The natural resource curse originally estimated using cross-sectional data from the 1970s and 1980s has disappeared when applying the same econometric model to the same sample of countries but using more recent data. In fact, the most recent data find that natural resources improve economic growth. Explaining the reasons for this gradual reversal of the role natural resources play in economic growth (from curse to asset) is largely understudied in the economics literature . Perhaps the natural resource sector within countries has been decreasing relative to the size of a country's overall economy. Or perhaps global prices of natural resources have increased over the past several decades. Third, perhaps the increasing capital-to-labor ratio associated with natural resource extraction has allowed workers involved in resource extraction to gain transferable skills.
The first paper to make use of a cross-section sample of countries to estimate the effect of natural resources on economic growth was Sachs and Warner [5][6][7]. The data were used to estimate the following model, which deserves some attention.
GDPit denotes country i’s gross domestic product in period t. Thus, the dependent variable is the natural log of country i’s ratio of GDP in year t + 20 to its GDP in year t. This ratio represents the total growth rate between year t and t + 20. The important independent variable in the model is the ratio of country i’s exports of primary resources to its GDP in year t and only in year t. This specification is essentially allowing for 20 separate lagged effects on GDP from a single year’s natural resource exports. The coefficient 𝛽1 represents the total of these 20 lagged effects. A negative 𝛽1 implies that the export of these natural resources reduces 20-year GDP growth.
Control variables include each country’s capital investment expenditures (as a portion of GDP), each country’s reliance on international trade (imports plus exports as a portion of GDP), and an index of each country’s institutional quality. Instead of a single value from year t, each of these three variables is defined as the 20-year average commencing in period t, , for k = 3, 4, and 5. The model’s final control variable is the level of GDP in year t, a control variable included in many long-run growth models. Including the initial level of GDP controls for the possibility that low-GDP countries may grow at different rates than high-GDP countries. The expected sign on 𝛽2 is negative, suggesting that low-GDP countries grow faster than high-GDP countries—growth rates converge over time. Controlling for initial GDP also holds constant the denominator of other control variables and especially the natural resource exports. Holding GDP constant is important. Otherwise, if high-GDP countries consume rather than export their own natural resources, then the estimate of 𝛽1 would be biased.
To summarize, the model is designed to estimate the difference in 20-year GDP growth rates among countries with the same initial GDP, the same 20-year average of physical capital investment, the same 20-year average level of trade openness, and the same 20-year average of institutional quality but with different initial ratios of natural resource exports to GDP. Sachs and Warner estimate that, controlling for these variables, a one standard deviation change in the ratio of primary exports to GDP leads to a 1% annual decrease in the averaged 20-year GDP growth rate. Instead of growing at, say, a rate of 3% over a 20-year period, an identical country with those extra natural resources will grow at a rate of only 2%. This econometric specification raised several questions in the subsequent literature. Some papers argued that the level of some future value of GDP should replace the average growth rate as the dependent variable [9]. Others questioned how to best measure natural resource dependency. Natural resource exports have been replaced by natural resource abundance [10] and the stock of natural resources [11][12][13][14]. Papers have also questioned dividing any measure of natural resources by GDP when GDP is the dependent variable in the model. Dividing instead by the population is used as a substitute [15]. The literature has also questioned the assumption that institutional quality and GDP are exogenous and used various methods to control for possible endogeneity. Finally, the papers vary with respect to the scope of the data set. Whereas many use data from all nations, others use data from just Africa [16], just the Middle East [17], or just China [18]. A number of papers focus on the role of natural resources on economic outcomes in just developing countries, including [19][20][21][22][23]. One group of papers uses the S&W data to better understand the reason for the curse, a question left unanswered by S&W. Ref. [24] replaces S&W’s “Rule of Law” with a corruption index and then disaggregates the natural resource variable into four categories and finds that only food exports generate a resource curse. Commodity price variation is found to reduce GDP but only for Africa. Ref. [11] redefines resources as share of natural capital in national wealth and adds several human capital variables. Ref. [11] finds that a 10% increase in natural capital share reduces GDP growth by 1%—partly due to its effect on how resources affect the availability of public education. The model does not control for institutional quality. Ref. [25] uses the S&W data and redefine resources as a share of natural capital in national wealth (World Bank) to find that the curse disappears for all resources except land area. Another set of papers focuses on the role of political and economic institutions in determining economic growth. Institutions that promote shared governance and respect for individual property rights and equality under the law are considered important to long-run growth. The political science literature [26] suggests that natural resources may compromise political institutions through rent seeking. Ref. [4] suggests that institutions are the only significant predictor of growth. Other variables, including natural resources, are insignificant once institutions, which may be endogenous, are controlled for. Ref. [27] also finds that only institutions, and not natural resources, matter to economic growth. Ref. [12] redefines resources as the share of resource rents in GDP. The natural resource curse exists only if resource rents are consumed by governments rather than invested. Ref. [28] considers forms of human welfare other than GDP and finds that the resource curse operates via its negative impact on institutional quality. Once institutional quality and initial GDP are controlled for, the natural curse disappears. Ref. [14] finds that natural resources increase GDP unless the population of the country is ethnically fractionalized. A number of papers in the literature redefine natural resources as a stock variable rather than a flow variable. Ref. [13] utilizes S&W data but replaces the export share of GDP with the share of natural capital in national wealth and add human capital. Resource exports become insignificant and natural capital is estimated to be positive and significant. Ref. [15] also redefines resources as the share of natural capital in national wealth to find that small non-resource sectors of the economy are responsible for slow growth. Once the size of the non-resource sector is controlled for, the natural resource curse dissipates. Ref. [15] also finds that replacing exports per USD of GDP with exports per person causes the coefficient on natural resources to become insignificant (both for exports and for natural capital endowment). Ref. [10] distinguishes natural resource abundance (stock) and natural resource dependence (export flow). Both are considered endogenous to the model. Resource dependence is found to be insignificant and resource abundance is estimated to be positive and significant. Ref. [29] responds directly to [10] and finds that natural resources have no impact (positive or negative) on GDP. Ref. [30] estimates that stocks of natural resources reduce institutional quality but not economic growth. Export flows do not affect institutions (when controlling for stocks) but do impact growth. Ref. [9] develops instruments for 1970 GDP to find that oil and minerals enhance economic growth and are neutral towards institutional quality. Ref. [31] utilizes a heterogeneous panel data approach and find that oil production and oil rents improve GDP, whereas oil reserves are neutral. A simple OLS model with cross-section data estimates a resource curse when not controlling for institutional quality. Ref. [32] redefines resources as the share of natural capital in national wealth (using World Bank data) and use instruments for institutional quality and openness to estimate that both resource stocks and resource exports reduce GDP growth. Finally, [33] estimates that major resource discoveries increase GDP by 40% in the long run. This increase is greater for non-OECD countries than for OECD countries. To summarize, only [32] finds evidence of a natural resource curse for broad categories of natural resources. Ref. [34] distinguishes between GDP growth and GDP levels. Using panel data from just the United States, natural resource abundance is estimated to decrease growth rates but increase income levels.Variable Name | Definition |
---|---|
GDPit |
Sample | |||||
---|---|---|---|---|---|
(1) | (2) | (3) | |||
Output-side real GDP at chained PPPs (in mil. 2011USD) for country | i in year | ||||
Variable | t (United Nations) | ||||
1970 S&W Data Set | All Available Data | All Available Data | lgrowthi | Ln (GDPit+20/GDPit) for country i | |
UNsxp | 1.40 | 3.18 *** | - | UNsxpit | United Nations measure of primary resource exports (SITC Codes 1, 2, 3, 4, and 68) divided by GDP for country i in year t |
(0.96) | (0.66) | CIDsxpit | Center for International Data measure of primary resource exports (SITC Codes 1, 2, 3, 4, and 68) divided by GDP for country i in year t | ||
CIDsxp | - | CapInvesti | Share of gross capital formation at current PPPs or country i averaged over years t to t + 20 (United Nations) | ||
- | 3.29 *** | ||||
(0.54) | Openi | Share of merchandise exports + imports at current PPPs four country i averaged over years t to t + 20 (united Nations) | |||
CapInvest | −1.00 | 0.84 | 1.85 ** | IQi | Heritage Foundation overall institutional quality score, comprised of property rights, freedom from corruption, fiscal freedom, government spending, business freedom, labor freedom, monetary freedom, trade freedom, investment freedom, and financial freedom for country i averaged over year t to year t + 20 |
(1.19) | |||||
(0.88) | |||||
(0.77) | |||||
Open | 0.13 | −0.21 * | −0.07 | ||
(0.14) | (0.12) | (0.12) | |||
IQ | −0.009 | −0.004 | −0.019 *** | ||
(0.008) | (0.006) | (0.005) | |||
pcGDP | −0.032 *** | 0.004 | −0.05 ** | ||
(0.37) | (0.025) | (0.03) | |||
Constant | 1.90 *** | 0.78 ** | 2.02 *** | ||
(0.52) | (0.38) | (0.32) | |||
Variable Name | Observations | Mean | Standard Dev. | Min | Max |
---|---|---|---|---|---|
GDPit | 156 | 114,297 | 436,613 | 16.76 | 4,912,720 |
(annual) growthit | 153 | 4.18% | 1.89% | −5.09% | 14.24% |
UNsxpit | 98 | 0.08 | 0.09 | 0.00 | 0.54 |
CIDsxpit | 132 | 0.09 | 0.14 | 0.00 | 1.04 |
CapInvestit | 157 | 0.22 | 0.12 | 0.02 | 0.68 |
Openit | 157 | 0.48 | 0.44 | 0.01 | 3.00 |
IQit | 98 | 58.50 | 11.19 | 27.4 | 88.6 |