketty’s historical dataset supports the existence of fundamental inequality. However, this assumption contradicts to the necessity of diminishing rate of return to capital. This seemingly significant contradiction and puzzle should be addressed within the scope of wealth and capital distinction that we discussed in Chapter 1. Indeed, rate of return to “wealth” is historically much higher than the rate of growth of the economy and this rate of return is not bound by the diminishing returns to capital because bulk of the rate of return to capital is determined by factors out of the accumulation of capital. We delineate in this important issue further in Chapter 3.4.

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3.3.3 Increasing Capital-Labor Ratio Should Lead to Higher Wages According to the mainstream economics, increasing capital-labor ratio is associated with increase in wage rates over time. The underlying basis for this assumption is the marginal productivity theory of distribution, which claims that payments to the factors of production in a competitive economy is equal to their contribution (=marginal productivity). In this regard, there are two interrelated consequences of increase in capital-labor ratio. Firstly, more capital compared to available labor means higher productivity thanks to, possibly, cheaper and more effective capital goods. Secondly, relatively scarce labor means higher marginal product of labor, which then means higher compensation for the labor force, wage rate. Therefore, increase in capital-labor ratio should be reflected in higher wages and higher standard of living per worker in a economy. This outcome is also a natural extension of the marginal productivity theory of distribution: Since each factor of production is paid concomitant to their contribution to the society, through their marginal productivity, there is no exploitation in a competitive market and “additions to capital would cause wages to increase, so workers would be better off thanks to the savings and innovation of those at the top” (Stiglitz 2015a, 6). As opposed to the expectations of the marginal productivity theory of distribution, median and average wages have stagnated in both advanced and developing countries while the capital-labor ratio has been increasing. Moreover, slower growth in median wages relative to economic growth is a norm in many countries (IMF 2017, 14). One compelling explanation for the stagnation is wages is so-called “skill-biased technological change” hypothesis, which states that the technological progress places a premium on high-quality workers. The premium increases the inequality in earnings. Thus, the hypothesis claims that the workers with high skills see their wages increase whereas those with low or medium skills see their wages fall (Stiglitz 2015a, 430). But there are still problems with this hypothesis. Firstly, marginal productivity theory of distribution takes the average wages inclusive of all skill levels and data shows that average wages have been stagnating (IMF 2017, 14). Even we assume that wage rates of the unskilled workers are the same or declining, increasing productivity and skill premium should lead to an upward shift in the wage rate of the skilled workers. Therefore, average wage rate should increase. Secondly, bulk of the increase in wage inequality has occurred within the groups with similar observable characteristics such as education, age and experience in many countries (Piketty 2015e, 73). Thirdly, there is no a generalized upskilling of jobs. Therefore, the explanation for the puzzle of stagnating wages with increasing capital-labor ratio should be somewhere else. According to Stiglitz (2015a, 439) a more plausible explanation is increasing rents to the holders of the rent-generating assets such as land, intellectual property rights and monopoly power. Hence wealth increases

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without necessarily concomitant increase in productive capital, marginal product of labor and wage rate. This is because, as opposed to the saving-based wealth distribution models, rent-generating assets don’t need to be related to the productive capacity of the economy and accumulation of capital. It is even possible that increase in unproductive wealth crowds out productive capacity of the economy by giving rise to anomalies in wage rates.

3.3.4 Elasticity of Substitution Between Capital and Labor The functional distribution theory defines inequality as the opposition between who owns the capital and who do not. Thus, the fundamental source of inequality is unequal ownership of capital. In this picture, substitution between capital and labor has important consequences. If the quantities of capital and labor is fixed in production process (namely, fixed proportions production function), market forces and the price system play no role in inequality between capital and labor (Piketty 2015e, 39). In such a case, productive capacity of the economy, institutional factors and the degree of negotiation powers of the factors of production determine the distribution of income and wealth. For instance, power of the labor unions play a much important role compared to the relative prices of the factors of production. However, capital can be substituted for labor or vice versa in a market economy. In such a case, factor prices play not only an allocative but also a distributive role (Piketty 2015e, 28). The degree of substitutability between capital and labor is measured by the notion of elasticity of substitution between capital and labor, which is the percentage change in the capital stock when the relative price of capital (ratio of rate of return to capital to wage rate) increases by 1%. If elasticity of substitution between capital and labor is high (it means greater than 1), it is easy to substitute capital for labor as the relative productivity or the relative cost of these two factors changes. A typical Cobb-Douglas production function has elasticity of substitution equals to 1. If elasticity of substitution is less than 1, the economy comes closer to the fixed proportions production function, meaning that institutional factors play a bigger role. The elasticity of substitution brings one more puzzle to the table. If there is sustained increase in capital stock to labor ratio (capital deepening), as we discussed previously, elasticity of substitution between capital and labor should be bigger than unity (Stiglitz 2015a, 34). This would also imply increase in wages. However, average wages have diminished for the last several decades. This can happen only if the elasticity of substitution between capital and labor is less than unity. Indeed, many empirical studies support that elasticity of substitution is less than unity. Again, this puzzle stem from conflation of productive capital with wealth.

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3.3.5 Divergence Between Wealth-Income Ratios and Savings The fifth puzzle is that upward shift in the wealth to income ratios in many countries doesn’t match with the trends in national savings. To reiterate, mainstream models tend to explain wealth inequality through accumulation of productive capital and savings. However, increase in the wealth to income ratios leaves a large bulk which can’t be explained by the saving models. This large unexplained component is called wealth residual. Since it is one of the core arguments in this book, we analyze this concept separately in Chapter 3.4. Here we only give a quotation from Behhabib et al. (2017, 3–4) below that succinctly explains the logical inconsistency of the saving based models: The literature has largely emphasized the role of earnings inequality in explaining wealth inequality. Indeed, Bewley-Aiyagari economies, which focus on precautionary savings as an optimal response to stochastic earnings, represent the most popular approach of introducing heterogeneity into a representative consumer framework to study the distribution of wealth. [. . .] working with models in which earnings and precautionary savings are the main determinant of wealth accumulation, a much thicker distribution of earnings than the observed distribution is required to fit wealth data. This is exactly what the awesome state estimates, introduced with great success by Castaneda et al. (2003), effectively achieve. More precisely, an awesome state is a state added to the observed stochastic process for earnings whose properties are estimated in order to better match the wealth distribution. Castaneda et al. (2003), in a rich overlapping-generation model with various demographic and lifecycle features, obtain estimates of the awesome state which requires the top 0.039% earners to have about 1, 000 times the average labor endowment of the bottom 61%. With the recent availability of earnings data which have not been top coded we can assess the reliability of this estimate. In fact, the ratio between even the top .01% and the median is at most of the order of 200 in the World Wealth and Income Database (WWID) by Alvaredo, Atkinson, Piketty, Saez, and Zucman (since 2011).6 Similarly, Kindermann and Krueger (2014)’s awesome state in their Aiyagari-Bewley model requires the top 0.25% earnings to be 400 to 600 larger than the median. Instead, according to the WWID, even the top 0.1% are just about 34 times larger than the median. Finally, Diaz et al. (2003) estimate a top 6% of the population to earn 46 times the labor earnings of the median, while the top 5% in WWID earns about 5 times the median.

3.4 The Concept of Wealth Residual We have repeatedly stressed that saving-based models don’t perform well in explaining the determinants of wealth inequality, especially at the tails of the wealth distribution. The saving-based models have indeed improved much over time with a significant increase in detecting the drivers of wealth inequality but their weakness (at least currently) doesn’t stem from how well they are set up. These robust and complex models can explain the wealth inequality well only if the underlying cause of the

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wealth inequality is related to accumulation of capital and savings. If out of market forces, such as rents, are also in operation, their explanatory power automatically dilutes. To sum up, even improvement in the saving-based models, such as bequest motives, heterogeneity in preferences, and entrepreneurship don’t change their inherent weakness in explaining wealth inequality if savings are not the primary determinant of the wealth inequality. This section explains the concept of “wealth residual” against the backdrop of low performance of traditional saving-based models in explaining the surge in wealth inequality in the second inequality crisis era. The literature of economics has lately realized this important fact that savings motives become insufficient in explaining the wealth inequality. As stated by Stiglitz (2015g, 431), “much of the increase in wealth has little to do with savings in the usual sense. Rather it is the result of capital gains – especially the increased value of land – and an increase in the capitalized value of other rents”. One important prerequisite towards understanding the concept of wealth residual is to realize that typically mainstream models of wealth inequality, even including Piketty’s (2014, 25) fundamental inequality, are based on flow variables, such as savings in the Bewley models. As we explained before, income as a flow variable is restricted to certain point in time and related to labor market activity but wealth is a stock variable which is accumulated over a longer time span. Thus, flow data is insufficient in explaining the distribution of wealth as an inherent feature although it is relatively much more successful in explaining income inequality. The problem is then to explain the bulk of the wealth inequality which can’t be explained by income inequality. This unexplained part of the wealth inequality is named as “wealth residual” (Basu and Stiglitz 2016, 1–13). One can also define wealth residual as part of the wealth inequality which can’t be explained by the capital accumulation and/or savings. Alternatively, wealth residual is the increase in wealth without concomitant increase in capital. Again, as we highlight throughout the book, wealth and capital distinction makes the difference. The wealth residual concept is analogous to the Solow residual, which is the part of growth that is not accounted for factors of production. Solow (1957, 320) showed that only 13% of the total average productivity could be explained by the factors of production, the rest was attributed to total factor productivity (=residual). In the light of the definition of the Solow residual, we can also define wealth residual as the bulk of the wealth inequality, which can’t be explained by “factors of” income inequality. Based on real data, we provide a simple exercise here to give a concrete conceptualization on the relationship between savings and wealth residual. Let, K, Y, s, i, g, β, ω, stand for effective capital, income level, net saving rate, capital inflows over GDP, real growth rate, wealth to income ratio and capital to wealth ratio, respectively. In steady state, a simple Solow model defines capital deepening (growth rate of the capital-income ratio) as follows (Stiglitz 2015b, 11):

3.4 The Concept of Wealth Residual

   d K Y 1 = ð s + iÞ − g * = ð s + iÞ log − g* dt Y K βω

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(6)

It then implies that capital deepening requires: s > g* βω − i

(7)

Equation (7) states that, in the long run, saving rate must be bigger than multiplication of steady-state growth rate, wealth to income ratio and capital to wealth ratio, adjusted by capital inflows. In this framework, comparison of the realized saving rate over the long-run and the saving rate assuming the capital to wealth ratio is equal to unity (let’s call it required saving rate) gives the implausibility of the saving rates that could account for the observed level of the wealth to income ratios. If the difference between the realized and required saving rate is large, it means it is implausible that the realized level of the saving rate has produced that much of the wealth. It is then an indication of the low power of explaining wealth level with a saving-based model. Figure 13 shows realized average net saving rates for countries for which we also have data for wealth to income ratios. The required saving rates in the figure are calculated based on the assumption that capital to wealth ratio is equal to unity. This variable can also be considered as the level of savings in a counterfactual world in which there is no difference between capital and wealth (an assumption in the many saving-based models).24 The required saving rates are more than twice of the realized saving rates for several countries in the dataset. The figure clearly shows that, in general, “it is hard to generate plausible increases in the real capital stock that could account for the observed increases in the wealth income ratios in recent decades” (Stiglitz 2015b, 12). Besides, as opposed to the requirement of the equation, there is a secular decline in savings and productive capital in the world for the last few decades. Factual evidence shows us that wealth accumulation has soared as opposed to the fact that capital-income ratios on a declining path. A crucial question is then what determines the wealth residual. Stiglitz (2015b, sec. 2.4) points out four main underlying drivers of the wealth residual: (i) increase in the value of land, (ii) increase in the value of other inelastically supplied factors, (iii) increase in value of intellectual property, and (iv) increase in exploitation rents. In the rest of Chapter 3.4, we shortly explain the relevance of these four drivers to the formation of the wealth residual.

24 We use average values of the net saving rates, GDP growth rate and capital flows between 1970– 2016 as a proxy for the long-run levels of the variables. The wealth to income ratios are extracted from the World Inequality lab. It should be noted here that the calculation is very rough and aims at just giving a proxy picture of the realized and required saving rates.

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50

(Saving rate, %)

40 30 20

Required

Japan

US

Czech Rep.

Australia

Canada

Greece

UK

France

Italy

Spain

Denmark

Germany

Finland

Mexico

Netherlands

Norway

Korea

0

China

10

Realized

Figure 13: Realized and Required Saving Rates. Source: own calculations based on World Development Indicators (World Bank 2018) and WID.world (2017).

3.4.1 Increase in the Value of Land There are two main underlying factors for the soaring up in the real estate prices for the last few decades both in the advanced and developing economies. Firstly, most of the real estate purchased by the super-rich can be counted as positional goods, which represent luxury spending (Stiglitz 2015e, sec. 2). As the real estate is in fixed supply, higher demand for the real estate, as a positional good, increases its price. Secondly, low interest rate policies recently implemented by the central banks have played a significant role in soaring real estate bubble (Stiglitz 2015e, 18). In Chapter 3.6, we examine the relationship between interest-based debt contracts and the real estate in detail. 3.4.2 Increase in the Value of Other Inelastically Supplied Factors As the wealth level increases, the demand for positional goods, which derive their value from the fact that consumers prefer them to demonstrate their status, typically increases (Stiglitz 2015b, 23). These goods also related to the interest rate since their cost is linked to the opportunity cost, which is the interest rate. But we will not delve into this factor because its effect on total wealth residual is limited.

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3.4.3 Increase in the Value of Intellectual Property Changes in the property rights regime, patents and intellectual property rights have contributed to the wealth of the economic agents who hold rights of the intellectual property (Stiglitz 2015b, 27). 3.4.4 Increase in the Exploitative Rents Exploitative rents can be defined as the market power stemming from ownership of an asset, which cannot be in any way eliminated by competition (Stirati 2016, 4). Exploitative rents lead to the prices that are quite different from optimal market prices, which are determined by marginal product of capital in a competitive economy. These rents are not considered in the traditional growth and distribution models because, those working in the neoclassical tradition, assumed markets were competitive, and that output was produced with labor and capital, with constant returns to scale production function. In that theory, rents played no role, because under those assumptions, there were no rents (Stiglitz 2015b, 3).

The exploitative rents can take many forms, some of which are specified by Stiglitz (2015b, 23–28) as follows: – Changes in market power and exploitation: There is a rising monopoly and concentration trend among the firms in the world. A staggering example, which is based on a dataset of 28,000 firms formed by the McKinsey Global Institute, indicates that the ratio of average market capitalization of the top 100 firms to that of the bottom 2,000 firms was 7,000 in 2015. This ratio was only 31 in 1995 (UNCTAD 2017, 126). Besides explicit forms of exploitation (privatizations, tax evasion, market manipulation, etc.), subtle forms of exploitation also matter such as bail outs by the government in the form of transferring the risk to the public. – Exploitation of consumers: These rents include taking advantage of the consumers due to their limitations to process information and irrationalities in behaviors. Indeed, corporations and financial institutions have extensively used behavioral techniques to exploit the customers. – Knowledge and Information rents: These rents occur due to abuse of information asymmetries by the informed party such as insider trading and market manipulations. – Changes in discount rates and risk management: For instance, fall in the rate of discount and subsequent relative price changes induce important wealth gains for the holders of certain assets. As stated by Stiglitz (2015b, n. 67):

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This was the essential issue in the Cambridge-Cambridge controversy some half a century ago, where it was observed that the value of capital and the choice of technique may be nonmonotonic in the interest rate. [. . .] Thus, in models with the production of commodities by means of commodities, the economy at a low interest rate and a high interest rate may look the same (the same technologies are employed), while at an intermediate interest rate a different technology is employed. Even if the value of wealth has changed in going from the low to the high interest rate, there has not been capital deepening, at least in any meaningful real sense. There are a variety of other reasons that there can be changes in intertemporal pricing, with large consequences to the valuation of assets.

–

Improved risk management: Improved risk management has changed wealth income ratios. As indicated by Stiglitz (2015b, 28): At the same mean and variance of the return to an asset, such changes lead to an increase in the certainty equivalent return, and therefore of the market value. If the improved risk management/ ability to absorb risk leads to a lower discount rate, the increase in market value can be even larger.

Arguably, the sub-components of the four main factors (= rents) that give rise to wealth residual can also be re-grouped under two rubrics. The first part, which we call as “malfunctioning rents”, are the ones that can be eliminated by implementing appropriate regulation and enforcement of the rules of the game. For instance, consumer protection measures can eliminate knowledge and information rents. Securing rules of the game in privatization can impede formation of excessive market power. The second type of the rents, which we call as “inherent rents”, can’t be eliminated by better implementation of rules or elimination of tax evasion in the system since they exist as an inherent feature of the system. For instance, capital gains in real estate and riskshifting through bail-outs are inherent features of the interest-rate based systems (Askari et al. 2012, 248). Our main argument is that the interest-based debt contracts are the main driver of the inherent rents and they comprise the bulk of the wealth residual. Figure 14 is a demonstration of how interest rate, debt and financialization affect the wealth inequality through many channels. We start by decomposing the total wealth inequality into two components. The saving-based models imply that income inequality, through accumulation of capital, leads to wealth inequality over time (right part of the figure). In such a world, there is no wealth residual, and capital equals to wealth. As explained before, life-cycle savings and bequests allow for wealth accumulation. In this picture, interest rate mainly affects the variables that form the life-cycle savings such as rate of return differences among different sectors. Once wealth residual enters the picture, interest rates affect the wealth residual both directly and indirectly. Firstly, interest rate itself affects the three of the four types of rents directly. For instance, low interest rates have a direct effect on real estate values. Secondly, debt contracts, which are functions of the interest rate, have effects on these factors. For instance, debt and leverage are the drivers of the rentier capitalism in which corporate sector rent-seeking behavior in the stock market.

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Wealth Inequality

Wealth Residual

Land value

Positional goods

Intellectual property

Income Inequality

Exploitative rents

Saving

Life-cycle Saving

Inherited Saving

Financialization

Debt

Interest Rate

Figure 14: Components of Wealth Inequality.

Thirdly, financialization is a direct result of the interest based system and debt. For example, predatory lending and over-expansion of credit are the repercussions of the financialization. In Chapter 3.6, we explain the interest rate and wealth residual nexus through different channels in detail.

3.5 The Nature of Capital, Interest Rate, and Rate of Return to Capital In this section, we delve into the nature of capital and then review the debate on the nature of interest and profits. We also discuss the theory of capital from the lens of Islamic economics and finance. There is already a vast amount of conventional and Islamic literature on the theory of capital. Instead of reviewing the whole topic in this short section, we particularly focus on the nature of the capital, the difference between rate of return to capital and the interest rate, and the main impediments led by the interest-based contracts on the proper functioning of the markets. Throughout the book we use the term “interest-based debt contracts” but we haven’t clarified what we mean by this term. The main line of divergence between the risk-sharing contracts and interest-based debt contracts is the existence of the interest rate. Therefore, understanding the nature of the interest rate, the nature of the capital in conventional and Islamic economics, and clarification of the differences between the rate of return to capital and the interest rate are good starting points to conceive of the role of the interest-based debt contract in formation of the wealth

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residual. As we highlight throughout the book, savings and accumulation of capital are the key factors to economic growth and in the mainstream models. In a wellfunctioning market economy with no rents, these factors also determine, to a large extent, the degree of inequality. This assumption is the basis for the Bewley-type models that we discuss in Section 3.2. In such a world, the notion of capital is the key concept in explaining savings and inequality. We already define the notion of capital and its difference from the notion of wealth in Chapter 1. Here, we focus on the definition of capital from the different strands of economic thought by focusing on the notion of capital as a pool of money or a fund, as opposed to the idea of capital as a set of heterogeneous physical goods. Capital, in the history of economic thought, refers to two interrelated definitions, capital in the form of physical goods and capital as a pool of financial assets, including money. They are intimately related because capital which indicates physical goods is the other side of the capital which indicates pool of financial assets. It means capital in the form of financial assets and money is backed by the physical goods that are defined as the capital. As indicated by Krichene (2013, 8) “financial stability could be undermined when banks issue more money claims than are backed by the stock of real capital or when there is misalignment between money interest rates and the real return to capital. When financial capital multiplies independent of real capital, inflation results and speculative bubbles in real assets and commodities accelerate.” The term factors of production refer to the inputs used in the production such as land, labor and capital. Each factor of production receives its remuneration: landowners receive rent, workers receive wage and capital owners receive rate of return to capital. Among the factors of production, capital has a special place and it has been the subject of the most debate in the history of economic thought. In the early classical economic theory, the notion of capital refers to two broad definitions: One definition sees the capital as a concrete physical good, whereas the other definition means the market value of the first definition. Adam Smith redefines these two concepts “as a means of acquisition for the individual, and capital as a means of social production” (Askari et al. 2010, 14). According to Ricardo, capital is part of a nation’s wealth and is employed in production. Indeed, capital is an important ingredient of Ricardo’s labor theory of value, which considers the labor as the underlying value of commodities (Askari et al. 2010, 14). In his theory of value, capital indirectly supports the labor through providing more food and other inputs to the labor production. Böhm-Bawerk also considers the capital as a subsistence fund similar to Ricardo but he adds that capital supports landlords and money capitalists (Askari et al. 2010, 14). Another influential name, John Bates Clark, treats capital “as a fund rather than as an array of heterogeneous capital goods and offered a general definition of rent as the income from all capital goods, rather than just the income from land. There is a permanent fund of productive wealth, expressible in money but not embodied in money, and it is this which businessmen designate as capital” (Askari et al. 2010, 15). Here, we specifically focus on the

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definition of capital in the mainstream economics because understanding these two definitions of capital, capital as a physical good and capital as a monetary fund, has important implications on the nature interest rate and inequality. As underlined by Krichene (2013, 8): The dichotomy of the definition of capital in terms of real commodities versus money fund is of paramount importance in the conduct of macroeconomic policy, growth, and financial stability. The object of economic growth is to increase the quantity of real. capital and output. An overriding goal of macroeconomic policy is to achieve financial stability. Real capital may face constraints for its expansion because of limits to saving, natural resource availability, or entrepreneurship. However, money capital can lose contact and association with real capital and may expand disproportionately in relation to real capital when fiscal and monetary policies are unduly expansionary. If fiscal deficits are financed through bank credit (i.e., monetization), there will be an inflationary expansion of money capital that is inconsistent with the stock of real capital. Similarly, central banks or the banking system may expand credit in an uncontrolled manner, leading to an inflationary expansion of money capital accompanied by slow growth or even contraction of real capital. In the same vein, financial innovation can lead to the creation of instruments that are pure debt-trading instruments and have no connection to real capital. For instance, through securitization or credit derivatives, money capital can expand at phenomenal rates that bear no relationship to the stock and availability of real capital. Disproportionate increase of money capital has often led to high inflation; it financed speculative booms in real assets and commodities, with the burst of speculative bubbles resulting in banking bankruptcies and large redistributions of wealth from savers in favor of debtors. The financial crisis of 2007–2008 illustrates how uncontrolled expansion of credit can lead to financial chaos and bankruptcies. In all cases of disproportionate increase of monetary capital, inflation in the price of food and consumer necessities would intensify; it could be regarded as an increase in the price of capital and a contraction of real capital. Suppliers of commodities generally reduce real supplies in an inflationary environment and hoard commodities. Inflation reduces real wages. It also reduces real saving and depresses demand for capital goods as well as the demand for nonnecessities. Such an inflationary effect acts as a depressant for the real economy and triggers an economic recession. In Islamic finance, interest and credit are inoperative. Monetary capital is fully anchored by real capital and maintains full and direct connection to it. There is no inflationary pressure on capital prices and therefore there is full macroeconomic stability. Real supplies of commodities remain always forthcoming in a competitive manner. Real wages are not depleted. Saving remains high in real terms, as do investment and capital accumulation.

In Islam, the concept of capital differs from the one in the mainstream economics significantly in several respects. The fundamental difference is that Allah (swt) is the sole creator and owner of everything. The Holy Qur’an indicates that “And He has subjected to you, as from Him, all that is in the heavens and on earth: behold in that are signs indeed for those who reflect.” [45:13]. In addition, Allah (swt) warns the human beings not to have excess love toward the “wealth with inordinate love” [89:20]. The Holy Qur’an also warns against “appropriating unjustly the capital of other people, including the wealth of orphans” (Askari et al. 2010, 20). Since everything belong to Allah (swt) in essence, misuse of capital is also condemned.

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Askari et al. (2010, 20) explains the main features of the Islamic theory of capital as follows: The classical distinction between land and capital is not essential to Islamic capital theory. Land and real commodities could be easily treated as wealth or capital. The distinction between labor and capital, however, is made explicit. Besides designating capital in terms of commodities, capital also has been defined in money terms. In both domestic and international trade, commodities are sold for money. In turn, money reserves are used to acquire commodities. Money eliminates the double coincidence of wants and thus saves considerably on transaction costs. Because of its wide acceptance and its real purchasing power, money is considered as wealth. Money capital is referred to under the general name kanz, and includes gold, silver, other precious metals and jewelry. Capital can be used in trade, production and for lending. The notion of capital as a roundabout production process and advances in knowledge and technologies are inherent to Islamic capital theory. Capital accumulation sustains economic growth, increases output and enhances human comfort. It expands cities and enriches people. However, classical capital theory’s emphasis on capital productivity as an explanation for interest is irrelevant in Islamic capital theory. Loan transactions are perfectly legitimate; however, they have to be qardh hassan – free of interest. A loan has to be written by a notary in the presence of two witnesses, irrespective of its amount. The real value of the loan has to be preserved in terms of quantity, quality and time. The debtor should never fail to repay his debt. The repayment of a loan has priority over other spending such as performing the pilgrimage of hajj or umra. It also has priority before an inheritance is passed on to heirs. Thus trust is stipulated as a fundamental element of Islamic finance and economics and of how well economics and finance function in practice.

The nature of interest and whether the interest rate and rate of return to capital are significantly different concepts are subject to pervasive debate in the mainstream, as well as, the Islamic economics literature. The main source of confusion is to consider the concept of interest exactly same as the concept of profit. For instance, both Adam Smith and Ricardo think that interest rate is mainly governed by the rate of profit. Böhm-Bawerk, Irving Fisher, and Alfred Marshall also consider the concept of interest same as the concept of profit in their theories of capital (Krichene 2013, 10). Wicksell proposes the idea of natural rate of interest, which is a measure of the rate of return on capital as opposed to the money-market rate, which is determined by the financial sector. He argues that booms and contradictions in an economy are reflections of the deviations between natural rate of interest and money-market interest rate (Askari et al. 2010, 17). Frank Knight, on the other hand, defines profit as a residual of production process after all other payments to the factors of production are paid. “Hence, Knight considered that interest remunerates capital and, in contrast to classical capital theory, he did not confuse profit with interest” (Askari et al. 2010, 17). Furthermore, Frank Fetter points out that there are inconsistencies in the previous literature on the theory of rent and interest, and indicates that “roundaboutness is an important aspect of the productivity of capital goods. However, while this productivity may increase the rents to be derived from capital goods, it cannot account for an increase in the rate of interest, that is, the ratio between the annual

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rents derived from these capital goods and their present price” (Krichene 2013, 11). Although, Fetter wants to separate the concept of marginal productivity from the concept of interest, “another principle is needed to explain why and on what basis these rents are discounted to get the present capitalized value of the factor: whether that factor be land, or capital goods” (Krichene 2013, 11). The missing principle that Fetter needs to add to his theory is the concept of time preference, which states that individuals prefer to consume or to invest now compared to the future. It should be noted here that the notion of time preference is one of the most basic concept in mainstream economics. Indeed, “among a collection of surmises made by economists about the necessity for and justification of interest is one that rests on the belief in the sufficiency of time preference for a positive rate of interest” (Toutounchian 2011, 118). In the first half on the twentieth century, there have been many other important discussions on the role of interest in market economy and the distinction between rate of return to capital and interest rates in the Western economic thought. For an extensive literature review, see Toutounchian (2011, chap. 2). Another important contribution to the topic of interest rate determination comes from Kenneth Arrow, who was to win a Nobel Prize in 1972, and two of his coauthors, Gerard Debreu and Frank Hahn. They produce an elegant mathematical model that reflects Smith’s conception of a market economy. The model economy, which is called Arrow-Debreu economy, is composed of contingent markets, which exist for every state of the economy, for all commodities (Mirakhor and Bao 2013, 29). In such an economy, it is the budget constraint of the participants that determines how much of each contingent commodity at prices prevailing in the market they can buy. Because these commodities are contingent on future states, they are risky. Therefore, the budget constraint of individuals determines the risk-bearing ability of each market participant (Mirakhor and Bao 2013, 29). However, Arrow (1972, 127) admits that requiring such a market is unrealistic: One can work out the implications of this model. Clearly, the contingent commodities called for do not exist to the extent required, but the variety of securities available on modern markets serves as a partial substitute. In my own thinking, the model of general equilibrium under uncertainty is as much a normative ideal as an empirical description. It is the way the actual world differs from the criteria of the model which suggests social policy to improve the efficiency with which risk-bearing is allocated.

Since the ideal market is unrealistic, he instead suggests instruments that can be used as substitutes to the contingent markets in the Arrow-Debreu economy (Arrow 1972, 127). These substitute securities are referred to as Arrow Securities (Mirakhor and Bao 2013, 29), whose payoffs could be used to purchase commodities, would reduce the number of markets required while replicating the efficiency of risk allocation of complete contingent markets. Associated with complete markets are complete contracts. These are agreements contingent on all states of nature. In the real world, not all contracts can cover all future contingencies.

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Therefore, they are said to be incomplete contracts and may indicate inefficiencies in exchange. However, as suggested above, optimal contracts can be devised provided there is mutual trust between the parties to the contract. This could be a simple contract with provisions for modification of terms and conditions should contingencies necessitate change. A compelling case can be made that insofar as the financial instruments are Arrow Securities (their payoff is contingent on the “state of nature” – that is, dependent on the outcome, not fixed or predetermined – and represents the reward to risk sharing), this ideal system would share many characteristics of an ideal Islamic system (Mirakhor 1993). However, not all Arrow Securities would satisfy Islamic requirements, as some may well represent contingent debt contracts to deliver a fixed predetermined amount of money if a given state of world occurs. Thus, these may not represent an ownership claim, either. Shares of common stock of open corporations do meet these requirements. They are residual ownership claims and receive a return contingent on future outcomes; they are “proportionate claims on the payoffs of all future states” (Fama and Jensen 1983, 328). These payoffs are contingent on future outcomes. Stock markets that are well organized, regulated, and supervised are efficient from an economic point of view because they allocate risks according to the risk-bearing ability of the participants. In essence, this is the contribution of the Arrow- Debreu model of competitive equilibrium (1954; see also Arrow 1971), according to which, efficient risk sharing requires that the risks of the economy are allocated to market participants in accordance with their “respective degree of risk tolerance” (Hellwig 1998, 328–345).

However, the current market system is not a representation of the Arrow-Debreu world. This is not because the elegant model in the Arrow-Debreu world is unrealistic but because the current working mechanism of the system has diverged far away from what the Arrow-Debreu world envisages. The Arrow-Debreu economy is composed of contingent markets, which means the economy is based on risk-sharing contracts whereas the current financial system is dominated by the interest-based debt contracts which are based on the notion of risk shifting and risk transfer at best. The financialization process since 1980s is the most explicit reflection of this divergence between the Arrow-Debreu economy and the current financial system. What the Arrow-Debreu economy attempts to demonstrate is that “a decentralized economy motivated by self-interest and guided by price signals would be compatible with a coherent disposition of economic resources that could be regarded, in a welldefined sense, as superior to a large class of possible alternative dispositions” (Arrow and Hahn 1971, vi). On the other hand, as Evensky (1993, 204) writes, the Smithian story told by Arrow and Hahn – and they are representative of modern economists – is an abridged edition. The spring that motivates action in Smith’s story has been carried forward, but much of the rest of his tale has been forgotten. Unfortunately, this has become the standard treatment of the works of the great economists of the past. They are not read for the fullness of their vision; they are cited for the pieces we have inherited.

Lack of rule compliance (rules of institutional scaffolding) may be one of the missing elements of the Smithian story, “abstracting from them would be unlikely to change the outcome of the mathematical analysis of Arrow-Debreu and/or Arrow-Hahn” (Mirakhor and Bao 2013, 30). Arguably, another missing element of the Smithian

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story is the existence of ex-ante determined interest-based debt contracts. In spite of the fact that the notion of the rate of interest is considered to be one of the fundamental elements of the market economy, there hasn’t been a rigorous theoretical explanation in the literature on why there is ex-ante determined interest rate in an economy. As underlined by Mirakhor and Bao (2013, 30), [a]ll so-called theories of interest from the classical economists to contemporary finance theories explain interest rate as the price that brings demand for and supply of finance into equilibrium. This clearly implies that interest rates emerge only after demand and supply forces have interacted in the market and are not ex-ante prices. In fact, in some theoretical models there is no room for a fixed, ex-ante predetermined rate of interest. For example, introducing such a price into the Walras or Arrow-Debreu-Hahn models of general equilibrium (GE) leads to the collapse of the models as they become overdetermined. Tyler Cowen (1983), for instance, argued that Arrow-Debreu-Hahn models of general equilibrium (GE) cannot accommodate predetermined rates of interest. The argument is that since the prices of all commodities for present and future delivery are already explicitly included in the system of Arrow-Hahn-Debreu equations, there is no room for the imposition of a discount rate on the economy. The inclusion of interest rates is conducive to the over determination of the system of equations. Indeed, the prices of all goods and services under all states of nature are already described by the original set of equations. An overdetermined system is characterized by more equations than unknowns, and it either has no unique solutions, when some equations represent linear combinations of others, or it is inconsistent, leading to no solution at all. It is not clear how the interest rate should enter the system of equations, but when the system is not inconsistent, the rates of interest determined within the system should nevertheless be explained with reference to the relative prices and intertemporal price ratios.

What Mirakhor and Bao (2013, 30) accentuate is that the ex-ante rate of interest is incompatible with the notion of contingent claims given in the Arrow-Debreu economy. Incompatibility between interest rate and contingent securities is mostly overlooked even in the models developed after the Arroe-Debreu model, which assume there is a risk-free benchmark asset and interest rate of which, though it is called the rate of return on the risk-free asset, form a basis for all other rates of return in an economy. The rate of return on equity market is considered to depend on this benchmark rate. Even the basic theories of finance, such as the Capital Asset Pricing Model (CAPM) and the Modern Portfolio Theory (MPT), are based on the notion of risk-free rate of return. Mirakhor and Bao (2013, 30) highlights that: For all practical purposes, the assumption of a risk-free interest rate introduced an artificial floor into the pricing structure of the real sector of the economy and into all financial decisions. It can be argued that it is the existence of this exogenously imposed rate on the economy that transformed the Arrow-Debreu vision of a risk-sharing economy and finance. The resulting system became one focused on transferring or shifting risk rather than sharing it. Such a system needed strong regulation to limit the extent of both. Further developments in finance theory provided analytic rationale for strengthening interest rate-based debt financing as well as the emergence of aggressive deregulation of finance in developed economies.

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One of the further developments in finance theory that supported the rationale for the ex-ante rate of interest is the Modigliani-Miller Theorem, which assumes that the value of the firm is irrelevant to its capital structure, debt or equity. According to this theory, whether the firms prefer to incur higher debt rather than issuing more equity doesn’t affect its value. In other words, “the risk of additional debt would be shifted to other stakeholders” (Mirakhor and Bao 2013, 31). Another development in finance literature is the Efficient Market Hypothesis (EMH), which states that “it is impossible to beat the market because stock market efficiency causes existing share prices to immediately incorporate and reflect all relevant information” (Askari et al. 2012, 148). According to the EMH, the financial markets should be complete in order to reach at market efficiency. This could be achieved through innovation and financial engineering in financial markets. One result of the EMH in the regulatory and the policy arena is that the financialization process has been accelerated in the financial markets. Indeed, the spanning theory indicates that it is possible to generate infinite number of new financial instruments from one basic financial instrument (Maghrebi, Iqbal, and Mirakhor 2015, 44). These developments, “coupled with the high magnitude of leverage available from the money-credit creation process characteristic of a fractional reserve banking system, represented an explosive mix that reduced the vision of Adam Smith to the rubble of post-crisis 2007/2008” (Mirakhor and Bao 2013, 31).

3.6 Interest-Based Debt Contracts and Wealth Residual This section looks at the role of interest-based debt contracts in formation of the wealth residual. Before delving into the channels which leads to the wealth residual, we return back to Piketty’s fundamental inequality ðr > gÞ as a starting point. Piketty (2015d, 75) explains the fundamental inequality as follows: A central property of this large class of models is that, for a given structure of shocks, the longrun magnitude of wealth inequality will tend to be magnified if the gap r – g is higher. In other words, wealth inequality will converge towards a finite level. The shocks will ensure that there is always some degree of downward and upward wealth mobility, so that wealth inequality remains bounded in the long run. But this finite inequality level will be a steeply rising function of the gap r – g. Intuitively, a higher gap between r and g works as an amplifier mechanism for wealth inequality, for a given variance of other shocks. In other words: a higher gap between r and g facilitates a sustained level of wealth inequality that is higher and more persistent over time (i.e. a higher gap r – g leads both to higher inequality ad lower mobility). Technically, it can be shown that if shocks take a multiplicative form, then the inequality of wealth converges toward a distribution that has a Pareto shape for top wealth holders (which is approximately the form observed in real world distributions, and which corresponds to relatively fat upper tails and large concentration of wealth at the very top), and that the inverted Pareto coefficient (an indicator of top-end inequality) is a steeply rising function of the gap r – g.

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Theoretically, the interest rate should be in compliant with the rate of return to capital, which is then a reflection of the marginal productivity of capital. The theoretical nexus between the interest rates real sector activity through the marginal productivity of capital forms the basics of economic theory. For instance, see Ljungqvist and Sargent (2012, 7), and Wickens (2008, 56). A natural extension of this assumption is that rate of return to capital should be associated with the growth rate of the economy. In such a world, there should be no significant rents, especially inherent rents, in the system and the wealth grows in line with the growth rate of capital and economic activity. Assume that r (rate of return to wealth) is composed of two parts. One part is directly a function of the marginal product of capital, which we call as rr. Another part is the one which stems from the rents and is not directly a function of the marginal product of capital. Let’s call this as rw. In practice, the level of difference between long term rate of return to wealth and growth rate of the economy give a rough estimate of the degree of the second part (rw) of the rate of return to wealth. A new and comprehensive dataset for all major asset classes covering 16 advanced economies from 1870 to 2015 provides such a long-run data (Jordà et al. 2017, 6). Table 4 shows that rate of return to wealth is not only much higher than rate of real GDP growth but also the difference is higher in the post-1980 period compared to the post-1950 period. That the r and g difference is higher in the post-1980 period overlaps with the financialization era in the world. It should be noted that rate of return to equity outpaces the rate of return of all other components of wealth, including real estate, in the post-1950 period (Jordà et al. 2017, 47). Furthermore, equity and real estate holders are typically the rich, not the poor and the middle class. Table 4: Rate of Return to Wealth and GDP Growth in Selected Countries. Full sample

France Germany Japan Spain UK US

Post-

Post-

R

g

r

g

r

g

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

Notes: r stands for rate of return on wealth and g stands for real GDP growth rate. Average annual real returns. Real return on wealth is a weighted average of bonds, bills, equity and housing. The weights correspond to the shares of the respective asset in each country’s wealth portfolio. Period coverage differs across countries. Source: Jordà et al. (2017, 47).

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We decompose the interest-based debt contracts and wealth residual nexus into two interrelated channels. The first mechanism is called “volume channel”, which covers generation of wealth inequality due to access to credit, credit expansion/contraction and level of debt. The second mechanism is called “price channel”, which covers repercussions of interest rate mechanism and capital gains through asset price booms. These two mechanisms, which are then determined by the interest-based contracts, amplify wealth inequality through wealth residual.25

3.6.1 Volume Channel The volume channel can be defined as the change in the wealth residual due to differentiated access to debt contracts among heterogeneous economic agents stemming from their initial level of wealth. In other words, the volume channel is access to debt-contracts at more advantegous terms, as a source of wealth inequality, due to higher initial volume of wealth. There are arguably two main pillars of the volume channel, namely, credit constraints and collateral, and the level of debt, both of these pillars are highly interrelated though. Anyway, looking at these two pillars separately gives a more comprehensible picture of the volume channel and wealth inequality nexus. 3.6.1.1 Credit Constraints and Collateral In standard textbook world, the contracts are complete and fully enforceable. In such a world, income and wealth solely have an effect on the budget constraint of the economic agents. Hence, all agents in such an economy are exposed to the same contractual opportunities (Bowles 2012, 37). That is, both the poor and the rich are eligible for doing transactions under the same conditions. However, incomplete and unenforceable contracts are the norm in the real world, not exceptions as apposed to the standard textbook assumptions.26 In such a world, lacking in wealth not only constrains the size of the transactions but also directly affects whether an economic agent is able to take part in specific class of contracts. The initial level of wealth also determines whether the terms of the contracts are favorable to the parties.

25 It should be underlined here once again that these two channels also affect the non-residual part of the wealth inequality, such as savings and income inequality. For instance, interest-based contracts can affect capital income, which then affect the income inequality. Fully cognizant of these indirect effects, we only focus on the wealth residual, which is the focus of this chapter. 26 Wang (2013, 63) explains complete contract as follows: “complete contract is a contract in which the income-sharing rule is capable of handling all possible contingencies so that additional mechanism are unnecessary.”

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In the incomplete and unenforceable contracts world, credit constrains amplify wealth inequality through several channels (Bowles 2012, chap. 2). Firstly, credit constraints may prevent high-quality projects from being implemented. Secondly, credit-constrained agents typically have lower expected returns in their projects. Thirdly, access to credit increases risk-taking by the economic agents with higher expected returns. Fourthly, credit-constraints are related to economic agents’ degree of impatience, which affect saving and investment decisions. But these channels are common to all types of access to finance impediments, including equity finance. A pertinent question is then why interest-based debt contracts (=credit) are particularly more relevant to wealth inequality compared to other means of finance. One feature of the interest-based debt contracts is that monitoring cost is typically lower from the side of the creditors. As explained by Buiter and Rahbari (2015, 144): The issue of monitoring costs is intrinsically linked to the issue of asymmetric information between entrepreneurs and borrowers. The seminal work in this area is Townsend (1978). In his setup, information is asymmetric, as only the entrepreneur observes the state of the world (the success of his investment project) as a matter of course and provides the investor with a report. The outside investor has to pay a (fixed) monitoring cost to learn the state (in Townsend, 1978, this monitoring cost is deterministic, given a state), which is why this manifestation of asymmetric information is referred to as ‘costly state verification.’ Monitoring here can be thought of as the time, resource and opportunity cost of observing the actions of the firm, its financial position as well as the environment within which the firm operates. Monitoring costs can indeed be high as effective monitoring may require financial as well as operational expertise. But monitoring costs also include the costs associated with bankruptcy. Townsend (1978) showed that in such a setting a contract that features constant, state-independent payments from the entrepreneur to the investor and no monitoring in the ‘good states’ and a state-contingent payoff (equal to the value of the project minus the cost of monitoring) in the ‘bad states’ is ‘optimal’, defined as maximising the payoff or utility of the entrepreneur, subject to satisfying a reservation (or participation) constraint for the investor.

Even though debt contracts economize in monitoring costs in theory, this is not enough. To reduce moral hazard, collateral should also be pledged in order to induce borrowers to increase their efforts. Once pledging their assets, the borrowers are more prone to costly actions and efforts to increase the likelihood of repayment. As collateral has a direct positive effect on the repayment of the loans, it is a key determinant of access to credit (Serra-Garcia 2010, 2). Without collateral, it is very possible that there is credit rationing. But the poor usually doesn’t have the collateral even if s/he has a very good income-generating project. Reversely, the rich can easily have access to the credit even if s/he has a bad project. This is indicated by Bowles (Bowles 2012, 37): The most obvious reason why an individual’s amount of wealth influences the kinds of contract she can engage is that only those with sufficient wealth can undertake projects on their own account, that is, without borrowing. And among those who do borrow, those with more wealth

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borrow on better terms. This is because greater wealth on the part of the agent allows contracts which more closely align the objectives of principal and agent. This is the case, for example, when the borrower has sufficient wealth to post collateral or put her own equity in a project, and therefore has greater incentives to supply effort, to adopt the more prudent risk levels preferred by the lender (the principal), to reveal information to the principal, and to act in other ways that advance the principal’s interests but that cannot be secured in a contract.

This means the rich can much more easily make profitable investments compared to the poor even at the cost of optimal allocation of resources. In sum, it is existence of collateral in the interest-based debt contracts that pave the way for wealth inequality over time. One area in which collateral is a fundamental driver of inequality is real estate. As home purchasing is usually the biggest asset for the households, those with high wealth can purchase real estate with more favorable conditions. Once, real estate becomes an investment class with high returns, the wealth begets wealth through collateral by distorting the wealth distribution. Moreover, less wealthy can purchase their homes in an environment with continuously increasing prices through bank lending but having high debt-low collateral combination is very dangerous mix as shown many prominent authors, such as Mian and Sufi (2014), Hintermainer and Koeniger (2015, 6). 3.6.1.2 Level of Debt The Chapter 3.6.1.1 emphasizes that limited access to credit through collateral mechanism is a driver for wealth inequality. On the other hand, over-access to credit, which means accumulation of debt, is also a generator of wealth inequality.27 Buiter and Rahbari (2015, 151) explains that [t]he academic literature has emphasized that debt, particularly when it is relatively large, can cause either underinvestment (the debt overhang problem of Myers (1977)) or excessive risktaking. For the former (debt overhang), the presence of existing (relatively large) debt means an entrepreneur faced with a capital call for an investment project or considering a new (positive NPV) investment project, may refuse to invest because the capital injection (by reducing the probability of default) would also lead to a wealth transfer to the existing lenders. For the latter, the presence of debt coupled with limited liability aggravates the issue that equity has a convex payoff function: the equity owner is able to capture all of the upside (after costs, including debt service), but her losses are limited to losing her equity investment. Debt, particularly if it is large enough, can, therefore, systematically induce adverse behavioral distortions in the decisions of individual households and businesses.

27 It should be noted here that when it comes to existence of interest-based debt contracts both limited access and over-access are problems. It means not the quantity or pervasiveness of the debt contracts but the debt contracts themselves are the source of the problem.

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One of the most important empirical regularities in macroeconomics is that household debt expansion is closely linked to severity of subsequent recessions and decline in real GDP growth (Mian and Sufi 2018, 32). Such recessions and decline in GDP growth have distributional consequences. A big run-up of household debt leads to a decline in household spending (with the bigger the run-up in debt, the bigger the decline in household spending) during a recession because of defaults and loss of wealth. Households cannot service the money they have borrowed from banks resulting in losses for banks, leading to job losses. At the same time, businesses incur losses and run into financial difficulties as household demand declines and they lay off more workers. The financial problems of businesses reverberate further on banks and business activity declines even more followed by a banking crisis with credit freezing up. Moreover, the monetary and fiscal authorities invariably bail out lenders. As a result, severe financial crises and recessions exacerbate wealth inequalities by exposing borrowers (the less fortunate) and protecting lenders (the fortunate). Because of the deteriorating distributional wealth effect of debt contracts and job losses, it is the expenditures of poorer households that get specially affected because they have a higher marginal propensity to consume from housing wealth. This impact is further corroborated by the fact that the dot-com bubble in the US did not lead to the same decline in spending, job losses, and recession. There is fraud in both debt and equity markets but it is more prevalent in debt markets and because lenders feel that they have a senior claim in debt contracts and they don’t think fraud is as important. The proper policy response should have been to restructure household debt, given levered losses, and not to bail out bank shareholders. Use of credit has other ominous implications. Credit can be issued to finance consumption, and hence may rapidly deplete savings and investment, whereas equity finance finances investments. The depletion of savings could be significant if credit finances large fiscal deficits. Hence, credit is no longer directly related to the productive base as in the equity based system, and the income stream from credit is no longer secured by real output as shown for the equity system. Credit expands through the credit multiplier, leading to increased default risk, whereas equity in the equity-based system cannot expand more than real savings. And with securitization, credit can expand theoretically to an infinite degree as debt can be packaged and sold with the proceeds used to finance new loans.

3.6.2 Price Channel Another channel is called the price channel. It can be defined as the change in wealth residual due to capital gains resulting from interest rate and leverage. There are

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arguably two main pillars of the price channel, namely, leverage and asset booms. The price channel is particularly important in the era of low interest rates in the aftermath of the Global Financial Crisis as a reflection of seeking for return by the rich and institutional investors. It should be noted here that, arguably, existence of the interest rate mechanism is seemingly more relevant to the change in the wealth residual compared to the volume channel.

3.6.2.1 Leverage The very nature of interest-based debt contracts is leverage, in which debt claims can be multiple of current output (Bezemer 2011, 6). Let’s start with a staggering observation to understand size of the leverage in advanced economies. For the last several decades, leverage, which is defined as credit to GDP ratio, has tripled in advanced economies. Specifically, in the two decades before the GFC, credit growth rate was around twice of the nominal GDP growth rate (Turner 2016, 7). The same pattern now can be observed in the developing and emerging economies, such as China. Nearly two units of credit growth in order to secure only one unit of nominal GDP growth doesn’t seem to be an efficient mix of finance and real sector growth. As highlighted by Turner (2016, 7), credit growth appears necessary to drive the economies forward. But if that is really true, we face a severe dilemma. We seem to need credit to grow faster than GDP to keep economies growing at a reasonable rate, but that leads inevitably to crisis, debt overhang, and post-crisis recession. We seem condemned to instability in an economy incapable of balanced growth with stable leverage.

The main role of leverage in increasing inequalities is to allow the rich to access to credit in large quantity in a world of volatile asset prices (Turner 2016, 121). In such a world, collateral, leverage and asset booms are the perfect complements for the benefit of the rich. It should also be noted that money creation as a source of leverage and lending not for investment as a repercussion of leverage increase inequalities further and further. Firstly, money creation is much different from what is explained in the standard textbook model, in which starting point of the money creation is the central bank, which changes its monetary base through changes in reserves. Increase in the monetary base also increases reserves of the banking sector. If the banking sector doesn’t want to hold excess reserves, then they want to give more loans to get rid of their excess reserves. Each new loan account produces an equivalent deposit account. As loans are made, additional deposits are created as loans are re-deposited within the banking system. In the end of the process, deposits are multiplied by the same amount as given by money multiplier. Because the money supply is the sum of deposits and cash balances, the money supply is also increased by the same amount as the change in the deposits. In the real world, the banking sector is not simply an

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intermediary that transfers the savings into the loans, indeed, the mechanism does not run from the savings-deposits-loans but from the loans to the deposits. In fact, the banking system creates money concurrently with debt. In the money creation process, when an individual bank extends a loan to a customer, it simultaneously creates an equivalent deposit in the accounting entries. This means credit and money are created concurrently so the direction of causality is from loans to deposits, not vice versa (Mahmud, Yamaguchi, and Yülek 2017, 47). Secondly, most of the credit created by the banking sector flows into consumption related activities and purchase of already existing assets as opposed to the finance theory explanations which emphasize credit is crucial for investment and optimal allocation of capital (Turner 2016, 3–4). The most important avenue for leveraged flows is the real estate. Existence of leverage amplifies demand for real estate as an asset class, which then increases its price. Easy access to credit by the rich allows them to invest in real estate. However, the poor suffers from ever increasing prices. This is an important effect of leverage on increasing wealth inequality when the prices are rising. There is another effect of leverage on wealth inequality. After a threshold level, borrowers may suddenly realize that they are overleveraged and then cut their consumption and/or investment in order to secure their solvency. Turner (2016, 77) explains the process as follows: [W]hen house prices fall, borrowers suffer a fall in net worth, and the higher their leverage is, the greater the percentage loss they experience. With a 90% loan to value mortgage, a 5% fall in house prices wipes out 50% of the household’s equity in their house. Faced with falling net worth, many households cut consumption. This follows in part from a simple ‘wealth effect’: when people feel less wealthy, they tend to consume less and save more. But it is amplified if debtors are worried that the fall in their net worth could go so far as to make them insolvent, facing them with the additional costs of bankruptcy, repossession, and the sale of their home at a fire sale price. Fear that default might make it impossible to borrow in the future (except at exorbitant rates) may also be an important concern. So when house prices fall, highly leveraged households focus strongly on reducing their debt levels – the household equivalent of the Japanese companies that Richard Koo analyzed – and their reduced expenditure depresses demand in the economy. But this reduction is not offset by increased expenditure on the part of net creditors elsewhere in the economy: indeed, if asset house prices mean falling prices for credit securities, or concerns about bank solvency, net creditors may themselves reduce expenditure.

The whole process simply means that the rich saves its net worth during a downturn whereas the poor disproportionally loses its net worth.

3.6.2.2 Asset Prices Speculation and ensuing bubbles, both supported by monetary policy and excessive supply of credit, are inherent features of debt-based and financialized economic system. Indeed, financial system can’t easily make profits from the real sector without speculation and high price volatility. The unlimited expansion of debt, stemming from leverage and money creation process, leads to upward pressure on prices, particularly

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on asset prices such as stocks, housing, and commodities. The demand for goods and assets are financed by abundant credit, not from income, with the result that pressure built up in asset prices. Indeed, not only the speculators but also rational individuals participate in asset price bubbles if they expect rising asset prices (Askari and Mirakhor 2015, 13). Disconnection between the financial and the real sector, due to debt and leverage, validates expectations of ever-increasing asset prices. This is a vicious cycle. The relevance of asset prices to wealth inequality is that heterogeneous income and wealth groups benefit from the asset booms unequally. As highlighted by Turner (2016, 122), “superior access to credit in volatile economic circumstances has often been crucial to the accumulation of large fortunes”. There are typically two mechanisms that give rise to accumulation of fortunes related to asset prices. Firstly, asset portfolios are different among the income and wealth groups. Asset portfolios are more diversified and holdings of equities in total portfolios are much higher in this at the top of the wealth distribution. On the other hand, the main portfolio items for the lower segments of the wealth distribution are real estate and deposits (Domanski, Zabai, and Scatigna 2016, 53). Secondly, differences in access to credit, given differed collateral requirements and leverage, give rise to differentiated levels of indebtedness and portfolios between the rich and the others. Leverage is much lower for the top of the wealth distribution, whereas the lower segments of the wealth distribution are usually indebted for their real estate purchases.

9 8 7

(%)

6 5 4 3 2 1 0 Bills

Bonds Full sample (1870–2015)

Equity Post-1950

Figure 15: Global Real Rates of Return. Note: Arithmetic average real returns per annum, unweighted, 16 countries. Source: Own graph based on Table 3 in Jordà et al. (2017, 13).

Housing

3.6 Interest-Based Debt Contracts and Wealth Residual

77

Since equities and real estate are the two most important assets which are prone to speculation and high price volatility and form the bulk of the financial portfolios at the top of the wealth distribution, we focus on these two assets in the rest of this section. Let’s start with an observation to give one aspect of why equities and real estate increase inequalities. Figure 15 show average annual asset returns for 16 countries over 150 years, a first such dataset compiled by Jordà et al. (2017, 13). The figure clearly show that real estate and equity, which are usually owned by the top segment of the wealth distribution, have been the best long-run asset classes for investment over the course of modern history. As we assume the main financial asset of the poor, other than real estate, is deposits and we expect deposit rates don’t much converge from bonds, there is a clear rate of return heterogeneity among the different segments of the wealth distribution. Stock market is typically a risky investment but more risk means, at least in theory, higher expected return. Risk-seeking individuals, who are usually at the top of the wealth distribution, hold disproportionally higher share of their portfolios in the form of equity. To give an example, fifth quintile in the wealth distribution in the US has 15.1% of their financial assets in equities. This number is only 0.6% for the second quintile of the wealth distribution deposits (Domanski, Zabai, and Scatigna 2016, 54). This indicates that (Gomez 2017, 2), in periods when stocks enjoy large realized returns, investors at the top of the wealth distribution gain more than the rest, i.e. wealth inequality increases. In turn, as a larger share of wealth falls into the hands of risk-tolerant households, the aggregate demand for risk increases, which lowers risk premia and pushes up asset prices, i.e. higher wealth inequality predicts lower future returns.

Historically, capital gains in real estate stemming from asset booms are the main element of the price channel. Although equities have disproportionally smaller share at the middle and low segments of the wealth distribution compared to the top segment, this is not exactly true for the real estate, which usually have a higher share in the middle and low segment’s portfolio. The difference comes from the net worth position of the rich and the rest. Real estate is typically the most important asset for the households. The main mode of finance for real estate is bank credit. Indeed, in the last half century, banks’ loan portfolio has moved from productive and investment related activities to real estate (Turner 2016, 7–8). Furthermore, bulk of the credit for real estate goes to purchase of real estate assets that already exist. Since supply of land is fixed, more demand means higher prices. Once real estate prices increase due to the asset boom, it produces a vicious cycle as rising prices gives further impetus for buying more, which then gives rise to higher prices. This is formation of a bubble (Bacha and Mirakhor 2013, 263). But such a bubble has asymmetric consequences for the rich and the rest. The rich certainly benefits from higher prices. The rich also benefits from easier access to credit in favorable terms as it allows purchasing more real estate when the prices are rising. On the other hand,

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ever increasing real estate prices means resorting to more bank credit, higher collateral and lower net worth for the rest of the society. The rich gains disproportionally while the asset prices are increasing. Reversely, it is the highly-leveraged poor who have big losses while the asset prices are falling. This is because debt magnifies the fall in asset prices because of foreclosures and concentrating losses on the indebted. Thus, depending on the extent of the asset price collapse, the borrower may be forced to absorb most, if not all, of the losses, while the lenders equity (the rich) is senior and may be totally protected or bailed out by the government. As a result, severe financial crises and recessions exacerbate wealth inequality by exposing borrowers (those whose capital ownership is small) and protecting lenders (the fortunate ones with positive net capital) (Askari and Mirakhor 2015, 31).

4 Risk-Sharing Asset-Based Redistribution 4.1 Introduction Chapter 4 introduces the idea of risk-sharing asset-based redistribution as a potentially effective mechanism to deal with the wealth inequality problem. Chapter 4.2 starts with introducing the concepts of risk and risk-sharing. Chapter 4.3 gives an overview of the redistribution policies as a response to the second inequality crisis. The section also explains why and how risk-sharing asset-based redistribution can be an effective solution to the wealth inequality problem. Chapter 4.4 gives an overview of the concept of redistribution in Islam. Instead of reviewing the whole redistribution policy literature in Chapter 4, the specific focus is given to the role of risk-sharing contracts in asset-based redistribution. There are two main reasons for focusing exclusively on the risk-sharing contracts within the context of wealth inequality. Firstly, as we will show in the rest of the chapter, most of the redistribution policies out of the scope of the risk-sharing frameworks do not provide a viable answer to the increasing wealth inequality. Indeed, the redistribution policies prevalent in the literature are more effective in dealing with the income inequality problem. Wealth is different from income in the sense that it is a command over the use of resources. Hence, implementing policies based on leaving the asset concentration intact dilute the effectiveness of the redistribution policies in reducing wealth inequality. Secondly, redistribution proposals based on risk-sharing framework in the conventional economics have counterparts in Islamic economics. For instance, Piketty’s (2014, chap. 15) global and coordinated wealth tax proposal to cope with the increasing wealth inequality problem resembles the notion of zakah in Islam. As the concept of risk-sharing is one of the core elements of Islamic economics and finance, focusing on risksharing proposals in the conventional literature in the second inequality crisis era helps us understand prospective consequences of implementing risk-sharing redistribution policies within the context of Islamic economics.

4.2 The Concept of Risk and Risk-Sharing Risk is an indispensable part of human life. As stated by David Bartholomew (2008, 230): It could be plausibly argued that risk is a necessary ingredient for full human development. It provides the richness and diversity of experience necessary to develop our skills and personalities. This does not mean that the risks are always welcome at the time. We can all look back and identify occasions of great uncertainty which we would have gladly avoided but which are seen, in retrospect, to have contributed to our development.

https://doi.org/10.1515/9783110586664-004

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He then continues that: We value our free will above almost everything; our human dignity depends upon it and it is that which sets us apart from the rest of creation. But if we as individuals are free, then so is everyone else, and that means the risks created by their behaviour, foolish or otherwise, are unavoidable. To forego risk is to forego freedom; risk is the price we pay for our freedom. (Bartholomew 2008, 240)

While risk inherently encompasses a possibility of loss, whether small or big, it also contains opportunity, realization of which then is a propulsive power for development and technological progress. Understanding the conceptual and semantic differences among the risk, uncertainty and ambiguity should be the starting point of the concept of risk-sharing. Indeed, a good understanding of risk-sharing and its connection with Islamic finance require separation of the concept of risk from the concepts of uncertainty and ambiguity. The mainstream economics, as discussed in Chapter 2, embraces rationality as the essence of human behavior. As a natural extension of rationality, economic structures are assumed to be stationary over time. In other words, as reflected in the Rational Expectations Hypothesis (REH), economic agents’ expectations are fulfilled on the average. This assumption significantly simplifies economic analysis in the sense that anticipation of the future is only constrained by random errors with pre-specified distributions (Erbas and Mirakhor 2013, 99). As highlighted by Erbas and Mirakhor (2013, 100), economic agents, also anticipate the impact on economic variables of changes in the economic environment and structure, presumably also subject to other error terms. The implication is that there are errors on errors (probabilities on probabilities) in forming expectations. For the dynamic theory to be valid in essence, complex randomness of economic prospects over time needs to be reducible to simple error terms at the initial point in time. This is the equivalent of the reduction axiom of expected utility theory.

In other words, rationality requires an ergodic, stable and stationary, stochastic world. In case of non-ergodic world, the future is ontologically uncertain; realization of systematic risks can occur but can never be predicted in advance (Davidson 2009, 328). The formalized distinction between the risk and the uncertainty has arguably started with Frank Knight (1921, 233) who states that risk applies to situations in which the odds can be measured while the outcome is not known. He adds that the uncertainty applies to situations in which even the odds are not available. According to him, “there is a fundamental distinction between the reward for taking a known risk and that for assuming a risk whose value itself is not known. It is so fundamental, indeed, that, as we shall see, a known risk will not lead to any reward or special payment at all” (Knight 1921, 43). Regarding the implications of the risk, the distinction is very important since it is the risk with known probability distributions to be insured. On the contrary, uncertainty is not subject to insurance because

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there is no known probability distribution with prospective payoffs to be insured. As asserted by Askari and Mirakhor (2014, 348) developments in probability theory over the last century have led to a semantic alteration in the sense that uncertainty has come to mean what Knight referred to as risk and the uncertainty has become the term “ambiguity”, which ultimately stems from “impossibility of cognitive completeness”, which is then a reflection of missing information (Askari and Mirakhor 2014, 348). As a direct implication of the cognitive completeness, ambiguity renders decision-making quite difficult (Erbas and Mirakhor 2013, 41). Patience and acquiring more knowledge are the two strategies to stand against the ambiguity. Behavioral economics and bounded rationality pioneered by Daniel Kahneman and Amos Tversky have contributed much to our understanding of behavior under uncertainty (see Kahneman 2003; Kahneman and Tversky 1979). Kahneman (2003, 702–4) explains that through Framing Effects and the Prospect Theory economic agents respond to the risk different from what the rationality assumes. The Framing Effects state that the ambiguity is suppressed in perception in the sense that same situations result in different outcomes due to the differences in perceptions among economic agents (Kahneman 2003, 702). It means the economic agents respond differently to the same situations when they frame the situation differently as a reflection of their different perceptions. Prospect Theory, on the other hand, states that the abrupt shift from risk aversion to risk seeking can be explained by change in the attitudes toward gains and losses with respect to a reference point, not by a utility function alone (Kahneman 2003, 704). This reference point is the status quo, which is called “endowment effects” according to which people can divert from the status quo, rather than holding it, in case the prospect of gaining overcomes that of losing with respect to the status quo (Kahneman, Knetsch, and Thaler 1991, 198). As stressed in the Framing Effect, the way prospects are framed leads to different choices of the economic agents (Tversky and Kahneman 1981, 453). The Framing Effects and the Prospect Theory give a guidelines to address the risk and risk-sharing in public policy. The guidelines are juxtaposed by Askari and Mirakhor (2014, 349) as follows: (i) when it comes to a choice between certain and uncertain gains, people generally prefer certainty even if the prospect of uncertain gains is objectively much larger than certain gains; (ii) in choosing between certain and uncertain losses, people generally prefer uncertain alternatives even if the prospective loss is larger than the certainty case; and (iii) people generally overestimate small short-term risks and underestimate long-term risks. The risks can be classified under two main rubrics: Systematic and idiosyncratic risks (Rizvi, Bacha, and Mirakhor 2016, 69). The former one is defined as the risk encountered by the whole system or market. The systematic risk is also called as undiversifiable risk because it is not possible to diversify or to transfer the risk among the entities (members, agents, sectors, etc.) of the system. It is therefore uninsurable by nature. Resilience to realization of such a kind of risk is a function

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of the risk management at the macro level, as well as, institutional and policy infrastructure of the society or markets. The latter type of risk is endemic to specific segments of the society, assets or markets such as supply shock to a specific sector, bankruptcy of individual banks or loss of job for people. While the idiosyncratic risks can play havoc with the economic agents/entities who/which are exposed to the risk, it is diversifiable as the inflicted parties can be compensated by the uninflected parties. One important instance of the idiosyncratic risk is the high dependence of consumption to income, which can be effectively mitigated by the risk-sharing instruments, such as capital markets. Apart from classification of the risks with respect to their effects on systemwide (systematic) or individual (idiosyncratic) level, how the risk is distributed among the parties of the transactions brings about another classification. Ideally, the main role of the financial system is to facilitate the financing of the economic activity as the grease of the economic system, as well as, to mediate between the economic agents who have surplus of funds and the ones who need financing. Fulfilling the aforementioned roles, financial transactions can, and usually do, generate risks among the parties involved. These risks can basically be transferred, shifted or shared. The risk-transfer is an arrangement among the intermediary, depositor and borrower in which the risk is transferred from the surplus unit to the deficit unit (Bacha and Mirakhor 2013, 255). The main mode of risk management of the banks has been risk-transfer by purely channeling savings from the surplus units to finance the investments of the deficit units. “Thus, risk transfer in principle amounts to the “sale” of risk. Risk transfer takes place when the party that transfers a given risk has faced the risk to begin with, even if only for a short period of time” (Abdullah 2013, 282). On the other hand, risk-shifting happens when risk of a financial transaction is shifted to a third party that directly has no stake in the transaction (Mirakhor and Bao 2013, 38). Worse than having no stake, risk is shifted without knowledge and consent of the ultimate risk bearer. A prominent incidence of the risk-sifting is excessive risks taken by financial institutions with the expectation that none of them but the taxpayers bear the losses through bail outs. This is what has happened in the Global Financial Crisis of 2007/2008 and its aftermath. Risk-sharing basically can be defined as a contractual or societal arrangement whereby the outcome of a random event is borne collectively by a group of economic agents involved in a contract, a transaction or a community (Askari et al. 2012, 70). Parties in the contractual or social arrangement undertake risk-sharing with the expectation that involvement of many participants, resources and skills result in lesser individual risk, as well as, greater output. Instances of risk-sharing arrangements range from purchasing stocks of domestic or foreign business entities, participating in cooperatives to insuring against idiosyncratic risks. One famous example of the consequence of risk-sharing on economic development is

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role of equity-based commenda on development of trade and business in Medieval Europe.28 The Arrow-Debreu-Hahn general equilibrium models, which are discussed in Chapter 3.5.3, are the risk-sharing conceptualizations in which contingent claims are shared concomitant to the risk bearing ability of the economic agents. Hence, risk-sharing contracts have the advantage of working through and within market forces and not against them (Bacha and Mirakhor 2013, 270). Moreover, Mirakhor and Bao (2013, 54) highlight that equity share claims represent first-best instruments of risk sharing and satisfy characteristics required of Arrow Securities. It would appear that had the financial markets in industrial countries developed their financial sector along the lines suggested by the Arrow-DebreuHahn model, they could have had much more efficient risk sharing, and perhaps avoided the crises that have plagued the conventional financial system.

Within the context of economics of inequality, the risk-sharing mechanisms provide shared prosperity through several channels. Maybe the most important contribution of the risk-sharing mechanisms is its role in mitigating the pro-cyclicality in financial system. This contribution is pertinently related to the inequality-financial crises nexus explained in the previous section. In theory, one of the main roles of the financial system is to mitigate shocks emanating from fluctuations in economic cycle. In effect, such a system produces counter-cyclicality between the financial and economic cycles. However, the reverse (pro-cyclicality) is the norm and intrinsic to the financial system mostly due to the co-movements between economic activity and credit growth, as well as, liquidity and expectation shocks to the banking system (Rochet 2008, 96). As underlined by Mian (2013, sec. 2) the fundamental driver of financial recessions is a failure of risk-sharing. He also argues that the workhorse macroeconomic models assume that households can shield themselves against asset price shocks, this is proven false in the real-world data, though (Mian 2013, 1). Against the debt-based system, risk-sharing system render the financial system counter-cycle due to its inherent nature that the returns depend on contingent realizations (Askari et al. 2010, 67). In this sense, risk-sharing can overcome the equitable economic growth and financial stability trade-off of the debt-based system. Another contribution of risk-sharing is to secure allocative efficiency with equitable growth. As highlighted in Chapter 2.5, the mindset in the post-GFC era has changed from pursuing efficiency to automatically end up with equity to need for securing allocative efficiently and equity together for long-term growth. But this is difficult to achieve in the debt-based financial system. In theory, the economy-wide price in the economic system (interest rate) should reflect the return of capital, which is marginal productivity of capital, which should be then tied to the economic growth rate. As the core idea of

28 Commenda is an equity-based contract form of maritime ventures in medieval Italy. Commenda is the ancestor of the modern day limited liability corporation in Europe (Brouwer 2016, 151).

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Piketty’s (2014, 25) book, the fundamental inequality ðr > gÞ is the norm in the capitalist system and this inequality is the main cause of increasing wealth inequality. It can then be argued that the ex-ante interest rate pre-determined in the financial system is the main destabilizing force, which diverges from the return to capital in the real economy, determined in the ex-post. As opposed to this, risk-sharing system can be panacea to the fundamental inequality as the return on contingent claims are determined, by nature, on ex-post and depends on the observed growth rate of the economy, such that ðr = f ð gÞÞ. As expressed by Maghrebi and Mirakhor (2015, 106), since the payoffs are contingent on the realization of a particular state of nature, the realized return on real investment is known only on ex post basis. The growth rate can be positive or negative depending on the realization of favorable or unfavorable states of nature. This implies that capital is not allowed to increase irrespective of growth rates, and that it is bound to decrease with negative growth. The systematic risks entailed by economic activities are thus shared by investors in capital markets insofar that equity markets, rather than bond markets, are concerned.

4.3 Risk-Sharing Asset-Based Redistribution Given the repercussions of the so called second inequality crisis (see Chapter 2.5) and role of the risk-sharing mechanisms in providing more effective solution to the increasing wealth inequality in the post-GFC era, this section discusses how risk-sharing based redistribution policies could be implemented in the policy arena. In this regard, we give an overview of the redistribution policies discussed in the post-GFC era to mitigate the wealth inequality and then specifically focus on the asset-based redistribution policies as an alternative to the redistribution policies proposed in the literature. The literature is already cognizant of the need for implementing appropriate redistribution tools to fight against the ever-increasing wealth inequality problem. Fernholz and Fernholz (2014, 252) argue that the distribution of wealth grows increasingly right-skewed until the distribution reaches at its absolute inequality level (one individual or household owns everything). This outcome occurs even if the economic agents in the economy have identical opportunities, preferences and abilities. As a result, implementation of the redistribution policies is not an option but a necessity to mitigate the increasing wealth inequality in the age of the inequality crisis. Alvaredo et al. (2017, 8), based on the World Wealth and Income Database (WID.world), conclude that steady-state wealth inequality depends on the (i) saving rates, (ii) labor income, (iii) rates of return to wealth, and (iv) income and wealth taxes. These determinants are intimately linked to the redistribution. In addition, simulations in their paper indicate that even small changes in the structural parameters can have big effects on the steady-state level of wealth inequality. In the postGFC literature, the redistribution policies targeting at the determinants of the steady-

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state wealth inequality juxtaposed above fall into two categories: (i) income-based redistribution, which aims at mitigating income inequality as an important driver of the wealth inequality and (ii) asset-based redistribution, which aims at redistribution of wealth by changing contractual framework of economic exchange. There are basically two types of income-based redistribution, namely, hard and soft. Proposals under the income-based redistribution involve hard income-based redistribution such as the taxing income and wealth (including inherited wealth) at high rates. According to the OECD (2017, 4) less distributive tax and benefits system is one of the important causes of increasing inequalities. Apart from transformation in the welfare state, an important driver for the less redistributive tax system is that “increased levels of capital mobility have led to certain reductions in statutory income tax rates and in some cases to a greater reliance on less mobile tax bases, which has reduced the progressivity of tax systems and contributed to increased inequalities” (OECD 2017, 28). A recent and controversial, yet important, response to the linkages among mobility of capital, less redistributive policies and increasing wealth inequality comes from Piketty’s proposal of coordinated progressive wealth tax at the global level (Piketty 2014, chap. 15, 2015c, sec. III). Piketty proposes a tax rate of 1% on wealth between 1 and 5 million Euros, 2% above 5 million Euros, 5% on additional top bracket over 1 billion Euros, an additional 0.1% on all wealth below 200,000 Euros and 0.5% on wealth between 200,000 and 1 million Euros (Piketty 2014, 517). Political viability and coordination problems, as well as, compliance by the rich are the main impediments of implementing such a proposal, though. It should be noted here that zakah, as one of the five pillars of Islam, provides a similar but more powerful alternative to Piketty’s wealth tax proposal in mitigating the wealth inequality problem. Basically, zakah is subject to 2.5% of a Muslim’s wealth as long as her/his wealth is above nisab.29 The rate of zakah is, indeed, quite close to the optimal rate of wealth tax proposed by Piketty. However, zakah has an important advantage over the wealth tax (it should be noted here that zakah is not a tax): It is an injunction is Islam so that rule-compliance ensures the implementation issues of Piketty’s proposal (political viability, global coordination and compliance) are lessened. We further discuss the role of zakah in detail in Chapter 4.4. There are also “soft” income-based redistribution proposals such as the shared prosperity (inclusive growth) proposal led by the World Bank. Shared prosperity is measured as the growth in the average income/consumption of the bottom 40% in the income scale (World Bank 2016, 6). Soft income-based redistribution proposals take the current distribution of income and wealth as given and focus on attenuating the consequences of market operations by distributing the additional income more equally through public investment in health, education, infrastructure, financial inclusion, pro-poor transfers and wage policies to reduce the impact of wealth concentration.

29 Nisab is the minimum amount of wealth over which a Muslim is obliged to give zakah.

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Since wealth has command over use of resources, leaving current asset concentration intact means that market-determined rewards going to the concentrated wealth will be highly unequal thus reinforcing rather than weakening inequality. Furthermore, the income-based redistributions have perverse incentive effects such as less participation in labor markets (OECD 2017, 53) and distorting investment decisions in education (Seshadri and Yuki 2004, 1434). It should also be noted here that the underlying theoretical underpinning of this approach, which assumes that increasing the income of the poor through income redistribution will stimulate aggregate demand, has little empirical support (Bowles and Boyer 1995, 143). Overlooking current asset inequality, income-based redistribution proposals also leave the underlying governance structure with all its inefficiencies stemming from incomplete contracts, such as coordination failures, intact. The solutions available are change in the economic rules of the game (Stiglitz 2015f, 5), changing the underlying property right claims such as the asset-based approach Bowles (2012, 18) and, risk-sharing approach (Askari et al. 2012, 51). The main feature of the asset-based redistribution is the change in contractual framework of economic exchange rather than implementing hard redistribution tools. Bowles (2012, 6) argues that highly unequal distribution of assets hinder implementation of productivity-enhancing governance structure through three main channels. Firstly, the asset-poor can’t enter contracts available to the assetrich. To give an example, the asset-poor is typically constrained by fixed-price loan contracts rather than equity-based financing contracts which may fit better to the opportunity cost of their economic activity. Indeed, fixed-price contracts impede productivity-enhancing behavior, such as putting full effort to work, providing full information, risk taking, and cooperation, in many respects. One feature of the fixedprice contracts, such as debt contracts, is that productivity-enhancing behavior can’t be internalized in these types of contracts because there is high monitoring cost. In effect, fixed-price contracts don’t incentivize the borrower to elicit maximum levels of efforts. Secondly, it is difficult to decrease principal agent and coordination problems in case of high inequality because there is a trade-off between high inequality and incentive for productivity-enhancing behavior. Thirdly, maintaining high level of inequality in the society is costly for the wealthy because it requires expensive supportive institutional structure, such as security forces. These costs divert resources away from productive activities thus in-volve allocative inefficiencies. High asset concentration also means lost opportunity for the entrepreneurs, investors, and innovators who could well enhance the productivity of the economy if they had not been asset-poor. There is compelling evidence that many asset-poor entrepreneurs are either shut out of credit markets or must pay higher interest rates than the asset-rich. Furthermore, asset-poor investors are forced to accept much lower rates of return on their assets than their wealthier counterparts. There is also evidence that the asset-poor have much higher rate of time preference, as well as,

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higher level of risk aversion (Carney and Gale 2005, 203; Hopkins 2018, 316; Lawrance 1991, 54; Moseley 2001, 317). Bowles (2012, 37) argues that, where contracts in financial markets are incomplete or unenforceable, individuals lacking in wealth are either precluded from engaging in a class of contracts that are available to the wealthy, or enter into these contracts on unfavorable term. [. . .] why an individual’s amount of wealth influences the kinds of contract she can engage in is that only those with sufficient wealth can undertake projects on their own account, that is without borrowing. And, among those who borrow, those with more wealth borrow on better terms. This is because greater wealth on the part of the agent allows contracts which more closely align the objectives of principal and agent. This is the case, for example, when the borrower has sufficient wealth to post collateral or put her own equity in a project, and therefore has greater incentive to supply effort, to adopt more prudent risk levels preferred by the lender (the principal), to reveal information to the principal, and to act in other ways that advance the principal’s interests but that cannot be secured in a contract.

It is the contention of the asset-based redistribution that there is a class of contractual relationships in economic exchange that is incentive-compatible, enhances productivity and generates higher economic growth (Bowles 2012). The chief characteristic of this class of contracts is that they reduce or eliminate the distinction between principal and agent. Asset-based redistribution rewrites the property rights by allowing agents to share in the three crucial dimensions of property rights: (a) right to control access to the asset; (b) right to control the disposition over its use; and (c) right of claim on the residual income produced by the asset (Bowles 2012, 15). An example of these forms of contracts is a joint-partnership where the ownership of an economic venture is shared between two or more partners. They share the property rights claim and residual income jointly. Each has residual claim but also the control that is involved in the property rights claim. These types of contracts are referred to as risk-sharing contracts. The “share economy” proposal of Martin Weitzman (1984, 87–88) is another example of the incentive-compatible contracts in which labor receives a base wage and a share of the profit. From the point of view of asset-based redistribution, the shortcoming of this type of proposal is that while labor is a residual claimant, it has no ownership control contrary to Bowles’ proposal that eliminates the principal-agent distinction. Bowles (2012, 18) argues that there are distinct advantages of asset-based redistribution compared to income-based redistribution, such as its potential for combining equity and efficiency. He also adds that (Bowles 2012, 18) in contrast to income-based egalitarian strategies, which are rarely better than productivityneutral (and often a lot worse), asset-based egalitarianism can in principle be productivityenhancing. This is true both because it can implement more efficient distributions of residual claimancy and control rights and because redistributing assets addresses a major cause of unequal incomes, and thus gives greater scope for markets to do what they are good at: identifying losers – firms that fail to produce good products at competitive prices – and getting them out of the game.

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Asset-based redistribution has the potential to enhance efficiency as each party in a contract has “skin-in-the-game” thus eliminating or minimizing principle-agent differences. In doing so, it can minimize monitoring, supervisory and disciplinary costs leading to efficiency gains. As a result, participants in a contract of an economic undertaking can choose higher risk-higher return projects thus increase the efficiency and productivity of the system. Finally, asset-based redistribution can create a reciprocal and trusting environment that strengthens social cohesion, promotes social mobility and reduces income inequality without perverse incentive effects and resentments that would lead to resistance to changes in status quo that marks income-based redistribution efforts. While Bowles’ (2012) asset-based redistribution proposal represents a compelling substitute for income-based redistribution proposals, it does not provide a blue print of policies and procedures for its implementation. However, he suggests a roadmap by (Bowles 2012, 18) first identifying those aspects of concentrated ownership of assets that can give rise to perverse incentive and costly enforcement strategies and then to devise asset redistributions that can attenuate the resulting co-ordination failures without introducing their own costly incentive problems.

Overall, Islam provides the risk-sharing mechanism through (i) risk-sharing instruments in the financial sector; (ii) redistributive risk-sharing instruments within the society; and (iii) the inheritance rules (Mirakhor and Bao 2013, 32). A concrete blueprint for assetbased redistribution in Islamic finance is proposed through the risk-sharing mechanism, which is also the essence of Islamic finance as confirmed by Kuala Lumpur Declaration of 2012 (ISRA 2012, 1). Risk-sharing basically can be defined as a contractual or societal arrangement whereby the outcome of a random event is borne collectively by a group of economic agents involved in a contract, a transaction or a community (Askari et al. 2012, 70). Parties in the contractual or social arrangement undertake risk-sharing with the expectation that involvement of many participants, resources and skills result in lesser individual risk, as well as, greater output. Instances of risk-sharing arrangements range from purchasing stocks of domestic or foreign business entities, participating in cooperatives to insuring against idiosyncratic risks. As opposed to the debt-based system, risk-sharing financial arrangements render the financial system counter-cyclical due to its inherent nature that the returns depend on contingent realizations (Askari et al. 2010, 67). In this sense, risk-sharing can overcome the equitable economic growth and financial stability trade-off of the debt-based system. Another advantage of risk-sharing is to secure allocative efficiency with equitable growth. One important risk-sharing instrument in Islamic finance is sukuk, which has important advantages over debt-based instruments in corporate and public finance. Sukuk is a prospective candidate for Shiller’s (1993) “macro-market” securities, which are issued by the public sector and can help people to mitigate their risks to their income (Askari, Iqbal, and Mirakhor 2014, 51). A type of sukuk, which also resembles the macro-market securities, is GDP-linked sukuk. The notion of the GDP-linked

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sukuk is based on the idea of sharing of a country’s output (Bacha, Lahsasna, and Diaw 2014, 196; Bacha, Mirakhor, and Askari 2015, 206; Ismath Bacha and Mirakhor 2017, 7). Three advantages of the GDP-linked sukuk is specifically related to the asset-based redistribution to mitigate wealth inequality (Bacha, Mirakhor, and Askari 2015, 207). Firstly, investors prospectively have higher and more stable returns that co-moves with the national output growth. This is an important alternative to thedebt-based instruments which are pro-cyclical. Secondly, these instruments reduce the systematic risks stemmed from public sector debt, which is an important cause of financial crises, because they are equity in the balance sheet of the public sector. Thirdly, inherently regressive feature of the debt contracts, which is a primary reason for increasing inequality, is eliminated. The GDP-linked sukuk and its variants, such as revenue generating sukuk for the infrastructure projects, can be designed as an effective risk-sharing asset-based redistribution policy tool. This instrument can be used as an asset-based redistribution tool in many ways. One idea to use GDP-linked sukuk as an asset-based redistribution tool is the mass issuance of the GDP-linked sukuk in small denominations to fund the public finance needs and to distribute the sukuk certificates to the middle-class as an investment instrument and to the (asset) poor as a complement or substitute for the regular transfer payments. Since there are not many alternative financial investment opportunities and/or rate of return to the financial instruments, which are mostly consist of bank deposits, are usually not high, the demand for the GDP-linked sukuk is expected to be high for the middle-class in many OIC countries. In addition, the poor will benefit from having the GDP-linked sukuk as a transfer tool because the will have income generating assets to surpass the problems caused by being the asset-poor. Beyond implementing asset-based redistribution solely in public finance, an ideal risk-sharing financial system from the lens of Islamic finance eliminates debt from the system altogether. In such a system, economy-wide asset redistribution is secured for all the people. In such a case, returns are determined ex-post and pertinently co-move with the rate of return to capital. As mentioned before, rate of return in the financial instruments are function of the growth rate of the economy ðr = f ð gÞÞ (Maghrebi and Mirakhor 2015, 106). In such a framework, Piketty’s fundamental inequality ðr > gÞ will not hold systematically as there should be a co-integration relationship between the rate of return to financial instruments and growth rate of the economy. This result is quite important from the lens of wealth inequality in the sense that, as opposed to many other income or asset based redistribution policies proposed in the conventional literature, the co-integration relationship is a steadystate equilibrium so it is stable and sustainable.30 That is, in a risk-sharing Islamic

30 As early as mid-1980s, a series of IMF working papers showed that an economy in which the rate of return to finance is derived directly from the rate of return to the real sector and financial assets represent equity positions produces a stable equilibrium (Khan and Mirakhor 1989, 47).

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financial system economic policies do not need to interfere with the markets in order to secure re-distribution because the system itself works in the direction of morally/ economically acceptable distribution of wealth.

4.4 Redistribution in Islam: An Overview We already give an overview of Islam’s stance on existence of inequalities in Chapter 2. In this section, as a continuum of Chapter 2.6, we give an overview of the notion and foundations of redistribution in Islam. The focus is wealth and its redistribution. The starting point for understanding the notion of distribution and redistribution in Islam is the notion of justice, which is arguably the gist of Islamic system. The importance of the justice stems from the fact that justice is a prerequisite to secure the central economic tenet of Islam: A prosperous and just economic structure in which members of the society can live in harmonious existence to reach at an ideal Islamic system. Since justice is a prerequisite for reaching at the ideal Islamic system and just distribution and redistribution are another prerequisite to secure justice, how to guarantee just distribution and redistribution of the resources, as well as, wealth is clearly explained through divine injunctions and the scripture with moral values including well-defined rights. Besides charity and sympathy, rights of the poor and less-able in the rich’s and more-able’s wealth is also emphasized. Indeed, as underlined by Iqbal and Mirakhor (2017, 91): Redeeming these rights is a manifestation of belief in the Oneness of the Creator and its corollary, the unity of the creation in general and of mankind in particular. It is the recognition and affirmation that Allah (swt) has created the resources for all of mankind who must have unhindered access to them. Even the abilities that make access to resources possible are due to the Creator. This would mean that those who are less able or unable to use these resources are partners of the more able.

The notion of justice may be broadly divided into two, “commutative” and “distributive” justice. The former type secures “the effective prevention of harm to members of society and to their property by others” and the latter type means “the just division of the economic pie (production and wealth) among the members of society that includes owners of capital, workers, those that cannot provide for themselves and the animal species” (Mirakhor and Askari 2017, 199). The notion of distributive justice is inherent in the Islamic framework so there is not a separate theory of justice in Islam alike the Western thinking on distributive justice. Indeed, “compliance with rules of behavior handed down in the Qur’an and interpreted by the Prophet (sawa) assures the emergence of justice as a natural outcome of the practice of a rule-compliant society” (Mirakhor and Askari 2017, 199). Hence, as long as the society remains fully rule-compliant the natural outcome

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is a just distribution of the resources and wealth. The Messenger (pbuh) summarizes Islam’s view on distributive justice by saying that “a society may survive in disbelief but never in injustice” (Nasr 2009, 240). As mentioned earlier, a comprehensive set of rules prescribed in the Qur’an constitute the institutional scaffolding of a society that choses to organize on the basis of such structure. It is within this structure that Islam addresses distributional questions. Given the context of discussion of inequality in this book, this section focusses on the rules that govern distributional relationships. Rules governing property relations and contract formation and enforcement are at the center of the distributional framework in Islam. Prescribed in the Holy Qur’an, these rules include the following: (i) all resources, natural as well as human physical and mental, belong to their Creator; (ii) this ownership is immutable regardless of the number of stages of transformation that take place in converting natural resources, in combination with human physical and mental capacities to add value, into new products; not only the resources themselves but the stage-wise value added too belong to the Creator 31; (iii) The Creator has granted the right of possession of resources He has created to the collective humanity all of whose members have equal opportunity to combine these resources with their own labor and ingenuity to develop the earth and produce the products they need32; (iv) once individuals combine their physical and mental capacities with natural resources, they gain a right of possession to the product thus created; this right of ownership remains inviolate as long as there is compliance with rules governing the exchange and use of property and assets33; (v) since resources are created for the human collectivity, society has priority rights if the interests of private property rights comes into conflict with the collective interests and wellbeing of the society; (vi) there are only two ways of gaining private property rights: through ones labor and/or legitimate transfer (such as gifts or inheritance); all other instantaneous property rights possessions without commensurate combination of work, labor and resources, such as theft, bribery, interest charges, and income from illicit activities, are prohibited; (vii) because resources are created for the benefit of all humans, every human has the right of access to them; the inability (physical or otherwise) of a person to do so does not negate their right of access and possession; (viii) the general right of every human to the created resources writes into the property rights rules of Islam the principle of sharing, that is, the inviolable right of the less able to the income and

31 For related Verse in the Qur’an see, for example, Verse107: Chapter 2; Verse 23: Chapter 3; Verse 120: Chapter 5; Verse 16: Chapter 40; and Verse 180: Chapter 3. 32 See, for example, Verse 29: Chapter 2; and Verse 61: Chapter 11. 33 These rules include prohibition against waste, want on destruction, negative externalities, use in prohibited activities, opulence and extravagance (see, for example, Verse 42: Chapter 8; Verse 35: Chapter 45; Verses 1–9: Chapter 83; Verse 188: Chapter 2; Verse 40: Chapter 24). For a detailed discussion see Mirakhor and Askari (2017, chap.5).

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wealth of the more able; in effect, since the more able access the resources more extensively than their share (that is they also use resources meant for the less able), these rights have to be redeemed at some point in the process of use, production and exchange of resources and products through the redistributive mechanisms enumerated in the Qur’an; (ix) these transfers in no way can be interpreted as “charity” since they are redemption of the rights of the less able34; (x) Private property rights must not lead to private accumulation of wealth as the latter is considered the life blood of the economy that must continuously flow without obstruction35; (xi) all private property rights seize at the death of the owner; (xii) the wealth, legitimately earned, of all individuals is divided at the time of their death according to the rules of inheritance specified in the Qur’an; the purpose is to institute another barrier against accumulation of wealth as it transfers from one generation to next.36 The rules governing property relations are buttressed by rules governing formation of contracts and the necessity of remaining faithful in relationships, to the terms and conditions of contracts, promises as well as being trustworthy, even in relationships with nonMuslims.37 In Islam, the market is an indispensable part of the economy but it is just an instrument. Thus, income and wealth earned through market mechanism is not the sole property of the earner; it is advised, even ordained, through the religious precepts that it should be shared. The ultimate owner of all property is Allah (swt). The wealth should then be used for the benefit of the society through productive investment in the market mechanism. The wealth which is earned through legitimate market transactions should be redistributed in compliance with the rules governing property rights. Although the concept of the property rights is sacred in Islam, redistribution of some part of the wealth aims at sharing what ultimately belongs to Allah (swt) with others and cleansing the property rights of the others. It should also be noted here that “Islam leaves free will in the hands of the individual to accept or decline the prescribed rules for redistribution of wealth at her own merit or peril at the final judgment of Allah (swt)” (Erbas and Mirakhor 2013, 117). Mirakhor and Askari (2010, 176) explain how distribution and redistribution mechanism work given that the market is instrument as follows:

34 See, for example, Verses 177, 215, and 273: Chapter 2; Verse 41: Chapter 8; Verse 60: Chapter 9; Verse 26: Chapter 17; Verse 7: Chapter 59; Verse 51: Chapter 19; Verse 24: Chapter 33; Verse 180: Chapter 3; Verses 36–37: Chapter 4; Verse 5–11: chapter 92. 35 See, for example, Verse 34: Chapter 9; and Verses 15–18: Chapter 70. 36 For rules governing the distribution of inheritance see, for example, Verses 11–12: Chapter 4. The Messenger (sawa) permitted individuals the freedom at the time of their death to choose how onethird of their wealth will be distributed after them. Even here, many Muslims choose to allocate their third to establish trusts (Waqf) to provide public services and assistance to the less able. 37 See, for example, Verses 172 and 235–237: Chapter 2; Verses 7, 33 and 58: Chapter 4; Verses 1 and 89: Chapter 5; Verse 27: Chapter 8; Verse 4: Chapter 9; Verse 91: Chapter 16; and Verse 34: Chapter 17.

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In a market where there is full rule-compliance, the price that prevails for goods, services, and factors of production is considered just. The resulting incomes are considered justly earned. Therefore, the resulting distribution is just. However, participants will not be allowed to keep their full earnings simply because their income was justly earned. There are rights and entitlements of others in the resulting post-market distribution of income and wealth that must be redeemed. This is the function of post-market redistribution, which is governed by its own set of rules. There are levies such as khums (on income) and zakat (on wealth) that must be paid. But redistribution does not end here. There is infaq (expenditures in the way of Allah), qardh hassan (beautiful loan), sadaqat (payments to redeem others’ rights and to demonstrate the veracity of one’s claim to Islamicity), and waqf (designated assets whose underlying income flows are used to support building and maintaining public infrastructure). Any remaining wealth that is accumulated is broken up at the end of the person’s life and distributed among a large number of beneficiaries spanning at least four generations, according to rules specified in the Quran. This is designed to avoid the concentration of income and wealth in the hands of a few.

Among the post-market redistribution instruments, zakah plays a relatively more important role. It is not a charity but an ordained rule aimed at redistribution of wealth, and one of the Five Pillars of Islam. Zakah resembles a flat-rate wealth tax similar to the Piketty’s capital taxation proposal (2015c, 453) and aims at securing sharing among all members of the society. As indicated by Erbas and Mirakhor (2013, 117), Prescribed for all faithful of means, zakah may well be the first of its kind as an institutionalized form of social insurance at a time when none other than familial and tribal association provided security for the individual, excluding charity at the mercy of others. Before the advent of Islam, those insured by tribal affiliation benefitted from the mutuality of such insurance, but it might not reliably extend to the indigent who did not belong to a tribe or to those who were excommunicated.

5 Stock-Flow Consistent Modeling: A Primer 5.1 Introduction This chapter gives an overview of the Stock-Flow Consistent Approach (SFCA) with the aim of preparing the reader for the next chapter in which a stock-flow consistent (SFC) model is constructed to quantitatively show that interest-based debt contracts play an importat role in formation of the wealth inequality and selected risk-sharing asset-based redistribution proposals perform better in reducing wealth inequality compared to the traditional redistribution proposals. The SFC macroeconomic models coherently integrate all stocks and flows in an economy through behavioral equations and accounting framework, which reproduces the balance sheets and transaction matrices. In other words, the SFCA integrates national accounting data into dynamic macroeconomic modelling through interconnections between sectoral stocks (balance sheets) and flows (transaction matrices). The approach is based on the principle that every transaction by a sector must have an equivalent transaction by another sector and every change in the balance sheet of a sector must give rise to an equivalent change in the balance sheet of another sector. In effect, there is always a counter-party change in the flow and stock of a sectoral transaction. This “everything comes from somewhere and everything goes somewhere” principle allows describing evolution of the whole economic system in a consistent way, linking real and financial sector and setting up linkages between income and wealth (Nikiforos and Zezza 2017, 2). As discussed in Chapter 3 and Chapter 4, interest-based debt contracts and their direct outcomes (debt, leverage, etc.) are important drivers of rising income and wealth inequalities. Simulating quantitative effects of these drivers on a steadystate economy requires employing appropriate tools that allow interdependencies among the macroeconomic variables and feedback effects dynamically. In this regard, Chapter 5 and Chapter 6 aim to show that the SFCA is an appropriate tool to set up the linkages between the debt, interest rate and wealth inequality by linking real and financial activities in a coherent way. Many conventional modeling strategies, such as the Bewley models discussed in Chapter 3.2, lack of coherence in linking financial and real variables. This then render even the most sophisticated models of the mainstream economics insufficient in capturing the deeper determinants of increasing wealth inequality. Another relevance of the SFCA for the goals of the book is that the SFCA is based on Keynesian analysis of interest and money, which then provides the best rationale for some of the basic precepts of Islamic economic system (Iqbal and Mirakhor 2013, xi). Chapter 5.2 discusses the relevance of the SFCA to the economic theory within a historical context. Chapter 5.3 identifies the main features of the generic SFC models https://doi.org/10.1515/9783110586664-005

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and shows the main elements of a generic SFC structure. The chapter then concludes by showing the basics of constructing a simple SFC model. Having the main features of the SFCA at hand, the reader then can grasp the structure, rationale and the goals of Chapter 6.

5.2 Stock-Flow Consistent Modeling in Historical Perspective Since Keynes passed away, there have been two fundamentally different paradigms of macroeconomic research with their own interpretation of Keynes’ works. The first paradigm, mainstream neoclassical, states that rational and self-interest economic agents drive the economic activity (Godley and Lavoie 2007, 1). The neoclassical production function assures that there is no involuntary unemployment. In such an economy, optimality in agents’ behavior results in optimal allocation of resources between wages and profits. The role of money in the neoclassical model is unimportant, as well. Godley and Lavoie (2007, 2) continue that [a]s production is instantaneous, while supply is brought into equivalence with demand through the market-clearing process, there is no systemic need and therefore no essential place for loans, credit money or banks. The concept of ‘money’ is indispensable, yet money is an asset to which there is not, in general, a counterpart liability and which often has no accounting relationship to other variables.

The second paradigm, named as the “post-Keynesian” or “structuralist” approach, recognizes importance of institutions and regularities through some stylized facts (Godley and Lavoie 2007, 2). In the post-Keynesian and structuralist world, production decisions of the firms may be separate from the rest of the economy and the price system determines the distribution of national income. Expectations play an important role, as well. Moreover, “as production and investment take time while expectations are in general falsified, there is a systemic need for loans from outside the production sector which generates acceptable credit money endogenously – in other words (in accordance with common observation) there must exist a banking sector” (Godley and Lavoie 2007, 2). As opposed to the fact that this paradigm is more realistic, it has failed in being the dominant economic paradigm in the literature. Among many others, one important reason for the failure of the post-Keynesian tradition is that there is scarcity of formal models showing how the economic system as an organic whole works (Godley and Lavoie 2007, 3). Given the failure of the post-Keynesian models compared to their neoclassical counterpart, relatively new family of models, the socalled stock-flow consistent (SFC) models, have been introduced to the literature by a group of economists at Cambridge (UK), MIT and Yale starting from 1970s

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(Dos Santos 2007, 2).38 While the roots of the stock-flow consistency go back to the works of Morris Copeland (1949) on flow of funds and social accounting matrices, the SFC models have culminated in a coherent theory thanks to the works of James Tobin in the 1980s. As one of the pioneers in stock-flow consistent models, James Tobin in his Nobel Prize lecture (1982, 1) stated that: With the publication of J. M. Keynes’s General Theory in 1936 and the mathematical formalizations of his theory by J. R. Hicks (1937) and others, the language of macro-economic theory became systems of simultaneous equations. [. . .] Hicks’s (1937) “IS-LM” version of Keynesian and classical theories has been especially influential, reaching not just professional economists but, as the standard macro-model of textbooks, also generations of college students. [. . .] But the framework has a number of defects that have limited its usefulness and subjected it to attack.

Given the defects of the standard macro models, Tobin then identified defining features of the SFCA, which is still central to the SFC modeling (Caverzasi and Godin 2013, 6): – Tracking of stocks and precision regarding time: This feature stresses that modeling should follow stock-flow consistent approach. As pointed out by Turnovsky (1977, 3) certain logical relationships between stocks and flows can lead to intrinsically dynamic behavior in the models. – Several assets and rates of return: A genuine and comprehensive model should encompass several financial assets with several rates of return other than the bill rate and money like in the IS/LM model. A modern economy has complex financial relationships with many sophisticated interactions among financial instruments and economic agents. In such a sophisticated environment various

38 Dissatisfaction with the naïve Keynesian models was also expressed by other prominent names in economics. Solow (1983, 146), for instance, wrote that [t]here are however important gaps in Keynesian macroeconomic theory. [. . .] perhaps the largest theoretical gap in the model of the General Theory was its relative neglect of stock concepts, stock equilibrium and stock-flow relations. It may have been a necessary simplification for Keynes to slice the time so thin that the stock of capital goods, for instance, can be treated as constant even while net investment is systematically positive or negative. But those slices soon add up to a slab, across which stock differences are perceptible. Besides, it is important to get the stock-flow relationships right; and since flow behaviour is often related to stocks, empirical models cannot be restricted to the shortest of the short runs. Inserting stock-flow consistent framework into the model implies recognition of dynamics through stock accumulation as an implicit response to the issues raised by both Solow and Tobin. As accentuated by Turnovsky (1977, xi), stock and flow interactions “necessarily impose a dynamic structure on the macroeconomic system, even if all the underlying behavioural relationships are static”. Against these critics, the SFC models didn’t receive much attention from the mainstream economics.

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interactions among lenders, borrowers and the banking system emerge (Godley and Lavoie 2007, 13). Modeling of financial and monetary policy operations: Supply of various financial assets and financial/monetary rules should be modelled properly (Godley and Lavoie 2007, 14). Walras’ Law and adding up constraints: Budget constraints and system-wide consistency requirements of the economic agents should be considered properly. As we will show in the subsequent sections concretely, the transaction flows of a sector are determined by the transaction flows of the other sectors, which then implies existence of redundant equality in line with the Walras’ Law (Godley and Lavoie 2007, 14). As highlighted by Godley and Cripps (1983, 18) “the fact that money stocks and flows must satisfy accounting identities in individual budgets and in an economy as a whole provides a fundamental law of macroeconomics analogous to the principle of conservation of energy in physics”.

The stock-flow consistent models use national accounts and many of the models in the SFC modeling literature generically decompose the whole economy into five main sectors, namely, (i) households, (ii) non-financial firms, (iii) financial sector, (iv) public sector and (v) rest of the world. Depending on the nature of the analysis and requirement of the sectoral details, this generic classification can be more aggregated or detailed. The crucial factor in sectoral decomposition is that it must represent the economy well (as opposed to the representative agent models in which the economic agents are the focus of the analysis) and the accounting conventions should be based on theory (Dos Santos 2007, 10). In other words, the discretion in sectoral decomposition and the definitions with regards to the national accounting items “are in last resort arbitrary” (Godley and Cripps 1983, 23). National accounts are the basic source of data in many macroeconomic models since they are usually easily accessible to the public and give a comprehensive picture of the economy. In this regard, using the constraint and closure rules based on the national accounts are of significant importance to set up the SFC model correctly. Thanks to the closure rules, endogenous and exogenous variables in a large macroeconomic model, as well as, the direction of influence among a set of variables are determined (Lance Taylor 1991, 41). He further adds that (Lance Taylor 2004, 7) macroeconomics is framed by social accounting and social relations. The social accounts form a skeleton, and social relations change the skeleton’s position over real, historical time. Specifying just which relations drive the motions is not a trivial task, as subsequent chapters at- test. But the objects that move – the observable phenomena in macro – are mostly the numbers making up the national income and product ac- counts (NIPA), the flow of funds (FOF) accounts, and allied systems.

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Similarly, Godley (1996, 3) writes that it is a matter of ascertainable fact that the real world is characterized by a huge and complex structure of interdependent institutions such as governments, firms, banks and households. I do not accept that these institutions are “veils” with nothing more to do than passively sponsor or facilitate the optimizing aspirations of individual agents; and wish, rather, to start from a conceptual framework which has cognizance of (something remotely approaching) the real world as we know it.

Divergence between the internal consistency and description of the real world is one of the distinguishing features of macroeconomics today. The SFC models seek to model the whole economy in a way that clearly shows why the outputs come out as they do, as opposed to the mainstream macro models that “rely solely on a narrative method which puts a strain on the reader’s imagination and makes disagreements difficult to resolve” (Godley 1999, 394). Tobin (1989, 65) indicates that the macroeconomic discussions become a “babble of parables”, which are only specific to a stylized fact, not generalization of facts. He further adds that these parables “differ in the arbitrary institutional restrictions they specify on technology, markets, or information” (Tobin 1989, 65). What Tobin stated works clearly as opposed to the modelling methodology of the SFCA because the goal in the SFCA is to capture the “essential interdependences” among the sectors of interest at the expense of added complexity and large number of accounting equations in the model (Brainard and Tobin 1968, 99). The SFC models have the potential to be close substitutes to the dynamic stochastic general equilibrium (DSGE) models. Indeed, the SFC models have several advantages compared to the DSGE models as argued by Burgess et al. (2016, 3): the SFC models can be used to analyze the evolution of gross and net positions of financial stocks; feedbacks from financial positions to real economic decisions can be tracked; imposition of a policy quickly or slowly can generate different steadystates; money, credit and banks have realistic roles; agents are more heterogeneous compared to the DSGE models. On the other hand, DSGE models have the advantage of based more directly on the economic theory, more invariant to the Lucas critique issue, have a simpler structure and have well defined techniques to estimate and to solve these models. Table 5 summarizes the advantages and disadvantages of using the SFCA compared to the DSGE Approach. In contrast to the Marshallian partial equilibrium analysis, which considers only one sector or aspect of the economy by assuming that the rest of the economy stays constant, the SFCA attempts to explore the whole system and its dynamics (Godley and Lavoie 2007, 3). The SFCA also differs from the Walrasian general equilibrium world in the sense that the latter is resulted in market equilibrium via instant adjustment (tâtonnement) process of the prices while the latter follows the principle of adjustment to observed disequilibria through changes in real and financial variables (Meadway 2013, 28).

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Table 5: A Comparison of the SFC Approach with the DSGE Approach. Pros

Cons

The model framework is based on national accounting constraints. Allow modelling of gross flow and balance sheet positions by sector. Can be used to model feedback from financial asset and liability positions to the paths for production and spending. Can include an important role for money, credit and the financial system.

The model equations are not explicitly linked to the optimization problems of particular agents. The framework is not well-established, which makes it harder to take on board insights from other work. The models are complicated, which makes it hard to explain the main economic mechanisms at work.

Can offer a framework for exploring different specifications for agents’ expectations.

Arguably SFC models have more realistic behavioral assumptions than many models which are micro-founded.

They are hard to take to the data: the data requirements are large relative to those in more standard DSGE models. The model parameters suffer from the Lucas critique: they can be affected by changes in policy regime or time series properties of the driving processes. The models are not so clearly linked to economic theory.

Source: Adapted from Burgess et al. (2016, 3).

As a rediscovered approach to the macroeconomic modeling, which is gaining traction in the literature, the SFCA has two main potential use related to economic modelling and policy-making (Dos Santos 2007, 33). On the one hand, the “destructive” aspect of the SFC can be employed to unveil inconsistencies in the mainstream macroeconomic models. Taylor (2004, 6), for instance, argued that the MundellFleming framework is logically inconsistent because considering the stock-flow consistency requirement it “has one fewer independent equation than one usually thinks”. Godley and Shaikh (2002, 431) also added that the standard neoclassical macroeconomic model is also stock-flow inconsistent. On the other hand, the “constructive” aspect of the SFCA can be utilized for policy-making. There are many such policy related and empirical works based on SFCA such as United Nations Environment Programme (UNEP) Stock-flow Consistent Ecological Model (2015), Van Treek (2007), Le Heron and Mouakil (2008), Skott and Ryoo (2013), Sawyer and Passarella (2017), Nikolaidi (2014), Caverzasi and Godin (2013), Khalil and Kinsella (2015) and Caiani et al. (2016). Besides, Burgess et al. (2016) estimate and calibrate UK’s economy by employing a very complex SFC model. Regarding the financialization and inequality nexus, there are important recent studies that employ SFCA as the underlying modeling strategy such as Zezza (2008), Detzer (2016), Dafermos and Papatheodorou (2015), Naqvi (2015), and König (2016).

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5.3 Setting up A Generic SFC Model In this section, we derive a simple benchmark SFC model à la Dos Santos (2007). Godley and Lavoie (2007, 9) succinctly explain how to construct a SFC model as follows: [T]he method will be to write down systems of equations and accounting identities, attribute initial values to all stocks and all flows as well as to behavioral parameters, using stylized facts so well as we can to get appropriate ratios (e.g. for the proportion of the national income taken by government expenditure). We then use numerical simulation to check the accounting and obtain a steady state for the economy in question. Finally, we shock the system with a variety of alternative assumptions about exogenous variables and parameters and explore the consequences. It will be our contention that via the experience of simulating increasingly complex models it becomes possible to build up knowledge, or ‘informed intuition’, as to the way monetary economies must and do function.

The postulated economy is closed, composed of four sectors (households, government, firms and banks), and six asset classes (high-powered money, demand deposits, time deposits, government bonds, loans and equity). There is only one good in the model. There is also a small set of assumptions in this generic model. The households (i) do not have access to credit, (ii) do not invest, (iii) hold cash, money and time deposits, equity and government bonds as their wealth, (iv) do not pay taxes, and (v) have their income in the form of wages, dividends and interest receipts. The firms are assumed to shoulder the production activity in the economy by leaving no room for the financial sector to be a direct ingredient of the GDP. In addition, firms finance their operations only through loans, equity and internal funds. Government receives its tax revenue from the firms whereas interest payments go to the banks and the households. Government bonds are assumed to only be consisting of perpetuities, bonds that pay a fixed amount of return per period. Banks are assumed to have no operational costs and they distribute profits to the owners and shareholders (Dos Santos 2007, sec. 2.1.1). Setting up the generic model requires three consecutive stages, namely, – determination of the accounting identities in order to secure consistency between flows and stocks, – equations describing the laws of motion of the system, the closure rules and the behavioral patterns of the agents, – calibration of base scenario and then running simulations. 5.3.1 Accounting Identities The starting point of any SFC model is the accounting entries, which consist of initial stocks, current transactions and flow of funds matrices. There are four main accounting principles in SFCA (Nikiforos and Zezza 2017, sec. 2.1):

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Flow consistency: One of the pillars of the SFCA is the principle that every flow comes from somewhere and goes somewhere. This makes the flows consistent. Two consistency conditions should be simultaneously secured, namely, horizontal and vertical consistency. The horizontal consistency means that every flow coming from an economic unit goes to another economic unit. The vertical consistency means every transaction at least affects two entries within the accounts of the economic unit. For instance, when an economic unit makes expenditure, one or more of its financial accounts are debited by the same amount. Stock consistency: Like the flow consistency requirement, every stock (asset/ liability) for an economic sector has a counterparty stock (liability/asset) for another sector. For example, credit is an asset for the banking sector and liability for other sectors. It means net wealth of the whole system is zero (except capital stock which has no counterparty liability). Stock-flow consistency: Relevant to the previous two accounting principles, every flow gives rise to change in stocks. It means, change in the wealth stock in a period must equal to net saving in that period (assuming there is no capital gains). Quadruple entry: Given these three consistencies, accounting rules then imply quadruple entry, which means every transaction in stocks requires four entries in balance sheets. This is because one entry stemming from a transaction leads to another entry vertically, which then leads to another entry horizontally and finally another entry vertically (Minsky 1996, 77). Let us here also mention about a peculiar feature of the quadruple-entry system related to money creation process (Godley and Lavoie 2007, 48): In the mainstream framework, money is sometimes said to fall from the sky, thrown out of an helicopter, as in the famous parable by Milton Friedman. In that mainstream framework, which is highly popular in mainstream intermediate macroeconomic textbooks, money is a given stock, which seems to appear from nowhere, and which has no counterpart in the rest of the economy. Despite changes in the real economy, and presumably in financial flows, the stock of money is assumed to remain at all time constant. The quadruple-entry system shows that such a conception of money is meaningless.

The stock-flow consistency principle of the SFCA requires the initial stocks for the economy must be identified. Table 6 shows the components of the initial balance sheet of our simple and generic economy. Given the initial stock level of the economy, the stock values in the next period is updated by using the flows and valuations changes within the period of interest. The rows of the balance sheet in Table 6 stands for the financial instruments and the columns indicate the sectors in the economy. In the balance sheet, row totals sum to zero as reflection of SFC principle that “everything comes from somewhere and goes to somewhere” (Godley and Lavoie 2007, 38). Column totals in the balance sheet gives sectoral net wealth. Indeed, vertical rows can be considered as sectoral budget constraints. That is, any current income/ expenditure flow under the “current” columns must equal to changes in the stocks

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Table 6: Balance Sheet Matrix. Households Cash Demand deposits Time deposits Loans Bonds Equities Net Wealth

Firms

+ Hh + Md + Mt −L + PB Bh + PE E Wh

− PE ΔE Wf

Government

Banks

Total

−H

+R − PB B

+ Hb − Md − Mt +L−R + PB Bb

Wg

Wb

      W

Source: Adapted from Dos Santos (2007, 75).

represented by the “capital” columns. This equality ensures stock-flow consistency and shows which stock-flow relationship in a sector is unsustainable. In the balance sheet, the convention is to assign plus sign (+) to a stock variable which denotes an asset and to assign minus sign (−) to a stock variable which denotes a liability. The first row of the balance sheet matrix in Table 6 shows how the high-powered money (or cash) is distributed among the sectors in the economy. Following the sign convention, households and banks have cash as their assets whereas it is a liability for the government. Similarly, demand and time deposits are assets (+) for the households and liability (−) for the banks. Loans are assets for the government and liability for the firms. Regarding the banking sector, it is both an asset and liability since the banks use credit from the government (in this hypothetical economy, the government and the central bank is consolidated). Hence, we enter the netted-out loan stock to the loans cell under the banks column. Asset prices enters the balance sheet for the bonds and the equities, stock of which then changes because of not only within-period transactions (flows) but also valuation changes stemming from price changes. After constructing the balance sheet, the next step is to construct the transactions matrix, which shows receipts and payments by the economic agents resulting from current transactions (flows). In the transaction matrix, the convention is to assign plus sign (+) to a flow variable which denotes a receipt and to assign minus sign (−) to a flow variable which denotes a payment. Table 7 shows such a transaction matrix for our hypothetical economy. The upper block of Table 7 indicates the sources of expenditures for each of the sectors in the economy. Private consumption is an expenditure for the households (minus sign) and an income for the firms, who sell goods and services to the household sector. Similarly, the government buys goods and services from the firms. Investment and inventories, on the other hand, are both an expenditure and a receipt for the firms. So, these rows are netted out within the firms sector column. The lower part of Table 7 shows how the expenditures are distributed among the factors of production. As opposed to the consumption, wages are a payment for the firms and an income form the households. The forms pay out taxes to the government. As a reflection of the loans in the balance sheet matrix, the firms and the banks pay out interest whereas the banks

−C

Private Consumption Government Consumption Investment Change in Inventories

Capital

− ΔIN

+ ΔIN

Sg

Sb = 0

Sf

Banks

+ il  L − 1 − ig  R − 1 − Fb + ib  Bb − 1

Current

Sh

− ib  B − 1

+T + ig  R − 1

Capital

− im  Mt − 1

− WB −T − il  L − 1 − Ff

−G

Current

Government

+ im  Mt − 1

+F + ib  Bh − 1

+ WB

Source: Adapted from Nikiforos and Zezza (2017, 10).

Current savings

Wages Taxes Interest on loans Dividends Interest on bonds Interest on time deposits

Capital

− PΔK

Firms

+ PΔK

+G

+C

Current

Accounting memo: Y ≡ C + G + PΔK + ΔIN ≡ National Income

Current

Households

Transactions

Sectors

Table 7: Transaction Matrix.

Capital

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and the government receive interest payments. Dividends, interest on bonds and interest on time deposits are distributed among the sectors in the same vein. After constructing the balance sheet and the transaction matrix, the final step in identification of the accounting identities is the construction of the flow of funds matrix. In the flow of funds matrix, the convention is to assign plus sign (+) to a flow variable which denotes sources of funds and to assign minus sign (−) to a flow variable which denotes uses of funds. Table 8 gives the flow of funds matrix, which shows financial implications of the transactions given in the upper half of the table. In other words, the lower half accounts for the change in the financial wealth of the sectors in the economy. The balance sheet, the transaction matrix and the flow of funds matrix allow us to write down the constraints and identities of the economy of interest in equational form. To give an example, let’s go back Table 7, in which households earn wages (WB), dividends (F), receive interest on bonds (ib  Bh − 1 ) and interest on time deposits (im  Mt − 1 ). They spend part of their income via consumption (C). The net savings of the household sector is then calculated by summing up these entries. The net savings by the households are then used for accumulation of financial wealth, which is given in Table 8. Equation (8) shows that the households use their savings by accumulating the row entries of the flow of funds matrix, namely, cash ðΔHh Þ, demand deposits (ΔMd), time deposits (ΔMt), bonds (PB ΔBh ), and equities (PE ΔE). In equational form, household net savings (Sh ) and financial wealth accumulation is as follows: Sh = WB + F + ib  Bh − 1 + ig  Mth − 1 = ΔHh + ΔMd + ΔMt + PB ΔBh + PE ΔE

(8)

Let’s continue with the government sector, which earns taxes ðT Þ, and interest
 income from loans to the banks ig  R − 1 . It has government expenditures ðGÞ and pays out interest on bonds ðib  B − 1 Þ. The net savings by the government are then used for accumulation of financial wealth, which is given in Table 8. Equation (9) shows that the government uses its savings by accumulating the row entries of the flow of funds matrix, namely, money issuance ðΔHh Þ, loans to the banks (ΔR), net borrowing (PB ΔB). In equational form, the government net savings (Sg ) and financial wealth accumulation is as follows: Sg = T − G + ig  R − 1 ib  B − 1 = ΔH − ΔR + PB ΔB

(9)

To sum up, the consistency of the system requires that (i) all saving accounts and (ii) all capital transactions, by definition, add up to zero. The matrix framework then brings some theoretical issues with respect to the accounting identification and macroeconomic methodology on the table. 5.3.2 Behavioral Equations Setting up the accounting framework, the subsequent step is to determine behavioral equations for the model. The accounting framework gives the identities of the model.

− ΔMt

Δtime deposits

Source: Adapted from Nikiforos and Zezza (2017, 10).

Sh + net capitaltransactions=0

− PE ΔE

Δequities

Sum

− PB ΔBh

Δbonds

Δloans

− ΔMd

Δdemand deposits

Capital − ΔHh

Current

Households

Δcash

Transactions

Sectors

Table 8: Flow of Funds Matrix.

+ PE ΔE

+ ΔL

Capital

Sf + net capitaltransactions=0

Current

Firms

+ PB ΔB

− ΔR

+ ΔH

Capital

Sg + net capitaltransactions=0

Current

Government

− PB ΔBb

− ΔL + ΔR

+ ΔMt

+ ΔMd

− ΔHb

Capital

Sb + net capitaltransactions=0

Current

Banks

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5 Stock-Flow Consistent Modeling: A Primer

However, how the sectors behaves in the model is as important as determination of the accounting identities. As stated by Nikiforos and Zezza (2017, 14), “from a technical point of view, if a model needs to determine n endogenous variables, and its accounting skeleton provides us with k independent accounting identities, we need n-k more equations to solve the model. These equations are provided by the specification of the behavior of the various agents and sectors of the model.” To give an example, the connection between consumption and saving partakes in the transaction matrix while the determinants of the consumption are revealed through behavioral equations, such as Haig-Simons type consumption function. There can be numerous behavioral equations for the same variable as per theoretical background, assumptions and the research question. For instance, an investment function can use either of domestic demand, cost of financing, capacity utilization, or some combination of them. The degree of complexity and number of parameters in the behavioral equations are quite difficult tasks per se. Imposing more parameters can even end up with more subtle behaviors. So, there is generally a trade-off between more complexity and subtle outputs in modeling the behaviors. As indicated by Caverzasi and Godin (2013, 27): If one believes that SFC modeling should be used merely as a reference within an argumentative theoretical debate [see Section 3], or for simple didactical purposes, a just balance between realistic behaviors and the number of parameters has to be found. This joins the call of Dos Santos and Zezza (2008), among others, for simple models targeted at specific subjects, rather than large models including numerous sectors or assets.

Determination of the closure rules is also an important ingredient of the SFCA within the behavioral equations. There are two main closure approaches in the literature, namely, Yale-type and Cambridge-type models. In the former approach, the artificial economy is modelled as functions of the sectors’ budget constraints and the variables that determine the sectors’ “long-run target asset and wealth positions” (Dos Santos 2007, 47). Cambridge type models, on the other hand, emphasize on theorizing the aggregate variables. The focus is especially on “the level of aggregation in which the stronger empirical regularities can be found about the minimum possible number of behavioral hypotheses (generally about stock-flow ratios)” (Dos Santos 2007, 71). Once the set-up the model is complete, the next step is to determine the long-run behavior of the benchmark model and to calculate the steady state conditions. The process starts with choosing the initial conditions depending on plausible stock to flow ratios and parameters. It is important to note that the initial values must ensure that the model convergence and consistency requirements are secured. Moreover, the goal of the models here is to analyze the evolution of the economy and how it behaves after a certain shock. In this respect, the values can be hypothetical and not need to be real as long as it satisfies the consistency and convergence requirements (Khalil 2011, 12).

6 The Model 6.1 Introduction The previous chapters underscore that there is a close association between debt and rising inequalities through different channels. Specifically, we discuss that the rate of return independent from the real sector activities (i.e. fixed interest rate) and housing prices play significant role in rising inequalities. In addition, alterations in the households’ balance sheets add to the distortion in the distribution of wealth and income. Given the identified channels from finance to inequality, a pertinent question then becomes how much and to what degree each of the channels contribute to the formation of the inequalities and which redistribution policies, some of which are discussed previously, are better at dealing with the inequality problem. In this respect, this chapter seeks to delve into the channels by employing a complex model and then shows that asset-based redistribution policies in line with the Islamic precepts are effective tools to cope with the increasing wealth inequality. As explained in Chapter 5, the stock-flow consistent approach (SFCA) provides such a tool to gauge the prospective impact of redistributive policies on reducing inequalities, mainly the wealth inequality. This chapter constructs a SFC model with several household classes and other sectors within a prototype economy. The model structure allows for evolution of the economy from low degree of inequality to high level one due to the financialization process. Then the model is simulated in order to gauge comparative effectiveness of the redistribution policies, including the risk-sharing asset-based redistribution.

6.2 Structure of the Model The formulation of the model connects functional distribution of income and wealth to personal distribution by separating the household sector into various groups that are defined by different income sources and composition of the balance sheets. While the model structure is inspired from Dafermos and Papatheodorou (2015), the whole model is original in many respects. Firstly, the model structure is one of the few models in the SFC literature that explicitly connects household assets not only to the income inequality but also to the wealth inequality. Secondly, it is the first model in the literature (both Islamic finance and conventional) that explicitly introduces tenant versus homeowner distinction so as to reveal important contribution of real estate prices in the formation of wealth inequality. Thirdly, focusing on wealth inequality also shows that financialization has incomparably higher effects on wealth inequality compared to the income inequality. This diversion between the two inequalities is also in line with the highlights of the past chapters that income https://doi.org/10.1515/9783110586664-006

108

6 The Model

and wealth inequalities have become different concepts with decreasing correlation for many countries over time. Fourthly, existence of housing capital allows bifurcation of the productive capital from housing capital. In effect, evolution of these two stock variables differently provides a basis for evaluating how speculation in housing is detrimental to the long-term formation of productive capital, a driver of long-term and sustainable growth. Finally, the model is novel in the sense that it quantifies impact of asset-based and Islamic redistribution policies compared to the incomebased redistribution policies, which lost their effectiveness in the medium to long run. The postulated economy is composed of four sectors in the model, namely, the households, the firms, the banking sector and the government (or public sector). As the goal is to construct a simplified benchmark model to gauge sources of the inequality, we assume a closed economy with no transactions with the rest of the world. Further extensions of the model can encompass the external sector in the future works but the contribution of such addition will not change much the findings from the benchmark model. Moreover, the structure does not have a separate central bank sector. Instead, main roles of a central bank are distributed between the public and the banking sector. As we assume banks have the power to generate money and have perfectly elastic supply of loans to the rest of the economy, introduction of the central bank would be of moderate contribution to the findings of the model. The household sector consists of three classes (the poor, the middle-class and the rich) and five groups (the poor and the middle-class are further decomposed into the tenants and the home-owners). To avoid unnecessary complications, we assume each household has one unit of labor and there is no unemployment so the total number of individuals equals to the labor force. The number of the classes is comparable to the definition of the classes in inequality and poverty literature: the poorest 40% of the population is defined as the poor, the middle 50% of the population comprise the middle-class and the upper 10% of the population is defined as the rich. It should also be mentioned here, we excluded the poorest segment of the population who has no financial wealth and live in subsistence conditions. The assumption is in line with the empirical regularity that this segment is usually financially excluded and live in abject poverty condition so direct impact of financialization through wealth effect are of relatively smaller importance. In effect, all the classes have some form of financial wealth and have regular labor income. Regarding the tenant and homeowner distinction, we assume a tenant enters financial transactions to buy a home during the period. Once the tenant buys home, s/he moves to the homeowner group in the end of the period with his or her financial wealth. So, the homeowner population grows over time because of both exogenously determined population growth but also mobility among the groups. One simplification is that the tenants can only purchase home within their classes.

6.2 Structure of the Model

109

For instance, a middle-class tenant can only move to the middle-class homeowners group. Similarly, a rich individual cannot buy house from the poor segment. A tenant can buy at most one home for dwelling purpose until becoming a member of the homeowners group. We also assume that there are no rich tenants, while this assumption is partly correct in the real world. The logic is that the rich segment has enough wealth to be able to speculate on the housing prices. Their real estate purchases are not restricted to home buying, they mainly deal with real estate and land transaction to be able to maximize their capital gains. As the land prices and other real estate transactions have economy-wide price effects on the rest of the economy, their capital gains compensate their loss of rent revenue from not having tenants in the rich class. Firms encompass manufacturing, services and construction entities. So they run productive and construction investment projects using both internal sources (saving and current deposits) and bank loans. Despite many other SFC models in the literature the model does not explicitly have equities as a form of financial instrument. Instead of having firm ownership through stock market, the households have direct ownership and they receive a pre-determined share of the firm profits as a proxy for income from the equities. Only the rich class owns the firms so other household groups do not receive profit income. The banking sector is portrayed as passive intermediary that supply all loans demanded by the households and the firms. The structure is in compatible with the idea that the banks create money through loans as stressed by Kumhof et al. (2015). Inflation is assumed away so that all the nominal variables equal to their real values. Finally, the government receives tax income and distributes its revenues through social transfers, investment, current consumption and interest payments for its bond stock. The government finances its budget deficit (net dissaving) by issuing bonds. One important caveat here is that we use the terms bonds and bills interchangeably in the model. Indeed, there is no price for the bonds but periodical interest return for the investors. As there is no short and long term borrowing distinction in the model, all bills are also the bonds. Table 9 shows the balance sheet and the financial assets/liabilities in our benchmark model. It is salient feature in the model that total capital stock in the economy is composed of housing stock and productive capital. Such a type of distinction allows for testing sources of surge in the capital stock over the last several decades a la Piketty (2014; 2014a). Table 10 gives a detailed description of the transaction matrix. The matrix also clearly shows the sources and uses of the funds and the residual items within a coherent framework. As we delve into the details of the transaction matrix and the balance sheet in the following sections of this chapter, we do not give the detailed description of the component of the Tables 9 and 10.

Current Deposits Time Deposits Bonds Loans Real Estate Stock Capital Stock Net Worth

+ NWpt

+ DCpt + DTpt + Bpt − Lpt

Poor Tenants

+ NWph

+ HVph

+ DCph + DTph + Bph − Lph

Poor Homeowners

Table 9: Balance Sheet of the Model.

+ NWmt

+ DCmt + DTmt + Bmt − Lmt

Middle Class Tenants

+ NWr

+ HVr

+ HVmh + NWmh

+ DCr + DTr + Br − Lr

Rich

+ DCmh + DTmh + Bmh − Lmh

Middle Class Homeowners

+K + HV + K + NWba

   

− DC − DT + Bba +L

+K + NWf

Total

Banks

+ HV

+ NWg

−B

Govn.

+ HVf

− Lf

+ DCf

Firms

110 6 The Model

Consumption − Cpt Rent − REpt Investment Govn. Consumption Wages + WBpt Govn. Transfers + TRpt Private Transfers + TPpt Income Taxes − τ p  WBpt Firm Profits Bank Profits Loan Repayment − LRpt Interest on deposits + id  DTpt −1 Interest on bonds + ib  Bpt −1 Interest on loans − il  Lpt −1 Net Lending NLpt Investment in − IHpt Housing Current Deposits − DCwpt Time Deposits − DTwpt Bonds − Bwpt Loans + Lwpt Sum 

Poor Tenants

− TPmh − τ m  WBmh

− TPmt − τ m  WBmt

− LRmt − LRmh + id  DTmt −1 + id  DTmh −1 + ib  Bmt −1 + ib  Bmh −1 − il  Lmt −1 − il  Lmh −1 NLmt NLmh − IHmt − DCwmt − DTwmt − Bwmt + Lwmt 

− LRph + id  DTph −1 + ib  Bph −1 − il  Lph −1 NLph − IHph − DCwph − DTwph − Bwph + Lwph 

− DCwmh − DTwmh − Bwmh + Lwmh 

− IHmh

+ WBmh

+ WBmt

+ WBph + TRph + TPph − τ p  WBph

− Cmh + REmh +I +G − WB

+C

− DCwr − DTwr − Bwr + Lwr 

− IHr



+ Lwf 

− DCwf

+ IH

NLf

+ PR

− If

Firms Firms (Current) (Capital)

− TPr − τ r  WBr + PF − PT + PBF − LRr − LRf + id  DTr −1 + ib  Br −1 − il  Lr −1 − il  Lf −1 NLr 

+ WBr

− Cr

Middle Rich Class Home owners

− Cmt − REmt

Middle Class Tenants

− Cph + REph

Poor Home owners

Table 10: Transaction Matrix of the Model.



+ Bw

NLg

− ib  B −1

+ TAX

− TR

− Ig −G

Govn.



− PB + LR − id  DT −1 + ib  Bba −1 + il  L −1 

Banks (Current)

+ DCwba + DTwba − Bwba − Lw 

NLba

+ PBR

Banks (Capital)

    



              

Total

6.2 Structure of the Model

111

112

6 The Model

6.3 Model Equations After identification of the balance sheet and the transaction matrix, the following task towards setting up the SFC model is to define the behavioral and stock-flow consistent equations. In this regard, this section defines stock and flow equations which secure consistency of the dynamic model, as well as, the behavioral variables of the sectors in the economy. Before delving into the equations in each sector, one important issue is the formation of the expectations imposed in some of the behavioral equations. We follow adaptive expectations assumption throughout the model as it is the general form of the expectations in the SFCA literature. The adaptive expectations for the variable ð XÞ at time t are formed as follows: Xte = β  Xt −1 + ð1 − βÞ Xte−1

(10)

The adaptive expectations equation, by construction, gives importance to the past events and errors in the past expectations to predict the future realizations. In this respect, the equation points out that expected value of for the current value of the variable ðXÞ is weighted average of the previous value of the variable and expected value of that previous value. It indicates that if there is a prediction error in the past, some portion of that prediction error is carried forward. Indeed, adaptive expectations are formed as an average of past observations with geometrically declining weights. The following sub-sections identify the equations within each of the sectors and groups in the economy. It should be noted here from the outset that while the equations in the rest of the chapter seem to be tedious, they mean to ensure that we are getting the dynamics of the balance sheets right. Else, the long-run behavior of the model and the subsequent simulations would be wrong due to dynamically miscalculated balance sheets over time. 6.3.1 Households The household sector is divided into five groups (poor tenants, poor homeowners, middle-class tenants, middle-class homeowners, entrepreneurs & rentiers) under three classes (the poor, the middle-class, the rich) as per their income level, home ownership, wealth portfolio and other behavioral features. 6.3.1.1 Poor Tenants The poor tenants group represents the bottom segment of the population with respect to the per capita wealth level while their disposable income is at a level similar to their homeowner peers, except paying out rent.

6.3 Model Equations

113


 Equation (11) summarizes the components of disposable income YDpt for the poor tenants. The main source of income for this group is the wage income net of income taxes. The model assumes that both the poor tenants and the poor homeowners earn the same wage rate ðWBp Þ per period. Since the new agents enter the workforce at the rate of population growth (pop) at the beginning of the period, the total wage payment for the current period is calculated as the multiplication of wage


 rate net of taxes WBp  1 − τp with size of the population ð1 + popÞ Npt −1 during period t. The poor tenants also receive transfers ðTRpt Þ from the government and private social transfers ðTPpt Þ from the middle and the rich classes. Every period the poor tenants pay out rents ðREpt Þ to the poor homeowners and amortize their outstanding loans ðLRpt Þ. They also receive interest income from their bond holdings

 i B and time deposits id  DTpt −1 , as well as, they pay out interest
b pt −1 il  Lpt −1 for their loans outstanding.
 YDpt = WBp  1 − τp  ð1 + popÞ Npt −1 + TRpt + TPpt − REpt − LRpt (11) + ib  Bpt −1 + id  DTpt −1 − il  Lpt −1 Equation (12) depicts the dynamic evolution of the wage rates over time. Here we follow a simple rule in which the wage rate grows at the class-specific productivity rate which is imposed exogenously. The parameter differs for all wage classes (the poor, the middle-class and the rich) slightly to indicate that institutional and economic factors make the wage rate growth differ among these classes. WBp = pdp  WBp −1

(12)

Equation (13) shows how the total government transfers are dispersed between the poor tenants and the poor homeowners since the total government transfers solely go to the poor. The government transfers to the poor tenants ðTRpt Þ are composed of two parts. Firstly, the poor tenants receive a pre-determined share of government transfers ðTRÞ commensurate with their size of population in the total poor class population in the previous period. In addition, the welfare state compensates part of their rent payments, which is not paid to the homeowners. We assume such rent compensation is a pre-determined proportion ðhallÞ of the rent payments in the previous period because the transfer decision (within the budget process) by the government is given at the beginning of the period. TRpt =

Npt −1  TR + hall REpt −1 Npt −1 + Nph −1

(13)

Equation (14) shows the private social transfers received from the middle-class and the rich class to the poor class. The supply of funds used for social transfers are commensurate with the gross wealth of the middle-class and the rich and can also be considered as a proxy for wealth tax argued by Piketty (2014) in previous chapters and zakah ordained by Islam. On the demand side, each poor group receives the

114

6 The Model

transfers as per their relative size in the poor class. In effect, per capita transfer is the same for all members of the poor class. There is no rental adjustment for the private social transfers as everyone in the poor class is assumed to be eligible for the transfers. TPpt =

Npt −1  ðTPmt + TPmh + TPr Þ Npt −1 + Nph −1

(14)

Equation (15) indicates that rental rate is determined in the poor homeowners group and exogenous to the poor tenants. The loan repayment by the poor tenants in eq. (16) is a pre-determined multiple (rep), which represents average payment periods, of loan stock outstanding ðLpt −1 Þ. REpt = REph

(15)

LRpt = rep Lpt −1

(16)

Throughout the model equations for household classes, we implement HaigSimons consumption function which relates consumption of each population group ðCi Þ to expected disposable income ðYDei Þ and wealth accrued up to the previous period ðWi −1 Þ. The marginal propensity to consume out of disposable income and wealth are assumed to be fixed in all periods. In this respect, eq. (17) shows that current consumption level of the poor tenants is function of subsistence
 level of consumption Cpt , expected income and gross wealth of the previous period. Cpt = Cpt + mpcpt  YDept + mpwpt Wpt −1

(17)

Net lending (saving or dissaving) of the poor tenants ðNLpt Þ is defined in eq. (18) as the difference between disposable income and consumption. Primary balance ðPBALpt Þ of the poor tenants in eq. (19) is defined as the net lending net of interest payments and amortization of debt. The net lending is of primary importance throughout the model equations as it determines whether the agents can make transactions in financial instruments and are able to repay their loans. NLpt = YDpt − Cpt

(18)

PBALpt = YDpt − Cpt + il Lpt −1 + LRpt

(19)

Equations (20–22) describe determinants of investment in housing by the poor tenants.39 By construction, tenants deal with real estate transactions to reside in

39 In many of the stock-flow consistent models, investment items are located as the above-the-line item. That is, they are considered as one of the components of disposable income and net lending. Here we follow a slightly different approach. Because the homeowners (poor, middle-class and rich) treat investment in housing as a portfolio item by construction of the model, investment in housing by the tenants (poor and middle-class) is also located in the below-the-line of the current account

6.3 Model Equations

115

dwelling and can buy at most one dwelling in a period. Once they buy their dwellings, they then move to the homeowners group in the beginning of the next period. To keep the model simple, there is no default in home ownership so once they join in the homeowners group, they stay in that group forever. If they have difficulty in paying out their instalments or interest payments after moving to the homeowners group, they liquidate their financial assets or reduce their consumption. Equation (20) shows that total investment in housing by the poor tenants ðIHpt Þ equals to the number of dwellings purchased during the period ðHwpt Þ multiplied by the current market price of one unit of dwelling ðPHp Þ in the poor class.40 Equation (21) calculates the share of tenants who purchase home during the period. The number of homebuyers in each period is determined by institutional factors represented by a constant coefficient ðα1pt Þ, as well as, the expected rate of return   RHpe of having a dwelling in the poor class. On the other hand, buying a dwelling is constrained by both Ponzi condition and availability of collateral as given in eq. (22). If either of the conditions given through the indicator variable (z11) is not fulfilled, the share of homebuyers will be zero until both of the conditions are met in the following periods. The first condition ðPBALpt > 0Þ accentuates that there are sufficient funds to ensure repayment of debt obligations so long as primary balance is positive. This condition is a reflection of Minsky’s (1982, 1992) well-known taxonomy of hedge, speculative and Ponzi finance. It also implies either the poor tenants do not buy home as they do not have prospect of repaying their debt or the banks do not provide credit as they consider the group very risky. In any case, the investment in housing transactions will not take place. The second condition states that sum of gross financial wealth (it is multiplied by (1+pop) to reflect accumulation of wealth stemming from new entrants into the poor tenants in the current period) and net lending in the current period must be at least as
 much as the collateral ð1 − ltvÞ  IHpt to invest in housing. Here we use gross wealth instead of just the current deposits because we assume realistically that once a tenant decides to buy home s/he is eager to liquidate other financial assets, if needed.

tables. This makes no difference from the lens of the consistency of the model because it enters into the current account framework as a liability, which still must be financed by either positive net lending and/or financial transactions. 40 Here we depart from the conventional notation in defining the flow variables that define change in the stock variables. In the conventional notation, change in the stock variables is denoted by the delta ðΔÞ symbol. In the equations, we instead use ðwÞ suffix attached to the stock variable symbols. This is because the mobility between the income groups of the households lead to change in end-ofperiod stock variables in addition to within-group flow transactions. So the total change equals to the sum of within group transactions and mobility between the groups. In the equations, ðwÞ suffix therefore stands for within group transaction during the period.

116

6 The Model

The sum to the gross wealth and net lending is also multiplied by the share of home
 buyers in total population of poor tenants Hwpt =ð1 + popÞ Npt −1 in order to reflect the fact that the equation only applies to the home buyers, not whole population of poor tenants. IHpt = PHp  Hwpt  Hwpt = z11  α1pt + α2pt RHpe  ð1 + popÞ Npt −1

(20)



( z11 =

 1; if PBALpt > 0 AND

Hwpt ð1 + popÞ  Npt − 1

(21)


  ð1 + popÞ  Wpt − 1 + NLpt > ð1 − ltvÞ  IHpt

0; otherwise (22) Equations (23–27) show how the bond and time deposit flows are determined. Starting with eq. (23), the flow in bonds stems from separate behaviors by the tenants and
 home-buyers in the poor tenants group. Non-home-buyers 1 − Hwpt =ð1 + popÞ Npt −1 allocate part of their savings in tandem with the portfolio decision of the poor home    T owners.41 They follow the allocation decision BTph −1 = BTph −1 + DTph by the poor −1 homeowners to replicate their portfolio allocation since the portfolio decision is based on rational analysis of relative returns and other factors.42 The second part of eq. (23) indicates that home-buyers also deal with bond transaction. As implied by eq. (22), poor home-buyers may resort to bonds or time deposits to meet the collateral requirement in case their current deposit stock is not sufficient. Here we assume that home buyers liquidate some part of their bond holdings if the collateral requirement is higher than availability of the current deposits. How much bond stock is liquidated is commensurate with the share of bond stock in gross wealth. Realization of the bond flows in the poor tenants group depends on fulfilment of two conditions. Firstly, sum of bond flow and bond stock must be positive, which then eliminate the possibility that negative bond flow may exceed stock level (see eq. (24)). Secondly, liquidation of the bond stock by the home buyers requires total of current deposit stock and net lending not to meet the collateral requirement, as given in eq. (25). Else, there would be no need of liquidating some portion of the bond stock.

41 We assume the non-home-buyers do not allocate all their savings to portfolio formation. Instead, they transfer ð1 − ncdÞ of their savings to the current deposits, as they are risk-averse and allot part of their wealth in the form of liquid assets. So, they only allot ðncdÞ of their net lending for portfolio accumulation. 42 The formulas in eqs. (23) and (24) also imply that the bonds and time deposits flows become negative by indicating contraction in the stock level in case net lending is negative.

6.3 Model Equations

Bwpt = z12 

BTph −1 T BTph −1 + DTph −1

  ncd NLpt  1 −

Bpt −1  ð1 − ltvÞ IHpt − z13  Wpt −1 ( z12 = ( z13 =

Hwpt ð1 + popÞ  Npt −1

117



!

(23)

1; if Bwpt + Bpt − 1 > 0

(24)

0; otherwise

1; if ð1 + popÞ DCpt − 1 + NLpt < ð1 − ltvÞ  IHpt

(25)

0; otherwise

Flows in time deposits follow the same pattern as the flows in bonds. So, eqs. (26) and (27) are just reiteration of the equations given for the bond flows above. In this sense,
 eq. (16) is composed of two parts. Non-home-buyers 1 − Hwpt =ð1 + popÞ Npt −1 allocate part with the portfolio decision of the poor home  of their savings in tandem  T T T =B + DT . Home buyers liquidate part of their time deposits in owners DTph −1 ph −1 ph −1 commensurate with the share of time deposits in gross wealth in case they do not have sufficient current deposits. Similar to the bonds, realization of the flows in time deposits depends on fulfilment of two conditions. Firstly, sum of flow in time deposits and time deposit stock must be positive, which then keep aside the possibility that negative flow may exceed stock level (see eq. (27)). Secondly, liquidation by the home buyers requires total of current deposit stock and net lending not to meet the collateral requirement (see eq. (23)). DTwpt = z14 

T DTph −1 T BTph −1 + DTph −1

 ncd  NLpt  1 −

DTpt −1  ð1 − ltvÞ IHpt − z13  Wpt −1 ( z14 =

!

1; DTwpt + DTpt − 1 ≥ 0 0; otherwise

Hwpt ð1 + popÞ Npt −1

 (26)

(27)

Loan usage by the poor tenants is again composed of two parts as shown in eq. (28).
 The first part ltv IHpt represents loan usage stemming from investment in housing by the home-buyers. The second part indicates that non-home-buyers take loans to fund their negative net lending in case the condition in eq. (29) is fulfilled. The indicator variable (z15) in eq. (29) takes the value of one if the two following subconditions are concurrently fulfilled: (i) sum of current stock of current deposits and net lending must be negative to ensure that current deposits are not sufficient to cover funds needed for closing the net lending gap. This assures resorting to loans, (ii) current stock of current deposits is at least as big as the collateral so that the group

118

6 The Model

is eligible for taking loans. If either of the two conditions is not met, there will be no transaction in bond or time deposits.43 The first sub-condition above also implies that as long as the stock level of the current deposits is higher than the negative net lending (dissaving), there is no need to resort to using credit. Internal funds are enough to cover the financing need stemmed from dissaving.     Hwpt (28) Lwpt = ltv  IHpt + z15 ltv  NLpt   1 − ð1 + popÞ Npt −1 (   ð1 + popÞ  DCpt − 1 + NLpt h0 and ð1 + popÞ  DCpt − 1 ið1 − ltvÞ  NLpt  z15 = (29) 0; otherwise We assume, following Godley & Lavoie (2007), that bank deposits act as a buffer. In effect, both residuals stemming financial transactions and ex-post adjustments resulting from mistaken expectations partake in the current deposits formula. Equation (30) reflects the fact that change in current deposits in the current period equals to the net lending and change in other financial transactions. DCwpt = NLpt − IHpt − Bwpt − DTwpt + Lwpt

(30)

Writing out the equations for flows, the rest of the section defines how the stock variables are updated in a dynamic sense. As given in eq. (31), stock of current
 deposits DCpt is composed of current deposits stock of the previous period owned by non-home-buyers (adjusted for population growth) and flows in current deposits net of transactions by home buyers.   Hwpt DCpt −1 + DCwpt DCpt = ð1 + popÞ  1 − ð1 + popÞ Npt −1 (31)  

Hwpt  NLpt − ð1 − ltvÞ IHpt − ð1 + popÞ  Npt −1
 Equation (32) shows that stock of time deposits DTpt consists of time deposits stock in the previous period (adjusted for population growth) and flows in time deposits by the non-home-owners. Similarly, eq. (33) calculates formation of the bond stock for the non-home-buyers. It should be reminded here that the indicator variable (z13) in eqs. (32) and (33) guarantees that as long as the stock of current deposits and net lending is more than the collateral, there is no need to liquidate other financial assets.

  Hw does not partake in 43 The reader may wonder why share of the hon-home buyers 1 − ð1 + popÞpt N pt −1 eq. (29). Indeed, we have it implicitly in the equation. Since they exist both sides of the inequalities, they cancel out each other. So, we delete them altogether to save space.

6.3 Model Equations

 Hwpt DTpt −1 DTpt = ð1 + popÞ  1 − ð1 + popÞ Npt −1   T DTph Hwpt −1 + z13  T ncd  NL  1 − pt T ð1 + popÞ Npt −1 Bph −1 + DTph −1

119



 Hwpt Bpt −1 ð1 + popÞ Npt −1   BTph −1 Hwpt + z12  T ncd  NLpt  1 − T ð1 + popÞ Npt −1 Bph −1 + DTph −1

(32)

 Bpt = ð1 + popÞ  1 −

(33)

As both home buyers and the rest of the group take part in loan transactions, the set
 up for the loan stock Lpt is similar to that for the stock of current deposits as given in eq. (34). Loan stock at the end of the period is adjusted for home buyers.   Hwpt  Lpt −1 + Lwpt − ltv  IHpt (34) Lpt = ð1 + popÞ  1 − ð1 + popÞ  Npt −1
 Gross wealth Wpt in eq. (35) is the sum of stock values of current deposits, time
 deposits and bonds. Population size Npt in eq. (26) is calculated as the population size in the previous period multiplied by population growth coefficient net of home buyers who move to the poor homeowners group at the end of the period. Wpt = DCpt + DTpt + Bpt

(35)

Npt = ð1 + popÞ Npt −1 − Hwpt

(36)

6.3.1.2 Poor Homeowners Poor homeowners are alike their tenant peers with respect to their level of income, consumption patterns and gross wealth levels whereas the two groups differ in their home-ownership and income from the housing wealth. Equation (37) summarizes the components of disposable income for the poor
 homeowners YDph , which consist of the wage income net of income taxes

  WBp  1 − τp  ð1 + popÞ Nph −1 , government transfers ðTRph Þ, private social transfers ðTPph Þ from the middle class and the rich class, rent income from the poor tenants
 ðREph Þ, interest income from their bond holdings ib Bph −1 and interest income from
 time deposits id  DTph −1 , and payments for amortization ðLRph Þ of loans and interest
 payments il  Lph −1 for loans.
 YDph = WBp  1 − τp  ð1 + popÞ Nph −1 + TRph + TPph + REph − LRph + ib  Bph −1 (37) + id  DTph −1 − il  Lph −1

120

6 The Model

Total government transfers ðTRph Þ and private transfers ðTPph Þ to the poor homeowners are calculated as the residual items of the total transfers after poor tenants receive their shares, as given in eqs. (38) and (39), respectively. Whilst these two equations are calculated as residual items, they indeed equal to the share of the homeowners among the poor adjusted for extra rental aid to the poor tenants. TRph = TR − TRpt

(38)

TPph = ðTPmt + TPmh + TPr Þ − TPpt

(39)

Equation (40) calculates rent price in the poor class. Economic theory states that optimal rental rates approximately equal to user cost of capital which represent cost of using capital (real estate here) for one period and can also be regarded as the instalments in calculating the present value of capital.44 Poterba (1992) suggests calculating the user cost of capital (UCC) as UCC = P  ðia + τ + f − πÞ where ðia Þ represents after-tax nominal mortgage interest rate, ðτÞ is the property tax rate, ð f Þ is the holding costs (depreciation, maintenance and the risk premium) and ðπÞ is the expected capital gains. Finally, ðPÞ is the house price. While the user cost of capital changes every period as reflection of changes in its determinants, we impose a fixed rate, which approximately equals to the long-run average of the user cost of capital, to overcome prospective problems in calculation and corner solutions. In this sense, total rent income equals to the current market
 value of the houses used by the tenants PHp  ð1 + popÞ  Npt −1 multiplied by the rental rate (re). The formula also implies that any price change or excess demand by the tenants is reflected in rental income. REph = re  PHp  ð1 + popÞ Npt −1

(40)

One of the key variables that, directly or indirectly, affects many other variables in the model is per unit price of real estate. In the model, we set different average unit prices for each income class of the households (the poor, the middle-class and the rich).
 Equation (41) assumes that unit real estate price in the poor households group PHp is proportional to the unit price in the rich households group ðPHr Þ. The idea is that rich household group buys any type of real estate, including the land. As their investment in real estate drives the cost, demand and pricing decision in the whole economy, the prices set in the rich household group diffuse to the other income groups in different proportions. The proportion is quite simple, the ratio of disposable income per capita in the poor homeowners group to the disposable income per capita

44 As stressed by Himmelberg et al. (2005), “A correct calculation of the financial return associated with an owner-occupied property compares the value of living in that property for a year – the “imputed rent”, or what it would have cost to rent an equivalent property – with the lost income that one would have received if the owner had invested the capital in an alternative investment – the ‘opportunity cost of capital’.”

6.3 Model Equations

121

in the rich households group. It is also time-varying by allowing the relative prices of real estate among the groups to change as per other developments in the economy such as relative wealth level as an ingredient to interest income. In addition, relative disposable income per capita is a proxy for the relative purchasing power for the income group which then determines their investment in housing, renting vs. purchasing real estate and portfolio decisions. The determination of the unit price of real estate in the rich income group is delineated in the rich households section.   YDph −1 =Nph −1 PHr (41) PHp = YDr −1 =Nr −1 Equation (42) describes rate of return in real estate, which can also be regarded as opportunity costs of acquiring real estate. The total net return is the sum of difference between percentage change in prices and interest rate paid on loans and rent income
 as share in total value of real estate REph =HVph −1 .     REph PHp −1 (42) −1 − il + RHp = PHp − 2 HVph −1

 As given in eqs. (43–46), amortization on debt LRph , consumption Cph , net

 lending NLph and primary balance PBALph equations in the poor homeowners are calculated alike the calculations in the poor tenants group. LRph = rep  Lph −1

(43)

Cph = Cph + mpcph  YDeph + mpwph Wph −1

(44)

NLph = YDph − Cph

(45)

PBALph = YDph − Cph + il  Lph −1 + LRph

(46)

Although most of the equations describe similar behavioral and stock-flow consistent patterns between the tenants and the homeowners, the main line of divergence occurs in the motivation for acquiring financial assets. As accentuated before, the tenants first decide on whether to buy a home and then distribute the rest of the financial wealth and income between bonds and time deposits. On the other hand, there is an obvious portfolio choice behavior for the homeowners as they already have dwellings and form the portfolio, including investment in housing, based on relative returns and institutional/behavioral determinants. In the homeowner equations, the allocation of financial wealth and savings are then decided along simple Tobinesque lines. In other words, the ex-ante allocation of the financial assets depends linearly on the relative rates of return of the financial assets within the portfolio. A detailed elucidation on Tobin Portfolio Model and its extensions partake in Appendix 1.   T In the beginning of each period, homeowners devise a target portfolio PORTph level that they expect to have in the end of the period. To reach at their target

122

6 The Model

portfolio, they make transactions on real estate, bonds, time deposits and net lending throughout the period.45 As given in eq. (47), target portfolio level is composed of the    e expected stock values of real estate portfolio PHpe  Hph − ð1 + popÞ Nph −1 ,     e bonds Beph , time deposits DTph and a pre-determined share of net   lending ncd  NLeph g. As the households are risk-averse and decide to allot some of their wealth in the form of liquid assets, they do not transfer their entire (positive of negative) net lending to the portfolio account. It should be noted here that the real estate portfolio does not equal to total real estate value of the poor homeowners class. This is because the homeowners cannot put their dwellings into the portfolio so that they only put their units of houses in excess of their units of dwellings (one for each household  by assumption) into the portfolio.  Inthe formulation, total number of e and current real estate prices PHpe are based on expectations while units Hph constant population growth assumption allows the homeowners to correctly estimate
 their number of dwellings ð1 + popÞ Nph −1 .   T e e = PHpe  Hph − ð1 + popÞ Nph −1 + Beph + DTph + ncd NLeph PORTph

(47)

In tandem with the Tobinesque approach (see Appendix 1 for details), share of each of the target stock level of financial assets in total portfolio is function of a constant coefficient, reflecting institutional and behavioral patterns in portfolio formation, own rate of return andrate ofreturn of other financial assets, as given in eqs. (48–50). T in total portfolio is positively associated with constant Target housing stock HVph coefficient and own rate of return whereas negatively associated with rate  ofreturn in bonds and time deposits (see eq. (48)). Similarly, target bond stock BTph in total portfolio is positively associated with constant coefficient and own rate of return

45 The rationale behind forming target portfolios for each of the homeowner class in the model is that they allocate their expected stock of financial assets in proportion to the expected returns of each asset. As the expectations are set to be adaptive in the model equations, the expectations come from the past experience but the realizations diverge from the ex-ante expectations so the mistake is reflected in current deposits. This is a realistic imitation of the reality, indeed. One issue pertaining to the target portfolios is that there is no equity in the portfolio. One reason is that introduction of equity and stock market in the model requires determination of the equity price and volumes of stocks but such revision in the current model would introduce many new equations. This would then expand the model, which is already at the border of tractability. In addition, we know almost for certain that introduction of equity prices and related asset booms would increase the inequality further. If the current model can show that debt and financialization have effects on inequality, then the modified (equity added) model should automatically confirm this result. As a result, the author prefers to keep the model simple by excluding the equity market, inclusion of which would only amplify the current outputs. In the future modifications of this benchmark model, the authors are planning to add the stock market into the structure.

6.3 Model Equations

123

whereas negatively associated with rate of return  in real  estate and time deposits (see T in total portfolio is positively eq. (49)). Finally, target time deposits stock DTph associated with constant coefficient and own rate of return while negatively associated with rate of return in real estate and bonds (see eq. (50)). T HVph T PORTph

BTph T PORTph T DTph T PORTph

= λp10 + λp11  RHpe + λp12 ib + λp13 id

(48)

= λp20 + λp21  RHpe + λp22 ib + λp23  id

(49)

= λp30 + λp31  RHpe + λp32 ib + λp33  id

(50)

As highlighted before, investment in housing decision by the poor homeowners
 IHph are behaviorally different from that of the poor tenants as the former group consider housing as one of the financial portfolio instruments. In effect, equations for investment in housing between the tenants and the homeowners are set differently. Equation (51) states that investment in housing by the poor homeowners consist of two parts. Firstly, the investment   volume is the difference between ex-ante target real T estate portfolio value HVph and the realized value in the end of previous peri
 od PHpe  Hph −1 − Nph −1  . In addition, constant population growth requires new  investment on dwellings PHpe  pop Nph −1 , which is also imposed into the equation. h i
 T − PHpe  Hph −1 − Nph −1 + PHpe pop Nph −1 IHph = z21  HVph

(51)

Similarly, flows in bonds and time deposits are calculated as the difference between ex-ante target level of these assets and their realized value in the end of the previous period, as given in eqs. (52) and (53), respectively.   (52) Bwph = z21  BTph − Bph −1   T − DTph −1 DTwph = z21  DTph

(53)

Realization of transactions in equations (51–53) depends on fulfilment of the positive primary balance condition ðPBALph > 0Þ. The condition assures that there are sufficient funds to ensure repayment of debt obligations by preventing Ponzi finance à la Minsky.46

46 The binary nature of financial transactions set through indicator variable (z21) is a strong condition as in the real world the economic agents most possibly retrench their total portfolio size

124

6 The Model

( z21 =

1; if PBALph > 0 0; otherwise

(54)

Poor homeowners take loan either to finance their net lending gap or to meet their target financial portfolio in case their net lending and current deposits combined are not sufficient to solely resort to internal funds. Equation (55) shows that the max
 imum amount of loan usage is the total financing need IHph + Bwph + DTwph − NLph multiplied by loan-to-value ratio (ltv) if the conditions in eqs. (46) and (57) are fulfilled. The indicator variable in eq. (56)states that (i) expected stock value of e > 0 , and (ii) sum of expected stock value of current deposits is always positive DCph the current deposits and net lending must be greater than collateral level   e e DCph + NLph > DCCwph so as to guarantee that poor homeowners have sufficient collateral to use credit. Equation (57) indicates that if expected stock value of the  e current deposits can cover the financing need IHph + Bwph + DTwph − NLph > DCph then there is no need to resort to take loan. In such a case, the transaction takes place solely in the current deposits.

z23 =


 Lwph = z22  z23  ltv  IHph + Bwph + DTwph − NLph ( e e 1; if DCph > 0 and DCph + NLph > DCCweph z22 = 0; otherwise ( e e 1; if DCph > 0 and IHph + Bwph + DTwph − NLph < DCph 0; otherwise

(55) (56)

(57)

On meeting the requirements given in eqs. (56) and (57), poor homeowners can take
 loans, which also means putting collateral DCCwph into the loan agreement as shown in eq. (58). The collateral rate is (1-ltv) which is set by the banking sector and/ or government as part of the macro-prudential policy.
 (58) DCCwph = z22  z23  ð1 − ltvÞ  IHph + Bwph + DTwph − NLph
 Equation (59) defines residual transactions in current deposits DCRwph other than the collateral usage. Given the stock-flow consistency requirement, it is also the residual financial transaction for the poor home-owners class. It residual can also be defined as the residual flow item in the transaction flows. Total change in current deposits then can be calculated as the sum of collateral and residual current deposit flows as given in eq. (60).

up to the point where primary balance is positive. Instead the structure here imposes that in the negative primary balance case the agents do not deal with transactions and strengthen their liquid (current deposits) position.

6.3 Model Equations

125


 DCRwph = NLph − IHph + Bwph + DTwph − DCCwph + Lwph

(59)

DCwph = DCCwph + DCRwph

(60)

Following the equations defining flows during the given period, the rest of the section then identifies equations on how the stock variables are updated. Equation (61) calculates number of houses purchased by poor homeowners
 Hwph during the period by dividing investment in housing by unit price of
 houses in the poor group. Equation (62) then calculates the housing stock Hph as the sum of housing stock of the previous period adjusted for population growth, investment in housing by the poor homeowners and investment in housing by the poor tenants who then move to the homeowner group in the
 end of the period. Finally, housing value HVph is calculated by multiplying the housing stock with the current price level in the poor income group, as given in eq. (63). Hwph =

IHph PHp

(61)

Hph = ð1 + popÞ Hph −1 + Hwph + Hwpt

(62)

HVph = PHp  Hph

(63)


 Equation (64) shows that stock of current deposits DCph in the poor home-owners class is composed of two parts. One part identifies stocks and flows of the within
 group transactions ð1 + popÞ DCph −1 + DCwph . The subsequent part adds to the stock due to the mobility of the home-buyers from the poor tenants group in the previous group.   Hwpt DCpt −1 DCph = ð1 + popÞ DCph −1 + DCwph + ð1 + popÞ  ð1 + popÞ Npt −1 (64)  

Hwpt NLpt − ð1 − ltvÞ IHpt + ð1 + popÞ Npt −1
 Similarly, current stock of time deposits DTph is composed of within-group transactions and stock of poor tenants in the previous period who then move to the homeowners group as given in eq. (65). The last formula in the equation indicates that home buyers in the tenant group liquidate part of their time deposits in commensurate with the share of time deposits in gross wealth in
 case they do not have sufficient current deposits. Current stock of bonds Bph in the poor homeowners group is calculated alike the time deposit stock as given
 in eq. (66). Loan stock of the poor homeowners Lph consists of within-group borrowing transactions and loan transactions by poor tenants who purchase houses (see eq. (67)).

126

6 The Model



 Hwpt DTpt −1 DTph = ð1 + popÞ  DTph −1 + DTwph + ð1 + popÞ  ð1 + popÞ Npt −1 + z13 

DTpt −1  ð1 − ltvÞ IHpt Wpt −1



Bph = ð1 + popÞ Bph −1 + Bwph + ð1 + popÞ  Bpt −1 + z13   ð1 − ltvÞ IHpt Wpt −1 Lph = ð1 + popÞ Lph −1 + Lwph + ð1 + popÞ 



 Hwpt Bpt −1 ð1 + popÞ Npt −1

(65)

 Hwpt Lpt −1 + ltv IHpt ð1 + popÞ Npt −1

(66)

(67)


 Finally, gross wealth Wph in eq. (68) is the sum of stock values of current deposits,
 time deposits and bonds. Population size Nph in eq. (69) is calculated as the population size in the previous period multiplied by population growth coefficient plus home buyers who move to the poor homeowners group at the end of the period. Wph = DCph + DTph + Bph + HVph

(68)

Nph = ð1 + popÞ Nph −1 + Hwpt

(69)

6.3.1.3 Middle Class Tenants The model structure for the middle-class tenants is quite similar to their poor peers, except that the middle-class tenants do not receive government transfers but pay private social transfers as a pre-determined share of their gross wealth. Equation (70) summarizes the components of disposable income ðYDmt Þ for the middle-class tenants, which consist of the total wage payments net of taxes ðWBm  ð1 − τm Þ  ð1 + popÞ  Nmt −1 Þ, private transfers ðTPmt Þ to the poor class, rents paid ðREmt Þ to the middle-class homeowners, amortization of their outstanding loans ðLRmt Þ, interest income from their bond holdings ðib  Bmt −1 Þ and time deposits ðid  DTmt −1 Þ, as well as, their interest payments for their loans outstanding ðil Lmt −1 Þ. YDmt = WBm  ð1 − τm Þ  ð1 + popÞ Nmt −1 − TPmt − REmt − LRmt + ib Bmt −1 + id DTmt −1 − il Lmt −1

(70)

Equation (71) depicts the dynamic evolution of the wage rates over time. Similar to the determination of the wages in the poor group, we follow a simple structure in which the wage rate grows ðWBm Þ at the productivity rate which is imposed exogenously. WBm = pdm WBm −1

(71)

6.3 Model Equations

127

Equation (72) shows that the middle-class tenants pay out a pre-determined share of their gross wealth to contribute to the poor classes as long as wealth level per capita of the middle-class tenants are above the wage rate of the poor as given in eq. (73).47

z31 =

TPmt = z31  zk  Wmt −1 ( 1; if Wmt − 1=Nmt − 1 > WBp 0; otherwise

(72) (73)

Rental rate is determined in the middle-class homeowners class so total rent payments ðREmt Þ in eq. (74) is given for the middle-class tenants. The loan repayment by the middle-class tenants in eq. (75) is a pre-determined multiple (rep), which represents average payment periods, of loan stock outstanding ðLmt −1 Þ. REmt = REmh

(74)

LRmt = rep  Lmt −1

(75)

Consumption pattern for the middle-class tenants follows the Haig-Simons consumption function which relates total consumption ðCmt Þ to expected disposable income
e  YDmt and wealth accrued up to the previous period ðWmt −1 Þ in eq. (76). The marginal propensities to consume out of disposable income and wealth are assumed to be fixed in all periods. The constant subsistence level of consumption indicates that the consumption level cannot be below the subsistence level. Cmt = Cmt + mpcmt  YDemt + mpwmt Wmt −1

(76)

Equation (77) defines net lending of the middle-class tenants ðNLmt Þ. Primary balance ðPBALmt Þ of the middle-class tenants is defined in eq. (78). NLmt = YDmt − Cmt

(77)

PBALmt = YDmt − Cmt + il Lmt −1 + LRmt

(78)

Equation (79) states that total investment in housing by the middle-class tenants ðIHmt Þ equals to the number of houses purchased during the period ðHwmt Þ multiplied by the current market price of one unit of houses ðPHm Þ in the poor class. Equation (80) calculates that number of houses purchased is a percentage share of the size of the middle-class tenants population and that share is a function of constant coefficient ðα1mt Þ, representing institutional/social factors in home ownership, and
e expected rate of return on house ownership RHm . Equation (81) puts the conditions of buying a home, which are fulfillment of the Ponzi condition and availability of collateral through an indicator variable ðz32Þ.

47 As the model is based on a benchmark and hypothetical economy, nisab level is considered as the annual wage of a poor individual. A detailed exposition is given in the simulations section.

128

6 The Model

If either of the conditions is not fulfilled, the share of homebuyers will be zero until the conditions are met in the following periods. (79) IHmt = PHm Hwmt  e (80) Hwmt = z32  α1mt + α2mt  RHm  ð1 + popÞ Nmt −1   ( Hwmt  ðð1 + popÞ  Wmt − 1 + NLmt Þ > ð1 − ltvÞ  IHmt 1; if PBALmt > 0 and ð1 + pop ÞN


z32 =

mt − 1

0; otherwise (81)




Equations (82–84) indicate that non-home-buyers 1 − Hwpt =ð1 + popÞ Npt −1 allocate their savings in tandem with the portfolio decisions of the home owners. This is because, allocation of bond and time deposits by the poor homeowners is based on some rational analysis of relative returns and other factor and the poor tenants follow the allocation decision by the poor homeowners so as to replicate their portfolio allocation. Time deposit flows in eq. (85) and conditions in eqs. (84) and (86) are similar to the bond transactions. 

BTmh −1  ncd  NLmt  T T Bmh −1 + DTmh −1 ! Bmt −1  ð1 − ltvÞ IHmt − z34  Wmt −1

Bwmt = z33 

(

z34 =

DTwpt = z35 

Hwmt ð1 + popÞ Nmt −1

 (82)

1; if Bwmt + Bmt − 1 > 0

z33 = (

1−

(83)

0; otherwise

1; if ð1 + popÞ  DCmt − 1 + NLmt < ð1 − ltvÞ  IHmt

(84)

0; otherwise T DTph −1 T BTph −1 + DTph −1



Hwpt  ncd  NLpt  1 − ð1 + popÞ Npt −1 !

 (85)

DTpt −1  ð1 − ltvÞ  IHpt − z34  Wpt −1 ( z35 =

1; DTwept + DTpt − 1 ≥ 0 0; otherwise

(86)

Equation (87) indicates that loan usage by the middle-class tenants is composed of investment in housing ðltv IHmt Þ and the loans for funding part of negative net lending in case the condition in eq. (88) is fulfilled. The indicator variable in eq. (88) takes the value of one if the two following sub-conditions are fulfilled: (i) sum of

6.3 Model Equations

129

current stock of current deposits and net lending must be negative to ensure that current deposits are not sufficient to cover funds needed for closing the net lending gap, (ii) current stock of current deposits is bigger than the collateral so that the group is eligible for taking loans. In addition, the first sub-condition of the second condition also implies that as long as the stock level of the current deposits is higher than the negative net lending (dis-saving), there is no need to resort to using credit. Internal funds are enough to cover the financing need stemmed from dissaving.   Hwmt (87) Lwmt = ltv IHmt + z36  ltv  jNLmt j  1 − ð1 + popÞ Nmt −1 ( ð1 + popÞ  DCmt − 1 + NLmt h0 and ð1 + popÞ  DCmt − 1 ið1 − ltvÞ  jNLmt j (88) z36 = 0; otherwise Current deposits are the residual item to balance the financial transactions. In this regard, eq. (89) reflects the fact that change in current deposits ðDCwmt Þ in the current period equals to the net lending and change in other financial transactions. DCwmt = NLmt − IHmt − Bwmt − DTwmt + Lwmt

(89)

Stock of current deposits in the middle-class tenants group ðDCmt Þ is equal to the sum of stock of previous period adjusted for population growth and mobility of the poor home buyers. As given in eq. (90), current stock of current deposits is composed of current value of the previous stocks after home buyers leave from the group and flow of current deposits net of the current deposit transactions by home buyers.  DCmt = 1 + popÞ  1 −  −

 Hwmt  DCmt −1 + DCwmt ð1 + popÞ Nmt −1 

NLmt − ð1 − ltvÞ IHmt

(90)

Hwmt ð1 + popÞ  Nmt −1

Stock values for time deposits ðDTmt Þ and bond ðBmt Þ are constructed alike their peers in the poor tenants group (see eqs. (91) and (92), respectively).   Hwmt DTmt −1 DTmt = ð1 + popÞ  1 − ð1 + popÞ Nmt −1 (91)   T DTmh Hwmt −1 + z34  T  ncd  NLmt  1 − T ð1 + popÞ Nmt −1 Bmh −1 + DTmh −1   Hwmt Bmt = ð1 + popÞ  1 −  Bmt −1 ð1 + popÞ Nmt −1 (92)   BTmh −1 Hwmt + z34  T  ncd  NLmt  1 − T ð1 + popÞ  Nmt −1 Bmh −1 + DTmh −1

130

6 The Model

As both home buyers and the rest of the group take part in loan transactions, the set up for the loan stock ðLmt Þ is similar to that for the stock of current deposits as given in eq. (93). Loan stock at the end of the period is adjusted for home buyers.   Hwmt Lmt −1 + Lwmt − ltv IHmt (93) Lmt = ð1 + popÞ  1 − ð1 + popÞ Nmt −1 Gross wealth ðWmt Þ in eq. (94) is calculated as the sum of financial assets. Population size ðNmt Þ in eq. (95) is calculated as the population size in the previous period multiplied by population growth coefficient net of home buyers who move to the poor home-owners group at the end of the period. Wmt = DCmt + DTmt + Bmt

(94)

Nmt = ð1 + popÞ  Nmt −1 − Hwmt

(95)

6.3.1.4 Middle-Class Homeowners Middle-class homeowners are alike their tenant peers with respect to their level of income, consumption patterns and gross wealth levels whereas the two groups differ in their real estate ownership and income from the housing wealth. Equation (96) summarizes the components of disposable income ðYDmh Þ, which consist of the total wage payments net of taxes ðWBm  ð1 − τm Þ  ð1 + popÞ Nmh −1 Þ, private transfers ðTPmh Þ to the poor class, rents income ðREmh Þ from the middle-class tenants, amortization of outstanding loans ðLRmh Þ, interest income from bond holdings ðib Bmh −1 Þ and time deposits ðid  DTmh −1 Þ, as well as, interest payments for loans outstanding ðil  Lmh −1 Þ. YDmh = WBm  ð1 − τm Þ  ð1 + popÞ  Nmh −1 − TPmh + REmh − LRmh + ib  Bmh −1 + id  DTmh −1 − il Lmh −1

(96)

Equation (97) shows how much the middle-class homeowners contribute to the private social transfers which goes to the poor class. They give a predetermined portion ðzkÞ of their gross wealth in the previous period in the form of private social transfers. The threshold to contribute for such transfers is the wage rate of the poor, as given in eqs. (98).

z41 =

TPmh = z41  zk Wmh −1 ( 1; if Wmh − 1=Nmh − 1 > WBp

(97) (98)

0; otherwise Equation (99) shows how the rent payments are determined in the middle-class group. Similar to the rental determination in the poor group, the current market

6.3 Model Equations

131

value of the houses used by the tenants ðPHm  ð1 + popÞ Nmt −1 Þ is multiplied by the rental rate (re) to reach at the total rent payments. REmh = re PHm  ð1 + popÞ Nmt −1

(99)

Equation (90) assumes that unit real estate price in the middle-class households group ðPHm Þ is concomitant to the unit price in the rich household class ðPHr Þ. The proportion term is calculated as the ratio of disposable income per capita in the middle-class homeowners to that of the rich group.   YDmh −1 =Nmh −1  PHr (100) PHm = YDr −1 =Nr −1 Equation (101) calculates rate of return in real estate, which can also be regarded as opportunity costs of acquiring real estate. The total net return is the sum of difference between percentage change in prices and interest rate paid on loans and rent income as share in total value of real estate ðREmh =HVmh −1 Þ.     PHm −1 REmh (101) RHm = −1 − il + PHm − 2 HVmh −1 As given in eqs. (102–105), amortization on debt ðLRmh Þ, consumption ðCmh Þ, net lending ðNLmh Þ and primary balance ðPBALmh Þ equations in the middle-class homeowners are calculated alike the calculations in the poor tenants group. LRmh = rep  Lmh −1

(102)

Cmh = Cmh + mpcmh  YDemh + mpwmh  Wmh −1

(103)

NLmh = YDmh − Cmh

(104)

PBALmh = YDmh − Cmh + il  Lmh −1 + LRmh

(105)

The allocation of financial wealth and savings in the middle-class homeowners are based on the simple Tobinesque lines alike the allocation of which by the poor homeowners (see the relevant section and Appendix 1 for the details). In the beginning of each period, given in eq. (106), homeowners devise a target portfolio
 T which is composed of the expected stock values of real estate PORTmh
e
e 

e  portfolio PHm  Hmh − ð1 + popÞ  Nmh −1 , bonds Bemh , time deposits DTmh and a
 pre-determined share of net lending ncd NLemh .
e  T e e = PHm  Hmh − ð1 + popÞ Nmh −1 + Bemh + DTmh + ncd NLemh (106) PORTph In tandem with the Tobinesque approach, share of each of the target stock level of financial assets in total portfolio is function of a constant coefficient, reflecting institutional and behavioral patterns in portfolio formation, own rate of return and rate of return of other financial assets, as given in eqs. (107–109).

132

6 The Model

T HVmh e = λm10 + λm11 RHm + λm12 ib + λm13 id T PORTmh

(107)

BTmh e = λm20 + λm21 RHm + λm22  ib + λm23 id T PORTmh

(108)

T DTmh e = λm30 + λm31 RHm + λm32  ib + λm33 id T PORTmh

(109)

Equation (110) states that investment in housing by the poor homeowners consist of two parts. Firstly, the investment volume is the difference between ex-ante target real
T  and the realized value in the end of previous periestate portfolio value HVmh od ðPHm −1  ðHmh −1 − Nmh −1 ÞÞ. In addition, constant population growth requires new
e  pop Nmh −1 . investment on dwellings, which is also imposed into the equation PHm  T e e − PHm  ðHmh −1 − Nmh −1 Þ + PHm pop Nmh −1 IHmh = z42  HVmh (110) Flows in bonds and time deposits are calculated as the difference between ex-ante target level of these assets and their realized value in the end of the previous period, as given in eqs. (111) and (112), respectively.
 (111) Bwmh = z42  BTmh − Bmh −1
T  − DTmh −1 (112) DTwmh = z42  DTmh At the same time, realization of transactions in eqs. (110–112) depend on fulfilment of the positive primary balance condition ðPBALmh > 0Þ alike we impose for the middleclass tenants in investment in housing. The condition in eq. (113) assures that there are sufficient funds to ensure repayment of debt obligations by preventing Ponzi finance a la Minsky. ( 1; if PBALmh > 0 (113) z42 = 0; otherwise Middle-class homeowners take loan either to finance their net lending gap or to meet their target financial portfolio in case their net lending and current deposits combined are not sufficient to solely resort to internal funds. Equation (114) shows that the maximum amount of loan usage is the total financing need ðIHmh + Bwmh + DTwmh − NLmh Þ multiplied by loan-to-value ratio (ltv) if the conditions in eqs. (115) and (116) are fulfilled. The indicator variable in eq. (115) states that
e  > 0 , and (ii) (i) expected stock value of current deposits is always positive DCmh sum of expected stock value of the current deposits and net lending must be
e  + NLmh > DCCwemh to guarantee that middlegreater than collateral level DCmh class homeowners have sufficient collateral to use credit. Equation (116) indicates that if expected stock value of the current deposits can cover the financing need

6.3 Model Equations

133




 e then there is no need to resort to take loan. In IHmh + Bwmh + DTwmh − NLmh > DCmh such a case, the transaction takes place solely in the current deposits.

z44 =

Lwmh = z43 z44 ltv  ðIHmh + Bwmh + DTwmh − NLmh Þ ( e e 1; if DCmh > 0 and DCmh + NLmh > DCCwemh z43 = 0; otherwise ( e e 1; if DCmh > 0 and IHmh + Bwmh + DTwmh − NLmh < DCmh 0; otherwise

(114) (115)

(116)

Once middle-class homeowners take loans, they also have to put collateral ðDCCwmh Þ into the loan agreement as given in eq. (117). DCCwmh = z43 z44  ð1 − ltvÞ  ðIHmh + Bwmh + DTwmh − NLmh Þ

(117)

Equation (118) defines residual transactions in current deposits ðDCRwmh Þ other than the collateral usage. Total change in current deposits then can be calculated as the sum of collateral and residual current deposit flows as given in eq. (119). DCRwmh = NLmh − ðIHmh + Bwmh + DTwmh Þ − DCCwmh + Lwmh

(118)

DCwmh = DCCwmh + DCRwmh

(119)

Following the equations defining flows during the given period, the rest of the section then identifies equations on how the stock variables are updated. Equation (120) calculates number of houses purchased by poor homeowners ðHwmh Þ during the period by dividing investment in housing by unit price of houses in the middleclass group. Equation (121) then shows the housing stock ðHmh Þ, which is the sum of housing stock of the previous period adjusted for population growth, investment in housing by the poor homeowners and investment in housing by the poor tenants who then move to the homeowner group in the end of the period. Finally, housing value ðHVmh Þ is calculated by multiplying the housing stock with the current price level in the poor income group, as given in eq. (122). Hwmh =

IHmh PHm

(120)

Hmh = ð1 + popÞ Hmh −1 + Hwmh + Hwmt

(121)

HVmh = PHm  Hmh

(122)

Stock of current deposits in the middle-class homeowners group ðDCmh Þ is composed of two parts in eq. (123). One part identifies stocks and flows of the within-group transactionsðð1 + popÞ DCmh −1 + DCwmh Þ. The subsequent part adds to the stock due to the mobility of the home-buyers from the middle-class tenants to the homeowners class.

134

6 The Model



 Hwmt  DCmt −1 DCmh = ð1 + popÞ DCmh −1 + DCwmh + ð1 + popÞ  ð1 + popÞ  Nmt −1  

Hwmt NLmt − ð1 − ltvÞ  IHmt + ð1 + popÞ Nmt −1

(123)

Similarly, current stock of time deposits ðDTmh Þ is composed of within-group transactions and stock of middle-class tenants in the previous period who then move to the homeowners group as given in eq. (124). The last formula in the equation indicates that home buyers in the tenant group liquidate part of their time deposits in commensurate with the share of time deposits in gross wealth in case they do not have sufficient current deposits. Current stock of bonds ðBmh Þ in the middle-class homeowners group is calculated alike the time deposit stock as given in eq. (125). Loan stock of the middle-class homeowners ðLmh Þ consists of within-group borrowing transactions and loan transactions by poor tenants who purchase houses (see eqs. (126)).   Hwmt DTmt −1 DTmh = ð1 + popÞ DTmh −1 + DTwmh + ð1 + popÞ  ð1 + popÞ Nmt −1 (124) DTmt −1 + z34   ð1 − ltvÞ  IHmt Wmt −1   Hwmt Bmt −1 Bmh = ð1 + popÞ Bmh −1 + Bwmh + ð1 + popÞ  ð1 + popÞ Nmt −1 (125) Bmt −1 + z34   ð1 − ltvÞ  IHmt Wmt −1   Hwmt  Lmt −1 + ltv IHmt (126) Lmh = ð1 + popÞ Lmh −1 + Lwmh + ð1 + popÞ  ð1 + popÞ Nmt −1 Gross wealth ðWmh Þ in eq. (127) is calculated as the sum of financial assets. Population size ðNmh Þ in eq. (128) is calculated as the population size in the previous period multiplied by population growth coefficient net of home buyers who move to the poor home-owners group at the end of the period. Wph = DCph + DTph + Bph + HVph

(127)

Nph = ð1 + popÞ Nph −1 + Hwpt

(128)

6.3.1.5 The Rich Class (Entrepreneurs and Rentiers) The rich class differs from the other household classes through relatively higher income, high level of capital gains and income from firm and bank profits. In effect, the rich class is both income earner and rentier. The rich class can also speculate on housing prices as the housing prices in the economy are directly determined by rich class and then diffuses to other classes.

6.3 Model Equations

135

Equation (129) summarizes the elements of disposable income for the rich ðYDr Þ, which consist of the wage income net of income taxes ðWBr  ð1 − τr Þ  ð1 + popÞ Nr −1 Þ, private transfers ðTPr Þ to the poor, profits from firms ðPF Þ and banks ðPBF Þ, interest income from their bond holdings ðib  Br −1 Þ and time deposits ðid DTr −1 Þ, as well as, amortization of loans ðLRr Þ and interest payments ðil Lr −1 Þ. YDr = WBr  ð1 − τr Þ  ð1 + popÞ Nr −1 − TPr + PF + PBF + LRr + ib Br −1 + id  DTr −1 − il Lr −1 (129) Equation (130) gives the dynamic evolution of the wage rates over time. Similar to the determination of the wages in the poor and middle-class groups, we assume that the wage rate grows ðWBr Þ at the group-specific productivity rate which is imposed exogenously. WBr = pdr  WBr −1

(130)

Equation (131) shows how the rich group makes transfers to the poor out of their wealth. They allot ðzkÞ of their gross wealth for private transfers as long as the wealth level per capita of the rich are above the wage rate of the poor, as given in eq. (132). TPr = z51 zk  Wr −1 ( 1; if Wr − 1=Nr − 1 > WBp

(131)

(132) 0; otherwise
 The rich also receives a pre-determined portion sp of the expected firm profits from the firms if the expected profit rate is positive, else there will be no profit income from the firms (see eqs. (133–134)). Similarly, they also receive the same portion of the bank profits as long as the expected profit rate by the banks is positive (see eqs. (135–136)). z51 =

PF = z52  sp  PT e ( 1; if PT e > 0 z52 = 0; otherwise PBF = z53  sp  PBe ( 1; if PBe > 0 z53 = 0; otherwise

(133) (134) (135) (136)

As given in eqs. (137–140), amortization on debt ðLRr Þ, consumption ðCr Þ, net lending ðNLr Þ and primary balance ðPBALr Þ equations in the rich group are calculated alike the other household classes. LRr = rep  Lr −1

(137)

136

6 The Model

Cr = Cr + mpcr  YDer + mpwr Wr −1

(138)

NLr = YDr − Cr

(139)

PBALr = YDr − Cr + il Lr −1 + LRr

(140)

In the model structure, real estate prices are determined within the rich class and then diffuse to the rest of the economy. The real estate composition of the rich class is tilted towards land, development areas, business centers, etc. rather than dwellings. We assume change in the prices of the land trigger price chances in housing for the rest of the economy through cost channel, imitation and the speculation. In this regard, eq. (141) gives an account of how the prices in the rich class ðPHr Þ are
 determined in the model structure. The prices are multiple γ0 + γ1  ln Y e − γ2 il of
 the long-run price level PHr . The multiple coefficient is a function of a constant,48 as a proxy for institutional determinants, expected GDP, as a proxy for level of demand in the economy, and interest rates, as a proxy for the cost of purchasing real estate. Such a construction allows for the current price level to fluctuate around the long run price level in case interest rates move in tandem with the heating up of the economy. Once the current price level of the real estate is determined, it diffuses to the rest of the economy through eqs. (41) and (100), as explained before. Equation (142) defines the rate of return for the real estate in the rich group ðRHr Þ alike the other rates of return in other income groups except there is no rental return in the equation because we assume that there is no rich tenant group in our hypothetical economy.49  PHr = γ0 + γ1  ln Y e − γ2 il  PHr   PHr −1 −1 − il RHr = PHr − 2

(141) (142)

The allocation of financial wealth and savings in the rich class are based on the simple Tobinesque lines alike the allocation of which by the middle class and poor homeowners (see the relevant section and Appendix 1 for the details). In
 the beginning of each period, homeowners devise a target portfolio PORTrT which is composed of the expected stock values of real estate portfolio
e
e 

 PHr  Hr − ð1 + popÞ  Nr −1 , bonds Ber , time deposits DTre and a pre-determined
 share of net lending ncd NLer as given in eq. (143).

48 Prices are also affected by long-run rush to the land (Piketty and Zucman 2014b), within-country mobility (Stiglitz 2015d), and demand for higher priced land as the economies getting richer (Turner 2016). 49 This is mostly due to the assumption that the rich group mostly invest in real estate and land, through which they affect all the construction, housing and rental rates in the rest of the economy.

6.3 Model Equations


 PORTrT = PHre  Hre − ð1 + popÞ  Nr −1 + Ber + DTre + ncd  NLer

137

(143)

As before, share of each of the target stock level of financial assets in total portfolio is function of a constant coefficient, reflecting institutional and behavioral patterns in portfolio formation, own rate of return and rate of return of other financial assets, as given in eqs. (144–146). HVrT = λr10 + λr11  RHre + λr12 ib + λr13 id PORTrT

(144)

BTr = λr20 + λr21  RHre + λr22 ib + λr23  id PORTrT

(145)

DTrT = λr30 + λr31  RHre + λr32 ib + λr33  id PORTrT

(146)

Equation (147) states that investment in housing by the rich class consist of two parts. Firstly, the investment volume is the difference between ex-ante target real estate
 portfolio value HVrT and the realized value in the end of previous period ðPHr −1  ðHr −1 − Nr −1 ÞÞ. In addition, constant population growth requires new invest
 ment on dwellings, which is also imposed into the equation PHre pop Nr −1 .  IHr = z54  HVrT − PHre  ðHr −1 − Nr −1 Þ + PHre  pop  Nr −1 (147) Similarly, flows in bonds and time deposits are calculated as the difference between ex-ante target level of these assets and their realized value in the end of the previous period, as given in eqs. (148) and (149), respectively.
 (148) Bwr = z54  BTr − Br −1
T  (149) DTwr = z54  DTr − DTr −1 Realization of transactions in eqs. (147–149) depends on fulfilment of the positive primary balance condition ðPBALr > 0Þ alike we impose for the other classes. ( 1; if PBALr > 0 (150) z54 = 0; otherwise Similar to the other groups, the rich class takes loan either to finance their net lending gap or to meet their target financial portfolio in case their net lending and current deposits combined are not sufficient to solely resort to internal funds. Equation (151) shows that the maximum amount of loan usage is the total financing need ðIHr + Bwr + DTwr − NLr Þ multiplied by loan-to-value ratio (ltv) if the conditions in eqs. (152) and (153) are fulfilled. Lwr = z55  z56  ltv  ðIHr + Bwr + DTwr − NLr Þ

(151)

138

6 The Model

( z55 = ( z56 =

1; if DCre > 0 and DCre + NLr > DCCwr 0; otherwise

1; if DCre > 0 and IHr + Bwr + DTwr − NLr < DCre 0; otherwise

(152)

(153)

Meeting the requirements given in eqs. (152) and (153), the rich class can take loans, which also means putting collateral ðDCCwr Þ into the loan agreement as given in eq. (154). DCCwr = z55 z56  ð1 − ltvÞ  ðIHr + Bwr + DTwr − NLr Þ

(154)

Equation (155) identifies flow in current deposits which is not covered by collateral usage, which is labelled as ðDCRwr Þ. This residual can also be defined as the residual flow item in the transaction flows. Total change in current deposits then can be calculated as the sum of collateral and residual current deposit flows as given in eq. (156). DCRwr = NLr − ðIHr + Bwr + DTwr Þ − DCCwr + Lwr

(155)

DCwr = DCCwr + DCRwr

(156)

Following the equations defining flows during the given period, the rest of the section then identifies equations on how the stock variables are updated. Equation (157) calculates units of real estate purchased by poor homeowners ðHwr Þ during the period by dividing investment in housing by unit price of houses in the rich class. Equation (158) then shows the housing stock ðHr Þ is the sum of previous period’s stock adjusted by the population growth and current period purchase of real estate. Finally, housing value ðHVr Þ is calculated by multiplying the housing stock with the current price level in the rich class, as given in eq. (159). Hwr =

IHr PHr

(157)

Hr = ð1 + popÞ Hr −1 + Hwr

(158)

HVr = PHr Hr

(159)

Stock of current deposits, time deposits, bonds and loans are updated in a simpler way compared to the other homeowner groups as there is no tenant segment in the rich class. The stock formations of these financial instruments are given in eqs. (160–163). DCr = ð1 + popÞ DCr −1 + DCwr

(160)

DTr = ð1 + popÞ DTr −1 + DTwr

(161)

6.3 Model Equations

139

Lr = ð1 + popÞ Lr −1 + Lwr

(162)

Br = ð1 + popÞ Br −1 + Bwr

(163)

Gross wealth ðWr Þ in eq. (164) is calculated as the sum of financial assets. Population size ðNr Þ in eq. (165) is calculated as the population size in the previous period multiplied by population growth coefficient net of home buyers who move to the poor home-owners group at the end of the period. Wr = DCr + DTr + Br + HVr

(164)

Nr = ð1 + popÞ Nr −1

(165)

6.3.2 Firms The firms in the model structure make set of decisions about how much to invest and in what ways to finance their production and investment whereas their production and employment decisions are determined by other sectors of the economy. Production is totally consumption-led in the sense that total production is equal to the sum of total consumption, which is determined by the household sector, total investment, which is not only determined by the firms but also government sector and housing demand, and government consumption. Employment is also exogenous as there is no unemployment in the model. In effect, total wage payment is exogenous, as well. Investment is also determined in tandem with the GDP and interest rates, which are partly outside of the scope of firm decision. The model equations in the firm sector starts with the determination of the GDP and its components. Equation (166) states that total output ðY Þ equals to total demand ðGDPÞ, which then equals to the sum of total consumption ðCÞ, total investment ðI Þ and government consumption ðGÞ. As this is a closed economy there is no external sector, so that net exports are zero by construction. Total private consumption is equal to the sum of consumption in all the household groups, as given in eq. (167). Equation (168) shows that total investment is sum of investment in productive

 capital If , investment in housing ðIh Þ and public investment Ig . Equation (169) indicates that total wage payments ðWBÞ is the sum of all wage payments to the
 households. Loan amortization LRf is like the amortization payments in the household sector, as given in eq. (170). Y = GDP = C + I + G

(166)

C = Cpt + Cph + Cmt + Cmh + Cr

(167)

I = If + Ih + Ig

(168)

140

6 The Model


 WB = WBp  Npt + Nph + WBm  ðNmt + Nmh Þ + WBr Nr

(169)

LRf = rep Lf −1

(170)

Total firm profits are calculated as the difference between total revenues ðY Þ and total
 costs WB + LRf + il Lf −1 (see eq. (171)). As we assume that the firms have only current deposits in the form of financial assets, there is no interest income but interest payments for the loans outstanding. Equation (172) states that the amount of firm profits kept ðPRÞ is the amount of total firm profits net of profits paid to the rich class. PT = Y − WB − LRf − il  Lf −1

(171)

PR = PT − PF

(172)

In the model structure, the firms have target level of investment for each period, which is set ex-ante depending on state of the economy and other determinants. On the other hand, ex-post or realized investment may diverge from the target level depending on the state of the economy and financial constraints. That is the firms may not meet their target level if they cannot finance their target level or there is no sufficient demand. This line of reasoning in investment starts with determination of the target level of productive investment as given in eq. (173). The target level of productive investment is a linear multiple of the productive capital stock in the previous period. The linear multiple is a function of a constant term, expected level of total consumption and interest rate. The constant term stands for autonomous part of the investment, which is independent of the current cycle of the economy and cost of financing. The expected consumption is a proxy for the demand in the economy that requires higher level of investment to meet the expected demand. Interest rate on corporate debt represents the cost of borrowing for investment purposes. An increase in interest rates raises the cost of borrowing and affects negatively the level of real investment. IfT = ðd0 + d1  ln Ce − d2 il Þ  K −1

(173)

The target level of investment in housing is calculated as the product of the unit of houses constructed and unit price of each construction as given in eq. (174). (174) IhT = Hwf PHf
 Units of housing constructed by firms Hwf are intimately related to the stock of houses ðHSÞ under the firms’ portfolio. Firms have a target housing stock at the end of each period. Equation (175) states that houses constructed by the firms each period is
 the difference between target level of firm housing stock HST and the stock of houses in the previous period ðH −1 Þ. In effect, firms must construct new houses that are equal to the difference given in eq. (175). The indicator variable in eq. (176) just

6.3 Model Equations

141

guarantees that the number of houses constructed is not a negative number. Equation (177) indicates that firms’ housing stock is multiple ð1 + hstÞ of expected stock of houses. It means the firms want to have unsold stock of houses above expected stock of real estate as inventory. Equation (178) defines total housing stock owned by the households. Similarly, eq. (179) calculates the investment in housing flow during the given period.
 (175) Hwf = z62  HST − H −1 (
 1; if HST − H − 1 > 0 z62 = (176) 0; otherwise HST = ð1 + hstÞ  H e

(177)

H = Hph + Hmh + Hr

(178)

IH = IHpt + IHph + IHmt + IHmh + IHr

(179)

Besides the number of units of houses constructed by firms, price of each construction is an important part of the investment in housing equation. Unit price of
 constructions PHf in each period is a multiple of previous period’s unit price level of the constructions as given in eq. (180). The multiplier consists of two parts. The first part is the growth rate of the wage rate in the poor sector because the price reflects the cost of construction for the firms and the most important cost factor of construction is the wage payments, most of which goes to the poor segment. The second part is the general price level in the economy which is also reflected in the construction profits.     WBp −1 PHr  (180) PHf = PHf −1  WBp − 2 PHr −1 As indicated before, target levels of investment are not necessarily equal to the realized levels of investment. Equation (181) shows that the productive  realized 
 investment If equals to either target level of investment IfT or maximum of expected profit rate ðPRe Þ or autonomous part of the investment ðd0 K −1 Þ. The realized level of investment then is determined by eq. (182). In case target level of investment is less than the profits, then there is no need for resorting to external financing and the target level can be reached. In such a case the indicator variable (z63) takes one. Alternatively, if the expected stock of current deposits is at least as much as the collateral need, which is collateral rate multiplied by target level of investment in excess of expected profits, the target level can again be met so that the indicator variable takes the value of one. If = z63  IfT + ð1 − z63Þ  maxðPRe , d0  K −1 Þ

(181)

142

6 The Model

  IfT − PRe < 0   z63 = 1; ifDCe > ð1 − ltvÞ  I T − PRe > f f > > : 0; otherwise 8 > > >
<
 z64 = 1; ifDCfe > ð1 − ltvÞ  IhT − IH e > : 0; otherwise

(183)

(184)

Finally, net lending in the above-the-line items is calculated as the difference between profits kept by the firms and the sum of investment in productive capital and housing as given in eq. (185).50 NLf = PR − If − Ih

(185)

Firms do not hold time deposits and bonds by assumption. The below-the-line financial assets of the firms are composed only of loans, current deposits and their
 income from houses sold. Equation (186) indicates that the firms use loans Lwf to finance their total investment needs on the condition that productive investment and investment in housing are eligible for resorting to loan. The eligibility is satisfied by the indicator variables. As eqs. (182) and (184) show the firms resort to bank loans if their internal funds are more than collateral requirements.   h
i (186) Lwf = ltv  z63  IfT − PRe + z64  IhT − IH e The transactions in current deposits equal to the sum of net lending, investment in housing receipts and loan usage as given in eq. (187). DCwf = NLf + IH + Lwf

(187)

50 The definition is an unconventional way of stating the net lending. As current account part of the firms is always zero by definition, as given in balance sheet and transaction flow tables previously, we calculate net lending from the capital account column. Indeed, adding the current account column items would make no difference in the final form of the equation.

6.3 Model Equations

143

The following equations in the firm sector show how the stocks of variables are updated. Total capital stock is sum of previous period’s stock of capital and total productive investment (private and public) in the economy, as given in eq. (188). Equation (189) shows that total housing stock, as inventory for the firms, equals to
 units of unsold houses Hwf − IH multiplied by the price of each unit of construction. Total housing capital is then given in eq. (190).


K = K −1 + If + Ig

(188)

 HVf = Hwf − ðH − H −1 Þ  PHavg

(189)

HV = HVph + HVmh + HVr + HVf

(190)

Finally, the financial stocks are updated as the sum of previous period’s stock value and flows in the current period, as given in eqs. (191–193). Lf = Lf −1 + Lwf

(191)

DCf = DCf −1 + DCwf

(192)

Wf = DCf + Kf + HVf

(193)

6.3.3 Government The government sector in the model structure has relatively simple equations. All of the government decisions related to spending are on the basis of expected national income as the decision to spend is set at the beginning of the period through budget process. Equations (194–196) state that government consumption ðGÞ, government
 investment Ig and government transfers to the poor ðTRÞ are set as a share of expected national income. G = tc Y e

(194)

Ig = ig Y e

(195)

TR = tg  Y e

(196)

Tax income solely comes from the wage payments in the economy adjusted for population growth as there is only income tax in our model, as given in eq. (197).
 Equation (198) defines net lending of the government NLg as the difference between tax income and government expenditures. 
 TAX = ð1 + popÞ  τp  WBp  Npt −1 + Nph −1 + τm WBm  ðNmt −1 + Nmh −1 Þ + τr  WBr Nr −1 (197) NLg = TAX − Ig − G − TR − ib  B −1 − rep B −1

(198)

144

6 The Model

The government sector issues bonds to finance its budget deficit, which equals to the net lending. So, in case there is budget deficit the flow in bond is positive indicating issuing bonds whereas the bond flows is negative in case there is budget surplus, as given in eq. (199). Bw = − NLg

(199)

Bond stock can be defined in two equivalent ways as given in eq. (200). It can be defined as the sum of previous period’s bond stock and bond flow in the current period. Alternatively, it is the cum of all bond stocks held by other sectors. Because the stock-flow consistency of the model equates both definitions in all periods, either method works in calibration. Finally, the net worth of the government sector equals to the negative of the bond stock (see eq. (201)). B = B −1 + Bw = Bpt + Bph + Bmt + Bmh + Br + Bba

(200)

Wg = − B

(201)

6.3.4 Banks The banking sector in the model structure does not play an active role in the sense that almost all of the transaction matrix items are closed in the banking sector. In effect, they do not make strategic decisions on lending, borrowing and asset management but they work based on the demand from other sectors of the economy. This makes the model tractable and allow for closing the equations. It can also be argued that a passive banking sector resembles a financial system in the pre-financialization period which is the proposed benchmark environment in which the variables are initialized. Because the banking sector closes the model, there is no behavioral equation in the banking system. All the equations below indicate how the accounts in the rest of the economy are closed. Equation (202) shows that banking profits are calculated as the difference between revenues and expenditures. Net lending in eq. (203) is the difference between banking profits and the profits distributed to the households. Equivalently, it is the sum of the transactions in financial instruments. Equations (204–207) calculate the loan repayments, transactions in current deposits and time deposits as the sum of their equivalent definitions in other sectors. PB = LR + ib Bba −1 + il L −1 − id DT −1

(202)

NLba = PB − PBF = Bwba + Lw − DCw − DTw

(203)

LR = LRpt + LRph + LRmt + LRmh + LRr + LRf

(204)

6.4 The Benchmark Model

145

DCw = DCwpt + DCwph + DCwmt + DCwmh + DCwr + DCwf

(205)

DTw = DTwpt + DTwph + DTwmt + DTwmh + DTwr

(206)

Lw = Lwpt + Lwph + Lwmt + Lwmh + Lwr

(207)

Flows in bonds for the banks are different from the other financial instruments as the banks have the residual amount of the flows in bonds transactions as given in eq. (208). Bwba = Bw − Bwpt − Bwph − Bwmt − Bwmh − Bwr

(208)

Stocks of financial assets for the current deposits, time deposits and loans are calculated as the sum of their peers in other sectors (see eqs. (209) and (210)), whilst the bond stock is the sum of previous period’s bond stock and flows in bonds in the current period as given in eq. (212). Gross wealth of the baking sector equals to the sum of loans and bond stock. Finally, interest rate on loans is calculated by adding a spread term to the interest rate on deposits (see eq. (214)). DC = DCpt + DCph + DCmt + DCmh + DCr + DCf

(209)

DT = DTpt + DTph + DTmt + DTmh + DTr

(210)

L = Lpt + Lph + Lmt + Lmh + Lr + Lf

(211)

Bba = Bba −1 + Bwba

(212)

Wba = L + Bba − DC − DT

(213)

il = spr + id

(214)

6.4 The Benchmark Model The previous section writes down the complete model structure. The next step is to run the benchmark model given the initial values and parameters for the aim of tracking how the financialization, leverage and debt distort the initial income and wealth inequality. Although the complete model is large and complicated, even at the upper limit of tractability, it allows us to see how different redistribution policies affect the evolution of the wealth and income inequality. In this sense, we simulate the model by changing the parameters and some of the key variables of the model structure after running the benchmark model. This section starts with introducing the initial values and parameters for a hypothetical economy that is at the stage of low degree of financialization. The subsequent sub-section runs the benchmark model and discusses evolution of some of the key variables, including the income and wealth inequality. The last

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6 The Model

sub-section then simulates the benchmark model to see how the redistribution policies discussed in the previous chapter give rise to different inequality paths toward the steady-state.

6.4.1 Initialization of the Benchmark Model – Initial Values and Parameters As discussed before, the aim of the model structure, calibration and simulations is to analyze the evolution of the proposed economy and how it behaves after certain shocks or policy shifts. In this regard, we present a hypothetical economy nonetheless drawn from empirical data to illustrate some general conclusions for a wide range of countries. As long as the initialization process depends on empirical data, plausible stocks, flows and parameters, the results can be considered as replication of the reality. The initial values of the hypothetical economy resemble a developing country which shows the characteristics of non-financialized state of the development. The economy has a low level of income and wealth inequality, the production and sophistication level of which is simple. The prices (assets, housing, etc.) do not show any sign of boom and go in line with the real sector developments. Moreover, the public redistribution mechanism, which is based on income-based redistribution, is generous through progressive taxation and high transfers. On the other hand, the model equations secure financialization throughout the calibration and simulation periods in the benchmark model. We then can see how the financialization affects the basic macroeconomic variables and distribution of income and wealth. The initialization strategy has a few pillars. Many of the exogenous values, such as the number of households, housing stock, and productivity of labor, are set hypothetically in a coherent and realistic way. The empirical values for the predetermined and endogenous variables, such as the GDP, bond stock and housing investment, are set to mimic, to some extent, the stock-flow ratios and long-run trends in a plausible for the Turkish economy. We harness the literature for determination of the parameters and some of the key ratios. The rest of the section discusses some of the key initial values in the model. The full initialization values for the transaction, flow of funds and balance sheet matrices are illustrated in Table 11 (stocks) and Table 12 (flows). Appendix 2 and Appendix 3 explains the variables and parameters in the model with their initial values and the sources of the initial values. The total number of households, which is further decomposed into five groups, is set to 10,000 and the total population exogenously grows at the rate of 2% per period. In line with the definition of the class in the literature, the bottom 40%, the middle 50% and the top 10% of the population are classified as the poor, the middle-class and the rich, respectively. Homeownership rates also matters for the poor and the

Current Deposits Time Deposits Bonds Loans Real Estate Stock Capital Stock Net Worth

+, +, +, –, +,, +,,

+,

Poor Homeowners

+, +, +, –,

Poor Tenants

Table 11: Balance Sheet of the Benchmark Model.

+,

+, +, +, –,

Middle Class Tenants

+,,

+, +,, +,, –,, +,,

Middle Class Homeowners

+,,

+, +,, +, –, +,,

Rentiers

–,, +,, +,, +,,

+,,

Firms

–,,

–,,

Govn.

    +,, +,, +,,

–,, –,, +,, +,,

+,,

Total

Banks

6.4 The Benchmark Model

147

–, +, , +,

–,

–, +,

–, –,

+, 

–, +,

+, +

+, 

Loans Sum

Time Deposits Bonds

Current Deposits

+, 

–,

–,

–,

–,

+,

–,

Net Lending Inv. in Housing

+,

+,

–,

+,

+,

bonds Interest on loans

–,

–,

+,

 –,

deposits Income from

Loan Repayment Interest on

Firm Profits Bank Profits

 –,

 –,

Private Transfers Income Taxes

+,, 

–, –,

–, +,

–,

–,

+,

+,

–,

 –,

+,,

,

–,,

+,,

–,, +,,

,,

+, 

–. –,

–, +,

–,

–,

+,

+,

–,

+, +,

 –,





–,

–,

–,,

–,,

+, +,

+, +,

Govn. Transfers

+,,

–,

–,

+,, 

+,, –,,

+,

+,,

–,,

Firms (Capital)

Firms (Current)

+,

,

Rentiers

Consumption Wages

–,

–,

Tenants

owners

–,

owners

Class

Investment Govn.

Consumption Rent

Middle Class Home

Middle

Poor

Home

Poor

Tenants

Table 12: Transaction Matrix of the Benchmark Model. Banks

Banks



,

–,

–,

,

–,





+,

   

–,, 

 





+,, –,

+,,

,



 +,

 +, –,

 

 

 





Total

–,

+,

(Capital)

 

–,

(Current)

–,

Govn.

148 6 The Model

6.4 The Benchmark Model

149

middle-class. Housing statistics and home-ownership surveys around the world (see OECD) and Turkey indicates that home ownership rate of 60% is quite appropriate (TurkStat 2011). Initial values of the unit of houses (or interchangeably real estate) for the poor and middle-class segments are set to equate number of dwellings to the units of houses. That is, houses owned by the homeowners are either used by the homeowners in the form of dwelling or rented to the tenants. So, there is no speculative purchase of real estate in the initial period. As the rich class does not have tenants, they buy real estate either to live in or to make capital gains in the future (As discussed before we may assume most of their purchases are in the form of land). We assume that the ratio of their total real estate assets to their units of dwellings is commensurate with other classes in the initial period. Estimation of the consumption function for the household groups requires parameters for autonomous consumption, marginal propensity to consume (mpc) and marginal propensity to consume out of wealth (mpw). Studies show that mpc is 0.53 and mpw is 0.04 with big difference among the income deciles (Barlas Ozer and Tang 2009; Colak, Ozturkler, and Tokatlioglu 2008). Thus, the mpc and the mpw parameters revolve around these averages with higher parameter values for the poorer groups. Autonomous consumption is set to 0.15, which is close to the literature for Turkey. Initial values for the wage rate for each of the income classes are set hypothetically whereas their levels approximate to the disposable income of each class and the wage rate differs among the income classes in line with the wage rate difference extracted from household income surveys published by TurkStat. Productivity coefficients are set hypothetically, too. They differ as per the income classes to reflect the well-known fact that wages of the poor class and the middle-class have stagnated all over the world in the last 30 years. Thus, wage rate grows below the average growth rate of the economy in the poor class and middle-class while it outperforms other classes in the rich class. Initial values for the financial assets (deposits, bonds, loans) approximate to the statistics in “Financial Accounts” published by the Central Bank of Turkey (TCMB 2016). We take long-term averages of the ratio of corresponding accounts to the GDP for the households. So, for instance, ratio of households’ total current deposits to national income is reflected in the initialization process. On the other hand, the “Financial Accounts” publication does not decompose the data over the income deciles so they differ among the groups and classes. Firms have relatively fewer number of initial values as compared to the households. One of the most important entries for the firms is the wage rates. The wage rates are not equivalent, even comparable, to the wages in the real world but their share in the GDP and their evolution goes in line with the wage rates in the real world. There are three levels of wages in the economy as they differ with respect to the classes. Relative proportion of these wages approximate to the average wage rates in

150

6 The Model

the real economy and these relative proportions are have been gotten from the Household Consumption and Income Surveys in Turkey (TurkStat 2014). Regarding capital stock and investment rates, we resort to the Penn World Tables to get an average investment rate to GDP and capital stock to GDP ratios (Feenstra, Inklaar, and Timmer 2015). Around the world, capital stock is approximately 250%–300% of the GDP so we follow suit in assigning initial value for the capital stock, which only encompasses stock of productive capital not housing stock. Similarly, long-run average investment to GDP rates are applied for the initial values of the productive investment, government investment and investment in housing values. Finally, financial assets and liabilities of the firms approximate “Financial Accounts” published by the Central Bank of Turkey (TCMB 2016). Similarly, financial variables of the government sector and the banks are extracted from the “Financial Accounts” published by the Central Bank of Turkey (TCMB 2016). While the numbers are not equal to the published numbers from official data, key ratios of the financial and real variables, as well as, their long-term trends values are quite similar to the real world realizations.

6.4.2 The Base Scenario – Evolution of the Inequalities under Financialization This section runs the base scenario to show that how accumulation of debt and housing give rise to increasing income and wealth inequality. The variables and parameters are set to resemble an economy in the state of pre-financialization bout but the evolution of the variables ensures that the economy evolves into a debtintensive state. As the goal is not to estimate the variables of a specific economy, the variables may diverge from their historical realizations to some extent but the important point is that the variables evolve in a coherent way under the stock-flow consistency requirements. One important note is that inequality indices are not one-to-one equal to the inequality realizations of the base economy. The most important reason for divergence is that our inequality indices are formed on the basis of five household groups whereas the inequality indices in the real world take the whole population composed of millions of people and income/wealth deciles. On the other hand, the quick rise in the inequality indices goes hand in hand with the increase in inequality over the last 30–40 years in the world. In the base scenarios, we employ three of the most widely used inequality indices, namely, Gini index, Atkinson index and the squared coefficient of variation. As all these indices are explained in detail in Chapter 1, we directly give the outputs and discuss their evolution in this section. Besides, some of the key variables in the economy and how the financialization distorts their evolution over time are discussed.

6.4 The Benchmark Model

151

Figure 16–18 show the evolution of the Gini, Atkinson and Squared Coefficient of Variation indices, respectively, for income and wealth. All three indices indicate stepup in the income and wealth inequality over time. While the inequality indices are not directly comparable to their realizations in the real world,51 the story goes in line with the real world data: A very step (and nonlinear) increase in inequality over time, which even approaches to the ceiling of the inequality index, as the financialization moves forward. But why? Evolution of the per capita wealth levels in Figure 19 emphasizes that the underlying cause of the step rise in the inequality indices are the explosion in the relative wealth of the top 10% in a non-linear manner. As indicated in Figure 19, increase in the relative wealth of the top 10% come along with the decline in the wealth share of the middle-class. The figure simply says it is the degradation of the middle-class in the financialization process. The story in the figure indeed accompanies to the demise of the middle-class for the last few decades all over the world. The “winnertakes-all” phenomenon is realized in the figure. Figure 20 shows how components of the GDP evolve over time. There are three stylized developments pronounced in the figure. Firstly, there is a secular decline in the share of the private consumption. As taking part in financial transactions are quite profitable and loan usage is limitless as long as the collateral requirement is satisfied, more financial and real sources are devoted to the financial activities even at the expense of consumption. The trend of the consumption in the figure coincides with the fact that lack of effective demand and over-accumulation of capital (both productive and housing capital). While the decline in the effective demand in the world economy is not as sharp as given in the model, it has declined considerable, especially in the developed and financialized economies, in the last 30–40 years anyway. Secondly, decline in the share of the productive capital is pronounced in the model. Lack of productive investment, alike the effective demand, is an acute problem for the world economy. Thirdly, and most importantly, investment in housing booms in the model, which partly substitutes productive investment and partly does consumption. Both in the decline in the share of the productive capital and boom in the investment in housing, to a large extent, overlaps with the Piketty’s return of capital thesis, which asserts that share of total capital (productive and housing) has been in an increasing trend since the WWII whereas there is lack of productive capital. From these three stylized facts from the figure, it can be confidently argued that the base scenario catches up the evolution of the main characteristics of the world

51 The real world data comes from all the income and wealth deciles whereas our artificial indexes are based on five household groups so the absolute levels of the real data and the data coming from our hypothetical economy are not comparable but their evolution and rate of change over time.

152

6 The Model

1.0

(Index Value)

0.8

0.6

0.4

0.2

84

88

92

96

84

88

92

96

80

76

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

12

8

4

0

0.0 Periods Gini Index (Income)

Gini Index (Wealth)

Figure 16: Gini Index in the Base Scenario.

1.0

(Index Value)

0.8

0.6

0.4

80

76

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

12

8

4

0.0

0

0.2

(Periods) Atkinson Index (Income) Figure 17: Atkinson Index in the Base Scenario.

Atkinson Index (Wealth)

153

6.4 The Benchmark Model

8.0

(Index Value)

6.0

4.0

96

92

88

84

80

76

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

8

12

4

0.0

0

2.0

(Periods) Coef. of Square Index (Income)

Coef. of Square Index (Wealth)

Figure 18: Coefficient of Square Index in the Base Scenario.

100%

80%

(Share)

60%

40%

20%

96

92

88

84

80

76

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

12

8

4

0

0% (Periods) Poor Tenant

Poor Homeowner

Middle-Class Tenant

Middle-Class Homeowner

Figure 19: Relative Wealth Shares of the Household Groups in the Base Scenario.

Rich

154

6 The Model

100%

80%

(Share)

60%

40%

96

92

88

84

80

76

72

68

64

60

56

52

48

44

40

36

28

32

24

20

16

8

12

4

0%

0

20%

(Periods) Consumption

Productive Inv.

Housing Inv.

Government Inv.

Government Coms.

Figure 20: Components of GDP in the Base Scenario.

economy since the financialization process outpaced the real dynamics of the economies. One direct reflection on the distortion in the components of the GDP is suppression of the private saving rates in the economy, as given in Figure 21. Over-borrowing, channeling of the scarce sources to the financial activities and speculative real estate activities are resulted in sharp decline in the saving rates, which then affects productive investments negatively. Another reflection of the distortion in the components of the GDP is over-debt syndrome in the sector. Figure 22 indicates that government net lending to GDP ratio skyrockets over time and reaches at very high levels. It should be noted here that the numbers are not compelling in the figure but this is mostly due to the fact that we do not introduce a crisis into the model so the debt can increase without disruption as long as the economy has financial funds, unlimited credit creation ability by the banks. Finally, evolution of the real estate prices in the model is of significant importance due to their direct link with the housing wealth, indebtedness and opportunity cost of productive investment. Figure 23 indicates that unit price of real estate in the rich class has increased significantly relative to the prices in the other classes. Such a divergence in relative prices is one of the core causes of increase in the relative share of housing capital and distortion in the income/wealth distribution, as discussed in the chapters of literature review.

155

6.4 The Benchmark Model

25

20

(%)

15

10

76

80

84

88

92

96

76

80

84

88

92

96

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

8

12

4

0

0

5

(Periods) Figure 21: Household Saving Rate in the Base Scenario (% of GDP).

0

(%)

–50

–100

(Periods) Figure 22: Government Net Lending in the Base Scenario (% of GDP).

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

12

8

4

–200

0

–150

156

6 The Model

300

250

(%)

200

150

100

96

92

88

84

80

76

72

68

64

60

56

52

48

44

40

36

32

28

24

20

16

8

12

4

0

0

50

(Periods) PH_r/PH_m

PH_r/PH_p

Figure 23: Change in Relative Real Estate Prices (%).

6.5 Simulations Under Alternative Redistribution Policies This section implements different redistribution policies discussed in the previous chapter and then shows their relative impact on the evolution of inequalities stemmed from debt, leverage and financialization. Chapter 4 discusses several proposals to mitigate the current inequality crisis from both conventional and Islamic views. The proposals (see Chapter 4 for details) can be juxtaposed as follows: – Income-based redistribution proposals Progressive income taxes Pro-poor transfers –

Asset-based redistribution Wealth tax Zakah GDP-linked public finance Transformation of the system from debt-based to risk-sharing instruments

We run several simulations over the base model in order to gauge relative effectiveness of each of these proposals in mitigating the income and wealth inequalities

6.5 Simulations Under Alternative Redistribution Policies

157

in the rest of this chapter. It should be noted here that the simulations do not implement the proposals in Islamic finance (zakah, GDP-linked sukuk, economywide risk-sharing) with their full features. This is an inevitable weakness in the model. For instance, zakah is treated as a kind of wealth tax with approximated nisab while it is not in reality. Similarly, we interchangeably use the word debt even after simulating the transformation of the interest-based system into a risk-sharing system (all financial instruments are linked to the GDP growth) whereas there is no debt, except qardh hassan, in the ideal Islamic system. We are fully aware of the conceptual vagueness in the simulations but, as an abstraction of the real world, a model can’t handle all aspects of the real life. The idea is that inequality mitigating effects of the risk-sharing finance in conventional models can be a guide for the prospective benefits of implementing Islamic instruments. Indeed, the Islamic framework has additional pillars, other than risk-sharing instruments, such as rule-compliant economic agents and asset-based finance. These additional pillars even strengthen the prospective benefits of implementation of the risk-sharing instruments. In the simulations, the graphs indicate change in the inequality index with respect to the index in the base scenario. In this regard, positive value in the graphs indicates that inequality increases compared to the base scenario. If the index value is denoted as ðxÞ, then the value in the graph indicates 100x% increase for the Gini and Atkinson indices. – Role of Government Transfers to the Poor: The first simulation exercise increases the government transfers to the poor from 4 to 6% of GDP, amounting to 50% increase as compared to the base scenario. This is a significant increase that shifts up the consumption and saving level of the poor. Interestingly, the simulations conclude that there is a pronounced increase in inequality as given in Figure 24. Transferring to the poor gives rise to higher level of budget deficit while it does not change saving, real estate buying and return to capital dynamics in the rich class. The result is ever increasing inequality, though this inequality diminishes towards the end of the simulation periods. – Role of Income Taxes: One of the distinguished features of the welfare state is the progressive tax rates, which has degraded over the last several decades in many of the developed countries. As discussed in the literature review chapters, tax rates are one of the most common redistributive policy tools. As argued by Piketty and other authors, imposing higher level of income taxes in a progressive manner may not be effective in taming the wealth inequality since the underlying factors causing wealth inequality have diverged from the ones causing income inequality, for which the income taxes can be effective policy tools. To see if the income taxes have significant effect on the wealth inequality, we change the income taxes for all household classes, except the poor. In the simulation exercise, tax rate for the middle-class is increased from 20 to 25% and the tax

158

6 The Model

0.08

0.04 0.02 0.00

–0.04

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–0.02

Time Gini Index (Income) Gini Index (Wealth)

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

Index Value

0.06

0.10 0.08 0.06 0.04 0.02 0.00 –0.02 –0.04 –0.06 –0.08

Time Atkinson Index (Income)

Atkinson Index (Wealth)

2.00

Index Value

1.50 1.00 0.50 0.00

–1.00

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–0.50

Time Coeff of Sq. Var.(Income)

Coeff of Sq. Var.(Wealth)

Figure 24: Inequality Effects of 50% Increase in Government Transfers (Deviation from the Base Scenario).

–

rate for the rich class from 25 to 30%. The tax rate for the poor is kept constant at the rate of 15%. Figure 25 shows the deviation of the new inequality indices as compared to the base scenario. Imposing taxes in a progressive way, indeed, are effective tools in taming the inequalities. It reduces the wealth Gini as much as 16 points whereas this pattern is reversed towards the end of the simulation periods. Effect of progressive taxation on income is more pronounced than on wealth, as discussed in the literature. Role of the GDP-linked Bonds: The third simulation exercise explores the effect of switching from fixed-interest rates to the GDP-linked payments on income and wealth inequalities. As discussed in the literature chapters, the GDP-linked bonds are considered as effective tools in reducing the government debt in the medium to long term, yet, there is no study on the nexus between the GDP-linked bonds and inequality. Figure 26 reveals interesting results in this area of research. As opposed to the outputs from the previous simulation exercises, switching to the GDP-linked bonds seems to be effective policy tools in taming the inequalities, especially the wealth inequality. The decline in Gini index is as much as 16 points, which stands for a significant shift-down given the relative size of the government bond stock in the base model. Other inequality indices also support the trend in Gini index.

0.02 0.00 –0.02 –0.04 –0.06 –0.08 –0.10 –0.12 –0.14 –0.16 –0.18

159

0.05

Index Value

0.00 –0.05 –0.10 –0.15 –0.20 –0.25 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

6.5 Simulations Under Alternative Redistribution Policies

Time Gini Index (Income)

Atkinson Index (Income)

Gini Index (Wealth)

Time Atkinson Index (Wealth)

0.00

Index Value

–0.50 –1.00 –1.50 –2.00 –2.50 –3.00 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–3.50 Time Coeff of Sq. Var. (Wealth)

Coeff of Sq. Var. (Income)

0.02 0.00 –0.02 –0.04 –0.06 –0.08 –0.10 –0.12 –0.14 –0.16 –0.18

0.05

Index Value

0.00 –0.05 –0.10 –0.15

–0.25

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–0.20 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

Figure 25: Inequality Effects of Increase in Income Taxes (Deviation from the Base Scenario).

Time Gini Index (Income)

Gini Index (Wealth)

Atkinson Index (Income)

Time Atkinson Index (Wealth)

0.50

Index Value

0.00 –0.50 –1.00 –1.50 –2.00 –2.50 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–3.00

Coeff of Sq. Var. (Income)

Time Coeff of Sq. Var. (Wealth)

Figure 26: Inequality Effects of Switching to GDP-linked Bonds (Deviation from the Base Scenario).

160

Role of Tax on Wealth and Zakah: Piketty and his successors argue that taxing the wealth is the most effective redistributive policy to reduce the wealth inequality. Similar to Piketty’s arguments, Islam also consider zakah, a proxy for tax on wealth, as one of the most effective tools to cope with the poverty, as discussed in the previous chapters. To see how effective the injunction of zakah by Islam on inequalities we impose the zakah rate of 2.5% on gross wealth as a proxy for the nisab. Figure 27 underlines the inequality effects of imposing zakah on wealth. The simulation outputs are quite staggering as it supports both role of zakah as the main redistributive policy in Islamic economics and Piketty’s arguments. In almost all of the periods, there is decline in inequality supported by all three inequality indices. Role of Ex-Post Rate of Return in Financial Transactions: The final (and partial) simulation exercise explores role of switching to rate of return tied to GDP-growth in loan and deposit transactions. As argued in the previous sections, ex-ante fixed interest rate independent of rate of return in the real economy is one of the underlying causes of the economic illnesses, including the economic inequality. As the fixed interest rate impedes realization of the real transactions in the financial transactions, the result is a misallocation of both real and financial resources. In the simulation exercise, we assume that there is a perfect profit-and-loss sharing directly linked to the GDP growth for the loans. This is the 0.00 –0.02 –0.04 –0.06 –0.08 –0.10 –0.12 –0.14 –0.16

0.10 0.05 0.00 –0.05 –0.10 –0.15 –0.20 –0.25 –0.30

Time Gini Index (Wealth) Gini Index (Income)

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

–

Index Value

–

6 The Model

Atkinson Index (Income)

Time Atkinson Index (Wealth)

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

0.00 –0.50 –1.00 –1.50 –2.00 –2.50 –3.00 –3.50 –4.00

Coeff of Sq. Var. (Income)

Time Coeff of Sq. Var. (Wealth)

Figure 27: Inequality Effects of Tax on Wealth (Zakah) (Deviation from the Base Scenario).

6.5 Simulations Under Alternative Redistribution Policies

161

0.02

0.01 0.00 –0.01 –0.02 –0.03 –0.04 –0.05 –0.06 –0.07 –0.08

Index Value

0.00 –0.02 –0.04 –0.06

Time Gini Index (Income) Gini Index (Wealth)

–0.10

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–0.08 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

case for the ideal Islamic banking. For the loans, though, we assume that the lenders pay long-run average interest rate in case the GDP growth rate is below the long-term average interest rate. Else they pay the GDP growth rate as the rate of return on loans. Figure 28 indicates that such a set up mitigates wealth inequality considerable, while the reduction in income inequality is relatively milder.

Atkinson Index (Income)

Time Atkinson Index (Wealth)

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Index Value

0.20 0.00 –0.20 –0.40 –0.60 –0.80 –1.00 –1.20 –1.40

Coeff of Sq. Var. (Income)

Time Coeff of Sq. Var. (Wealth)

Figure 28: Inequality Effects of Benchmarking Rate of Return (Deviation from the Base Scenario).

–

Imposing All of the Redistribution Policies at All: Whereas all of the redistribution policies considered in the simulation exercises mitigates inequalities at different degrees, imposing these policies together can have non-linear effects on the formation of the inequalities. Indeed, policy-making towards mitigating the inequalities should focus on different policies concomitantly to increase their effectiveness. In this regard, we impose all of the policies together to see whether there are extra benefits and feedback mechanisms. As shown in Figure 29, the results are quite staggering. The wealth inequality declines sharply to the levels even much lower than the initial inequality state of the economy. The reduction in the income inequality is also pronounced. Moreover, reduction in inequalities is below the base simulation indicating that the policy results are sustainable even in the long-run.

162

6 The Model

–0.10

Index Value

Index Value

–0.05

–0.15 –0.20 –0.25 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–0.30 Time Gini Index (Income) Gini Index (Wealth)

0.10 0.05 0.00 –0.05 –0.10 –0.15 –0.20 –0.25 –0.30 –0.35 –0.40 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

0.00

Atkinson Index (Income)

Time Atkinson Index (Wealth)

0.00 Index Value

–1.00 –2.00 –3.00 –4.00 –5.00 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

–6.00

Coeff of Sq. Var. (Income)

Time Coeff of Sq. Var. (Wealth)

Figure 29: Inequality Effects of Imposing All of the Redistribution Policies at All (Deviation from the Base Scenario).

7 Summing-up and Final Thoughts As we underscore throughout the book, Kaldorian stylized facts, which refer to a stable and sustainable economy, are no more valid. Instead, the world is living in a period of growing inequalities, which seems to be unsustainable in many respects. Beside of the literature and relevant empirical evidence that high inequality has negative social, public finance, and short-run growth effects, recent evidence indicates that high inequality is an important cause of the financial crises and detrimental to long-term economic growth. Given the role of inequality on other macroeconomic variables and concerns about its sustainability, economics of inequality, especially wealth inequality, has been a core area of research in economics. Thomas Piketty’s Capital in the Twenty-First Century has also contributed to reemergence of interest in wealth inequality. Thanks to his unique collection of data, Piketty sketched out that wealth accumulation has concentrated in the hands of a small group of super-rich and this trend is unsustainable. Moreover, he argued that increasing wealth inequality is an inevitable by-product of capitalism through the fundamental inequality mechanism. The mainstream economics still lacks in giving a full picture of why wealth inequality has increased so much. A typical defense for income and wealth inequality is meritocracy and role of capital accumulation. However, the current level of wealth inequality and its dissociation from income inequality indicate that distribution of wealth is driven by factors other than meritocracy and capital accumulation. A compelling idea to explain the divergence between wealth and income inequality is the notion of wealth residual, which is the wealth increase without concomitant increase in capital. We discuss in the book that the interest-based debt contracts are one of the fundamental drivers of the existence of wealth residual. What causes wealth residual is one part of the whole story. The next step should be how to address the wealth inequality problem. This question brings us to the rethinking the redistribution policies. The book discusses that neither the soft nor the hard redistribution proposals fully take account of the underlying drivers of wealth inequality. An alternative proposal to the wealth inequality problem stems from the notion of risk-sharing. We discuss in-depth how risk-sharing based redistribution policies can give effective response to the increasing wealth inequality problem. The risk-sharing framework secures allocative efficiency with equitable growth and mitigation of pro-cyclicality in financial system. Asset-based redistribution harnesses the benefits of the risk-sharing framework by changing the contractual framework of economic exchange. Such contractual relationships in economic exchange are incentive-compatible, enhance productivity and generate higher economic growth. Assetbased redistribution reduces or eliminates the distinction between principal and agent when and where it is due to ownership structure. A corollary of the change in the ownership structure is that the economic agents share three crucial dimensions of https://doi.org/10.1515/9783110586664-007

164

7 Summing-up and Final Thoughts

property rights: (a) right to control access to the asset; (b) right to control the disposition over its use; and (c) right of claim on the residual income produced by the asset. Islamic finance provides a comprehensive approach to asset-based redistribution through (i) risk-sharing instruments in the financial sector; (ii) redistributive risk-sharing instruments within the society; and (iii) the inheritance rules. Two important asset-based redistribution tools within the context of Islamic finance are zakah and profit-and-loss sharing macro market instruments, such as sukuk. In the book, we also develop a stock-flow consistent model to show that debtbased financial system give rise to very unequal distribution of wealth even if the initial state of the prototype economy does not have high level of income and wealth inequalities. This means the system dynamics of the debt-based economy is inherently biased towards generating income and wealth inequalities. This result then implies that elimination of the debt, interest and leverage from the system should be a priority in order to effectively cope with the wealth inequality problem. The model simulations support the view that risk-sharing instruments are quite effective in mitigating the wealth inequality. As per the simulations in the base model, risksharing instruments considered are relatively more successful in reaching out the more equitable distribution of wealth targets compared to the many proposals in the conventional literature such as pro-poor transfers and more progressive income tax schemes. Given superiority of risk-sharing proposals in the model in dealing with the inequality problem, a subsequent question then arises: How to implement the risksharing framework. In the book, we consider three risk-sharing instruments to deal with increasing wealth inequality: Zakah, GDP-linked bonds (or sukuk) and transforming the whole financial system from a debt-based regime to equity-based (or profit-loss-sharing) one. Although wealth inequality in the OIC countries is especially high, seemingly there is no economy-wide initiative on reducing the debt-based economy in their financial system. Even the zakah has not been institutionalized in many of the OIC countries yet. In such a picture, it is naïve from government to fully harness redistributive role of zakah or to fully render debt-based economies into the risk-sharing based ones, at least, in the short to medium run. One serious alternative could be to issue GDP-linked sukuk by the governments. Risk-sharing frameworks are especially easy to implement for development projects that produce revenue. Projects like tolled highways, railroads, mass rapid transit systems, airports, can be subjected to risk-sharing public finance instruments. Indeed around 30% to 40% of the government budgets in the developing world are used for financing development projects. Thus, there is a big opportunity for using the risk-sharing based instruments in the developing countries. Although it is not easy for the governments to fully implement the zakah system and changing the whole financial system from debt to equity, issuing macro-market or other risksharing instruments by the governments such as GDP-linked sukuk might be a good starting point to change the whole system in the long-run. Issuing such instruments

7 Summing-up and Final Thoughts

165

in low denominations for the poor and the-middle-class also adds to mitigate the wealth inequality because such an initiative not only increase risk-sharing instruments in the portfolios of these classes but also provide a good substitute for the dead instruments such as gold stock. As the OIC countries need to invest in infrastructure and other development related projects in huge amount in the future, financing these projects with risk-sharing instruments should be considered very seriously given benefits of risk-sharing on stable, as well as, equitable growth.

Appendix 1: Household Portfolio Choice In the Tobinesque portfolio choice model proportion of each asset in a portfolio is determined by the expected rate of returns of the assets. Economic agents hold a certain proportion of their assets based on institutional set-up, behavioral parameters and preferences. Certain proportion of the ith asset is represented by the coefficient ðλi0 Þ but this proportion is then modified by the expected rates of return on these assets and by the level of expected disposable income. The matrix form of the Tobinesque portfolio choice model is given as follows:

2

HV1mh

3

2

λi10

3 2

λi11

6 7 6 7 6 4 B1mh 5 ¼ 4 λi20 5 + 4 λi21 D1mh

λi30

λi31

λi12

λi13

32

rhm

3 2

λi14

3

λi22

76 7 6 7 λi23 5:4 iB 5 + 4 λi24 5:YDemh

λi32

λi33

iD

λi34

where the first column represents proportion of the assets in the portfolio. To be able to solve the model in a coherent system of equations, vertical and horizontal adding-up constraints are also added. The vertical adding-up constraints indicate that:

λi10 + λi20 + λi30 = 1 λi11 + λi21 + λi31 = 0 λi12 + λi22 + λi32 = 0 λi13 + λi23 + λi33 = 0 λi14 + λi24 + λi34 = 0 The above equations say that the total of the shares of each asset must sum to unity. It simply means people can only have more of one thing by having less of another in a completely coherent system. The horizontal adding-up constraints say that the sum of all the coefficients on rates of return, should also sum to zero. That is the coefficient on each own rate of return should equal the reverse sign of the sum of all the other coefficients in the row.

Note: This appendix is adapted from Godley and Lavoine (2007, 142). https://doi.org/10.1515/9783110586664-008

Appendix 2: Endogenous and Pre-Determined Variables Variable

Definition

Bpt

Bond stock held by the poor tenants

Bph

Bmt

Bmh

Br

Bba B

Bwpt Bwph Bwmt Bwmh Bwr Bwba BTmh

BTr Cpt Cph

Remarks

Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Bond stock held by the poor Initial values are approximated to the homeowners data in the Financial Accounts Report by the Central Bank of Turkey Bond stock held by the Initial values are approximated to the middle-class tenants data in the Financial Accounts Report by the Central Bank of Turkey Bond stock held by the Initial values are approximated to the middle-class homeowners data in the Financial Accounts Report by the Central Bank of Turkey Bond stock held by the rich Initial values are approximated to the class data in the Financial Accounts Report by the Central Bank of Turkey Bond stock held by the banking Initial values are calculated as the sector residual of the row bond entries Total bond stock in the Calculated hypothetically that initial economy government debt to GDP is around %, which is a good number before financialization Bond transactions by the poor Calculated from stock variables and tenants within-mobility of the groups Bond transactions by the poor Calculated from stock variables and homeowners within-mobility of the groups Bond transactions by the Calculated from stock variables and middle-class tenants within-mobility of the groups Bond transactions by the Calculated from stock variables and middle-class homeowners within-mobility of the groups Bond transactions by the rich Calculated from stock variables and class within-mobility of the groups Bond transactions by the Calculated from stock variables and banking sector within-mobility of the groups Target bond stock in the Calculated from Tobinesque portfolio portfolio by the middle-class model homeowners Target bond stock in the Calculated from Tobinesque portfolio portfolio by the rich model Consumption level of the poor Determined by the parameters, no need tenants for initialize Consumption level of the poor Determined by the parameters, no need homeowners for initialize

https://doi.org/10.1515/9783110586664-009

Initial Value ,

,

,

,

,

N/A ,,

N/A N/A N/A N/A N/A N/A N/A

N/A N/A N/A

168

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

Remarks

Initial Value

Cmt

Consumption level of the middle-class tenants Consumption level of the middle-class homeowners Consumption level of the rich class Total consumption in the economy Current deposits held by the poor tenants

Determined by the parameters, no need for initialize Determined by the parameters, no need for initialize Determined by the parameters, no need for initialize Determined by the household consumption, no need for initialize Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization

N/A

Cmh Cr C DCpt

DCph

Current deposits held by the poor homeowners

DCmt

Current deposits held by the middle-class tenants

DCmh

Current deposits held by the middle-class homeowners

DCr

Current deposits held by the rich class

DCf

Current deposits held by the firms

DCwpt

Current deposit transactions by the poor tenants Current deposit transactions by the poor homeowners Current deposit transactions by the middle-class tenants Current deposit transactions by the middle-class homeowners Current deposit transactions by the rich class Current deposit transactions by the firms Total current deposit transactions Collateral paid by the poor homeowners Collateral paid by the middleclass homeowners

DCwph DCwmt DCwmh DCwr DCwf DCw DCCwph DCCwmh

N/A N/A N/A ,

,

,

,

,

,,

N/A N/A N/A N/A N/A N/A N/A N/A N/A

169

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

Remarks

Initial Value

DCCwr

Collateral paid by the rich

N/A

DCRwph

Residual transactions in current deposits by the poor homeowners Residual transactions in current deposits by the middleclass homeowners Residual transactions in current deposits by the rich Total time deposit stock in the economy

Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization

N/A

Calculated from other variables, no need for initialization Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Time deposits held by the poor Initial values are approximated to the tenants data in the Financial Accounts Report by the Central Bank of Turkey Time deposits held by the poor Initial values are approximated to the homeowners data in the Financial Accounts Report by the Central Bank of Turkey Time deposits held by the Initial values are approximated to the middle-class tenants data in the Financial Accounts Report by the Central Bank of Turkey Time deposits held by the Initial values are approximated to the middle-class homeowners data in the Financial Accounts Report by the Central Bank of Turkey Time deposits held by the rich Initial values are approximated to the class data in the Financial Accounts Report by the Central Bank of Turkey Target time deposits stock in Calculated from Tobinesque portfolio the portfolio by the poor model homeowners Target time deposits stock in Calculated from Tobinesque portfolio the portfolio by the middlemodel class homeowners Target time deposits stock in Calculated from Tobinesque portfolio the portfolio by the rich model Units of real estate purchased Calculated from other variables, no need in the current period by the for initialization poor tenants Units of real estate purchased Calculated from other variables, no need in the current period by the for initialization poor homeowners Units of real estate purchased Calculated from other variables, no need in the current period by the for initialization middle-class tenants

N/A

DCRwmh

DCRwr DT

DTpt

DTph

DTmt

DTmh

DTr

T DTph

T DTmh

DTrT Hwpt

Hwph

Hwmt

N/A

N/A

,

,

,

,

,

,

N/A

N/A N/A

N/A

N/A

170

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

Remarks

Initial Value

Hwmh

Units of real estate purchased in the current period by the middle-class homeowners Units of real estate purchased in the current period by the rich class Units of real estate constructed

Calculated from other variables, no need for initialization

N/A

Calculated from other variables, no need for initialization

N/A

Calculated from other variables, no need for initialization Initial values equal to the total poor class population. Initial values equal to the total poor class population. Initial values equal to the . times of poor class population. Calculated from other variables

N/A

Hwr

Hwf Hp Hm Hr Hf H HST HVph HVmh HVr HVf HV T HVph

T HVmh

HVrT I IfT

Units of real estate stock in the poor class Units of real estate stock in the middle-class Units of real estate stock in the rich class Units of real estate stock in the firms sector Total stock of real estate Calculated as the sum of units of real estate for each class Target level of housing stock Calculated as a constant share of under firms’ portfolio expected real estate stock in the economy Total real estate value owned Calculated from other variables, no need by poor homeowners for initialization Total real estate value owned Calculated from other variables, no need by middle-class homeowners for initialization Total real estate value owned Calculated from other variables, no need by the rich for initialization Total real estate value owned Calculated from other variables, no need by the firms for initialization Total real estate value in the Calculated from other variables, no need economy for initialization Target housing stock in the Calculated from Tobinesque portfolio portfolio by the poor model homeowners Target housing stock in the Calculated from Tobinesque portfolio portfolio by the middle-class model homeowners Target housing stock in the Calculated from Tobinesque portfolio portfolio by the rich model Total investment in the Determined by the investment by sectors, economy no need for initialize Target productive investment Determined by the investment function, by the firms no need for initialize. Approximately % of GDP historically in Turkey.

, , , N/A N/A N/A ,, ,, N/A N/A N/A ,

N/A

N/A N/A N/A

171

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

IhT

Target investment in housing (construction expenditures)

If Ih Ig

IHpt IHph IHmt IHmh IHr IH

K

LRpt

LRph

LRmt

LRmh

LRr

LRf

Remarks

Determined by the investment in housing function. Approximately % of GDP historically in Turkey. Realized productive Calculated from other variables, no need investment by the firms for initialization Realized investment in housing Calculated from other variables, no need (construction expenditures) for initialization Government investment Determined by the last year’s GDP. Approximately % of GDP historically in Turkey. Investment in housing by the Calculated from other variables, no need poor tenants for initialization Investment in housing by the Calculated from other variables, no need poor homeowners for initialization Investment in housing by the Calculated from other variables, no need middle-class tenants for initialization Investment in housing by the Calculated from other variables, no need middle-class homeowners for initialization Investment in housing by the Calculated from other variables, no need rich for initialization Total investment in housing by Calculated from investment in housing by the households each household group, no need for initialization Productive capital stock Penn World Tables indicates that capital to GDP ratio is around .– for a large set of countries. Amortization of loan stock – Assumption is % of the total stock is poor tenants repaid every period. It means average repayment period is  periods. Amortization of loan stock – Assumption is % of the total stock is poor homeowners repaid every period. It means average repayment period is  periods. Amortization of loan stock – Assumption is % of the total stock is middle-class tenants repaid every period. It means average repayment period is  periods. Amortization of loan stock – Assumption is % of the total stock is middle-class homeowners repaid every period. It means average repayment period is  periods. Amortization of loan stock – Assumption is % of the total stock is rich class repaid every period. It means average repayment period is  periods. Amortization of loan stock – Assumption is % of the total stock is firms repaid every period. It means average repayment period is  periods.

Initial Value N/A

N/A N/A N/A

N/A N/A N/A N/A N/A N/A

,,

N/A

N/A

N/A

N/A

N/A

N/A

172

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

Remarks

Initial Value

LR

Total amortization of loan

N/A

Lpt

Loans outstanding by the poor tenants

Lph

Loans outstanding by the poor homeowners

Lmt

Loans outstanding by the middle-class tenants

Lmh

Loans outstanding by the middle-class homeowners

Lr

Loans outstanding by the rich

Lf

Loans outstanding by the firms

Lwpt

Lwr

Loans usage by the poor tenants Loans usage by the poor homeowners Loans usage by the middleclass tenants Loans usage by the middleclass homeowners Loans usage by the rich

Lwf

Loans usage by the firms

Npt

Population of the poor tenants at time t Population of the poor homeowners at time t Population of the middle-class tenants at time t Population of the middle-class homeowners at time t Population of the rich at time t Net lending of the poor tenants group Net lending of the poor homeowners group

Calculated as the sum of all loan repayments by the sectors Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Initial values are approximated to the data in the Financial Accounts Report by the Central Bank of Turkey Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups Calculated from stock variables and within-mobility of the groups We set the initial value hypothetically but the share of each group in total population is in line with the definition of the class (the bottom % is the poor, the middle % is the middle-class and the top % is the rich) and the homeownership rates reflect the OECD averages and Turkish household surveys.

Lwph Lwmt Lwmh

Nph Nmt Nmh Nr NLpt NLph

Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization

,

,

,

,,

,

,,

N/A N/A N/A N/A N/A N/A , , , , , N/A N/A

173

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

NLmt

Net lending of the middle-class tenants group Net lending of the middle-class homeowners group Net lending of the rich class

NLmh NLr NLf NLg NLba PBALpt PBALph PBALmt PBALmh PBALr PHp PHm PHr PHf

PT PF PR PB PBF REpt REph

Remarks

Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Net lending of the firms Calculated from other variables, no need for initialization Net lending of the government Calculated from other variables, no need for initialization Net lending of the banking Calculated from other variables, no need sector for initialization Primary balance of the poor Calculated from other variables, no need tenants group for initialization Primary balance of the poor Calculated from other variables, no need homeowners group for initialization Primary balance of the middle- Calculated from other variables, no need class tenants group for initialization Primary balance of the middle- Calculated from other variables, no need class homeowners group for initialization Primary balance of the rich Calculated from other variables, no need class for initialization Unit price of real estate in the Calculated from other variables, no need poor class for initialization Unit price of real estate in the Calculated from other variables, no need middle-class for initialization Unit price of real estate in the Calculated from other variables, no need rich class for initialization Price of each unit of Initial value is multiple of the long-run construction average real estate price level in the economy. Total firm profits Calculated from other variables, no need for initialization Firm profits distributed to the Calculated from other variables, no need shareholders (the rich class) for initialization Firm profits reinvested/kept by Calculated from other variables, no need the firms for initialization Total bank profits Calculated from other variables, no need for initialization Profits distributed to the Calculated from other variables, no need households (rich class) for initialization Rent payment by the poor Equals to rent received by the poor tenants homeowners by definition Rent received by the poor Calculated from other variables, no need homeowners for initialization

Initial Value N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A  N/A N/A

N/A N/A N/A N/A N/A N/A N/A

174

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

Remarks

Initial Value

REmt

Rent payment by the middleclass tenants Rent received by the middleclass homeowners Rate of return in real estate for the poor homeowners Rate of return in real estate for the middle-class homeowners Rate of return in real estate for the rich homeowners Total tax revenue of the government Private social transfers to the poor tenants (inflow) Private social transfers to the poor homeowners (inflow) Private social transfers from middle-class tenants (outflow) Private social transfers from middle-class homeowners (outflow) Private social transfers from the rich (outflow) Government transfers to the poor tenants Government transfers to the poor homeowners Total government transfers

Equals to rent received by the middleclass homeowners by definition Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization By assumption, there is only income tax in the economy. Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization

N/A

REmh RHp RHm RHr TAX TPpt TPph TPmt TPmh

TPr TRpt TRph TR

Wr

Gross wealth of the poor tenants Gross wealth of the poor tenants Gross wealth of the poor homeowners Gross wealth of the middleclass tenants Gross wealth of the middleclass homeowners Gross wealth of the rich

Wf

Gross wealth of the firms

Wg

Gross wealth of the government

Wpt Wpt Wph Wmt Wmh

Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated as proportional to the expected GDP Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization Calculated from other variables, no need for initialization

N/A N/A N/A N/A N/A N/A N/A N/A N/A

N/A N/A N/A N/A N/A , ,, , N/A N/A N/A N/A

175

Appendix 2: Endogenous and Pre-Determined Variables

(continued ) Variable

Definition

Wba

Gross wealth of the banking sector Average wage rate in the poor class Wage rate for the middle-class group in the population Wage rate for the rich group in the population Total wage payments to the labor force by the firms Gross domestic product

WBp WBm WBr WB Y YDpt YDph YDmt YDmh YDr

Remarks

Calculated from other variables, no need for initialization Values set hypothetically but they anyway resemble the real data from the Household Surveys by the TurkStat. So wage rates approximate to the disposable income and their relative levels are in line with the household surveys. Determined by the wage payments to the classes, no need for initialize Calculated from other variables, no need for initialization Disposable income of the poor Calculated from other variables, no need tenants group for initialization Disposable income of the poor Calculated from other variables, no need homeowners for initialization Disposable income of the Calculated from other variables, no need middle-class tenants for initialization Disposable income of the Calculated from other variables, no need middle-class homeowners for initialization Disposable income of the rich Calculated from other variables, no need for initialization

Initial Value N/A   , N/A N/A N/A N/A N/A N/A N/A

Appendix 3: Parameter Values Variable

Definition

Remarks

Initial Value

α1pt

Autonomous share of the poor tenants buying real estate

.

α2pt

Sensitivity of return in real estate to real estate purchases by the poor tenants Autonomous share of the middleclass tenants buying real estate

The coefficient is set to hypothetically to ensure the number of homebuyers is at least as many as the new entrants into the group. The coefficient is set hypothetically to ensure that rate of return leads to more home purchases The coefficient is set to hypothetically to ensure the number of homebuyers is at least as many as the new entrants into the group. The coefficient is set hypothetically to ensure that rate of return leads to more home purchases Studies show that is is around . of the total consumption. So we set the initial subsistence level as the . of the initial consumption level.

α1mt

α2mt

ib

Sensitivity of return in real estate to real estate purchases by the middle-class tenants Subsistence (or minimum) level of consumption for the poor tenants Subsistence (or minimum) level of consumption for the poor homeowners Subsistence (or minimum) level of consumption for the middle-class tenants Subsistence (or minimum) level of consumption for the middle-class homeowners Subsistence (or minimum) level of consumption for the entrepreneurs&rentiers Constant term (autonomous investment) Sensitivity of expected income to productive investment Sensitivity of interest rates to productive investment Interest rate on bond

id

Interest rate on time deposits

il

Interest rate on loans

Cpt Cph

Cmt

Cmh

Cr

d0 d1 d2

https://doi.org/10.1515/9783110586664-010

.

.

.

, ,

,

,

,

The coefficients are calculated from simple regression of long-term investment function for Turkey

. . .

The rate is in line with the other variables in the model and the real world The rate is in line with the other variables in the model and the real world The rate is in line with the other variables in the model and the real world

.

.

.

Appendix 3: Parameter Values

177

(continued ) Variable

Definition

ig

Average propensity to government Calculated from the long-run investment government investment to GDP ratio for Turkey Share of rent payments repaid Based on assumption back to the tenants in the form of social transfers Loan-to-value ratio It shows collateral amount in loan transactions. Historically, it is around  %- % in Turkey. Marginal propensity to consume Different studies show that the mpc is (poor tenants) around .–. in Turkey. Economics Marginal propensity to consume literature indicates that as income (poor homeowners) level increases, mpc declines. In Marginal propensity to consume developing countries, mpc for the (middle-class tenants) poor is as much as . while it Marginal propensity to consume decreases to . for the top income (middle-class homeowners) groups. So we change mpc for the Marginal propensity to consume income groups with the condition that (rich) average mpc will be around .. Marginal propensity to consume out of gross wealth (poor tenants) While there are a few studies on Marginal propensity to consume Turkey that calculates marginal out of gross wealth (poor propensity to consume out of wealth, homeowners) they reach at quite consistent results. Marginal propensity to consume Studies show that mpw to be around  out of gross wealth (middle-class %- % for the whole population. As tenants) opposed to the mpc assumption, we Marginal propensity to consume assume that mpw is higher for the out of gross wealth (middle-class higher income groups as they switch homeowners) to luxurious consumption due to the Marginal propensity to consume wealth effect. out of gross wealth (rich) Share of net saving deposited to The coefficient is set hypothetically the current deposits in the form of pre-cautionary Productivity multiplier for the It is a fact that wage rate of the poor wage rate of the poor class and the middle class have stagnated Productivity multiplier for the over the last  years. To reflect this wage rate of the middle- class fact we assign different multiplier Productivity multiplier for the numbers, which are consistent with wage rate of the rich class ILO’s () Global Wage Report. Exogenously set population Average population growth in growth rate developing world. Source: World Development Indicators

hall

ltv

mpcpt mpcph mpcmt mpcmh mpcr mpwpt mpwph

mpwmt

mpwmh

mpwr ncd

pdp pdm pdr pop

Remarks

Initial Value .

.

.

. . . . . . .

.

.

. .

. . . .

178

Appendix 3: Parameter Values

(continued ) Variable

Definition

Remarks

re

Proxy for user cost of capital coefficient Share of firm profits disbursed to the shareholders Interest rate spread (lending rate minus deposit rate, %) Average propensity to government consumption

Calculated as the long-run average . rent payment to total real estate value Assumption of  % is quite close to . the realization The coefficient is set to hypothetically .

sp spr tc

tg

τp τm τr

γ0 γ1 γ2 zk β

Calculated from the long-run government consumption to GDP ratio for Turkey Average propensity to government Calculated from the long-run transfers government transfers to GDP ratio for Turkey Income tax rate paid by poor class Given the assumption of progressive taxation and actual tax brackets data, Income tax rate paid by middlethe parameter values consistent with class Income tax rate paid by rich class the empirical data. See OECD () Table I.: Central government personal income tax rates and thresholds Constant term The coefficient is set to hypothetically Sensitivity of expected GDP to The coefficient is set hypothetically home prices in the economy Sensitivity of interest rates to The coefficient is set hypothetically home prices in the economy Zakah rate out of gross wealth Both Piketty’s work and zakah rate are around the same rate Adaptive expectations parameter Set in line with the literature

Initial Value

.

.

 %  %  %

 . . . .

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Index adaptive expectations 112 Allah (swt) 38, 39, 63, 92 allocative efficiency 83, 88, 163 ambiguity 41, 80, 81 Arrow-Debreu economy 65, 66, 67 Arrow-Hahn-McKenzie 19 Arrow Securities 65 asset price bubbles 76 asset price cycles 37 asset prices 1, 37, 70, 74, 75–78, 83, 102 asset redistribution 89 asymmetric information 71 Atkinson index 10, 11, 150 average propensity to save 37 bail-out 36, 60 balance sheet matrix 102, 146 Behavioral economics 81 Bewley models 45–51, 56, 94 bonds 77, 100, 102, 104, 109, 113, 116–119, 121–123, 125, 126, 128–132, 134–138, 142, 144–146, 158, 164 bounded rationality 20, 81 capital accumulation 22, 25, 56, 163 Capital Asset Pricing Model 67 capital gains 5, 56, 60, 70, 73, 77, 101, 109, 120, 134, 149 Capital in the Twenty-First Century 3, 18, 27, 34, 163 capital markets 7, 82 capital to output ratio 50 Classical economics 19, 62 Coefficient of Variation 9, 150, 151 cognitive completeness 81 competitive general equilibrium model 19, 20 complete contracts 19, 23 complete information 20 complete markets 19, 23–24 consumption 5, 11, 17, 20, 36, 37, 46, 48, 50, 73, 75, 82, 85, 102, 104, 106, 109, 114, 115, 119, 121, 127, 130, 131, 135, 139, 140, 143, 149–151, 157 contingent securities 19, 67 contract theory 23 credit markets 23, 24, 45, 86

https://doi.org/10.1515/9783110586664-012

debt-based economy 164 deposits 3, 74–77, 82, 89, 100, 102, 104, 113, 115–119, 121–126, 128–138, 140–142, 144, 145, 149, 160 distribution of wealth 1–3, 8–14, 16, 20, 33, 45, 47, 49, 50, 56, 84, 90, 92, 107, 163, 164 distributive justice 38, 90, 91 down payment 36 dynamic stochastic general equilibrium 98 Efficient Market Hypothesis 68 elasticity of substitution 2, 54 equitable growth 38, 83, 88, 163, 165 equity-efficiency trade-off 19, 21–23 ex-post rate of return 160 factors of production 2, 22, 28, 51, 53, 54, 56, 62, 64, 102 financial assets 1, 3–5, 7, 11, 36, 62, 77, 89, 96, 97, 109, 115, 118, 121, 122, 130, 131, 134, 137, 139, 140, 142, 145, 149, 150 financial crises 34–37, 73, 78, 83, 89, 163 financial repression 25, 26 financialization 5, 18, 19, 36, 50, 60, 61, 66, 68, 69, 99, 107, 108, 122, 144, 145, 146, 150–156 first fundamental law of capitalism 29 first inequality crisis 35 First Welfare Theorem 19 foreclosure 36, 78 Framing Effects 81 functional income distribution 50, 51 fundamental inequality 3, 29–32, 52, 56, 68, 84, 89, 163 GDP-linked sukuk 88, 89, 164 Gini index 9, 10, 150, 158 Global Financial Crisis 34, 74, 82 global imbalances 34 government transfers 113, 119, 120, 126, 143, 157 gross domestic product (GDP) 3, 23, 35, 36, 56, 57, 69, 73, 74, 88, 89, 100, 136, 139, 146, 149, 150, 151, 154, 156, 157, 158, 160, 161, 164

194

Index

hard redistribution 86, 163 heterodox economics xvii Holy Qur’an 38, 39, 63, 91 households 3, 5, 45, 46, 48, 72, 73, 77, 83, 97, 100, 102, 104, 107–109, 112–139, 141, 144, 146, 149 idiosyncratic risk 81, 82, 88 incentive-compatible contracts 23, 24, 87 inclusive growth 37 Income inequality 2, 15, 16, 17, 23, 27, 32, 44, 45, 51, 52, 56, 60, 70, 79, 85, 88, 107, 145, 157, 161, 163 inequality problem 44, 45, 79, 84, 85, 107, 163, 164 inherent rents 60, 69 institutions 1, 19, 24–26, 31, 40, 59, 82, 95 intangible capital 4 interest-based debt contracts 2, 44, 45, 58, 60, 61, 66–78, 94, 163 interest rates 25, 26, 50, 58, 60, 61–70, 73, 74, 84, 86, 94, 107, 120, 121, 131, 136, 139, 140, 145, 158, 160, 161 International Monetary Fund 89 investment 5, 17, 22, 27, 33, 36, 37, 41, 71–75, 77, 82, 85, 86, 89, 92, 95, 102, 106, 109, 114, 115, 117, 120, 121, 123, 125, 127, 128, 132, 133, 137–143, 146, 150, 151, 154 Islam 34, 38–43, 63, 79, 85, 88, 90–93, 113, 160 Islamic economics 18, 61, 64, 79, 94, 160 Islamic finance 80, 88, 89, 107, 157, 164 Kaldor 21, 22, 29, 49 Kaldorian Stylized Facts 49, 50, 163 karamah 42 Keynes 50, 95 khilafah 42 Kuala Lumpur Declaration 88 Kuznets Curve 22 labor productivity 37, 50 law of diminishing returns 52 leaky bucket 22 leverage 35, 37, 60, 68, 73–76, 78, 145, 156, 164 life-cycle savings 45, 47, 60 loans 71, 73–75, 95, 100, 102, 104, 108, 109, 113, 114, 117–119, 121, 124, 126, 128–131, 133, 135, 138, 140, 142, 145, 160, 161 long-term growth 34, 37

Lorenz curve 9, 10 lump-sum transfers 20 macro-market instruments 164 malfunctioning rents 60 marginal product of capital 5, 33, 52, 59, 69 median voter theorem 20, 24 meethaq 42 meritocracy 30, 44, 45, 50, 163 Mian, Atif 36, 72, 73, 83 middle-class 36, 89, 108, 109, 112, 113, 120, 126–135, 146, 149, 151, 157, 165 Minsky, Hyman 101, 115, 123, 132 Modern Portfolio Theory 67 Modigliani-Miller Theorem 68 mortgage 35, 120 national accounts 3, 5, 50, 94, 97 national income 31, 33, 50, 52, 95, 143, 149 Neoclassical Economics 18, 19 net lending 114–118, 121, 122, 124, 127–129, 131, 132, 135–137, 142–144, 154 New Classical Macroeconomics xiii New Institutional Economics 24, 26, 40 new normal 31 non-financial assets 1, 3–8, 11 non-parametric approach 9 OECD 3, 4, 16, 34, 35, 37, 85, 86 OIC 89, 164, 165 ownership structure 163 parametric approach 9 pareto optimal 20 patrimonial capitalism 30 perfect foresight 19, 20 personal income distribution 51 Piketty, Thomas 5, 13, 18, 21–23, 25–37, 50, 52–54, 56, 68, 79, 84, 85, 89, 93, 109, 113, 136, 151, 157, 160, 163 Ponzi condition 115, 127 precautionary savings 46 price channel 70, 73–78 primary balance 114, 115, 121, 123, 124, 127, 131, 132, 135, 137 principal-agent problems 23 private debt 35 productive capital 1, 4, 5, 32, 33, 34, 50, 51, 54, 55, 57, 108, 109, 139, 140, 142, 150, 151

Index

profit-and-loss sharing 160 progressive income tax 156, 164 property rights 24, 26, 53, 59, 86, 87, 91, 92, 164 Prospect Theory 81 public finance 88, 89, 156, 163, 164 qardh hassan 157 quadruple entry 101 Rational Expectations Hypothesis 80 rationality 19–21, 80, 81 real estate 3, 4, 5, 32, 36, 37, 45, 58, 60, 69, 72, 75–78, 107, 109, 114, 120–123, 130–132, 136–138, 141, 149, 154, 157 real estate boom 36 redistribution mechanism 38, 92, 146 redistribution policies 20, 21, 27, 34, 35, 79, 84, 89, 107, 108, 145, 146, 156–162, 163 rents 21, 44, 45, 50, 53, 56, 57, 59–62, 64, 65, 69, 113, 126, 130 risk 19, 23, 24, 33, 34, 40, 44, 46–48, 59, 60, 61, 65–68, 71, 73, 77, 79–93, 94, 107, 122, 156, 157, 163–165 risk aversion 46, 47, 81, 87 risk-sharing 24, 33, 34, 61, 66, 79–93, 94, 107, 156, 157, 163, 164, 165 risk-sharing asset-based redistribution 79–93, 94, 107 risk-sharing contracts 24, 61, 66, 79, 83, 87 risk-shifting 82 risk-transfer 79, 82 rule-compliance/ compliant 39–42, 85, 90, 157 rules of the game 26, 40, 60, 86 saving behavior 45, 46 Say’s Law xiii second fundamental law of capitalism 29 second inequality crisis 34–37, 56, 79, 84 Second Welfare Theorem 20 semi-parametric approach 9 shared prosperity 27, 83, 85 soft redistribution 85, 163

195

Solow, Robert 2, 21, 22, 29, 31, 32, 56, 96 state-dependent payoffs 19 stock-flow consistency 96, 99, 101, 102, 124, 144, 150 stock-flow consistent accounting stock-flow consistent approach 94, 96, 107 strong reciprocity 20, 21 sukuk 88, 89, 157, 164 System of National Accounts (SNA) 3, 4 systematic risk 80, 81, 89 tangible capital 4 The Great Depression xiii the Messenger (sawa) 39, 92 The Organization of Islamic Cooperation (OIC) 89, 164, 165 The World Bank 12, 27, 85 Tobin, James 96, 98, 121 Tobinesque approach 122, 131 top 1% 9, 12, 13, 16, 49 úbudiyyah 43 uncertainty 40, 41, 46, 80, 81 volume channel 70–72, 74 wages 52, 53–54, 62, 85, 87, 95, 100, 102, 104, 113, 119, 126, 127, 130, 135, 139, 141, 143, 149 walayahh 42, 43 Walras’ Law 97 Washington Consensus 25, 26 wealth inequality 1–17, 18, 24, 27, 29, 31, 33, 34, 36–43, 44–78, 79, 84, 85, 89, 94, 107, 145, 146, 150, 151, 157, 158, 160, 161, 163–165 wealth residual 2, 44–78, 163 wealth tax 34, 79, 84, 85, 93, 113, 156, 157 wealth to income ratio 1, 5, 28, 29, 55, 56, 57 World Income Lab 13 zakah 79, 85, 93, 113, 156, 157, 160, 164