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Mining Away the Preston Curve World Development Vol. 78, pp. 22–36, 2016 0305-750X/Ó 2015 Elsevier Ltd. All rights reserved. www.elsevier.com/locate/worlddev http://dx.doi.org/10.1016/j.worlddev.2015.10.013 Mining away the Preston curve RYAN B. EDWARDS* Australian National University, Canberra, Australia Summary. — I estimate the long-term national health and education impacts of having a larger mining share in the economy. By instru- menting the relative size of the mining sector with the natural geological variation in countries’ fossil fuel endowments, I provide evi- dence suggestive of a causal relationship. The findings suggest that countries with larger mining shares tend to have poorer health and education outcomes than countries with similar per capita incomes, geographic characteristics, and institutional quality. Doubling the mining share of an economy corresponds to, on average, the infant death rate being 20% higher, life expectancy being 5% lower, total years of education being 20% lower, and 70% more people having no formal education. Divergences from the Preston curve—the con- cave relationship between cross-country income and life expectancy that has long been of interest to economists, demographers, and epidemiologists—are thus partly explained by the size of the mining sector. Within-country evidence from Indonesia paints a similar picture. My results provide support for a growing body of evidence linking mining to poorer average living standards, particularly vis-a`-vis other types of income. I also estimate the effects of national mining dependence on non-mining income, health and education investment, and institutions. Ó 2015 Elsevier Ltd. All rights reserved. Key words — mining, fossil fuels, natural resources, health, education, human capital 1. INTRODUCTION is on average better for health and education than income from the mining sector. Resource-rich Equatorial Guinea had a gross national The findings help to understand divergences from the Pre- income of $14, 320 per capita in 2013, yet more than three- ston curve, the concave relationship between cross-country quarters of its population lives below the poverty line, and life income and life expectancy that has long been of interest to expectancy at birth is 20 years less than other high-income, economists, demographers, and epidemiologists (Deaton, non-OECD countries. Africa grew fast on the back of the glo- 2013; Preston, 1975). In Figure 1, I plot life expectancy at birth bal commodity boom in the 2000s, but progress in reducing against per capita income, with countries weighted by contri- poverty has been disappointing (World Bank, 2013). The fate bution of mining to value–added. I do the same for years of of much of the world’s poor is tied to mining, with at least half education in Figure 2. Countries with larger mining sectors of the world’s known oil, natural gas, and mineral reserves in tend to have poorer health and education outcomes than non-OECD, non-OPEC countries. Resource-driven economies expected at their income level. are home to around 70% of the world’s extreme poor. The article proceeds as follows. In Section 2, I provide a The economic and institutional effects of natural resources conceptual framework linking mining to health and education. and a booming resource sector have been well studied, but evi- Section 3 explains the instrumental variable (IV) strategy used dence on how extractive industries relate to social outcomes in my main estimates. Section 4 presents the national-level remains thin (see van der Ploeg (2011) and Wick and Bulte results, compares the health and education elasticities of min- (2009) for reviews). Human capital is typically seen as a chan- ing income with income from other sectors, and explores nel for natural resources to stunt economic growth (Gylfason potential mechanisms. Section 5 presents similar evidence & Zoega, 2006), although primary commodities can also from Indonesian districts. Section 6 concludes. directly impede social development (Carmignani, 2013). To my knowledge, an international study is yet to focus on mining sector output or examine its effects on national health and edu- 2. LINKING MINING TO HEALTH AND EDUCATION cation outcomes. In this article I compare countries with different structural Why would the mining sector affect a country’s infant mor- compositions to look at the ‘‘social productivities” of different tality rate or life expectancy? The size of the mining sector can types of economic activity, focusing on fossil fuel extraction. I be linked to national health and education outcomes through exploit geological variation in countries’ fossil fuel endow- three main channels: income and Dutch disease effects; ments to identify the long-term effects of mining on health investment in health and education, by individuals and and education. I find that countries with more mining tend to have poorer health and education outcomes than countries with similar per capita incomes, geographic characteristics, and institutional quality. My estimates suggest that doubling * I am grateful to Paul Burke, Fabrizio Carmignani, Stephen Howes, the mining share of an economy corresponds to the infant Robert Sparrow, Budy Resosudarmo, Hal Hill, Max Corden, Richard death rate being 20% higher, life expectancy being 5% lower, Denniss, Kimlong Chheng, Jessica Hughes, three anonymous reviewers, total years of education being 20% lower, and 70% more peo- colleagues at the Arndt-Corden Department of Economics and the ple having no formal education. Within-country evidence from Crawford School of Public Policy, and participants at the World Congress Indonesian districts reveals similar patterns. Just as some types for Environmental and Resource Economists 2014 for helpful comments of economic growth are better at reducing poverty and discussions. I have no relevant funding or industry affiliations to (Christiaensen, Demery, & Kuhl, 2012), non-mining income declare. Final revision accepted: October 2, 2015. 22 MINING AWAY THE PRESTON CURVE 23 from a larger mining sector depend on the size of the mining Norway 80 expansion, its effects on other sectors of the economy, and Brunei Qatar the distribution of mining and non-mining income. The Dutch Kuwait UAE Saudi Arabia disease occurs when resource exports generate large balance of Seychelles 70 payments surpluses, appreciating the real exchange rate and Trinidad and Tobago Kazakhstan increasing relative prices for non-tradable inputs. Coupling Russia these price and exchange rate effects with higher demand from PNG Gabon a mining boom, other trade-exposed sectors tend to be less 60 competitive and are often permanently displaced (Corden, Botswana 1984). In extreme cases, booming mining sectors can have sim- Life expectancy at birth (years) ilar effects on non-resource sectors as large tariffs (Gregory, Equatorial Guinea 50 1976). Because manufacturing and other tradable sectors tend to more intensively use human and physical capital, booming sector dynamics often lead to less capital in the economy Note: countries are weighted by contribution of mining to value−added 40 (Mikesell, 1997). Positive health and education impacts of a 0 20000 40000 60000 80000 mining-driven income boost are also likely to be offset by GDP per capita the unequal distribution of new income, as countries with Figure 1. Preston curve, 2005. greater primary commodity dependence tend to have higher inequality, which in turn affects social development trajecto- ries (Carmignani & Avom, 2010). The second main channel is human capital investment, 15 which tends to be lower in mining-dependent countries due to lower expected returns to skills, education, and knowledge Canada (Blanco & Grier, 2012). At the micro level, a booming mining Russia Norway Australia sector alters the incentives for human capital development. Trinidad and Tobago Trade exposed modern sectors are typically more labor and 10 human capital intensive, with higher wage premiums for edu- UAE Bahrain Brunei cated workers and greater innovation. Conversely, primary Saudi Arabia Qatar commodity sectors tend to use less skilled labor and have Gabon Algeria Kuwait fewer linkages to other sectors of the economy, effectively tax- Congo, Rep. 5 Years of schooling ing human capital if they divert people and resources away Iraq from higher skilled activities (Matsuyama, 1992). For exam- Papua New Guinea Yemen ple, oil resources tend to orient university students toward spe- Mali cializations providing better access to resource rents (Ebeke, Omgba, & Laajaj, 2015). With poorer micro-level incentives Note: countries are weighted by contribution of mining to value−added 0 for investment in skills and education, private health invest- 0 20000 40000 60000 80000 ment is unlikely to respond much differently. Long-term GDP per capita positive spillovers from natural resources typically hinge on Figure 2. Educational attainment, income, and mining, 2005. resource revenues strengthening governments’ fiscal positions, enabling increased investment in health and education, and ‘‘spreading the benefits” (Arezki, Gylfason, & Sy, 2011). governments; and various institutional channels (Figure 3, Volatility—the ‘‘quintessence of any resource curse” (van der dotted lines and clear boxes). Ploeg & Poelhekke, 2009)—makes this difficult, as short- A larger share of mining in the economy could benefit health term political and economic horizons in volatile economies and education by boosting income. While a substantial body provide little incentive to prioritize long-term health and edu- of research
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