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Property rights and the stock market-growth nexus
North American Journal of Economics and Finance 32 (2015) 48–63
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North American Journal of Economics and Finance
Property rights and the stock market-growth nexus Adam Ng a,b,∗, Ginanjar Dewandaru c, Mansor H. Ibrahim a a International Centre for Education in Islamic Finance (INCEIF), Lorong Universiti A, 59100 Kuala Lumpur, Malaysia b Oxford Centre for Islamic Studies, Oxford OX1 2AR, United Kingdom c General Council for Islamic Banks and Financial Institutions (CIBAFI), Manama, Bahrain
a r t i c l e
i n f o
Article history: Received 21 May 2014 Received in revised form 16 January 2015 Accepted 17 January 2015 Available online 30 January 2015 Keywords: Property rights Minority shareholders Stock market development Growth Thresholds JEL classification: K11 O16 O43 P14
a b s t r a c t Using threshold estimation techniques, this study examines whether the growth effect of stock market development differs according to the different levels of property rights and minority shareholders protection in a cross-section of 85 jurisdictions during the post-crisis period. The results demonstrate that the impact of stock market liquidity on growth is positive and significant only in jurisdictions where there is high level of property rights protection. Similar effect is discerned in the case of strong minority shareholders protection. Using the market size as a measure of stock market development, the paper also documents a positive growth effect of market size when property rights and minority shareholders protection are strong. However, there is mixed evidence in the low to medium degrees of protection. Further analyses using other broader governance indicators as threshold variables and instrumental variable threshold regressions reaffirm the main findings. The study upholds the “better finance, more growth” proposition and contributes to the identification of thresholds above which institutional quality can positively shape the impact of stock market on economic growth. © 2015 Elsevier Inc. All rights reserved.
∗ Corresponding author at: International Centre for Education in Islamic Finance (INCEIF), Lorong Universiti A, 59100 Kuala Lumpur, Malaysia. Tel.: +60 376514000. E-mail addresses: [email protected] (A. Ng), [email protected] (G. Dewandaru), [email protected] (M.H. Ibrahim). http://dx.doi.org/10.1016/j.najef.2015.01.004 1062-9408/© 2015 Elsevier Inc. All rights reserved.
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1. Introduction The importance of financial development as a catalyst for long-term economic growth is an issue that has received intense discussion in recent years. It is generally viewed that well-developed financial markets and financial intermediaries facilitate the allocation of capital to the corporate sector, enhance resource allocation, and enable firms to raise capital essential to long-term investment, and consequently bring positive ramifications on economic growth (King & Levine, 1993; Levine, 1997; Beck, Levine, & Loayza, 2000; Wurgler, 2000). Despite extensive early cross-country evidence establishing positive contributions of finance to economic growth, country-specific studies have noted vast variations in the finance-growth causal nexus. In recent years, two trends of studies have emerged. First, concerns over financial systems in some countries being too large vis-à-vis the size of the economy and being a drag on growth have reignited the interest of policymakers and academics to reexamine whether too much finance is growthimpeding. Research at the Bank for International Settlements (BIS) and the International Monetary Fund (IMF) suggests that financial development may only be good up to a certain level, after which it becomes a drag on growth (Arcand, Berkes, & Panizza, 2012; Cecchetti & Kharroubi, 2012). A more recent research by Law and Singh (2014) also reinforces these findings on the importance of having an optimal level of financial development in order to spur growth. Second, the recent recurrences of financial crises and economic meltdowns have called into question whether finance is necessarily growth-enhancing. These developments have prompted the inclusion of policy conditionality such as inflation, income and institutional quality to complement the study of the finance-growth nexus. For example, high and volatile inflation distorts the ability of financial development in efficiently allocating capital, thereby reducing the positive effects of finance on economic growth (Huang, Lin, Kim, & Yeh, 2010). Evidence on the relationship between financial development and growth becomes mixed when income level is used as a policy condition. While Deidda and Fattouh (2002) find evidence of a significant and positive relationship between financial development and growth in high-income countries, Huang and Lin (2009) demonstrate that the positive effect is larger in the low-income countries. Particularly relevant to the restoration of trust and confidence in the financial system is the role of institutional quality as a necessary enabler of a positive finance-growth relation. In the presence of strong institutions and good governance that protects investors’ rights and attract investment, financial development can efficiently allocate capital to productive use in the economy as evidenced in several empirical studies. Arestis and Demetriades (1999) suggested that the presence or absence of good governance is likely to affect the causal relationship between financial development and economic growth. Negative correlation between economic growth and financial development (measured in terms of currency, narrow money and broad money) can also be attributed to a weak regulatory environment that hampers the efficiency of financial institutions in the allocation of their resources (Al-Yousif, 2002). Building on the idea by Arestis and Demetriades (1997) and Demetriades and Andrianova (2004), empirical works such as Demetriades and Law (2006) show that financial development has greater effect on growth when the banking system is operating within a sound institutional framework. The effect is particularly prominent in middle-income countries which are characterized by higher institutional quality. However, in low-income countries, financial development in the absence of strong institutions may not yield the desired economic outcomes in the long term. In a more recent study, using in a cross-country analysis for 85 countries, Law, Azman-Saini, and Ibrahim (2013) reaffirm the importance of institutions in shaping the relations between financial development and economic growth. Specifically, they find that the growth effect of banking sector development is contingent on formal institutional qualities such as control of corruption, rule of law, bureaucratic quality or government effectiveness. According to their results, banking sector development exerts positive influences on real growth only when these institutional quality indicators exceed certain thresholds. All the aforementioned studies suggest that “better finance, more growth” has become the more appropriate proposition than “more finance, more growth”. The former is grounded on the notion that a financial system entrenched in a sound institutional framework promotes economic growth. Where such institutions are in place, the financial system can play a pivotal role in the optimization of resource allocation. It is therefore the quality rather than the quantity of finance that matters more to
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economic growth. On this premise, previous studies have examined institutional quality in the form of government quality and public governance at the macro level. In the present paper, a further attempt is made to unearth the roles of institutional quality in defining the finance-growth nexus. The paper contributes to the existing literature in the following ways. First, the analysis is geared toward aspects of corporate governance widely noted to be relevant. These are property rights protection and minority shareholders protection. The protection of property rights provides the “foundation for both economic freedom and the efficient operation of markets” and gives incentives to engage in productive activity (Gwartney, Lawson, & Hall, 2013; Fraser Institute, 2013, p.5)1 . In particular, the examination of the effects of property rights on financial market is based on the contractual view of the firm (Jensen & Meckling, 1976; Harris & Raviv, 1988; Shleifer & Vishny, 1997). This view stresses the importance of protecting the property rights of financiers from expropriation by corporate insiders in order for firms to attract capital flows. Hence, better protection of the legal rights of investors, particularly minority shareholders in the case of stock markets, makes investors willing to provide capital to firms at lower costs (Shleifer & Wolfenzon, 2002). This will, in turn, positively promote the trading of equities and widen investment opportunities, thereby contributing to more economic growth (Beck & Levine, 2005). Second, while previous studies use the sample of pre-crisis or crisis periods, our study has an additional contribution of using the post-crisis era as sample. Notably, the need for a stable financial system underpinned by sound institutional quality is not only desirable but also necessary in the postcrisis era (Ng, Ibrahim, & Mirakhor, 2014). This is a critical period where the emphasis on institutional quality in finance has reemerged in the radar of policymakers, firms, investors and households (Ng, Ibrahim, & Mirakhor, 2015). As to the focus of the study, we examine the stock market development and its relation to economic growth. Over the years, stock markets have developed rapidly in parallel with banking sector development in many parts of the world especially to serve as a direct conduit for efficient resource allocation (Dewandaru, Rizvi, Bacha, & Masih, 2014). Finally, in keeping up with the literature, the analysis adopts the threshold regressions by Hansen (2000) as well as the recent threshold regression by Caner and Hansen (2004) taking into account the potential endogeneity issue to address the institutional conditionality of stock market-growth nexus. The threshold regression in our study allowed for the identification of three property rights and minority shareholders protection thresholds (low, medium and high thresholds) and the classification of countries into three groups. It follows that this would help to provide policy targets for the development of institutions to further promote growth-enhancing stock markets. The rest of the paper is structured as follows. Section 2 outlines the threshold regression approach and data used in the analysis. Estimation results are presented in Section 3. Finally, Section 4 concludes with the main findings and some concluding remarks. 2. Empirical approach and data 2.1. Model specification and estimation Since the studies by Levine and Zervos (1998a), Levine and Zervos (1998b) and Beck et al. (2000), it has become commonplace to examine the empirical relationship between financial development (in our case, stock market development) and growth using the following linear cross-country growth equation: Yi = ˇ SMDi + ˛Xi + εi
(1)
where Yi is the real per capita GDP growth rate in country i, SMDi is the country’s stage of stock market development, X is a vector of controlled variables, and εi is a noise term. Following the empirical
1 The interaction between property rights and institutional arrangements in shaping economic behavior has been an important aspect in the institutional economic literature and earlier studies (Alchian & Demsetz, 1973; North, 1989, 1991). Property rights is an important rule of behavior and principle in risk sharing, which is the essence of Islamic finance (Askari et al., 2011).
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literature, the list of controlled variables usually includes initial per capita income, years of schooling, inflation rate, government final consumption expenditure, and trade openness. The model above posits that the stock market development–economic growth nexus is invariant to differing institutional qualities across countries. To uncover whether the effect of stock market development on economic growth is contingent on a country’s institutional quality (i.e., property rights protection and minority shareholder protection), Eq. (1) is modified to allow for the presence of institutional quality threshold as:
Yi = ˇ1 SMDi + 1 Xi I (P ≤ ) + ˇ2 SMDi + 2 Xi I (P ≥ ) + εi
(2)
where is the unknown threshold parameter and P is a measure of institutional quality. I(.) is the indicator function that takes the value of 1 if the argument in the indicator function is valid and 0 if otherwise. This modeling technique allows the role of stock market to differ depending on whether the level of institutional quality is below or above an unknown threshold level of . The effect of stock market development on growth will be captured by ˇ1 and ˇ2 , respectively, for countries with institutional quality below the threshold and countries with institutional quality above the threshold. Note that Eq. (2) reduces to Eq. (1) if ˇ1 = ˇ2 and 1 = 2 . In order to estimate Eq. (2), we first apply the threshold regression estimation method due to Hansen (2000) to obtain the threshold estimate as well as estimates of the model parameters. The method is essentially the least squares estimation approach of the model for any given . The threshold parameter is chosen by minimizing the concentrated sum of the squared residuals (SSE) over various thresholds. Then, given the estimated threshold, we can obtain the estimates of the model parameters accordingly by the least squares method. In the implementation, the heteroskedascity-consistent Lagrange multiplier (LM) test for a threshold as provided by Hansen (1996) is used to evaluate the significance of the threshold. Note that, since the threshold parameter is not identified under the null of no threshold effect, there is a “nuisance” parameter or non-standard inference problem. In the presence of such problem, the conventional asymptotic distributions of standard tests become non-standard. We follow the suggestion by Hansen (1996) by relying on the bootstrap procedure to simulate the asymptotic distribution of the likelihood ratio test for inferences. We first start with one threshold regime and then search for the possibility of two threshold regimes, if they exist. It is possible that the Hansen’s (2000) procedure may suffer from simultaneity bias due to the endogeneity of stock market development. Accordingly, to control for potential endogeneity of stock market development, this study further employs the threshold regression with instrumental variables developed by Caner and Hansen (2004). This approach enables the examination of the causal effects of the exogenous component of stock market development and identification of potential threshold effects on the nexus. To this end, Eq. (2) can be modified into the following form:
Yi = ˇ1 SMDi + 1 Xi I (Pi ≤ ) + ˇ2 SMDi + 2 Xi I (P > ) + εi
(3)
SMDi = ı1 Zi + ϕ1 Xi I (Pi ≤ ) + ı2 Zi + ϕ2 Xi I (P > ) + i
(4)
where Zi is a vector of instrumental variables and the order condition is satisfied. The instruments considered are domestic credit to the private sector, financial openness, portfolio equity flows, portfolio equity assets and liabilities. The instrumental variables estimation within endogenous threshold models is applicable with certain restrictions. For example, while the exogeneity assumption for the explanatory variables is relaxed, Caner and Hansen (2004) continue to assume the exogeneity of the threshold variable. This assumption is noted by recent studies such as Bose, Murshid, and Wurm (2012) and Law et al. (2013) as an important condition of the instrumental variable threshold regression. A three-step procedure is followed in estimating the regression coefficients. First, SMDi is regressed on Zi using the ordinary least square (OLS) approach to obtain the fitted values of SMDi . Second, substituting the predicted values of SMDi into Eq. (4), the threshold parameter is estimated with the OLS method similar to that in Hansen (2000). Finally, the whole sample is divided in two or three sub-samples based on the estimates of and the slope parameters are estimated using the generalized method of moments (GMM).
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2.2. Data In the analysis, averaged data over the post-crisis period (i.e. 2009–2012) for 85 jurisdictions are used. This period was selected given the focus of the study and availability of property rights and minority shareholders protection data. The cross-sectional data are used for threshold estimations since it allows us to divide countries into different level of property rights and minority shareholders protection without having to deal with the time effects, considering that institutional quality does not vary significantly over the short to medium term (see, for example, Ding & Jinjarak, 2012). Table 1 provides the list of variables used in the present analysis, their descriptive statistics and sources. We capture economic growth using the averaged real per capita GDP in 2005 US dollar growth rate. Two measures of stock market development are used. These are turnover ratio and market capitalization. Turnover ratio, i.e. the trading volume of the stock market relative to its size, is associated with market ˇ liquidity and transaction costs (Li, 2007; Cihák, Demirgüc¸-Kunt, Feyen, & Levine, 2012). It is generally viewed to be a more appropriate measure of stock market development than market capitalization (Beck & Levine, 2004). While many studies on stock market development use market capitalization as an indicator, theories do not unequivocally claim that listing of shares alone will impact resource allocation or predict growth (Levine & Zervos, 1998a). Capturing the market size, the market with larger market capitalization may not necessarily function more effectively due to, for example, distortions
Table 1 Descriptive statistics.
Economic growth (GDP per capita growth) Initial income Schooling
Government consumption Inflation Trade openness Turnover ratio
Market capitalization Property rights protection Minority shareholders protection Legal structure/system Institutions
Maximum
Source
Unit of measurement
Mean
Std. dev.
Minimum
%
1.1717
2.6152
−7.3910
8.2915
WDI
US$ 2005 constant price (logarithm) Average years of total schooling (logarithm) % Of GDP (logarithm) Annual % % Of GDP (logarithm) % of market capitalization (logarithm) % of GDP (logarithm) 1–7 (Best)
8.8061
1.5076
5.5197
11.2965
WDI
2.1548
0.3029
1.1756
2.5855
WDI
2.7603
0.3510
1.7083
3.3645
WDI
4.8581 4.4073
4.0004 0.5306
−0.5958 3.1813
25.6069 6.0522
WDI WDI
2.8826
1.7471
−1.6130
5.3588
WDI
3.4267
1.1966
−0.9637
6.0394
WDI
4.6254
1.0512
1.8434
6.4614
GCI
1–7 (Best)
4.4044
0.7251
2.8549
5.9487
GCI
1–10 (Best)
6.1898
1.4521
2.4767
8.8366
Fraser Institute
1–7 (Best)
4.2484
0.9199
2.3999
6.1147
GCI
Jurisdictions: Armenia, Australia, Austria, Bangladesh, Belgium, Bolivia, Botswana, Brazil, Bulgaria Canada, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, Czech Republic, Denmark, Ecuador, Egypt, El Salvador, Estonia, Finland, France, Germany, Ghana, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Ireland, Italy, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyz Republic, Latvia, Lithuania, Luxembourg, Malawi, Malaysia, Mexico, Mongolia, Morocco, Namibia, Nepal, Netherlands, New Zealand, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Saudi Arabia, Serbia, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, South Korea, Sweden, Switzerland, Tanzania, Thailand, Turkey, Uganda, Ukraine, United Arab Emirates,United Kingdom, United States, Uruguay, Venezuela, Vietnam, Zambia. Source: World Development Indicators (WDI), Global Competitiveness Index (GCI), Fraser Institute.
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from taxes and other market imperfections2 . For the control variables, we use the initial (i.e. 2009) real per capita GDP in 2005 US dollar to account for economic convergence, the average years of schooling of population over the 25 years old for human capital accumulation, the inflation rate to represent macroeconomic stability, the general government final consumption expenditure to capture government size, and the export plus import to GDP ratio to represent trade openness (Barro, 1991; Mankiw, Romer, & David, 1992; Miller & Upadhyay, 2000; Danquah, Moral-Benito, & Ouattara, 2013; Assibey-Yeboah & Mohsin, 2014). These data are obtained from the World Development Indicators (WDI) database. Four different measures of institutions are employed as the institutional threshold. Our main institutional variables are property rights protection and minority shareholders protection. Data on the protection of property rights are obtained from the Global Competitiveness Index (GCI) published by the World Economic Forum. This measurement is derived from the executive opinion survey pertaining to the following question: “How would you rate the protection of property rights, including financial assets, in your country?” Answers are scaled from 1 to 7, where higher values imply that protection of property rights are among the best in the world where assets are clearly delineated and protected by law. An important feature of the indicator is that it does not simply reflect laws on the books, but the overall legal environment related to the protection of property rights and the overall quality of legal institutions (Bose et al., 2012). The indicator has been adopted by the Economic Freedom of the World (EFW) published by the Fraser Institute (rescaled from 1 to 10) and referred to by Claessens and Laeven (2003) and Bose et al. (2012), among others. Data on the protection of minority shareholders was also obtained from the GCI. It is derived from the executive opinion survey pertaining to the following question: “In your country, to what extent are the interests of minority shareholders protected by the legal system?” Scale 1 represents no protection at all while scale 7 implies full protection. It is expected that higher level of protection would enable stock market to contribute positively to economic growth. In addition to these two main indicators, we also explore two broader measures of institutions, namely, legal structure/system from the EFW and institution from the GCI. Legal institutions play an important role in determining the degree of expropriation and the confidence with which investors participate in financial markets (Beck & Levine, 2005). The legal structure/system indicator has nine components, namely judicial independence, impartial courts, protection of property rights, military interference in rule of law and politics, integrity of the legal system, legal enforcement of contracts, regulatory restrictions on the sale of real property, reliability of police, and business costs of crime. The GCI’s institution indicator encompasses 21 components, namely property rights, intellectual property protection, diversion of public funds, public trust in politicians, irregular payments and bribes, judicial independence, favoritism in decisions of government officials, wastefulness of government spending, burden of government regulation, efficiency of legal framework in settling disputes, efficiency of legal framework in challenging regulations, transparency of government policymaking, business costs of terrorism, business costs of crime and violence, organized crime, reliability of police services, ethical behavior of firms, strength of auditing and reporting standards, efficacy of corporate boards, protection of minority shareholders’ interests, and strength of investor protection3 . 3. Estimation results We first present the results from the Hansen’s (2000) least squares threshold regression approach using the property rights protection and minority shareholders protection as the threshold indicators. Then, we provide the results from our further analyses using broader measures of institutions and instrumental variable threshold regression of Caner and Hansen (2004).
2 This study does not use stock value traded as it is a measurement of trading relative to the size of the economy rather than the liquidity of the market itself. Value traded, as product of quantity and price, can increase without growth in the number of transactions. Turnover ratio does not suffer from this shortcoming (Levine & Zervos, 1998a; Levine & Zervos, 1998b; Beck & Levine, 2004). 3 Preference was given to the GCI’s institution indicator over the Worldwide Governance Indicators of the World Bank as the former encompasses more institutional components.
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Table 2 Threshold estimates of property rights and minority shareholder. Property right protection
Minority shareholders protection
Turnover ratio
Market cap
Turnover ratio
Market cap
First sample split (Regimes 1 & 2) LM test for no threshold Bootstrap p-value Threshold estimate 95% Confidence interval
14.2173 0.0920 1.4630 [1.4589, 1.4817]
15.4029 0.0460 1.4630 [1.4589, 1.4817]
21.4549 0.0000 1.4488 [1.3657, 1.5413]
17.4629 0.0040 1.4625 [1.4625, 1.4625]
Second sample split (Regime 3) LM test for no threshold Bootstrap p-value Threshold estimate 95% Confidence interval
15.1465 0.0160 1.6383 [1.6354, 1.6383]
14.5911 0.0370 1.6222 [1.5221, 1.7030]
17.8777 0.0100 1.6049 [1.6030, 1.6049]
15.0091 0.0660 1.5059 [1.5023, 1.6049]
Stock market indicators
Third sample split LM test for no threshold Bootstrap p-value
8.1535 0.7640
11.2366 0.10250
8.2206 0.7390
10.1327 0.3030
Notes: Results correspond to trimming percentage of 15% and 1000 numbers of bootstrap replications. The null hypothesis of LM test (bootstrap p-value) is no threshold effect.
3.1. Least squares threshold regressions Table 2 presents the threshold estimates of Eq. (2) using the property rights protection and minority shareholders protection for models with the turnover ratio and market capitalization as measures of stock market development. The statistical significance of the threshold estimate in the regression allowing for heteroskedasticity is evaluated using the bootstrap method with 1000 replications and 15% as trimming percentage. Examining the presence of one threshold, there is evidence rejecting the null hypothesis of no threshold for all cases. In assessing whether the sample can be split further, the additional test reveals that the third regime exists in all regressions. With the existence of thresholds, the influence of property rights and minority shareholders protection on the stock market development-economic growth relations can thus be examined. Table 3 reports the empirical results of Eq. (2) using the turnover ratio for the three identified regimes, henceforth, low, intermediate and high protection regimes. Meanwhile, Table 4 provides the corresponding results using market capitalization as the stock market development indicator. For comparison, we also estimate the linear growth model without the threshold (i.e. Eq. (1)). On our main theme, we find the coefficients of stock market development indicators to be positive and significant in high property rights and minority shareholders protection regimes. As for the low and intermediate protection regimes, the results tend to depend on the stock market development measures. Using the turnover ratio, we find its coefficients in the growth equation of low and intermediate protection regimes to be indistinguishable from zero. The finding suggests that property rights and minority shareholders protection can replicate non-linear relationships between financial development as measured by the turnover ratio and economic growth, which is broadly consistent with the general empirical works by Deidda and Fattouh (2002), Rioja and Valev (2004), Huang and Lin (2009), Huang et al. (2010), Cecchetti and Kharroubi (2012), Law et al. (2013). When the market capitalization indicator is used, we find the relations between finance and growth to be positive in the low property rights protection regime and insignificant in the intermediate regime. Meanwhile, adopting the minority shareholders protection, we find their relations turn from being insignificant in the low regimes to being negatively significant in the intermediate regime. While plausible explanation of the results is not easy, it could be that market capitalization is less suitable as a measure of stock market development. In view of these, the analyses that follow will be based on using the turnover ratio as a measure of stock market development. As for other variables, we document evidence consistent with the conditional growth convergence and distorting effect of government consumption expenditure. The coefficients on initial income are negative and significant in all regressions and regimes. Likewise, the government consumption
Independent variables
Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Turnover ratio R-squared No. of observations
Linear model without threshold
Property rights protection as threshold
Minority shareholders protection as threshold
Regime 1 (PR ≤ 1.4630)
Regime 2 (PR ≤ 1.6383)
Regime 3 (PR > 1.6383)
Regime 1 (MS ≤ 1.4488)
Regime 2 (MS > 1.4488)
Regime 3 (MS > 1.6049)
0.1281*** (0.0333) −0.0116*** (0.0026) 0.0094 (0.0114) −0.0142* (0.0078)
0.1341*** (0.0342) −0.0084*** (0.0029) −0.0012 (0.0113) −0.0249*** (0.0072)
0.2277* (0.1150) −0.0352*** (0.0075) 0.0341* (0.0176) −0.0015 (0.0298)
0.1450*** (0.0401) −0.0087*** (0.0020) −0.0009 (0.0128) −0.0235*** (0.0049)
0.1122*** (0.0367) −0.0089*** (0.0029) 0.0074 (0.0111) −0.0243*** (0.0089)
0.2276*** (0.0363) −0.0160*** (0.0046) 0.0264* (0.0129) −0.0338*** (0.0096)
−0.3952*** (0.1287) −0.0185** (0.0067) 0.1509*** (0.0370) 0.0204 (0.0154)
−0.0150 (0.0877) −0.0001 (0.0046) 0.0021 (0.0015) 0.4182 85
−0.0289 (0.0664) 0.0038 (0.0067) 0.0008 (0.0015) 0.4942 37
0.2085 (0.2482) 0.0051 (0.0151) 0.0012 (0.0046) 0.6916 19
0.1381 (0.1282) −0.0001 (0.0024) 0.0034** (0.0013) 0.6575 29
0.0001 (0.0617) 0.0041 (0.0073) 0.0015 (0.0014) 0.5108 36
−0.0959 (0.1421) −0.0050 (0.0044) −0.0014 (0.0034) 0.6166 30
1.6484*** (0.4761) 0.0258** (0.0091) 0.0031** (0.0013) 0.7004 19
Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
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Table 3 Regression results of LS threshold regressions using the turnover ratio.
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Independent variables
Linear model without threshold
Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Market capitalization R-squared No. of observations
Property rights protection as threshold
Minority shareholders protection as threshold
Regime 1 (PR ≤ 1.4630)
Regime 2 (PR ≤ 1.6222)
Regime 3 (PR > 1.6222)
Regime 1 (MS ≤ 1.4625)
Regime 2 (MS > 1.4625)
Regime 3 (MS > 1.5059)
0.1148*** (0.0339) −0.0121*** (0.0026) 0.0144 (0.0116) −0.0139* (0.0076)
0.1023** (0.0378) −0.0086*** (0.0026) 0.0033 (0.0111) −0.0261*** (0.0062)
0.4260*** −0.0271*** 0.0344*** −0.0759***
−0.0365 (0.1093) −0.0126*** (0.0041) 0.0389 (0.0252) 0.0055 (0.0166)
0.0848** (0.0402) −0.0085*** (0.0026) 0.0098 (0.0118) −0.0245*** (0.0078)
0.5137*** (0.0773) −0.0411*** (0.0080) 0.0829** (0.0312) 0.0112 (0.0128)
−0.0051 (0.0780) −0.0165*** (0.0049) 0.0476** (0.0177) −0.0023 (0.0156)
0.0157 (0.0885) −0.0012 (0.0044) 0.0046** (0.0022) 0.4359 85
0.0163 (0.0694) 0.0066 (0.0064) 0.0047** (0.0022) 0.5256 37
−0.1817 (0.2618) −0.0056 (0.0156) 0.0013 (0.0034) 0.7897 17
0.3602** (0.1547) 0.0009 (0.0040) 0.0104* (0.0051) 0.4716 31
0.0254 (0.0696) 0.0065 (0.0072) 0.0039 (0.0024) 0.5106 38
−1.015*** (0.2359) −0.0625** (0.0201) −0.0111*** (0.0017) 0.8495 10
0.4599** (0.1785) 0.0028 (0.0038) 0.0107** (0.0059) 0.6268 36
(0.1204) (0.0049) (0.0105) (0.0255)
Notes: The standard errors are reported in parentheses (white corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
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Table 4 Regression results of LS threshold regressions using the market capitalization.
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coefficients are significant and negative most apparently for the countries in the low protection regimes. We also document evidence of the growth-enhancing role of human capital in higher protection regimes. The evidence for the significant roles of inflation and trade openness are, however, rather limited. At this juncture, the empirical findings highlight that improvement in institutional quality in the form of better protection of property rights and minority shareholders can facilitate efficient financing by reallocating the flow of finance toward growing firms and preventing capital divergence to unproductive investments (Wurgler, 2000; Beck & Levine, 2002). Consequently, firms would have more supply of financing to invest more and grow faster. Higher protection also results in greater confidence and encourages people to participate in the market. In fact, more protective environments are associated with lower information asymmetries, narrow bid-ask spreads and more market depths. Overall, the results of this study are broadly consistent with previous studies that have demonstrated that better investor protection tends to lower liquidity cost, increase market liquidity and promote financial development (Brockman & Chung, 2003; Lesmond, 2005; Eleswarapu & Venkataraman, 2006; Bose et al., 2012). 3.2. Further analyses We further assess the stock market development-economic growth nexus by considering broader measures of institutions. Table 5 presents Hansen’s (2000) threshold regression results using legal structure and institutions as the threshold indicators. Unlike the preceding sub-section, the test for the threshold reveals the presence of only two regimes (i.e. one threshold). It is comforting to note that, using broader measures of institutions, we find the results to be roughly similar to the one documented above. More importantly, the institutional conditionality of the market turnover and growth is further substantiated. The effect of market liquidity on growth is only significant and positive in the regime of high level of legal structure and high level of institutions, respectively. Market liquidity is however negative and insignificant in the low regimes. Hence, the results indicate that market liquidity has beneficial effects on growth when the level of institutional quality is above a certain threshold. It may be argued that the aforementioned results suffer from simultaneity bias due to the joint determination of financial development and economic growth4 . However, there are cases where simultaneity bias does not exist (as discussed, for example, in Beck et al. (2000), Levine, Loayza, and Beck (2000), and Levine (2003)). Hence, preference should be given to the least square results as it is the most efficient estimation. The Hausman test statistics for endogeneity presented in Table 6 indicate that market liquidity variable is exogenous in almost all regimes, which lend support to the preceding results. As there is no evidence of endogeneity in all the three regimes in the minority shareholders protection threshold model, we proceed to re-estimate the other remaining models using the instrumental variable threshold regression (IVTR) due to Caner and Hansen (2004) for robustness check. It has been noted in the literature that the identification of instruments that capture the exogenous cross-country variation in financial development is difficult in many macroeconomic datasets. To address this challenge, we made an attempt by using the initial value of the appropriate instruments for the IVTR to minimize the potential endogeneity of the instruments. In the implementation, the initial values of the following instruments for the market liquidity variable are considered: domestic credit to the private sector, financial openness, portfolio equity flows, as well as portfolio equity assets and liabilities. They are chosen based on the theoretical and empirical works in the literature as well as the joint instruments validity test. Variables that have a significant effect on the stock market and fulfill the instruments validity test are assumed to be exogenous with respect to economic growth. Countries with better developed stock markets also have better developed banks, while countries with weak stock markets tend to have weak financial intermediaries (Demirguc-Kunt & Levine, 1996). Banking sector development has also been identified as a precondition for stock market development (Chinn & Ito, 2006). Banking sector development is proxied by domestic credit to the private sector (% of GDP) from the WDI. With regard to financial openness, there
4
Demetriades and Hussein (1996), Calderón and Liu (2003), and Bangake and Eggoh (2011).
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Independent variables Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Turnover ratio R-squared No. of observations
Legal structure as threshold Regime 1 (LS ≤ 1.6306) ***
0.1416 (0.0470) −0.0015 (0.00340) −0.0042 (0.0135) −0.0386*** (0.0121) −0.1482** (0.0663) 0.0016 (0.0137) −0.0013 (0.0020) 0.5476 21
Institutions as threshold Regime 2 (LS > 1.6306) *
0.1105 (0.0576) −0.0146*** (0.0038) 0.0179 (0.0117) −0.0099 (0.0134) 0.1436 (0.1358) 0.0011 (0.0045) 0.0035* (0.0020) 0.5152 64
Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
Regime 1 (INS ≤ 1.3548) ***
0.1370 (0.0361) −0.0066** (0.0036) −0.0028 (0.0132) −0.0317*** (0.0105) −0.0461 (0.0725) 0.0054 (0.0081) −0.0005 (0.0017) 0.4547 35
Regime 2 (INS > 1.3548) 0.1155** (0.0486) −0.0162*** (0.0037) 0.0326*** (0.0110) −0.0137 (0.0144) 0.2104 (0.1285) −0.0015 (0.0039) 0.0040* (0.0021) 0.5989 50
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Table 5 Regression results using broader institutional indicators.
Independent variables
Durbin–Wu–Hausman chi-sq test Hausman p-value
Threshold indicators Property rights protection
Minority shareholders protection
Regime 2 Regime 3 Regime 1 (PR ≤ 1.6354) (PR ≤ 1.7030) (PR > 1.7030)
Regime 1 Regime 2 Regime 3 Regime 1 (MS ≤ 1.4488) (MS > 1.4488) (MS > 1.6049) (LS ≤ 1.6094)
Legal structure Regime 2 (LS > 1.6094)
Institutions Regime 1 Regime 2 (INS ≤ 1.5736) (INS > 1.5736)
1.285
5.9648
0.2841
0.0287
0.0113
0.4209
0.3381
9.4237
0.0094
13.4654
0.257
0.0146
0.594
0.8654
0.9155
0.5165
0.5609
0.0021
0.9227
0.0002
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Table 6 Endogeneity tests.
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60
Independent variables
Threshold indicators Property rights protection
Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Turnover ratio J-stat J-stat p-value No. of observations
Legal structure
Institutions
Regime 1 (PR ≤ 1.6354)
Regime 2 (PR ≤ 1.7030)
Regime 3 (PR > 1.7030)
Regime 1 (LS ≤ 1.6094)
Regime 2 (LS > 1.6094)
Regime 1 (INS ≤ 1.5736)
Regime 2 (INS > 1.5736)
1.1448*** (0.2738) 0.0374*** (0.0082) −0.0288 (0.0261) 0.0985*** (0.0237) 1.0848*** (0.3036) 0.0917*** (0.0144) 0.0047 (0.0064) 2.3549 0.1814 55
2.0182*** (0.5749) 0.0068 (0.0144) 0.1149* (0.0639) 0.1571*** (0.0354) −0.4891 (0.9379) 0.0410*** (0.0133) 0.0090 (0.0173) 6.9493 0.0538 12
0.6720 (0.4996) 0.0762*** (0.0052) 0.0423* (0.0240) 0.0107 (0.0161) 0.2387 (0.3492) 0.0052 (0.0039) 0.0084*** (0.0020) 6.7170 0.0584 18
1.1277*** (0.3100) 0.0274** (0.0109) 0.0656* (0.0366) 0.0490 (0.0322) 0.6663*** (0.1721) 0.1013*** (0.0193) 0.0075 (0.0082) 3.2567 0.1598 18
1.1627*** (0.3743) 0.0282*** (0.0076) 0.1051** (0.0385) 0.0761*** (0.0195) 2.2045*** (0.2873) 0.0340*** (0.0100) 0.0113* (0.0064) 4.9813 0.1032 67
1.2898*** (0.2983) 0.0377*** (0.0083) −0.0024 (0.0271) 0.1068*** (0.0239) 1.1196*** (0.3211) 0.0721*** (0.0117) 0.0028 (0.0078) 3.5081 0.1293 60
0.0051 (0.5538) 0.0387*** (0.0074) 0.2043*** (0.0495) −0.0077 (0.0262) 1.8957*** (0.5604) 0.0148 (0.0094) 0.0083** (0.0034) 2.5007 0.1807 25
Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
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Table 7 IVTR using initial value of instruments.
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is evidence that capital account liberalization contributes to stock market development after attaining a threshold level of legal and institutional infrastructure development (Chinn & Ito, 2006; Ito, 2006). Foreign portfolio investment and capital flows are also important determinants in the development of stock markets, serving as a proxy of de facto openness (Levine & Zervos, 1998b; El-Wassal, 2005; Yartey, 2010). Data on financial openness, portfolio equity flows, and portfolio equity assets and liabilities are obtained from the Chinn–Ito index developed by Chinn and Ito (2006), WDI, and the External Wealth of Nations dataset constructed by Lane and Milesi-Ferretti (2007), respectively. Again, in this further exercise, we identify the presence of thresholds that are in line with previous results. The results of the IVTR are presented in Table 7. Furthermore, the Hansen J-statistics validates the list of the instruments used in the estimation at 5% significance level. It is pleased to note that the results remain similar to the ones reported above. Hence, they strengthen our previous conclusions that strong institutional quality are needed for the stock market development to have positive contribution to economic growth. 4. Concluding remarks This paper examines whether high levels of property rights and minority shareholders protection can enable stock market to positively spur economic growth using a cross-section of 85 jurisdictions. To this end, the paper adopts threshold regression techniques developed by Hansen (2000) and Caner and Hansen (2004). The empirical results demonstrate that stock market liquidity has a significant and positive influence on GDP growth only after the attainment of a certain threshold level of property rights protection. Until then, the effects of stock market liquidity on GDP growth are found to be negligible. Market liquidity is also significant and positive in promoting GDP growth in cases of strong minority shareholders protection. The results for the high protection regimes are further supported when the market capitalization is used as a measure of stock market development. Furthermore, employing broader measures of institutions, we arrive at a similar conclusion pointing to the importance of institutional quality in shaping the positive relations between stock market development and economic growth. Hence, this paper upholds the “better finance, more growth” proposition and contributes to the identification of thresholds above which property rights and minority shareholders protection can positively shape the impact of stock market on economic development. It provides a policy target for countries to strengthen their institutional framework in order for their respective stock market to have positive and significant effect on growth. The empirical evidence also serves as a basis for policy makers to formulate initiatives to enhance property rights and minority shareholders protection measures particularly in cases where low levels of protection impede the role of stock market development in enhancing growth. Institutional reforms providing better formal mechanisms for the reliable enforcement of contracts and protection from expropriation can therefore create conducive conditions for the overall economic development. Acknowledgements We thank the editor, two anonymous referees, Mustafa Disli, and discussants at the 5th Financial Market and Corporate Governance Conference (23-24 April 2014, Brisbane) for their helpful comments and review of the draft. Financial support from the Central Bank of Malaysia Shari’ah scholarship and the International Centre for Education in Islamic Finance is gratefully acknowledged. References Alchian, A. A., & Demsetz, H. (1973). The property right paradigm. The Journal of Economic History, 33(1), 16–27. Al-Yousif, Y. K. (2002). Financial development and economic growth: Another look at the evidence from developing countries. Review of Financial Economics, 11, 131–150. Arcand, J., Berkes, E., & Panizza, U. (2012). Too much finance? Washington, DC: International Monetary Fund. Arestis, P., & Demetriades, P. (1997). Financial development and economic growth: Assessing the evidence. The Economic Journal, 107(442), 783–799.
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Contents lists available at ScienceDirect
North American Journal of Economics and Finance
Property rights and the stock market-growth nexus Adam Ng a,b,∗, Ginanjar Dewandaru c, Mansor H. Ibrahim a a International Centre for Education in Islamic Finance (INCEIF), Lorong Universiti A, 59100 Kuala Lumpur, Malaysia b Oxford Centre for Islamic Studies, Oxford OX1 2AR, United Kingdom c General Council for Islamic Banks and Financial Institutions (CIBAFI), Manama, Bahrain
a r t i c l e
i n f o
Article history: Received 21 May 2014 Received in revised form 16 January 2015 Accepted 17 January 2015 Available online 30 January 2015 Keywords: Property rights Minority shareholders Stock market development Growth Thresholds JEL classification: K11 O16 O43 P14
a b s t r a c t Using threshold estimation techniques, this study examines whether the growth effect of stock market development differs according to the different levels of property rights and minority shareholders protection in a cross-section of 85 jurisdictions during the post-crisis period. The results demonstrate that the impact of stock market liquidity on growth is positive and significant only in jurisdictions where there is high level of property rights protection. Similar effect is discerned in the case of strong minority shareholders protection. Using the market size as a measure of stock market development, the paper also documents a positive growth effect of market size when property rights and minority shareholders protection are strong. However, there is mixed evidence in the low to medium degrees of protection. Further analyses using other broader governance indicators as threshold variables and instrumental variable threshold regressions reaffirm the main findings. The study upholds the “better finance, more growth” proposition and contributes to the identification of thresholds above which institutional quality can positively shape the impact of stock market on economic growth. © 2015 Elsevier Inc. All rights reserved.
∗ Corresponding author at: International Centre for Education in Islamic Finance (INCEIF), Lorong Universiti A, 59100 Kuala Lumpur, Malaysia. Tel.: +60 376514000. E-mail addresses: [email protected] (A. Ng), [email protected] (G. Dewandaru), [email protected] (M.H. Ibrahim). http://dx.doi.org/10.1016/j.najef.2015.01.004 1062-9408/© 2015 Elsevier Inc. All rights reserved.
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1. Introduction The importance of financial development as a catalyst for long-term economic growth is an issue that has received intense discussion in recent years. It is generally viewed that well-developed financial markets and financial intermediaries facilitate the allocation of capital to the corporate sector, enhance resource allocation, and enable firms to raise capital essential to long-term investment, and consequently bring positive ramifications on economic growth (King & Levine, 1993; Levine, 1997; Beck, Levine, & Loayza, 2000; Wurgler, 2000). Despite extensive early cross-country evidence establishing positive contributions of finance to economic growth, country-specific studies have noted vast variations in the finance-growth causal nexus. In recent years, two trends of studies have emerged. First, concerns over financial systems in some countries being too large vis-à-vis the size of the economy and being a drag on growth have reignited the interest of policymakers and academics to reexamine whether too much finance is growthimpeding. Research at the Bank for International Settlements (BIS) and the International Monetary Fund (IMF) suggests that financial development may only be good up to a certain level, after which it becomes a drag on growth (Arcand, Berkes, & Panizza, 2012; Cecchetti & Kharroubi, 2012). A more recent research by Law and Singh (2014) also reinforces these findings on the importance of having an optimal level of financial development in order to spur growth. Second, the recent recurrences of financial crises and economic meltdowns have called into question whether finance is necessarily growth-enhancing. These developments have prompted the inclusion of policy conditionality such as inflation, income and institutional quality to complement the study of the finance-growth nexus. For example, high and volatile inflation distorts the ability of financial development in efficiently allocating capital, thereby reducing the positive effects of finance on economic growth (Huang, Lin, Kim, & Yeh, 2010). Evidence on the relationship between financial development and growth becomes mixed when income level is used as a policy condition. While Deidda and Fattouh (2002) find evidence of a significant and positive relationship between financial development and growth in high-income countries, Huang and Lin (2009) demonstrate that the positive effect is larger in the low-income countries. Particularly relevant to the restoration of trust and confidence in the financial system is the role of institutional quality as a necessary enabler of a positive finance-growth relation. In the presence of strong institutions and good governance that protects investors’ rights and attract investment, financial development can efficiently allocate capital to productive use in the economy as evidenced in several empirical studies. Arestis and Demetriades (1999) suggested that the presence or absence of good governance is likely to affect the causal relationship between financial development and economic growth. Negative correlation between economic growth and financial development (measured in terms of currency, narrow money and broad money) can also be attributed to a weak regulatory environment that hampers the efficiency of financial institutions in the allocation of their resources (Al-Yousif, 2002). Building on the idea by Arestis and Demetriades (1997) and Demetriades and Andrianova (2004), empirical works such as Demetriades and Law (2006) show that financial development has greater effect on growth when the banking system is operating within a sound institutional framework. The effect is particularly prominent in middle-income countries which are characterized by higher institutional quality. However, in low-income countries, financial development in the absence of strong institutions may not yield the desired economic outcomes in the long term. In a more recent study, using in a cross-country analysis for 85 countries, Law, Azman-Saini, and Ibrahim (2013) reaffirm the importance of institutions in shaping the relations between financial development and economic growth. Specifically, they find that the growth effect of banking sector development is contingent on formal institutional qualities such as control of corruption, rule of law, bureaucratic quality or government effectiveness. According to their results, banking sector development exerts positive influences on real growth only when these institutional quality indicators exceed certain thresholds. All the aforementioned studies suggest that “better finance, more growth” has become the more appropriate proposition than “more finance, more growth”. The former is grounded on the notion that a financial system entrenched in a sound institutional framework promotes economic growth. Where such institutions are in place, the financial system can play a pivotal role in the optimization of resource allocation. It is therefore the quality rather than the quantity of finance that matters more to
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economic growth. On this premise, previous studies have examined institutional quality in the form of government quality and public governance at the macro level. In the present paper, a further attempt is made to unearth the roles of institutional quality in defining the finance-growth nexus. The paper contributes to the existing literature in the following ways. First, the analysis is geared toward aspects of corporate governance widely noted to be relevant. These are property rights protection and minority shareholders protection. The protection of property rights provides the “foundation for both economic freedom and the efficient operation of markets” and gives incentives to engage in productive activity (Gwartney, Lawson, & Hall, 2013; Fraser Institute, 2013, p.5)1 . In particular, the examination of the effects of property rights on financial market is based on the contractual view of the firm (Jensen & Meckling, 1976; Harris & Raviv, 1988; Shleifer & Vishny, 1997). This view stresses the importance of protecting the property rights of financiers from expropriation by corporate insiders in order for firms to attract capital flows. Hence, better protection of the legal rights of investors, particularly minority shareholders in the case of stock markets, makes investors willing to provide capital to firms at lower costs (Shleifer & Wolfenzon, 2002). This will, in turn, positively promote the trading of equities and widen investment opportunities, thereby contributing to more economic growth (Beck & Levine, 2005). Second, while previous studies use the sample of pre-crisis or crisis periods, our study has an additional contribution of using the post-crisis era as sample. Notably, the need for a stable financial system underpinned by sound institutional quality is not only desirable but also necessary in the postcrisis era (Ng, Ibrahim, & Mirakhor, 2014). This is a critical period where the emphasis on institutional quality in finance has reemerged in the radar of policymakers, firms, investors and households (Ng, Ibrahim, & Mirakhor, 2015). As to the focus of the study, we examine the stock market development and its relation to economic growth. Over the years, stock markets have developed rapidly in parallel with banking sector development in many parts of the world especially to serve as a direct conduit for efficient resource allocation (Dewandaru, Rizvi, Bacha, & Masih, 2014). Finally, in keeping up with the literature, the analysis adopts the threshold regressions by Hansen (2000) as well as the recent threshold regression by Caner and Hansen (2004) taking into account the potential endogeneity issue to address the institutional conditionality of stock market-growth nexus. The threshold regression in our study allowed for the identification of three property rights and minority shareholders protection thresholds (low, medium and high thresholds) and the classification of countries into three groups. It follows that this would help to provide policy targets for the development of institutions to further promote growth-enhancing stock markets. The rest of the paper is structured as follows. Section 2 outlines the threshold regression approach and data used in the analysis. Estimation results are presented in Section 3. Finally, Section 4 concludes with the main findings and some concluding remarks. 2. Empirical approach and data 2.1. Model specification and estimation Since the studies by Levine and Zervos (1998a), Levine and Zervos (1998b) and Beck et al. (2000), it has become commonplace to examine the empirical relationship between financial development (in our case, stock market development) and growth using the following linear cross-country growth equation: Yi = ˇ SMDi + ˛Xi + εi
(1)
where Yi is the real per capita GDP growth rate in country i, SMDi is the country’s stage of stock market development, X is a vector of controlled variables, and εi is a noise term. Following the empirical
1 The interaction between property rights and institutional arrangements in shaping economic behavior has been an important aspect in the institutional economic literature and earlier studies (Alchian & Demsetz, 1973; North, 1989, 1991). Property rights is an important rule of behavior and principle in risk sharing, which is the essence of Islamic finance (Askari et al., 2011).
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literature, the list of controlled variables usually includes initial per capita income, years of schooling, inflation rate, government final consumption expenditure, and trade openness. The model above posits that the stock market development–economic growth nexus is invariant to differing institutional qualities across countries. To uncover whether the effect of stock market development on economic growth is contingent on a country’s institutional quality (i.e., property rights protection and minority shareholder protection), Eq. (1) is modified to allow for the presence of institutional quality threshold as:
Yi = ˇ1 SMDi + 1 Xi I (P ≤ ) + ˇ2 SMDi + 2 Xi I (P ≥ ) + εi
(2)
where is the unknown threshold parameter and P is a measure of institutional quality. I(.) is the indicator function that takes the value of 1 if the argument in the indicator function is valid and 0 if otherwise. This modeling technique allows the role of stock market to differ depending on whether the level of institutional quality is below or above an unknown threshold level of . The effect of stock market development on growth will be captured by ˇ1 and ˇ2 , respectively, for countries with institutional quality below the threshold and countries with institutional quality above the threshold. Note that Eq. (2) reduces to Eq. (1) if ˇ1 = ˇ2 and 1 = 2 . In order to estimate Eq. (2), we first apply the threshold regression estimation method due to Hansen (2000) to obtain the threshold estimate as well as estimates of the model parameters. The method is essentially the least squares estimation approach of the model for any given . The threshold parameter is chosen by minimizing the concentrated sum of the squared residuals (SSE) over various thresholds. Then, given the estimated threshold, we can obtain the estimates of the model parameters accordingly by the least squares method. In the implementation, the heteroskedascity-consistent Lagrange multiplier (LM) test for a threshold as provided by Hansen (1996) is used to evaluate the significance of the threshold. Note that, since the threshold parameter is not identified under the null of no threshold effect, there is a “nuisance” parameter or non-standard inference problem. In the presence of such problem, the conventional asymptotic distributions of standard tests become non-standard. We follow the suggestion by Hansen (1996) by relying on the bootstrap procedure to simulate the asymptotic distribution of the likelihood ratio test for inferences. We first start with one threshold regime and then search for the possibility of two threshold regimes, if they exist. It is possible that the Hansen’s (2000) procedure may suffer from simultaneity bias due to the endogeneity of stock market development. Accordingly, to control for potential endogeneity of stock market development, this study further employs the threshold regression with instrumental variables developed by Caner and Hansen (2004). This approach enables the examination of the causal effects of the exogenous component of stock market development and identification of potential threshold effects on the nexus. To this end, Eq. (2) can be modified into the following form:
Yi = ˇ1 SMDi + 1 Xi I (Pi ≤ ) + ˇ2 SMDi + 2 Xi I (P > ) + εi
(3)
SMDi = ı1 Zi + ϕ1 Xi I (Pi ≤ ) + ı2 Zi + ϕ2 Xi I (P > ) + i
(4)
where Zi is a vector of instrumental variables and the order condition is satisfied. The instruments considered are domestic credit to the private sector, financial openness, portfolio equity flows, portfolio equity assets and liabilities. The instrumental variables estimation within endogenous threshold models is applicable with certain restrictions. For example, while the exogeneity assumption for the explanatory variables is relaxed, Caner and Hansen (2004) continue to assume the exogeneity of the threshold variable. This assumption is noted by recent studies such as Bose, Murshid, and Wurm (2012) and Law et al. (2013) as an important condition of the instrumental variable threshold regression. A three-step procedure is followed in estimating the regression coefficients. First, SMDi is regressed on Zi using the ordinary least square (OLS) approach to obtain the fitted values of SMDi . Second, substituting the predicted values of SMDi into Eq. (4), the threshold parameter is estimated with the OLS method similar to that in Hansen (2000). Finally, the whole sample is divided in two or three sub-samples based on the estimates of and the slope parameters are estimated using the generalized method of moments (GMM).
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2.2. Data In the analysis, averaged data over the post-crisis period (i.e. 2009–2012) for 85 jurisdictions are used. This period was selected given the focus of the study and availability of property rights and minority shareholders protection data. The cross-sectional data are used for threshold estimations since it allows us to divide countries into different level of property rights and minority shareholders protection without having to deal with the time effects, considering that institutional quality does not vary significantly over the short to medium term (see, for example, Ding & Jinjarak, 2012). Table 1 provides the list of variables used in the present analysis, their descriptive statistics and sources. We capture economic growth using the averaged real per capita GDP in 2005 US dollar growth rate. Two measures of stock market development are used. These are turnover ratio and market capitalization. Turnover ratio, i.e. the trading volume of the stock market relative to its size, is associated with market ˇ liquidity and transaction costs (Li, 2007; Cihák, Demirgüc¸-Kunt, Feyen, & Levine, 2012). It is generally viewed to be a more appropriate measure of stock market development than market capitalization (Beck & Levine, 2004). While many studies on stock market development use market capitalization as an indicator, theories do not unequivocally claim that listing of shares alone will impact resource allocation or predict growth (Levine & Zervos, 1998a). Capturing the market size, the market with larger market capitalization may not necessarily function more effectively due to, for example, distortions
Table 1 Descriptive statistics.
Economic growth (GDP per capita growth) Initial income Schooling
Government consumption Inflation Trade openness Turnover ratio
Market capitalization Property rights protection Minority shareholders protection Legal structure/system Institutions
Maximum
Source
Unit of measurement
Mean
Std. dev.
Minimum
%
1.1717
2.6152
−7.3910
8.2915
WDI
US$ 2005 constant price (logarithm) Average years of total schooling (logarithm) % Of GDP (logarithm) Annual % % Of GDP (logarithm) % of market capitalization (logarithm) % of GDP (logarithm) 1–7 (Best)
8.8061
1.5076
5.5197
11.2965
WDI
2.1548
0.3029
1.1756
2.5855
WDI
2.7603
0.3510
1.7083
3.3645
WDI
4.8581 4.4073
4.0004 0.5306
−0.5958 3.1813
25.6069 6.0522
WDI WDI
2.8826
1.7471
−1.6130
5.3588
WDI
3.4267
1.1966
−0.9637
6.0394
WDI
4.6254
1.0512
1.8434
6.4614
GCI
1–7 (Best)
4.4044
0.7251
2.8549
5.9487
GCI
1–10 (Best)
6.1898
1.4521
2.4767
8.8366
Fraser Institute
1–7 (Best)
4.2484
0.9199
2.3999
6.1147
GCI
Jurisdictions: Armenia, Australia, Austria, Bangladesh, Belgium, Bolivia, Botswana, Brazil, Bulgaria Canada, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, Czech Republic, Denmark, Ecuador, Egypt, El Salvador, Estonia, Finland, France, Germany, Ghana, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Ireland, Italy, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyz Republic, Latvia, Lithuania, Luxembourg, Malawi, Malaysia, Mexico, Mongolia, Morocco, Namibia, Nepal, Netherlands, New Zealand, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Saudi Arabia, Serbia, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, South Korea, Sweden, Switzerland, Tanzania, Thailand, Turkey, Uganda, Ukraine, United Arab Emirates,United Kingdom, United States, Uruguay, Venezuela, Vietnam, Zambia. Source: World Development Indicators (WDI), Global Competitiveness Index (GCI), Fraser Institute.
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from taxes and other market imperfections2 . For the control variables, we use the initial (i.e. 2009) real per capita GDP in 2005 US dollar to account for economic convergence, the average years of schooling of population over the 25 years old for human capital accumulation, the inflation rate to represent macroeconomic stability, the general government final consumption expenditure to capture government size, and the export plus import to GDP ratio to represent trade openness (Barro, 1991; Mankiw, Romer, & David, 1992; Miller & Upadhyay, 2000; Danquah, Moral-Benito, & Ouattara, 2013; Assibey-Yeboah & Mohsin, 2014). These data are obtained from the World Development Indicators (WDI) database. Four different measures of institutions are employed as the institutional threshold. Our main institutional variables are property rights protection and minority shareholders protection. Data on the protection of property rights are obtained from the Global Competitiveness Index (GCI) published by the World Economic Forum. This measurement is derived from the executive opinion survey pertaining to the following question: “How would you rate the protection of property rights, including financial assets, in your country?” Answers are scaled from 1 to 7, where higher values imply that protection of property rights are among the best in the world where assets are clearly delineated and protected by law. An important feature of the indicator is that it does not simply reflect laws on the books, but the overall legal environment related to the protection of property rights and the overall quality of legal institutions (Bose et al., 2012). The indicator has been adopted by the Economic Freedom of the World (EFW) published by the Fraser Institute (rescaled from 1 to 10) and referred to by Claessens and Laeven (2003) and Bose et al. (2012), among others. Data on the protection of minority shareholders was also obtained from the GCI. It is derived from the executive opinion survey pertaining to the following question: “In your country, to what extent are the interests of minority shareholders protected by the legal system?” Scale 1 represents no protection at all while scale 7 implies full protection. It is expected that higher level of protection would enable stock market to contribute positively to economic growth. In addition to these two main indicators, we also explore two broader measures of institutions, namely, legal structure/system from the EFW and institution from the GCI. Legal institutions play an important role in determining the degree of expropriation and the confidence with which investors participate in financial markets (Beck & Levine, 2005). The legal structure/system indicator has nine components, namely judicial independence, impartial courts, protection of property rights, military interference in rule of law and politics, integrity of the legal system, legal enforcement of contracts, regulatory restrictions on the sale of real property, reliability of police, and business costs of crime. The GCI’s institution indicator encompasses 21 components, namely property rights, intellectual property protection, diversion of public funds, public trust in politicians, irregular payments and bribes, judicial independence, favoritism in decisions of government officials, wastefulness of government spending, burden of government regulation, efficiency of legal framework in settling disputes, efficiency of legal framework in challenging regulations, transparency of government policymaking, business costs of terrorism, business costs of crime and violence, organized crime, reliability of police services, ethical behavior of firms, strength of auditing and reporting standards, efficacy of corporate boards, protection of minority shareholders’ interests, and strength of investor protection3 . 3. Estimation results We first present the results from the Hansen’s (2000) least squares threshold regression approach using the property rights protection and minority shareholders protection as the threshold indicators. Then, we provide the results from our further analyses using broader measures of institutions and instrumental variable threshold regression of Caner and Hansen (2004).
2 This study does not use stock value traded as it is a measurement of trading relative to the size of the economy rather than the liquidity of the market itself. Value traded, as product of quantity and price, can increase without growth in the number of transactions. Turnover ratio does not suffer from this shortcoming (Levine & Zervos, 1998a; Levine & Zervos, 1998b; Beck & Levine, 2004). 3 Preference was given to the GCI’s institution indicator over the Worldwide Governance Indicators of the World Bank as the former encompasses more institutional components.
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Table 2 Threshold estimates of property rights and minority shareholder. Property right protection
Minority shareholders protection
Turnover ratio
Market cap
Turnover ratio
Market cap
First sample split (Regimes 1 & 2) LM test for no threshold Bootstrap p-value Threshold estimate 95% Confidence interval
14.2173 0.0920 1.4630 [1.4589, 1.4817]
15.4029 0.0460 1.4630 [1.4589, 1.4817]
21.4549 0.0000 1.4488 [1.3657, 1.5413]
17.4629 0.0040 1.4625 [1.4625, 1.4625]
Second sample split (Regime 3) LM test for no threshold Bootstrap p-value Threshold estimate 95% Confidence interval
15.1465 0.0160 1.6383 [1.6354, 1.6383]
14.5911 0.0370 1.6222 [1.5221, 1.7030]
17.8777 0.0100 1.6049 [1.6030, 1.6049]
15.0091 0.0660 1.5059 [1.5023, 1.6049]
Stock market indicators
Third sample split LM test for no threshold Bootstrap p-value
8.1535 0.7640
11.2366 0.10250
8.2206 0.7390
10.1327 0.3030
Notes: Results correspond to trimming percentage of 15% and 1000 numbers of bootstrap replications. The null hypothesis of LM test (bootstrap p-value) is no threshold effect.
3.1. Least squares threshold regressions Table 2 presents the threshold estimates of Eq. (2) using the property rights protection and minority shareholders protection for models with the turnover ratio and market capitalization as measures of stock market development. The statistical significance of the threshold estimate in the regression allowing for heteroskedasticity is evaluated using the bootstrap method with 1000 replications and 15% as trimming percentage. Examining the presence of one threshold, there is evidence rejecting the null hypothesis of no threshold for all cases. In assessing whether the sample can be split further, the additional test reveals that the third regime exists in all regressions. With the existence of thresholds, the influence of property rights and minority shareholders protection on the stock market development-economic growth relations can thus be examined. Table 3 reports the empirical results of Eq. (2) using the turnover ratio for the three identified regimes, henceforth, low, intermediate and high protection regimes. Meanwhile, Table 4 provides the corresponding results using market capitalization as the stock market development indicator. For comparison, we also estimate the linear growth model without the threshold (i.e. Eq. (1)). On our main theme, we find the coefficients of stock market development indicators to be positive and significant in high property rights and minority shareholders protection regimes. As for the low and intermediate protection regimes, the results tend to depend on the stock market development measures. Using the turnover ratio, we find its coefficients in the growth equation of low and intermediate protection regimes to be indistinguishable from zero. The finding suggests that property rights and minority shareholders protection can replicate non-linear relationships between financial development as measured by the turnover ratio and economic growth, which is broadly consistent with the general empirical works by Deidda and Fattouh (2002), Rioja and Valev (2004), Huang and Lin (2009), Huang et al. (2010), Cecchetti and Kharroubi (2012), Law et al. (2013). When the market capitalization indicator is used, we find the relations between finance and growth to be positive in the low property rights protection regime and insignificant in the intermediate regime. Meanwhile, adopting the minority shareholders protection, we find their relations turn from being insignificant in the low regimes to being negatively significant in the intermediate regime. While plausible explanation of the results is not easy, it could be that market capitalization is less suitable as a measure of stock market development. In view of these, the analyses that follow will be based on using the turnover ratio as a measure of stock market development. As for other variables, we document evidence consistent with the conditional growth convergence and distorting effect of government consumption expenditure. The coefficients on initial income are negative and significant in all regressions and regimes. Likewise, the government consumption
Independent variables
Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Turnover ratio R-squared No. of observations
Linear model without threshold
Property rights protection as threshold
Minority shareholders protection as threshold
Regime 1 (PR ≤ 1.4630)
Regime 2 (PR ≤ 1.6383)
Regime 3 (PR > 1.6383)
Regime 1 (MS ≤ 1.4488)
Regime 2 (MS > 1.4488)
Regime 3 (MS > 1.6049)
0.1281*** (0.0333) −0.0116*** (0.0026) 0.0094 (0.0114) −0.0142* (0.0078)
0.1341*** (0.0342) −0.0084*** (0.0029) −0.0012 (0.0113) −0.0249*** (0.0072)
0.2277* (0.1150) −0.0352*** (0.0075) 0.0341* (0.0176) −0.0015 (0.0298)
0.1450*** (0.0401) −0.0087*** (0.0020) −0.0009 (0.0128) −0.0235*** (0.0049)
0.1122*** (0.0367) −0.0089*** (0.0029) 0.0074 (0.0111) −0.0243*** (0.0089)
0.2276*** (0.0363) −0.0160*** (0.0046) 0.0264* (0.0129) −0.0338*** (0.0096)
−0.3952*** (0.1287) −0.0185** (0.0067) 0.1509*** (0.0370) 0.0204 (0.0154)
−0.0150 (0.0877) −0.0001 (0.0046) 0.0021 (0.0015) 0.4182 85
−0.0289 (0.0664) 0.0038 (0.0067) 0.0008 (0.0015) 0.4942 37
0.2085 (0.2482) 0.0051 (0.0151) 0.0012 (0.0046) 0.6916 19
0.1381 (0.1282) −0.0001 (0.0024) 0.0034** (0.0013) 0.6575 29
0.0001 (0.0617) 0.0041 (0.0073) 0.0015 (0.0014) 0.5108 36
−0.0959 (0.1421) −0.0050 (0.0044) −0.0014 (0.0034) 0.6166 30
1.6484*** (0.4761) 0.0258** (0.0091) 0.0031** (0.0013) 0.7004 19
Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
A. Ng et al. / North American Journal of Economics and Finance 32 (2015) 48–63
Table 3 Regression results of LS threshold regressions using the turnover ratio.
55
56
Independent variables
Linear model without threshold
Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Market capitalization R-squared No. of observations
Property rights protection as threshold
Minority shareholders protection as threshold
Regime 1 (PR ≤ 1.4630)
Regime 2 (PR ≤ 1.6222)
Regime 3 (PR > 1.6222)
Regime 1 (MS ≤ 1.4625)
Regime 2 (MS > 1.4625)
Regime 3 (MS > 1.5059)
0.1148*** (0.0339) −0.0121*** (0.0026) 0.0144 (0.0116) −0.0139* (0.0076)
0.1023** (0.0378) −0.0086*** (0.0026) 0.0033 (0.0111) −0.0261*** (0.0062)
0.4260*** −0.0271*** 0.0344*** −0.0759***
−0.0365 (0.1093) −0.0126*** (0.0041) 0.0389 (0.0252) 0.0055 (0.0166)
0.0848** (0.0402) −0.0085*** (0.0026) 0.0098 (0.0118) −0.0245*** (0.0078)
0.5137*** (0.0773) −0.0411*** (0.0080) 0.0829** (0.0312) 0.0112 (0.0128)
−0.0051 (0.0780) −0.0165*** (0.0049) 0.0476** (0.0177) −0.0023 (0.0156)
0.0157 (0.0885) −0.0012 (0.0044) 0.0046** (0.0022) 0.4359 85
0.0163 (0.0694) 0.0066 (0.0064) 0.0047** (0.0022) 0.5256 37
−0.1817 (0.2618) −0.0056 (0.0156) 0.0013 (0.0034) 0.7897 17
0.3602** (0.1547) 0.0009 (0.0040) 0.0104* (0.0051) 0.4716 31
0.0254 (0.0696) 0.0065 (0.0072) 0.0039 (0.0024) 0.5106 38
−1.015*** (0.2359) −0.0625** (0.0201) −0.0111*** (0.0017) 0.8495 10
0.4599** (0.1785) 0.0028 (0.0038) 0.0107** (0.0059) 0.6268 36
(0.1204) (0.0049) (0.0105) (0.0255)
Notes: The standard errors are reported in parentheses (white corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
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Table 4 Regression results of LS threshold regressions using the market capitalization.
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coefficients are significant and negative most apparently for the countries in the low protection regimes. We also document evidence of the growth-enhancing role of human capital in higher protection regimes. The evidence for the significant roles of inflation and trade openness are, however, rather limited. At this juncture, the empirical findings highlight that improvement in institutional quality in the form of better protection of property rights and minority shareholders can facilitate efficient financing by reallocating the flow of finance toward growing firms and preventing capital divergence to unproductive investments (Wurgler, 2000; Beck & Levine, 2002). Consequently, firms would have more supply of financing to invest more and grow faster. Higher protection also results in greater confidence and encourages people to participate in the market. In fact, more protective environments are associated with lower information asymmetries, narrow bid-ask spreads and more market depths. Overall, the results of this study are broadly consistent with previous studies that have demonstrated that better investor protection tends to lower liquidity cost, increase market liquidity and promote financial development (Brockman & Chung, 2003; Lesmond, 2005; Eleswarapu & Venkataraman, 2006; Bose et al., 2012). 3.2. Further analyses We further assess the stock market development-economic growth nexus by considering broader measures of institutions. Table 5 presents Hansen’s (2000) threshold regression results using legal structure and institutions as the threshold indicators. Unlike the preceding sub-section, the test for the threshold reveals the presence of only two regimes (i.e. one threshold). It is comforting to note that, using broader measures of institutions, we find the results to be roughly similar to the one documented above. More importantly, the institutional conditionality of the market turnover and growth is further substantiated. The effect of market liquidity on growth is only significant and positive in the regime of high level of legal structure and high level of institutions, respectively. Market liquidity is however negative and insignificant in the low regimes. Hence, the results indicate that market liquidity has beneficial effects on growth when the level of institutional quality is above a certain threshold. It may be argued that the aforementioned results suffer from simultaneity bias due to the joint determination of financial development and economic growth4 . However, there are cases where simultaneity bias does not exist (as discussed, for example, in Beck et al. (2000), Levine, Loayza, and Beck (2000), and Levine (2003)). Hence, preference should be given to the least square results as it is the most efficient estimation. The Hausman test statistics for endogeneity presented in Table 6 indicate that market liquidity variable is exogenous in almost all regimes, which lend support to the preceding results. As there is no evidence of endogeneity in all the three regimes in the minority shareholders protection threshold model, we proceed to re-estimate the other remaining models using the instrumental variable threshold regression (IVTR) due to Caner and Hansen (2004) for robustness check. It has been noted in the literature that the identification of instruments that capture the exogenous cross-country variation in financial development is difficult in many macroeconomic datasets. To address this challenge, we made an attempt by using the initial value of the appropriate instruments for the IVTR to minimize the potential endogeneity of the instruments. In the implementation, the initial values of the following instruments for the market liquidity variable are considered: domestic credit to the private sector, financial openness, portfolio equity flows, as well as portfolio equity assets and liabilities. They are chosen based on the theoretical and empirical works in the literature as well as the joint instruments validity test. Variables that have a significant effect on the stock market and fulfill the instruments validity test are assumed to be exogenous with respect to economic growth. Countries with better developed stock markets also have better developed banks, while countries with weak stock markets tend to have weak financial intermediaries (Demirguc-Kunt & Levine, 1996). Banking sector development has also been identified as a precondition for stock market development (Chinn & Ito, 2006). Banking sector development is proxied by domestic credit to the private sector (% of GDP) from the WDI. With regard to financial openness, there
4
Demetriades and Hussein (1996), Calderón and Liu (2003), and Bangake and Eggoh (2011).
58
Independent variables Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Turnover ratio R-squared No. of observations
Legal structure as threshold Regime 1 (LS ≤ 1.6306) ***
0.1416 (0.0470) −0.0015 (0.00340) −0.0042 (0.0135) −0.0386*** (0.0121) −0.1482** (0.0663) 0.0016 (0.0137) −0.0013 (0.0020) 0.5476 21
Institutions as threshold Regime 2 (LS > 1.6306) *
0.1105 (0.0576) −0.0146*** (0.0038) 0.0179 (0.0117) −0.0099 (0.0134) 0.1436 (0.1358) 0.0011 (0.0045) 0.0035* (0.0020) 0.5152 64
Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
Regime 1 (INS ≤ 1.3548) ***
0.1370 (0.0361) −0.0066** (0.0036) −0.0028 (0.0132) −0.0317*** (0.0105) −0.0461 (0.0725) 0.0054 (0.0081) −0.0005 (0.0017) 0.4547 35
Regime 2 (INS > 1.3548) 0.1155** (0.0486) −0.0162*** (0.0037) 0.0326*** (0.0110) −0.0137 (0.0144) 0.2104 (0.1285) −0.0015 (0.0039) 0.0040* (0.0021) 0.5989 50
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Table 5 Regression results using broader institutional indicators.
Independent variables
Durbin–Wu–Hausman chi-sq test Hausman p-value
Threshold indicators Property rights protection
Minority shareholders protection
Regime 2 Regime 3 Regime 1 (PR ≤ 1.6354) (PR ≤ 1.7030) (PR > 1.7030)
Regime 1 Regime 2 Regime 3 Regime 1 (MS ≤ 1.4488) (MS > 1.4488) (MS > 1.6049) (LS ≤ 1.6094)
Legal structure Regime 2 (LS > 1.6094)
Institutions Regime 1 Regime 2 (INS ≤ 1.5736) (INS > 1.5736)
1.285
5.9648
0.2841
0.0287
0.0113
0.4209
0.3381
9.4237
0.0094
13.4654
0.257
0.0146
0.594
0.8654
0.9155
0.5165
0.5609
0.0021
0.9227
0.0002
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Table 6 Endogeneity tests.
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Independent variables
Threshold indicators Property rights protection
Constant Initial GDP per capita Schooling Government consumption Inflation Trade openness Turnover ratio J-stat J-stat p-value No. of observations
Legal structure
Institutions
Regime 1 (PR ≤ 1.6354)
Regime 2 (PR ≤ 1.7030)
Regime 3 (PR > 1.7030)
Regime 1 (LS ≤ 1.6094)
Regime 2 (LS > 1.6094)
Regime 1 (INS ≤ 1.5736)
Regime 2 (INS > 1.5736)
1.1448*** (0.2738) 0.0374*** (0.0082) −0.0288 (0.0261) 0.0985*** (0.0237) 1.0848*** (0.3036) 0.0917*** (0.0144) 0.0047 (0.0064) 2.3549 0.1814 55
2.0182*** (0.5749) 0.0068 (0.0144) 0.1149* (0.0639) 0.1571*** (0.0354) −0.4891 (0.9379) 0.0410*** (0.0133) 0.0090 (0.0173) 6.9493 0.0538 12
0.6720 (0.4996) 0.0762*** (0.0052) 0.0423* (0.0240) 0.0107 (0.0161) 0.2387 (0.3492) 0.0052 (0.0039) 0.0084*** (0.0020) 6.7170 0.0584 18
1.1277*** (0.3100) 0.0274** (0.0109) 0.0656* (0.0366) 0.0490 (0.0322) 0.6663*** (0.1721) 0.1013*** (0.0193) 0.0075 (0.0082) 3.2567 0.1598 18
1.1627*** (0.3743) 0.0282*** (0.0076) 0.1051** (0.0385) 0.0761*** (0.0195) 2.2045*** (0.2873) 0.0340*** (0.0100) 0.0113* (0.0064) 4.9813 0.1032 67
1.2898*** (0.2983) 0.0377*** (0.0083) −0.0024 (0.0271) 0.1068*** (0.0239) 1.1196*** (0.3211) 0.0721*** (0.0117) 0.0028 (0.0078) 3.5081 0.1293 60
0.0051 (0.5538) 0.0387*** (0.0074) 0.2043*** (0.0495) −0.0077 (0.0262) 1.8957*** (0.5604) 0.0148 (0.0094) 0.0083** (0.0034) 2.5007 0.1807 25
Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). * ** *** , , Denote significance level at 10%, 5% and 1%, respectively.
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Table 7 IVTR using initial value of instruments.
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is evidence that capital account liberalization contributes to stock market development after attaining a threshold level of legal and institutional infrastructure development (Chinn & Ito, 2006; Ito, 2006). Foreign portfolio investment and capital flows are also important determinants in the development of stock markets, serving as a proxy of de facto openness (Levine & Zervos, 1998b; El-Wassal, 2005; Yartey, 2010). Data on financial openness, portfolio equity flows, and portfolio equity assets and liabilities are obtained from the Chinn–Ito index developed by Chinn and Ito (2006), WDI, and the External Wealth of Nations dataset constructed by Lane and Milesi-Ferretti (2007), respectively. Again, in this further exercise, we identify the presence of thresholds that are in line with previous results. The results of the IVTR are presented in Table 7. Furthermore, the Hansen J-statistics validates the list of the instruments used in the estimation at 5% significance level. It is pleased to note that the results remain similar to the ones reported above. Hence, they strengthen our previous conclusions that strong institutional quality are needed for the stock market development to have positive contribution to economic growth. 4. Concluding remarks This paper examines whether high levels of property rights and minority shareholders protection can enable stock market to positively spur economic growth using a cross-section of 85 jurisdictions. To this end, the paper adopts threshold regression techniques developed by Hansen (2000) and Caner and Hansen (2004). The empirical results demonstrate that stock market liquidity has a significant and positive influence on GDP growth only after the attainment of a certain threshold level of property rights protection. Until then, the effects of stock market liquidity on GDP growth are found to be negligible. Market liquidity is also significant and positive in promoting GDP growth in cases of strong minority shareholders protection. The results for the high protection regimes are further supported when the market capitalization is used as a measure of stock market development. Furthermore, employing broader measures of institutions, we arrive at a similar conclusion pointing to the importance of institutional quality in shaping the positive relations between stock market development and economic growth. Hence, this paper upholds the “better finance, more growth” proposition and contributes to the identification of thresholds above which property rights and minority shareholders protection can positively shape the impact of stock market on economic development. It provides a policy target for countries to strengthen their institutional framework in order for their respective stock market to have positive and significant effect on growth. The empirical evidence also serves as a basis for policy makers to formulate initiatives to enhance property rights and minority shareholders protection measures particularly in cases where low levels of protection impede the role of stock market development in enhancing growth. Institutional reforms providing better formal mechanisms for the reliable enforcement of contracts and protection from expropriation can therefore create conducive conditions for the overall economic development. Acknowledgements We thank the editor, two anonymous referees, Mustafa Disli, and discussants at the 5th Financial Market and Corporate Governance Conference (23-24 April 2014, Brisbane) for their helpful comments and review of the draft. Financial support from the Central Bank of Malaysia Shari’ah scholarship and the International Centre for Education in Islamic Finance is gratefully acknowledged. References Alchian, A. A., & Demsetz, H. (1973). The property right paradigm. The Journal of Economic History, 33(1), 16–27. Al-Yousif, Y. K. (2002). Financial development and economic growth: Another look at the evidence from developing countries. Review of Financial Economics, 11, 131–150. Arcand, J., Berkes, E., & Panizza, U. (2012). Too much finance? Washington, DC: International Monetary Fund. Arestis, P., & Demetriades, P. (1997). Financial development and economic growth: Assessing the evidence. The Economic Journal, 107(442), 783–799.
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