Economic growth in Latin America: Are stock market and
banking development significant determinants?
June 2019
Abstract: This research assesses by an empirical analysis the link between economic growth and its determinants with particular emphasis on the stock market and banking development in Latin America. Working with a data set for the timeframe 1980-2017 and applying different panel data models, the findings suggest a positive relationship between stock market development and economic growth, whilst banking development is not robustly linked with it. These results are consistent with the views that stock markets provide different services from banks and the importance of strengthening stock markets as a means of speeding up economic development.
JEL Classification: G00, O16, F36
Keywords: Economic Growth; Stock Markets; Banks
2
1. Introduction
Finance studies the interactions of economic agents as companies, individuals, investors, etc. on the financial system, which one enables lenders and borrowers to exchange funds through financial transactions. Two of the most important means of financing are the stock markets and banks. Stock markets allow investors to buy and sell shares in publicly traded companies. They are one of the most vital areas of a market economy as they provide companies with access to capital and investors with a slice of ownership in the company and the potential of gains based on the company's future performance. On the other hand, a bank is a financial institution licensed to receive deposits and make loans. Banks may also provide financial services, such as wealth management, currency exchange, and safe deposit boxes. In most countries, banks are regulated by the national government or central bank.
Since stock markets have an active role in the economy of any country, it can be inferred that the development of the stock markets along the years should be related to the economic growth, but, actually, there is a considerable debate about last affirmation that has been held by many scholars. For instance, historically, economists had focused on banks. Schumpeter (1911) and Bagehot (1873) emphasize the critical importance of the banking system in economic growth and highlight circumstances when banks can actively spur innovation and future growth by identifying and funding productive investments. Conversely, Lucas (1988) terms the relationship between financial and economic development badly over-stressed.
Complementing the historical discussion about the contribution of banking within the economic growth, there is an expanding literature that sustains a direct link between the economic growth and the stock market development, as it has been stated by Levine and Zervos (1998) or Atje and Jovanovic (1993). In contrast, there are some scholars that postulate the opposite (or at least that economic growth is not a consequence of the development of the stock markets). Some of this literature can be attributed to Singh (1997), Robinson (1952), inter alia.
Most of the latter studies have been focused on developed countries, except Levine and Zervos (1998), since their study was based on an assortment of different type of countries. Thus, the present research will intend to base its analysis in Latin America, a region with important presence of developing countries. In this way, undertaking this investigation may contribute with additional findings and empirical evidence (for instance extending the Levine and Zervos study period more than two times) about the role of the stock market and banking development in the growth of the economies, and in the same line, it could be a valid resource to understand the economic growth of this region during the last four decades (period in which some countries as Chile, turned from one of the poorest economies to one of the most prosperous and stables in Latin America).
3 period measured, including personal consumption, government purchases, private inventories, paid-in construction costs and the foreign trade balance. Increase in GDP means an increase in all the values of goods and services produced in an economy. Consequently, the GDP per capita of Chile (based on purchasing power parity or PPP) has increased almost six times from 1990 until current days. In order to make it more graphic, the indicator value for 2017 was $24.634,97 (at international dollars), whilst Romania’s one was $26.656,71 (at international dollars). As can be contrasted, the Chilean economy would be achieving the same level of a member of the European Union, something totally unthinkable some decades ago, especially from a country belonging to Latin America. Unlike the Chilean economy, not all the countries from the region have followed the same path, and therefore dissimilar situations can be depicted within Latin America, particularly from the economic approach taken by each government. In this way, what are the determinants of a boost in the economy? Could it be attributed in part to the development and strengthening of the stock market and bank sector?
Most of the countries of Latin America, at least as economies, are recognized abroad as open ones, but it has not been always like that. This liberalization process started more than 30 years ago as a result of a disappointing economic management that led to high inflation rates, scarce economic development and high levels of foreign debt versus the earning power of the countries. Consequently, the economic situation exploded along the continent in the early of 1980s in the denominated Latin American debt crisis. This debt crisis was the most serious of Latin America's history. Incomes and imports dropped; economic growth stagnated; unemployment rose to high levels; and inflation reduced the buying power of the middle classes. In order to respond the crisis, most of the governments implemented a gradual plan based on the liberalization of the economy, with special focus in privatization and deregulation. But not all the countries did it at the same level, for instance Chile carried out a complete reform of the economic policy, while other countries adapted their old models with the liberalization principle (something additional to mention is that almost whole Latin America at the moment of the debt crisis was under political dictatorships). With the return of the democracy for most of the countries around 1990, the new governments were empowered to continue with the recovery of the economic disaster happened during the last decade.
4 As outlined before, the main goal of this paper is to study the implication of some determinants on the economic growth, with a distinctive focus on the effects of the stock market and banking development in Latin America. In order to carry out this study, they have been selected the current six most important economies of the region1 previously mentioned, i.e. Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The time frame to be evaluated will be from the year the debt crisis started in Latin America, i.e. 1980 and extending it until 2017. Next section intends to answer the research question about the relevance of these determinants through the existing literature. Section 3 describes the methodology to be carried out, whilst section 4 gives a description of the data and section 5 contains the main testing results. Section 6 concludes.
2. Literature review
The main fundamentals of the economic growth have been studied historically because of the relevance that it has in the short and long term in our lives. Economic growth has a ripple effect by expanding the economy, businesses start to see a surge in profits, which means stock prices also see growth. Companies can then raise more money in order to invest more, therefore adding more jobs to the labor force. That leads to an increase in incomes, inspiring consumers to open up their wallets and buy more. In order to understand how an economy can grow, diverse models have been introduced, being a starting point the Solow (1956) model. This concept of exogenous growth grew out of the neoclassical growth model. The exogenous growth model factors in production, diminishing returns of capital, savings rates and technological variables to determine economic growth. The exogenous growth model differs from the endogenous growth model in that the exogenous model requires forces outside of capital investment and a growing working population to continually grow an economy. The endogenous model suggests that an economy can continue to grow indefinitely using already available items such as existing technology or investment in education. The main focus of this study is to assess the relationship between some of these endogenous variables, specifically the link between stock market and banking development with the economic growth. In this way, there is a significant debate and literature about the relationship of the topics previously mentioned.
Levine and Zervos (1998) revealed a positive and significant relationship between stock market and banking development in the long run with regards to economic growth. Their research took into account 47 countries from 1976 to 1993 period, and the study is based on
5 cross country method. They stress that the liquidity of the stock market, measured as the value of stock traded relative to the market capitalization and the size of the economy, is a significant and positive determinant of economic growth. On the other hand, they emphasize that the level of banking development, gauged as the ratio of bank loans to the private sector over the size of the economy, is directly linked with the rates of growth. The results are consistent with the views that financial markets provide important services for growth, and that stock markets provide different services from banks. The paper also finds that stock market size, volatility, and international integration are not robustly linked with growth, and that none of the financial indicators is closely associated with private saving rates.
As indicated previously, the historical approach about financial system and its influence in the economic growth has been based on banks. Schumpeter (1911) argued about the services provided by financial intermediaries- mobilizing savings, evaluating projects, managing risk, monitoring managers, and facilitating transactions- stimulate technological innovation and economic development. In the same line, Bagehot (1873) emphasizes the critical importance of the banking system in economic growth and highlights circumstances when banks can actively promote innovation and future growth. A more updated perspective is provided by King and Levine (1993), as they intended to endorse Schumpeter (1911) affirmations. In this way, they examined a cross-section of about 80 countries for the period 1960-89, they found that various measures of financial development are strongly associated with both current and later rates of economic growth. So overall, they showed that the level of financial intermediation is a good predictor of long-run rates of economic growth, capital accumulation, and productivity improvements.
Besides the historical focus on banking, there is an expanding theoretical literature on the links between stock markets and long-run growth. For instance, Atje and Jovanovic (1993) have upheld that there is major link between the level of stock market development and the economic growth. They found a significant correlation between growth over the period 1980-1988 and the value of stock market trading divided by GDP for 40 countries. In addition, they also conclude that it is even more surprising that more countries are not developing their stock markets as quickly as they can as a means of speeding up their economic development, especially because they were not able to find an important relationship from banking perspective.
6 of market services leads markets to form in a way that is perceived to be efficient by market participants. In addition, as argued by North (1981), the provision of liquidity and the sharing of risk associated with financial market development substantially reduced the perceived costs of investing in innovation.
Beck and Levine (2001) investigated the impact of stock markets and banks on economic growth using a panel data set for the period 1976-98. They declare that the development of banks and stock markets significantly impacts growth, with a special mention to stock market liquidity (the ability to trade equity easily) and bank credit. Unlike to Levine and Zervos (1998) study based on cross country methodology, the findings of Beck and Levine (2001) are not due to potential biases induced by simultaneity, omitted variables or unobserved country-specific effects. In the same line, they argue that Levine and Zervos (1998) empirically assess the relationship between growth and both stock markets and banks, but their study suffers from an assortment of econometric weaknesses. The ordinary least squares (OLS) approach taken by Levine and Zervos (1998), however, does not account formally for potential simultaneity bias, nor does it control explicitly for country fixed effects or the routine use of lagged dependent variables in growth regressions. Further, while theory stresses the potential relationship between economic growth and the contemporaneous level of financial development, Levine and Zervos (1998) use initial values of stock market and bank development. This not only implies an informational loss in relation to using average values, but also a potential consistency loss. As it can be perceived, moving to a panel from pure cross-sectional data allowed Beck and Levine (2001) to exploit the time-series dimension of the data and deal rigorously with simultaneity.
Endorsing one more time the concept of liquidity and its importance, Levine (1991) and Bencivenga et al. (1995) came up with findings where more liquid stock markets, therefore, it is less expensive to trade, diminish the disincentives to investing in long-duration projects because investors can easily sell their stake in the project if they need their money back before the project matures. Thereby, enhanced liquidity eases investment in longer-run, higher-return projects that boost productivity growth.
So far, it has been depicted how different scholars support the thesis about banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development, commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. Consequently, the size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems.
7 proposition that the development of financial markets and institutions facilitates economic growth, as advanced by King and Levine (1993) and Levine and Zervos (1998). Consistent with Levine and Zervos, they found that the size of the stock market by itself is not as important in mobilizing financing as is the level of activity of the market.
The theory also provides conflicting predictions about whether stock markets and banks are substitutes, complements, or whether one is more conducive to growth than the other. For instance, Stiglitz (1985) and Bhide (1993) stress that stock markets will not produce the same improvement in resource allocation and corporate governance as banks. On the other hand, Allen and Gale (2000) emphasize that markets mitigate the inefficient monopoly power exercised by banks and stress that the competitive nature of markets encourages innovative, growth-enhancing activities as opposed to the excessively conservative approach taken by banks. Finally, some theories sustain that it is not banks or markets, it is banks and markets jointly; these different components of the financial system enhance different information and transaction costs. Following that, Demirguc-Kunt and Levine (2001) show that banks and securities markets tend to become more developed as economies grow and that securities markets tend to develop more rapidly than banks. Thus, financial systems generally become more market-based during the process of economic development. Complementing that, Huybens and Smith (1999) concluded, as an empirical matter, there is a strong positive association between measures of both bank lending activity and the volume of trading in equity markets on the one hand and real activity on the other.
With regards to international integration of the financial system across countries, Devereux, Smith and Obstfeld (1994) state that greater international risk sharing through internationally integrated stock markets induces a portfolio shift from safe, low-return investments to high-return investments, thereby accelerating productivity growth.
Nevertheless, there are some scholars who contribute to the debate with a different opinion with respect to the influence of the development of the stock markets and banking with the economic growth. For instance, Robinson (1952) contends that financial development simply follows economic growth, and articulated this causality argument by suggesting that where enterprise leads, finance follows. In the same side, Lucas (1988) declares that economists badly over-stress the role of the financial system within the economy.
8 in developing countries, which, despite their many difficulties, have been successful in several countries.
On the other hand, the liquidity of a stock market has been pointed out several times during this section as a feature that enhances the economic growth, but for instance there are some detractors about this evidence. Shleifer and Vishny (1986), sustained that since more liquidity makes it easier to sell shares, thus more liquidity decreases the incentives of investors to carry out the costly task of monitoring managers. In turn, weaker corporate governance hinders effective resource allocation and slows productivity growth, with obvious consequences in the economic performance.
According to previous literature exhibited, it can be derived that the relationship between the stock market and banking development with economic growth has been discussed for a while, and there are many researchers who argue in favor of this direct link, while others, in a lower quantity, think totally opposite. Taking into account this debate, this investigation work pursues to validate, in some certain way, the approach of the financial system development, with special focus on the stock markets and banks, can be attributed as one of the determinants of the economic growth, specifically from Latin America’s perspective. In this way, and aligned with all the literature reviewed from before, it is expected to confirm along this study the significant role that stocks market and banking development play in the growth of the economies.
3. Methodology
9 Complementing previous findings, Miller (1996) argues that cross-country regression approach implicitly assumes that countries possess similar structural characteristics (e.g. production technologies and institutional patterns). Structural differences, be they political, economic, social or other, between countries, therefore, do not condition the growth process. Or if they do, then the effects are randomly distributed with zero mean. If structural differences between countries do matter significantly and non-randomly in the growth process, it implies the existing cross-country research is potentially flawed. In order to address these kinds of problems, the use of panel data techniques attempts to accommodate country and across-time structural differences.
As it can be denoted from latter argumentation, the most suitable methodology to undertake this research is based on panel data analysis. It is important to stand out that there are important advantages of the use of panel data, (see Brooks, 2014): First, and perhaps most importantly, we can address a broader range of issues and tackle more complex problems with panel data than would be possible with pure time series or pure cross-sectional data alone. Second, it is often of interest to examine how variables, or the relationships between them, change dynamically (over time). To do this using pure time series data would often require a long run of data simply to get a sufficient number of observations to be able to conduct any meaningful hypothesis tests. But by combining cross-sectional and time series data, one can increase the number of degrees of freedom, and thus the power of the test, by employing information on the dynamic behavior of a large number of entities at the same time. The additional variation introduced by combining the data in this way can also help to mitigate problems of multicollinearity that may arise if time series are modelled individually. Third, as will become apparent below, by structuring the model in an appropriate way, we can remove the impact of certain forms of omitted variables bias in regression results.
The simplest way to deal with this data would be to estimate a single pooled regression on all the observations together, denominated pooled OLS, but pooling the data assumes that there is no heterogeneity, i.e. the same relationship holds for all the data. Since panel data deal with different entities over time, there is bound to be heterogeneity in these units, which may be often unobservable. In order to counter this potential problem, it can be used two classes of panel data models: fixed effects and random effects. It is possible to test whether the panel approach is necessary at all, carrying out the Redundant Fixed Effects test and the Breusch-Pagan Lagrange multiplier test. The first one is useful to decide between pooled OLS and fixed effects approach, while the second one works to decide between pooled OLS and random effects model. In the case that the pooled OLS is discarded, a Hausman (1978) test should be carried out in order to define which panel data model is more adequate. Thereby, and afterwards the perform of these tests2, the null hypothesis for Redundant Fixed Effects test of observed and unobserved fixed effects are equal to zero, i.e. they are equal across all units, it cannot be
10 rejected, meanwhile Breusch-Pagan Lagrange multiplier test has as null hypothesis that variances across entities are zero, i.e. no random effects, it was not rejected either, thus Pooled OLS method becomes the most suitable one for this research.
According to the latter, ordinary least squares regressions will be used to test the relationship between stock market and banking development with the economic growth. Where the generic equation is defined as following:
Y𝑖𝑡 = 𝛼 + 𝛽1X1𝑖𝑡+ 𝛽2X2𝑖𝑡 + 𝛽3X3𝑖𝑡+ 𝜀𝑖𝑡 i = 1, 2, …, N (cross sections)
t = 1, 2, …, N (time series)
Where the dependent variable Y is represented by the economic growth. Regarding the independent variables, that in this particular research are the stock market and banking development, they were defined in some cases more than one indicator in order to measure it. In this way, the stock market development will be measured through two indicators: size and liquidity, whilst the banking development through the domestic credit to private sector by banks. Moreover, in order to assess the strength of the independent link between both stock markets and bank development with economic growth, some control variables have been added in the regressions since they could be other potential determinants of economic growth.
Part of the next section describes in detail the measuring of the stock market and banking development, as well for economic growth and the control variables to be included in the model.
4. Data and descriptive statistics
At the previous section it was mentioned that the stock market and banking development will be measured by some specific indicators. In this way, it is possible to turn from the standardized Equation 1 to one more specific for this research:
GDPPC = 𝛼 + 𝛽1MC𝑖𝑡+ 𝛽2TR𝑖𝑡 + 𝛽3VT𝑖𝑡+ 𝛽4BC𝑖𝑡 + 𝛽5FDI𝑖𝑡 + 𝛽6IR𝑖𝑡+ 𝜀𝑖𝑡 i = 1, 2, …, N (cross sections)
t = 1, 2, …, N (time series)
Where the dependent variable GDPPC is the real Gross Domestic Product per capita growth, used as well by Levine and Zervos (1998), Beck and Levine (2001) and other scholars as the best proxy for economic growth.
With regards to the stock market development, and according also to the work done by Levine and Zervos (1998), it will be measured through two indicators: size and liquidity. Thus,
[1]
11 the size of the stock market measure corresponds to the Market Capitalization over GDP, and it is represented in the model as MC. The assumption behind this measure is that overall market size is positively correlated with the ability to mobilize capital and diversify risk on an economy-wide basis. Something relevant to mention, is that a large stock market is not necessarily a liquid market, and therefore it is included the variable TR, which stands for Turnover ratio and equals the value of total shares traded divided by market capitalization. A high turnover ratio is often used as an indicator of low transaction costs. The turnover ratio complements the market capitalization one, and it is a proper way to estimate the liquidity of a stock market. A large but inactive market will have a large market capitalization ratio but a small turnover ratio. Another liquidity measure can be used, and it is defined as Value Traded, VT in Equation 2. It is calculated as the value of the trades of domestic shares on domestic exchanges divided by GDP. While not a direct measure of trading costs or the uncertainty associated with trading on a particular exchange, theoretical models of stock market liquidity and economic growth directly motivate Value Traded (Levine, 1991; Bencivenga et al.,1995). Value Traded measures trading volume as a share of national output and should therefore positively reflect liquidity on an economy wide basis. Value Traded may be importantly different from Turnover as shown by Demirgiig-Kunt and Levine (1996). While Value Traded captures trading relative to the size of the economy, Turnover measures trading relative to the size of the stock market. Thus, a small, liquid market will have high Turnover but small Value Traded.
With respect to the banking development, it is intended to be measured in the model as variable BC, which stands for Bank Credit. It can be defined as the domestic credit to private sector by banks as percentage of GDP. This indicator refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. The reasoning behind this indicator is that allows to identify where the financial system allocates the capital, and is also aligned with the work of Levine and Zervos (1998), and as well Beck and Levine (2001).
12 The second control variable is the Inflation Rate (IR), that is a valid way to measure macroeconomic instability and economic activity (William Easterly and Sergio Rebelo, 1993; Stanley Fischer, 1993; Michael Bruno and Easterly, 1998).
As mentioned earlier along this document, the main purpose of this research is to test the significance of the stock market and banking development in Latin America, taking as sample the current six largest economies of the region. The data period will comprise from 1980 to 2017, and all the yearly data needed has been collected from World Development Indicators (WDI), World Bank Open Data and Global Financial Development Database, International Financial Statistics from IMF and World Federation of Exchanges database.
Before continuing with statistics and analysis of the variables considered in the model, it is relevant to understand if the data series are stationary or non-stationary. Spurious regression possibilities arise when the data series under consideration are non-stationary, and therefore misleading inferences. Thus, in order to examine whether the data series are stationary or non-stationary, an augmented Dickey-Fuller3 test was carried out across each six panels and variables. The null hypothesis of this statistic test is that all panels contain unit roots (it is an extension of the time series original test take it to a panel data approach). In this way, the null hypothesis for most of the variables was rejected at the 0.01 significance level, i.e. no presence of unit root, whilst for Market Capitalization and Value Traded, the null hypothesis was also rejected but at 0.1 and 0.05 significance level respectively.
Having discarded the non-stationarity issue across the data series, descriptive statistics of the different variables are presented in Table 1, while Figures 1-4 provide a breakdown by indicator and country. Lastly, Table 2 exhibits correlations.
Table 1. Summary Statistics: Annual Averages 1980 – 2017. Table shows mean, median, maximum,
minimum, standard deviation, kurtosis, skewness and number of observations for Latin America sample.
Mean Median Max Min St. dev Kurtosis4 Skewness No.
GDPPC 0.015 0.020 0.102 -0.142 0.041 1.765 -0.348 228
Market Cap 0.330 0.227 1.564 0.004 0.309 1.886 1.004 211
Turnover 0.303 0.188 4.140 0.019 0.439 36.794 0.780 211
Value Traded 0.074 0.047 0.462 0.001 0.088 4.822 0.948 219
Bank Credit 0.328 0.281 1.331 0.069 0.209 3.383 0.676 228
Notes: GDPPC = real GDP per capita growth; Market Cap = value of domestic shares as a share of GDP; Turnover = value of
the trades of domestic shares as a share of market capitalization; Value Traded = value of the trades of domestic shares as a share of GDP; Bank Credit = bank credit to the private sector as a share of GDP.
3Please refer to the appendix B to review in detail the tests performed.
13 Figure 1. Real GDP per capita growth. Graph Figure 2. Market Capitalization. Graph shows
shows the average value by country for 1980-2017 the average value by country for 1980-2017
Figure 3. Liquidity indicators. Graph shows the Figure 4. Bank Credit. Graph shows the
average value by country for 1980-2017 average value by country for 1980-2017
From both, tables and figures, can be denoted substantial variance among the countries for economic growth, stock market and banking development indicators. Previous affirmation can be noticed from some statistics provided in Table 1. For instance, all the explanatory variables are positively skewed, that means the bulk of the data is at the left and the right tail is longer. It draws the attention the high values of skewness for Market Capitalization, and as well for Value Traded. On the other hand, the dependent variable real GDP per Capita Growth is completely the opposite, since it is negatively skewed, but in a reasonable range of symmetry. As a complement, the distribution for all indicators shows positive kurtosis that it implies that compared to a normal distribution, its tails are longer and fatter, and often its central peak is higher and sharper. In addition, from standard deviation values may be also inferred a high variability among the data set, since the values are in most of the cases even higher than the mean of each variable. With regards to the balance of the data panel, it may be observed that for some variables there are some missing observations, nevertheless, it is a strongly balanced panel taking into account the extension in terms of time and the different entities considered.
As a way to illustrate the wide variation across the different countries, Figures 1-4 are attached. From these plots, it can be mentioned, for instance, that Chile has double real gross domestic product per capita growth than the average of the region, followed by Peru and Colombia that are slightly above the mean. On the other hand, Mexico exhibits the lowest average growth of 1% in 1980 – 2017 period. Regarding the independent variables, Chile also outperforms almost two times in comparison to the sample in certain indicators as Market Capitalization and Bank Credit, with 73% and 59% respectively. In terms of the control
14 variables, Chile also stands out in comparison with Latin America, since presents a value of Foreign Direct Investment (FDI) that almost duplicate the region, and as well it has been the country with best control of the inflation during the last four decades. Latter results might be a valid way to strengthen what has been stated along this study, that Chile is one of the most consolidated economies within the region. With respect to the indicators related to liquidity, i.e. Turnover Ratio and Value Traded, Brazil leads the region with considerable distance against the rest of the countries with 51% and 17% respectively. In contrast, the region shows average values of 29% and 7% for each liquidity indicator.
Table 2. Correlations. Matrix presents correlation coefficients among the different variables
considered in the model.
GDPPC Market Cap Turnover Value Traded Bank Credit
GDPPC 1.000
Market Cap 0.289 1.000
Turnover 0.052 -0.175 1.000
Value Traded 0.141 0.506 0.355 1.000
Bank Credit 0.043 0.522 -0.096 0.333 1.000
Notes: GDPPC = real GDP per capita growth; Market Cap = value of domestic shares as a share of GDP; Turnover = value of
the trades of domestic shares as a share of market capitalization; Value Traded = value of the trades of domestic shares as a share of GDP; Bank Credit = bank credit to the private sector as a share of GDP.
It is noticed that the Gross Domestic Product per Capita growth is correlated in some level with Market Capitalization and Value Traded, whilst with Turnover Ratio and Bank Credit a weak relationship is depicted. Value Traded is strongly correlated with Market Capitalization, while a similar relationship exists between Bank Credit and Market Capitalization.
Next section is intended to evaluate, through the methodology previously described, whether measures of stock market and banking development are significantly related with the economic growth in Latin America.
5. Results
15 In order to compute these results, it was carried out a Pooled OLS, which one was indicated as the most suitable model to evaluate the relationship among the variables. This kind of model assumes there are no unique attributes of individuals within the measurement set, and no universal effects across time, and it is undertaken by the technique of Ordinary Least Squares. Consequently, Table 3 shows the main results of the regression of the model developed.
Table 3. Stock Markets, Banks, and Growth, 1980 – 2017. Dependent variable: GDPPC. P-values
smaller than 0.01, 0.05 and 0.10 are indicated by ***, ** and *, respectively.
Independent Variables Coefficients
Market Cap 0.047*** (0.014) Turnover 0.012** (0.005) Value Traded -0.040 (0.030) Bank Credit -0.021 (0.019) FDI 0.009 (0.176) Inflation -0.002*** (0.001) Constant 0.010 (0.006) R-squared 0.184 Observations 211 Countries 6
Notes: Heteroskedasticity consistent standard errors in parentheses. GDPPC = real GDP per capita growth; Market Cap = value
of domestic shares as a share of GDP; Turnover = value of the trades of domestic shares as a share of market capitalization; Value Traded = value of the trades of domestic shares as a share of GDP; Bank Credit = bank credit to the private sector as share of GDP. Control variables included in the regression: Foreign Direct Investment (FDI) and Inflation.
According to these results, it can be noticed that some indicators used as reference for stock market and banking development enter significantly in the regression model at the 0.01 and 0.05 significance level at the moment of assessment of their relationship with the dependent variable, which is the economic growth measured as real gross domestic product per capita (GDPPC). Thereby, it is possible to address each indicator and dimension used in the model and in the same way to interpret the empirical results coming from the regression.
16 Besides being statistically significant, the estimated coefficients suggest that the relationships between stock market development and future rates of long-run growth are economically large. For instance, the estimated coefficient implies that a one standard deviation increase in market capitalization (0.3) would increase the real gross domestic product per capita growth by 1.4 percentage points per year (0.047 * 0.3) over this period. Accumulating over 38 years, this implies that real GDP per capita would have been over 70 percent higher by 2017 (exp {38 * 0.014}). On the other hand, regarding the Turnover Ratio it is also possible to do same exercise as before, therefore one standard deviation increase in this indicator (0.4) would increase the real gross domestic product per capita growth by 0.5 percentage points per year (0.012 * 0.4) over this period. Taking into account this effect over 38 years, it would imply that real GDP per capita would be over 20 percent higher by 2017 (exp {38 * 0.005}).
Another similar approach can be done for instance with a particular country of the sample, which could be Mexico since shows the lowest average GDP per capita growth in the timeframe. In this way, and taking as reference the Market Capitalization coefficient, if Mexico had had the Latin America mean of this indicator (0.330) instead of its actual value (0.228), the annual per capita growth would have been 0.5 percentage points faster (0.047 * 0.102) over the sample period, such that real GDP per capita growth would have been 20.1 percent higher by 2017 (exp {38 * 0.005}). Same could be done for Argentina, which shows the lowest mean in terms of Market Capitalization in the region (0.115), turning out in 46.8 percent higher by 2017 regarding the economic growth indicator. With respect to the remaining measure of stock market liquidity, i.e. Value Traded, it can be mentioned that the ratio enters in the regression with a negative impact, but with a significance level far away of the minimum thresholds set as acceptable as 0.1 at least.
The second determinant that was intended to evaluate this document is the bank development and its influence within the economic growth. That dimension is measured in this model through the variable Bank Credit, which, as it has been described before, stands as the domestic credit to private sector by banks as percentage of GDP. From Table 3, it is depicted that the variable does not enter significantly in the regression, discarding a relevant relationship between this indicator with the real GDP per capita growth. Furthermore, the associated coefficient is negative, and therefore an unexpected result.
17 to 1990, Latin America countries had to deal with incredible high levels of inflation and as long as they managed to control it, they were also able to unlock the growth in their economies.
Previous interpretation of the results comes from a Pooled OLS method, which one was defined as the best one to tackle the relationship under evaluation, and it assumes that there is no heterogeneity. This assumption at first instance is a bit surprising since, as it has been described along this study, the different countries have shown dissimilar indicators. According to the latter, and as a way to contrast the model previously chosen, the following Table 4 compares the outcomes from Pooled OLS and Random Effects model, which one is considered more suitable than Fixed Effects after having performed a Hausman test5.
Table 4. Stock Markets, Banks, and Growth, 1980 – 2017. Pooled OLS and Random Effects results. Dependent variable: GDPPC. P-values smaller than 0.01, 0.05 and 0.10 are indicated by ***,
** and *, respectively.
Independent Variables Pooled OLS Random Effects
Market Cap 0.047*** 0.047*** (0.014) (0.011) Turnover 0.012** 0.012*** (0.005) (0.004) Value Traded -0.040 -0.040 (0.030) (0.027) Bank Credit -0.021 -0.021 (0.019) (0.019) FDI 0.009 0.009 (0.176) (0.263) Inflation -0.002*** -0.002*** (0.001) (0.000) Constant 0.010 0.010 (0.006) (0.009) R-squared 0.184 - Observations 211 211 Countries 6 6
Notes: Heteroskedasticity consistent standard errors in parentheses. GDPPC = real GDP per capita growth; Market Cap = value
of domestic shares as a share of GDP; Turnover = value of the trades of domestic shares as a share of market capitalization; Value Traded = value of the trades of domestic shares as a share of GDP; Bank Credit = bank credit to the private sector as share of GDP. Control variables included in the regression: Foreign Direct Investment (FDI) and Inflation
From Table 4 it can be observed that the results are quite similar between both panel models, where the main difference is that Turnover enters in a more significant way with Random Effects, but the latter does not change at all the interpretation provided before. In this way, it can be confirmed that for this sample is not observed country-specific or random-specific effects, thus Pooled OLS is the best way to tackle the data series of this research.
In sum, and bearing in mind the statistical and economic significance detailed above, the main findings of this study are that the stock market development has an important role as
18 a determinant of the economic growth in Latin America, either by size or by liquidity dimension. These results are consistent with the view that stock market liquidity facilitates long-run growth argued by Levine and Zervos (1998), Atje and Jovanovic (1993), Beck and Levine (2001), inter alia. Hence, these results are not supportive of models that emphasize the negative implications of stock market liquidity (Shleifer and Vishny, 1986; Shleifer and Lawrence Summers, 1988). Besides liquidity dimension, this document also found a significant relationship between the size of the stock market, measured as market capitalization over GDP, and the economic growth. Latter indicator was discarded as a good predictor by Levine and Zervos (1998), but one feasible reason to have different results are that both studies deal with different kind of countries, period of time, and methodology. In this way, both dimensions studied in the model endorse the significant relationship of the stock market development with future economic growth.
With regards to the role played by banking development as a promoter of economic growth, the empirical results show that at least for Latin America and for the period under evaluation, it cannot be attributed significantly. This outcome is aligned with Atje and Jovanovic (1993), where they could not find a relevant effect linking banks development with the economic growth (unlike with market liquidity as they found it). Nevertheless, it is not consistent with the findings from Levine and Zervos (1998), Beck and Levine (2001), and with the historical approach claimed by Schumpeter (1911) and Bagehot (1873). Something that draws the attention about Bank Credit measure, it is that for Latin America the mean value is 33% along the sample period, whilst in the models of Levine and Zervos (1998) and Beck and Levine (2001), it is 80% and 50% respectively, thus there is an important difference which could be attributed surely because of different countries and time studied. One of the possible reasons of this low value for Latin America is because the depth level in terms of banking for the region still being a pending topic to address, since the presence of informal credit business still having an important incidence for many sectors of the population (most of the time because of a low creditworthiness). Complementary, a first difference transformation for the variable Bank Credit was also tested in the regression model without major changes in terms of significance or sign either. In that way this results just strengthen the first ones that come from the original regression.
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6. Conclusion
Throughout this document it has been studied the empirical relationship between stock market development, banking development, and long run economic growth for Latin America in the last four decades (based on a sample of the largest six economies of the region). Latter determinants were gauged by different measures as in the case of stock market development through size and liquidity dimensions, while banking development by the domestic credit to private sector. On the other hand, as a proxy of long run economic growth it was considered the real gross domestic product per capita growth.
Among the main findings of this paper are that one of the determinants under study, being more specific the stock market development, is related significantly with the economic growth through both dimensions previously mentioned, i.e. by size and liquidity. In this way, a significant relationship was found between market capitalization over GDP (size measure), and as well turnover ratio (liquidity proxy) with the real GDP per capita growth. Whilst the main assumption behind size indicator is that it is positively correlated with the ability to mobilize capital and diversify risk on an economy-wide basis, a high turnover ratio is often used as an indicator of low transaction costs. These results are aligned with the vision that more liquid stock markets ease long-run growth argued by Levine and Zervos (1998), Atje and Jovanovic (1993), Beck and Levine (2001), inter alia. In addition to liquidity feature, and as previously mentioned, it has been also found a significant relationship between the size of the stock market and the economic growth. The prior indicator was not considered a relevant one by Levine and Zervos (1998), but one probable reason to have a different interpretation is that both studies deal with different kind of countries, timeframe, and methodology. Thereby, both dimensions studied in the model endorse the significant relationship of the stock market development with future economic growth.
20 creditworthiness in many sectors. The prior fact clearly affects banking levels across the economies of Latin America, and therefore the access to domestic credits which can spur somehow the economic growth.
In sum, and taking into account the assessment of the determinants previously described, it could be mentioned that this document also validates the view provided by Allen and Gale (2000), where they emphasize that stock markets mitigate the inefficient monopoly power exercised by banks and stress that the competitive nature of markets encourages innovative, growth-enhancing activities as opposed to the excessively conservative approach taken by banks. Lastly but not least, it is important to remind one of the most important takeaways that Atje and Jovanovic (1993) come up regarding how surprising is that more countries are not developing their stock markets as quickly as they can as a means of speeding up their economic development. More concretely for this research, it can be noticed from Latin America sample that Chile stands out in terms of economic growth, and part of this can be explained by the strong indicators related to stock market development of the country, especially regarding to market capitalization, one of the significant dimensions where Chile presents values 120% higher than the mean of the region. Moreover, the countries that follow Chilean economy, in terms of GDP per capita growth, are Colombia and Peru, which ones have established and consolidated the same economic model based on neoliberalism and free market capitalism than in Chile.
Beyond the results obtained in this research, there were also limitations and learnings that came up during the development of this study. For instance, at the moment that it was decided to focus on one particular region as Latin America, the initial idea it was to extend the scope of the sample at least for ten countries in order to have a larger representation for the territory, but it happened that the data available to work with it was quite incomplete, thus the risks to work with an unbalanced panel were too high. Taking into account the reference to the panel data method, it was also unforeseen that results prove to be generally consistent across the ordinary least-squares (Pooled OLS), fixed-effect and random-effect models. Of course, the fixed- and random-effect models capture the effects, if any, of omitted country-specific (or time-specific) variables, but it seems that the sample taken for Latin America countries may not have significant structural differences. If so, then the fixed- and random-effect modelling may not lead to significant changes in the findings for the standard ordinary least squares model, something that happened in this particular paper and it is aligned with the findings of a similar research undertook by Miller (1996), where OECD member countries did not show substantial structural divergences either.
21 based on a particular region where developing countries prevail as in Latin America. One drawback of this extension would be the lack of data that it exists with regards to the indicators used in this paper, so one additional challenge could be to find new ways of measure for stock market and banking development so that with the available data the research could be carried out.
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Appendix
A. Panel data tests
In order to assess the existence of fixed effects and random effects within the dataset, it is necessary to carry out the Redundant Fixed Effects and the Breusch-Pagan Lagrange multiplier tests. Firstly, it will be examined the fixed effects:
The last F-test has as null hypothesis that all entities effects are equal to zero. Thus, as the P-value is 0.1657 the null hypothesis cannot be rejected and therefore Pooled OLS is more suitable than Fixed Effects. Now it is time to evaluate random effects:
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B. Unit root test by variable
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C. Hausman test
In order to decide which model is more suitable between fixed effects and random effects, a Hausman test is performed where the null hypothesis is that the error term is uncorrelated with the explanatory variables.
