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The resource drag

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Abstract

Between 1995 and 2001, Jeffrey Sachs and Andrew Warner published a series of influential empirical studies examining mining and energy’s role in economic growth. Their principal finding was that economies heavily dependent on extractive activity in 1971 grew more slowly than comparable non-extractive economies over the next 19 years. This result has been deemed ‘the resource curse’. The result is generally robust across differing country samples and across extended sample periods. Many have sought to explain the phenomenon, but without unified success. Sachs and Warner suggest that crowding out of a sector or activity with production externalities is the most likely explanation. This paper demonstrates that the relatively slower growth in mineral and energy economies may simply reflect a resource drag whereby optimally managed per capita resource production does not grow substantially over time and hence introduces a drag on the measured growth of per capita economic output. If the resource curse is indeed only a resource drag, this has implications for trade and industrial policies implemented on the presumption that there are growth-reducing market failures associated with mineral and energy production.

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Notes

  1. Here and henceforth, we use the term ‘mineral’ to refer to mining, oil and gas products.

  2. For a more extensive review of the literature, see Davis (2005), Stevens (2005), and Frankel (2010).

  3. According to Google Scholar, the three working papers had been cited a total of 1,776 times as of 7 September 2010.

  4. Excluded are six poorly performing oil economies—Bahrain, Iraq, Kuwait, Qatar, Saudi Arabia, and United Arab Emirates—and one adequately performing oil economy—Oman. Included in the sample are 11 countries that did export significant amounts of crude oil or crude oil products.

  5. In their 2001 paper, Sachs and Warner report the independent variable as “real growth per person between 1970 and 1990” (p. 830). We have replicated their results in that paper, and the independent variable is in fact real growth per economically active population, as it is in the 1997a paper.

  6. There were, however, some errors in the 1997a paper’s reporting of the results, which we log here: Table 1—independent variable is LINV7089; Table III—dependent variable is GEA7090, t statistic on Land in regression 3.4 is −4.08; Table IV—dependent variable is GEA7090; Table VI—dependent variable is GEA7090; Table VII—dependent variable is GEA7090; Table VIII—independent variable is LGDPNR70, not LGDPEA70; Table IX—SXP80 and not SXP70 is used in the second regression, column headings should be Growth 1970–1980 and Growth 1980–1990.

  7. We use GEA7089 in the absence of the GEA7090, taken from the same data sources used by Sachs and Warner.

  8. Sachs and Warner regression 3.2 repeats regression 1.5, only with SNR replacing SXP. The coefficient on SNR is negative and highly significant. The results are not directly comparable, however, as three outliers (Gabon, Guyana, and Malaysia) omitted from the sample in regression 1.5 are added back to the sample in regression 3.2.

  9. Zambia should have grown 2.5% faster than Japan as a result of its lower initial per capita income and 1.9% slower than Japan as a result of rule of law and investment differences. Adding these to the trade policy, terms of trade, and mineral intensiveness growth differences leads to an explained growth difference of 4.1%. This leaves 1.4% of the difference unexplained.

  10. This same result is obtained when we include labour explicitly in the production of non-mineral output, though the derivation is more cumbersome.

  11. These are Botswana, Niger, Zaire, Somalia, Tanzania, Barbados, Haiti, Myanmar, Oman, and Saudi Arabia. We use GEA7089 in the absence of the GEA7090 data.

  12. \( \frac{{{\text{M}}71/{\text{EAPOP}}71}}{{{\text{GDPEA}}70}} \cong {\text{SNR}}71 = \frac{\text{M71}}{{{\text{GNPD}}71 \times {\text{POP}}70}} \), where GNPD71 is GNP per capita in 1971 dollars and POP70 is population in 1970. We have more data for \( \frac{{{\text{M}}71/{\text{EAPOP}}71}}{{{\text{GDPEA}}70}} \) and \( \frac{{{\text{M}}90/{\text{EAPOP}}90}}{{{\text{GDPEA}}70}} \) than for SNR71 due to better availability of the data that go into making up the ratio.

  13. This is the value of g for the USA over this period (Jones 2002, Table 2.1). The average for all of the economies in our sample is probably higher than this (Jones 2002, Fig. 2.15), but we stay with 1.13 to be conservative. Hall and Jones (1999) note that by 1988, productivities differed dramatically across countries. This is consistent with all countries having only small differences in the level of g since the differing productivities are a result of compounding differences in g over perhaps 100 years.

  14. We are aware of the criticism that the ratio of mineral production to level of GNP, as in SNR71, suffers from an endogeneity problem in the sense that economies that are growing more slowly over the long run will tend to have a higher SNR71 value for a given level of mineral output (Alexeev and Conrad 2009). Alexeev and Conrad nevertheless concede to adding mineral output per unit of economic output to their regressions as a conditioning variable, and so do we, since the purpose of our paper was to revisit the Sachs and Warner regressions and to test the robustness of their results after taking into account the resource drag effect.

  15. We are able to determine the 23 mineral products used by exactly matching Sachs and Warner’s reported total value of mineral production across these 23 minerals, and so differences are either due to reported mineral production for each country or imputed US import prices. In some cases, Sachs and Warner report ‘0’ SNR values for countries that are not included in the Minerals Yearbook database. We instead remove these countries from the sample. In other cases, we are not able to find 1971 GNP per capita data for a country using the sources cited in Sachs and Warner. The end result is the addition of SNR71 data for three countries and the removal of SNR71 data for 11 countries. Most other SNR71 values differ only slightly from the SNR values originally computed by Sachs and Warner.

  16. We determined Botswana to be an outlier using a Grubbs test since its change in mineral production during the sample period is over 10 standard deviations from the sample mean and over 9.9 standard deviations from the next value in the sample. It has been removed from the sample in all regressions. Sala-i-martin et al. (2004) also note that Botswana is an outlier that is driving their results, but do not appear to exclude it from their regressions.

  17. Source: RGDPL, Penn World Tables v. 6.2. We lose Guyana in regression 1.5d due to the absence of growth data over the sample period.

  18. Neumayer (2004), in a similar cross-sectional study, replaces GDP growth with growth in genuine income adjusted for capital depreciation and mineral depletion. He finds that the resource drag slowed growth by about 0.06 percentage points per year for the most resource-intensive economies and increased growth by 0.12 percentage points per year for the initially least resource-intensive economies. There remains, however, a considerable and statistically significant crowding out effect even in growth in genuine income. Neumayer only adjusts GDP for the production of 13 minerals, compared with our 23. This and the fact that he controls for SXP rather than SNR is probably why he does not reject a crowding out effect.

  19. The coefficient on SNR71 is insignificant whether or not we include the resource drag term. The coefficient on SXP when we exclude the resource drag term is −5.32, with a t statistic of −2.00.

  20. Zambia, like the oil producers, suffered from a collapse of its mineral sector from 1970 to 1990 (Davis 2009).

  21. In confirmation of our proposal, Stijns (2005, p. 112) states that his results are unaffected by whether he uses stock or flow measures of resource intensity.

  22. Mineral value added as a share of GDP in 1988 is Hall and Jones’ (1999) variable ‘Mining’.

  23. See also Sala-i-martin (1997a, b).

  24. Whilst they refer to mining, the independent variable includes oil and gas extraction.

  25. While Oman’s oil output per worker has fallen according to Table 1, its per worker output of all mineral and energy products has risen according to the US Bureau of Mines data that we use in the regressions.

  26. Canada has also been said to have escaped the resource curse, which it has, in the long run, if one looks at GDP per capita (Keay 2007). Keay (2007) estimates that Canada’s mineral production has had no perceptible resource drag effect in the short run. Using the results of regression 1.5c, we estimate that the resource drag reduced Canada’s growth by an average of 0.12 percentage points per year from 1970 to 1990. Crowding out reduced it by another 0.20 percentage points per year.

  27. National Public Radio (United States), All Things Considered, 22 September 2003.

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  1. Colorado School of Mines, 1500 Illinois St., Golden, CO, 80401, USA

    Graham A. Davis

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Davis, G.A. The resource drag. Int Econ Econ Policy 8, 155–176 (2011). https://doi.org/10.1007/s10368-011-0193-0

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