“Natural Resources, Development and Democracy: The Quest for Mechanisms”

Erik Wibbels Department of Political Science

University of Washington

Ellis Goldberg Department of Political Science

University of Washington

Abstract

The correlation between natural resource abundance, authoritarianism and poor developmental

outcomes—typically referred to as the resource curse—is one of the few findings in comparative politics and political economy on which there is something approaching a scholarly consensus. That the case, there is widespread disagreement as to the causal mechanisms behind the correlation. Indeed, we count 11 distinct causal stories linking resource abundance to political-economic outcomes. This dissensus persists in large part due to the empirical difficulty of testing alternative hypotheses cross-nationally and limits our understanding of the causal processes at work. We address this difficulty with two innovations: first, we integrate insights from the traditional “enclave economy” argument with work on economic geography to develop a new hypothesis to account for the negative relationship between resource dependence and poor developmental outcomes; second, we test our hypothesis against the 11 hypotheses culled from the literature in a new, better empirical context. We find support for our hypothesis and several others while discarding most of the conventional wisdom. In doing so, we serve to sharpen the debate on the factors underpinning the resource curse.

"A place in the throes of an energy boom isn't so different from a person in the throes of addiction: there's the denial that things are out of control; there's the sleeplessness and the moral carelessness, and the fact that you're doing something that you know isn't good for you but you just can't stop."1 Over the course of three decades, researchers have gathered mounting evidence that natural

resource abundance contributes to all manner of dysfunctional outcomes, ranging from poor and uneven

economic development (Sachs and Warner 1995) to authoritarianism (Ross 2001) to civil war (Collier

and Hoeffler 2001). Collectively, these findings have become known as the resource curse. Some

common problems in the literature aside (Ross 1999), these findings represent some of the most

consensual in comparative politics and political economy. They also represent some of the most

important. The empirical effect of natural resources on some of the most central dependent variables in

the social sciences aside, the findings have considerable theoretical importance. The association between

endowments in natural resources and poor growth, for instance, represents a serious challenge to the

most fundamental theory of international trade, namely comparative advantage.

Given the consistency and importance of the findings, it comes as something of a surprise that

there is little agreement as to the precise reason or reasons that resource abundance contributes to poor

outcomes. Indeed, below we identify 11 separate hypotheses linking resource wealth to authoritarianism

and poor developmental outcomes. Political scientists, for instance, have suggested that mineral

abundance contributes to authoritarianism via its detrimental impact on everything from the

development of a robust civil society to public institutions to corruption to repression. Meanwhile,

researchers have chalked up the negative effects on economic development to exchange rate effects,

poor institutions, economic volatility, poor fiscal management, and the problems associated with state

ownership of economic assets. These hypotheses are not necessarily mutually exclusive, and it might be

that many (or even all) of them contribute to the resource curse.

That the case, most of the hypotheses in the resource curse literature suffer from one common 1 Alexandra Fuller “Boomtown Blues: How Natural Gas Changed the Way of Life in Sublette County” The New Yorker Feb. 5, 2007: 38-44. Page 44.

flaw—they imply that mineral wealth inevitable leads to poor outcomes. Economic theory and economic

history suggest differently. Trade theory provides no basis for suspecting a consistent resource curse.

The impact of oil or mineral booms should be positive or negative depending on the sectoral makeup of

an economy.2 Indeed, an entire class of open economy models suggests that a booming sector can

generate a level of domestic demand sufficient to generate spillovers to other sectors and ultimately

increasing returns to a wide range of economic activity (Murphy, Shleifer, and Vishny 1989; Krugman

1991; Corden 1984). Empirically, there are simply too many cases for which natural resource wealth

provided the foundations for protracted economic booms and/or were consistent for democracy. Indeed,

mineral wealth seems to have done nothing to preclude the emergence and sustainability of democracy

in Norway or Botswana, and Chile’s recent decades of economic growth and democratic deepening have

occurred at the same time as a boom in natural resource based exports. Likewise, economic historians

emphasize the developmental foundations of easily extracted coal in early 19th century Great Britain

(Pomeranz 2000), the comparative per capita resource wealth of the U.S. and Australia in the latter

decades of the 19th century (Wright 1990), and the positive long-term implications of the California

gold rush (McLean 2005).

We address this shortcoming by developing a new hypothesis linking natural resources to

developmental outcomes via economic geography. Rethinking the traditional emphasis in the literature

on the propensity for natural resources to be produced in enclaves (Sachs and Warner 1995; Hirschman

1958) from the point of view of research on economic geography (Krugman 1991), we argue that: first,

geography (broadly understood to include local endowments) conditions the costs of researching and

developing natural resources with consequences for the initial level of local demand at the time of the

resource boom; and second, geographically-determined access to external markets conditions the

2 A resource boom has two key effects—the reallocation of resources and increased incomes. Import-competing sectors benefit from the income effect as demand increases but are hurt by the higher wages associated with the reallocation of resources. For these sectors, the net impact is ambiguous. Non-booming traditional exports will be hurt by the rise in wages brought about by the boom sector and benefit little from the income effect. Nontradables, on the other hand, can adjust to higher wages with increased prices and will benefit from increased demand via the income effect of the boom. In this sectoral approach, the overall impact of a resource boom is positive when the economy is weighted to non-tradables and negative to the degree that it relies on non-boom exports.

likelihood that natural resource wealth will generate externalities and increasing returns to other kinds of

economy activity (and thereby ‘development’). Where local demand is low and transportation is costly,

natural resource production is likely to remain isolated in an enclave. As local demand increases and

transportation costs fall, the prospects for resource wealth to spillover into other forms of production and

foster development improve. Such mechanisms, we suspect, offer the potential to account for apparent

outliers—the Norways and Californias of the world.

Our own hypothesis added to the mix, the proliferation of hypotheses represents a serious

shortcoming in our understanding of resource curse and certainly precludes developing coherent policy

advice for addressing natural resources’ supposed detrimental impact on societies. There are several

reasons for this divergence of opinion. The quantitative research has been plagued by a dearth of data

that might allow for detailed hypothesis testing on the mechanisms linking resource wealth to political

and economic outcomes. There are, for instance, no (or very scarce) reliable measures of the richness of

civil society, levels of corruption, the quality of institutions, and even real exchange rates across

countries—all channels that researchers have identified as causing the resource curse. Qualitative

research on the subject has produced myriad studies in diverse empirical contexts from diverse

theoretical perspectives. While some such research may simply reflect a disregard for hypothesis testing,

most has suffered from the common small-n challenge of establishing external validity (Ross 1999). The

result of these shortcomings in the comparative literature is that hypotheses have proliferated in the

absence of any means to falsify them.

We address this problem by turning to a different empirical setting that is considerably more

data rich: the U.S. states. The U.S. states offer a number of advantages over the cross-national research

on the resource curse including a far richer data environment, a relatively uniform set of rules governing

data collection across the states, cultural differences between governments substantially less than in

cross-national settings, and a longer time-series dating back as far as 1929 (and therefore containing

several international business cycles). These advantages allow us to do two things: first, establish

whether or not the resource curse exists in a sample of cases different from that which generated the

hypothesis; and second, test the mechanisms underpinning the findings in a much more systematic

fashion than is possible in the cross-national setting.

We proceed in four parts. In the next section, we overview the resource curse literature with

specific attention to the numerous competing hypotheses linking resource dependence to economic and

political outcomes. Thereafter, we outline our chief theoretical innovation, namely a hypothesis linking

economic geography to the impact of natural resources on development. In the fourth section, we

provide the first systematic test of the 11 competing hypotheses that we extract from the resource curse

literature. Finally, in the concluding section, we consider the theoretical implications of our findings for

the literatures on the resource curse and trade more generally and the implications for future, cross-

national work aimed at uncovering the causal relationship between resource abundance, development

and democracy.

II: The Resource Curse and the Proliferation of Hypotheses

Initially articulated in the economic realm as part of the structuralist rethinking of classical

economics (Prebisch 1950) and later extended to the political realm in the context of the Middle East

(cites), the resource curse literature argues that reliance on natural resources contributes to a number of

dysfunctional outcomes. The conventional wisdom is composed of two general propositions—that

resource abundant nations grow more slowly than resource poor ones and that politics in resource rich

societies are authoritarian. Over the last decade, the amount of research lending credence to various

aspects of the resource curse is impressive (Ross 2001; Jensen and Wantchekon 2004; Chaudry 1997;

Isham et al 2003; Leite and Weidmann 1999; Karl 1997; Smith 2004; Sala-I-Martin & Subramanian

2003; Sachs and Warner 1999; Mehlum, Moene, and Torvik 2006; Morrison 2006).

Widespread agreement on the finding aside, there is very little agreement as to the mechanisms

or causal processes behind the findings. Indeed, little has changed since Michael Ross wrote nearly 10

years ago that “There is now strong evidence that states with abundant resource wealth perform less well

than their resource-poor counterparts, but there is little agreement on why this occurs.”3 This is

unfortunate, because figuring out why there seems to be a resource curse is of crucial importance for a

number of broad literatures. Indeed, the importance of the resource course finding transcends a narrow

concern with mineral wealth itself. For instance, the dominant theory of trade is comparative

advantage—the notion that countries benefit by producing goods employing locally abundant factors of

production and engaging in trade. That the production of some locally abundant factors of production

might detract from growth and development is an important claim, particularly in light of the emphasis

on the advantages of trade openness in much of the research on development in recent decades.4 As

Sachs and Warner note “one of the surprising features of modern economic growth is that economies

abundant in natural resources have tended to grow slower than economies without substantial natural

resources.”5 Likewise, the conventional wisdom holds that higher per capita incomes are the single most

important determinant of the sustainability of democracy. That oil-rich countries consistently appear as

outliers in such studies underscores the importance of understanding the resource curse for broader

theories of regime type. In both literatures, the apparent exceptionalism of resource abundant countries

has produced sharp debates around the causes (and existence) of the resource curse.

Despite the importance of the finding, increased research on the political economy of natural

resource abundance over the last decade has only served to proliferate the number of causal hypotheses.

Detailed, though important, concerns with methodology aside (see Stijns 2005), the empirical challenges

associated with testing the competing hypotheses in cross-national settings are manifold. First, cross-

national economic data is available for a relatively short time period (approximately 30 years) that

provides limited traction for studying the impact of resource abundance on long-run dynamics

associated with democratization and development. Second, the period includes in many ways the most

anomalous era in the history of natural resource markets—one in which an international cartel was able

3 Ross 1999: 297. 4 See, for instance, Williamson 1993, Edwards 1998, and Frankel and Romer 1999. See Rodríguez and Rodrik 2001 for a skeptical view. 5 Sachs and Warner 1995: 1.

to limit the supply of oil and radically increase prices. Third, even where cross-national data is available,

the more detailed data necessary to test competing explanations is not. Fourth and finally, there are

considerable cross-national differences in everything from data collection standards to legal practices

that can be very difficult to measure and thereby might limit the generalizability of the findings.

For all of these good reasons, the resource curse literature is stuck in neutral—with an

apparently strong finding and limited capacity to explain it. In short, while it might be the case that

“countries become what they produce” (Rodrik and Hausman 2006: 2), we do not know much about

why that would be the case. While hypotheses mushroom and various kinds of empirical support mount,

the challenge remains of actually testing competing hypotheses against each other. In this section, we

briefly overview the various hypotheses as a precursor to the first systematic test of the contending

arguments in the resource curse literature.6

II.2: Politics

We begin with the impact of natural resources on political competition. One widespread view as

to the mechanisms governing the relationship between resource wealth and political competition

emphasizes the rentier effect (Crystal 1990). In the rentier state literature, easy revenues accrue to the

state from natural resource production. Because production is concentrated at a small number of sites,

governments have an easy time controlling and extracting rents from such production. Thanks to

resource rents, public officials have little need to develop taxing capacity. Indeed, these “cheap”

revenues allow officials to buy public support and build patronage networks. Lightly taxed, the citizenry

only weakly demands representation in government, thereby stunting the development of democratic

institutions and attenuating political accountability. There are two steps in the causal chain: first, from

natural resources to “easy” fiscal expansion and patronage networks; and second, from patronage to the

capacity to survive under otherwise adverse circumstance—to insulate incumbents from the political

business cycle.

6 For other good recent overviews of the diverse causal accounts that emerge from the resource curse literature, see Ross (1999), Isham et al (2003), and Sarraf and Jiwanji (2001)

Building on the classic work of Moore (1966), a related argument links natural resource

dependence to delayed processes of modernization and the failure to develop the social constituencies

and cultural practices that provide the foundation for robust, competitive politics. In some of these

accounts, states reliant on natural resources actively resist the process of industrialization as a means to

head off the creation of alternative sources of social and economic power, such as urban labor, middle

classes and industrialists. In the absence of such actors, the government faces limited demands for

increased literacy, union organization, education spending, political pluralism, and the like. The net

result is that many resource rich societies lack the social capital that is a precursor to democracy (Bellin

1994).

Yet a third hypothesis links natural resource abundance to political contestation via its impact on

asset inequality and the mobility of capital. In several recent accounts (Boix 2003; Acemoglu and

Robinson 2006), democratization occurs when the redistributive demands of labor are muted and elites

can, therefore, foresee relatively few threats to their economic power under a democratic order.

According to Boix, two factors impact the intensity of redistributive demands: first, the level of

economic inequality; and second, the capacity of capital to exit. Consistent with the long-standing

median voter tradition (Meltzer and Richard 1981), economic inequality serves to exacerbate

redistributive demands, thereby encouraging elites to maintain constraints on political pluralism. Capital

mobility has the opposite effect as it reduces the redistributive demands of labor which recognizes that

“excessive” redistributive demands will cause capital to flee. The result is a political elite more

predisposed to democratize. According to such an account, natural resource wealth reduces the

likelihood of democratic politics by both increasing the level of interpersonal inequality (Leamer et al.

1999) and eliminating capital’s exit threat since natural resources are the quintessential fixed asset (Boix

2003). 7

One final hypothesis bears attention, namely that linking resource wealth to poor institutions and 7 In Leamer et al’s (1999) account, natural resources serve to exacerbate inequality by pulling physical capital into sectors that require little human capital. The result is a workforce heavily oriented toward low skill, low wage activity and education systems poorly adapted to the accumulation of equality-inducing human capital.

thereby to undemocratic politics. The typical argument suggests that natural resource wealth encourages

society to engage in rent seeking which militates against the development of institutions and discourages

the state from developing the institutional capacity to gather systematic information about society. Both

state leaders and large portions of civil society come to share a preference for discretion in the

production and distribution of private goods by the state. The result is a tendency for incumbents to view

the state as a tool for rewarding supporters and heading off opponents, while government opponents see

little opportunity for altering state behavior through institutionalized politics. In such environments,

incumbents accrue tremendous benefits from weakly institutionalized constraints and are able to

marginalize any potential opposition. Different researchers emphasize the negative impact of natural

resources on different institutions ranging from fiscal (Jones-Luoung and Weinthal 2006; Karl 1997) to

regulatory institutions (Ross 2001). In these accounts, poor institutions increase the likelihood of rent-

seeking, corruption and repression and thereby delay the emergence of robust democratic competition.

II.3: Growth and Development

Previous research has discussed a plethora of plausible mechanisms linking natural resource

dependence to poor economic growth and developmental outcomes. One of the most prominent of these

arguments links natural resource booms to exchange rate effects commonly referred to as “Dutch

Disease”. The most common version of the argument suggests that natural resource booms lead to

appreciation of the real exchange rate and a resulting decline in the competitiveness of traditional

exports and import-competing sectors (Corden 1984; Auty 1998; Usui 1997; Mikesell 1997; Sachs and

Warner 1995). Related accounts emphasize the role of booming mineral sectors in drawing labor and

capital away from traditional sectors of the economy. Particularly where traditional sectors are

associated with manufacturing, the resulting process of deindustrialization is likely to contribute to poor

developmental outcomes over the long term, and politically inspired attempts to shield negatively

affected producers via trade protection and subsidies often serve to exacerbate the problem (Auty 1994;

Sachs and Warner 1995; Matsuyama 1992). Although there is some evidence of real exchange rate

appreciations in the presence of resource windfalls, opinion is divided as to the long-term developmental

costs of reliance on natural resources. While proponents of Dutch disease style arguments typically

assume the superior developmental characteristics of reliance on manufacturing and the externalities

related to it (Sachs and Warner 1995; Matsuyama 1992), critics cite the lack of evidence that reliance on

any particular type of endowment is superior to any other (Stijns 2001; de Feranti et al. 2001; Wright

2001) and emphasize the ease with which governments can sterilize the impact of mineral-inspired

inflows on the real exchange rate. The overall cross-national evidence on this count is mixed.8

A second set of arguments suggest that poor developmental outcomes are a result of the terms of

trade associated with commodity exports. The oldest version of this argument suggests that the terms of

trade for natural resources will decline through time, resulting in relatively poorer economic

performance in commodity-reliant states (Prebisch 1950; Singer 1959). Recent research on commodity

markets sheds considerable doubt on these claims (Cashin and McDermott 2002), but a related argument

persists, namely that it is the volatility of international commodity markets that contributes to

unpredictable export earnings, weak growth and poor development (Nurske 1958; Levin 1960; Isham et

al. 2003; Auty 1998; Mikesell 1997). Under conditions where commodity exports crowd out non-

resource tradables, these effects are particularly pronounced (Hausmann and Rigobon 2003). Volatility,

it is argued, produces an unstable environment for investment, with the result being poor long term

economic performance. These arguments go hand in hand with evidence that more volatile economies

grow more slowly (Ramey and Ramey 1995) and that the terms of trade are an important determinant of

economic growth (Mendoza 1997). That the case, the international evidence on trends in the terms of

trade for commodities is mixed, with some arguing there has been a long-term decline (Powell 1991;

Bleaney and Greenway 1993) while others paint a more benign picture (Cuddington 1992; Cashin and

McDermott 2002).

8 Fardmanash (1990) finds evidence that while resource booms hurt agriculture, they do not hurt manufacturing. Sala-i-Martin and Subramanian (2003), using Easterly and Levine’s overvaluation variable, find no evidence of a dutch disease effect. Likewise, in an overview of several papers, McMahon (1997) finds no support for an exchange rate effect. Auty and Evia (2001), on the other hand, suggest that the Bolivian economy showed signs of Dutch disease as evidenced by limited diversification, an overly small agriculture and a protected, uncompetitive manufacturing sector.

A third hypothesis suggests that resource abundant nations grow slowly because they

systematically overspend and invest in inefficient ways that non-mineral rich nations do not (McMahon

1997). Three interrelated factors might contribute to this dynamic. First, government spending

accelerates rapidly during boom periods, but because of the political difficulty of retrenching spending

in subsequent bust periods, politicians respond by borrowing. Though this disjuncture between fiscal

policy during up- and downturns in the business cycle is a common finding in the public finance

literature, McMahon (1997) suggests the scale of the effect is distinctive in mineral states and refers to it

as the “irreversibility of government expenditure.” Second, the negative long-term effects of over-

indebtedness are likely to be exacerbated by the inefficiency of investments that increase very rapidly in

boom time, particularly when pursued via government spending (Sachs and Rodriguez 1999). Such

spending, for instance, often goes to inefficient, but politically attractive, capital projects and the

expansion of the public bureaucracy. Third and finally, because natural resources represent obvious and

easily measureable collateral, banks are willing to lend more to such countries than to others

(Cuddington 1989).9 When easy access to credit is combined with boom-driven spending cycles and

inefficient government spending, the result is excessive indebtedness and the misallocation of capital

which jointly lower economic performance.

Yet if exchange rates, economic volatility and overspending are prominent lines of

argumentation, probably the most prevalent hypothesis in contemporary research linking resource

dependence to poor growth is that which emphasizes the quality of institutions (Robinson, Torvik and

Verdier 2006; Sala-i-Martin and Subramanian 2003). Consistent with a long line of work suggesting that

good institutions represent a key first-order ingredient of long-term economic development (North 1981;

Olson 1982; Acemoglu and Robinson 2006), such accounts emphasize the role of natural resources in

limiting the incentives of states to develop the institutional capacity to gather information from society

(through tax collection or regulatory oversight) that might contribute to better public policy. Nor do such

9 Auty and Evia (2001), for instance, provide evidence from Bolivia which used the government revenues accrued as result of a commodity boom in the 1970s as collateral for unsustainable foreign borrowing.

states develop institutional mechanisms for members of society to provide systematic feedback into the

policymaking process. A recent variant of this argument suggests that state ownership of natural

resources militates against the development of institutions since the state can rely on resource rents

rather than developing extractive capacity (Weinthal and Jones-Luong 2002). In these accounts, poor

institutions are endogenous to resource wealth. In yet other accounts, the quality of institutions are

considered exogenous, and it is institutional quality that mediates the relationship between mineral

wealth and growth (Mehlum, Moene, and Torvik 2006). When institutions are “good”, resource booms

contribute to income growth. When combined with large rents from natural resource production,

however, poor institutions for gathering, receiving and processing information foster rent-seeking (Leite

and Weidmann 1999; Tornell and Lane 1999), corruption (Leite and Weidman 1999), weak rule of law,

low state investments in bureaucratic capacity (Sala-i-Martin and Subramanian 2003), and even civil

war (Collier and Hoeffler 2000)—all of which are associated with poor economic growth. In short, in

the absence of institutional constraints, interests in society compete to extract from a fundamentally

distributive state rather than focusing on market competition (Bates 2005).

Finally, a longstanding argument suggests that natural resource exploitation results in a sector

poorly linked to the rest of the economy resulting in an “enclave” economy (Hirschman 1958; Seers

1964; Baldwin 1966). Because such sectors are highly capital intensive, employ relatively few people,

rely on imported inputs, and are oriented overwhelmingly toward export markets they fail to foster

upstream and downstream investments that might promote a broad-based economic boom. The result is

an economy lacking in diversification and profoundly dependent on the whims of the natural resource

sector.

All of these hypotheses suffer from one key problem. As formulated, they suggest that natural

resource abundance always and inevitably leads to bad outcomes. This is problematic because there are

many cases both historic and contemporary that do not fit easily within the resource curse paradigm.

Historically, natural resource endowments play a central role in the developmental successes of the U.S.,

Canada, Australia and England. In none of these cases, moreover, did natural resources block or delay in

any obvious way the development of competitive, democratic politics. Likewise, the contemporary

experiences of countries ranging from Norway to Botswana to Chile show little evidence of suffering

from a resource curse. The challenge, therefore, is to account both for cases in which natural resources

detract from growth prospects and others in which resource wealth serves as a catalyst for broad-based

development.

III: Geography, Natural Resources and Development

In response to this challenge, we develop a new hypothesis to account for the apparently

divergent impact of natural resources on growth across the world. Our hypothesis builds on traditional

arguments linking natural resource extraction with undiversified enclave economies. Traditional

discussions of enclave economies have emphasize that natural resource extraction is capital intensive,

involves a small, specialized workforce, and often relies on imported inputs (Hirschman 1958). The

result is weak linkages with the rest of the economy. As with most of the hypotheses outlined above,

however, the enclave economy provides no foundation for explaining the many cases in which natural

resource wealth served as a catalyst for broader economic development. Thus, while traditional enclave

accounts bear strongly on the West Virginian and Bolivian economies, they fail to explain the role of

natural resource wealth in fostering broad-based development in places like California and Australia.

With specific reference to our empirical cases, we must ask: Why does the coal boom of West Virginia

look so different from the gold boom of California from the vantage point of 2007?

Though all of the hypotheses outlined above provide logical accounts that might contribute to

the negative relationship between natural resources and development, we believe they do not account for

one of the most powerful factors mediating the impact of resource abundance, namely geography.

Rethinking the traditional emphasis in the literature on the propensity for natural resources to be

produced in enclaves (Sachs and Warner 1995; Hirschman 1958) from the point of view of research on

economic geography (Krugman 1991), we argue that topography has two potential implications: first,

geography (broadly understood to include local endowments) conditions the costs of researching and

developing natural resources with consequences for the initial level of local demand at the time of the

resource boom; and second, geographically-determined access to external markets conditions the

likelihood that natural resource wealth will generate externalities and increasing returns to other kinds of

economy activity (and thereby ‘development’). Where local demand is low and transportation is costly,

natural resource production is likely to remain isolated in an enclave. As local demand increases and

transportation costs fall, the prospects for resource wealth to spillover into other forms of production and

foster development improve. Such mechanisms, we suspect, offer the potential to account for apparent

outliers—the Norways and Californias of the world.

Consistent with the insights of Krugman (1991), we expect that natural resource extraction can,

under certain conditions, generate positive externalities for other forms of production, thereby

generating increasing returns to a rich array of economic activity, urbanization, and development. In

explaining when this is likely to happen, Krugman emphasizes two key factors: the size of the local

market and transportation costs. As local market size increases, incentives mount for other producers to

locate nearby. Transportation costs work in a parallel manner. As economies of scale mount, the

incentives to produce in any given location increase as its transportation networks improve—only thus

will large initial investments be recouped by serving a broader market through trade.

How does this discussion bear on the relationship between resource wealth and development?

Natural resource deposits are randomly located around the world. This means both that the sites and the

sizes are normally distributed—a lot of the resource will be in a large number of very small sites and a

lot will be in a small number of very large sites. Search and development, however, is determined by

cost, so the cheapest (easiest) deposits will be located first. These will usually be deposits near existing

use, i.e. where local demand is quite high. Only over time (as more geological and technological

information is gained and transportation technology also improves) will more distant sites be located and

developed. These are in "harsher environments" on average because they are not where people preferred

to live at the earlier stages of search and development. Because populations will be smaller and less

dense, local demand will be lower. The returns to natural resources will ensure extractive investments,

but the paucity of local demand will militate against increasing returns to other economic activity. Such

an economy will be linked via trade to external markets and become an enclave.10

The geography of transportation costs functions similarly. Broad-based investments and

economic concentration (another way of talking about development) will only occur if the costs of

serving a wider market are relatively low. Thus, while a large resource boom may generate some initial

level of demand, whether that demand is served by investments in local production and external

economies resulting from other firms’ decisions will depend in large part on the costs of transporting

other products to wider markets. Again, where the costs of transporting ancillary products to broader

markets are high, investments will likely be concentrated only in the high return resource sector—the

value to weight ratio of other products will simply be too low to warrant producing them for outside

markets. In contrast, if transportation costs are low, the local demand and capital generated by a resource

boom are more likely to generate spillovers for other sectors of the economy.

This is precisely the story told by McLean (2005) of California, where the gold rush generated

population inflow, high wages and considerable local demand, but also where propitious geography

combined with mineral capital to finance capital intensive, mechanized, large-scale investments in wheat

and other agricultural production for outside markets shipped via an emerging San Francisco. The

growth of San Francisco strengthened local demand and stimulated early industries, generating

increasing returns to a broad swath of economic activity. Thus, the gold boom rippled through the

agricultural, service, nascent manufacturing, construction, and transport sectors. Los Angeles emerges as

a manufacturing center as part of this process.

IV: Data, Methods and Results

A decade ago, Chaudhry noted that “theories of the rentier state far outstrip detailed empirical

10 If we think of development in terms of GDP/population, harsher environments are thus likely to reduce both the numerator and the denominator. This might help explain the apparently strange finding in the comparative literature, whereby resource endowments are associated with weak growth but high per capita incomes. In the presence of a large resource boom, the depressing effect of “bad” geography on the denominator of GDP/population will generate high per capita incomes. At the same time, the absence of strong local demand and transportation networks (themselves also a function of geography) is likely to facilitate weak growth.

analysis of actual cases.”11 In this section we address that problem by bringing a new dataset to bear on

the resource curse literature. In the analysis, we focus on the relationship between natural resource

dependence, economic development, and the competitiveness of electoral politics in the U.S states from

1929 through 2002 (see Appendix for data sources). A focus on the U.S. states has a number of

advantages over the traditional approaches taken in the literature. First and most important, it allows us

to analyze a much longer time-series of data than any previous study. As noted above, existing cross-

national research provides little leverage on many national cases that were authoritarian and/or poor

before and after the oil-induced swelling of state coffers. Second, the U.S. states evince considerable

diversity in their resource abundance, levels of development, and experiences with electoral

democracy.12 Third, the U.S. states provide considerable variation with regards to a number of

alternative explanations for political and developmental outcomes. For example, the colonizing nation,

factor endowments, and transportation networks vary considerably across the states. Finally, inherent to

varying degrees in statistical comparative politics work is a considerable amount of unmeasured cross-

national variation that is consumed by either the error term or country dummies—this is what

researchers either do not know, do not understand, or cannot measure but that has a bearing on

explaining outcomes across nations. By analyzing states within a federation, we control for legal

practices, institutions of government, party systems, cultural differences, and data collection standards

that might impact findings but that are often un- or poorly-measured in cross-national work.

In the following pages, we pursue two lines of analysis: first, we provide evidence of both

political and economic resource curses in the U.S. states; second, we use detailed state-wise data to

examine the four hypotheses linking resource wealth to uncompetitive politics and similar data to test

the causal mechanisms commonly posited to impact economic growth as well as our own geographic

hypothesis. In the hypothesis testing sections, we first assess whether or not there is any relationship

between resource abundance and the mechanism under investigation (institutional quality, for instance) 11 1997: 187. 12 Mean resource dependence across the U.S. states is very similar to that across countries around the world and shows a higher standard deviation.

and then utilize either simultaneous equations or two stage least squares to assess if natural resources

work through the hypothesized mechanisms to impact political and economic outcomes.

IV2: The Resource Curse in the U.S.—SEMINAR PARTICIPANTS CAN SKIP THE NEXT THREE

PAGES IF THEY’VE READ PAPER 1

Here we present initial evidence that there is a resource curse in the U.S. states. The mere

existence of such a curse in an empirical setting entirely different from previous research provides

considerable support to the core finding in the comparative politics and political economy literatures.

We estimate two sets of models, one designed to assess the prevalence of an economic resource curse

and the other focused on the political aspect of the resource curse literature. Where time-series data is

available and appropriate, we estimate random effects, cross-sectional time-series models with state

fixed effects, a lagged dependent variable and define the errors as clustering on the cross-sections. In the

event only a single data point is appropriate for hypothesis testing or available for each state, the report

results for simple OLS models with robust standard errors. The results are robust to alternative

specifications.

Turning first to growth and development, Table 1 reports the results for three different

dependent variables: logged per capita income in 2002 (Model 1), the ten year average of log annual

differences of state per capita income (Model 2), and annual percent change in state per capita income

(Model 3). 13 Per capita income in 2002 is a more direct assessment of long-term development while the

latter two assess short- and medium-term economic growth.

The key independent variable, natural resource dependence, is measured as annual oil and coal

production as a share of state income as our measure of resource dependence.14 This approach mirrors

the typical approach in the resource curse literature and facilitates comparisons with existing findings.

13 There is some debate as to the appropriate measure of growth. See, for instance, Chatterjee and Shukayev’s (2006) critique of Ramey and Ramey’s (1995) use of average log differences, the standard in the growth literature. They recommend using annual percent change. 14 Focusing on either oil or coal separately has little impact on the results which are not very sensitive to the operationalization of the dependent variable.

Nevertheless, some have criticized the use of GDP in the denominator since a high resource/GDP ratio

could be the result of a high numerator or a low denominator. An alternative measure, resource

production per capita, suffers from its own problems.15 In any case, our results are robust when using

this alternative measure of resource dependence. The measure of resource dependence in Model 1 is the

average for the entire period, while in Models 2 and 3, resource dependence is measured as the ten year

average and annual lag, respectively.

We control for factors commonly emphasized in each body of research. All models control for

lagged wealth following Barro’s (1989) evidence and theorization of a return to the mean in growth

rates.16 Model 1 also controls for factor endowments, access to external markets, and colonial heritage.

Factor endowments have important implications for development. In the context of the U.S. states,

Engerman and Sokoloff (2000) argue that the key factoral determinant of long term growth trajectories

is the degree to which geographic and climatic conditions created the foundations for either plantation or

smallholder agriculture. Plantation agriculture led to slavery, extractive property rights institutions,

exclusionary political institutions, and weak human capital development—all of which contributed to

poor long-term development. Smallholder agriculture in the northeast did the opposite. Thus, we

introduce a measure of the percentage of the state population that was enslaved in 1860.17 A second

factor oft-associated with development is access to external markets (Hausmann 2001; Acemoglu,

Johnson and Robinson 2005). To control for market access, we include a dummy variable for states that

have access to rivers, lakes, or an ocean upon which to transport goods to and from foreign markets. 18

Finally, a prominent line of work suggests that colonial origins have implications for long-run

15 Most importantly, if resource abundance affects growth and growth has implications for birth rates (Przeworski et al 2000), the numerator and the denominator of a resource per capita measure are not independent. 16 In Model 1 this is measured as the initial per capita income in 1929, though for Hawaii and Alaska it is per capita income in the year they achieve statehood. In model 2, income is measured as the initial level at the beginning of the 10 year period. In model 3, it is the one year lag of per capita income. 17 Data from Mitchener and Mclean (2003). Note that this measure significantly improves on the atheoretical standard practice of including a dummy for ‘southern states’ in statistical work on the U.S. 18 Data from Mitchener and Mclean (2003).

developmental trajectories (North 1979). The 50 U.S. states had eight different colonial experiences.19

The most common theme running through the literature is the negative implications of Spanish

colonialism. As such, we create a dummy variable taking on a value of 1 for any state in which the

Spanish were not involved. In the growth equations, Models 2 and 3 control for each state’s capital

stock, human capital endowment and government spending consistent with standard models in the cross-

national growth literature (see Barro 1997).20

The findings are consistent—natural resource dependence has a negative impact on growth and

development. Models 2 and 3 increase our confidence in the findings by spotlighting growth. Focusing

on the easier to interpret coefficient in Model 3, the results suggest that a 10 percent increase in natural

resource dependence reduces annual growth by 1.4 percent relative to a state with no natural resources.

Lest the reader think these cuts in growth rates trivial, they imply that relying on natural resources to the

tune of 30 percent of the state economy (think Louisiana or West Virginia) would reduce the average

state per capita income by $5,000 over the next decade when compared to a similar state without such

resources. Unreported results suggest that these findings are robust to additional controls for which we

have less data, the time period under analysis, the use of a per capita resource endowment measure, and

the estimation procedure. In short, we find substantial support for an economic resource curse in the

context of the American states—a fact that lends credence to the cross-national resource curse literature.

Table 1 Here

Table 2 turns to politics. We measure the competitiveness of the electoral environment with two

indicators: the margin of victory in gubernatorial elections and the vote share of the incumbent governor.

We are not claiming that resource dependence transform polities into one-party dictatorships. The logic

19 The eight different experiences were: colonized by the English alone, the French alone, the Spanish alone, the English and Dutch, the French and Spanish, the English and Spanish, the English, French and Spanish, and those that were not colonized (or rather were colonized by the U.S.). 20 Capital stock is measured on a per capita basis and is from Garofalo and Yamarik (2002). Human capital is measured as the share of the population with a high school diploma and comes from the U.S. Census. State spending is measured as a share of state income and comes from the Book of the States. The educational attainment data is reported by decade beginning in 1940. We interpolate the data for the intervening years. The state spending data is reported at mostly two year intervals from 1940 to 1982 and annually thereafter. We interpolate the missing data.

of the resource curse argument does not, however, imply such a claim. Recall that the argument is that

leaders of resource-rich states are able to maintain lower direct taxes on their citizens while mineral

rights provide them with rents to lavish on key constituencies. As a result, they are expected to survive

in office for long periods. It is generally accepted in the business-cycle literature on democracies that

incumbents lose votes during periods when the economy either ceases to grow or experiences decline.

The presence of state income generated as an external rent should allow political elites to remain in

power without regard to the business cycle, and thus a rentier effect should appear in a democratic

context and be evidenced by a party or governing elite winning by large electoral margins and winning

higher vote shares across the business cycle than for politicians without access to mineral rents. Controls

include the same slave state, colonial heritage, and wealth indicators noted above. Given the importance

of growth in retrospective election models, we also introduce a control for state-level economic growth

the year prior to the election. In Model 2 we also control for the incumbents vote share in the prior

election.

Table 2 Here

Given the poor economic performance of resource dependent states noted above, the U.S. voting

behavior and comparative literature on elections would suggest that such states should see significant

political turnover. From research on elections in the U.S. states to those across established democracies

to newer democracies in poorer regions of the world, weak economic growth is associated with

declining electoral fortunes of incumbent governments. The results in Table 2 provide further support

for the resource curse hypothesis, though the scale of the impact is relatively small. Turning first to

electoral margins, each percent increase in natural resource dependence increases the margin of

gubernatorial victory by about .15 percent. Put differently, a U.S. rentier state (defined as one in which

resources constitute 20 of state product) is predicted to have gubernatorial victories 3 percent larger than

a state without natural resources. Likewise, Model 2 shows a statistically significant, postitive impact of

resource abundance on incumbent vote shares. The scale of the effect is relatively small but that it

appears at all after controlling for the lagged vote of the incumbent, the business cycle and state

dummies suggests a consistent effect that would make a real difference in elections that are relatively

close.

IV3: The Impact of Natural Resources on the Hypothesized Mechanisms

But what exactly is the means through which resources are having this impact? In this section,

we present the results of 11 different models designed to measure the impact of resource wealth on the

many mechanisms we discuss above. As formulated in the literature, each mechanism is hypothesized to

work in two stages: first, mineral wealth has implications for the mechanism, and second, thereby

impacts economic and political outcomes. The intuition behind the empirics here is that if resource

abundance does not have an impact on the hypothesized mechanism, it cannot thereby impact outcomes.

In each model, the hypothesized mechanism serves as the dependent variable and natural resource

wealth as the key independent variable. The other independent variables vary from model to model in a

manner consistent with the separate literatures on each dependent variable. Our dependent variables

assess each of the mechanisms discussed above—the rentier/tax, modernization, inequality and asset

specificity arguments linking resource abundance to uncompetitive politics; the real price, economic

volatility, and fiscal performance arguments linking resource wealth to poor growth; and the hypotheses

linking mineral wealth to poor economic and political outcomes via their impact on institutional quality.

We measure the rentier effect as the difference between actual taxation and taxing capacity. As

argued in the rentier state literature, resource wealth should be associated with limited tax effort. We use

Berry and Fording’s (1997) annual data from 1960 to 1991. We proxy modernization with two measures

of social development—the first is Putnam’s state-wise index of social capital and the second is the

richness of the interest group environment as collected by Lowery and Gray (1993). Both measures are

increasing in social capital.21 Inequality is measured by each state’s annual gini coefficient from 1963

21 Lowery and Gray measure the interest group environment as the ratio of state GSP divided by the absolute number of interest groups, which is decreasing in social capital. We invert the scale to make interpretation more intuitive. Lowery and Gray’s measure of interest groups is available for 1980, 1990 and 1997. Putnam’s measure is available for a single year in 1992. See Putnam (2000: 290-91) for details on the variable. There is no social capital

to 2002. The level of asset specificity (hypothesized to negatively impact democratic politics) is

measures as the sum of agriculture, mining and government as a share of state GSP.

Turning to the economic resource curse, we assess the impact of resource abundance on real

prices, using Berry, Fording and Hanson’s (2004) cost of living indices for the states from 1960-2003.

Because the literature is somewhat unclear on the time periods necessary for real exchange rate

appreciations to impact broader economic performance, we analyze a number of variants of the

argument. One possibility is that resource rich states simply have higher real prices. To test this

proposition, we estimate a cross-sectional model for real prices as of 2002 (“Prices—CS Levels”). We

also analyze the impact of natural resources on changing price levels through time using an error

correction model in which annual percent changes in real prices are a function of both lagged levels

(“Prices—PTS Levels”) and changes (“Prices—PTS Change”) in resource dependence as well as control

variables. To assess the impact of natural resource wealth on economic volatility we measure the latter

as the standard deviation of annual per capita income growth from 1945-2002.22 To test the argument

that resource dependence results in systematic and economically inefficient overspending, we measure

fiscal performance as deficits as a share of state GSP. The data is available biannually from 1945 to

1981 and annually from 1982 to 2002. Finally, we have two proxies for institutional quality, which is

prevalent in both the economic and political versions of the resource curse literature. First, we use the

measure of legislative professionalism developed by Berry, Berkman, and Schniederman (2000)—per

capita state spending per member of the state legislature. Second, we use the measure of corruption in

state politics as reported in Boylan and Long (2002).

Given the large number of models, we present tables with results in Appendix 3, preferring here

to summarize the findings in a more easily consumable manner. Figure 1 presents the point estimates

and 90 percent confidence intervals for natural resources in each of the models. In order to present the

results in a single figure, many of the coefficients have been rescaled. As a result, the figure does not

data for Hawaii or Alaska. 22 Data from the Bureau of Economic Analysis.

convey the scale of natural resources’ impact on each dependent variable. As Figure 1 shows, our

findings suggest that natural resources have a significant impact on only about half of the hypothesized

mechanisms, and in some cases the impact is opposite to that hypothesized in the resource curse

literature. The top half of the figure focuses on the political hypotheses. There is preliminary evidence

supporting a rentier effect as mineral wealth has a significant, negative impact on fiscal effort even after

controlling for the size of state government. Likewise, the significant coefficients in the inequality and

asset specificity models provide preliminary evidence supporting Boix’s account of democratization—

mineral wealth has a positive impact on both. In contrast, the modernization hypothesis holds up less

well. The coefficient on the richness of the interest group environment is in the expected direction but is

not significant at traditional levels (though nearly so), and mineral wealth has no impact on state-wise

social capital. There is mixed evidence, at best, with regards to the institutions hypothesis. The sign on

the resource dependence coefficient in the corruption model is in the right direction and nearly

significant at 10 percent, but the sign in the legislative professionalism model is in the wrong direction

(suggesting improved institutional quality) and also nearly significant.

Turning to the economics of the resource curse at the bottom half of the figure, we find little

evidence in support of any of the hypothesized mechanisms. Resource dependence actually looks to

decrease real price levels both in the cross-sectional and cross-sectional time-series analyses. Short term

(one year) increases in mineral dependence do temporarily increase price levels, but that overall price

levels remain lower suggests that the our economic findings can not be explained by a Dutch Disease

effect. Resource abundance likewise does not contribute to higher levels of economic volatility or poorer

fiscal performance. In unreported analysis it is also that case that mineral wealth does not contribute to

larger state governments. Finally, as noted above, resource abundance has a mixed and weak effect on

our measures of institutional quality. In short, extant hypotheses show limited promise for explaining the

negative relationship between mineral wealth and economic outcomes.

Figure 1 Here

The net result of the analyses is that we have a reduced number of candidate mechanisms

whereby resource wealth impacts political competition and economic performance. Nevertheless, that

resource dependence has an impact on tax effort, inequality and the like does not mean that it is thereby

that mineral wealth generates a resource curse. Next we turn to a series of simultaneous equations to test

the impact of resource dependence on growth and political competitiveness via these hypothesized

mechanisms.

IV4: Simultaneous Equations and Other Tests

We use the results from the previous section to direct us in the formulation of a series of

simultaneous equations designed to assess the direct and indirect impact of mineral wealth on the

outcomes of interest. We do so only for the variables on which resource wealth had a significant impact

above. Thus, we model political competition as a function of several exogenous variables, mineral

wealth, asset specificity, inequality and tax effort where the latter three are endogenous. Similarly, we

model growth as a function of a series of exogenous variables, mineral wealth, prices and institutions

(we include the corruption indicator of institutions as the coefficient on mineral wealth above is very

nearly significant above), where both prices and institutions are endogenous.23 Consistent with the

approach taken in Sala-i-Martin and Subramanian (2003), this allows us to assess both the direct and

indirect of mineral wealth via the various mechanisms. Summary results appear in Table 3 which

presents the impact of a one standard deviation increase in natural resources on the intervening variables

and the subsequent impact on the dependent variable.

Table 3 About Here

Turning first to political competition, resource abundance has no direct impact on either

electoral margins or the incumbent’s vote share. It does, however, have an indirect impact via its

influence on asset specificity, inequality and tax effort. As hypothesized, mineral wealth does indeed

contribute to more asset specific state economies and asset specificity does decrease electoral

competition (as evidenced by higher vote margins and vote shares for incumbents). While mineral 23 Models estimated using three stage least squares.

wealth continues to have the expected impact on inequality and tax effort, however, each of these

intervening variables contributes to increased rather than decreased electoral competition. Indeed, these

results help explain the relatively modest direct impact of mineral wealth on political competition

reported in Table 1 above—resource abundance has a significant impact on these intervening variables

which themselves have significant, but contradictory, effects on political competition. Indeed, the net

effect of resource wealth on electoral margins is only slightly positive once one sums the quite large

indirect effects via asset specificity (positive), inequality (negative) and tax effort (also negative). Take

electoral margins, for example. A standard deviation increase in logged mineral abundance works

through asset specificity to increase electoral margins by 12.4 percent, though inequality to reduce

electoral margins by 3.7 percent, and through tax effort to reduce margins by 8.3 percent. The net impact

of natural resources via these various mechanisms is to increase electoral margins by .4 percent. Though

the total effect is quite small, the mechanisms by which that effect is produced appear much more

sophisticated than previous research on the resource curse has led us to believe.

The results with respect to growth are more straight forward. Mineral abundance has no direct

impact on growth, nor does it have any through its impact on prices. Resource wealth does, however,

have an impact via its influence on institutional quality. Substantively, a one standard deviation increase

in natural resource dependence produces a .30 standard deviation decline in institutional quality (as

proxied by the corruption indicator) which in turn reduces the average rate of growth in the 1990s by

about .33 percent. These findings are remarkably similar to those in Sala-i-Martin and Subramanian

(2003) who take a similar approach in a cross-national setting.

An alternative approach—using instrumental variables on each of the indicators on a

hypothesis-by-hypothesis basis—produces roughly similar results to those presented here. At this point,

we have trimmed the initial 11 indicators representing 9 hypotheses linking resource abundance to poor

economic and political outcomes to two. We find evidence that mineral wealth increases asset

specificity and that asset specificity decreases electoral competition. This is consistent with Boix’ (2003)

account, though we find no evidence that inequality matters as he suggests nor do we find evidence that

an interaction between asset specificity and inequality has implications for political competition. We

also find some evidence that institutions matter, with resource wealth reducing institutional quality and

institutional quality contributing to poorer economic growth.

IV5: Economic Geography

Having examined extant hypotheses in the resource curse literature, we turn to our own

geography hypothesis. The simplest hypotheses to emerge out of our discussion of economic geography

is that natural resources will not foster development in contexts of sparse domestic markets and when

they are far removed from broader markets (and therefore face high transportation costs). Under such

conditions, spillovers to the broader economy will be minimal and many of the dysfunctions associated

with enclave economies are likely to set in. Under more propitious conditions, on the other hand, trade

in natural resources should contribute to growth in much the same manner as any other factor of

production.

As such we estimate a growth model below in which resource dependence is conditioned on

access to broader markets and the scale of a state’s market. As Hausmann (2001) notes, even the

transportation of goods over land is seven times more expensive than over water. As such, “access to

markets” is measured with a dummy variable coded as 1 where states have access to navigable rivers,

lakes, or oceans. We measure market size using population density. The full results are presented in the

appendix, and the interactive relationships of interest are graphed in Figures 2a and 2b which show

predicted growth rates at various combinations of values across the independent variables. The

interaction is jointly significant, and the figure shows the impact to be considerable. Interestingly, the

results suggest that all three theoretical accounts linking mineral wealth to growth are correct under

certain conditions. Big boom accounts of mineral wealth generating widespread growth occur when

resource wealth occurs in contexts of large domestic markets and limited access to external markets (the

upper right hand quadrant of Figure 2a). In such cases, it seems likely that the profits from mineral

booms are reinvested in other local firms, thereby generating productive ripples through the economy.

In contrast, the resource curse holds in contexts when mineral abundance occurs in contexts of

geographic isolation and small local markets (the bottom left of Figure 2a). In these cases, the lack of a

local market and expensive transportation costs combine to produce poor performing enclave

economies. Finally, the standard trade story appears to hold when natural resources are combined with

access to broader markets (Figure 2b). In such cases, mineral wealth has no impact on growth, which is

uniformly positive. Under such conditions, natural resources seem to impact growth just as any other

endowment would.

These findings go a long way toward explaining the mixed experience with mineral wealth

across the American states and the world. Figure 3 plots the results for Model 1 in Table 1 discussed

above. It is suggestive that most of the states that appear toward the bottom-right are also either land-

locked (West Virginia, New Mexico, Oklahoma, Wyoming, etc) or have large distances between the

resources and external markets (Texas). In this regard, those economies during their mineral rich days

bear resemblance to quintessential enclave economies in places like Bolivia and Ecuador. That Alaska

appears as an outlier with regards to per capita income (though note that it is not in any of the growth

models) is also instructive and underscores the importance of market size, if mineral wealth is to provide

the foundations for broad-based growth. Alaska’s difficult environment and restrictions on labor

mobility to the state resulting from restrictive rules on access to the Alaska Permanent Fund (Alaska’s

oil fund) preclude the development of the kind of economic concentration that might lead to a broad-

based boom while simultaneously lifting per capita income for the few people that live there. In this

regard, Alaska bears resemblance to the oil monarchies of the Middle East, most of which have imported

foreign labor to work the oil fields but have precluded the integration of those immigrants into the

broader economy. The resulting domestic markets are smaller and narrower than they would be

otherwise, and limit the extent to which oil wealth will generate spillovers into other forms of economic

activity. Indeed, our emphasis on geography helps explain a heretofore anomalous finding in the cross-

national resource curse literature, namely that resource wealth is associated with high per capita incomes

but low growth. Our theoretical account and the experiences of Alaska and Saudi Arabia help explain

why the two might go together over the short- and medium-term.

Figure 3 Here

Our theoretical account and empirical results also help explain the success stories. As Walker

(2001: 172-3) notes, “Gold, silver, copper, and petroleum provided spectacular bursts of wealth that

propelled California’s expansion along the fast track of capitalist development”, and California was the

world’s leading oil producer between 1905 and 1930. Speculative bubbles in gold and later oil did not

prevent reinvestment and the creation of a high-wage, capital-intensive economy. Indeed, they were

fundamental to the establishment of that economy. California was distinct from the West Virginias and

Bolivias of the world in that it was from the beginning an almost autonomous region economically: it

“became an isolated province separated by barriers of distance and freight costs from competition with

other areas of production in the United States and abroad” (White 1970: 138).24 Almost the only

mineral California lacks is coal and consequently the state developed an entire transport infrastructure

based on liquid fuel and an economy centered at first on San Francisco.

California’s population growth during the period of the gold and oil booms was, moreover,

spectacular. The population of the state as a whole grew 365% between 1900 and 1940, but the

population of the areas around the oil fields in Southern California grew even faster. The five counties

of Los Angeles, Orange, Riverside, San Bernardino and Ventura grew 1200% between 1900 and 1940 in

a period in which US population as a whole grew by 73.3% (Coons 1942: 137). There was,

consequently, a local market for California oil, and “cheap oil gave ‘a decided impetus’ to industry

statewide” (Walker 1970: 143). As a consequence, California’s internal market provided (along with

military spending in World War II) the basis for increased industrialization and continued economic

growth (Parsons 1949). When we look around the world, perhaps the most analogous country is Iran,

where a large population relatively distant from major markets is rapidly increasing its domestic demand

for oil. Indeed, demand is increasing so quickly that there is talk of the country ceasing to export oil

within a generation.

Conclusions 24 Also see Johnson 1970: 155 for a description of California as a “self-contained petroleum province”.

To summarize our findings, we find little support for most of the standard hypotheses in the

resource curse literature. Consistent with some recent cross-national research (McMahon 1997), the

exchange rate mechanism seems less important than some have suggested. Likewise, the economic

volatility that is often associated with dependence on natural resources has no implications for

development, at least in the context of the U.S. states. The impact of mineral wealth on real prices does

not account for the negative relationship between growth and resource abundance in our sample. Among

standard hypotheses, only the institutions one seems to help explain the economic resource curse.

Similarly, our findings suggest that the hypotheses linking mineral wealth to uncompetitive politics via

their impact on social modernization do not explain the political resource curse. We do, however, find

evidence that resource abundance affects political competition indirectly via its impact on tax effort,

inequality, and asset specificity. The causal story is more complex than standard accounts suggest,

however, with weak tax effort and increased inequality contributing to more competitive politics and

asset specificity detracting from electoral competition. These findings suggest the need for cross-

national research to take a more nuanced approach to the political impact of mineral wealth.

Our hypothesis and findings also suggest the need to return to cross-national research with

careful attention to economic geography. We suggest that the absence of growth associated with raw

material production is a feature of geography, an admittedly poorly understood feature of the economy.

We note, however, that even in relatively advanced economies such as that of the US, mineral

production rarely leads to the creation of urban centers and more specifically does not create the kind of

pooled markets for labor, capital goods, or information that since Marshall have been associated with

intense economic growth (Krugman 36). In the absence of such localized markets (cities), we

hypothesize that the only way to integrate mineral production into a larger economic system is through

free trade which, in general, will tend to reproduce the so-called “enclave” or rentier economy. Texas,

West Virginia and New Mexico have grown slowly because they have not had the local demand or

transportation networks to provide the setting for investments in other capital goods. In addition, we

note that as long as the returns to mineral production are higher than the returns to other forms of

investment in a region, we would expect more investment to flow into the activity with the higher return

than the one with the lower return.

Finally, this research points to the value of looking for tests in unlikely places. At this point in

time, cross-national data sets simply lack the richness necessary to test many important hypotheses in

comparative politics. Subnational governments in federal systems can provide an excellent alternative

setting for exploring arguments in comparative politics. Indeed, a growing list of scholars are using the

subnational diversity of large, federal states such as India or the United States to test hypotheses

developed in studies of nation-states in the international system (see, e.g., Remmer & Wibbels 2000,

Besley & Coate 2003, Chhibber & Nooruddin 2004, Besley et al. 2005). Such approaches have

important methodological advantages. Inherent in comparative politicsis some degree of unmeasured

cross-national variation that is difficult to control for in small-n studies and is consumed by either the

error term or country dummies in large-n settings. This is the stuff that researchers do not know, do not

understand, or cannot measure but that has a bearing on outcomes across nations. By analyzing states

within a federation, we are able to control for legal practices, institutions of government, party systems,

etc. that might impact outcomes of interest but that are unmeasured or poorly measured in cross-national

work. Who knows? Given the emerging popularity of the U.S. states as a laboratory for comparativists,

such studies might even begin to bridge the gap between studies of American politics and those of

comparative politics—that is, the rest of the world.

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Mendoza, Enrique. 1997. “Terms-of-Trade Uncertainty and Economic Growth.” Journal of Development Economics 54: Mikesell, R. 1997. “Explaining the Resource Curse, with Special Reference to Mineral-Exporting Countries.” Resources Policy 23: 191-199. Morrison, Kevin. 2006. “Oil, Non-Tax Revenue, and the Redistributional Foundations of Regime Stability.” Working Paper. Department of Political Science, Duke University. Nurske. 1958. “Trade Fluctuations and Buffer Policies of Low-Income Countries.” Kyklos 11: pages. Powell, Andrew. 1991. “Commodity and Developing Country Terms of Trade: What Does the Long Run Show?” Economic Journal 101: Prebisch, Raul. 1950. The Economic Development of Latin America and its Principal Problems. Lake Success: United Nations. Ramey, Valerie and Garey Ramey. 1995. “Cross-Country Evidence on the Link between Volatility and Growth,” with Garey Ramey. American Economic Review 85: 1138-1151. Robinson, James. Ragnar Torvik and Thierry Verdier. 2006. “Political Foundations of the Resource Curse.” Journal of Development Economics 79: 447– 468. Rodríguez, Francisco and Dani Rodrik. 2001. “Trade Policy and Economic Growth: A Skeptic's Guide to the Cross-National Evidence.” Macroeconomics Annual 2000, eds. Ben Bernanke and Kenneth S. Rogoff, MIT Press: Cambridge, MA. Ross, Michael. 1999. “The Political Economy of the Resource Curse.” World Politics 51: 297-322. Ross, Michael. 2001. "Does Oil Hinder Democracy?" World Politics 53: 325-361. Sachs, Jeffrey D. and Andrew M. Warner. 1995. “Natural Resource Abundance and Economic Growth.” NBER Working Paper No. 5398. Singer, Hans. 1950. “The Distribution of Gains from Investing and Borrowing Countries.” American Economic Review 40: pages. Stijns, J.P. 2005. “Natural Resource Abundance and Economic Growth Revisited." Mimeo, Department of Economics, Northeastern University. Weinthal, Erica and Pauline Jones-Luong. 2002. “Energy Wealth and Tax Reform in Russia and Kazakhstan.” Resources Policy 27: 215-28. Williamson, John. 1993. "Development and the "Washington Consensus." World Development 21:1239-1336. Wright, Gavin. 1990. “The Origins of American Industrial Success, 1879-1940.” American Economic Review 80: 651-68. Wright, Gavin. 2001. “Resource Based Growth, Then and Now.” Manuscript, Stanford Department of Economics.

Table 1: Natural Resource Dependence, Development and Growth

Model 1: Per Capita Income

Model 2: Ten Year Average Growth

Model 3: Annual % Growth

Resource Dependence -.031** (.013)

-.001* (.001)

-.613*** (.007)

Per Capita Income (logged)a

.287*** (.047)

-.013* (.004)

-6.606*** (1.064)

Slave Population, 1860 .002 (.001)

Access to Markets .029 (.039)

Colonizing Nation .025 (.029)

Capital Stock -.123 (.152)

-97.430*** (33.417)

Human Capital -.000 (.000)

.133*** (.026)

State Spending .000** (.000)

.001** (.000)

Lagged DV -.078 (.062)

N= 50 248 2671 R-squared .60 .62 .06

Note: Dependent variable in Model 1 is annual growth in real per capita state income. In Model 2 the dependent variable is the 10 year average growth for the decades of the 1950s, 1960s, 1970s, 1980s and 1990s. In model 3 the dependent variable is annual percent change in per capita income. In models 2 and 3, logged per capita income is measured at the beginning of the time period. All models include state dummies. a In model 1, per capita income is the initial per capita income in 1929 for all states except Hawaii and Alaska. In those two cases, initial per capita income is for their first year as states. In model 2 per capita income is for the initial year of the 10 year period. In model 3, per capita income is lagged one year.

Table 2: Natural Resource Dependence and Electoral Competition, 1929-2002

Model 1: Electoral Margin

Model 2: Incumbent Vote Share

Resource Dependence .142* (.078)

.050* (.026)

Economic Growth .109** (.051)

.093*** (.033)

Per Capita Income -3.768*** (1.768)

-1.322*** (.770)

Colonizer (non-Spanish= 1) -12.933*** (1.288)

-1.557*** (.402)

Slave Population, 1860 .285*** (.040)

.117*** (.028)

Winner’s Lagged Vote .737*** (.077)

N= 1077 1077 R2 .59 .66

Note: The dependent variable in Model 1 is the difference between the winner’s vote share and the runner up’s vote share. The dependent variable in Model 2 is the incumbent’s vote share.

Table 3: The Direct and Indirect Effect of Mineral Wealth on Growth and Electoral Competition Growth Electoral Margin Incumbent Vote Share Direct Effect None None None Intervening Effect None -0.002 0.008 5.803 -8.505 .008 5.806

-14.466

Substantive Impact None -0.33 -3.72 12.4 -8.275 None 8.634 -8.007 Intervening Variable Prices Institutions Inequality

Fixed Assets

Tax Effort Inequality

Fixed Assets

Tax Effort

Note: "Direct Effect" refers to the direct impact of natural resources on the dependent variable. "Intervening Effect" refers to the impact of a one standard deviation increase in resource wealth on the intervening variable. "Substantive Impact" refers to the impact of a one standard deviation increase in natural resources on the dependent variable via its impact on the intervening variable.

Figure 1: The Relationship between Natural Resources and 11 Mechanisms

Professionalism

Corruption

Deficits

Volatility

Prices: PTS Change

Prices: PTS Levels

Prices: CS Levels

Asset Specificity

Inequality

Social Capital

Interest Groups

Tax Effort

-.1 0 .1 .2Point Estimates and 90 % Confidence Intervals

Polit

ics

Econ

omic

s

Note: These are the point estimates of natural resource wealth from models in which each of the y-axis variables serve as a dependent variable. The point estimates have been rescaled and are, therefore, not directly comparable. Models are specified as described in the text. “Fixed Assets” is the sum of agriculture, mining and government as a share of GSP. “Inequality” is the state-wise gini coefficient. “Tax Effort” is the gap between actual taxes raised and taxing capacity (Berry and Fording 1997). “Interest Groups” is an indicator for the richness of the interest group environment, measured as recommended by Lowery and Gray (1993)—note that as per their measure, the scale is inverted (natural resource wealth detracts from the interest group environment). “Social Capital” is the state-wise social capital index as collected by Putnam (2000). “Prices: PTS Levels” is the coefficient on the lagged level of natural resources in a cross-sectional time-series model of annual price changes. “Prices: PTS Change” is the coefficient on the annual change in natural resource dependence in the same cross-sectional time-series model of annual price changes. “Prices: CS Levels” is the coefficient on the level of resource dependence in a cross-sectional model of state-wise real prices as of 2002. The price data comes from Berry, Fording and Hanson (2003). “Volatility” is the standard deviation of annual per capita income growth from 1945 to 2002. “Deficits” is annual deficits as a share to state GSP. “Corruption” is a measure of the level of corruption in state politics as reported in Boylan and Long (2002). “Professionalism” is the measure of per legislature state spending as collected by Berry, Berkman, and Schniederman (2000). Both “Corruption” and “Professionalism” are proxies for institutional quality.

Figure 2: The Impact of Resource Abundance, Population Density and Market Access on Predicted Economic Growth

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.80

100

200

300

-10

-5

0

5

10

15

20

Predicted Annual Growth

Resource Dependence (ln)

Population Density

Figure 2a: Without Market Access

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.80

100

200

300

-10

-5

0

5

10

15

20

Predicted Annual Growth

Resource Dependence (ln)

Population Density

2b: With Access to Markets

Figure 3: Natural Resources and Per Capita Income

AL

AK

AZ

AR

CA

CO

CTDE

FL

GA

HI

ID

IL

INIA

KS

KYLA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NVNH

NJ

NM

NY

NC

ND

OH

OK

OR PA

RI

SC

SDTNTXUT

VT

VA

WA

WV

WI

WY

10.1

10.2

10.3

10.4

10.5

Logg

ed P

er C

apita

Inco

me

2002

0 1 2 3 4Logged Resources / GDP

Note: The graph shows the relationship between the predicted value of GDP per capita (from Model 1 in Table 2) and logged natural resource dependence.