Boon or bane: Examining divergent development outcomes among oil- and mineral-dependent countries in the Global South

Abstract

Under neoliberal conditions that privilege foreign investors and call for the retreat of the state, some oil- and mineral-dependent countries in the Global South outperform others. To investigate what accounts for this variation in economic development among these countries, this study tests hypotheses derived from resource curse and dependency/world systems literatures using a dataset of 36 oil- and mineral-dependent countries in the Global South from 1984 through 2010 and panel methods of data analysis. The results show that state capacity and debt dependence shape uneven development outcomes among these countries. The implications for resource curse and dependency/world systems theories are discussed.

Keywords

Cross-national dependency development neoliberalism resource curse state capacity

Introduction

Oil and minerals are the most important primary commodities on the world market. Over the past two decades, fuel and mineral products recorded the highest price increases, despite sharp price fluctuations (World Trade Organization, 2015). These resources are largely extracted from countries in the Global South, which account for over 80 percent and 60 percent of global oil and mineral exports, respectively (United Nations Conference on Trade and Development (UNCTAD), 2014). Moreover, for the overwhelming majority of oil- and mineral-exporting countries in the Global South, their economies are heavily dependent on revenues from these resources (these countries are hereafter referred to as oil- and mineral-dependent countries¹). This raises important concerns about how these countries can translate their resource income into economic development.

Although this concern took hold amid the optimism of the postwar period firm in the belief that formerly colonized countries could use their natural resources as a platform for economic independence, the same question has taken on a sense of urgency in the context of neoliberal restructuring since the 1980s. The neoliberal wisdom of transferring control of extractive sectors to foreign ownership through privatization and marketization and aggressively promoting natural resource exports went hand-in-hand with reducing the state investment and regulation that were part and parcel of development strategies in the immediate postwar era. Given that these neoliberal conditions impeded the development of other raw material exporters (Bradshaw and Huang, 1991; Mahutga, 2006), we might expect neoliberalism to pose even greater obstacles to the development of oil- and mineral-dependent countries in the Global South for two main reasons.

First, these countries are already in a position of extreme dependence on richer nations in the Global North. No other primary commodities match the strategic importance of oil and minerals to powerful nations for military power and as inputs for production (Amuzegar, 2001; Radetzki, 2008). Unlike other primary commodities such as plantation crops and timber, oil and minerals are non-cultivatable, non-renewable ‘point-source’ resources that are extracted from a narrow geographical base in a small group of countries across the world (Isham et al., 2005; Sala-i-Martin and Subramanian, 2003). This makes these countries particularly important targets to core nations (Perelman, 2003). In addition, these resources require great capital and technology to procure, which renders these countries especially dependent upon transnational investors from richer countries. Together, these forces drastically reduce their autonomy to create development policies that may not be in line with more powerful nations’ interests (Bunker and Ciccantell, 2005; Perelman, 2003). Second, research also shows that, of all primary commodity exporters, oil- and mineral-dependent countries in the Global South possess the world’s weakest, most corrupt states that undermine economic growth and development (Bulte et al., 2005; Isham et al., 2005; Sala-i-Martin and Subramanian, 2003). Based on such extraordinary dependence and weak state institutions, we may expect neoliberal policies to exacerbate the development problems of oil- and mineral-dependent economies.

Yet, even under these extreme conditions, there is significant diversity in their development performances. On one hand, there are economically low-performing countries, such as Togo and Mozambique, that rank among the world’s poorest countries. On the other hand, countries such as Kuwait and Chile are classified as high-income economies (World Bank, 2017). In addition, a large group of oil- and mineral-dependent countries, such as Mexico and Morocco, are among the world’s middle-income countries (World Bank, 2017). The question is, ‘Why are some oil- and mineral-dependent countries in the Global South more successful in achieving economic development than others under the conditions of neoliberalism?’

To answer this question, I draw on two branches of scholarship on natural resources and development. The first – the resource curse literature – highlights the importance of domestic institutions, in particular state capacity, in explaining uneven development among resource-dependent countries (Acemoglu et al., 2003; Kurtz, 2009). Likewise, I examine whether variation in state capacity accounts for the divergent performances of oil- and mineral-dependent countries. However, fewer works in this tradition explicitly consider not just how the international economic environment shapes the development of resource exporters (Dunning, 2005; Rosser, 2007) but how structural inequalities and historical legacies of exploitative relations between states in the world system impact these countries. According to Rajan (2011), there ‘appears to be a prominent blind spot’ in this research tradition with respect to international geopolitics and the power of global capital. Therefore, I also draw on the dependency/world systems perspective. These scholars argue that a European-dominated world system emerged in the 1450s where countries in the periphery became dependent on and exploited by industrialized countries (Wallerstein, 1974). In the postcolonial period, these countries’ sustained dependence on resource exports, foreign direct investment (FDI), and foreign loans continue to impede their development (Bornschier and Chase-Dunn, 1985; Bunker and Ciccantell, 2005; Chase-Dunn and Grimes, 1995; Curwin and Mahutga, 2014). According to Mahutga (2006), the consequences of dependency are far worse for the periphery in the neoliberal era. While dependency/world systems scholars take a long view of development, their perspective suggests that dependence on resource exports, FDI, and foreign loans continues to negatively impact oil and mineral exporters.

I develop hypotheses based on the insights from these two bodies of work and test them on an unbalanced panel dataset of 36 oil- and mineral-dependent countries in the Global South for the period from 1984 to 2010 (annually) using panel methods that account for unmeasured heterogeneity between countries. I find that strong state capacity positively impacts economic development in these countries, while debt dependence impedes economic development. These findings hold across several statistical models, including random-effects (RE) models, and models that include a number of controls and endogeneity tests.

These results contribute to the literature on natural resource-based development in three ways. First, most works on oil- and mineral-dependent countries are either in-depth case studies (Bunker and Ciccantell, 2005; Kaup, 2010) or quantitative studies that compare oil- and mineral-dependent countries to other natural resource exporters (Isham et al., 2005; Sala-i-Martin and Subramanian, 2003). This study provides cross-national empirical evidence for what accounts for uneven development among oil- and mineral-dependent countries, thereby filling this gap in our understanding of these countries’ development trajectories. Second, for the resource curse literature specifically, the findings emphasize not only the wide variation among these states and that state capacity is crucial for these countries’ development in the neoliberal era but also the importance of attending to the global structures of dependency that also influence their development, in particular dependence on external debt. Third, for dependency/world systems arguments, I find that of the various forms of economic dependency, debt dependence is the primary impediment to the development of these oil- and mineral-dependent countries in the neoliberal period. Dependency/world systems perspectives should concentrate on this mechanism in order to fully capture its profound impacts on these nations.

In the following sections, I summarize and extract testable hypotheses from the resource curse and dependency/world systems literatures. I then describe the data used to operationalize the key variables to test the hypotheses. Following a discussion of the results, I conclude by highlighting the contributions of this research to the advancement of comparative research on development in oil- and mineral-dependent countries.

Explaining the variation in development

How have oil- and mineral-dependent countries in the Global South fared in the neoliberal era? Figure 1 shows the variation in per capita income among these countries. On one hand, there are the economically low-performing countries, such as Niger and Mozambique, where, for over 20 years, the per capita income remains below approximately US$1000 (Heston et al., 2012). These nations rank among the countries in the world with the lowest income (World Bank, 2017). On the other hand, countries such as Kuwait and Chile rank among the high-income nations of the world (World Bank, 2017) with per capita incomes of approximately US$41,000 and US$12,525, respectively, in 2010 (Heston et al., 2012). In addition, the worlds’ middle-income nations include oil- and mineral-dependent countries, such as Mexico and Morocco. Moreover, Trinidad and Tobago and Malaysia elevated their per capita incomes almost threefold since the 1980s. To investigate what might account for the varied development experiences of these countries, I draw on two bodies of work within the mainstream development literature – resource curse theory and dependency/world systems theory.

Figure 1.

Variation in economic development among 36 oil- and mineral-dependent countries in the Global South.²

Resource curse, states, and development

The traditional view that dominated early resource curse theory was that resource-dependent countries exhibit lower economic development than their resource-poor countries (Auty, 1993; Sachs and Warner, 1995), weaker democracies (Ross, 2001, 2015), and experience more political instability and civil war (Collier and Hoeffler, 1998). With respect to economic development, the outcome of interest in this study, traditional resource curse explanations by economists initially cited the Dutch Disease, that is, the corrupting effects of natural resources on exchange rates and the weakening of agriculture and manufacturing sectors (Gelb et al., 1988; Gylfason et al., 1999; Matsuyama, 1992). Political scientists contended that oil and minerals inhibit development by weakening the bureaucratic capacity of the state to formulate and implement development policies (Beblawi and Luciani, 1987; Karl, 1997, 2007; Mahdavy, 1970). These ‘rentier states’ receive external ‘rents’ or income from taxes and royalties on natural resource production and exports rather than from taxing their citizens, which tend to foster increased corruption and clientelism (Leite and Weidmann, 2002), excessive spending on unprofitable firms and programs (Auty, 1990; Auty and Gelb, 2001; Chaudhry, 1994; Karl, 1997, 2007; Robinson and Torvik, 2005; Sachs and Warner, 1995; Tornell and Lane, 1999), and authoritarian regimes and repressive apparatus to suppress dissent (Bellin, 2004; Jensen and Wantchekon, 2004; Ross, 2001, 2015; Smith, 2006). Thus, traditional resource curse scholarship largely agreed that natural resources harmed development.

However, beginning in the early 2000s, confidence in this claim was partially reversed as scholars turned attention to evidence of development winners and losers among resource-dependent countries (Humphreys et al., 2007; Torvik, 2009). The contemporary turn in resource curse studies aims to unearth the factors that condition whether proceeds from natural resource exports would promote or inhibit economic growth (Dunning, 2005; Rosser, 2007). The findings generated broad consensus around two main points: (1) that the quality of state institutions mediates the relationship between natural resources and development, although more fixed factors such as geography and historical factor endowments also shape how resource wealth is invested (Schrank, 2004; Sokoloff and Engerman, 2000), and (2) that types of natural resources matter – oil- and mineral-dependent countries in particular have weak state capacity.

A long tradition of historical-institutional scholarship establishes the importance of state capacity, defined in the Weberian sense, as high quality/robust public sector bureaucracies that are meritocratic, autonomous from the elite, able to restrain state officials from exploiting state coffers, and capable of coordinating multiple actors in order to promote industrial transformation and provide developmental goods on a nationwide scale, regardless of government change (Amsden, 2001; Evans, 1989, 1995; Kohli, 2004; Rueschemeyer and Evans, 1985). According to Kaup and Gellert (2017), states can successfully engage in ‘resource nationalism’ – the effort to enhance their share of benefits from resource extraction. Recent evidence in the resource curse literature shows that resource-dependent countries with stronger state institutions outperform those with weaker states. State institutions, in these studies, refer not only to the state’s ability to guarantee private property rights (Acemoglu et al., 2003) but more importantly to the state’s capacity to promote economic development, which involves mainly the quality of the bureaucracy and also rule of law and transparency and accountability mechanisms (Atkinson and Hamilton, 2003; Boschini et al., 2007, 2013; Bulte et al., 2005; Dunning, 2005; Kurtz, 2009; Mehlum et al., 2006; Orihuela, 2013; Rosser, 2007). At the same time, recent studies also assert that weak bureaucracies with high levels of corruption are intrinsic to oil- and mineral-dependent countries, and not to all raw-material-dependent nations (Brollo et al., 2013; Vicente, 2010). Several studies find an inverse correlation between oil/mineral dependence and various measures of institutional quality (Beck and Laeven, 2006; Bulte et al., 2005; Isham et al., 2005) and that these oil- and mineral-dependent countries tend to grow more slowly than other raw material exporters (Leite and Weidmann, 2002; see also Isham et al., 2005; Sala-i-Martin and Subramanian, 2003).

These results do not bode well for the ability of the oil- and mineral-dependent state to direct development under conditions of neoliberalism. Neoliberalism demands a dramatic reduction of the public sector and bureaucratic capacity. It promotes abandoning regulation of the economy and social service provision, and therefore abrogating the state agencies responsible for these tasks (Babb, 2005; Evans, 1997). The countries that experienced positive development in the neoliberal era are those that resisted pressures to downsize and disengage from economic regulation (Amsden, 2001; Evans, 1997; Wade, 1990, 2003). Still, oil- and mineral-dependent countries’ development performances are quite varied. Therefore, given that previous research shows resource-exporting states have different capacities to promote development and that those with stronger states outperform those with weaker states, one would expect there to also be variation in state capacity among oil- and mineral-dependent states. In addition, because the state literature suggests that development still requires a strong state in the era of neoliberalism, I examine the role of state capacity in development with the presumption that stronger state capacity would positively impact economic development in oil- and mineral-dependent countries.

Natural resources, dependency, and development

While recent resource curse perspectives focus on and highlight the domestic institutional factors driving development in resource-dependent countries, apart from a few studies (Dunning, 2005; Kurtz and Brooks, 2011; see also Rajan, 2011; Shaxson, 2007), less attention is paid to understanding how global dynamics of unequal power between states in the world system also impacts development outcomes. A rich and long intellectual tradition of dependency/world systems theory argues that while state capacity is important for development, the economic performances of poorer countries are intrinsically linked to structural inequality in the global capitalist system (Amin, 1974; Arrighi et al., 2003; Bornschier and Chase-Dunn, 1985; Cardoso and Faletto, 1979; Evans, 1979; Frank, 1969; Kentor and Boswell, 2003; Mahutga et al., 2011; Snyder and Kick, 1979; Wallerstein, 1974). Dependency/world systems theorists take a deeply historical and global view of development and are primarily interested in what accounts for the development gaps between the tiers in the world system and upward/downward mobility in the system. From the 1450s, a European-dominated world system emerged and persisted where the powerful, industrialized countries in the core exploited the raw materials from less powerful countries in the periphery and semi-periphery (Wallerstein, 1974). In the postcolonial world, the world system is maintained through the persistence of these exploitative relations via dependence on FDI, external debt, and structural adjustment (Bornschier and Chase-Dunn, 1985; Bunker and Ciccantell, 2005; Chase-Dunn and Grimes, 1995; Curwin and Mahutga, 2014). While a much longer time period would be needed to really test world systems theory and the data in this study do not extend that far back, the insights about how a country’s structural economic ties to the world system shape its development can be applied to the neoliberal era to make certain predictions about oil- and mineral-dependent countries (see, for example, Kaup and Gellert, 2017).

In this perspective, raw material export dependence is one of the main drivers of underdevelopment. Powerful nations in the core control entire global commodity chains in key raw materials from ownership of the resources to control of processing, transport, and global marketing, and are able to secure favorable terms of trade (Bunker, 1984; Bunker and Ciccantell, 2005, 2007). Consequently, according to the theory of unequal exchange, resource-exporting countries in the periphery benefit comparatively less for their resources but bear greater costs of environmental degradation (Bunker, 1984; Hornborg, 2009; Jorgenson et al., 2009; Rice, 2007). The negative impacts are greater when they depend on a single commodity or single trading partner (Galtung, 1971; Jaffee, 1985). This form of dependency may also be more harmful based on the type of commodity. In comparison with other raw materials, rich countries have a strategic interest in fuel and mineral products; they seek them to uphold military power, for energy and material inputs for merchandise production, and to maintain a high standard of living (Amuzegar, 2001: 12; Radetzki, 2008). Oil and minerals are also non-cultivatable, non-renewable, and heavily concentrated in a small group of countries across the world (Isham et al., 2005; Sala-i-Martin and Subramanian, 2003). This makes these countries especially important targets to more powerful nations, which may result in particularly exploitative trade relations that harm their development.

Dependency/world systems research also highlights how dependence on FDI impedes development. Many natural resources, particularly oil and gas, require extraordinary technological and infrastructural investment to extract and produce (Barham et al., 1994; Boyd et al., 2001; Bunker, 1989; Bunker and Ciccantell, 2005, 2007). Poorer countries often lack the necessary capital and expertise to access the resources and, therefore, rely upon foreign investors (Bunker, 1989; Frank, 1969). This dependence affords foreign transnational corporations a significant advantage over the dependent resource-exporter (Bornschier and Chase-Dunn, 1985; De Graaff, 2012; Kentor and Boswell, 2003). This results in profit expatriation rather than local re-investment (Bornschier, 1980) and limited state autonomy to pursue development interests that may not align with foreign corporations’ interests (Amin, 2001; Perelman, 2003).

External debt is another form of dependency that inhibits development in the periphery and semi-periphery. In addition to FDI, these countries also finance industrialization and development projects with loans from a variety of external sources – from private commercial banks to multilateral and bilateral sources. Debt and interest payments drain countries of already scarce capital that could go toward development projects. A number of studies show that debt dependence negatively impacts economic growth and human health in the Global South (Bradshaw and Huang, 1991; Bradshaw and Tshandu, 1990; Bradshaw and Wahl, 1991; Shen and Williamson, 2001). Similarly, even though Manzano and Rigobon (2007) do not use the framework of structural inequalities in the world system, they argue that these countries are not afflicted by a ‘resource curse’ but rather by debt overhang.

Furthermore, structural adjustment programs only further compounded dependency and retarded economic development. For countries seeking debt relief in the 1980s and 1990s, the International Monetary Fund (IMF) and World Bank assumed a leading role in renegotiating and restructuring debt to external creditors and evaluating creditworthiness for future loans (Bradshaw and Huang, 1991; McMichael, 2004; Peet, 2003). These institutions mandated that future loans be conditional upon neoliberal structural adjustment reforms, such as currency devaluation, the reduction in health and social services, and opening up domestic markets to foreign investors. Empirical research shows that these IMF-imposed austerity measures lowered living standards and impeded economic development in these countries (Babb, 2005; Bradshaw et al., 1993; Bradshaw and Huang, 1991; Kaup, 2010; Walton and Ragin, 1990). This argument implies that countries facing less pressure from the IMF to engage in neoliberal structural reforms would outperform those facing higher levels of IMF pressure.

While the economic restructuring of the 1980s resulted in the global reorganization of manufacturing (Frobel et al., 1980), neoliberal ideology emphasized an increase in natural resource production and exports in resource-exporting countries (McMichael, 2004). According to Mahutga (2006), ‘the expansion of neo-liberal trade regulations is associated with less upward mobility than the period before this policy expansion’ (p. 1882). Overall, dependency and underdevelopment of the periphery intensified (Mahutga, 2006; Peet, 2007) and income inequality increased (ElGindi, 2017). Resource-dependent countries face great difficulty counteracting this neoliberal push (Kaup, 2010). Therefore, in addition to state capacity, it is possible to derive hypotheses from the overarching claim that variations in dependency explain the diverse development performances of oil- and mineral-dependent countries. We expect those with less dependence on oil/mineral exports, FDI, external debt, and structural adjustment loans to outperform those that are more dependent.

Data and methods

To test these hypotheses, I compile an unbalanced panel dataset of 36 oil- and mineral-dependent countries in the Global South, that is, outside the Organisation for Economic Co-operation and Development (OECD) and former Soviet bloc, for which longitudinal data are available on an annual basis from 1984 to 2010 – variables that capture the potential explanatory factors, along with some key controls. The sample includes countries that meet three criteria. First, the countries are independent, sovereign states in the Global South throughout the period of study. The Soviet successor countries, such as Azerbaijan and Kazakhstan, are excluded because these states lacked autonomy over their energy and development policies and experienced unique economic changes following the fall of communism that are beyond the scope of this study (Jones Luong and Weinthal, 2010; Nee, 1989).

Second, only countries that are oil- and/or mineral-dependent are included. This data on oil and mineral exports by country and year comes from UNCOMTRADE and World Bank’s World Development Indicators (WDI). Oil corresponds to the UNCOMTRADE Standard International Trade Classifications (SITC) for fuels and natural gas, and minerals correspond to the SITC for crude fertilizer, mineral ores, metalliferous ores and scrap, and non-ferrous metals. Following the resource curse literature, oil and mineral dependence is measured as the share of fuel and mineral exports in the gross domestic product (GDP; Sachs and Warner, 1995). According to the standard criteria in the literature, a country is resource-dependent if natural resource exports account for 10 percent or more of GDP (Auty, 1993).

Finally, in order to examine the changes within and between countries over time, the data for each country included in the sample must be available for at least two time points. Given this criterion, ideally I would have 49 countries. However, based on data availability for the independent variables, the final sample consists of 36 countries: Algeria, Angola, Bahrain, Bolivia, Brunei Darussalam, Cameroon, Chile, Republic of Congo, Ecuador, Egypt, Gabon, Guinea, Guyana, Indonesia, Iran, Jamaica, Jordan, Kuwait, Malaysia, Mexico, Mongolia, Morocco, Mozambique, Niger, Nigeria, Oman, Papua New Guinea, Peru, Sudan, Syria, Togo, Trinidad and Tobago, Tunisia, Venezuela, Yemen, and Zimbabwe.³

The time period 1984–2010 covers the neoliberal era. While the dominance of the state-led order was already being modestly challenged by the 1970s, the outbreak of the 1982 Third World debt crisis intensified the neoliberal order (Babb, 2003, 2005; Centeno and Cohen, 2012; Portes, 1997). Neoliberal market policies – the withdrawal of state management of the economy, opening to foreign trade, privatizing state enterprises, market deregulation, capital market liberalization, and drastic reductions in public spending and down-scaling of state-supported social programs – were linked to a new variety of IMF lending packages that conditioned loan disbursements for balance of payments relief upon neoliberal ‘structural adjustments’ (Babb, 2003, 2005; Centeno and Cohen, 2012; Portes, 1997). About half the countries in the sample adopted neoliberal reforms in the early 1980s (1982–1986; 1984 is the earliest date for which data on all independent variables are available; IMF, 2017). The analysis ends in 2010 so as not to capture the after-effects of the 2008–2009 global financial shock, the worst economic crisis of global proportion since the Great Depression of the 1930s. While neoliberal ideology remains unchallenged by alternatives, the crisis weakened neoliberalism’s hold on policy as governments responded with a series of state interventions to stabilize the economy, followed by a resurgence of greater state regulation (Centeno and Cohen, 2012; Dale, 2012). Thus, this study is confined to the period covering the heyday of neoliberalism.

Dependent variables

Economic development is measured using per capita GDP (purchasing power parity (PPP) in constant 2005 US dollars, logged to reduce skew). I use a PPP measure because it captures the cost of a domestic basket of goods, which is a more ‘welfare-oriented’ way of reflecting national income (Firebaugh, 2003). These data come from the Penn World Tables (PWT) (Heston et al., 2012).

Independent variables

The dataset includes independent variables that capture the main explanatory factors: state capacity and dependency (oil/mineral export dependence, foreign capital dependence, debt dependence, and IMF pressure). Measures of state capacity are notoriously difficult to agree upon and develop in the social science literature. State capacity can be operationalized as the tax–GDP ratio or tax–total government revenue ratio (Delacroix and Ragin, 1981: 1325; Lee et al., 2007). However, these measures are relatively high in oil- and mineral-dependent countries due to resource rents and not necessarily because these states have the ability to extract taxes from the general population (Karl, 1997). The exclusion of rents from the tax data results in very limited data points for the countries in this sample, which reduces the ability to draw meaningful conclusions from the regressions.

State capacity is also often operationalized using the World Bank’s ‘government effectiveness’ measure from the World Governance Indicators (WGI). However, this indicator suffers from perception biases of firms, investors, and citizens and conflates the conceptualization of good governance with controversial policy preferences that favor the business elite (Kurtz and Schrank, 2007). In addition, different sources are sometimes used for different years, thereby reducing internal consistency and making it difficult to compare countries across space and time, which is critical in this research. Finally, these data are only available from 1996. Such a short time frame is insufficient for historical analyses covering the neoliberal era, which rules it out from being adopted here.

To test the state capacity hypothesis, I use two other standard measures – the International Country Risk Guide (ICRG) Quality of Government indicator (PRS Group, 2005) and total government expenditure (% of GDP). While the bureaucratic quality indicator is also criticized for containing perception and policy biases (Kurtz and Schrank, 2007), which may raise questions about its validity, I use it for substantive and practical reasons. Substantively, the ICRG variable is consistent with how historical-institutional scholars characterize the quality of the state bureaucracy that effectively promotes development (Amsden, 2001; Evans, 1989, 1995; Kohli, 2004; Rueschemeyer and Evans, 1985).⁴ It captures the extent to which the bureaucracy is more autonomous from political pressure, has an established mechanism for recruitment and training, and can ‘govern without drastic changes in policy or interruptions in government services’ after elections (PRS, n.d.: 7). On a 4-point scale, countries that are perceived to perform well in these areas receive higher points. In addition, studies show that ICRG bureaucratic capacity scores are strongly correlated with other less subjective measures of state capacity, such as ratio of taxes to GDP and government spending in health and education (Cole, 2016).

Second, using this variable also allows this study to be consistent with previous research on resource-dependent countries, the vast bulk of which use this measure (Arezki and Van der Ploeg, 2007; Mehlum et al., 2006), and recent cross-national work on state capacity (Charron and Lapuente, 2011; Cole, 2016). For practical reasons, this indicator also has the added advantage of broader availability (back to 1984 for 140 countries) and internal consistency as the researchers who compile it use the same criteria, definitions of concepts, and scoring decisions every year across all countries.

The other measure of state capacity used in research on states and development is total government expenditure (% of GDP; from the WDI) (Garrett, 2001; Lee et al., 2007; Lin, 2015; Shen and Williamson, 2005). This measure includes all government expenditures for purchases of goods and services, including compensation of employees (wages and salaries) and excludes government military expenditures.⁵ This indicator reflects state capacity with respect to how much resources are allocated toward enhancing citizens’ general welfare. Neoliberalism calls for a reduction in public sector spending. However, a greater capacity to provide goods and services to the population is hypothesized to positively impact on development.

The second set of independent variables in this study operationalizes arguments from the dependency/world systems theories. First, reliance on resource exports is one of the main mechanisms of dependency that impedes economic development. To evaluate this hypothesis, I use the percent of oil and mineral exports as a percentage of GDP (Sachs and Warner, 1995).

Second, I use inward FDI stocks as a percentage of GDP from the UNCTAD World Investment Directory to examine foreign capital dependence (Curwin and Mahutga, 2014; Dixon and Boswell, 1996; Kentor, 1998; Soysa and Oneal, 1999).⁶ FDI can also be measured as flows. I use stocks rather than flows because while inward FDI flows may positively impact economic growth in the short-term, long-term dependence on foreign capital, measured by inward FDI stock, may have negative effects (Bornschier and Chase-Dunn, 1985; Bornschier et al., 1978). Nevertheless, I crosscheck the models with inward FDI flows as a percentage of GDP, and the results are found to be essentially the same (see Table 5).

This study is interested in examining a country’s increasing burden of debt relative to the size of the national economy. Therefore, to measure the impact of debt dependence, I use total external debt stocks/gross national income (GNI) from the WDI database (Bradshaw and Wahl, 1991; Ha, 2015). Finally, to examine the impact of the IMF in the neoliberal period, I operationalize IMF pressure with the ‘Use of IMF credit’ variable from the WDI dataset (in proportion to GDP), which includes purchases and drawings under Stand-By, Extended, Structural Adjustment, Enhanced Structural Adjustment, and Systemic Transformation Facility Arrangements, together with Trust Fund loans. These are the loans that come with ‘conditionalities’ and require countries to undergo structural adjustment (Jiang, 2014).⁷

Controls

Following resource curse and dependency/world systems studies, a number of other variables are added to the models to control for other potential sources of variation in development. For consistency, I use the same variables as these studies (all are taken from the WDI database). The first control variable is national savings rates measured as domestic savings as a percent of GNI, adjusted for natural resource depletion. Matsen and Torvik (2005) and Atkinson and Hamilton (2003) argue that there is a correlation between higher national savings rates and development in resource-dependent countries.

Second, this study controls for human capital since previous research links human capital in the form of secondary education enrollment (as a percentage of the population) to development (Curwin and Mahutga, 2014; Kentor and Boswell, 2003; Shen and Williamson, 1997), particularly in resource-dependent countries (Bravo-Ortega and De Gregorio, 2007; Ding and Field, 2005; Kurtz and Brooks, 2011; Stijns, 2001). Third, similar to other dependency/world system and resource curse studies, population is included to control for any demographic effects on economic growth, such as those caused when rapid population growth affects the number of workers in the population and, in turn, economic growth (Bjorvatn and Farzanegan, 2013; Curwin and Mahutga, 2014). Finally, as is customary for panel data analysis, year is included in the models as a linear time trend to guard against spurious associations among factors with common trends and to control for linear increases in per capita income in these countries.⁸

This annual cross-sectional time-series data form an unbalanced panel where 36 countries contribute a total of 487 observations for the 1984–2010 period.⁹ Table 1 lists the number of observations and years that each country in the sample contributes to the analysis. Table 2 presents the descriptive statistics and the sources of the variables used in the analysis, and Table 3 shows all the correlation coefficients among the variables.¹⁰

Table 1.

Countries and years included in the analysis.

Country	Obs (n)	Years
Algeria	26	1984–1997, 1999–2010
Angola	2	1990, 1991
Bahrain	11	1986–1996
Bolivia	11	1998–2003, 2006–2010
Brunei Darussalam	4	1991–1994
Cameroon	16	1986, 1987, 1989, 1990, 1995, 1997, 2000–2009
Chile	8	1986, 1988–1993, 1998
Republic of Congo	3	1985, 1986, 1994
Ecuador	21	1984–1988, 1990–1993, 1996, 1998, 1999, 2000–2007, 2010
Egypt	20	1984, 1986–1989, 1991–1997, 1999–2004, 2009, 2010
Gabon	2	1994, 1996
Guinea	8	1996, 1999, 2001, 2002, 2004–2006, 2008
Guyana	8	1999, 2001, 2003, 2005, 2007–2010
Indonesia	25	1984, 1986–1999, 2001–2010
Iran	9	1997, 1999, 2000–2006
Jamaica	17	1986–1989, 1991, 1993, 1999–2005, 2007–2010
Jordan	21	1984–1994, 1999, 2000, 2002–2008, 2010
Kuwait	8	1986–1990, 1996–1998
Malaysia	20	1984–1993, 1996–2005
Mexico	25	1984, 1985, 1988–2010
Mongolia	6	1998–2003
Morocco	27	1984–2010
Mozambique	13	1994, 1995, 1999–2002, 2004–2010
Niger	15	1996–2010
Nigeria	11	1984, 1986, 1987, 2000–2002, 2006–2010
Oman	11	1987, 1989, 1990–1998
Papua New Guinea	10	1984, 1986–1993, 1998
Peru	27	1984–2010
Sudan	2	2005, 2010
Syria	17	1985–1987, 1989, 1990, 1992, 1995–1997, 2000–2007
Togo	18	1986–1991, 1994–1997, 1999–2005, 2007
Trinidad and Tobago	8	1986–1993
Tunisia	27	1984–2010
Venezuela	8	1986–1993
Yemen	7	1999, 2001, 2003–2005, 2008, 2010
Zimbabwe	15	1984–1986, 1990–1997, 1999–2002

Table 2.

Descriptive statistics.

Variable	Description	Mean	SD	Min.	Max.	Source
Real GDP per capita	PPP GDP per capita at 2005 constant prices in US dollars, logged	8.270	0.918	5.885	10.902	Penn World Tables, Heston et al. (2012)
State capacity	Bureaucratic quality	1.915	0.752	0	3.5	Quality of Government Dataset, International Country Risk Guide (ICRG) (PRS Group, 2005)
Government expenditure	Total government expenditure (% of GDP)	15.026	5.474	5.694	50	World Development Indicators, World Bank (2016)
Oil/mineral export dependence	Oil and mineral exports (% of GDP)	12.135	12.335	0.292	71.987	World Development Indicators, World Bank (2016)
Foreign capital dependence	Inward FDI stocks (% of GDP)	23.315	19.978	0.053	111.117	United Nations Conference on Trade and Development, UNCTADstat (2014)
Debt dependence	Total external debt stocks (% of GNI)	66.392	42.597	3.681	356.151	World Development Indicators, World Bank (2016)
IMF pressure	Use of IMF credit (% of GDP)	2.158	3.053	0	24.619	World Development Indicators, World Bank (2016)
National savings	Savings adjusted for natural resource depletion (% of GNI)	9.142	8.353	0.002	37.681	World Development Indicators, World Bank (2016)
Human capital	Gross enrollment ratio, both sexes (% of total population of official secondary education age)	57.521	24.375	5.132	106.237	World Development Indicators, World Bank (2016)
Population	Population (thousands)	32,900	49,300	264,365	242,000	World Development Indicators, World Bank (2016)

SD: standard deviation; GDP: gross domestic product; PPP: purchasing power parity; FDI: foreign direct investment; GNI: gross national income; IMF: International Monetary Fund.

Table 3.

Matrix of correlations for all variables.

	1	2	3	4	5	6	7	8	9	10
1. GDP per capita (log)	1.000
2. Bureaucratic capacity	.537	1.000
3. Government expenditure	.271	.158	1.000
4. Oil/mineral export dependence	.493	.123	.222	1.000
5. Foreign capital dependence	−.230	−.223	−.063	−.057	1.000
6. Debt dependence	−.400	−.353	−.029	−.106	.677	1.000
7. IMF pressure	−.343	−.313	−.068	−.130	.928	.776	1.000
8. National savings	.284	−.015	.188	.784	−.032	.003	−.161	1.000
9. Human capital	.734	.355	.069	.262	.079	−.272	−.168	.079	1.000
10. Population	−.119	−.068	−.328	−.193	−.097	−.159	−.110	−.051	−.029	1.000

GDP: gross domestic product; IMF: International Monetary Fund.

Methods

I utilize fixed-effects (FE) models with panel-clustered robust standard errors for theoretical and methodological reasons. For analyses of unbalanced panel data that encompass both within- and between-country variation, Pooled Ordinary Least Squares (POLS) may be inappropriate because errors are likely to be correlated within the panels due to unmeasured heterogeneity, and this can bias regression coefficients (Beck and Katz, 1995; Greene, 2000; Wooldridge, 2002).

Scholars often use FE and RE models to deal with this problem (Hsiao, 2003; Wooldridge, 2002). The FE models explain historical variation within countries by controlling for time-invariant differences between countries using country-specific constants and differencing away all unmeasured between-country variation (Halaby, 2004). In so doing, the FE approach is robust against missing control variables, thereby guarding against the possibility that enduring/slow-changing cross-national differences (such as electoral formats and ethnic diversity) impact development. The RE approach, conversely, includes a normally distributed error term to adjust for within-panel error correlation, thereby preserving both cross-national (between-country) and historical (within-country) variation. However, RE models are less capable of controlling for within-country unobservables (Hsiao, 2003). The Hausman test for any heterogeneity bias reveals that the FE is the preferred technique, so I present these as the main results. To help ensure unbiased and consistent parameter estimates, I also control for serial correlation and heteroskedasticity using robust-cluster standard errors at the country level (Wooldridge, 2002).

To ensure that the models are not sensitive to the FE model specification or to particular variables, Table 5 displays an array of sensitivity analyses using the RE approach and alternative variables and the conclusions remain consistent. I also use RE methods to examine whether the main results still hold with the inclusion of some important time-invariant factors. While the FE methods are unable to deal with variables that either do not or barely change over time, the RE models can accommodate these factors in addition to taking into account unmeasured heterogeneity. Finally, influential cases and outliers can skew the results. I examine residual versus predicted plots and test for and drop data points with residuals greater than ±2 in each model. I present the results without the outliers for greater robustness, but the findings with the outliers included are consistent.

Results

Table 4 reports the results of the FE analyses that control for unmeasured country effects. Model 1 includes only the linear time trend to obtain a baseline estimate of the trend in economic development. In an effort to examine the relative impacts of state capacity and dependency arguments on development, I present the models that regress development on each of these groups of independent variables separately before combining them in the final model. Model 2 examines only the state capacity variables – measured as bureaucratic capacity and government expenditure – and Model 3 investigates whether these findings hold with the introduction of the control variables. Model 4 tests the dependency/world systems hypotheses, that is, whether oil/mineral export dependence, foreign capital dependence, debt dependence, and IMF pressure explain this trend, and Model 5 introduces the control variables to those in Model 4. Finally, Model 6 includes all the key independent and control variables.

Table 4.

Fixed-effects regression of real GDP per capita (log) on dependency, state capacity, and control variables: 36 oil- and mineral-dependent countries in the Global South, 1984–2010.

	(1)	(2)	(3)	(4)	(5)	(6)
State capacity
Bureaucratic capacity				.066*** (.021)	.050** (.022)	.062*** (.021)
Government expenditure				−.002 (.028)	−.019 (.029)	−.016 (.026)
Dependency
Oil/mineral export dependence		.002 (.016)	.012 (.016)			.008 (.016)
Foreign capital dependence		−.028 (.131)	.040 (.122)			−.012 (.096)
Debt dependence		−.157*** (.042)	−.143*** (.038)			−.169*** (.040)
IMF pressure		.012 (.079)	−.021 (.066)			.021 (.072)
Controls
National savings			.014 (.028)		.010 (.032)	.010 (.029)
Human capital			.101* (.055)		.092* (.050)	.077 (.058)
Population			.140*** (.051)		.087** (.041)	.064 (.045)
Year	.012*** (.002)	.009*** (.003)	.002 (.004)	.011*** (.002)	.005 (.003)	.003 (.003)
Constant	−.491*** (.088)	−.439*** (.134)	−.183 (.162)	−.466*** (.081)	−.282** (.119)	−.244* (.125)
Observations	487	487	487	487	487	487
R ²	.402	.483	.541	.468	.499	.588

GDP: gross domestic product; IMF: International Monetary Fund.

Robust standard errors are in parentheses.

significant at 10%; **significant at 5%; ***significant at 1%.

The results of Model 2 show that of the state capacity variables, the measure for bureaucratic capacity shows a positive, statistically significant correlation with economic development at the very high p < .01 level. The positive impact of bureaucratic capacity remains consistent even with the addition of controls (Model 3). Even in the full model (Model 6), with all the dependency variables and the controls, bureaucratic capacity remains positive and significant with a standardized coefficient of .062 that is significant at the .01 level. This suggests that oil- and mineral-dependent states with higher bureaucratic capacity, that is, greater autonomy and internal coherence, the ability to minimize corruption, and the expertise to effectively channel resource revenue toward development, are likely to outperform those with weaker bureaucratic capacity.

However, the relationship between government expenditure and economic development is not significant. This might be reflective of two issues. First, as Evans (1989) points out, it is not so much the size of the public sector that matters, but what it is capable of doing. For example, industrialized countries in the core typically have large public sectors that are capable of providing extensive welfare provisions (Quadango, 1987). The East Asian developmental states possess comparably smaller public sectors but a high capacity to direct economic development (Amsden, 1989; Wade, 1990). Another possibility is there may be shortfalls with the measure itself. The WDI draws these data from the IMF Government Finance Statistics. Ideally, I would also subtract spending on public order and safety since this expenditure may be channeled toward repressive and/or war efforts, which detracts from development (Le Billon, 2001; Ross, 2006). However, there is poor time-series and country coverage for these data in the IMF Government Finance Statistics for the countries in this sample (only available for 10 countries in this sample and often only for one or a few years out of the 26 years under examination here). Utilizing what data are available and subtracting this amount from the total government expenditure do not alter the results. When more disaggregated data for these countries become available, this dynamic might be explored more closely.

Model 4 includes the variables that test the dependency/world systems hypotheses. Consistent with arguments that debt dependence is negatively associated with economic development, debt stocks/GNI is negatively correlated with per capita income. These results are robust to the introduction of the controls and the state capacity variables (Models 5 and 6). Substantively, this finding suggests that, net of other factors, a .1 increase in debt dependence in oil- and mineral-dependent countries worsens the per capita income (log) by a little under two points. However, the effects of oil/mineral exports, foreign capital dependence, and IMF pressure on the dependent variable are not statistically significant for this group of countries.

There are a number of explanations for the lack of evidence to confirm the other dependency/world systems hypotheses. With respect to oil/mineral export dependence, for example, as Clark (2008, 2010) notes, resource dependency is not necessarily harmful as trade relations with a greater number of partners can reduce the negative effects of dependency and enhance development. Since these countries are uniquely positioned in the global capitalist system to export natural resources to higher income, equivalent, and lower positioned countries, they may be resource dependent but not partner dependent – a promising area of study using network-based methodologies. Relatedly, as Kaup and Gellert (2017) show, competing hegemons provide new possibilities for states seeking to increase their share of benefits from resource extraction. In particular, the rise of China may be opening up different possibilities for these nations. China accounts for much of the recent growth in demand for fuels and metals. Moreover, in the US-centered hegemonic regime, the United States as a raw material producer itself competed with lower income resource-exporting countries. Conversely, China specializes in labor-intensive manufacturing, so trade relationships with oil- and mineral-dependent countries are more complementary to the latter’s development rather than competitive (Bonini, 2012).

Similarly, the impact of conditional IMF loans might warrant further investigation. While some of these countries such as Kuwait, Malaysia, and Syria managed to entirely avoid IMF conditionality (IMF, 2016), for those that received these loans, perhaps the implementation processes varied quite significantly (see, for example, Fourcade-Gourinchas and Babb (2002) for a comparison of Mexico and Chile). Therefore, for these countries, it may be more enlightening to examine how structural adjustment was enacted rather than the quantity of IMF loans.

Table 5 displays a range of sensitivity tests showing that these results are consistent. The results of the RE regression are substantively identical to those from the FE full model shown in Table 4. The coefficients for state capacity and debt dependence are approximately the same size in both models (.062 and .074 and −.169 vs −.191, respectively), and both are statistically significant at p < .01 level. I also note that of the control variables, human capital is also found to be positively associated with economic development in this sample in the RE model, consistent with research underscoring the necessity of including it as a control variable.

Table 5.

Sensitivity analyses.

	(1)RE model	(2)Outliers not removed	(3) Gov. Fraction instead of ICRG variable	(4)Debt service/exports instead of debt stocks/GNI	(6)Inward FDI flows/GDP instead of FDI stock/GDP	(6)T − 1 time dummies instead of linear year term	(7)Additional controls (RE)	(8)Endogeneity test
State capacity
Bureaucratic capacity	.074*** (.022)	.069*** (.025)	−.032** (.012)	.043** (.020)	.066*** (.022)	.063*** (.018)	.091*** (.027)	.053** (.022)
Government expenditure	−.007 (.025)	−.006 (.024)	−.021 (.035)	.011 (.030)	−.023 (.026)	−.016 (.026)	.031 (.027)	−.028 (.033)
Dependency
Oil/mineral export dependence	.014 (.017)	.018 (.018)	.003 (.025)	.012 (.016)	.011 (.018)	.010 (.019)	.027 (.017)	.028 (.021)
Foreign capital dependence	−.005 (.096)	−.014 (.022)	.016 (.028)	.001 (.019)	−.012 (.011)	.003 (.024)	.006 (.021)	.020 (.026)
Debt dependence	−.191*** (.042)	−.122*** (.033)	−.093** (.035)	−.158*** (.027)	−.137*** (.030)	−.145*** (.040)	−.200*** (.051)	−.116*** (.034)
IMF pressure	.041 (.075)	.012 (.020)	.001 (.016)	−.024* (.012)	.004 (.022)	.007 (.023)	.042 (.039)	.004 (.018)
Controls
National savings	.034 (.033)	.005 (.033)	.012 (.033)	−.022 (.029)	.007 (.036)	.034 (.048)	.035 (.048)	.029 (.027)
Human capital	.137*** (.052)	.054 (.069)	.078 (.077)	.133* (.076)	.089 (.068)	.106 (.070)	.244*** (.064)	.121* (.062)
Population	.033 (.038)	.048 (.076)	.238*** (.087)	.160*** (.041)	.084 (.056)	.098* (.057)	−.018 (.056)	.174*** (.049)
OPEC membership							.218 (.256)
Democracy index							−.007* (.003)
Commodity price index							.020 (.015)
Natural resource dummy							−.515** (.212)
Manufacturing							.016 (.035)
Year	.001 (.003)	.008 (.006)	.005 (.006)	−.002 (.003)	.004 (.004)		−.004 (.006)
Constant	−.087 (.151)	−.340 (.217)	−.327 (.228)	−.246* (.125)	−.236 (.147)	−.111*** (.040)	.295 (.289)	−.034** (.014)
Observations	487	512	468	384	487	487	310	487
R ²	.637	.569	.551	.683	.592	.613	.716	.524

RE: random-effects; ICRG: International Country Risk Guide; GNI: Gross National Income; FDI: foreign direct investment; GDP: Gross Domestic Product; IMF: International Monetary Fund; OPEC: Organization of Petroleum Exporting Countries.

significant at 10%; **significant at 5%; ***significant at 1%.

Table 5 also shows that the results hold when the outliers are not removed (Model 2), when inward FDI flows (% GDP) replace inward FDI stock (% GDP), and when T − 1 time dummies substitute for the linear year term. Alternative variables for bureaucratic capacity and debt dependence also do not alter the main findings. I replace the ICRG bureaucratic quality variable from the main analysis with an alternative ‘government fractionalization’ indicator used in other empirical studies (Bjorvatn et al., 2012). The index is taken from Beck et al. (2001) and goes from 0 to 1 where higher values indicate a highly fractionalized government comprising a large number of parties, which signals a weaker capacity to govern; lower values denote a more cohesive government, consisting of a small number of parties (possibly only one), suggesting a strong government (Bjorvatn et al., 2012). The results show that the higher levels of government fractionalization negatively impacts economic development, confirming the hypothesis that a weak state inhibits development. Likewise, the models are not sensitive to different measures of debt dependence. Some scholars use debt service payments/exports of goods and services, arguing that this measure represents the pressure a country faces to repay its loans since exports help countries obtain hard currency to pay off their debt (Bradshaw and Tshandu, 1990; Walton and Ragin, 1990). The results hold with this indicator, with the exception that the coefficient for IMF pressure also becomes negative and significant (Model 4). This may be partly a function of the smaller sample as the data for debt-to-exports ratios is missing for a number of countries and years in this study’s sample, reducing the number of observations from 487 to 384. Nonetheless, overall, the bureaucratic quality and debt dependence variables remain significant.

The findings also remain robust when a number of additional variables are added to the model. These include (1) a dummy variable for a country’s membership in the Organization of the Petroleum Exporting Countries (OPEC), which can impact its development trajectory through coordinated price increases for their petroleum exports (Hallwood and Sinclair, 2016; Levy, 1978); (2) manufacturing (% of GDP) to control for the positive effect of manufacturing on economic growth, although scholars argue that Dutch Disease effects shift labor and capital away from manufacturing during oil and mineral booms (Matsuyama, 1992; Sachs and Warner, 1995); (3) ‘democratic quality’ using the polity2 variable from the Polity IV dataset since less democratic countries are associated with greater corruption, which, in turn, may negatively impact development (Bhattacharyya and Hodler, 2014; Boschini et al., 2013; see also Barro, 1996); (4) a dummy variable to capture the dominance of either oil or mineral exports in these economies to control for effect of oil exporters, which benefit from much higher commodity prices than mineral exporters (Auty, 1993); and (5) a commodity price index calculated following Deaton (1999) to capture the impact of price fluctuations for a country’s specific basket of resource exports, which can boost or lessen resource income and affect economic growth.¹¹ Even when controlling for all of these factors, the results of the main model hold – bureaucratic capacity has a positive and significant relationship with development and debt dependence has a negative and significant relationship with development in these nations.

The only controls that are statistically significant are the natural resource dummy and democracy index. The negative, statistically significant coefficient of the natural resource dummy variable, consistent with other studies’ findings, shows that oil-dependent countries outperform mineral-dependent countries (Auty, 1993; ElGindi, 2017). This may be because oil rents are higher than any other primary commodity rents, and therefore brings in larger government revenues per unit than minerals (Amuzegar, 2001). In addition, most mineral exporters must still import fuel. Consequently, when oil prices rise, they suffer like other oil importers (Auty, 1993).¹² The result that higher democratic quality is associated with lower levels of development falls in line with other research that shows that resource-dependent autocracies outperform democracies (Collier and Hoeffler, 2009) and reflects a broader uncertainty in the literature regarding the link between political democracy and economic performance (Przeworski et al., 2000).

Finally, it is important to check for the possibility of endogeneity. In other words, while bureaucratic capacity may affect per capita income, per capita income may also impact bureaucratic capacity as more developed economies may have greater resources to invest in recruiting and better compensating civil servants and improving the organizational capacity of the state to meet the needs of the population. Similarly, while debt dependence may reduce per capita income as countries channel national income toward debt repayments, poorer economies may also rely more heavily on external debt. One standard approach to endogeneity is the two-stage least square FE or first differenced regressions using instrumental variables. The main obstacle to using this approach here is that the standard instruments for bureaucratic capacity in the literature – the log of European settler mortality (Acemoglu et al., 2001), latitude of country, historical origin of a country’s laws (La Porta et al., 1999), and fraction of the population speaking English or a European language (Hall and Jones, 1999) – are all time-invariant. While used in ordinary least squares and cross-sectional regression analyses, it is not possible to estimate FE or first differenced estimators since they will get wiped out by the within-transformation.

Another method of dealing with endogeneity is to lag the independent variables by 1 year to remove the contemporaneous effect if the regressor is endogenous (Alcacer and Ingram, 2013; Ferree and Singh, 2006; Yoo and Koo, 2014).¹³ Running a FE estimation using these lagged independent variables ‘provides superior protection against omitted variable bias’ because FE methods control for all unmeasured, stable characteristics of the countries (England et al., 2007: 1240; see also Allison, 1990; Halaby, 2004). The results hold (Table 5, Model 8): higher bureaucratic capacity of the state is likely to promote economic development, but there is a greater propensity for debt dependence to impede development.¹⁴

Discussion and conclusion

Oil and minerals are important commodities, not only on the global market but also to the countries that depend on their exports for national income. Despite the prevailing view that these countries possess the weakest states and face acute levels of dependency and exploitation in the world system, under neoliberal pressures to aggressively export their resources, open up domestic markets to foreign capital, and reduce state regulation of the economy, some of these oil- and mineral-dependent economies fare better than others. Most of our understanding of oil- and mineral-dependent countries’ economic development comes from in-depth qualitative case studies and cross-national statistical analyses that compare these countries to those that depend on other commodities. However, there is very little cross-national evidence concerning what accounts for the divergent performances among oil- and mineral-dependent countries. This study fills this important gap using a sample of 36 oil- and mineral-dependent countries and systematically testing hypotheses derived from the resource curse literature, which provides rich insights into domestic state-level characteristics, and dependency/world systems research, which attends to the global structures of inequality and economic dependency in the world system. The results show that of all the factors hypothesized to produce uneven development outcomes among these countries, bureaucratic capacity and debt dependence account for much of the variation among these countries over the neoliberal period examined in this analysis: Higher bureaucratic capacity can enhance economic development, while debt dependence is likely to hinder development.

These results contain important implications for the future study of oil- and mineral-based economies. The finding regarding bureaucratic capacity aligns with previous research about robust bureaucracies (Evans, 1995; Mehlum et al., 2006). Under neoliberal conditions, the state remains crucial (Evans, 1997; Wade, 2003) – in this case for ensuring that resource revenues are effectively channeled toward enhancing per capita income. In countries such as Nigeria, weak state bureaucracies, characterized by extensive patron–client networks, insufficient trained bureaucrats for managing oil wealth, and a lack of cohesion between agencies and government administrations, citizens are less likely to benefit from resource extraction relative to those in Trinidad and Tobago, for example, where the state has greater bureaucratic capacity. States with higher bureaucratic capacity – those that are better able to mitigate patronage, have meritocratic systems for recruiting and training personnel, and cohesive agencies that run smoothly even when government changes – are more likely to effectively channel resource revenue toward economic development.

In addition, in strong support of dependency/world systems perspectives, there is a strong, negative relationship between dependence on external debt and economic development in this sample. Just as debt dependence obstructed development from the mid-1970s to the mid-1980s in middle- and low-income countries (Bradshaw and Wahl, 1991; Glasberg and Ward, 1993), this study shows that debt dependence continues to harm oil- and mineral-dependent countries well beyond the 1980s. Greater debt burdens, such as in Bolivia and Guyana, divert capital away from much-needed development programs and toward debt repayments. This reflects an inherent structural problem in the world capitalist system that shapes these countries’ development trajectories.

Together, these findings provide critical intervention into resource curse theory by demonstrating the importance of external structural constraints. In other words, a holistic view of the development of these nations must examine and go beyond internal institutions and incorporate an analysis of unequal power and dependency in the world system. While dependency/world systems scholars already agree that state capacity is critical, this study nevertheless provides important implications for dependency/world systems theory. The effect of external debt on development in conjunction with the non-significant effects of resource exports, foreign capital dependence, and IMF pressure presents provocative nuances for dependency/world systems theory as it pertains to this particular subset of raw-material-exporting countries, which again underscores the importance of disaggregating these countries from other raw material exporters. This study shows that dependency in the form of debt may be more pernicious than other forms of dependence based on trade, investment, or IMF loans for these countries. This supports Gereffi’s (1989) assertion that different forms of dependency have different effects on development for different groups of countries and Kaup and Gellert’s (2017) findings about the importance of considering the opportunity structures for development within the world system. Of the mechanisms by which global economic engagement can adversely affect countries in the Global South, the oil- and mineral-dependent countries may need to especially guard against the negative effects of debt.

Finally, these findings raise questions about the relationship between the state and debt dependence. The moderate negative correlation between these measures (r = −.358) suggests that states with stronger bureaucratic capacity are less likely to take on greater debt and vice versa. In this case, one must carefully assess the impact of bureaucratic capacity on economic development as it relates to debt. For example, South Korea accumulated external debt to finance growth in manufacturing (Frieden, 1981). Therefore, it is important to ask the following questions: When and why do some of these countries take on more debt in the neoliberal period? Does the source of debt (multilateral, bilateral, or commercial banks) matter? How is debt used or misused, accrued, or serviced? Exploring these dynamics can provide further insight into the dynamics behind the results of this cross-national study.

Overall, this research emphasizes the profound importance of further investigating the dynamics of state capacity and debt dependence under neoliberalism for oil- and mineral-dependent countries in the Global South. As long as these countries must rely on capital held by richer nations, they will continue to be locked in a cycle of underdevelopment. For those countries that managed to evade the negative effects of debt dependence and experience positive economic development, we also need to investigate the mechanisms that enabled them to do so and the role of the state in that process. Unlocking this code may be the key to brighter development prospects for more oil- and mineral-dependent countries.

Footnotes

Acknowledgements

I am thankful to Jason Beckfield, Elyas Bakhtiari, Daryl Carr, Dino Christenson, Kara Cebulko, Orly Clerge, Cedric de Leon, John Gerring, Julian Go, Eric Hirsch, Brandon Martinez, Simeon Newman, Shiri Noy, Sigrun Olafsdottir, Charlotte O’Kelly, and Trina Vithayathil for their helpful feedback at various stages. Thanks also to the editor David A Smith and the anonymous reviewers for their comments and suggestions.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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