MINING, OIL, AND INEQUALITY
Graham A. Davis
Division of Economics and Business
Colorado School of Mines
1500 Illinois St.
Golden, Colorado 80401
U.S.A.
+1 303 273 3550
May 1, 2014
ABSTRACT
It is commonly presumed that economies specializing in mining and oil supply have high income
inequality. Policy prescriptions follow. I use five different data sets on income inequality, three of
which are recent and have good coverage of mining and oil economies, to examine whether there is a
structural relationship between mining and oil production and income inequality. Overall, I find no
robust evidence pointing to persistently higher income inequality in mining and oil economies.
JEL Classifications: O13, O15, Q32, Q33, Q38
Keywords: mining, oil, income inequality, resource curse, economic development.
Statement on Conflict of Interest: I am currently a short-term consultant for the World Bank and
provide expert consulting services to law firms representing international mining companies during
disputes over land holdings. None of these parties were involved in funding this research, and nor were
they involved in any other way.
1
INTRODUCTION
Over the past four decades many economies have experienced an increase in income inequality. The
study of the origins of increasing inequality often focuses on productive activity. Why, for example,
are developing economies that have become heavily involved in mining and oil production particularly
prone to high inequality? Is it because of their relatively closed trade policies, neglect of public
education, or poor institutional quality, all of which have been shown to increase income inequality
(Spilimbergo et al. 1999, De Gregorio and Lee 2002, Chong and Gradstein 2007)? Is it because the
inequitable land holdings established during colonization have persisted to the present day via
institutions set up to maintain the status quo (Engerman and Sokoloff 2002)? Or is it because these
economies have a consistently high ethnic polarization that prevents income redistribution (Fum and
Hodler 2010)?
These are all interesting questions, but this paper seeks to answer none of them. Instead, it investigates
the premise driving these questions, that economies heavily involved in mining and oil supply have
high income inequality. I start here because this is an outcome, despite the certitude with which it has
been presented, that has yet to be empirically demonstrated. There are arguably 40 to 50 economies in
which mining or oil production is significant enough to affect their paths of development. This is a
quarter of the world’s economies. Establishing whether or not there is high income inequality across
this group is an important step in guiding modern development policy. As one NGO warns, mining is
not “just another industry” (Power 2002, p. 6).
I first review the case for high income inequality in mining and oil economies, and then synthesize the
existing empirical studies. The main body of the paper examines income inequality in mining and oil
economies using two new proxies for oil and mining intensiveness, the revenues from oil and mining
production per unit GDP and the revenues per economically active population, and five data sources
for income inequality: Gini Indexes compiled by Chen and Ravallion (2001), Fum and Hodler’s (2010)
selection of Gini Indexes from the UNU-WIDER World Income Inequality Database (WIID), a Theil
Index of wage inequality (Conceição and Galbraith 2000), an Estimated Household Inequality Index
(EHII) that combines the Gini Index measurements by Deininger and Squire (1996) with wage
inequality data (Galbraith and Kum 2005), and an estimated Gini series by Solt (2009) that applies a
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missing-data algorithm to the WIID data set. The latter three data sets have unusually good coverage
for mining and oil economies, yet have not been previously applied to the question at hand.
IS HIGH INCOME INEQUALITY A REASONABLE PROPOSITION?
High income inequality in mining and oil economies is taken as fact in academic writings (e.g.,
Engerman and Sokoloff 1997, 2002; Sokoloff and Engerman 2004, Karl 2004, Page 2006), the popular
press (e.g., Stiglitz 2006, p. 137), publications by non-governmental organizations (e.g., Ross 2001,
Power 2002, Society for International Development 2013, Slack 2009), and at multilateral
development agencies (e.g., Nankani 1979, de Ferranti et al. 2004). Income inequality can be a sign of
a well-functioning economy transitioning along its optimal growth path (Leamer et al. 1999). In this
case, however, the income inequality is alleged to be the kind that slows long-run growth and leads in
the short run to high poverty and civil unrest.1 The ultimate concern is that inequality causes mining
and oil economies to be at a development disadvantage, a far cry from the “distinct advantages” once
thought to be held by countries like Libya, Zambia, Chile, and Peru (Johnston and Kilby 1975). The
main focus of current academic analysis is appropriate corrective policy.2
My review of this and related literature has found that the claims of high inequality are for the most
part based on interpretive analyses or casual empiricism. The academic literature on mining, oil, and
inequality is sparse and inconclusive. Yet the claims have had bite. In June of 2001 the World Bank,
responding to civil society’s criticisms of its support for extractive industry and the inequality and
poverty that follow, initiated both an internal and an external Extractive Industries Review (EIR). The
mining industry itself established the Minerals, Mining, and Sustainable Development (MMSD)
initiative in 2000 and subsequently formed and funded the International Council on Mining and Metals
(ICMM), which has conducted a series of case studies to examine mining’s contribution to
development.3 Both the Bank and academics have called for continued studies on the effects of mining
and oil extraction on the poor (Weber-Fahr 2002, Karl 2007, Ross 2007).
The claim of high inequality, even if anecdotal, is not provocative at first glance. There are three paths
by which productive activity can affect income inequality.4 The first is through direct factor receipts
from production. Production that is concentrated in a few hands, either due to economies of scale or
3
the geography of endowments, could lead to income inequality (Leamer et al. 1999). Because of the
point-source nature of mine and oil deposits and the large amounts of equity required for entry into the
market, one could indeed imagine rents being disproportionately distributed to private or public sector
elites. The rents from mining and oil have been shown to increase per capita national incomes
(Alexeev and Conrad 2009), but they could also increase the spread around that mean by expanding
the right tail of the income distribution. At the same time concentrated production could strengthen
institutions with the power to maintain or worsen these direct effects in the long run (Engerman and
Sokoloff, 1997, 2002). Leamer et al. (1999) posit an endowment-related story that has inequality
temporarily rising in mining and oil economies as their development proceeds, a type of Kuznets
effect. In their model economies without nonrenewable resources or capital-intensive agriculture, on
the other hand, would see a decline in income inequality over time.
A second, more complete analysis recognizes that resource extraction creates complex factor
movement and spending effects associated with a booming mineral sector. Goderis and Malone (2011)
present a structural model of a resource-producing economy with learning by doing and these
additional spending effects. They find that wage inequality can rise or fall during and subsequent to a
resource price shock. The increase or decrease in inequality depends on the initial distribution of labor
in the economy. They propose that wage inequality will fall in developing economies during a price
boom because of increasing wage income of unskilled workers relative to skilled workers. Gylfason
and Zoega (2003), on the other hand, build a model that predicts that discoveries of mining and oil
endowments unequivocally raise income inequality during a boom, though with the possibility that
taxes that protect the shrinking manufacturing sector can mitigate the impact by reducing the size of
the discoveries and through this the increase in inequality.5
A third view recognizes the power of government, or institutions more broadly, to affect the
distribution of income via rent-concentrating rent seeking or rent-disbursing taxation. Government
often receives a large portion of mining and oil rent through direct equity participation or royalties and
taxes. An additional effect on factor receipts is government spending on infrastructure financed by this
taxation or by spending from accumulated Sovereign Wealth Funds.6 Using rents to reduce income
inequality and build a social safety net was, for example, a common goal in the OPEC member
countries (Amuzegar 1999, p. 8). Redistribution programs were also undertaken in some mining
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economies, even at the expense of economic growth (Donaldson, 2008).7 There is also the
countervailing thought that once governments avail themselves of mining and oil income they no
longer need to tax citizens’ income or consumption (Bornhorst et al. 2009). Any progressive
redistribution that was in place would dissipate. The effects of different redistribution policies are
evident in the data; in an empirical investigation of income inequality in 44 countries, Latin American
countries’ average Gini Index is 7 to 9 points higher than other countries in the sample after controlling
for stage of development and mix of endowments (Leamer et al. 1999).8 Leamer et al. attribute these 7
to 9 points in part to differences in government policy across regions. The implication is that Latin
America’s penchant for redistributive taxation is lower than in other regions. Adelman and Morris
(1973), one of the first to find quantitative evidence linking nonrenewable resource abundance with
high income inequality, note that resource-abundant countries with government programs aimed at
health and education had significantly lower income inequality than those without such programs.
In the end, once the indeterminate effects of mining and oil production on factor receipts are added to
indeterminate effects of income redistribution that range from inequality-reducing social welfare
programs to inequality-increasing rent-grabbing, there is no a priori reason to believe that income
inequality in mining and oil economies will be unusually high. This makes claims that there is a
structural link between resource extraction and income inequality all the more interesting, as they must
reflect a story that data is revealing.
THE EXTANT EMPIRICAL LITERATURE
Thirteen peer-reviewed papers examine the relationship between natural resource wealth or production
and income inequality. Only four of the papers have specifically tested whether mining and oil
economies have unusually high levels of income inequality. I take some time here to review these 13
papers because I can find no existing synthesis and because I find that their record on mineral
production and income inequality is far from clear.
In the first of the four papers that specifically test the impact of mining and oil production on income
inequality, Bourguignon and Morrisson (1990) find that the 10 mining and oil exporting countries in
their sample of 35 small- and medium-sized developing economies had significantly higher income
5
inequality than the rest of the sample. Income inequality is measured by 1970 income shares of
economically active individuals prior to redistribution, testing the pure factor receipts model of
resources and inequality. Human capital accumulation was the only other significant determinant of
income inequality, though its effect on the regression R2 is small compared with that of mining and oil
exports. As an interesting side note, nine of the ten mining and oil economies in the sample had
nationalized their resource industries by 1970, and yet the income inequality associated with resource
production in that year persisted. Bourguignon and Morrison suggest that the inequality at that point
was bequested from the time when the resources were discovered, which was under private ownership.
This is a frightening proposition, as it means that any income inequality established during a resource
boom may persist for decades after the boom has ended. A second paper that revises the raw data,
increases the sample size from 35 to 38, and changes the empirical specification slightly finds that
mining and oil exports are in fact not a statistically significant determinant of income inequality
(Bourguignon and Morrisson 1998). The small sample size and sparse coverage of mining and oil
economies in these two studies is typical.9
Leamer et al. (1999) use 1980 and 1990 Gini Index data from Deininger and Squire (1996) to measure
inequality.10 They find that after conditioning for stage of development, tropical agriculture production
creates income inequality while manufacturing production results in a more equitable income
distribution. There is evidence that fertilizer, coal, natural gas, and metals production as a group
promotes inequality, but that result is by no means definitive due to weak statistical significance.
Surprisingly, oil production does not promote inequality. Since the Deininger and Squire (1996) Gini
Index data includes pre-tax and post-tax income surveys as well as expenditure surveys (see Galbraith
and Kum 2005, Table 2), Leamer et al. are likely picking up some post re-distribution effects rather
than the pure factor receipt effects that they model. Their sample size is small, at less than 50
developed and developing countries.
Easterly (2001) tests Engerman and Sokoloff’s proposition that tropical agricultural commodity
exporters and oil and mining exporters have high income inequality. In a sample of 102 countries he
finds the share of income accruing to the middle three income quintiles to be low in nonfuel
commodity exporters compared with non-commodity exporters. Oil exporters have higher middle
income shares than the nonfuel commodity exporters, but lower middle income shares than non6
commodity exporters. My reproduction of his analysis reveals that the sample includes only six oil
producers (Algeria, Iran, Nigeria, Trinidad and Tobago, Turkmenistan, and Venezuela) and that many
income share estimates are based on a single survey year over the 1960 to 1996 sample period.11
The next two papers regresses Gini Indexes on broad, aggregate measures of natural resource stocks.
Gylfason and Zoega (2003) pool World Bank WDI Gini Indexes that again contain pre-tax and posttax observations of income inequality. They find that income inequality is positively correlated with
natural resource wealth as share of total national wealth in 1994 for a sample of 75 countries, but that it
is not correlated with natural resource wealth per capita. The resource wealth estimates come from the
World Bank (1997).12 Gylfason and Zoega do not separately analyze the inequality associated with
subsoil wealth, which is typically less than 30% of natural resource wealth (World Bank 1997).13
Fum and Hodler (2010) use WIID Gini Indexes and World Bank subsoil resource wealth estimates in
an analysis of endowments and inequality. They specifically exclude Gini Indexes computed from pretax income surveys in order to measure inequality “after the political processes of rent seeking and
redistribution.” They also exclude low-quality Gini estimates. In a cross-sectional analysis of income
inequality for 75 developed and developing countries they find that having subsoil wealth (as averaged
across the World Bank data for 1994 and 2000, in per capita terms) has decreased or increased income
inequality, depending on the degree of ethnic polarization present in an economy. Subsoil wealth
lowered inequality in economies with an ethnic polarization index of less than 0.70, and raised
inequality otherwise. Fum and Hodler theorize that this is because of a penchant for equal sharing of
the rents to support the poor in low ethnic polarization economies and a winner-take-all competition
for rents in polarized economies. Of the 81 countries with ethnic polarization data and nonzero subsoil
wealth, at the ethnic polarization cutoff of 0.70 there are 18 countries that will have experienced
increasing income inequality as a result of their mineral endowments and 63 that will have experienced
decreased income inequality.14 There is nothing notable about those 18 economies that have higher
income inequality as a result of their mineral endowments – the grouping does not explain Latin
America’s high income inequality, for example, since only 30% of the Latin American and Caribbean
economies with subsoil wealth have ethnic polarization indexes greater than 0.70. That there can be
both increased and decreased income inequality in mining and oil economies, depending on ethnic
polarization, lends support to the idea that even if the pure endowment effect on the equality of factor
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receipts is negative, as may be the case for coal, natural gas, and metals (Leamer et al. 1999), strategic
redistribution efforts by the state can lead to positive net income outcomes.
Fum and Hodler do not test whether subsoil wealth results on average in higher net income inequality,
which is the claim that my paper is investigating. Using their data, regression 1 in Table 1 shows that
more subsoil wealth on average lowers income inequality. This may be because of the inclusion of
countries like Norway and Canada in the sample. Using Leamer et al.’s suggestion that less developed
economies have structurally higher income inequality, regression 2 controls for stage of development
using income per capita. It also controls for survey type, since income-based surveys have been shown
to reveal higher inequality than expenditure-based surveys (Deininger and Squire 1996).15 Subsoil
wealth now has no overall effect on income inequality. I do not control for additional factors that may
influence inequality since the claims I refer to in the Introduction of the paper are made
unconditionally.16
Controlling for level of development using income per capita may create a false development peer
group for mining and oil economies, which have temporarily high GDP per capita due to their
booming resource sector (Alexeev and Conrad 2009, Davis 2009). Regression 3 controls for stage of
development using regional and economic dummies. Again, there is no indication that subsoil wealth
increases income inequality on average. The adjusted R2 of the regression rises considerably once the
regional dummies are added. This is not surprising since dramatic regional differences in income
inequality have been noted elsewhere (Besley and Burgess 2003, Galbraith and Kum 2005).
Regression 4 confirms that my control variates in regression 3 still produce Fum and Hodler’s finding
that country-specific inequality outcomes depend on a proclivity for rent redistribution that varies with
ethnic polarization.17
While the results in Table 1 may appear to definitively reject claims of overall higher income
inequality in mining and oil economies, I do not put much faith in the World Bank subsoil wealth data
used as the independent variable to detect oil and mining economies, even though its ubiquity in
analyses of resource curse issues led me to take this short investigative detour. The World Bank data is
not useful because of the limited number of minerals that it measures, its methodology for computing
resource wealth, and the fact that resource wealth has no obvious relationship to mining and oil supply.
8
For example, Ireland is measured to have more subsoil wealth per capita than Peru or Zambia, two
mining stalwarts. Canada’s economy is measured to have ten times the per capita subsoil wealth as
Botswana, the most mining intensive country in the world. This downgrading of Botswana is because
the World Bank measure does not include diamond production. Nor does the data include platinumgroup metal production, and so the subsoil wealth measure is likely to seriously underestimate South
Africa’s and Russia’s endowments.18
Another three papers conduct a dynamic (changes in levels) analysis. Being interested in dynamics
rather than levels, none of these studies measures whether the cumulative effects of booms and busts
that resource economies experience over time yields a high ultimate level of income inequality, which
is the question at hand. Davis (2009) examines changes in income inequality during 240 positive and
negative growth episodes in 21 mining and oil economies and 67 non-mineral economies from 1956 to
1999. The mining and oil economies have slightly more frequent unambiguous reductions in income
inequality than non-mineral economies and slightly less frequent unambiguous increases in income
inequality.19 Goderis and Malone (2011) examine changes in Gini Indexes during a commodity price
index boom.20 They find that wage inequality falls during an oil and gas price boom and via symmetry
find the opposite in commodity price busts. 21 There is no response to an agricultural price boom. This
does not test for the equilibrium level of wage inequality in mining and oil economies, only that the
level will temporarily go down when prices rise and will temporarily go up when prices fall. The effect
is very small, with a maximum decline of 0.37 Gini Index points for the most resource intensive
country in the sample, Zambia, when faced with a two-sigma price shock.22 The effect measured by
Goderis and Malone dissipates to zero after 20 years. Given that most current mining and oil
economies came to their economic status as extractive economies as a result of sustained production
booms that originated more than 20 years ago, the inference from Goderis and Malone’s results is that
these economies should not have steady state wage inequality that is any different from non-mineral
economies. In the last dynamics paper Davis and Vásquez Cordano (2013) conduct a random effects
panel analysis of mining and oil booms in 57 developed and developing countries. They find no
evidence that booms in mining and oil production worsen income inequality.
The last four papers look specifically at local and regional effects of mining. Output booms at the
Yanacocha gold mine in Peru increase incomes equally across the local population (Aragón and Rud
9
2013, fn 48), though Loayza at al. (2012) suggest that this is a best-case scenario due to international
pressures for redistribution at that mine site. Loayza at al. find that mining in Peru as a whole has
increased income inequality, as measured by the Gini Index, across and within districts proximate to
mining activity, though the effect is very small; they measure a roughly 1 point increase in the Gini
Index in mining districts compared with non-mining districts. Reeson et al. (2012) measure a 3 point
Gini Index increase in the Statistical Local Areas within Australia where significant numbers of mining
employees reside. Buccellato and Mickieviz (2009) use a panel estimation of within-region income
inequality in Russia and find that oil and gas production per capita is associated with higher income
inequality as measured by productions’ effect on quintile income shares and differences between low
and high quintiles shares. They also find that oil and gas production contributes to between-region
income inequality in Russia.
In a recent summary of the literature on natural resources and growth, including the papers by
Gylfason and Zoega (2003), Fum and Hodler (2010), and Goderis and Malone (2011), van der Ploeg
(2011, p. 406) opines that “The best available empirical evidence suggests that countries with a large
share of primary exports in GNP have bad growth records and high inequality, especially if quality of
institutions, rule of law, and corruption are bad. This potential curse is particularly severe for pointsource resources such as diamonds and precious metals.” In my view the evidence in these and the
other papers I have reviewed is mixed. Certainly nothing in particular can be said of inequality in
economies that produce point-source resources like diamonds and precious metals because the data in
the papers that examine inequality do not include diamonds or platinum-group metals. Moreover, the
small sample size in these papers, with a bias away from the top mining and oil producers due to their
listwise deletion, is likely to lead to unreliable results. The studies of regional effects in Peru,
Australia, and Russia are more compelling since one may reasonably expect localized income
inequality in and around producing districts. But this does not imply or confirm a general tendency
towards income inequality at the national level or across all mining and oil producers.
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A NEW LOOK AT THE DATA ON MINING, OIL, AND INCOME INEQUALITY
To recap, the question is a simple one: is income inequality unconditionally higher than expected in
countries identified as mining and oil intensive? Given the casual empiricism that pervades statements
about income inequality and mineral wealth we are not looking for some hard-to-find residual effect in
a comprehensive system of equations. We are looking for bald evidence that is obvious to all. In this
light I initially simply divide developing economies into mining economies, oil economies, and nonmineral economies and then test for differences in mean income inequality across these groups. The
groups are formed by measuring the revenues from mining and oil production circa 1990 at 1990 US
dollar import prices and then ranking countries by revenues per economically active population or
revenues per unit of 1990 GDP measured in current US dollars.23 Categorizing mining and oil intensity
using GDP as the numeraire risks having the groupings influenced by the effect of oil and mining
production on GDP. The resource curse, for example, may result in mining and oil economies having
lower than normal levels of income. A categorization based on mining and oil revenues per GDP will
then tend to include failing economies with low GDP and only moderate mineral revenues. It will
likewise exclude successful mineral economies with high GDP. A better numeraire is population
(Alexeev and Conrad 2009). I elect to use working-age population to avoid eliminating African
countries from the oil and mining country groupings due to Africa’s burgeoning numbers of youth and
resultant high population counts. Mining and oil production in 1990 is taken from the US Bureau of
Mines Minerals Yearbooks. I consider production in 22 mined products, including coal, diamonds and
platinum-group metals, and three petroleum products (oil, natural gas, and natural gas liquids).24 I
assembled three different samples for my groupings of mining and oil economies: the top 15 mining
economies and the top 20 oil economies worldwide; the top 20 mining economies and top 25 oil
economies; and the top 25 mining economies and top 30 oil economies. The first sample includes only
the most intensive mining and oil producers, while the last adds less intensive producers. After
reviewing the countries included and excluded in each sample my preferred grouping is the most
inclusive grouping, the top 25 mining economies and top 30 oil economies, as this ensures that Norway
and Kazakhstan are included as oil producers, and that Russia and Zambia are included as mining
producers. This also matches well what the IMF considers to be resource-intensive economies
(Baunsgaard et al. 2012). The Appendix lists the country groupings for this sample and the income
inequality coverage provided by each of the inequality data bases that I use in this paper. In the
11
analysis that follows I elect to separately analyze the mining and oil producers because the rents
attributable to oil production are so much higher than the rents attributable to the same value of mining
production and because the impacts of oil production on societies appears to be different from that of
mining production (Ross 2004, Karl 2007, Petermann et al. 2007).25 This separation also prevents a
non-effect from mining production clouding the effect from oil production where countries produce
both products.
In the next two sections I look for high pre-redistribution income inequality and post-redistribution
income inequality in developing economies with extensive mining and oil production using a
comparison of means. This is followed by a more traditional least-squares analysis.
1. Inequality Prior to Redistribution Effects
The periodic nature of household income surveys combined with the unusually high volatility of the
Gini Index values computed across sequential surveys has led researchers to look for other measures of
income inequality. Conceição and Galbraith (2000) have constructed a “long and dense” time series
Thiel Index of manufacturing wage inequality. Manufacturing includes the 26 two-digit ISIC
classifications, which range from Agriculture and Hunting to Insurance. The data, available at the
University of Texas Inequality Project (UTIP) web site, covers 154 countries over 4,030 observations
from 1963 to 2008.26 Wage inequality is useful because it reflects the pure endowment effect on factor
receipts prior to redistributive policies that vary by country. An added benefit to this data series is that
it includes inequality data for all of the Middle East oil producers except for Saudi Arabia. Other
measures of income inequality for these countries either do not exist or are of poor quality. Galbraith et
al. (2000) suggest that this measure of inequality may behave differently from the more comprehensive
income inequality that Gini Indexes are trying to measure, but that it has the advantage of coverage,
consistency, and accuracy.27
Figure 1 presents a first look at differences in mean income inequality between 1963 to 2008 for
developing mining economies, developing oil economies, and developing non-mineral economies
using the per worker numeraire, along with inequality data for the 23 OECD economies. I separate out
the OECD countries in an effort to control for stage of development. Wage inequality is measured by
12
the log of the Conceição and Galbraith Thiel Index of manufacturing wage inequality. A more negative
number indicates less wage inequality. The two lines at the bottom of the chart show the total number
of mining and oil economies for which wage inequality data exists in a given year. Given that I have
identified a total of 25 mining and 30 oil economies in the sample, this gives an indication of the lack
of data completeness that plagues research on resource wealth and income inequality, even when using
this broad-based data set. Since the sample is not consistent across years, this wage inequality data
should not be viewed as a time series. Rather, it is a series of separate year-by-year cross-country
inequality comparisons.
The data in Figure 1 show that the developing mining economies for which I have data on average
have a tendency for lower wage inequality than non-mineral developing economies. This is contrary to
the expectation in Leamer et al. (1999), who predict that wage inequality should be high in these
economies during their mining boom. On the other hand, the oil economies for which I have data do
have a tendency for higher wage inequality compared to non-mineral economies. A SatterthwaiteWelch two-tail t-test shows that the oil economies have a statistically higher average wage inequality
than the non-mineral economies in three years, 1998, 2000, and 2001. A non-parametric
Wilcoxon/Mann-Whitney test of medians confirms the results of the t-test. The fact that some years
show a statistically significant difference while others do not is due to varying countries in the sample
from year to year rather than changing conditions in these economies. The mean wage inequality in the
mining economies is never statistically higher than in the non-mineral economies. The wage inequality
in the OECD economies is lower than in all three of the developing economy groupings.
Figure 2 repeats the exercise for mining and oil economies categorized by mining and oil revenue per
unit GDP rather than per worker. This categorization has moved some low inequality mining
economies with high GDPs from the mining group to the non-mineral group and replaced them with
high inequality mining economies, the failed states that have low GDPs given their mineral output. The
coverage of wage inequality data for the mining group, which now includes more failed states with no
data, has dropped as a result. The inequality for those mining economies for which I have data has
risen substantially, with income inequality now statistically higher than the non-mineral group for
1963, 1964, 1965, and 2003 using a Satterthwaite-Welch two-tail t-test. The oil grouping has
13
statistically higher wage inequality in 1997, 1999, and from 2003 to 2006. A Wilcoxon/Mann-Whitney
test of medians confirms the t-tests in Figure 2 except for oil in 2003, 2004, and 2006.
My conclusion from this data is that the direct (i.e., pre-redistribution) factor receipts from mining and
oil production are in some samples more unequally distributed than in non-mineral economies. The
evidence is not strong enough to support a claim that the higher inequality was a general tendency
from 1963 through 2008. I also find that it matters whether one classifies an economy based on its
mining and oil output per worker or per unit GDP. The latter, which has a bias towards including failed
states in the mining and oil aggregate, shows stronger support for claims that the inherent nature of
mining and oil production increases inequality in factor receipts, though again the evidence is not
overwhelming. Overall, the preponderance of the evidence shows no persistent statistically different
level of income unequality in mining and oil economies.
2. Inequality After Redistribution Effects
As noted in the Introduction, governments in oil and mining economies, perhaps recognizing the
inherent tendency for mining and oil production to worsen income inequality, may have attempted to
use tax revenues from such production to improve the lot of the poor. There are arguments, on the
other hand, that rent-seeking and a ruling elite have instead concentrated that wealth. The wage
inequality data used in the previous section is unlikely to pick up these effects. It will also miss income
inequality generated by unemployment or investment income. To examine a more comprehensive link
between mining and oil production and income inequality I use an income-based Gini Index developed
by Galbraith and Kum (2005). They generate a set of Estimated Household Inequality Indexes (EHII)
that take advantage of the household pre-tax and post-tax income and expenditure surveys reported by
Deininger and Squire (1996) but that also include the accuracy and consistency of the wage inequality
data used in the previous section. The Index is the forecast from the regression
I = α + βT + γ X + ε
where I is the log of the Deininger and Squire (1996) Gini Index, T is log of the Thiel index of
manufacturing pay inequality referred to in the previous section, and X is a vector of conditioning
14
variables including dummies for the various types of household surveys (e.g., income versus
expenditure). I downloaded the estimated Gini Index data from the University of Texas Inequality
Program web site.28 It contains 3,872 estimated Gini Index measurements over 149 countries from
1963 to 2008, only slightly lower than coverage of wage inequality used in the previous section. It
again includes inequality data for all of the Middle Eastern oil producers save Saudi Arabia.
Figures 3 and 4 repeat my analysis in the previous section using this measure of income inequality.
Once again, when countries are grouped according to per capita mining and oil revenues the inequality
in the oil economies is higher than the inequality in the non-mineral economies (Figure 3). The
difference in means is statistically significant in seven of the samples using a t-test, though a
Wilcoxon/Mann-Whitney test of medians confirms the t-tests only for 1964, 1965, and 2000. The
income inequality in the mining economies is for the most part lower than the income inequality in the
non-mineral economies, though the difference is not statistically significant for any year. The same
analysis when done using GDP as the numeraire when measuring mining and oil intensity (Figure 4)
reveals that the mining economies have a tendency towards higher than average income inequality.
Once again it matters how one measures whether an economy is heavily engaged in mining and oil
activity. Under this measure there are more samples for which both mining and oil economies have
statistically higher income inequality than in the last section. A t-test shows higher mining economy
inequality in 1963, 1964, 1965, and 2003, though the difference in inequality in 1963 and 1964 is not
robust under a Wilcoxon/Mann-Whitney test of medians. There is higher inequality of oil economies in
1963, 1964, 1965, 1969, 1970, 1992, and from 1998-2006 based on a t-test, though a Wilcoxon/MannWhitney test of medians only finds higher income inequality in 1998, 2000, and 2002-2006.
I have thus far measured mining and oil intensity two different ways and measured income inequality
two different ways, resulting in 184 separate comparisons of mean income inequality between 1963
and 2008. Four results are evident. First, there is no indication that the average level of income
inequality in oil or mining economies is always or even generally higher than in non-mineral
economies. Second, normalizing mining and oil revenue by GDP creates a very different set of
economies within the mining and oil groups than when normalizing by population (see the Appendix).
Egypt, Nigeria, and Yemen, for example, are denominated as oil suppliers under the ‘oil per GDP’
measure, but not in the ‘oil per capita’ measure. Given that these countries’ income inequality is
15
unusually high, their inclusion in a data set that parses countries based on energy revenue per unit GDP
strengthens the claim that energy economies are more unequal. If the analysis instead parses countries
according to oil revenue per capita, these countries’ inclusion in the non-mineral grouping will
strengthen the counterclaim that oil economies have unusually low income inequality. This is evident
in my analysis: Figures 2 and 4 give very different impressions of inequality in mining and oil
economies than Figures 1 and 3. I and others have argued that normalization by population avoids
biasing the mining and oil groupings with a disproportionate number of failed economies, and so I put
more faith in the analysis shown in Figures 1 and 3. Third, by comparing Figures 1 and 3 and 2 and 4,
redistribution of factor incomes in mining and oil economies due to taxes or other programs has if
anything increased the level of income inequality. Finally, the average level of income inequality in all
three groups of developing economies – mining, oil, and non-mineral – is markedly higher than that of
the OECD economies, though the gap is narrowing over time as inequality in the OECD rises
(Galbraith and Kum 2005).
3. Mining and oil supply as continuous variables
The analysis so far has compared simple averages across discrete country groupings. I now present
linear regressions in the style of Fum and Hodler (2010) that make use of the continuous measure of
mining and oil revenue that I have compiled. Four different measures of income inequality are used as
the independent variable. I first use the post-redistribution WIID Gini Indexes compiled by Fum and
Hodler (2010). The availability of this Gini Index data for the major mining and oil producers is shown
in the Appendix. Note the limited data available for most of the oil producing nations. Table 2 repeats
the analysis in regressions 3 and 4 in Table 1 but now using mining or oil revenue as the resource
intensity variable instead of the stock of subsoil wealth as measured by the World Bank. There is no
evidence that higher mining or oil revenue is associated with higher post-redistribution income
inequality.29 Higher oil revenue per unit of GDP is actually associated with lower income inequality,
with a two-standard-deviation change in log oil revenue resulting in a 4 point drop in the Gini Index.30
Surprisingly, ethnic polarization has no explanatory power in these regressions. If we are to believe
that oil production inherently worsens income inequality, as hinted at in Figures 1 and 2, redistributive
efforts within these economies have more than offset these nefarious effects of production. The lack of
16
significance of ethnic polarization indicates that such redistributive efforts are not dependent on ethnic
homogeneity.
In Table 3 I repeat the analysis using the EHII Gini Indexes from UTIP, as introduced in Figures 3 and
4 above. I average the Index for each country over a 1980-2008 sample period, a period that spans the
year in which I measure mining and oil revenue.31 As I noted above, the advantage of this series is its
availability for a large number of countries of interest to this paper (see the Appendix, which reports
the mining and oil countries with at least one inequality data point in the 1980-2008 period). This
inequality measure is internally adjusted for household survey type, so I no longer need the survey
dummies. In Table 3 there is evidence that oil production is associated with increased income
inequality, as was previously indicated in Figures 3 and 4 and which drew on the same data set. The
impact of a two-standard-deviation increase in log oil revenue is 3 Gini Index points. There is no
evidence of higher income inequality in mining economies.
Solt (2009) produces a similar “estimated” Gini Index series based on WIID survey data. His series has
about the same mining and oil country coverage as the EHII Gini but has the advantage of supplying
both pre-tax and post-tax indices.32 Recomputing the four regressions in Table 3 using this data series,
with the Gini indices for each country again averaged from 1980 to 2008, produces statistically
insignificant coefficients on mining and oil revenue for both the pre-tax and post-tax Gini series
(results not shown).
The last measure of income inequality within countries is a Gini Index created by Chen and Ravallion
(2001). Chen and Ravallion’s data was published on a now defunct World Bank web site
http://www.worldbank.org/research/povmonitor/, and was updated from time to time.33 It is based on
primary data from household surveys of income or expenditure in the poorer developing and
transitional countries of the world.34 Besley and Burgess (2003) use it in their landmark study of
regional income inequality. Over the years I monitored the site and collated the data. In the end, after
cross-checking my data against Besley and Burgess’s compilation, there are 295 valid Gini Indexes
over 85 countries from 1980 to 2002. These 85 countries represent about 89% of the population of the
developing world. The richest country in the data set is Chile, at $3,864 per capita in 1993 PPP dollars.
My inspection of the data does not find the type of questionable inequality volatility in countries from
17
year to year as found in the Deininger and Squire (1996) inequality data that preceded it (Galbraith and
Kum 2005). To nevertheless smooth possible errors across surveys within each country I take
Easterly’s (2007) suggestion and average the Gini Index values for each country, producing 85 data
points. The availability of this Gini Index data for the mining and oil producers is shown in the
Appendix. Dummies are again used for averages that include only income surveys, only expenditure
surveys, or both. I now exclude the OECD dummy as none of the countries in the Chen and Ravallion
data set are from the OECD. To continue to control for income and hence development differences
across countries I replace the OECD control with controls for countries in East Asia and the Pacific,
the Middle East and North Africa, and South Asia. These dummies reflect regional inequality relative
to the base case of Eastern Europe and Central Asia, which have low income inequality. Table 4
produces the estimates using this data. Now mining production is associated with higher income
inequality, while oil production is not. A two-standard-deviation increase in log mining output results
in a 2 point increase in the Gini Index. Because of the small amount of ethnic polarization data for this
85 country sample I do not produce the regressions testing for the effect of ethnic polarization on
inequality.
CONCLUSIONS
Measuring income inequality is fraught with difficulty, and I am keenly aware of the missing income
inequality data for some of the mining and oil economies thought to be most problematic. Measuring
whether or not an economy is mining or oil intensive is also problematic, the measure at a minimum
being sensitive to the way one conditions for scale. I have sought to deal with these difficulties by
including two measures of resource production and multiple measures of income inequality. I can find
no robust evidence pointing to the higher income inequality in mining and oil economies that much of
the interpretive literature alludes to; whether or not mining and oil production is associated with higher
or lower income inequality depends on the way that mining and oil production is defined, the way
inequality is measured, and, no doubt, the country sample available for each pair of measures. At best,
a two standard deviation increase in log oil revenue creates a 4 point Gini Index decrease (mining
production was never found to decrease income inequality). At worst, a two standard deviation
increase in log mining or oil revenue creates a 2 to 3 point Gini Index increase.
18
My descriptive study of the data is by no means complex or exhaustive. It may well be that reverse
causality or omitted variables are masking a strong relationship between mining, oil, and inequality. It
may be that inequality is found only in diamond producers, or in countries with dictators. Or it may be
that redistributive, educational, and institutional policies have already corrected what would otherwise
be high inequality. Until those nuances are uncovered, the absence of any obvious statistical regularity
suggests that caution be exercised when alleging a deterministic relationship between mining or oil
production and economy-wide income inequality. It also raises immediate questions as to the need for
broad-based policies that further reduce inequality at the national level. The more prudent approach
may well be targeted programs aimed at reducing the inequality that has been convincingly measured
at the regional level.
19
Appendix: Mining and Oil Cohorts and their Gini Index Coverage
Measure of Production Intensity: Mining and Oil Revenue per Unit GDP
Top 30 Oil
Top 25 Mining Economies
FH EHII
S
CR Economies
FH EHII
Albania
Albania
Botswana
Algeria
Central African Republic
Angola
Chile
Azerbaijan
China
Bahrain
Gabon
Brunei
Guinea
Congo
Guyana
Ecuador
Jordan
Egypt
Gabon
Kazakhstan
Liberia
Indonesia
Macedonia
Iran
Iraq#
Mauritania
Mongolia
Kazakhstan
Namibia
Kuwait
New Caledonia
Libya
Papua New Guinea
Malaysia
Poland
Nigeria
Sierra Leone
Norway*
South Africa
Oman
Suriname
Qatar
Togo
Russian Federation
Ukraine
Saudi Arabia
Syria
Zaire
Trinidad and Tobago
Zambia
Turkmenistan
United Arab Emirates
Uzbekistan
Venezuela
Yemen
Totals
11
17
23
15 Totals
6
24
S
CR
22
13
#
*OECD member as of 1990. Iraq is included in this grouping even though there is no GDP data for this country.
FH = Fum and Hodler Gini Index, EHII = Galbraith and Kum estimated Deininger and Squire Gini Index, S = Solt
estimated WIID Gini Index, CR = Chen and Ravallion Gini Index.
20
Measure of Production Intensity: Mining and Oil Revenue per Economically Active Population
Top 30 Oil
Top 25 Mining Economies
FH EHII
S
CR Economies
FH EHII
S
Australia*
Algeria
Bosnia And Herzegovina
Angola
Botswana
Australia*
Bulgaria
Azerbaijan
Canada*
Bahrain
Chile
Brunei
Czechoslovakia/Czech Rep.
Canada*
Gabon
Congo
Greece*
Ecuador
Gabon
Jamaica
Jordan
Iran
Iraq
Kazakhstan
Macedonia
Kazakhstan
Mauritania
Kuwait
Mongolia
Libya
Namibia
Malaysia
New Caledonia
Mexico
Papua New Guinea
Netherlands*
Poland
Norway*
Russian Federation
Oman
South Africa
Qatar
Suriname
Russian Federation
Ukraine
Saudi Arabia
Syria
United States*
Trinidad and Tobago
Zambia
Turkmenistan
United Kingdom*
United Arab Emirates
United States*
Venezuela
Totals
13
21
24
15
Totals
9
25
CR
22
9
*OECD member as of 1990. FH = Fum and Hodler Gini Index, EHII = Galbraith and Kum estimated Deininger
and Squire Gini Index, S = Solt estimated WIID Gini Index, CR = Chen and Ravallion Gini Index.
21
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Table 1: World Bank subsoil wealth estimates and income inequality
C
Log subsoil wealth
Log GDP per capita
Income-based survey
Expenditure-based survey
Sub-Saharan Africa
Latin America and Caribbean
OECD
Ethnic polarization
Ethnic polarization x log
subsoil wealth
Dependent variable: Average Fum and Hodler WIID Gini Index, 1990 - 2004
(3)
(4)
(1)
(2)
46.32***
67.36***
34.05***
38.59***
(3.89)
(10.28)
(3.14)
(3.99)
-0.86**
0.17
0.20
-1.08**
(0.35)
(0.38)
(0.33)
(0.51)
-4.35***
(1.30)
8.30**
5.56**
5.42**
(3.24)
(2.14)
(2.11)
3.55
2.99
2.15
(3.49)
(1.96)
(2.04)
11.27***
8.99***
(2.78)
(2.55)
10.59***
8.80***
(2.55)
(2.50)
-9.79***
-8.62***
(2.67)
(2.88)
-4.29
(4.57)
1.89**
(0.81)
Adjusted R2
0.05
0.26
0.59
0.63
Observations
78
78
78
75
Notes: Data from Fum and Hodler (2010). Income dummy = 1 if all Gini surveys for a country are income based or if
surveys are a combination of income and expenditure. Expenditure dummy = 1 if all surveys for a country are expenditure
based or a combination of income and expenditure. Significance at 1% ***, 5% ** and 10% *. Robust standard errors in
parentheses.
28
Table 2: Income inequality as measured by Fum and Hodler
C
Log mining revenue/worker
Log oil revenue/worker
Log mining revenue/GDP
Log oil revenue/GDP
Income-based survey
Expenditure-based survey
Sub-Saharan Africa
Latin America and Caribbean
OECD
Ethnic polarization
Ethnic polarization x log
mining revenue/cap
Ethnic polarization x log oil
revenue/cap
Ethnic polarization x log
mining revenue/GDP
Ethnic polarization x log oil
revenue/GDP
Dependent variable: Average Fum and Hodler Gini Index, 1990 – 2004
(3)
(4)
(1)
(2)
37.14***
38.33***
38.97***
38.37***
(2.78)
(3.40)
(2.30)
(2.64)
0.78
-0.05
(0.44)
(0.84)
-0.43
-0.51
(0.29)
(0.62)
7.06
-15.41
(7.11)
(51.26)
-17.02***
-29.26
(6.20)
(21.99)
3.15
3.80
2.97
3.67
(2.81)
(2.40)
(2.59)
(2.40)
-0.21
-1.11
-0.34
-1.95
(1.87)
(1.94)
(1.71)
(1.96)
10.40***
9.19***
10.03***
9.21***
(2.29)
(2.29)
(2.28)
(2.46)
10.59***
9.00***
10.84***
8.97***
(3.01)
(2.67)
(2.77)
(2.58)
-11.22***
-11.45***
-11.19***
-12.81***
(3.17)
(2.87)
(3.41)
(3.16)
1.69
4.83
(4.51)
(3.67)
0.74
(1.30)
0.23
(1.05)
25.63
(78.33)
14.91
(32.69)
Adjusted R2
0.62
0.63
0.60
0.63
Observations
78
75
78
75
Notes: Income dummy = 1 if all surveys for a country are income based or if surveys are a combination of income and
expenditure. Expenditure dummy = 1 if all surveys for a country are expenditure based or a combination of income and
expenditure. Significance at 1% ***, 5% ** and 10% *. Robust standard errors in parentheses.
29
Table 3: Income inequality as measured by Galbraith and Kum
C
Log mining revenue/worker
Log oil revenue/worker
(1)
41.96***
(1.03)
-0.09
(0.18)
0.43***
(0.15)
Log mining revenue/GDP
Log oil revenue/GDP
Sub-Saharan Africa
Latin America and Caribbean
OECD
5.72***
(1.13)
3.11***
(1.12)
-7.55***
(1.04)
Ethnic polarization
Ethnic polarization x log
mining revenue/cap
Ethnic polarization x log oil
revenue/cap
Ethnic polarization x log
mining revenue/GDP
Ethnic polarization x log oil
revenue/GDP
Dependent variable: Average EHII Gini Index, 1980 – 2008
(3)
(4)
(2)
40.08***
41.67***
41.64***
(1.93)
(0.98)
(1.54)
0.26
(0.63)
0.45
(0.38)
3.14
-9.28
(2.67)
(26.10)
15.51***
12.16
(4.38)
(11.29)
4.81***
5.31***
3.99***
(1.18)
(1.17)
(1.28)
2.51**
3.49***
2.07*
(1.03)
(1.16)
(1.10)
-7.93***
-6.33***
-7.38***
(1.25)
(1.14)
(1.26)
4.72
3.74*
(3.24)
(2.06)
-0.16
(1.00)
-0.34
(0.66)
17.18
(40.03)
-6.73
(18.04)
Adjusted R2
0.41
0.55
0.45
0.55
Observations
141
113
135
110
Notes: Significance at 1% ***, 5% ** and 10% *. Robust standard errors in parentheses.
30
Table 4: Income inequality as measured by Chen and Ravallion
C
Log mining revenue/worker
Log oil revenue/worker
Log mining revenue/GDP
Log oil revenue/GDP
Income-based surveys
Expenditure-based surveys
Sub-Saharan Africa
Latin America and Caribbean
East Asia and the Pacific
Middle East and North Africa
Southern Asia
Dependent variable: Average Chen and
Ravallion Gini Index, 1980 – 2002
(2)
(1)
26.07***
27.47***
(2.80)
(2.80)
0.80*
(0.44)
-0.33
(0.36)
12.16***
(4.30)
-3.89
(6.17)
2.87
2.90
(2.34)
(2.32)
1.72
2.35
(2.29)
(1.93)
16.55***
15.76***
(2.97)
(2.75)
20.08***
19.95***
(1.95)
(1.87)
9.25***
8.44***
(2.75)
(2.82)
7.67***
7.61***
(2.68)
(2.73)
1.68
3.16
(2.36)
(2.02)
Adjusted R2
0.56
0.55
Observations
85
84
Notes: Income dummy = 1 if all surveys for a country are income based or if surveys
are a combination of income and expenditure. Expenditure dummy = 1 if all surveys
for a country are expenditure based or a combination of income and expenditure.
Significance at 1% ***, 5% ** and 10% *. Robust standard errors in parentheses.
31
Mining
Oil
OECD
Mining Count
Oil Count
Ln (Thiel)
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Non Mineral
-1.0000
70
-2.0000
60
50
-3.0000
40
-4.0000
30
-5.0000
-6.0000
10
-7.0000
0
Count
20
Figure 1: Average manufacturing wage inequality, mining economies, oil economies, non-mineral
economies, and OECD Economies, 1963-2008. Mining and oil economies are identified based on
mining and oil revenue per worker. Statistically significant differences in means at the 5% level
between the oil group or mining group and the non-mineral group are shown by square data markers.
32
Mining
Oil
OECD
Mining Count
Oil Count
Ln (Thiel)
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Non Mineral
-1.00
70
-2.00
60
50
-3.00
40
-4.00
30
-5.00
-6.00
10
-7.00
0
Count
20
Figure 2: Average manufacturing wage inequality, non-OECD mining economies, oil economies, and
non-mineral economies, and OECD Economies, 1963-2008. Mining and oil economies are identified
based on revenue per unit GDP. Statistically significant differences in means at the 5% level between
the oil group or mining group and the non-mineral group are shown by square data markers.
33
Oil
OECD
Oil Count
Mining Count
55.00
70
50.00
60
45.00
50
40.00
40
35.00
30
30.00
20
25.00
10
20.00
0
Count
Mining
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
EHII Gini
Non Mineral
Figure 3: Average EHII Gini Index, non-OECD mining economies, non-OECD oil economies, nonOECD non-mineral economies, and OECD Economies, 1963-2008. Mining and oil economies are
identified based on revenue per worker. Statistically significant differences in means at the 5% level
between the oil group or mining group and the non-mineral group are shown by square data markers.
34
Oil
OECD
Oil Count
Mining Count
55.00
70
50.00
60
45.00
50
40.00
40
35.00
30
30.00
20
25.00
10
20.00
0
Count
Mining
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
EHII Gini
Non Mineral
Figure 4: Average estimated Gini Index, non-OECD mining economies, non-OECD oil economies,
non-OECD non-mineral economies, and OECD Economies, 1963-2008. Mining and oil economies are
identified based on revenue per unit GDP. Statistically significant differences in means at the 5% level
between the oil group or mining group and the non-mineral group are shown by square data markers.
35
Endnotes
1
This is probably a good place to raise the inevitable concern about the endogeneity of mining and oil resources and
production. Given that resources are distributed randomly and unequally around the world and that income inequality
cannot influence this geological lottery, resources and production can only be endogenous to the extent that income
inequality somehow affects the exploration for resources and their conversion into proven reserves and production. I have
yet to see a compelling model in this regard, David and Wright’s (1997) examination of America’s rise to resource
abundance notwithstanding. The broadest geological measure of physical stocks of resources and the flows of minerals
from those stock are almost perfectly correlated (Stijns 2005), providing a strong indication that it is geology, and not
institutions, that causes production.
2
Aside from the usual remedies of redistributive taxation, education reform, and the development of institutions that
contain inequality, there is also special emphasis on corruption, fiscal transparency, state and community capacity building,
and development of agriculture and rural non-resource sectors (Davis and Vásquez Cordano 2013). Curiously, policy
prescriptions for mining economies are generally proposed in separate texts from those for oil economies (e.g., Humphreys
et al. 2007, Richards 2009).
3
In full disclosure, I have received research funding from the ICMM in the past and am currently a short-term consultant
for the World Bank. Neither institution is associated in any way with this research paper.
4
If resource production weakens institutions, income inequality may worsen for reasons independent of the direct impacts
of mining and oil production (Gylfason and Zoega 2006). Consistent with the new empirical work showing the “deep roots”
of economic development (Spolaore and Wacziarg 2013), Alexeev and Conrad (2009) find no evidence that mining and oil
wealth reduces institutional quality, and so effects via this channel are unlikely. In this same vein, Acemoglu and Robinson
(2012) do not implicate mining or oil extraction in their sweeping review of the political economy of failed states.
Economic volatility associated with mining and oil production has also been suggested to especially harm the poor and
increase income inequality. This proposal has not found support in the data (Goderis and Malone 2011).
5
That the inequality-inducing rents emanate at the time of the resource discovery in this model, rather than at the time of
production, is an important distinction when it comes to testing these theories empirically. The former requires
measurement of the value of reserves and resources discovered in a given year, while the latter involves measurement of the
annual rents from production, as often proxied by revenues.
6
Government spending on infrastructure is a major component of Gelb’s (1985) modeling of the effects of an oil windfall
on economic growth and welfare.
7
Even so, radical changes to mining and oil economy tax structures may well do little to change de facto income inequality
(Burgess and Stern 1993, Engel et al. 1999, Segal 2011). Moreover, mining rents are much lower than oil rents. Given this,
it would be surprising if the impact of any redistributional government programs is statistically identifiable in mining
economies.
8
In a similar analysis Gylfason and Zoega (2003) find a 10 point increase for Latin America.
9
This lack of data has been noted by Gelb and Associates (1988), Askari et al. (1997), and Ross (2007).
10
It is well known that an increasing Gini Index can provide an ambiguous measure of the change in income inequality.
Davis and Vásquez Cordano (2013) provide an illustrative example. Measuring income inequality is in general far from
exact (Deaton 2003).
11
I am unable to exactly replicate the analysis due to lack of original data. My replication recovers the data from the listed
sources, but by inspection it is not identical to the data Easterly used in his original analysis (2001, Table 1).
12
From footnote 5 above, their model actually requires measurement of the additions to resource wealth in a given year, not
the total resource stock in a given year.
13
Natural resource wealth includes pastureland, cropland, timber resources, nontimber forest resources, protected areas, and
subsoil assets. In a related paper, Carmignani (2013) tests for income inequality in resource-rich economies conditional on
institutional quality and ethnic fractionalization. In a sample of 80 developing countries that mainly exclude the extensive
oil producers he finds a statistically weak but positive residual correlation between both resource stocks and flows and
income inequality. The conditionality of the test makes this paper less useful given the posed research question.
14
I thank Roland Hodler for sharing his data and code with us. While I can replicate their results, I note that my WDI
downloads for GDP per capita in 1990, population in 1990 and trade as a percent of GDP are not the same as those in their
data set. I use their values here.
15
Fum and Hodler (2010) find the average difference between post-tax income and expenditure surveys from WIID to be
7.0.
16
Results finding unconditional inequality would motivate a search for the channels by which the inequality manifests. But
we must put the horse before the cart.
36
17
Fum and Hodler additionally control for Kuznets effects on income inequality via a squared income term. For a trenchant
critique of the very idea of Kuznets effects see Gallup (2012). As I noted above, Leamer et al. (1999) argue that the
development path of mining and oil economies is likely to be subject to a Kuznets effect while non-mineral economies will
see continuously declining income inequality as they develop. Since one does not want to control for the very effect one is
testing for, I omit the squared income term. Fum and Hodler also control for population. There is no a priori reason to
believe that population count would affect income inequality. Yet population is strongly significant in all of Fum and
Hodler’s regressions. My analysis of their regressions shows that population is proxying regional differences in inequality.
18
Segal (2011), who uses the World Bank data in a study of hypothetical resource rent distribution, complains about its
derivation and describes it as “indicative rather than authoritative” (p. 478). van der Ploeg and Poelhekke (2010) provide a
comprehensive criticism of the methodology by which the Bank computes subsoil wealth. The World Bank sample is also
highly skewed towards the richer economies of the world.
19
An unambiguous change in income inequality is defined as a shift in the entire Lorenz curve.
20
The index does not include precious metals or diamonds.
21
They use a Gini Index produced by Galbraith and Kum (2005), which is deterministically normalized to household pretax income to make time series inequality measures comparable. The deterministic normalization means that index will still
contain effects of the idiosyncratic redistribution policies of each economy, and so is an imprecise measure of wage
inequality. Goderis and Malone weight each price shock by the commodity export share in GDP in each country so that
resource exporting countries are more impacted by the price shock. Their Table 2 lists the commodity export shares that
they use for weights. Kuwait is shown to have a 0.0 commodity export share, making it more resource poor than Italy.
Singapore and Iceland are measured to be resource rich because of their gasoline (Singapore) and aluminum (Iceland)
exports. That manufactures like gasoline and aluminum are included in the same index as natural endowments like oil and
copper when identifying countries with “an exogenous gift of resource income” (p. 390), combined with the erroneous
weighting for Kuwait, leads me to treat their empirical results with caution.
22
This small impact is consistent with the analysis by Loayza et al. (2012) of regional inequality increases in and around
mining districts in Peru.
23
The revenue per GDP measure is similar to the series SNR that Sachs and Warner (1997) use as a measure of mining and
oil intensity, though they create the variable for 1971 and aggregate mining and oil products.
24
It would be more desirable to describe mining and oil intensity via cumulated revenues from, say, 1970 to 1990 so as to
reflect the full impact of rent accumulation over time. There are two impediments to producing such a series. First, the US
Bureau of Mines data is spotty, and many countries would have to be dropped for lack of data in certain years. Second,
there is a lack of consistency in reporting production across periods (e.g., some years report total production of ores and
concentrates, and other years report metal in concentrate). I have been able to determine that in each country the per worker
revenues from these 25 mining and oil products in 1971 are highly correlated with the 1990 values: the Spearman Rank
correlation across 147 countries is 0.84 and the logged values have a correlation of 0.85. In that sense, my 1990 figure for
each country is likely a good proxy for the intensity of rents over a number of decades.
25
Botswana, arguably the wealthiest mining economy in the world, with mining revenues per capita equivalent to the oil
revenues per capita of the Middle Eastern countries, has managed nothing more than graduation from least developed
country status, in 1994. Saudi Arabia, on the other hand, had so much value-added from their production that they enjoyed
PPP income per capita levels that were twice those of the United Kingdom in the early 1980s (Davis 2009).
26
UTIP-UNIDO updated 2013b.xls.
27
The Thiel Index measures wage inequality across UNIDO industrial classification codes (e.g., average wages in
extracting petroleum and natural gas), not within groups (differences in wages within petroleum and natural gas). Because it
is missing the within group inequality it is a lower bound on wage inequality.
28
EHII-UPDATED-10-30-2013 using 2004 DATA.xlsx
29
Galbraith and Kum (2005) find that income-expenditure differences in survey estimates are highly correlated with
regional differences in survey types, and so it is not unusual for the coefficients on survey type to be insignificant in these
and the remaining regressions.
30
In assessing the impact of revenues on Gini Indexes in this and the following tables, the sample standard deviation on log
mining revenues per capita and log oil revenues per capita is between 2.5 and 3, while the sample standard deviation on log
mining revenue per unit GDP and log oil revenue per unit GDP is about 0.1.
31
The results are similar when using a 1990 – 2008 average, though the sample size decreases.
32
Frederick Solt, 2013, "The Standardized World Income Inequality Database", http://hdl.handle.net/1902.1/11992
Frederick Solt [Distributor] V4 [September].
37
33
Selected data was also made available at http://wwwwds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2000/05/25/000094946_00050605490166/Rendered/PD
F/multi0page.pdf.
34
They note that all other compilations of distributional data use secondary survey data. Compilations from secondary data
have been criticized by Atkinson and Brandolini (2001) as potentially misleading.
38