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Introduction
Anecdotal evidence in the press and elsewhere suggests that natural resource-rich countries often fall victim to a ‘resource curse. Resource-rich countries such as Congo, Nigeria, Bolivia, Sierra Leone, and Venezuela have fared much worse than resource-poor countries like the Asian Tigers. Countries with a large share of natural resource exports typically have a relatively low income per capita, but there are notable exceptions. For example, Fasano (2002) documents that the United Arab Emirates have turned the resource curse into a blessing by investing massively in modern infrastructure and education. Also, Acemoglu, Johnson, and Robinson (2003) argue that ethnically homogenous and diamond-rich Botswana is a success story as it also uses resource revenues to invest in education and growth. Still, there is a wealth of systematic cross-country evidence suggesting that countries with large exports of natural resources have a worse growth performance than countries with little or no natural resources after correcting for the investment-GDP ratio, openness, and institutional quality as well as the initial level of income per capita. Most of these studies are based on the seminal work of Sachs and Warner (1995, 1997, 2001). An interesting extension is provided by Mehlum, Moene, and Torvik (2005).2 They argue and provide some evidence that resource dependence only affects growth performance adversely in countries with bad institutions (e.g., a poorly defined legal system or a high risk of expropriation), but may even boost growth in countries with good institutions. This literature is very interesting and potentially relevant from a policy point of view, but nevertheless suffers from a number of very serious shortcomings.
First, in their seminal work Sachs and Warner argue that resource dependence induces an appreciation of the real exchange rate which leads to a decline in the traded sector. If the traded sector enjoys more learning by doing than the sheltered sector, resource dependence harms growth.3 The problem is that the evidence for this interpretation of the resource curse is at best mixed and ignores other potentially more promising political economy explanations of the resource curse. The main ones that are offered in the literature are that substantial natural resource exports may worsen institutional quality and thus harm growth prospects and that resource dependence may aggravate the adverse effects of bad institutions on growth performance. For example, Lane and Tornell (1996) and Tornell and Lane (1999) highlight the voracity effect. In the absence of well-defined property rights, natural resources introduce a common pool problem and elicit rapacious rent seeking. As a result, a wealth of natural resources can hamper economic growth. Ross (1999), Baland and Francois (2000), Auty (2001), Busby et al. (2002), Isham et al. (2003), Torvik (2002), Mehlum, Moene, and Torvik (2005), Robinson, Torvik, and Verdier (2006), Wick and Bulte (2006), Caselli (2006) and many others also put forward political economy explanations of how natural resource dependence invites rent seeking and corruption and thus harms the economy.4
Other explanations of the resource curse highlight that resource abundance erodes the critical faculties of politicians and tends to keep bad policies in place. For example, Mansoorian (1991) and Mansano and Rigobon (2001) argue that countries rich in natural resources have a tendency to borrow excessively, especially if resources fetch a high price on international markets. However, once resources run out or if resource prices fall, they end up with financial crises that have dire consequences for economic growth. Countries rich in natural resources may also make the mistake of building a generous welfare state, which is not
sustainable when natural resources run out.5 Perhaps the most relevant example of natural resources engendering bad policies is when they generate political pressure to protect nonresource export sectors from the vigor of international competition, especially if they are hurt by the real appreciation of the exchange rate caused by substantial natural resource exports. Natural resource dependence may thus play a role in keeping restrictive trade policies in place, which in turn may harm growth prospects. The empirical resource curse literature thus suffers from the problem that it makes no serious attempt to disentangle what the main channels are by which substantial natural resource exports may harm economic growth.
There is ample evidence that resource dependence hurts growth prospects, but it is unclear whether this is due to forsaking learning by doing, worsening institutions, or keeping bad policies in place. It is also unclear whether natural resources are the root cause of bad institutions and bad policies or whether they aggravate the adverse effects of bad institutions and bad policies on economic growth. Without more information on the channels by which resources affect growth, the empirical evidence will be of limited use to policy makers.
Second, from an econometric point of view, the empirical evidence for the resource curse is flawed as no allowance is made for the endogenous character of explanatory variables such as the quality of institutions or the degree of the economy’s openness.6 This is in sharp contrast to the ever-growing literature on explaining differences in countries’ income per capita, where the main effort lies in the search for good instruments in order to disentangle the direction of causation and correct for endogenous explanatory variables. For example, Acemoglu and others (2001) stress the usefulness of colonial origins and settler mortality rates as instruments that affect institutional quality but not differences in income per capital directly. A much larger sample is possible if institutional quality is instrumented by the fraction of the population speaking English or Western European languages as a first language as in Hall and Jones (1999). Frankel and Romer (1999) use gravity equations for bilateral trade flows as instruments for international trade. Using this diverse set of instruments, Rodrik, Subramanian, and Trebbi (2004) conduct a ‘horse race.’ They find that institutions trump geography/climate and openness in explaining cross-country variations in income per capita, but geography/climate may affect income per capita indirectly through the quality of institutions.7
It is a serious defect of the empirical literature on the resource curse that it does not use instruments for institutions and trade and thus ends up with biased and misleading estimates. Furthermore, the existing literature on the resource curse does not distinguish between, on the one hand, the effect of resource dependence on institutional quality, and, on the other hand, the interaction effect of resource dependence and institutional quality. Of course, this is related to the problem of not being able to address the question of what the channel is by which substantial natural resources affect cross-country differences in income per capita. Furthermore, there is no evidence to explain the various channels by which substantial natural resource revenues may affect growth.
Third, as Islam (1995) has argued convincingly, cross-country regressions suffer from omitted variable bias. They do not allow for a correlation between the initial level of productivity and past income per capita. Since the correlation with past income per capita is likely to be positive, the coefficient on lagged income per capita is likely to be overestimated. As a result, cross-country regressions yield an underestimate of the speed of adjustment and an overestimate of the share of capital. As also pointed out by Parente and Prescott (1994), cross-country regressions can thus not explain ‘growth miracles’ as the high capital share implies slow adjustment speeds. One way out is to drop lagged income per capita and focus on explaining income per capita. A better solution is perhaps to use a panel regression in order to avoid these biases.
The purpose of this paper is to remedy, with the limited macro data we have at our disposal, some of the shortcomings mentioned above. We thus re-examine the cross-country evidence based on the seminal work of Sachs and Warner with an extended dataset.
Section 2 provides the ordinary least square (OLS) estimates of the original Sachs and Warner result that natural resources negatively affect growth even after allowing for the positive growth effects of the investment-GDP ratio, institutional quality, and openness. Section 2 confirms Mehlum, Moene, and Torvik (2005) and finds empirical evidence that resource dependence only negatively affects growth performance in countries where institutional quality is worse than a critical value. However, we also find support for the idea that the natural resource curse is less severe in countries with less restrictive trade policies.
Interestingly, if we extend the sample period, the interaction term with institutional quality becomes insignificant while the interaction term with trade openness remains significant at the 5 percent level. The natural resource curse may even become a blessing for very open economies.
In section 3 we re-estimate these equations where we instrument institutional quality and openness with bilateral trade shares, distance to the equator, settler mortality rates, legal origin, and fraction of the population speaking English. We find that the results of section 2 do not stand up to such scrutiny for a wide variety of instruments. Furthermore, we find that
the conditional speed of convergence implied by the estimates is unrealistically small.
Section 4 therefore chooses a different tack. It takes as a starting point the literature that explains cross-country variations in income per capita in terms of institutions, openness, and geography. Adding natural resource exports as an additional explanatory variable, we find evidence of a negative effect of resource exports on income per capita. We also find evidence of interaction terms, which imply that the natural resource curse particularly harms income per capita in countries with bad institutions or bad policies. When we estimate with instrumental variables techniques (IV) rather than OLS, we find that the results stand up although the estimates are less precisely determined.
Section 5 replaces the traditional flow measure of resource dependence (i.e., share of exports of natural resources in GNI) by the World Bank’s recent stock estimates of natural capital (World Bank, 2006b). This allows one to study the effects of natural resource abundance rather than dependence. We find that resource abundance depresses income per capita, but less severely for countries that are relatively open. Section 6 checks robustness with respect to alternative measures of institutional quality. Section 7 concludes.
Footnotes:
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We thank Andrea Barone, Fuad Hasanov, Amina Lahreche, Ahsan Mansur, Alessandro Maravalle, Steven Poelhekke for helpful comments and suggestions. We thank Luisa LaFleur and Sheila Tomilloso Igcasenza for excellent editorial assistance.
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Other examples are Mansano and Rigobon (2001) and Sala-i-Martin and Subramanian (2003). Isham et al. (2003), Murshed (2004) and Bulte, Damania, and Deacon (2005) provide evidence that point-based (geographically more clustered) resources harm growth more than diffuse natural resources.
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These arguments were based on van Wijnbergen (1984) and Krugman (1987). Earlier work on Dutch Disease by Forsyth and Kay (1980), Bruno and Sachs (1982), Neary and Purvis (1982), and Corden (1984) also discuss the decline of the traded sector, but learning by doing externalities are needed to have a rationale for government intervention.
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Some of these are based on the seminal work on the productive and unproductive use of talent by Murphy, Schleifer, and Vishny (1993) and on earlier work on corruption and growth by Mauro (1995) and Bardhan (1997). The pioneering work of North (1990) on the importance of good institutions for good growth has been a significant source of inspiration as well.
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It is often argued that the Netherlands used the revenues from the Slochteren gas source to build up an unsustainable welfare state during the 1970s and 1980s, which has taken many administrations to turn back.
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Lederman and Maloney (2002) allow for endogeneity and different time periods and cannot reproduce the
results of Sachs and Warner (1995, 1997). However, they only have a sample of 19 to 37 countries.
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Sachs (2003) disagrees and demonstrates that malaria transmission, strongly affected by ecological conditions,
directly affects the level of income per capita after controlling for the quality of institutions. Malaria risk is
instrumented by an index of malaria ecology (based on temperature, species abundance, etc.), which predicts
malaria risk well.
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