The Finance and Growth Nexus Re-Examined
Do All Countries Benefit Equally?
Author: Mr. Adolfo Barajas, Mr. Ralph Chami, and Mr. Seyed Reza Yousefi1
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Authors’ E-Mail Addresses: abarajas@imf.org; rchami@imf.org; s.reza.y@gmail.com
Publication Date:
29 May 2013
eISBN:
9781484372104
ISBN:
9781484372104
Language:
English
Keywords:
WP; MENA country; private sector; banking system; dependent variable
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A large theoretical and empirical literature has focused on the impact of financial deepening on economic growth throughout the world. This paper contributes to the literature by investigating whether this impact differs across regions, income levels, and types of economy. Using a rich dataset for 150 countries for the period 1975–2005, dynamic panel estimation results suggest that the beneficial effect of financial deepening on economic growth in fact displays measurable heterogeneity; it is generally smaller in oil exporting countries; in certain regions, such as the Middle East and North Africa (MENA); and in lower-income countries. Further analysis suggests that these differences might be driven by regulatory/supervisory characteristics and related to differences in the ability to provide widespread access to financial services.

Abstract

A large theoretical and empirical literature has focused on the impact of financial deepening on economic growth throughout the world. This paper contributes to the literature by investigating whether this impact differs across regions, income levels, and types of economy. Using a rich dataset for 150 countries for the period 1975–2005, dynamic panel estimation results suggest that the beneficial effect of financial deepening on economic growth in fact displays measurable heterogeneity; it is generally smaller in oil exporting countries; in certain regions, such as the Middle East and North Africa (MENA); and in lower-income countries. Further analysis suggests that these differences might be driven by regulatory/supervisory characteristics and related to differences in the ability to provide widespread access to financial services.

I. Introduction

It is well established that a vibrant, dynamic, and well-functioning financial sector leads to a host of improved economic outcomes. As surveyed first by Levine (1997a), then by Demirguc-Kunt and Levine (2008, 2009), there is a vast literature showing the benefits that accrue to countries in which financial development is greater. On the theoretical side, early work by McKinnon (1973) and Goldsmith (1969), among others, highlighted the key role in economic development that could be played by a banking system free of the types of controls on interest rates and quantities that were prevalent at the time. As the literature progressed, it began to recognize that the financial system in general—not exclusively banks—performed four basic functions essential to economic development and growth: mobilization of savings, allocation of resources to productive uses, facilitating transactions and risk management, and exerting corporate control. Through these functions, a country providing an environment conducive to greater financial development would have higher growth rates, with much of the effect coming through greater productivity rather than a higher overall rate of investment.

The empirical literature progressed in tandem, providing widespread evidence that financial depth—the extent to which an economy is making use of bank intermediation and financial market activity—is associated with higher rates of economic growth. In order to measure financial depth, several indicators have been used. For the banking sector, the ratio of liquid liabilities to GDP, or M2 to GDP, and of private sector credit to GDP. For stock market activity, market capitalization to GDP, the ratio of value of shares traded either to GDP or total capitalization—both measures of the turnover of market activity—have also been used.

Several different econometric methodologies have been employed to uncover this finance and growth nexus.2 Early studies such as King and Levine (1993) and Levine and Zervos (1998) used a cross-country regression—the former focusing on bank-based measures, and the latter on market-based ones—and controlled for other possible growth determinants and the Solow-Swan convergence effect. To deal with potential reverse causality—that some degree of financial development might possibly be induced by a greater demand for financial services as economies become richer—some studies have regressed growth rates over a relatively long period on initial values of financial depth. Later studies by Levine (1998) and Levine, Loayza and Beck (2000) use instrumental variable techniques to address the endogeneity issue in a panel data setting. Finally, other studies have used dynamic panel methodologies. Beck, Levine and Loayza (2000), Rousseau and Wachtel (2000), and Beck and Levine (2004) rely on GMM estimators to trace the effect of financial development in markets and banks on economic growth.

For the most part, the empirical studies on the determinants of growth have provided a single coefficient for all countries. However, there has also been increasing interest in examining possible sources of cross-country heterogeneity in these relationships. Khan and Senhadji (2000) and Khan, Senhadji and Smith (2001) use a wide sample of countries and find heterogeneity related to financial depth and inflation. The first paper finds threshold levels for inflation in industrial and developing countries above which inflation significantly slows growth, while the second one uncovers a threshold above which inflation impedes financial deepening. More recently, Arcand, Berkes, and Panizza (2011) detect a nonlinear growth impact of banking depth, finding that it becomes progressively weaker as depth increases to very high levels. Eventually, when private sector credit exceeds 110 percent of GDP, the marginal effect of additional deepening on economic activity becomes negative, both at the economy and industry level.

Another type of heterogeneity could arise from a finance-related “resource curse,” whereby growth underperformance by natural resource exporters would be partly explained by financial sector underperformance. The resource curse generally refers to negative externalities from the predominant resource-exporting sector to the rest of the economy, operating through either the real exchange rate channel (the Dutch Disease phenomenon), through poor fiscal discipline, or as a result of political economy effects that lead to weak institutions and greater prevalence of corruption and violence.3 Two recent studies described below go beyond these channels to examine the possible role played by the financial sector in resource-based economies, either ameliorating or contributing to the curse.

Nili and Rastad (2007) investigate a puzzle: the very low growth rates experienced by oil exporters over a 30-year period even while their investment rates are higher on average than in oil importing countries. The authors find that finance helps to explain the puzzle in two ways: oil exporters tend to exhibit lower financial depth, and the positive impact of their financial depth on aggregate investment—and presumably on growth—is substantially weaker than in non-oil exporting economies. Beck (2011) analyzes the case of resource-based economies in general, exploring whether there is a financial channel to the resource curse. He finds that, although the aggregate growth impact of banking depth is no different for resource-based economies, both private credit and stock market activity tend to be weaker, and access to credit for businesses is more limited in resource-based economies. There is evidence that banks in these countries are more profitable—possibly reflecting lower competition—but are not as engaged in intermediating funds to the private sector.

In this paper we explore three dimensions of possible heterogeneity in the finance-growth nexus: across regions, between oil and non-oil exporters, and across income levels. Our dataset encompasses the 1975–2005 period and takes non-overlapping five-year averages of all variables to smooth out short-term fluctuations in growth rates and to reduce the potential bias arising from having a large number of time observations in dynamic panel estimation. The sample includes up to 146 countries included in some regressions, grouped by income level according to the IMF classification, and by oil and non-oil exporters depending on the share of oil in total GDP, which is also included in some regressions as the measure of oil dependence.

We find that, across regions, in Middle East and North Africa (MENA) countries banking sector depth produces a lower growth impact than in the rest of the world, while in Europe and Central Asia the impact is greater. This provides an additional explanatory factor underlying the well-documented sub-par growth performance of the MENA region. For example, during 1975–2005, its real per capita GDP grew by an average 0.4 percent per year, compared to 2.4 percent for Emerging and Developing Countries (EDCs) on average, 5 percent in developing Asia, 1.1 percent in Latin America and the Caribbean, and 2.3 percent in Central and Eastern Europe (Figure 1). Previous studies have examined MENA growth underperformance and have linked it to such factors as shortfalls in institutional quality and ease of doing business, excessive government consumption, and in the case of oil importers, to lack of trade openness.4 One study, by Bhattacharya and Wolde (2010) identified the lack of access to credit as one factor driving growth differentials between MENA and other regions, along with a shortage of labor skills and of adequate supply of electricity.5 However, no other study had examined systematically whether the conventional positive link between finance and growth varies across regions, thereby at least partly explaining MENA’s disappointing growth performance. Our results also suggest that the underperformance of the MENA region, termed a “quality gap” in financial intermediation, could be related to strong state ownership, lack of competition, and lack of progress in financial reform.

Figure 1:
Figure 1:

Average Real Per Capita GDP Growth Rates Across Regions, 1975–2005

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

We also find that the growth impact of banking depth is weaker for oil exporters in general, and is progressively weaker as the degree of oil dependence increases. However, there is evidence that growth impact of stock market depth may actually be higher in oil-exporting countries.

Finally, we find that, indeed, the finance-growth nexus is weaker for Low Income Countries (LICs) as a group, and that it increases continuously with income level. In particular, the estimated growth impact of the credit-GDP ratio is about half as large for LICs relative to other countries with similar depth, and appears to be actually negative at the lowest income levels, becoming significantly positive at about the 73rd percentile of income per capita for LICs in 2008. Other country characteristics appear to influence these effects as well; as is the case for the full sample of countries, oil-exporting LICs derive weaker growth from banking depth but possibly higher growth from stock market depth. Estimations show that LICs with higher-quality supervision or those that are more open to international trade fare relatively better than the rest. While by no means conclusive, we also present supporting data showing that financial access and some regulatory aspects regarding ease of entry may be related to the identified quality gap experienced by LICs. Thus, the policy message should be more nuanced for LICs: while greater depth in undoubtedly desirable, the challenge is to engender high-quality deepening that facilitates greater access, competition, and with proper supervision in place.

This effect, of course, exacerbates the fact that LICs suffer from shallow financial systems. For example, in 2008 the average LIC had a ratio of private credit to GDP of just over 24 percent, compared to 47 percent for Middle Income Countries (MICs) and 110 percent for High Income Countries (HICs). Similarly, LICs had ratios of stock market capitalization to GDP of 23 percent, substantially lower than the levels of 73 percent for MICs and 130 percent for HICs in the same year. What the growth regression results imply is that these countries may also lack the supporting legal, institutional, regulatory or supervisory infrastructure that would allow the greatest benefit to accrue from their existing levels of financial depth. Lack of competition and efficiency, both in the financial and real sectors, could play a part in weakening the growth impact as well.

The organization of the paper is as follows. Section II provides a description of the data and some noteworthy stylized facts; Section III outlines the econometric methodologies used and Section IV presents the main results; Section V concludes and offers some plausible factors that might be driving the observed heterogeneity in the finance-growth relationship.

II. Data

A. Datasets

The data used in this study is composed of three datasets that provide annual country-specific observations from 1975 to 2005. The measures of financial development are provided by the Financial Structure Database constructed by World Bank. Standard financial depth indicators were employed: private credit and turnover. Private credit measures the ratio of private credit by deposit money banks to GDP and turnover is the ratio of the value of total shares traded to average real market capitalization.6

Some variables, such as non-oil GDP, total GDP, and population were obtained from the World Economic Outlook (WEO) April 2010 published database. WEO includes data from IMF staff’s projections and evaluations of economic development of all the member countries. In many cases this data was supplemented with series obtained directly from IMF desk economists on real non-oil GDP for oil-exporting countries.

The third database comes from the World Bank open source data. Total real per capita GDP of countries are extracted from this dataset to calculate the growth rate of countries as well as to use the initial levels of GDP in the regressions to control for the convergence effect. The values are in constant 2000 US dollars. Other variables include the percentage of gross secondary school enrollment to reflect human capital, and the ratio of FDI to GDP.

B. Stylized Facts

A list of the variables as well as their corresponding summary statistics is available in Table 1 for the full sample of countries, in Table 2 for the oil exporters, and in Table 3 for the regional and income-level groupings. Table 4 displays the results of tests for differences in means between: non-oil exporters and oil-exporters (first column), the Middle East and North Africa and all other countries (second column), LICs and all other countries (third column), and LICs and high-income countries (fourth column). Finally, Table 5 shows the correlations among the main variables. The list of countries is available in Appendix I, which also indicates which countries are oil exporters, as well as the country income group and regional classification.7 Oildep measures the degree of oil dependence, and is defined as the ratio of non-oil GDP to total GDP, both in real terms. The statistics confirm the Nili-Rastad finding that oil exporters have shallower banking systems on average, as measured by the ratios of deposits and private credit to GDP (Nili and Rastad, 2007). They also have significantly lower average growth rates—of both oil and non-oil GDP—than non-oil exporters.

Table 1a:

Summary Statistics

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Table 1b:

Cross-Country Summary Statistics*

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Computed from country means

Table 2a:

Summary Statistics – Oil Exporters

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Table 2b:

Cross-Country Summary Statistics – Oil Exporters*

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Computed from country means.

Table 3:

Sample Means by Region

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Table 4:

Tests for Differences in Means

(p-values)

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Table 5:

Unconditional Correlations – Full Sample of Countries

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The number of observations is shown below each correlation coefficient, and asterisks indicate significance at the 5 percent level or better.

The means tests also reveal that LICs are at a disadvantage in virtually every dimension with the exception of FDI. Financial depth is significantly lower compared to the average across all other countries, as is the level of secondary enrollment and the growth rate.

As for cross-region differences, over the entire study period the MENA region does not exhibit lower levels of secondary enrollment or FDI compared to other regions—the p-values of the tests of differences in means are all well above 10 percent—however, its growth performance has been significantly weaker (Figure 1). Moreover, the MENA countries on average do not appear to be particularly lacking in financial depth; average levels of bank deposits, private sector credit, or stock market turnover are not significantly different from those in the rest of the world. In fact, in 2008 the average private credit-GDP ratio for the region was, at 45 percent, higher than the emerging economy average of 38 percent, although well short of the 118 percent level typically observed in high-income countries (Figure 2a). Stock markets in MENA countries also appear to be relatively deep, with a turnover ratio of just under 40 percent in comparison to a world average of 54 percent and an EDC average of 40 percent.

Figure 2:
Figure 2:

Financial Depth Across Regions and Countries

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

Source: World Bank Database on Financial Structure, 2010, and International Financial Statistics.

However, three main qualifications should be made. First, there is considerable heterogeneity within the Middle East and North Africa. One way to see this is by slicing this region further, into a “Mediterranean Associated Countries,” or MEDA subregion, and the rest, which are primarily oil-exporting economies and several of which are also in the high-income GCC grouping.8 While the two subregions exhibit very similar levels of private credit, the MEDA group is visibly lagging in stock market depth, with a turnover of about half than that observed in the rest of the MENA region. On a country by country level, Bahrain, Jordan, Lebanon, Morocco, Tunisia, and the United Arab Emirates exhibit markedly deeper banking systems, with depth well above 50 percent of GDP, while others, such as Algeria, Libya and Syria, register depth below 15 percent of GDP (Figure 2b). With regard to equity markets, some GCC countries stand out as having a high level of activity—in particular, Saudi Arabia, with a turnover ratio of more than 130 percent—while Jordan, Egypt and Morocco are at about 30 percent, with the rest of the countries well below the EDC average.

Figure 2b.
Figure 2b.

Financial Depth in Individual MENA Countries, 2007-08

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

Source: World Bank Database on Financial Structure, 2008, and IFS.

The second qualification is that trends in bank deepening over time are not very encouraging for a number of MENA countries. Although the region on average deepened substantially from 1970 to 2008, the MEDA subregion stalled noticeably after 2005, losing about three percentage points of GDP. At the same time, other regions such as Europe and Central Asia were able to gain ground much more rapidly, gaining close to 20 percentage points of GDP (Figure 3). Although banking systems in other regions may have engaged in unsustainably high rates of bank lending in the run-up to the global financial crisis, the downward movement in MEDA should be cause for some concern, at the very least to merit further study to identify factors underlying this credit slowdown.

Figure 3:
Figure 3:

Deepening in the Banking Sector, Across Regions, 1975-2008

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

Third, MENA countries rank lowest in terms of converting bank deposits into private sector credit. For the average MENA banking system in 2008, credit represented 69 percent of bank deposits, as opposed to 90 percent for the average EDC (Figure 4). In particular, the bulk of the MEDA countries fall short; on average only about half of bank deposits were converted into loans to the private sector in 2008. Furthermore, over three decades the ratio has fallen more rapidly in the MEDA countries than anywhere else, and has continued to fall over the past decade, while beginning to recover in other regions (Figure 4). Thus, in these countries there is substantial untapped potential in the form of deposits that could be channeled into productive activities.

Figure 4:
Figure 4:

The Ratio of Private Credit to Deposits, 1975–2008

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

III. Empirical Methodology

The empirical objective is to obtain efficient, unbiased, and consistent estimates of the effect of financial development on growth. The general regression model used in most studies, as well as in this paper, can be summarized as:

gi⁢t=α+β⁢fi⁢t+γ⁢Xi⁢t+δ⁢yi,t−1+ci+μt+ϵi⁢t(1)

where yi,t is the GDP per capita of country i in period t and git is the growth rate of GDP per capita in the same period. The focus of the studies is on estimating β which indicates the effect of financial development, denoted by fi, on growth. The convergence effect is denoted by δ, as lagged income, yi,t–1 (or initial income yi,t0 in some cases) is expected to have a negative effect on growth rate. Xit is the set of control variables: as in Beck and Levine (2004), these include FDI and gross secondary school enrollment. Furthermore, the specification includes ci, denoting unobserved country-specific time-invariant variable, and μt, the time dummy variable in period t to capture common shocks affecting all countries simultaneously. Finally, εit is the error term, a white noise error with mean zero.

This paper focuses on the GMM dynamic panel methodology to present econometric estimates of β, given that the OLS estimator suffers from two deficiencies. First, because of (unobserved) omitted variables that may be correlated with the included covariates and drive economic growth at the same time, OLS estimates might be biased. This arises from the possible correlation of the lagged or initial value of the dependent variable with the error term, i.e., E[yi,t–1(μi + εit)] ≠ 0 or E[yi0(μi+εit)] ≠ 0, depending on which version of initial income is used in the regression. Second, the OLS method does not control for other sources of endogeneity such as reverse causality. Some instrumental variable estimations, such those in La Porta et al. (1998) use legal origin dummies as instruments for financial depth, but these require OLS to be applied purely at the cross-section level.

If one wishes to take advantage of time variation in the data and adopts the plausible assumption that the explanatory variables in the regression are weakly exogenous—they are affected only by the present and past levels of economic growth and uncorrelated with future innovations in growth—then the GMM dynamic panel methodology proposed by Arellano and Bover (1995) and Blundell and Bond (1998) provides unbiased estimators for the coefficients of interest. The method combines a regression in levels and a regression in differences. One must be careful to apply it to cases in which the number of periods is small relative to the number of cross-sectional observations, otherwise asymptotic imprecision and biases may arise.9 For this reason, and to smooth out cyclical variations in growth, this method is applied to non-overlapping five-year averages of the variables. Using 25 years of observations for 150 countries, the averaging produces five 5-year periods for each country, thus the number of time observations is very small relative to the number of countries.

By first-differencing equation (1) we obtain the following equation which eliminates country-specific variables, thus avoiding the potential omitted variable bias caused by time-invariant heterogeneity:

Δ⁢gi⁢t=βi⁢Δ⁢fi⁢t+γ⁢Δ⁢Xi⁢t+δ⁢Δ⁢yi,t−1+Δ⁢λt+Δ⁢ϵi⁢t(2)

where Δrit = rit – ri,t–1 for a given variable r. Although this differenced equation eliminates unobserved country-specific variables, it introduces a new correlation between the difference of lagged values of initial income and the error term (because of the correlation between ϵi,t–1 in the differenced error term and the covariates). Using the weak exogeneity assumption, Arellano and Bond (1991) propose that lagged values of the weakly exogenous (predetermined) and exogenous variables be used as instruments to the differenced equation:

E[fi,t−s⁢Δ⁢ϵi⁢t]=E[Xi,t−s⁢Δ⁢ϵi⁢t]=E[yi,t−s⁢Δ⁢ϵi⁢t]=0∀t≥3,s≥2⁢forweaklyexogenousands≥1forexogenousvariables

Furthermore, the Arellano and Bover method employs additional moments to be used in the GMM estimation. These are obtained from the equation for regression in levels, equation (1), using the intuition that lagged differences of the covariates are valid instruments for the regression in levels and are uncorrelated with the error term under the assumption that the correlations between the country specific term, ci, and the covariates are constant over time. For example, the lagged difference of financial development, the control variables, and lagged income, are uncorrelated with the error term and the fixed effects in equation (1), i.e.:

E[Δ⁢fi,t−s⁡(ci+ϵi⁢t)]=E[Δ⁢Xi,t−s⁢(ci+ϵi⁢t)]=E[Δ⁢yi,t−s⁢(ci+ϵi⁢t)]=0∀t≥3,s=2

Stacking all the moment conditions from the difference and level equations, a two-step GMM estimation is performed. In the first stage, it is assumed that the errors are homoskedastic and independent. The second stage takes the estimates of the variance-covariance matrix and performs a similar estimation to obtain final estimates under the assumption that the error terms are not necessarily homoskedastic and independent.10

The empirical model in this paper extends the conventional finance-growth equation to include an interaction term (Interact) between financial depth and one of three alternatives: (i) dummy variables to capture regional effects: Europe and Central Asia, MENA (or, alternatively, with MEDA or GCC subgroupings), South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, and the rest of the world (high-income countries);11 (ii) a dummy variable for oil exporters, Oilexp, as in Nili and Rastad (2007); and (iii) the degree of oil dependence, Oildep, measured as the share of hydrocarbons in total GDP. In contrast to Oilexp, this variable varies over time as well as across countries.

gi⁢t=α+β⁢fi⁢t+κ⁢Interacti×fi⁢t+γ⁢Xi⁢t+δ⁢yi,t−1+ci+μt+ϵi⁢t(3)

We use a similar set of control variables Xi as in Beck and Levine (2004): secondary school enrollment (“education”) to control for the effect of the level of human capital, and FDI as a percentage of GDP.12 All X variables are computed as the logarithm of their mean values over each five year period. κ measures the possible heterogeneity across groups of countries in the effect of financial development on economic growth. Finally, regressions are run with either total real GDP per capita or real non-oil GDP per capita as dependent variables.

The present paper introduces the following methodological and data improvements over previous studies: (i) in contrast to the Beck’s (2011) analysis of resource-rich economies, it uses a dynamic panel method (as in Nili and Rastad, 2007) rather than cross-country regressions to uncover differences for oil exporters; (ii) in contrast to the Nili and Rastad study of oil exporters, it uses a longer and more updated sample (1975–2005 vs. 1992–2001) and takes non-overlapping five-year averages of all variables, rather than annual observations; (iii) also in contrast to Nili and Rastad, it includes a more comprehensive country sample, with up to 146 countries included in some regressions. In particular, the sample of oil exporters has been expanded,13 and they are captured in the regressions not only through a dummy variable, but also in terms of a continuous variable measuring the degree of dependence on oil (as in Beck’s measures of resource dependence); (iv) in contrast to both of the above studies, it runs regressions for non-oil GDP in addition to total GDP growth. As economic diversification is a major issue for oil-dependent economies, the impact finance has on the long-run performance of the non-oil sector is of paramount importance; and (v) also in contrast to both studies, it not only examines the impact of the banking sector, but also that of stock market activity.

IV. Regression Results

A. Banking depth

The results of the system GMM estimator for the relationship between banking sector depth—as measured by the private credit-GDP ratio—and growth are shown in Tables 6–8. Specifically, we examine heterogeneity in this relationship across regions (Table 6), between oil exporters and other countries (Table 7), and across income levels (Table 8). In the first two cases, we run regressions for growth in non-oil as well as total per capita real GDP. In Table 6, the first and fifth columns present the baseline specification commonly used in the literature (such as in Beck and Levine (2004) or Beck (2008)), with one key modification: we also account for the possible effect of financial crises on the finance-growth relationship. As shown by Rousseau and Wachtel (2011), the empirical link between finance and growth weakens considerably once post-1990 data are introduced, primarily as a result of the proliferation of financial crises and their adverse effects on economic activity. Indeed, using the Laeven and Valencia (2012) definition of systemic banking crises, about 60 percent of all such episodes experienced during the 1970–2007 period occurred in the 1990s. Furthermore, to the extent that the incidence of crises varies across countries, accounting for these episodes is also crucial to disentangle cross-country differences in the growth impact of financial deepening.14 Across all specifications, financial crises reduce the growth impact of private credit by about one-half.

Table 6:

Private Credit and Growth: Heterogeneity Across Regions

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This table shows the results of dynamic panel regressions for growth of real total anf non-oil per capita GDP using a GMM procedure following Arellano and Bover (1995). The explanatory variables are Private credit, the ratio of bank credit to the private sector to GDP; Education, percentage of gross secondary school enrollment; Initial income, initial GDP per capita; and FDI expressed as a percentage of GDP. Some specifications also include interactions between private credit and regional dummy variables. Data are averaged over non-overlapping five year periods beginning in 1980. Robust t-statistics are shown in parentheses, and significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels are indicated.
Table 7:

Private Credit and Growth: Heterogeneity Between Oil Exporters and Other Countries

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This table shows the results of dynamic panel regressions for growth of real total and non-oil per capita GDP using a GMM procedure following Arellano and Bover (1995). The explanatory variables are: Oilexp, a dummy variable for oil exporting countries; Oildep, the share of oil GDP in total GDP; Private credit, the ratio of bank credit to the private sector to GDP; Education, percentage of gross secondary school enrollment; Initial income, initial GDP per capita; and FDI expressed as a percentage of GDP. Some specifications also include interactions between private credit and either Oilexp or Oildep. Data are averaged over non-overlapping five year periods beginning in 1980. Robust t-statistics are shown in parentheses, and significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels are indicated.
Table 8:

Private Credit and Growth: Heterogeneity Across Income Levels

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This table shows the results of dynamic panel regressions for growth of real total per capita GDP using a GMM procedure following Arellano and Bover (1995). The explanatory variables are Private credit, the ratio of bank credit to the private sector to GDP; Education, percentage of gross secondary school enrollment; Initial income, initial GDP per capita; and FDI expressed as a percentage of GDP. Some specifications also include interactions between private credit and a Low-Income Country (LIC) dummy variables and/or either the quality of bank bupervision, (from Abiad, et al, 2008) and the degree of trade openness (ratio of exports plus imports to GDP). Data are averaged over non-overlapping five year periods beginning in 1980. Robust t-statistics are shown in parentheses, and significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels are indicated.

The second and sixth columns in Table 6 report the previous results interacting private credit with the region dummy variables,15 showing that the growth effects are lower for the MENA region, as well as for Latin America and the Caribbean. With regard to total GDP growth, the results indicate that the same level of banking depth in the MENA region produces growth effects that are about one-third smaller than in other regions. When non-oil growth is considered, the MENA region appears to fare even worse, with a growth impact about one-half that of the rest of the world. In addition, there is evidence that Europe and Central Asia obtain relatively greater growth benefits benefit from private credit. Note that, by controlling for financial crises, the estimated heterogeneity refers to growth effects across regions during normal times.

Owing to the aforementioned heterogeneity within MENA, columns (3), (4), (7), and (8) introduce regional dummies once again, but distinguish further within MENA, following two alternative subgroupings: Mediterranean-Associated countries vs. the rest; and GCC vs. the rest. The results suggest that the GCC countries behave similarly to high-income countries;16 the coefficient on the interaction term between private credit and the GCC dummy is not statistically significantly different from zero. Furthermore, when the GCC countries are combined with a set of non-Mediterranean countries, the results are similar; the MEDA interaction coefficient with private credit is negative and significant, whereas the corresponding coefficient for other MENA countries is not statistically significant.17 Finally, once the GCC countries are accounted for separately, the interaction term for the Latin America and Caribbean region no longer becomes significant. That is, this region behaves relatively similarly to the full set of high-income countries.

In the lower portion of Tables 6–8 we report results of the Arellano-Bond test for autocorrelation and the Hansen test for over-identifying restrictions. The existence of autocorrelation would indicate that lags of the covariates used as instruments are actually endogenous, and therefore, not good instruments for the regressions. The test for autocorrelation, essentially an AR(2) test, 18 yields no evidence of significant autocorrelation among the set of instruments. The Hansen test checks the correlation between the residuals and exogenous variables to assess the validity of instruments.19 The results for our regressions indicate that the null hypothesis that the instruments are exogenous cannot be rejected.

In quantitative terms, the estimation results imply that the differences in growth potential across regions are not only statistically significant, but economically meaningful as well. Figure 5 shows the estimated impact on long-term total GDP growth from increasing banking sector depth. As one would expect from a log specification, greater growth benefits accrue to countries that begin their deepening from a lower initial level. In Figure 5a, countries are shown in which the current ratio of private credit to GDP is below the EDC, and therefore the figure depicts the estimated increase in growth rate obtained if each country were to reach the EDC average. Relative to countries outside the region, MENA countries would obtain a smaller increase in growth, with the difference amounting to a “quality effect” of their financial depth. For example, if Algeria were to increase its current depth from an initial level of 10 percent to the EDC average of 29 percent, its growth rate is estimated to increase by 112 basis points. However, a non-MENA country starting from the same initial depth could expect to increase its growth rate by 163 basis points, thus resulting in a quality effect of 51 basis points. Several non-MENA countries are shown for comparison purposes. For example, Armenia, which would obtain a full benefit of 160 basis points if it were to reach the EDC average depth. Figure 5a shows a group of MENA countries with initial depth above the EDC average, therefore the Figure displays the gains that would result from increasing depth by 20 percentage points of GDP, roughly the increase observed in high-income countries from 1995 to 2005. As before, for each MENA country there is the predicted effect and that which would accrue to a non-MENA country, with the difference corresponding to a quality effect.

Figure 5:
Figure 5:

Estimated Impact of Increases in Credit-to-GDP on Real Per Capita Growth

(Percentage Points)

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

Table 7 presents the results of regressions which distinguish oil exporters from the rest, confirming the Nili and Rastad finding that oil dependency weakens the finance-growth link, and thus providing evidence of a finance channel for the resource-curse. Oil exporters as a group obtain a smaller benefit from financial deepening, and the benefits fall continuously with the degree of oil dependence. Interestingly, both interaction terms are larger in absolute values in the regressions for non-oil GDP growth, thus indicating that banks in these countries have been particularly ineffective in generating productive activity outside the oil sector. Columns (3), (4), (7) and (8) present further interactions of private credit and Oilexp and Oildep with the GCC dummy. The results indicate that the GCC countries would tend to fare better in comparison to similarly oil-dependent countries outside the region. For example, Saudi Arabia—with an oil dependence of about 33 percent in 2005—would obtain a greater growth benefit from private credit than would a similarly oil-dependent country, such as Trinidad and Tobago. This result is consistent with the previous result that the growth benefits from banking depth in GCC countries are similar to those in high-income countries.

In Table 8 we summarize the findings on heterogeneity across income levels. There is evidence that LICs as a group obtain lower growth benefits from the same level of private credit, and that these benefits increase continuously with income level. Differentiating further, it is apparent that banking systems are more conducive to long-term growth in LICs which are more open to trade—as measured by the ratio of exports and imports to GDP20—and where bank supervision is of higher quality.21 In addition, these two characteristics only appear to affect the growth benefits of private credit in LICs, as the interaction terms for non-LICs are not statistically significant.

In Figures 6–8 we show the magnitudes of the above effects; how the growth impacts of banking depth vary across income levels and with respect to openness and the quality of bank supervision. In Figure 6 we see that at very low income levels the growth impact is not statistically significant, and only becomes positive (at a 95 percent confidence level) at a per capita income of $810, or roughly the 73rd percentile for LICs in 2008.22 Figure 7 illustrates the mitigating effect of the quality of bank supervision; at low levels, LICs are at a clear disadvantage, but as this quality improves, the growth impact LICs begins to approximate that of middle and high-income countries. As of 2005, the average value of the bank supervision indicator for a sample of 18 LICs s indicator was 1.4, compared to 1.8 for middle-income countries and over 2.5 for high-income countries. Finally, in Figure 8 we show how the lower growth impact of private credit in LICs is mitigated by the degree of trade openness of these countries. LIC banking performance begins to approximate that of other countries once total trade approaches 56 percent of GDP, or at the 47th percentile for LICs in 2008.

Figure 6:
Figure 6:

Estimated Marginal Impact of Increases in Private Credit-to-GDP on Growth at Different Income Levels

(Percentage Points)

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

Figure 7:
Figure 7:

Estimated Differences between LICs and non-LICs in the Growth Impact of Private Credit at Different Levels of Bank Supervision Quality

(Percentage Points)

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

Figure 8:
Figure 8:

Estimated Differences between LICs and non-LICs in the Growth Impact of Private Credit at Different Levels of Trade Openness

(Percentage Points)

Citation: IMF Working Papers 2013, 130; 10.5089/9781484372104.001.A001

B. Stock market activity

Tables 9–11 repeat the same exercises as in Tables 6–8, respectively, including a stock market-based, Turnover,23 rather than a bank-based measure of financial development as the relevant explanatory variable. As in the case of private credit, we account for banking crises and find that the coefficient on stock market turnover is positive and significant in normal times, while crises have a significant negative impact on the coefficient. However, virtually none of the cross-region heterogeneity observed for banks is present in the regressions for stock market activity, aside from weak evidence of a slightly larger growth impact in Europe and Central Asia (Table 9). Thus, it appears that greater deepening should be expected to generate roughly the same benefits across. The same can be said for oil exporters; neither the interaction with the oil exporter dummy nor with the degree of oil dependence yield significant coefficients, although there is weak evidence that oil exporters outside of the GCC might derive greater growth benefits from stock market activity (Table 10, fourth column). Regarding differences across income levels, there is also evidence that LICs obtain less growth benefits from stock market activity, an effect which is mitigated by a having higher quality bank supervision (Table 11, fifth column).

Table 9:

Stock Market Turnover Ratio and Growth: Heterogeneity Across Regions

article image
This table shows the results of dynamic panel regressions for growth of real total anf non-oil per capita GDP using a GMM procedure following Arellano and Bover (1995). The explanatory variables are: Turnover, the ratio of stock market val;ue traded to GDP; Education, percentage of gross secondary school enrollment; Initial income, initial GDP per capita; and FDI expressed as a percentage of GDP. Some specifications also include interactions between Turnover and regional dummy variables. Data are averaged over non-overlapping five year periods beginning in 1980. Robust t-statistics are shown in parentheses, and significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels are indicated.
Table 10:

Stock Market Turnover and Growth: Heterogeneity Between Oil Exporters and Other Countries

article image
This table shows the results of dynamic panel regressions for growth of real total and non-oil per capita GDP using a GMM procedure following Arellano and Bover (1995). The explanatory variables are: Oilexp, a dummy variable for oil exporting countries; Oildep, the share of oil GDP in total GDP; Turnvover, the ratio of stock market value traded to GDP; Education, percentage of gross secondary school enrollment; Initial income, initial GDP per capita; and FDI expressed as a percentage of GDP. Some specifications also include interactions between turnover and either Oilexp or Oildep. Data are averaged over non-overlapping five year periods beginning in 1980. Robust t-statistics are shown in parentheses, and significance at the 1 percent (***), 5 percent (**), and 10 percent (*) levels are indicated.