What Matters for Financial Development and Stability?
Author: Raja Almarzoqi, Samy Ben Naceur, and Akshay Kotak1
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: ralmarzoqi@imf.org; sbennaceur@imf.org; akshay.kotak@sbs.ox.ac.uk.
Publication Date:
24 Jul 2015
eISBN:
9781513501178
ISBN:
9781513501178
Language:
English
Keywords:
WP; d GMM; s GMM; banking sector; standard deviation; real GDP; growth rate; market power; dynamic generalized method of moments; policy variable
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This study aims to identify policies that influence the development of financial institutions as measured across three dimensions: depth, efficiency, and stability. Applying the concept of the financial possibility frontier, developed by Beck & Feyen (2013) and formalized by Barajas et al (2013a), we determine key policy variables affecting the gap between actual levels of development and benchmarks predicted by structural variables. Our dynamic panel estimation shows that inflation, trade openness, institutional quality, and banking crises significantly affect financial development. Our analysis also helps identify potential complementarities and trade-offs for policy makers, based on the effect of the policy variables across the different dimensions of financial development.

Abstract

This study aims to identify policies that influence the development of financial institutions as measured across three dimensions: depth, efficiency, and stability. Applying the concept of the financial possibility frontier, developed by Beck & Feyen (2013) and formalized by Barajas et al (2013a), we determine key policy variables affecting the gap between actual levels of development and benchmarks predicted by structural variables. Our dynamic panel estimation shows that inflation, trade openness, institutional quality, and banking crises significantly affect financial development. Our analysis also helps identify potential complementarities and trade-offs for policy makers, based on the effect of the policy variables across the different dimensions of financial development.

I. Introduction

The debate on the effects of finance on economic growth has been active for decades, raising opinions ranging from inconsequential and “very badly over-stressed” (Lucas, 1988) to seemingly obvious (Miller, 1998). Bagehot (1873) and Schumpeter (1911) have argued, more conservatively, that the interplay between finance and growth is positive and non-trivial. Goldsmith (1969) was one of the first studies to empirically investigate this relationship, finding a positive correlation between financial development (as measured by the size of the financial sector) and long-run growth. He (and later, Bencivenga & Smith, 1991) explained this correlation by asserting that intermediation leads to savings being better channeled into productive investment. In their treatises on financial liberalization, Shaw (1973) and McKinnon (1973) posited that eliminating financial repression would enhance growth, as the elimination of interest rate ceilings would increase the quantity of savings in the economy. However, these studies did not address the issue of causality in either direction.

One of the first attempts at demonstrating that the financial system promotes economic growth was by King & Levine (1993), who found that indicators of financial development strongly correlated with growth, and that these indicators also had significant power in predicting future growth rates. Rajan & Zingales (1998) found a similar causal link between financial development and growth by showing that the development of financial intermediaries and markets has a disproportionately large positive effect on sectors more dependent on external financing. Levine et al (2000) provided further evidence of this finance-growth causality through instrumented variable procedures, using the difference in legal and accounting systems across countries as instruments. Beck et al (2000) incorporated dynamic panel techniques (difference and system GMM estimators) to further strengthen their argument that financial intermediaries exert a large positive impact on factor productivity growth. Furthermore, Levine & Zervos (1998) examine the contribution of the development of financial markets to growth, finding that the development of financial institutions – banks and financial markets – promotes growth, capital accumulation, and increased productivity, even after controlling for economic and political factors.

It is therefore now widely accepted that financial institutions positively influence economic development and growth. A healthy financial system helps channel household savings into value-creating investments, monitors borrowers to increase efficiency, helps agents pool, share and diversify risk, and facilitates trade. Levine (1997), Levine (2005), Demirgüç-Kunt & Levine (2008), Beck (2012), and Barajas et al (2013b) provide detailed surveys of the literature on the finance-growth nexus.

In addition, there is another large body of literature that studies the role of financial development in reducing inequality. Beck et al (2007) find that the long-run impact of financial development on the income of the poorest quintile stems from both an increase in aggregate growth effect and a reduction in income inequality. Jalilan & Kirkpatrick (2002, 2005) find a similar poverty reducing effect of financial development in low-income countries. Jeanneney & Kpodar (2011) argue that financial development helps alleviate poverty through the aggregate growth channel and through the McKinnon (1973) conduit effect. However, they also warn that financial development breeds instability, which serves to negate some of the benefits of financial development on poverty reduction, and find evidence for both their hypotheses in a panel of data from developing countries. This highlights another fairly well-accepted theme in the finance-growth literature—that the relationship between financial development and growth is not monotonic.

Academic literature notes several different forms of non-linearity in the relationship between financial development and economic growth. Rioja & Valev (2004) find that the marginal effect of additional financial development on growth is dependent on the current level of financial development, with uncertain effects at the very low end of the spectrum (i.e. low levels of development), a large positive effect in economies with an intermediate level of financial development, and a smaller positive effect in developed financial systems. Aghion et al (2005) similarly find that economies above a certain threshold of financial development face a positive (and diminishing) return from additional financial development and converge to the same level of long-run growth, whereas those below the threshold attain a lower level of long-run growth. Demetriades & Law (2006) highlight the importance of institutions to the functioning of the finance-growth nexus. Using a panel data set from 72 countries, they find the magnitude of the effect of financial development on economic growth in an economy is directly tied to the quality of the institutional framework in that economy, with the relationship particularly weak in poor institutional settings.

While a burgeoning financial sector can boost growth opportunities, excessive and rapid expansion can also create instability and lead to crises. The empirical analysis of Arcand et al (2012) suggests that beyond a certain size, development of the financial sector starts having a significant negative effect on growth, even after controlling for volatility, crises, and other institutional factors. Dabla-Norris & Srivisal (2013) study the relationship between macroeconomic volatility and financial development in a sample of 110 countries and find that financial development acts as a shock absorber against volatility but only up to a point; beyond a certain level, financial systems exacerbate shocks and increase volatility. In a remarkably prescient paper, Rajan (2005) warned that rapid development of the financial sector increases its capacity to bear risk, but also increases the actual level of risk taken, increasing systemic risk and leaving the entire financial sector more vulnerable to left tail events. Beck et al (2012) analyze micro and macro data from 32 developed economies and find that increased levels of financial innovation between 1996 and 2006 were associated with both increased levels of economic growth, and increased levels of economic volatility and idiosyncratic bank fragility. In light of these tradeoffs, recent studies2 have put forward the idea of an “optimal” level of financial development for an economy.

Building on the work of Beck & de la Torre (2007) and Beck & Feyen (2013), Barajas et al (2013a) formalize this idea through the concept of the financial possibility frontier. They posit that the level of financial development in a country depends on both structural characteristics such as income, population, demographics, and other fundaments that are outside policy control in the short to medium run, and on policy and institutional variables.

To support this argument, they construct benchmarks based on such structural variables and relate the gaps between the predicted benchmarks and actual values of financial development, measured as the depth of the financial sector, to a host of policy and institutional variables using cross-country ordinary least squares (OLS) regressions. They also show that sustained overshooting of the benchmark is associated with a significant increase in the probability of a bad credit boom, lending further credence to the idea of optimizing financial development, instead of maximizing it.

Furthermore, financial systems are multidimensional. The very idea of financial development itself merits more elaborate treatment than to just proxy it with financial depth or the size of the banking sector3. While financial depth remains the most widely researched and widely reported measure of financial development, recent benchmarking studies4 outline a more well-rounded approach to assessing financial development by measuring it along four dimensions—depth, access, efficiency, and stability.

In this paper, we seek, in two ways, to extend and improve the analysis carried out by Barajas et al (2013a). Firstly, we extend the analysis to three dimensions of financial development—depth, efficiency, and stability—based on the availability of data and benchmarks. This provides a more rounded assessment of the effects of policy on financial development and also brings to light possible complementarities and trade-offs in the way different policy measures affect different dimensions of financial development. Our choice of indicator variables for the different dimensions is driven by data coverage and the availability of benchmarks. We use private credit as a percentage of GDP as the indicator for depth, net interest margin as the indicator for efficiency, and non-performing loans as a percentage of total gross loans as the indicator for stability. While these are standard, widely-used indicators of financial development, for robustness checks (available on request) we also re-ran the regressions reported in Tables 5-7b using other theoretically appropriate indicators, including domestic deposits to GDP, banks’ assets to GDP, banks’ cost to income ratio, lending to deposit spread, banks’ Z-score, and private credit to deposits ratio5. The size and the significance of the estimated coefficients vary, but these robustness tests serve to confirm our broad findings.

Table 2a.

Summary Statistics and Correlations: Banking Sector Depth (gap analysis), Global Sample, Annual Data

(2,382 observations, 115 countries)

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Table 2b.

Determinants of Banking Sector Depth (gap analysis)

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Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table 2c.

Robustness Checks: Determinants of Banking Sector Depth (gap analysis)

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Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table 3a.

Summary Statistics and Correlations: Banking Sector Efficiency (gap analysis), Global Sample, Annual Data

(1,176 observations, 103 countries)

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Table 3b.

Determinants of Banking Sector Efficiency (gap analysis)

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Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 3c.

Robustness Checks: Determinants of Banking Sector Efficiency (gap analysis)

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Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 4a.

Summary Statistics and Correlations: Banking Sector Stability (gap analysis), Global Sample, Annual Data

(927 observations, 85 countries)

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