Business Environment and Stock Market Development: An Empirical Analysis

Transcription

1 Business Environment and Stock Market Development: An Empirical Analysis Aleksandre Revia December 2013 to be presented at the Doing Business Research Conference: Past, Present, and Future of Business Regulation, February 2014, Washington DC, USA 1

2 Abstract This paper examines impact of regulatory environment on stock market development. The work uses sets of institutional variables: Investor Protection and Contract Enforcement indices from the Doing Business dataset, Worldwide Governance Indicators, and measures of legislative system origins to explain development of stock markets. The paper finds positive and robust relationship between institutional quality and level of sophistication of stock markets. The results are based on a sample of 71 countries, categorized by income country groups, and covering period from 2004 to For the empirical tests, Difference and System Generalized Method of Moments estimations techniques are used. Such dual analysis improves accuracy of inference about importance of quality of business regulations for stock market development. 2

3 1. INTRODUCTION Commerce and manufactures can seldom flourish long in any state which does not enjoy a regular administration of justice, in which the people do not feel themselves secure in the possession of their property, in which the faith of contracts is not supported by law, and in which the authority of the state is not supposed to be regularly employed in enforcing the payment of debts from all those who are able to pay. Commerce and manufactures, in short, can seldom flourish in any state in which there is not a certain degree of confidence in the justice of government. Adam Smith Why are we so rich and they so poor? - This is probably one of the most fundamental and frequently asked questions in development economics. For decades scientists have been researching and modeling causes of growth. Nevertheless, there has not been found any remedy to resolve the puzzle. Initial level, savings rate and innovations are considered as classical explanations of economic performance (Solow, 1956; Lucas, 1988; Romer, 1986; Mankiw et al., 1992). However, there still exists an undefined black box of factors affecting economic development. Over the time more attempts were made to fully disclose the inside of the box. To summarize, labor, physical and human capitals, as well as banking sector, stock markets, international trade, political regime, regional integration, and macroeconomic stability represent the most important variables to explain economic performance. However, all these factors are only weak determinants of growth if institutions and regulatory framework are not taken into the consideration. Environment, where business is done, represents one of the most crucial factors for sustainable economic growth. The main purpose of this paper is to address the problem of the black box of economic development. In particular, this work examines correlation between stock markets (stock exchanges) and institutions or more broadly quality of governance. The study assumes the following logical paradigm: high quality institutions lead to financial sector development, and expansion of financial sector causes better state of economy 1. Taking this logical pattern as given the paper focuses only on causal relationship between institutions and financial sector. There are many papers that address correlation of governance and financial sector, but the literature focusing exclusively on stock markets and institutional nexus is relatively scarce. Therefore, this work contributes to fill up the existing gap. The study captures classical measurement of stock market development (stock market capitalization divided by GDP) and broad and comprehensive measurements of institutions, regulations, and business environment. Institutions in economic literature are defined differently. North (1990) defines institutions as: (institutions) are the rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction. In consequence they structure incentives in human exchange, whether political, social, or economic 2. Another interesting definition and explanation of the role of institutions are given by Acemoglu et. al. (2004): 1 We do not research direction of causality; therefore in the paper we assume one way causality 2 Acemoglu, Johnson, Robinson

4 Economic institutions are endogenous. They are determined as collective choices of the society in large part for their economic consequences. Political power influences economic institutions. Economic institutions matter for economic growth, they shape the incentives of key economic growth because they shape the incentives of key economic actors in society, in particular, they influence investments in physical and human capital and technology, and the organization of production. In this paper institutions are defined as framework conditions (legislative system, laws and regulations and government services) that help develop entrepreneurship, stimulate innovations, and in general, create business friendly environment that leads to increase in overall welfare. To summarize, the main research question of this paper is to find causal relationship between development level of stock exchanges and quality of domestic institutions. The dependent variable in this study is stock market capitalization (SMC) rate 3, while main institutional variables consists of qualitative measurements of business legislation, legal procedures, government effectiveness, control for corruption, political stability, and the base of legislative system. The main limitation is related to the data used in this paper. In particular, sufficiently high quality data for such analyses is nonexistent; therefore the paper combines five different sources to build a unified dataset. The problem mainly arises because of inconsistency in time series elements. Some of the data has quite extensive time horizon, while others are going back only for a few years. The data limitations also became a main cause to shrink initial sample of 182 countries to 71. The rest of the work is organized in the following way: Section 2 presents brief overview of economic literature; Section 3 is devoted to theoretical model specifications; Section 4 describes the data; Section 5 talks about empirical methodology; Section 6 is devoted to the empirical analyses and results; Section 7 is conclusion and final remarks. 2. LITERATURE REVIEW The importance of stock markets for economic development has been widely discussed in modern economic literature (Levine, 1990), (Levine & Zervos, 1996; Capasso, 2006; Caporale et al., 2004). Although, there are many different philosophies how stock markets should develop, there is a consensus that existence of healthy and efficient stock markets is vital for persistent economic growth, (Levine and Zervos, 1998; Beck, 2006). Demirguc-Kunt and Levine (1996) and Levine et al. (1996) found that stock markets give a big boost to economic development. According to one of the leading financial economists, Levine (2011), finance and in particular stock markets, improve efficiency of capital, it is like an economy s central nervous system, choosing the best way of resource allocation. The markets play a critical role in redirecting excessive funds towards projects with the highest payoffs. Beck et al. (2000) 3 Stock market capitalisation level divided by Gross Domestic Product 4

5 describe three main channels through which financial development can enhance economic development. First of all, it can boost savings in an economy. Second, financial system can direct the accumulated funds into a real economy through investments. The last channel that works through the financial system is that tightens control over efficient use of investments and therefore allocates funds towards more productive uses. Many developing countries are heavily dependent on FDI inflows; however, after the recent financial crisis in , attracting FDI has become more challenging. Therefore, introduction of new financial institutions, such as stock markets, facilitates resource accumulation process. Dailami and Aktin (1990), Levine and Zervos (1993, 1996) argue that well established stock exchanges mobilize savings domestically as well attract international flows, lowers transactional costs, and facilitate investment into innovative and technology intensive sectors (Greenwood & Smith, 1997). One can argue that Schumpeterian approach of innovation-driven economy is well-achievable with developed stock markets. Stock exchanges, in general, are very important elements of financing private sector development. Equity markets are essential for the expansion of business in particular in the direction of funding innovative and growth-oriented projects. La Porta et al. (2000) argue that public equity financing, different from banking or even private equity system, not only contributes to expansion of business sector but it also allocates resources towards firms whose main assets are the growth opportunities. Stock markets enhance risk diversification and risksharing schemes from investors. However, in order to establish stable financial markets outside protection such as judiciary system, government institutions and legislative system, that minimize threat for investors, are essential (La Porta et al., 2000). One of the first seminal studies on the nexus of stock markets and economic growth was done by Levine (1991) and Levine and Zervos (1996, 1998). They found statistically significant relationship between economic performance and stock market development. However, their analyses did not take into consideration business regulations or governance in general. Consequently, researchers, studying similar topics, started to include institutional factors that affect business environment and concluded that quality of governance has robust and statistically significant explanatory power (Acemoglu, 2004; Glaeser et al., 2000; Doppelhofer et al., 2000). For a stable stock exchange system efficient business regulations are very important. Especially after the recent economic crisis this topic attracts much more attention from economic scholars. Some scholars advise about the need to enhance government regulation, and direct intervention into the market (Crotty, 2009; Stiglitz, 1989). But, more regulations not always mean better outcome. There are many examples when well-intentioned government interventions are frequently misused by the politically and economically powerful clans to increase their own slice of the economic pie, and not to increase the size of the pie itself (Levine, 2011). Roe (2006) argues that it s not so much the type of institutions the tools that have counted in the world s wealthier nations, but whether the nation has used them to support capital markets. Therefore, quality of government regulations matters more than the quantity (Beck, 2006; Levine, 2011). 5

6 Beck (2006) suggests introducing enabling market governance in order to achieve healthy and efficient stock market system. Governments should create institutional and informational framework that ensures market discipline, manages sound scheme of incentives and balances excessive risk-taking and probability of failures 4. La-Porta, Lopez-de-Silanes and Shleifer (2006) show that legislative system that obliges information disclosure, and therefore more private monitoring of financial system leads to stable stock market development. Governments should play a catalyzing role of enhancing social welfare 5 and not an artificial burden on economic expansion. According to Levine (2011), good governance means: high transparency of financial instruments, and sound incentive system for rational investment decisions. The efficiency of governance is studied extensively in the literature. La Porta et al. (1999) found that government institutions do play a very important role in country development. The authors introduced extensive set of indicators that capture not only the areas of government services but also broader setup of institutions such as cultural, linguistic, and religious background. Their analyses clearly stated that efficient government intervention that is associated with protection of property rights, low corruption and limited bureaucracy contribute a lot to countries overall development. From their analysis we can conclude that political freedom, democracy and political rights are the central elements for economic and in fact financial sector development. In recent economic literature scholars have used three broader measures of institutions. The first is the quality of governance, which includes corruption level, political rights, efficiency and regulatory burden. The second broad area consists of the extent of property rights and legal enforcement of these rights; and the third area measures the limits placed on the political and executive power (Edison, 2003; Yartey, 2008). In this paper all these three measurements of institutions are captured: the quality of governance is measured by Worldwide Governance Indicators; legal enforcements and property rights as well limits of executive power are measured by the Doing business indices- Investor Protection and Contract Enforcement. The degree of development of a market is strongly influenced by the regulatory system. Differences in regulatory environment are often used to explain differences in equity market development between countries such as the United Kingdom, the United States, and Canada on one side and Japan, Germany, France, and Italy on the other (Capasso, 2006). Beck (2006) found that Credit Information Index, and Creditors' Right Index have robust positive relationship with financial sector developments. Similarly, Claessens et al. (2000) argue that stock market development is more likely to accrue to countries with strong shareholders' rights, so that investors do not face expropriation fears. In other words, if country lacks sound property right system it is very unlike to attract investors. Another proof of the necessity of high quality institutions is captured in the work of Doidge et al. (2011). The authors showed existence of significant correlation between local 4 Beck Levine (2011a) 6

7 initial public offerings (IPOs) and institutional quality. In other words, the paper argued that in countries with poor institutions companies either would go to global markets for IPOs or would not be listed at all. In either case this negatively affects national stock market capitalization, and therefore the overall development level. Demirguc-Kunt et. al (2004) presented a comprehensive study of institutional and regulatory effect on financial sector. The authors used Worldwide Governance Indicators to assess institutional development, and found that regulatory framework has important and explanatory power of the differences in financial sector development 6. Even though the primary focus of the paper was banking sector, similar results are anticipated to be observed when using stock market development measurements as dependent variables. In economic literature (Djankov et al., 2008; La Porta et al., 1999) it is actively discussed that governance quality and efficiency of its institutions are primarily related to legal systems, in other words legislation that countries practice does influence the development of business sector. One of the seminal papers that studies relationship between financial development and legal basis was presented by Djankov et al. (2008). In particular, the authors focused on shareholders rights as a predictor of financial sector development. They used investor expropriation or selfdealing composite index as a main explanatory variable. Similar approach and findings were presented in the work of Shleifer and Wolfenzon (2002), who found that the share of publicly traded firms are much higher in countries that provide better legal protection of shareholders. Djankov et al. (2008) argue that development of financial sector is based upon legislative characteristics of countries. They proposed two main legislative families: common law - represented by an English law system, and civil law represented by French, German and Scandinavian systems. It is believed (La Porta et al., 1998) that a common law (which initially was developed in England) system is to defend property ownership against the attempt to public regulation and expropriation. Contrary, civil law (classical example is France) was designed to use as an instrument for state building and controlling economic life. Deeper historical perspectives on how differences in legislative system affect economy were presented by La Porta et al. (2000). They argue that, in England, common law was evolved to protect private property against the crown. Later, judiciary system applied similar ideology for protection of property owners to investors. On the other hand, civil law system was developed in France and Germany, where the central idea, for centuries was tight state regulation of economic activities. Studying closely the relationship between law origins and development of stock market, Djankov et al. (2008) conclude stock markets are on average more developed in common law countries than in civil law, particularly in French law system countries. La Porta et al. (2008) also found that common law countries on average have more sophisticated financial markets. The findings were confirmed by Doidge et al. (2011), who performed similar exercise and found number of IPOs is on average to be higher in countries with common law system. Therefore, four dummy variables were introduced to measure impact of the legislative system origin on stock markets. The dummies capture the following legislative systems: English (UK) legislative system, French legal system, German and Scandinavian legal systems. Shleifer and Woflenzon (2002) established a model that measures investors protection against expropriator actions from entrepreneurs. According to the theoretical model developed by the authors better investor protection reduces risk and costs of investments, therefore leads to 6 The authors also used bank specific indicators, which became insignificant as soon as the regulatory variables were added to regressions. 7

8 higher capital inflow into an economy. Another interesting finding of the paper was that Tobin s Q and dividends are higher in countries with better investor protection. Therefore when governments provide better security for shareholders through efficient institutional set up rate of return on investments is higher. Besides global analyses of stock markets nexus with institutions, there exist many studies focusing exclusively on developing and emerging economies. For instance Yartey and Adjasi (2009) researched equity markets based on evidence of African countries. The authors utilized difference GMM model and found that improvements in stock trading or liquidity of the stock markets, may contribute on average 3.7% of economic growth. The authors also underline the necessity of strong institutions for developing stock markets in Africa. Yartey (2007) finds that high quality institutions, such as rule of law, democracy, and in general efficiency of governance are significant determinants of low political risks and therefore create favorable conditions of external finances. These findings are in line with other works as well. For instance, Bekaert (1995) and Erb et al. (1996) state that high political risk determined by weak institutions significantly contributes to raising costs of local equity, which negatively influences stock market development in general. 8

9 3. THEORETICAL FRAMEWORK This chapter is devoted to the theoretical model used in this study. As it was discusses in the previous section economic literature on the topic exists, but it lacks detailed and comprehensive analysis of the nexus of stock market development and institutional set up. Therefore, the main contribution of this work is that it uses extensive set of institutional variables 7 for capturing impact of regulatory environment on SMC rate. In total the analyses is built upon 17 qualitative variables. Moreover, Difference and System Generalized Method of Moments (GMM) are used in one paper for more accurate and detailed inference of the results. This paper focuses on the three main areas of institutional impact. The first area covers specific laws and regulations that directly impact investors. The paper uses three different indices for capturing impact of investor protection on stock market development level. The second area focuses on commercial regulations and laws. In particular, three measurements of the efficiency of commercial contract enforcement are utilized in this study. The data for those two indicators were obtained from the Doing Business 8 project s database. The third research area focuses on broader measurements of institutions. Specifically, it captures overall quality of government services, democracy level, anticorruption measurements, legislative system bases, etc. The paper uses Worldwide Governance Indicators (WGI) and four legislative systems origins (La Porta et al., 2006) to measure broader governance impact. In light of review of the literature we found that there are not many papers using comprehensive set of indicators of governance and legislative systems; the Doing Business dataset, which provides measurements of business climate, has not been much utilized in such type of research. The following main question is formulated: What is the impact of regulatory environment and broader governance on stock market capitalization rate? To find answers to the questions above we use a behavioral model of stock market development. Based upon a classical model introduced by Calderon-Rossell (1991), we augmented it to address our particular research questions. Augmentation of the basic model gives an opportunity to explicitly underline impact of institutions on SMC rate. Modified Calderon- Rossell model provides foundation for the empirical estimations and the consequent analysis. The model also contains classical economic explanatory variables of stock market development, but due to the nature of the research questions, main focus is devoted to the institutional determinants 9. 7 Some of them were never used before for such research 8 Doing Business is a project of World Bank and International Financial Corporation. The main goal of the project is to collect and analyze the data on business regulations worldwide. 9 However, in the empirical results chapter and as well in the Appendix 2 we report the estimation results for all components of the model. 9

10 Basic Caldron-Rossell Model Theoretical set up that is used in the paper is built upon a behavioral model of stock market development introduced by Calderon-Rossell (1990, 1991) and later developed by Yartey (2008). Central hypothesis of the model is that stock market development is determined by level of economic development, which is captured by output growth, and market liquidity 10. The classical Calderon-Rossell model 11 states that stock market capitalization is a function of the number of firms listed and the value of those companies. According to, the basic model the prices of listed companies depend on number of listed companies and yearly output (measured in general by gross domestic product); and the number of listed companies is a function of output and liquidity available for the financial transactions. (1) Y=PV=Y(G,T) (2) V= V(G,P) (3) P= P (T,V) (4) where: Y- Market Capitalization (in Local Currency) P Number of companies listed on the stock market V - price of listed companies in local currency T- Turnover ratio, which measures liquidity on the market G- Measurement of output per year (GDP, GNP, or per capita measurements) As we can see Calderon-Rossell model represents an interrelated set of functions. We can express equation (3) and (4) in terms of growth rates LogV=α 1 LogG+ α 2LogT (5) LogP=ϕ 1 LogG+ ϕ 2 LogT (6) Combining equations (5) and (6) with equation (2) we get: Log Y=Log(PV)= α 1 LogG+ α 2LogT + ϕ 1 LogG+ ϕ 2 LogT (7) We can factor out similar terms in equation (7) Log Y= (α 1 +ϕ 1 )LogG + (α 2+ϕ 2 ) LogT (8) 10 measured by stock market turnover ratio (introduced by Levine and Zervos 1996, 1998) 11 This set up was provided in Yartey (2008) paper. 10

11 The model specification in (8) can be expressed as the reduced form behavioral model: LogY=β 1 LogG+ β 2 LogT (9) Where β 1 =(α 1 +ϕ 1 ) (10) β 2 =(α 2 +ϕ 2 ) (11) According to the basic Calderon-Rossell model development of stock markets is determined by the sophistication level of an economy and availability of liquidity sources. However, as the main topic of the paper is to study the impact of institutions and regulations on stock market development the basic Calderon-Rossell model is being modified to reflect the interest of this particular research. Augmented Caldron-Rossell (AC-R) Model In order to capture the role of institutions an additional element was introduced into the model. S=PVB (12) B=B(G,I) (13) Where: I - institutional factors, which includes: regulations, legislative systems, etc. B is a behavioral function of business confidence S- Stock Market Capitalization (the same as Y in the original Calderon-Rossell model) The behavioral function B is determined by the two factors: development level of economy (GDP, GNP, and per capita measurements) and overall business environment in a country 12. The augmented Calderon-Rossell model is based on the following assumptions: Assumption 1 Investors, in general, prefer doing business in a large 13 economy (size of output) as opposed to a small economy. Assumption 2 Investors, in general, prefer doing business in a country with better institutional quality (for instance to reduce a risk of repatriation, decrease transaction cost, etc) 12 These both factors might be related to each other, however the causal relationship is not straightforward; there are examples (such as China, Russia, etc) when big size of economy does not mean business friendly environment, and contrary some countries with relatively small economies (Singapore, Georgia) have much more business friendly regulations. Business friendly environment is measured by the Doing Business Easy of Doing Business Rank In terms of output, measured by gross domestic product, or GNI 11

12 Following the classical scheme of Calderon-Rossell model we can rewrite equation (13) into a logarithmic form: Log B = θ 1 LogG + θ 2 LogI (14) Now we can combine equations (7), (12) and (14): Log S=α 1 LogG+ α 2LogT + ϕ 1 LogG+ ϕ 2 LogT+ θ 1 LogG + θ 2 LogI (15) After factoring out similar terms we get: Log S= (α 1 + ϕ 1 + θ 1 ) LogG + (α 2 + ϕ 2 ) LogT + θ 2 LogI (16) We can rewrite (16) in a reduced form equation as follows: Log S= φ 1 LogG + φ 2 LogT + φ 3 LogI (17) Where: α 1 + ϕ 1 + θ 1 = φ 1 (18) α 2 + ϕ 2 = φ 2 (19) θ 2 = φ 3 (20) To avoid omitted variable bias, number of control variables will be included into the model. The matrix of control variables is being denoted by K, therefore the model will have the following form: LogS= φ 1 LogG + φ 2 LogT + φ 3 LogI + φ 4 LogK (21) The equation (21) represents an augmented Calderon-Rossell set-up which is the theoretical foundation of the paper. It is combination of the classical determinants of stock market development and the institutional factors. Because of the dynamic characteristics of the data for the empirical exercise the following regression was estimated: S i,t =α i +φs i,t-1 +β 1,k E it +β 2,jq I i,t +ϵ i,t (22) Subscript Definition i Country j,q institutional variable k economic variable t time period ( ) 12

13 where S it represents stock market capitalization as a percentage of a country s GDP. S it-1 is a lag variable of SMC rate, which is an important determinant of current capitalization rate 14, Variable E it stands for all economic factors that affect stock market development (such as GDP, liquidity, savings bank assets, etc ); I it is the matrix of the main interest variables such as institutional quality, regulatory framework and legislative bases. Appendix 1 contains detailed explanation of all variables used in all empirical exercises. 14 Yartey (2008, 2010) 13

14 4. DATA The purpose of this paper is to identify causal relationship between institutional quality and financial sector development, in this particular case, stock markets. Therefore, constructing comprehensive dataset based on reliable sources is crucial for the analysis. The database for the study combines five different sources. It consists of quantitative variables (dependent variable and number of control/economic variables), as well as qualitative variables measuring governance and institutional level. Stock market capitalization parameter was extracted from Financial Structure Dataset 15. The dataset presents trends in structure and development of financial sector and it contains data from 1960 to For this study the time horizon was shrunk to 6 years from 2004 to Beside the stock market capitalization, two additional explanatory variables were taken from the same dataset: index measuring domestic bank assets over GDP and another variable to control market liquidity- value of all shares traded on the stock markets to GDP. The economic control variables are constructed from the World Development Indicators and World Development Finance 17 datasets. The selection of control variables are influenced both by the existing economic literature as well as by number of empirical tests. The final regressions are based on the following control variables: real GDP and real GDP per capita, foreign direct investment inflow, private capital flow (which is mainly portfolio investments), gross savings as percentage of GDP and inflation. Key explanatory variables which are used to measure the institutional impact, are two indices constructed by the World Bank Group s Doing Business (DB) project. The Doing Business project was launched in 2002 with the goal of creating comprehensive measurements of business regulations worldwide. 18 Taking into account the nature of the thesis we focus on only two indices: Protecting Investors and Contract Enforcement indicators. The Doing Business Investor Protection index is a composite index of three sub dimensions, it combines 19 : Extent of disclosure index-measures transparency of investment transactions. Extent of director liability index- measures the liability for conflict of interest, such as using managerial position for self-benefit. Ease of shareholder suits index - captures ability of shareholders to sue managers for selfdealing 20. Investor Protection Index (IPI) is a mathematical average of all three above-mentioned indices. Doing Business collects data for these indices, through direct survey of business lawyers It was necessary to match the availability of other variables According to Doing Business web site about there were almost 900 articles published in peer-reviewed journals, and about 2,332 working papers utilizing Doing Business data. Business.pdf 19 Description provided by Doing Business methodology, 20 Self dealing is defined as misuse of corporate power of the managers for their own benefit. 14

15 and analysis of corporate and security regulations. The Investor Protection indices are measured on 0 to 10 scale higher value indicating better performance. Therefore, anticipated sign of these parameters has to be positive. The annex provides detailed information on the methodology of constructing the investor protection indices. Another Doing Business measurement used in this study is a parameter of quality and efficiency of judiciary system- contract enforcement. In particular, it consists of three measurements of commercial contract enforcement, measuring number of procedures, costs and time required to enforce contracts. The indices were built upon the evaluation of commercial sales disputes in national courts. It is based on comprehensive monitoring of local legislation system, court procedures and surveys of lawyers and judges 21. In this study we use the following indicators: Procedures of Enforcing Contracts- number of procedures needed to: file a claim and steps to file claim, obtain judgment and enforcement. Time for Enforcing Contracts-days required resolving commercial sale dispute through national court system. Cost of Enforcing Contracts- overall cost associated with contact enforcement, which may include expenses related to lawyer, court fees, etc. The cost is calculated as the percentage of a claim. According to, the methodology higher value of the indices represents lower efficiency of commercial contract enforcement system. Therefore, anticipated impact of each of these explanatory variables on stock market development should be negative. Other qualitative measurements of governance and institutions which are used in the paper are obtained from the following sources: Worldwide Governance Indicators (WGI) were taken from World Bank Institute. This data combines composite indices that measure six areas of governance. Each of these indexes is aggregate measure based on hundreds of specific variables measuring various dimensions of governance. They are taken from about 33 data sources provided by 30 different organizations. These data reflect views of public, private sector and NGO experts, as well as thousands of citizen survey respondents worldwide about governance and public sector services. In this paper all six indicators: rule of law, governance efficiency, control for corruption, political stability, rule of law and regulatory quality 22 are utilized. Beside the direct measurements of governance quality and effectiveness of the institutions some authors (La Porta et al., 2008) suggest using country s legislative system as a proxy of government efficiency. Based on the existing literature countries can be divided into four categories depending on the origin of their legislative systems. We created four dummy variables to capture the impact of English, Scandinavian, French and German based systems. According to, La Porta et al. (2008) findings English based legislative system is more favorable to stock market development than French and German based systems. Therefore, the anticipated 21 Doing Business methodology, 22 Description is given in the appendix 15

16 marginal impact of indicator for English based legislative system should be positive. This hypothesis was tested with the system GMM methodology 23. Initially the dataset was constructed for 182 countries with the time horizon of six years, from The countries in the dataset were divided into four categories: high income countries, upper middle income countries, lower middle income countries and low income countries 24. Categorizing the data gives more flexibility of researching group specific relation between dependent and explanatory variables. To capture the category specific impact regional indicator variables and several interaction terms with institutional variables have been created. As it is shown in the next chapter such approach gives an opportunity to observe interesting patterns especially using System GMM methodology. Although, initial dataset was more comprehensive and captured most of the sovereign states, because of the missing values, a more balanced sub-sample of 71 countries was derived 25 which consists of countries that fall within the following three income groups: high, upper middle and middle income countries. 23 Using difference GMM with dummy variables is inappropriate. 24 Per capita income level, the World Bank methodology used in the Financial Structure Dataset. 25 Missing values for Gross Domestic Savings, Gross Capital Formation and Gross Fixed Capital Formation for the following countries Australia ( average for 2009 ), Fiji ( average for 2009, Iran ( average for 2008 and 2009), Jamaica ( average for 2004), Montenegro, Serbia ( average of Private capital flow for period), were manually added year s value was used for Easy of Shareholder Suits Index in 2004 and 2005 years. static assumption for this index for the period of years. To fill the existing gap of Stock Market Value Traded to GDP index the following formula was used{ SMVT t=((stock Market Capitalization t+1- Stock Market Capitalization t)/ Stock Market Capitalization t +1)* SMVT t-1*ρ SMVT, Stock Market Capitalization*2.5} Using this formula 99% of correlation between modified and original data for Stock Market Value Traded to GDP index was achieved. 16

17 5. ESTIMATION METHODOLOGY In general, estimation of models using dynamic panel data can lead to multiple problems due to: Time invariant country fixed characteristics which can lead to inconsistent and biased estimators (Yartey, 2008). Possible endogeneity of regressors. Orthogonality condition between error term and regressors might not be satisfied. That is many variables are jointly determined with independent variable or have two-way causality. Dynamic process, meaning that the present value of dependent variable is determined by its previous realization, requiring inclusion of lagged dependent variable as an explanatory variable,, gives rise to autocorrelation hence biases estimator upward when using OLS. The number of time periods of available data,, is small. The problem that may arise with short time horizon is that once there is a shock it can be carried over to next periods, resulting in biased estimates. The idiosyncratic disturbances may feature heteroskedasticity and serial correlation, but are uncorrelated across individuals. There does not exist perfect instrumental variable that can be used to satisfy strict exogeneity requirement. The basic way is to use standard within transformation, such as first differencing, to tackle the problem of fixed effects. However, in this particular case 26 first differencing does not provide the remedy; on the contrary, it leads to a downward bias (Nickel, 1981) and inconsistency, caused by other problematic features of the model, such as endogeneity and presence of lagged dependent variable. Classical approach to tackle the problem of endogeneity is to use instrumental variables (IV) and perform two stage least square estimations (2SLS). But, taking into account the nature of regressors finding efficient and exogenous IV variables does not seem feasible. According to, the modern theory the most efficient way of addressing the problems above is to use Generalized Methods of Moments (GMM) techniques (Arellano & Bond, 1991; Bond, 2002; Roodman, 2006; Yartey, 2010). The GMM methodology is built upon two stage transformation process. On the first stage, we address the problem of country specific fixed effect by first differencing the data:, In this particular case the equation can be rewritten as: where ΔS it =αδs it-1 +Δβ 1 E it +Δβ 2 I it +Δβ 3 K it +Δϵ it Thus, we are able to eliminate the country specific effect; however, the problem of autocorrelation and endogeneity might still exist (such as correlation of regressors with error terms). 26 Mainly because of endogeneity problem 17

18 Therefore, on the second stage, in order to tackle endogeneity problem GMM uses instruments created from lags of the variables. As it is shown in Roodman (2006) Holtz-Eakin, Newey and Rosen (1988) such approach introduced more efficient way of creating instruments. In particular, they argue about constructing IV from the second lag of dependent variable for each t and assuming zeros for missing observations, this method is called GMM-style incrementing. [ ] it is assumed that where z is matrix of transformed instrumental variables, orthogonal to the disturbances. There exist two types of dynamic GMM estimators that are widely used by econometricians. The first relatively older version of dynamic GMM, Difference GMM was introduced by Arellano and Bond (1991). This approach uses classical procedures of differencing data and uses suitable lag values of each endogenous variable as its exogenous instrument. Lagged values of variables are argued to be strictly exogenous regressors which enter the first differenced equation. However later, in the literature it was argued that Arellano-Bond (1991) model might produce poor instruments for the first differenced variables which affects the efficiency of the results; moreover Blundell and Bond (1998) showed that this method might produce weak estimates for the variables that follow random walk, has poor finite sample properties, and it is downwards biased, especially when time horizon is small. Therefore, on a later stage augmented version of GMM- System GMM was introduced, derived from the estimation of a system of two simultaneous equations, one in levels where lagged first differences are used as instruments, and the other in first differences, having lagged levels as instruments. In particular, Arellano and Bover (1995) and Blundel and Bond (1998) and later Roodman (2006) have shown that adding original level equation to the system of firstdifferenced equations brings additional moment condition that increases the efficiency of the estimators and reduces finite sample bias. Nevertheless, the strict assumption that unobserved cross section specific effect is not correlated with the first differenced idiosyncratic error term has to be made. Bond et al. (2002) also argue that OLS and Within estimators, such as Fixed Effects estimator, should be considered as an upper and lower bounds of unbiased estimator; hence if the Difference GMM coefficient does not lie in these bounds, use of System GMM is recommended. 18

19 6. EMPIRICAL ANALYSIS AND RESULTS This chapter summarizes findings of the empirical exercise. As was described in the methodology section both Difference and System GMM estimations are used. Such sensitivity analysis 27 enhances accuracy of the statistical inference. Expected signs of the regressors are presented in Table 6.1. It shows anticipated impact of explanatory variables on stock market capitalization rate, built upon economic intuition and prediction of the theoretical model. Table 6.1. Expected impact Variables Lag of Stock Market Capitalization Log of constant GPD per capita Log of constant GDP Gross Savings/GDP Foreign Direct Investments/GDP Stock market Total Value Traded/GDP Inflation Domestic Bank Assets/GDP Private capital flows/gdp Protecting Investors - Extent of disclosure index Protecting Investors - Extent of director liability index Protecting Investors - Ease of shareholder suits index Protecting Investors Index Procedures for contract enforcement Time needed for contract enf Cost of contract enforcement Voice and Accountability Government Efficiency Political Stability Control for Corruption Rule of Law Regulatory Quality English legal system Scandinavian legal system German legal system French legal system Expected Sign Positive Positive Positive Negative Positive Positive Negative Negative Positive Positive Positive Positive Positive Negative Negative Negative Positive Positive Positive Positive Positive Positive Positive Postive/Negative Negative Negative Estimation results are presented in Table 6.2. For brevity, only the coefficients on the variables of interest are included, the tables with the complete output are given in the appendix. Before performing more comprehensive and precise analysis an auxiliary estimation was carried out where averages of the observations for each country over the time period were taken. Ordinary OLS cross section estimation was used to evaluate long term effect of the each indicator. First column of the Table 6.2 presents results of the auxiliary analysis. Most of the estimated coefficients are not statistically significant, except investor protection indicators, 27 Using Difference and System GMM estimation methodologies. 19

20 where we see positive and significant effects. Even though averaging gets rid of country specific fixed effects problems, we still have endogeneity in the regressors. The next sets of estimations are Pooled OLS estimations. Protecting Investors Index, as well as its main components (Extent of Disclosure Index, Extent of Director Liability Index and Ease of Shareholder Suit Index), has insignificant effect on the dependent variable and two of them have opposite signs compared to the predictions. Indicators that control for procedures and time needed for contract enforcement have positive significant effect while cost of contract enforcement has positive but insignificant effect, which also contradicts with our theoretical predictions. These coefficients are biased and inconsistent 28 ; nevertheless we can use these results in conjunction with the estimation results from Fixed Effects models to judge the validity of our estimates from GMM procedures. Table 6.2. Stock Market Capitalization and Institutional variables. Period Protecting Investors - Extent of disclosure index Protecting Investors - Extent of director liability index Protecting Investors - Ease of shareholder suits index Protecting Investors Index Procedures for contract enforcement Cost of contract enforcement Time needed for contract enforcement Voice and Accountability Government Effectiveness Political Stability Corruption Perception Rule of Law Regulatory Quality OLS Cross Section OLS t statistics in parentheses/ * p < 0.05, ** p < 0.01, *** p < Note: column 6 summarizes results when controlled for upper middle income countries FE Difference GMM System GMM System GMM' (1) (2) (3) (4) (5) (6) ** ** * *** (2.74) (0.27) (-1.18) (2.70) (-2.55) (4.61) ** *** *** (2.75) (-1.87) (0.19) (4.82) (-6.91) (0.99) *** *** ** (1.27) (-0.47) (0.92) (22.52) (-5.39) (2.87) 0.124*** *** *** *** (3.59) (-0.99) (-0.39) (8.08) (-4.99) (9.02) *** *** *** *** (-0.62) (3.71) (-0.81) (-8.69) (15.81) (-5.44) *** *** (1.33) (0.27) (-1.38) (-21.78) (9.91) (-1.64) *** *** *** *** (0.79) (4.49) (-1.30) (-7.74) (8.86) (-8.04) * *** *** *** (-1.60) (0.62) (2.05) (12.28) (7.31) (7.31) ** *** ** ** (0.55) (-2.62) (-0.79) (13.85) (2.96) (2.96) * *** *** *** (-1.07) (-2.05) (-0.25) (15.35) (6.56) (6.56) * *** (-0.17) (-2.24) (0.79) (-12.35) (2.02) (2.02) * *** *** *** (-0.71) (-2.01) (0.93) (6.19) (9.17) (9.17) * (0.27) (-2.44) (0.14) (0.43) (1.75) (1.75) 28 Because of the nature of our dataset, which contains lagged value, has a cross-section specific effect and also characterized with consistent endogeniety problem, estimation obtained from Ordinary Least Square methodology will be biased and inefficient. 20

21 Third column in the table provides estimation results of the Fixed Effect model. We can see that although, most variables are insignificant estimators now exhibit more intuitive signs by plausible direction of influence. Even though the overall Protecting Investors Index retains negative sign, two of its three components now have positive effect. The next three variables controlling for contract enforcement costs, time and procedures exhibit negative sign, conforming the theoretical predictions. Finally, three of WGI indicators reveal positive effect on market capitalization. As already discussed, the estimators of this model still suffer from bias, despite the fact that the country specific unobserved effects have been eliminated. As it was mentioned in the theoretical chapter OLS and FE provide an upper and lower bound for the unbiased estimators 29. The next three columns of the table show findings of GMM estimations. Column four describes results of Difference GMM estimation. All of the indicators have significant explanatory power; moreover the signs of the estimates mostly 30 conform to its theoretical predictions almost in every case. These results confirm the thesis of positive influence of institutional variables on the financial sector. Therefore, we can infer that institutional quality, including specific business regulations (captured by the Doing Business indicators) and broader Worldwide Governance Indicators are significant determinants of stock market development level. It should also be mentioned that the estimators hardly remain in the boundary between OLS and Fixed Effects. Therefore, as the next step 31 system GMM estimations are used for more accurate results and inference. The last columns describe estimation results of the System GMM model. First we analyze whole sample and record results in column five. We can see that all WGI indicators have statistically significant and positive effect; however coefficients of Protecting Investors and Contract Enforcement indices flip signs and run against our hypothesis, but when we control for country group effect System GMM estimations exhibit intuitive and robust results. Estimation outcome for upper middle income countries is presented in column 6. Overall, using both Difference and System GMM methods this paper finds that the variables exhibit correct direction of influence. The magnitudes of which are not overly sensitive to change in specifications between System and Difference GMM. Next step in the analysis is to further understand the coefficient estimates on System GMM and examine possibility that the effect of these indicators varies with the income groups. As it was described in the data section we have divided countries by categories based on income per capita level, and several interaction terms are employed for each of Doing Business indicators. Table 6.3 below depicts results of this exercise. First of all, when we control for each income group we see that the lower middle income countries have experienced the largest growth in market capitalization in this period, followed by upper middle income countries. 29 Roodman (2006) and Arellano and Bond (1991) 30 Except for Corruption Perception index 31 Bond et al. (2002) 21