CHAPTER 5 Finance and Oil: Is There a Resource Curse?

Amadou Sy, Rabah Arezki, and Thorvaldur Gylfason
Published Date:
January 2012
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Thorsten Beck


An extensive literature has identified financial sector development as a critical factor in inclusive economic development (see Levine, 2005, and Beck, 2009, for overviews). Countries with deeper financial systems grow faster, and it is the lowest income quintile that benefits most from this deepening (Beck, Levine, and Loayza, 2000; Beck, Demirgüç -Kunt, and Levine, 2007). Countries with deeper financial systems also experience faster reductions in income inequality and poverty rates. Financial sector development helps industries that are most reliant on external finance grow faster, and it helps enterprises, especially smaller and more opaque ones, overcome financing constraints (Rajan and Zingales, 1998; Beck, Demirgüç-Kunt, and Maksimovic, 2005). The positive effect of financial sector development on economic growth comes through improved resource allocation and productivity growth, rather than increased capital accumulation (Beck, Levine, and Loayza, 2000; Wurgler, 2000).

However, most of this literature is based on broad cross-country samples, following the assumption that the finance-growth relationship is linear and constant across countries.1 Meanwhile, many other papers in the finance and growth literature drop oil-exporting countries or natural-resource-based economies in general, arguing that their economic development is driven by different factors and that their financial sector has a role and structure that differs from what applies in other economies.

This chapter focuses on financial deepening in resource-based economies. Specifically, it will test whether the finance and growth relationship varies across countries depending on the degree to which they rely on natural resources and, second, will document the development and structure of financial systems in resource-based economies compared to other countries. In the first section, we will use standard cross-country growth regressions as well as industry-level regressions, allowing for a differential relationship between finance and economic growth depending on the degree to which an economy relies on natural resource exports or is abundant in natural resource wealth. In the second section, we will use aggregate bank-level and firm-level data to explore whether the depth, breadth, and efficiency of financial systems varies systematically across countries with different degrees of natural resource reliance.

Exploring the role financial-sector development plays in the growth of resource-based economies is interesting and important for both academics and policymakers. There is a large literature on the natural resource “curse” and the different channels through which resource abundance can influence growth; understanding the role of financial development in this context is critical. Policymakers who care about the development of their countries need to understand the relative importance of different policy areas and the effectiveness of specific policies. Understanding channels through which resource abundance can stimulate or dampen economic development can be important to developing policies to maximize the benefits of natural capital.

By exploring the role and structure of the financial system in resource-based economies, this chapter builds on the literature that has explored the above-mentioned curse of natural resource abundance (see van der Ploeg, 2011, for a recent survey), which refers to the crowding out of nonresource-based activities or investment through price and incentive effects. One form of this curse—also referred to as Dutch disease—works through the exchange rate mechanism: commodity exports will put upwards pressure on the real exchange rate, which will turn nonresource exports uncompetitive, ultimately depressing the traded goods sector. The decline of British manufacturing after the discovery of oil in the 1970s and the decline of the Dutch manufacturing sector after the discovery of a natural gas field in 1959 are prominent recent examples, although neither decline lasted.

Beyond price effects, the natural resource curse also refers to the distortion of incentives for investing in institutions, education, and other public services due to windfall gains from natural resources, which can ultimately have negative repercussions for political freedom and stability. It is generally easier to make short-term profits from natural resources such as oil than from fixed assets such as manufacturing plants, equipment, and machinery, because proceeds from natural resources depend less on the creation of a market, on human capital, and on research and development (R&D) investment. This in turn reduces incentives to invest in an institutional framework that supports broad domestic market- based exchange, private property rights, and the contractual framework supporting non-commodity production (Besley and Persson, 2010). Natural resource wealth also allows less than democratic governments to buy off political opposition, avoid accountability, and prevent transparency. Natural resources make it more profitable for the elites to hang on to power and block the development of an open society (Beck and Laeven, 2006). This, in turn, can foster conflict, as seen most prominently across Sub-Saharan Africa (Collier and Hoeffler, 2004). In addition, a commodity-induced bonanza can foster a shift from profit- making entrepreneurship toward socially inefficient rent seeking. However, there is also an interaction between institutional development and resource abundance, with countries above a threshold of institutional development able to reap benefits from natural resource wealth (Mehlum, Moene, and Torvik, 2006).

The empirical literature has provided ample evidence for the natural resource curse and the different channels through which it affects growth. However, this literature has also noted a wide cross-country variation in experiences. On the one hand, Nigeria has experienced negative growth since its independence, associated with exchange rate effects, rent seeking, and violence stemming from oil exports, while on the other hand Botswana has experienced positive growth over the past 50 years, despite being heavily reliant on diamond exports. However, according to Gylfason (2001), only 4 out of 65 resource-based economies can be considered success stories in terms of growth (Indonesia, Malaysia, Thailand, and Botswana), and the three Asian countries in that group still fared less well than their East Asian neighbors Hong Kong, Singapore, and South Korea. With few exceptions, however, the literature has not considered the effect of natural resource abundance on financial development or the role of financial institutions in mitigating the natural resource curse (van der Ploeg, 2011).2

In exploring whether there is a natural resource curse in financial development, this chapter also builds on a large literature on the determinants of financial deepening across countries. Boyd, Levine, and Smith (2001) show the importance of macroeconomic stability for financial deepening, while La Porta and others (1997, 1998) and Djankov, McLiesh, and Shleifer (2007) show the importance that contractual and information frameworks have for financial development.3 A related literature has explored the importance of historical factors, such as legal tradition, and geographic traits in forming institutional and specifically financial development (see Beck and Levine, 2005, for a survey).

Theory and the institutional literature provide different hypotheses on the effects natural resource abundance has on financial system development. Both demand-side and supply-side effects are analyzed. Take first the demand side. On the one hand, windfall gains from natural resource abundance and the consequent expansion of the nontraded goods sector can lead to higher demand for financial services, including consumer credit. On the other hand, there is lower demand for external financing from the natural resource sector than from the nonresource traded goods sector, which will suffer in a Dutch disease scenario. Further, the literature has documented lower savings and investment rates in resource-based economies, which in turn can also explain those economies’ lower demand for financial services. Specifically, resource-rich countries can use their resource revenues for consumption smoothing, which weakens the incentive to build an effective financial system to serve as a buffer to smooth consumption over the business cycle (Gylfason, 2004). Take next the supply side. Higher investment in the natural resource sector can lead to lower investment in the financial sector and draw away skills from the financial system. In addition, the fact that financial systems depend heavily on sound institutional frameworks, including effective contractual frameworks, can hamper financial deepening in countries where natural resource abundance undermines institutional development.

Theory also makes ambiguous predictions about the finance-growth relationship in resource-based economies. On the one hand, the financial system might be less important, since growth depends less on finance-intensive sectors. On the other hand, financial system development might be more important to compensate for the negative effects of Dutch disease and in order to diversify the economy. In addition, financial systems in resource-based economies can help counter the negative impact of real exchange rate volatility (Aghion and others, 2009).

The following empirical results show that financial development is as important for economic growth in resource-based economies as in other countries. On the other hand, resource-based economies do have less developed financial systems, and while their banks are more liquid, better capitalized, and more profitable, they give fewer loans to firms. Firms in these economies use less external finance than firms elsewhere, and a smaller share of them uses bank loans, although there is the same level of demand as in other countries, thus pointing to supply constraints. Overall, there is some indication of a natural resource curse in financial development, the weight of which falls more on enterprises than on households.

Since this chapter is one of the first to rigorously explore the role of financial systems in resource-based economies, several caveats are due. First, we work with very rough measures of natural resource dependence, although we test the robustness of our results across several indicators. Second, this is a very broad but also preliminary exploration of the role of financial systems in resource-based economies; what we gain in breadth, we miss in depth in the different dimensions. Several of the topics explored in this chapter could profitably be subjected to more in-depth explorations, which would also have to address issues of identification.

This chapter is related to a small literature on the institutional resource curse. Beck and Laeven (2006) show that variations in the extent of natural resources across transition economies can partly explain variations in their institution building after 1990, when all these countries faced the same challenge of building market-compatible institutions. Cross-country regressions have confirmed this negative relationship between natural resource abundance and the rule of law (Norman, 2009), control of corruption (Papyrakis and Gerlagh, 2004) and overall institutional capacity (Isham and others, 2005).

The remainder of the chapter is structured as follows. The next (second) section assesses whether the finance and growth relationship varies across countries according to the degree of importance that commodities have in the economy. The third section explores whether commodity-based economies have lower levels of financial development and is thus a test of the resource curse for financial system development. The fourth section analyzes banks’ balance sheets and income statements to show whether banks are different in resource-based economies. The fifth section uses firm-level survey data to explore differences in firms’ use of external finance and firms’ financing obstacles across countries with a different reliance on natural resources and aggregate outreach data. The last section concludes and provides some policy discussion.


This section explores whether the positive relationship between financial development and economic growth varies across countries depending on the degree of natural resource reliance. In order to do so, we use Barro-style standard crosscountry finance and growth regressions, adding a variable capturing natural resource reliance or abundance plus its interaction with financial development.

We use two indicators to gauge the reliance of economies on natural resources. The first indicator is Natural Resource Exports, which is the sum of fuel, ores, and metal exports relative to GDP.4 Data come from the World Bank’s World Development Indicators, and are available for a broad cross-section of countries on an annual basis over the period 1960 to 2007. The second indicator is Subsoil Assets per capita and refers to natural assets (World Bank, 2006). It is computed as the net present value of the income these resources are able to produce, calculated for the year 2000. Natural Resource Exports ranges from zero, in countries like Mauritius, to almost 100 percent in many oil-exporting countries. Similarly, Subsoil Assets per capita ranges from zero, in countries like Singapore, to US$80,000 in Saudi Arabia. Given the wide variation, we use the log of one plus Subsoil Assets in our regressions. It is important to note that there are important differences between these two measures, with Natural Resource Exports referring to the realized income stream based on the resources and Subsoil Assets referring to the actual wealth.5 However, the two measures are highly and significantly correlated with each other, suggesting that most economies that are abundant in natural resources also rely on natural resources as an export good. It is also important to note that both measures have their shortcomings. The ratio of Natural Resource Exports to GDP can be driven as much by the numerator as by the denominator and depends very much on the extraction rate. Subsoil Assets per capita is a more direct measure of natural resource wealth, but it relies heavily on assumptions about reserves and extraction costs (van der Ploeg and Poelhekke, 2010).

As an indicator of financial development, we use a standard indicator from the literature, Private Credit, which is the total claims by financial institutions outstanding on the domestic nonfinancial private sector, divided by GDP. This indicator ranges from less than 2 percent in the Democratic Republic of Congo to almost 150 percent in Switzerland. As an alternative indicator, we use Liquid Liabilities to GDP, which is defined as currency plus demand and interest-bearing liabilities of banks and nonbank financial intermediaries, divided by GDP, and thus focuses on banks’ liability side. Both indicators are from the World Bank’s Financial Development and Structure Database (Beck, Demirgüç-Kunt, and Levine, 2010). All other macroeconomic indicators are from the World Development Indicators (WDI) of the World Bank.

We average real GDP per capita growth over the period 1980 to 2007 and run the following regressions:

where β1 captures the general effect of financial development on growth, while β2 captures the differential effect in economies that are more resource based. Following the finance and growth literature, our set of conditioning information includes (i) the log of initial real GDP per capita to control for convergence, (ii) average years of schooling to control for human capital accumulation, (iii) the share of exports and imports to GDP, (iv) the inflation rate, and (v) the ratio of government expenditures to GDP.6 With the exception of initial GDP per capita, all explanatory variables are averaged over the sample period, 1980 to 2007.7

The results in Table 5.1 do not show any significant difference in the finance and growth relationship linked with the degree of natural resource reliance. The column 1 results confirm the findings in the cross-country finance and growth literature showing a positive relationship between financial development and long-run economic growth, while the column 2 results do not show any differential effect of financial development on growth in resource-based economies, since the coefficient on the interaction term enters negatively, but insignificantly. Columns 3 and 4 confirm our findings using our alternative indicator of natural resource abundance, Subsoil Assets, and our alternative indicator of financial development, Liquid Liabilities, respectively. Among the control variables, government consumption enters negatively and significantly, while years of schooling enters positively and significantly. Initial GDP per capita enters negatively, though it is not consistently significant, while trade openness enters positively, but not always with a significant coefficient. Finally, inflation enters insignificantly, which can be explained by the negative impact that inflation has on financial development (Boyd, Levine, and Smith, 2001) and which thus indirectly affects economic growth. In unreported regressions, we also use a dummy variable for countries with Natural Resource Exports greater than 10 percent of GDP and confirm our findings. Finally, we control whether the insignificant coefficient estimate for the interaction term is not driven by the absence of a nonlinear term of Private Credit to GDP; controlling for a squared term of Private Credit to GDP does not change our findings.

Table 5.1Finance, natural resources, and growth across countries
GDP pcGDP pcGDP pcGDP pcGrowthGrowth
growthgrowthgrowthgrowthin Giniin Gini
Initial GDP per capita-0.00354*-0.00356*-0.00274-0.00561***
Private credit0.00735***0.00798***0.0105***-0.00521**-0.00467*
Government consumption-0.0112**-0.0114**-0.0101**-0.0101**
Years of schooling0.00227***0.00222***0.00226**0.00308***0.0006850.000494
Natural resource exports-0.0284***0.0360**-0.0368**0.0212**0.00453
Natural resource exports*-0.00657-0.0108
Private credit
Liquid liabilities0.0101***
Natural resource exports*-0.00912
Liquid liabilities
Subsoil assets-0.00064
Subsoil assets*-0.00033
Private credit
Initial Gini-0.0173***-0.0173***
GDP pc growth0.04640.0533
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively

Columns 5 and 6 consider the relationship between finance and income inequality. Building on previous work by Beck, Demirgüç-Kunt, and Levine (2007), we regress the average annual growth rate in the Gini coefficient on financial development, Natural Resource Export, their interaction and a set of conditioning information.8 We find that financial development has a negative relationship with the growth rate in the Gini coefficient, while Natural Resource Reliance has a positive, thus inequality-increasing, impact. The interaction term between the two, on the other hand, does not enter significantly.

The results in Table 5.1 suggest that the finance and growth and the finance and inequality relationships hold as much for resource-based economies as for other economies. The insignificant interaction term between natural resource dependence and financial development, however, can also be interpreted as indicating that financial development does not have a dampening impact on the negative role of natural resources in the overall growth process.

In a second step, we test whether industries that are more dependent on external finance grow faster in countries with deeper financial systems and whether this relationship depends on a country’s reliance on natural resources. This test follows the seminal work by Rajan and Zingales (1998) who show that financial development is indeed beneficial for industries that depend more on external financing sources, where this demand is measured for large U.S. corporations that face a flat supply curve. Since financial deepening is especially relevant for man-ufacturing—a sector, on the other hand, that might easily be crowded out by natural resource abundance—this test seems especially relevant to our assessment whether the finance and growth relationship holds for natural resource countries as much as it does for other countries. Specifically, we extend the Rajan and Zingales (1998) test as follows:

where g(i, k) is growth of industry k in country i, averaged over the 1980s, External(k) is an industry-level measure of external dependence that does not vary across countries, a and X are vectors of country and industry dummies, respectively, and Share is the initial share of industry k’s value added in total manufacturing value added of country i. By including industry- and country-specific effects, the coefficient β measures the differential growth impact of financial development on high-dependence industries relative to low-dependence industries. While β1 captures the overall effect of financial development on industry growth dependent on the need of the industry for external finance, β2 measures the differential effect of this interaction depending on the abundance or reliance of the country on natural resources. We also include the interaction between external dependence and natural resources.9

Table 5.2 shows weak evidence that the finance and growth relationship might be even stronger for countries that rely more on natural resources. While the interaction between Private Credit and External Dependence enters positively and significantly, the triple interaction with Natural Resource Exports enters positively but insignificantly (column 1). We find similar findings when using Subsoil Assets, though here the interaction between Private Credit and External Dependence also enters insignificantly, possibly due to multi-collinearity with the triple interaction term (column 2). When we use the Natural Export Dummy (indicating Natural Exports greater than 10 percent of GDP), however, the triple interaction enters positively and significantly at the 10 percent level (column 3), providing some evidence that the role of the financial system in channeling funds to manufacturing industries that need them most might be even more important in resource-based economies.

Table 5.2Industry growth, finance, and natural resources across countries
Industry growthIndustry growthIndustry growth
Initial share-0.936***-1.080***-1.111***
Private credit*external dependence0.0804**0.05350.0766**
Private credit*external dependence*0.0408
Natural resource exports
External dependence*natural resource exports0.111
Private credit*external dependence*0.00922
Subsoil assets
External dependence*subsoil assets-0.0046
Private credit*external dependence*0.163*
Natural resource dummy
External dependence*natural resource dummy-0.0677
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively

Summarizing, this initial evidence does not provide strong evidence that the relationship between finance and growth differs across countries according to the degree of natural resource abundance, nor that the relationship between finance and changes in income inequality differs. Financial development is not less important for growth in resource-based economies, and it might be even more important. These results are certainly not conclusive. More work is needed in this area, especially using industry and firm-level data and disaggregating GDP into its resource-related and nonresource-related components. Preliminary work by Barajas, Chami, and Yousefi (2010) shows that there might be a differential effect if one considers panel rather than cross-country regressions, with financial development having lower if not negative impact on growth in oil-exporting countries. In related work, van der Ploeg and Poelhekke (2009) show that financial development has a dampening impact on volatility in resource-based economies, with positive repercussions for economic growth. Apparently, then, policymakers in resource-based economies should care about financial sector deepening as much as policymakers in other countries.

Having shown that financial development is as important for economic growth in resource-based economies as in other countries, we now explore whether the development and structure of financial systems differs across countries with different degrees of resource abundance.


This section explores whether there is empirical evidence for a resource curse in financial development. Specifically, we assess whether economies more reliant on natural resources have lower levels of financial development after controlling for standard factors associated with variation in financial development across countries.

Controlling for economic development, we find that countries that rely more on natural resource exports have lower levels of Private Credit. Figure 5.1 presents a partial scatter plot of Private Credit and Natural Resource Exports, controlling for GDP per capita. Here we present data across countries, with data averaged over the period 2000 to 2007. In the following, we will use multivariate regression analysis to assess the robustness of this finding while controlling for other determinants of financial development.

Figure 5.1Financial development and natural resource dependence

The literature has pointed to macroeconomic stability and the efficiency of the contractual and information frameworks as important determinants of financial sector development (Boyd, Levine, and Smith, 2001; Djankov, McLiesh, and Shleifer, 2007). In our analysis, we therefore control for (i) the log of real GDP per capita, averaged over the sample period (ii) the average inflation rate between 2000 and 2007, (iii) time to enforce a contract in number of days, and (iv) the efficiency of the credit information system, with the latter two measures averaged over the period 2003 to 2007. Specifically, we run the following regressions.

In addition to the two financial system indicators introduced above, we focus on several other indicators, all from the Financial Development and Structure Database (Beck, Demirgüç-Kunt, and Levine, 2010). The Loan- Deposit Ratio is a measure of intermediation efficiency and is the ratio of total bank claims outstanding on domestic nonfinancial sectors to total bank deposits. Higher ratios indicate higher intermediation efficiency; ratios above one, however, might indicate overheating of the financial system. We also use two indicators to gauge the development of the stock market. Specifically, Stock Market Capitalization to GDP is a measure of stock market size relative to real economic activity, and Stock Market Turnover is an indicator of stock market trading relative to stock market capitalization and therefore a measure of the liquidity of the market.

In addition to the financial development indicators defined above, we consider the relationship between natural resource reliance and two indicators of financial structure, that is, the degree to which a financial system is market-based or bank-based. Following Beck and Levine (2002), we define Structure-Size as the ratio of Stock Market Capitalization and Bank Assets, where the latter is defined as total banking claims on the nonfinancial (private and public) domestic sectors. Higher ratios would indicate a financial system that is more market-based. Structure-Efficiency is defined as the product of Stock Market Turnover and banks’ Net Interest Margin (a negative indicator of bank efficiency). Higher numbers would again indicate a financial system that is more market-based.

Table 5.3 shows that countries relying more heavily on natural resource exports have lower levels of financial development, even after controlling for other determinants of financial development. The effect is not only statistically large but also economically large. Take the example of column 1. One standard deviation higher Natural Resource Exports implies 10 percentage points lower Private Credit.10 Consistent with the literature, there is a negative relationship of inflation and contract enforcement inefficiency, while the efficiency of credit information sharing does not enter significantly. Consistent with Figure 5.1, the log of GDP per capita enters positively and significantly. The column 2 results confirm this finding using Subsoil Assets as the indicator of natural resources, while column 3 confirms the results using Liquid Liabilities. The column 4 results show that lower levels of financial intermediation do not imply lower intermediation efficiency, since Natural Resources does not enter significantly in the regression of the aggregate Loan-Deposit Ratio. Results in columns 5 and 6 show that in economies that rely more on natural resources, stock exchanges are not smaller but they are significantly less liquid. Natural Resource Exports enters insignificantly in the regression of Stock Market Capitalization to GDP, but negatively and significantly in the regression of Stock Market Turnover.

Table 5.3Financial development across countries
Loan-depositStock marketStock marketStructure-
Private CreditPrivate CreditLiquid LiabilitiesratiocapitalizationturnoverStructure-SizeEfficiency
GDP per capita0.181***0.188***0.178***0.03490.256***0.0911*0.128**-0.000205
Time to enforce contract-0.000178***-0.000132**-6.72E-05-0.000238**-0.000201-0.0002312.62E-05-1.37E-05
Information sharing0.005680.0197-0.0602**0.0654***-0.0430.0452*-0.02260.00203*
Natural resource exports-0.658***-0.733***0.3840.0148-0.704***1.557***-0.0251**
Subsoil assets-0.0145**
Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

Finally, the results in columns 7 and 8 show that when measured by size, resource-based economies have financial systems that are more market-based, while when measured by efficiency, they have systems that are more bank-based. Given the previous results, we can interpret this as suggesting that the market- based nature, in terms of size, stems from the smaller banking systems in resource- based economies, while the bank-based nature, in terms of efficiency, stems from the lower stock market liquidity in these countries. We confirm the findings of Table 5.3 using our alternative indicators of natural resource abundance, Subsoil Assets and the Natural Resource Export dummy.

The results so far have focused on cross-country variation in financial development, but there is also a large variation within countries over time. How do countries with different degrees of natural resource dependence develop their financial systems? Does natural resource abundance help or impede further financial deepening as demand for financial services increases with economic development?

Table 5.4 explores the within-country variation of financial development as a function of natural resource reliance. Specifically, here we present estimations with country-fixed effects to explore how Private Credit develops over time with GDP per capita. We focus on a longer sample period, using annual data over the period 1960 to 2007. We use this sample to assess how the financial system deepens as a function of economic development and other macroeconomic indicators, and whether these relationships vary according to the degree of natural resource reliance.

Table 5.4Financial development over time
Financial development over time
Private CreditPrivate CreditPrivate CreditPrivate CreditPrivate Credit
GDP per capita0.902***1.100***1.304***1.526***1.544***
Natural resource exports2.014***1.655**-4.034***
Natural resource exports*-0.418***-0.375***0.205
GDP per capita
Trade * GDP per capita-0.0433**-0.132***-0.112***-0.122***
Natural resource exports*-0.000642**-0.00279***
Real exchange rate
Real exchange rate*0.00608***
Natural resource exports
Subsoil assets*0.0232***0.000875
GDP per capita
Subsoil assets*0.000601***
Real exchange rate
Number of countries1481481538684
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Unlike in Table 5.3, we include all indicators in logs so that we can interpret the coefficient estimates as elasticities. While β1 shows the relationship between Private Credit and GDP per capita, β2 indicates whether this relationship is significantly higher or lower in countries with higher reliance on natural resources. We do not include indicators of the contractual or information framework, since the time- series variation and data availability in these indicators are limited. We do include country-specific fixed effects and other time-varying country variables as explained below. By including country-specific effects, we effectively explore relationships within countries over time and abstract from the cross-country variation.

The results in Table 5.4 indicate that Private Credit increases with GDP per capita, but to a lower extent in countries that rely more on natural resource exports. While the elasticity of Private Credit to GDP per capita is almost one for countries with no Natural Resource Exports, this elasticity is significantly lower in countries with higher Natural Resources. The column 2 results show that the significant interaction between GDP per capita and Natural Resource Exports is not driven by general trade openness. While there is a positive relationship between Private Credit and the trade share, this relationship is more muted in countries with a higher reliance on natural resources.11 The column 3 regression, on the other hand, shows that the relationship inverts when using Subsoil Assets, an indicator of natural resource abundance rather than exports. Countries with higher natural resource wealth deepen their financial systems at a faster rate than other countries. This points to an important difference between measures of resource dependence and measures of resource abundance, as already noted by Brunnschweiler and Bulte (2008), and indicates that natural resource abundance can actually be used to the advantage of countries in financial deepening. An important caveat, however, is that Subsoil Assets is measured in 2000, that is, it includes information that was available at this point in time, not necessarily in 1960.

The results in columns 4 and 5 show that while real exchange rate appreciation leads to deeper financial systems (although economically it is a very small effect), this relationship is reversed for countries with a higher share of Natural Resource Exports. This might be the clearest evidence of a Dutch disease effect, that is, a crowding out of nonresource exports through an appreciating exchange rate can also crowd out financial development. Interestingly, when controlling for real exchange rate changes and their interaction with natural resource indicators, the interaction between the natural resource indicators and GDP per capita turns insignificant, which would suggest that the resource curse in financial development does indeed work mainly through the Dutch disease effect of real exchange rate appreciation.

Summarizing, resource-based economies have smaller banking systems and less liquid stock exchanges than predicted by their level of economic development, their degree of monetary stability, and the efficiency of their contractual and informational frameworks. As resource-based economies develop economically, their financial systems deepen at a slower rate than in other countries, although this result holds for natural resource reliance (or dependence) rather than natural resource abundance. The fact that this result holds after controlling for the contractual and information frameworks suggests that the natural resource curse in financial development goes beyond the institutional natural resource curse documented in the literature (e.g., Beck and Laeven, 2006).

The findings so far are consistent with both a demand-driven and a supply- driven story, that is, with lower demand for financial services resulting in a smaller financial system or supply constraints preventing a financial system from developing. In the next two sections, we therefore focus first on indicators derived from banks’ financial statements to assess whether banks in commodity-based economies are different in their business model, efficiency, and stability, before turning to firm- level data to assess whether clients are underserved in resource-based economies.


While the previous section provides some evidence of a natural resource curse in financial development, this section digs deeper by exploring banks’ business models, efficiency, stability, and asset composition to assess whether there are significant differences across banks in countries that rely on natural resources to different degrees. We use data from Bankscope12 over the period 2000 to 2007 and construct and compare indicators of business orientation, efficiency, and stability across banks and across countries with different degrees of natural resource reliance. We only include banks with at least two observations and countries with data on at least four banks. We restrict our sample to the largest 100 banks (in terms of assets) within a country so that our sample is not dominated by any specific country. Finally, we eliminate outliers in all variables by winsorizing at the 1st and 99th percentiles.

To compare the business orientation of banks, we use four indicators suggested by Demirgüç-Kunt and Huizinga (2010): the ratio of fee-based income to total operating income; the importance of non-deposit funding to total funding; the traditional loan-to-deposit ratio; and the ratio of liquid assets to total assets. To compare bank efficiency, we use three indicators. Our first efficiency indicator is overhead cost, which is computed as total operating costs divided by total assets. Second, we use the cost-to-income ratio, which measures overhead costs relative to gross revenues, with higher ratios indicating lower levels of cost efficiency. And third, we use the net interest margin, which is net interest revenue relative to total earning assets. All three indicators decrease in efficiency, that is, higher numbers indicate less efficient banks. To compare the stability of banks across countries, we focus on the z-score, which is defined as the sum of capital-to-asset ratio and return on assets, divided by the standard deviation of return on assets. This score measures the distance—in number of standard deviations in return on assets—that separates a bank from insolvency, and so it increases as the stability of a bank increases. We also assess differences across banks and countries in the capital-to- asset ratio and in return on assets, two of the components of the z-score.

We average data over the sample period (2000 to 2007) and run the following regression:

where i stands for bank and j for country. B is a set of bank-level control variables, including size (measured in logs of millions of USD of total assets), the share of non-loan earning assets in total assets, and the ratio of fixed assets to total assets. We control for the log of GDP per capita to avoid confounding the relationship between economic development and natural resource dependence with the relationship between natural resource dependence and bank characteristics. We apply standard errors clustered on the country level, that is, we allow for correlation between error terms of banks within countries but not across countries, in order to control for unobserved factors across banks within a country.

The results in Table 5.5 show few significant differences across banks according to the reliance on natural resources in the countries where they operate. Regarding the business model, we find no significant differences in the share of fee income, the reliance on non-deposit funding, or the loan-deposit ratio across countries with different degrees of reliance on natural resources. However, we do find that the share of liquid assets in total assets increases as we move from countries with no natural resource exports to resource-based economies. In terms of efficiency, the only dimension where the degree of natural resource reliance seems to matter is the cost-to-income ratio, which is significantly lower in countries that are more resource-based. On the other hand, there are no significant differences in the net interest margin or overhead costs across countries with different reliance on natural resources.

Table 5.5Banks’business model, efficiency, and stability across countries
Fee incomeNon-deposit Loan-deposit Liquid assetsCost-incomeOverheadNet interestZ-scoreEquity-assetROA
Fixed assets1.439**-0.0232-0.0584***-0.2743.546***0.596***0.00269***-0.544*0.278**-0.0285
Non-loan earning assets0.192***0.00168-0.0159***0.543***0.03850.00125-0.000185***-0.0617***0.01130.000809
GDP per capita0.927-0.3140.0248-0.06592.066***-0.0142-0.00361***2.288***0.465**-0.0989***
Natural resource exports2.302-1.6360.22216.49***-24.86***0.3410.0161-0.9116.247***2.277***
Number of countries113114114114114114114114114114
*** p < 0.01, ** p < 0.05, * p < 0.1
*** p < 0.01, ** p < 0.05, * p < 0.1

Finally, we find no significant differences in the stability of banks across countries with different degrees of reliance on natural resources, but we do find a significant difference in capitalization and profitability. Banks in resource-based economies are significantly better capitalized and more profitable. The higher profitability also explains why we find a lower cost-to-income ratio for banks in resource-based economies, while there are no significant differences in the other two efficiency indicators. We confirm all our findings using Subsoil Assets and the Natural Export dummy as indicators of the resource nature of economies.

Turning to the control variables, we find that banks in richer countries have higher cost-to-income ratios but lower net-interest margins, and they are more stable due to higher capitalization and despite lower profitability. Banks with a higher share of fixed assets have higher fee income and a lower loan-to-deposit ratio, and they are less efficient and better capitalized. Banks with higher non- loan earning assets have higher fee income, lower loan-to-deposit ratio, higher liquid assets, and lower net-interest margins, and they are less stable. Finally, larger banks rely more on non-deposit funding, have lower loan-to-deposit ratios, hold fewer liquid assets, are more efficient, and have lower capital-to-asset ratios and returns on assets.

In a separate analysis, we compare the balance sheet composition of banks in resource- and nonresource-based economies using data from the IMF’s International Financial Statistics. Specifically, we compare the asset shares of (i) credit to the private sector, (ii) credit to national and subnational governments, (iii) credit to state-owned enterprises, (iv) foreign assets, and (v) liquid assets, comparing between banks in countries where Natural Resource Exports make up more than 10 percent of GDP and those in countries where these exports make up less than 10 percent of GDP.

Figure 5.2 shows that banks in resource-based economies invest a lower share of their assets in loans to the private sector or government, but a higher share in loans to state-owned enterprises. They also hold a larger share of their assets in both liquid and foreign assets. These differences are consistent with the previous findings reported in Table 5.5, but they also show a weaker tendency of banks to fulfill their intermediation function.

Figure 5.2Asset composition in resource-based economies

Summarizing, comparisons of bank-level indicators suggest that the only differences between banks in natural-resource-based economies and those in other economies is that banks in the former countries are better capitalized, more liquid, and more profitable. There are no significant differences in the business model, in the overall efficiency, or in their stability. Comparisons of asset composition across these two country groups also suggest that banks in resource-based economies are less engaged in financial intermediation. We will now turn to demand-side data to complement this analysis.


While the previous sections focused on aggregate and supplier data to explore differences across countries with different levels of natural resource reliance, we now explore whether these differences also translate into differences in firms’ financing patterns and financing obstacles. We rely on the World Bank/IFC Enterprise Surveys, which have been conducted over the past eight years in almost 100 countries with a consistent survey instrument.13 The surveys try to capture businesses’ perceptions of the most important obstacles to enterprise operation and growth, but they also include detailed information on management and financing arrangements of companies. Sample sizes vary between 250 and 1,500 companies per country, and data are collected using either simple random or random stratified sampling. The sample includes formal enterprises of all sizes, of different ownership types, and across 26 industries in manufacturing, construction, services and transportation.

We focus on several questions that capture firms’ financing patterns. First, we compute the share of enterprises with a loan or overdraft facility. Second, we compute the average share of working capital that is financed through external financial source across all enterprises in a country. Finally, we compute the average share of fixed assets that is financed with external financial source across all enterprises in a country. We also focus on a demand-side question, specifically, What share of firms in each country states that financing is a severe obstacle to its operation and growth?

Figures 5.3 through 5.6 show the correlation between Natural Resources and four indicators of firm finance. We see a negative relationship between reliance on natural resources and (i) the share of firms with loans or lines of credit, (ii) the average share of working capital financed externally, and (iii) the average share of fixed asset investment financed externally. We note, however, that these negative relationships are weak and noisy and driven by countries with a high share of natural resource exports. The share of firms that rate financing as a severe obstacle for their operation and growth, on the other hand, is not significantly correlated with Natural Resources (Figure 5.6).

Figure 5.3External finance in working capital and natural resource dependence

Figure 5.4.External finance in fixed asset investment and natural resource dependence

Figure 5.5.Share of firms with credit and natural resource dependence

Figure 5.6Financing obstacles and natural resource dependence

Table 5.6, Panel A, shows that the negative relationship between access to external finance and Natural Resources is consistent across firms of all sizes. To assess the relationship between firms’ financing patterns and natural resource reliance across different size classes, we recalculate the above-mentioned indicators within each country for small firms (fewer than 20 employees), mid-size companies (20 to 100 employees), and large enterprises (over 100 employees). For each size class, we compare the indicators, averaged across countries with Natural Resource Exports of less than 10 percent of GDP and averaged across countries with Natural Resource Exports of more than 10 percent. Unlike in the scatter plots, we find significant differences between firms in resource-based economies and those in other economies, across all size groups. Firms of all sizes use less external finance in resource-based economies than they do in other economies. The fact that in resource-based economies large firms use external financing as little as small firms do is in contrast to general cross-country findings, which show significantly less external financing by small firms than by large enterprises (Beck, Demirgüç-Kunt, and Maksimovic, 2008).

Table 5.6Firms’ financing patterns and obstacles across countries
Resource-based economiesNonresource- based economiesDifferencep-value T- stat
Panel A: Use of external finance and financing obstacles across different size groups
Small enterprises
External finance in working capital23.7030.74-7.0420.0272**
External finance in investment28.0637.48-9.4240.0122**
Share of firms with loan23.3233.02-9.7090.0059***
Share of firms with severe financing16.3116.84-0.5250.8047
Medium-size enterprises
External finance in working capital30.1339.51-9.3770.0034***
External finance in investment30.2941.90-11.6130.0004***
Share of firms with loan36.6949.00-12.3170.003***
Share of firms with severe financing13.5512.900.6540.7031
Large enterprises
External finance in working capital33.0542.82-9.7730.0095***
External finance in investment34.8343.40-8.5710.0216**
Share of firms with loan49.5959.83-10.2430.0243**
Share of firms with severe financing12.1411.160.9830.6147
Panel B: The demand for loans across countries
Do you have a loan?30.78342.079-11.2960.0066***
If you do not have a loan, did you apply13.27113.395-0.1240.933
for a loan?
Why did you not apply for a loan?
No need for a loan—establishment has47.28861.363-14.0750.001***
sufficient capital
Application procedures for loans or lines15.5618.1527.4090.0003***
of credit are complex
Interest rates are not favorable14.26213.0031.2600.510
Collateral requirements are too high6.8656.3420.5230.646
Did not think it would be approved7.3646.4880.8770.562
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.

Table 5.6, Panel B, shows additional significant differences between firms in the two types of economies regarding their access to finance. Here, we dig deeper into firms’ loan application process, splitting our sample again into countries with Natural Resource Exports, averaged over 2000 to 2007, below 10 percent of GDP and above 10 percent of GDP. Line 1 shows that firms in resource-based economies are significantly less likely to have a loan, consistent with Figure 5.2. Among the firms that do not have a loan, however, there is no significant difference in the tendency to apply for a loan across countries with and without resource abundance (line 2). Among the firms that decided not to apply, however, significantly more firms in resource-based economies stated that they did not apply because of cumbersome application procedures, while a significantly smaller share of nonap- plicants stated as their reason that they did not need a loan. Overall, the share of firms stating that they did not need a loan is about the same in both samples, which clearly suggests that it is not a lack of demand that drives the lower level of financial development in resource-based economies. There are no significant differences in other reasons for not applying for a loan. In summary, these data suggest that the lower use of external finance by firms in resource-based economies is not driven by demand but rather by supply-side constraints.

Table 5.7 shows weak evidence for lower bank outreach in resource-based economies and other countries. Here, we follow the model of Table 5.3 and regress indicators of branch penetration per capita and deposit accounts per capita on (i) log of GDP per capita, (ii) time to enforce a contract, (iii) efficiency of credit information sharing, (iv) inflation, and (v) Natural Resource Exports to GDP or Subsoil Assets. We focus on branch penetration, measured as branches per capita, and account penetration, measured as deposit accounts per capita. Both Natural Resource Exports and Subsoil Assets enter negatively in all regressions, but only Natural Resource Exports enters significantly in the regression of branches per capita. Overall, this seems to be weak evidence of a lower outreach in resource-based economies. It suggests that it is not the lack of geographic outreach, nor overall lower bank penetration, that drives the more limited access to external finance by firms in resource-based economies.

Table 5.7Banking sector outreach across countries
Branches perAccounts perBranches perAccounts per
GDP per capita5.175***546.8***5.387***532.9***
Time to enforce contract0.000966-0.2430.000662-0.137
Information sharing0.0824-130.8**-0.0671-113.6**
Natural resource exports-5.528-1.267**
Subsoil assets-0.0532-29.82
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.
Note: The symbols ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively.


This chapter tested for the existence of a natural resource curse in financial system development. We can summarize our findings as follows:

  • Banking systems are smaller in resource-based economies, and stock markets are less liquid, that is, they have lower trading activity.

  • Financial deepening is less income-elastic in resource-based economies, which suggests that resource-based economies invest less in their economies as they grow.

  • In resource-based economies, banks are more liquid,14 more profitable, and better capitalized, but they do not have different business models nor are they more or less efficient or stable than banks in other countries. They also engage less in intermediation with the real economy.

  • Firms in resource-based economies are less likely to have a loan, and they finance a lower share of their working capital and fixed asset investment using external finance; in addition, this gap is consistent across firms of all sizes. However, this is not due to a lack of demand.

  • Supply constraints, although not necessarily related to banks’ physical outreach, explain the more limited access firms have to external finance and their overall lower levels of financial development.

Overall, these findings point to a natural resource curse in financial development, with negative repercussions for resource-based economies. The finance and growth relationship seems as important for resource-based economies as for other economies, so that the under-investment in the financial sector will have long- term negative repercussions for economic growth. Country characteristics and policies related to financial sector deepening—macroeconomic stability, legal system efficiency, and an effective information sharing framework—hold true in resource-based economies as much as they do in other economies. It seems, rather, that lack of investment in the necessary financial and human resources in the financial sector can explain the natural resource curse of finance.

What are the policy implications of these findings? Policymakers in resource- based economies should care about the financial sector as much as policymakers in other economies, but they will have to “make the extra effort” in order to achieve the goals of inclusive financial deepening. In addition to the medium- to long-term policies—macroeconomic stability and an effective contractual and information framework—competition seems a fruitful area for policymakers to consider, given the high profitability of banks in resource-based markets, which might be partly due to lack of competition. Additional incentives for market- based lending to the private sector might be another important area, that is, through partial credit guarantees. It is important, however, that the necessary institutional framework first be in place in order to ensure the necessary governance structure for such interventions. It is also important to note that it is not the lack of resources that constrains intermediation in these countries, but rather missing incentives.

As mentioned in the Introduction, this is a first exploratory study of the role of financial systems in resource-based economies, with many further venues open for research. It will be important to analyze the role of financial sectors in resource-led boom and bust cycles, as well as the role of government interference and bank governance in resource-based economies. Disentangling financial intermediation into different components—such as enterprise lending and household lending—seems a promising approach to better understanding the role of financial systems in the growth process of natural resource based economies. Finally, sound policy advice will also require exploring the role of the financial systems in mitigating the effects of commodity price changes and ensuing exchange rate volatility.


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Thorsten Beck is professor of economics at Tilburg University and chairman of the European Banking Center. This chapter is based on the author’s presentation at “Natural Resources, Finance, and Development: Confronting Old and New Challenges,” a high-level seminar organized by the Central Bank of Algeria and the IMF Institute, which took place in Algiers, on November 4 and 5, 2010. The author gratefully acknowledges comments by Rabah Arezki, Thorvaldur Gylfason, Steven Poelhekke, and Marc Quintyn and research assistance by Mohammad Hoseini and Radomir Todorov. The author can be contacted at

There are several papers, however, that have shown nonlinear finance—growth relationships, including Aghion, Howitt, and Mayer-Foulkes (2005) and Rioja and Valev (2004a, 2004b).

Two exceptions are Bhattacharyya and Hodler (2010), who show a negative relationship between resource dependence and financing development in countries with low levels of democracy using country-level data, and Barajas, Chami, and Yousefi (2010), who explore the finance and growth relationship across countries with different degrees of resource dependence. Gylfason (2004) also offers some suggestive evidence of lower financial development in resource-based economies.

See Beck (2006) for an overview.

We therefore abstract from agricultural commodities.

Brunnschweiler and Bulte (2008) point to important differences in the effect of natural resource dependence and natural resource abundance on institutional and economic development.

Similar sets of conditioning information were used by Beck, Levine, and Loayza (2000) and Beck and Levine (2004).

In the context of this chapter, we will not address issues of causality and omitted variable. A large literature has shown that the relationship between financial development and growth is robust to controlling for biases due to endogeneity, measurement, and omitted variables. See Beck (2009) for a survey.

We focus on the change in income distribution rather than the level as complement to the GDP per capita regressions. Specifically, changes in relative and absolute poverty levels can be decomposed into changes in average income growth (i.e. GDP per capita growth) and changes in income inequality. While columns 1 to 4 of Table 5.1 focus on the former, columns 5 and 6 focus on the latter. See Beck, Demirgüç-Kunt, and Levine (2007) for a more detailed discussion.

We do not have to (and cannot) include the interaction between Private Credit to GDP and natural resources in the presence of country dummies.

It is important to note, however, that not all resource-based economies have a lower level of Private Credit than predicted by the other variables. Norway and other high-income countries have even higher levels of Private Credit than predicted by the other included variables, while many developing resource-based economies have significantly lower levels. This points to a need for further exploration of the differential effects of natural resource abundance in future research.

When computing trade share to GDP net of natural resource exports, our findings are confirmed.

Bankscope is a database owned by Bureau van Dijk.

See for more details. Similar surveys were previously conducted under the leadership of the World Bank and other IFIs in Africa (RPED) and the Central and Eastern European transition economies (BEEPS) in the 1990s, and then worldwide in 2000 (World Business Environment Survey).

Note that the concept of liquidity is a different one in the case of banks and stock markets. In the case of banks, it refers to the asset holdings, i.e., it is a stock variable, while in the case of financial markets, it refers to an activity, i.e., it is a flow variable.

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