Oil and Institutional Change: Is there a resource curse?

Oil and Institutional Change: Is there a resource curse?



THOMAS BRAMBORy Stanford University Draft - 03/28/2008 - Comments welcome.

Empirically, natural resource abundance has been found to be strongly associated with a host of negative outcomes in cross-country time-series analyses. We review the existing literature and nd that virtually all articles employing the ratio of primary commodity exports over GDP as the measure of resource abundance nd strong evidence for the 'resource curse', while those using per capita production or reserves as the measure of resource abundance nd evidence against the 'resource curse'. Contrary to claims in the literature that institutions are the result of resource abundance, we propose to view the quality of pre-existing intuitions as the origin of natural resource dependence. Natural resource abundance translates into natural resource dependence only in the presence of poor institutions. In an empirical analysis using a panel of 98 oil-producing countries from 1918-2000, we nd no evidence for the impact of crude oil production on the quality of institutions.

Prepared for the Annual Meeting of the Midwest Political Science Association, Chicago, IL, April 3-6, 2008. I thank Michael Albertus, David Laitin, Steve Haber, Natan Sachs, and Jeremy Weinstein for their comments on earlier drafts. This paper is a rough draft prepared for presentation only. The data and computer code necessary to replicate the results and gures in this analysis will be made publicly available at http://www. stanford.edu/~tbrambor upon completion of the paper. STATA 10 and R were the statistical packages used in this study. y Stanford University, Department of Political Science, 616 Serra St., Encina Hall West, Room 316, Stanford, CA 94305-6044, ([email protected]). 

1 Introduction Empirically, natural resource abundance has been found to be strongly associated with a host of negative outcomes in cross-country time-series analyses. In the rst part of this paper, we systematically review the current literature. In particular, we focus on the measures of natural resource abundance employed in existing cross-country empirical research on the eects of natural resources on political and economic outcomes. We nd that virtually all articles employing the ratio of primary commodity exports over GDP as the measure of resource abundance nd strong evidence for the 'resource curse', while using per capita production or reserves as the measure of resource abundance leads to evidence against the 'resource curse'. It appears that simply the way natural resource abundance is operationalized largely pre-determines the nding for or against a 'resource curse'. We claim that the familiar measure for resource abundance in the literature on the resource curse, commodity exports over GDP, is not exogenous to many of the outcomes of interest such as economic growth. It should be obvious that the denominator of the expression that is used to calculate this commonly used measure of resource abundance captures not only the activities in the resource sector, but also the size of the rest of the economy. Other work unrelated to the resource curse tells us emphatically that institutions are related to economic growth (Acemoglu et al. 2004; Rodrik et al. 2004). If these insights are correct, the ratio of commodity exports over GDP is not exogenous and thus inappropriate to draw inferences about the relationship between growth and resource abundance. Instead, both growth (and other variables) and "resource abundance" measured in this peculiar way are the result of the institutional environment of a country. In the second part of the paper, we propose a theoretical connection between pre-existing institutions and the dierent measures of resource abundance. In essence, a country with a strong set of institutions is able to attract investment into any sector of its economy, including the natural resource sector. As a result we nd a strong positive correlation between natural resource abundance, measured as per capita reserves or production, and good institutions. In contrast, we hypothesize that poor institutions have a strong negative eect on the development of the non-resource sector, but in most cases fail to deter investment into the resource sector. Natural resource rents serve to compensate investors for the additional risks they take on by investing in countries with poor institutions. Since these rents are largely absent outside the natural resource sector, poor institutions will hamper investment into the non-resource sector more than in the resource sector. The presence of poor institutions thus leads to a small non-resource sector relative to a potentially sizable resource sector. As a result, natural resources, if measured as commodity exports/GDP, are found to be negatively correlated with the quality of institutions. In sum, we propose that the quality of intuitions is not so much the result but rather the origin of natural resource dependence. It is the presence of 1

poor institutions that translates natural resource abundance into natural resource dependence because pre-existing institutions jointly determine natural resource dependence and economic development. Dependence, as measured by the ratio of commodity exports over GDP, then is not the result of the negative eects of primary commodities per se, but rather a result of poor pre-existing institutions. The commonly used ratio of commodity exports over GDP as a measure of 'resource abundance' thus selects countries with small GDP's. Based on this criterion of natural abundance, we are led to examine a set of countries in which the nonresource sector is relatively small compared with the resource sector. However, this poor performance of the non-resource sector compared to the resource sector may very well be due to poor institutions sti ing economic growth and not due to the presence of natural resources. It is no surprise then to nd that a sample selected on this basis performs poorer on many economic and political outcomes, a nding that represents the core evidence for the existence of a resource curse. In order for our insight to be valid, we need to show that pre-existing institutions are more or less unaected by the discovery and subsequent exploitation of natural resources, a claim directly contrary to the existing 'resource curse' literature. Using a new data set on petroleum production for almost all oil producing countries in the world1 from 1918 - 2004, we are able to provide empirical evidence on the impact of oil production on a country's political institutions. Unlike previous studies, which are mostly based on post-1970 data, our extended data set allows us to trace the impact of oil production back to the time of discovery and initial production for a large set of countries. We nd no evidence that the discovery and production of oil negatively aects political institutions. More importantly, we nd that this result holds independently of the strength of political institutions at the time of discovery and start of production.

2 Literature Review Natural resources appear to be more of a curse than a blessing to many countries. In the early 1990s, studies by Auty (1990, 1993, 1994) and others found little or no economic growth in many resource-intensive countries over extended periods. In uential works by Sachs and Warner (1995, 1997, 1999) substantiated these results by extending empirical analysis to a larger cross-section of countries and found that a greater dependence on natural resources was associated with slower economic growth after controlling for the usual determinants of growth. Early explanations centered on a phenomenon called "Dutch Disease"2 . This theory contends that a consequence of a booming natural resource sector is the appre1 We

have data on crude oil production for 98 countries in the world ranging from 1918 - 2004. Of these 98 countries, 11 started producing crude oil either in 1918 or before. 2 The term was coined in 1977 by "The Economist" to describe the decline of the manufacturing sector in the Netherlands after the discovery of natural gas in the 1960's. ("The Dutch Disease" (November 26, 1977). The Economist, p. 82-83.)

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ciation of the exchange rate due to primary commodity exports. The resulting reduction in manufacturing exports will lead to slow growth if, as Dutch Disease proponents argue, manufacturing is indeed more conducive to economic growth than resource extraction. Adding to this eect on the domestic currency is that a booming natural resource sector often leads to rising domestic wage rates due to an increased demand of labor, which is said to damage agriculture and manufacturing that have to compete in home or foreign markets with overseas competitors. This impedes economic diversi cation and increases dependence on volatile mineral markets. Another line of reasoning focuses on human capital. Gylfason (2001) argues that resource rich countries tend to underinvest in education which in turn leads to lower levels of schooling and thus lower growth rates. Bravo-Ortega and De Gregorio (2005) nd that the negative eect of natural resources can be oset by higher education levels. Another eect related to the volatility of commodity markets is excessive government borrowing. Fluctuating commodity prices lead to spending booms. Once expenditures are entrenched it becomes more dicult for governments to reduce their budgets in subsequent years. Since the resource can function as collateral, governments are enabled to borrow against future expected income. As a result, in both "boom" and "bust" cycles of the commodity market they start accumulating debt. This is further encouraged by real exchange rate increases in boom times resulting from capital in ows or the Dutch disease, because interest payments on the debt become cheaper. Once commodity prices and the real exchange begin to fall, governments are left with expensive debt that in turn will negatively aect economic growth (Manzano & Rigobon 2001). Beside such economic eects researchers have associated natural resource abundance with a host of negative political consequences as well. The main hypotheses here are built on the concept of the "rentier state", i.e. states that obtain the lion's share of their revenue from resource rents. Theories of the rentier state assert that states that rely mainly on revenue from external resources have no need and/or incentive to tax their own citizenries and as a result, so the story goes, become less accountable to the society they govern (Ross 1999). Ross correctly points out that it is not obvious why states would forgo the additional revenue they could obtain from taxation. Similarly, it is unclear why a lighter taxation should be correlated with the adoption of poor growth policies. Conversely, this would imply that states with higher taxes also choose better growth policies, which is far from obvious (Ross 1999). A related untested observation is the claim that natural resources break the link between taxation and state-building (Karl 1997). In practice, this hypothesis would imply that already established institutions, for example taxation systems, will wither once resource rents become available. Other papers have found that natural resource abundance is associated with a higher probability of civil war (Collier & Hoeer 2004)3 . Jensen and Wantchekon (2004) analyze a 3 While

the alleged direct eect of natural resources on civil war proneness has been disputed by Fearon (2005), he nonetheless accedes that "oil predicts civil war risk [..] because oil producers have relatively low state capabilities given their level of per capita income and because oil makes

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sample of 39 African countries and nd a negative correlation between the presence of a sizable natural resource sector and the level of democracy in Africa4 . Isham et al. (2003) dierentiate among types of natural resources and show that countries dependent on point-source natural resources and plantation crops do relatively poorly on a wide variety of governance indicators. Ramsay (2006) uses natural disaster estimates as an instrument for oil revenues and nds the negative relationship between oil rents and political institutions to be signi cant in the set of oil producing countries. By and large, these studies appear to strongly con rm the "natural resource curse". An interesting commonality of almost all of the studies cited above is that they have used SW's measure of resource dependence, the ratio of primary-product exports to GDP 5 . However, there exists another, small but growing literature on natural resources that uses dierent measures of natural resource abundance and comes to strikingly dierent results. Using subsoil wealth per capita as a measure of resource abundance, Stijns (2001) nds that not only do resource rich countries have higher levels of education but also that resource booms positively aect education spending. In Stijns words "a resource rent shock is all good" directly contradicting the results of Gylfason (2001) cited above. Lederman and Maloney (2003) nd positive growth eects using primary exports divided by total merchandise exports and primary exports over total labor force as their measures of resource abundance. Relying on mineral production over GDP, Davis (1995) nds little corroborating evidence for a resource curse and in fact estimates a positive relationship with economic growth. Based on the same measure, Papyrakis and Gerlagh (2004) nd both negative as well as positive growth eects. Jones-Luong and Weinthal (2006) emphasize the importance of the ownership structure of the oil industry in a country, and argue that only under state ownership will resource abundance lead to a 'resource curse'. Finally, Brunnschweiler (2006) examines several of the available measures, settles on subsoil wealth as her preferred measure, and concludes that there is a positive empirical relationship between natural resource abundance and economic growth between 1970 and 2000. At this point, an informed reader of the resource curse literature may point out that the review so far sells short some of the recent insights of existing research on the eects of natural resources on institutional quality. Indeed, the trend in the literature seems be going away from conjecturing that natural resources directly lead to negative outcomes. Instead, as already mentioned above in the section state or regional control a tempting prize." 4 The authors also present case study evidence showing that democratic reforms have only been successful in resource poor countries such as Benin, Mali, and Madagascar. 5 Sachs and Warner use the share of primary exports in 1970, the beginning of their period of investigation, as an indicator of natural resource dependence. Other studies have also used slight variants of SW's measure, such as the share of primary exports for every year, averages over the whole period under consideration or decade averages. Primary products in SW's analyses include food, agricultural products, fuels, and minerals - a heterogeneous mix that may not appropriate for some mechanisms, such as the rentier state, identi ed in the literature.

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on political consequences, scholars now tend to hypothesize that institutions are negatively aected by natural resource abundance and these institutional eects in turn cause the negative outcomes presented above (Karl 1997, Ross 2001a, Sokolo & Zolt 2004, Robinson, Torvik & Verdier 2002). To do justice to this trend, we will end our literature review with some insights of existing research on the eects of natural resources on institutional quality. For example, Mehlum et al. (2006) include an interaction term of resource abundance and an index of institutional quality and show that the resource curse only exists in countries with weak institutions. Similarly, Bulte et al. (2005) nd that once the eects of income and governance are accounted for, both point and diuse resource abundance typically have no signi cant impact on development. The authors conclude that the impact of resources on development is indirect and occurs only through channels of institutional quality6 . A weak point of almost all the papers discussed in the literature review so far is that they only use post-1970 data on natural resource abundance. Haber et al. (2007) correctly argue that in order to demonstrate the eect of natural resources on institutions, one needs to extend the data coverage back to the time of the discovery of the resource. The authors construct a novel dataset on taxation income from oil for ve major oil producers back to the time of independence and pair these with ve countries that are similar in many respects but do not produce oil. Using these long-run historical data, Haber et al. nd no support for the hypothesis that oil undermines democracy, contrary to the claim of other authors in the mainstream of the resource curse literature (Ross 2001, Jensen & Wantchekon 2004). Similarly Haber et al. nd no evidence that oil impedes democratization. Using an extended time frame, these authors thus provide some evidence that perhaps institutions are not as aected by natural resources as predicted by the existing literature on natural resources. In sum, it appears that support for the theory of a natural resource curse mainly comes from studies that use Sachs and Warner's proposed measure of primary exports over GDP. Challenges to this established view most often come from authors using dierent measures of natural resource abundance, such as subsoil wealth, and production or export gures normalized by population size. Most authors have simply argued for the superiority of their proposed measure for natural resource abundance on ad-hoc theoretical grounds. In contrast, we argue that these diering predictions can be reconciled when taking the pre-existing institutional framework into account. The following section presents the proposed theoretical connections between resource abundance, political institutions, and economic development. It is followed by empirical evidence of the eect of oil production on institutional change. 6 Curiously,

despite using the SW measure throughout their analysis they contend in the end that "future empirical analysis needs to be based on measures of resource stocks".

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3 Theory The theoretical framework of this paper does not agree with the consensus view that institutions themselves are aected by natural resources. Instead, we argue that the causality runs exactly in the reverse direction. Poor pre-existing institutions lead to the poor development of the non-resource sector, resulting in a small overall GDP. As a result, poor institutions are found to be associated with natural resource abundance, if measured in the peculiar way Sachs and Warner propose - the ratio of commodity exports over GDP. Natural resource abundance translates into resource dependence only in the presence of poor pre-existing institutions. Moreover, this is not due to deteriorating eects of natural resources on institutions, but rather due to detrimental eects of poor institutions on the non-resource sector of the economy. This paper's main focus is to highlight evidence against the impact of natural resources on institutions. Unfortunately, in both our discussion so far and the evidence to follow in the empirical section institutions remain aggregated. This is as much an admission of our ignorance about what institutions actually are as it is due to the absence of reliable measures of institutional quality for a longitudinal country panel study. The need to rely largely on the POLITY IV measures of institutional quality and derivatives of them, makes it dicult to test some more speci c hypotheses about how institutions are thought to aect the development of natural resource dependence. Nonetheless we will introduce one of these speci c theoretical accounts relating institutional quality and natural resource dependence through investment. 3.1

Natural resource dependence and the security of property rights

The extraction of mineral resources requires large up-front capital investments and technology. A substantial share of that investment is provided through foreign direct investment7 . Absent third-party enforcement or institutional constraints, the host government faces the problem to credibly commit to honor its promise not to expropriate after the investment is made. Note that expropriation here is meant in the widest sense ranging from direct seizures of capital, equipment, and reserves to increases in tax rates and other less obvious measures that reduce the value of the investment ex post. Especially transnational corporations that invest abroad thus face the risk that their investment may be expropriated. Yet even in environments where a genuine risk of expropriation exists, e.g. in many autocracies, one often observes investment in the natural resource sector. 7 Our

data set also contains information on the ownership structure of the oil sector for each country-year since 1945. The majority of crude oil reserves are held by domestic, state-owned companies today. Yet, the initial exploration, development, and production of oil was usually and often still is done by foreign multinational companies possessing the required expertise and capital.

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There are no laws directly prohibiting a host government from seizing the capital of a foreign direct investment in its borders and then denying compensation. The usual model setup for this situation in the economics literature is that the host country has an investment opportunity that it is unable to exploit itself, either because it is missing the technical expertise or its access to capital markets is restricted. The transnational corporation is able to exploit this situation as it has the necessary technology, capital, and managerial skills (for a review see Thomas et al. 1994). In general, the government has a short-term incentive to expropriate but also an incentive to foster long-term relations with the investor in order to reap the bene ts of future cooperation. The only feasible contracts then are selfenforcing in which the long-term discounted bene ts from cooperation exceed the short-term gains to be had from reneging on the contract. For a country, the costs from expropriating its investors are manifold. Beside the obvious loss of access to future capital, technology etc. Duncan (2006) shows that there is a cost for expropriations in the form of lower growth due to lower foreign direct investment into the sectors of countries that were expropriated in the past. So it appears investment is made possible and sustained largely by reputational concerns8 . In order to entice investors to invest in the face of a high perceived risk of expropriation, they need the prospect of higher rents9 . In general, poor institutional quality such as a high risk of expropriation negatively aects investment in both the resource and non-resource sectors. High potential rents in the natural resource sector, however, may allow investment even in a poor investment environment, but will deter investment in the non-resource sector. A strong assumption that is embedded in this theory of dierential investment patterns in the resource and non-resource sector is the exogeneity of institutions. This is not only a highly idealized assumption but more importantly it runs directly contrary to the mechanisms suggested by the existing literature on the eects of natural resources. The current consensus in the literature appears to suggest that the proposed negative eects of natural resources on economic and political outcomes work indirectly through the negative eects of natural resources on institutional quality. In contrast, we hypothesize that the production of natural resources does not aect the quality of institutions. In the empirical section to follow we will use data on one particular natural resource, petroleum, to test the impact of natural resource production on institutional quality. 8 Reputation

is not the only way host governments can signal their credibility. Transnational corporations can call on their home governments to apply political, economic, and even military pressure to enforce their property rights. Similarly, transnational corporations can threaten to withhold investment leading to a reduction of government revenue in the short run. 9 A high perceived risk of expropriation may just be one instantiation of poor institutions that aect investment decisions. For example, high corruption may similarly deter investment. In the same way as the risk of expropriation, however, its negative eects may be mitigated by the presence of high rents.

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4 Data Our data set contains information on oil and natural gas discovery, start of oil production, amount of crude oil production, crude oil reserves, ownership of oil industry, and a variety of measures of institutional quality of political institutions. The panel covers 197 countries for the period from 1918 to 2000. Of these, 98 countries produce crude oil in the period. 4.1

Production and Reserves of Crude Oil

Crude oil production gures are obtained from three dierent sources. The rst source is the International Energy Annual (IEA) provided by the U.S. Energy Information Administration (EIA) (2004). It contains data on 164 countries, of which 95 have crude oil production in the period from 1980 to 2004. The second source is the British Petroleum Statistical Review of World Energy (2006). It contains petroleum production data for 81 countries starting in 1965 up to 2005. Finally, the third source allows us to extend our data set further back in time. DeGolyer et al. (2005) provide crude oil production data from 1918 to 2004 for 68 countries. Using all three data sources, we are able to create a panel that contains crude oil production data for 98 countries10 . In order to test institutional change resulting from the onset of production, we need the starting year of crude oil production for every country in the data. Starting years are derived from the information on production discussed above. In addition, an excellent data set assembled by Lujala et al. (2006) on oil and gas eld discoveries and production starts in 111 countries from 1945 to 2000 allows us to crosscheck the starting years derived from the production data. We obtain starting years of production for 98 countries. Out of these 98 countries, 11 start oil production in or before 1918 and thus their actual production start cannot be determined from the data at hand. Another group of six post-communist countries have production starts in or before 1945 and are similarly left truncated11 . In sum, 10 Since

our three sources overlap substantially, we are able to compare the dierent crude oil production estimates. We nd extremely high correlations between the three sets of data (>0.99 respectively) and thus are con dent that the sources provide reliable estimates of crude oil production. 11 We nd the following starting dates for crude oil production: Afghanistan(1967), Albania(1937), Algeria(1958), Angola(1956), Argentina(1918), Australia(1964), Austria(1938), Azerbaijan(1965), Bahrain(1932), Bangladesh(1994), Belarus(1965), Benin(1983), Bolivia(1945), Brazil(1951), Brunei(1918), Bulgaria(1954), Cameroon(1978), Canada(1927), Chad(2003), Chile(1950), China(1943), Colombia(1923), Congo, Brazzaville(1957), Congo, Kinshasa(1976), Croatia(1970), Cuba(1955), Czechoslovakia(1951), Denmark(1972), Ecuador(1927), Egypt(1922), Equatorial Guinea(1992), France(1921), Gabon(1957), Georgia(1945), Germany, East(1980), Germany, West(1924), Ghana(1980), Greece(1981), Guatemala(1974), Hungary(1939), India(1947), Indonesia(1945), Iran(1918), Iraq(1928), Israel(1957), Italy(1952), Ivory Coast(1980), Japan(1918), Jordan(1984), Kazakhstan(1945), Kuwait(1938), Kyrgyzstan(1992), Libya(1961), Lithuania(1993), Malaysia(1968), Mexico(1918), Mongolia(1950),

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our data contains the initial year of production for 81 countries. That set of countries will be used to analyze the impact of oil production12 on institutional quality in the empirical section. 4.2

Quality of Institutions

It is not until the 1990s that the institutional environment for a large cross-section of countries has been measured in a dierentiated and comprehensive way. We selected a total of six measures which have been widely used in the literature and appear pertinent to the theory alluded to earlier. The Polity IV project (Marshall & Jaggers 2005) provides a 21 point regime type scale, ranging from -10 for stable autocracies to 10 for full democracies, for each independent country (with greater than 500,000 total population) since 1800. Additionally, the project provides six individual component measures that record key qualities of executive recruitment, constraints on executive authority, and political competition. For comparison in table 1, we include the combined regime type score and the component measure of executive constraints. The Fraser Institute index (Gwartney, Lawson & Easterly 2006) of legal structure and economic freedom is a composite index13 available in ve-year intervals from 1970 to 2000 and annually thereafter. The Heritage Foundation publishes a property rights index, which measures the degree to which a country's laws protect private property rights and the degree to which its government enforces those laws (Foundation 2008)14 . Morocco(1940), Myanmar(1950), Netherlands(1946), New Zealand(1970), Nigeria(1958), Norway(1971), Oman(1967), Pakistan(1948), Papua New Guinea(1992), Peru(1918), Philippines(1965), Poland(1918), Qatar(1939), Romania(1918), Saudi Arabia(1938), Senegal(1986), Slovakia(1990), Slovenia(1945), South Africa(1998), Soviet Union(1918), Spain(1967), Sudan(1993), Suriname(1986), Sweden(1975), Syria(1968), Taiwan(1968), Tajikistan(1979), Thailand(1981), Trinidad and Tobago(1914), Tunisia(1966), Turkey(1954), Turkmenistan(1945), Ukraine(1945), United Arab Emirates(1962), United Kingdom(1942), United States of America(1918), Uzbekistan(1945), Venezuela(1919), Vietnam(1987), Yemen Arab Republic(1986), Yugoslavia(1949). Note that starting years of 1945 and 1918 may simply be truncated values for earlier production starts. As a result in our section on the eects of oil production on institutional quality we leave out countries with starting years in 1918 and 1945. Future drafts will contain updated starting years for these countries. 12 Theories based on the rentier state's actual oil rents may be better suited to test the impact of natural resources on institutions than the value of crude oil production. Since in our data the value of produced oil and a measure of oil rents (1970-2001) provided by Hamilton and Clemens (1999) have a correlation of 0.94 we believe that oil production can serve as a valid proxy for oil rents. 13 It ranges from 0-10 where 0 corresponds to 'no judicial independence, 'no trusted legal framework exists, 'no protection of intellectual property, 'military interference in rule of law', and 'no integrity of the legal system' and 10 correspond to 'high judicial independence', 'trusted legal framework exists', 'protection of intellectual property', 'no military interference in rule of law', and 'integrity of the legal system'. 14 The index also accounts for the possibility that private property will be expropriated. In addition, it analyzes the independence of the judiciary, the existence of corruption within the judiciary, and the ability of individuals and businesses to enforce contracts. The less certain the legal protection

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Another widely used set of measures are the governance indicators published by the World Bank (Kaufmann, Kraay & Mastruzzi 2007). The data set provides six aggregate indicators measuring perceptions of governance. For this project, we focus on the 'rule of law' indicator, which measures the extent to which agents have con dence in and abide by the rules of society15 . The nal measure is an indicator of political constraints provided by Henisz (2002). Using insights from Tsebelis' (1995, 2002) work on veto players, this index measures the feasibility of policy change, i.e. the extent to which a change in the preferences of any one political actor may lead to a change in government policy16 . In table 1 we provide some information about the availability of these dierent measures across time and space, as well as a correlation matrix. Three of the measures, namely the ones published by the Fraser Institute, Heritage Foundation, and the World Bank are only available on an annual basis starting in the 1990s. Given that part of the motivation for this project is to provide within country evidence for the impact of natural resources on its institutions using before and after comparisons, these measures are ill-suited for our analysis. Most tightly related to the expropriations story in the theoretical section and available far enough back in time are the executive constraints measure from the Polity IV project and the political constraints measures provided by Henisz. From the correlations in table 1 it appears that Henisz' more extensive political constraints measure POLCON5 is most closely related to the other available institutional measures. It is substantially correlated with the the aggregate measures of property rights from the Heritage foundation (cor=0.52), the economic freedom index of the Fraser Institute (cor=0.61), and the survey measure of the rule of law from the World Bank governance indicators (cor=0.63). Given that some information from Polity IV is contained in Henisz' measures as well, the high correlation to the executive constraints measure (cor=0.85) is not surprising. Another advantage of Henisz' measures is that they are purely institutional measures of property is and the greater the chances of government expropriation of property are, the higher a countrys score is. The countrys property rights score ranges from 0 and 100, where 100 represents the maximum degree of protection of property rights. 15 These rules include perceptions of the incidence of crime, the eectiveness and predictability of the judiciary, and the enforceability of contracts. Together, the six indicators measure the success of a society in developing an environment in which fair and predictable rules form the basis for economic and social interactions and the extent to which property rights are protected. 16 The following information is used in order to create the index: the number of independent branches of government with veto power over policy change, counting the executive and the presence of an eective lower and upper house in the legislature (more branches leading to more constraint); the extent of party alignment across branches of government, measured as the extent to which the same party or coalition of parties control each branch (decreasing the level of constraint); the extent of preference heterogeneity within each legislative branch, measured as legislative fractionalization in the relevant house (increasing constraint for aligned executives, decreasing it for opposed executives). Henisz (2000) also provides a second indicator of political constraints, which adds two more veto points: the existence of an independent judiciary, and independent sub-federal entities (states, provinces, regions, etc.) when these institutions impose substantive constraints on national scal policy.

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Table 1: Correlations between institutional variables Plty

ExCon

Fras

Herit

WB

PC3

Polity IV - combined polity score

1.00

Polity IV - executive constraints

0.90

1.00

Fraser Inst. - Economic Freedom

0.50

0.53

1.00

Heritage Found. - Property Rights

0.41

0.45

0.79

1.00

World Bank - Rule of Law

0.50

0.56

0.92

0.85

1.00

Henisz Political Constraints III

0.80

0.79

0.42

0.40

0.47

1.00

Henisz Political Constraints V

0.86

0.85

0.61

0.52

0.63

0.90

1800-2000, n=13828, N=160, N =69, T =84 1800-2000, n=14008, N =161, N =70, T =85 1970-2003, n=1020, N =126, N =102, T =8 1994-2005, n=1793, N =160, N =149, T =11 1996-2005, n=1290, N =192, N =184, T =7 1800-2004, n=12955, N =161, N =63, T =79 1960-2004, n=6309, N =161, N =140, T =38

PC5

1.00

based on a formal model of institutional veto players. Their speci cation may thus avoid some of the pitfalls of endogeneity and reverse causality potentially associated with perceptions based measures. A drawback of Hensiz' more extensive measure is its more restricted time coverage starting in 1960. In sum, given the paucity of data for earlier years for some of the measures, our empirical analysis will focus on Henisz' two measures and the combined polity score from the Polity IV project. Although the regime type measure from Polity IV is theoretically not the correct measure to indicate concerns about expropriation, it can serve as a proxy for the concept. Moreover, since it is so widely used in political science, we feel that readers have a better sense about the meaning of the measure. All of the empirical analysis to follow is thus repeated for both Henisz measures as well as the combined Polity score17 . Results in the paper concentrate on the polity score; additional results can be found in the appendix.

5 Empirical Evidence 5.1

Level of Institutional quality

To see if natural resources aect the quality of the institutional environment of a country, the post-1970 data used in most existing articles is only of limited help. Since resources were often discovered before that period, the hypothesized eect of natural resources can often not directly be observed in these data sets. Instead researchers base their claims about within-country over time eects of 17 Results

not presented in this paper are available from the author upon request.

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natural resource abundance on cross-country evidence (Haber & Menaldo 2007). For example, of the 98 countries that have produced crude oil in between 1918 and 2003, more than two thirds (69 countries) started production before 1970. By extending crude oil production data back to 1918, we are able to see what impact, if any, the start of production had on the the institutional environment of a country at the time. To do so, we compare a country's institutional environment score before and after the start of production and check if the start of production introduces a signi cant structural break in the data. Instead of the usual t-test, we use a Wilcoxon paired signed-rank test to avoid having to make the distributional assumptions the t-test requires. All of our results remain unchanged if the more common t-test is used. In table 2, we compare the average polity score of oil producing countries over a time period of 3, 5, 10, and 20 years before and after the year of the start of oil production18 . We nd no evidence that the start of Table 2: Impact of Oil Production on Polity Score 3 years

5 years

10 years

20 years

Average Polity Score before production start

0.74

0.72

0.60

0.94

Average Polity Score after production start

0.75

0.66

0.54

0.42

Sample Size

56

56

47

40

Wilcoxon paired signed-rank test p-value

0.92

0.94

0.29

0.44

oil production negatively aected the institutional environment of the countries considered19 . Figure 1 further supports our results20 . Here we plot the average polity score of all countries in the sample in the ten years before and after they started producing crude oil. If the marker is located on the 45 degree line, the average polity score has not changed. Country markers located above the 45 degree line represent discon rming evidence of the resource curse, while countries below it represent evidence in support of the resource curse hypothesis with respect to institutional change. We nd that countries are located relatively close to the 45 degree line. Con rming what we found with the structural break analysis above, there appear to be no more countries below the 45 degree line as compared to above. Yet it could be the case that only if a sucient amount of oil is produced, can it aect the political institutions of a country. To account for this, we used red (grey) dots for countries that were among the Top 50 oil producers in the world in 2000. As before, even for these larger oil producers, there appears to be 18 The

lower sample size of n=56 compared to the number of all oil producing countries with known starting dates in the data (81) is the result of of insucient polity data before or after the year when production started. 19 The analog table for Henisz' P o l c o n 3 measure can be found in table 8 in Appendix II. 20 The analogous graph for the P o l C o n 3 measure can be found in gure 6 in Appendix II.

12

Figure 1: Impact of Oil Production on Level of Polity Score

no tendency to have a lower average polity score in the period after starting the production of oil. For a selected number of countries with a relatively large per capita production of oil we also plot individual time series of oil production value per capita and its polity score (see gure 5 in Appendix I). In none of the examples, the graphs reveal a clear pattern of worsening institutional impact with increasing production value of oil. While not a rigorous test, the failure to nd such patterns adds con dence to the simple evidence provided thus far. 5.2

Trends in Institutional quality

While the results so far support our hypothesis, it is possible that the comparison of pre- and post-production polity score averages is hiding changes in the trajectories 13

in institutional quality. For example, if a country's polity score increased from -5 to 5 in the ten years before production start and falls back to -5 in the ten years after production start, the average pre- and post-production polity score is unchanged. Yet clearly such a scenario would support the resource curse hypothesis because the positive trajectory towards a better institutional environment was reversed after oil production started. To account for changes in the trend of the quality of the institutional environment, we also compare the average annual change of the polity score in the periods pre- and post start of production. The results are presented in table 3. As before, we nd no evidence that starting oil production has any signi cant eect on the trend in polity scores of a country. Table 3: Impact of Oil Production on Trend of Polity Score 3 years

5 years

10 years

20 years

Avg. Annual Change in Polity Score before prod

0.09

0.05

0.14

0.07

Avg. Annual Change in Polity Score after prod

0.06

0.08

0.08

0.03

Sample Size

54

53

45

38

Wilcoxon paired signed-rank test p-value

0.68

0.33

0.81

0.95

For graphical representation of the analysis see gure 2. Note that data points in the fourth quadrant of the coordinate system (bottom right) provide evidence for the resource curse hypothesis, because a marker there indicates a country with a positive trend in polity scores before the start of oil production and a reversal of that trend afterwards. From the graph we see that most countries are located near the origin (no change in trend between before and after) and the graph does not appear to follow a particular pattern. In particular, there appear to be at least as many countries with an improvement in the trend their polity scores develop after the start of oil production as there are are cases with a deteriorating quality of institutions21 . Just to con rm that averages do not hide the truth here, in gure 3 we present a scatter plot of all the changes in the polity scores of the countries in the sample. A minuscule correlation of 0.0003 con rms our result, that the start of oil production does not alter a country's institutional environment. In sum, so far our empirical analysis has not provided any evidence that the institutional environment of a country is impacted by the start of production of petroleum. While this analysis has been helpful in seeing the trends in the data, so far we have only presented graphical evidence and dierence-in-means tests. None of this evidence controlled for overall time trends in the institutional environment of all countries. To make more rigorous assessments, we will now examine the same evidence in the framework of a simple panel data analysis. 21 The

corresponding results using the P o l C o n3 measure of institutions are presented in table 9 and gure 7 in Appendix II.

14

Figure 2: Impact of Oil Production on Trend of Polity Score

15

Figure 3: Impact of Oil Production on Change in Polity Score

16

5.3

Panel Regressions

We model the polity score of a country as a function of the average polity score in the previous ten years. If countries have started oil production already, we use the average polity score of the 10 years before production started. This is to prevent contamination by potential endogeneity if polity scores are indeed aected by oil production22 . These models also include time and country xed eects. The year xed eects help to account for any common global variation across countries over time, such as a trend toward higher polity scores over time. Country xed eects control for country-speci c, time-invariant dierences that are not accounted for by other variables in our regressions23 . In models 1 and 3, the errors are allowed to be correlated within panels, i.e. clustered on countries, and robust to heterogeneity. In models 2 and 4, we allow for rst-order autoregressive (AR1) disturbances with a common  for all countries. In models 1 and 2, we use an indicator variable, which takes on a value of 1 in years when oil production has started and zero otherwise, to test the eect of oil production. In both models, we nd the eect of oil production on a country's polity score to be positive and insigni cant. In models 3 and 4, we use the logged value of oil production per capita as a measure of the access to revenues from natural resources. Again we nd that the value of oil production in a country has no eect on its polity score. 5.3.1 Alternate Model Speci cations

The regression results presented thus far support our hypothesis that natural resource abundance does not have detrimental eects on the institutions of a country. As argued in the theoretical section and substantiated by the literature review, however, the evidence for the resource curse usually comes from studies using measures of resource dependence rather than resource abundance. Using ratio measures with overall GPD or total exports as denominators, we are led to examine a set of countries in which the non-resource sector is relatively small compared to the resource sector. We have argued that the small size of the non-resource sector may well be a result of investor-unfriendly intuitions rather than due to the eect of natural resource abundance. As a result, when employing these measures we often nd supportive evidence for the existence of a 'resource curse'. In contrast, using per capita measures of production and reserves, or subsoil wealth often leads to evidence against the 'resource curse' hypothesis. The short section to follow highlights these points using the data from our analysis so far. We now contrast dierent indicators of natural resources, model speci cations, error structures, and time periods (1970 - 2000 vs. 1918 - 2000). The 22 The

analysis is unaected if we use the average polity score in the past 10 years for all countries. that these xed eects, in essence, also exclude countries from the analysis, which never produced oil. This is because for these countries the country-speci c xed eect is collinear with the time-invariant information in the oil-production dummy. 23 Note

17

Table 4: Eect of Oil Production on Polity Scores Dependent Variable: Polity Score

Model 1

Model 2

Model 3

Model 4

Avg.Polity Score in past 10 years

0.837*** 0.152*** 0.840*** 0.163*** (0.034) (0.031) (0.033) (0.031) Oil Production Started 0.617 0.089 (0.579) (0.192) Log(Value Oil Production per Cap.) 0.158 -0.063 (0.167) (0.066) Constant -1.191* -3.023*** -1.175* 0.434*** (0.457) (0.146) (0.461) (0.124) Year Fixed Eects Yes Yes Yes Yes Country Fixed Eects Yes Yes Yes Yes N Number of Countries Avg. Number of Years Adj. R-Squared Error Structure

12250 140 88 0.58 clustered

12110 140 87 0.03 AR1

12128 140 87 0.58 clustered

11988 140 86 0.03 AR1

* p < 0:05 ** p < 0:01 *** p < 0:001 (two-tailed); standard errors in parentheses results are presented in tables 5 and 6. In each table, models 1-3 include no time and year xed eects and use heteroscedasticity robust standard errors. Models 4-6 include no xed eects either and use robust standard errors clustered on countries. The remaining models 7-8 use the same clustered error structure and also include both country and year xed eects. Since the last three columns present the most exible error structure and control for time and country speci c determinants, models 7-9 are our preferred set of speci cations. In table 5 we estimate models on the time period spanning from 1918 to 2000. As predicted, the choice of the natural resource indicator is decisive in us nding evidence for a 'natural resource curse'. While the percentage of GDP derived from oil production is negatively associated with polity scores (model 1), we nd a signi cantly positive eect when using logged oil production per capita (Model 2), and no eect when using a dummy variable indicating if oil production has started (model 3). Once we use clustered standard errors (Models 3 to 6) only the dummy variable indicator of production remains signi cant and positive at the ten percent level. When xed eects for years and countries are added (models 7 to 9), none of the indicators of natural resources is any longer signi cantly associated with polity scores. Taken at face value, this last set of models thus indicate no association between polity scores and either of the three natural resource measures. 18

Truncating our data set to the post 1970 time period, which most existing papers in the literature have looked at, we nd similar patterns. In our preferred speci cation, using clustered errors as well as time and year xed eects, however, we now nd a signi cant and positive eect of production/GDP on polity scores. In sum, we have shown that the choice of the indicator for natural resources is crucial for the results one obtains. Also,less restrictive error structures as well as the inclusion of xed eects often lead to insigni cant results24 .

24 Interestingly,

after the inclusion of country xed eects, the coecients on all three natural resource indicators are mostly negative though not signi cant.

19

20

N Number of Countries Avg. Number of Years Adj. R-Squared Error Structure

Country & Year dummies

Constant

Oil Production Started

log(Oil Prod/Pop)

Oil Prod/GDP

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

6074 119 51 0.62 robust

7132 130 55 0.66 robust

7254 130 56 0.66 robust

6074 119 51 0.62 cluster

7132 130 55 0.66 cluster

7254 130 56 0.66 cluster

6074 119 51 0.32 cluster

7132 130 55 0.34 cluster

7254 130 56 0.33 cluster

0.863*** 0.887*** 0.882*** 0.863*** 0.887*** 0.882*** 0.701*** 0.738*** 0.739*** (0.007) (0.006) (0.006) (0.029) (0.025) (0.025) (0.066) (0.057) (0.058) -1.194*** -1.194 -0.189 (0.199) (0.914) (0.583) 0.247*** 0.247 -0.138 (0.030) (0.160) (0.093) 1.221*** 1.221* -0.306 (0.132) (0.627) (0.611) 0.647*** 0.224*** 0.058 0.647** 0.224 0.058 -0.017 0.071 -1.933*** (0.061) (0.052) (0.048) (0.247) (0.152) (0.104) (0.788) (0.513) (0.716) No No No No No No Yes Yes Yes

Model 2

* p < 0:10 ** p < 0:05 *** p < 0:01 (two-tailed); standard errors in parentheses

Avg.Polity Score in past 10 years

Model 1

Table 5: Eects of Natural Resources on Polity Score - Comparison of Model Speci cations: 1918 - 2000

21

N Number of Countries Avg. Number of Years Adj. R-Squared Error Structure

Country & Year dummies

Constant

Oil Production Started

log(Oil Prod/Pop)

Oil Prod/GDP

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

3393 119 29 0.54 robust

3637 130 28 0.57 robust

3695 130 28 0.57 robust

3393 119 29 0.54 cluster

3637 130 28 0.57 cluster

3695 130 28 0.57 cluster

3393 119 29 0.34 cluster

3637 130 28 0.33 cluster

3695 130 28 0.33 cluster

0.813*** 0.833*** 0.828*** 0.813*** 0.833*** 0.828*** 0.594*** 0.602*** 0.600*** (0.010) (0.009) (0.009) (0.042) (0.039) (0.038) (0.077) (0.073) (0.074) -1.969*** -1.969** 0.977** (0.230) (0.830) (0.491) 0.124*** 0.124 -0.288 (0.036) (0.168) (0.186) 1.286*** 1.286* -0.326 (0.179) (0.702) (0.796) 1.034*** 0.762*** 0.360*** 1.034*** 0.762*** 0.360** -1.022*** 0.103 -0.787* (0.088) (0.085) (0.080) (0.332) (0.261) (0.178) (0.381) (0.364) (0.414) No No No No No No Yes Yes Yes

Model 2

* p < 0:10 ** p < 0:05 *** p < 0:01 (two-tailed); standard errors in parentheses

Avg.Polity Score in past 10 years

Model 1

Table 6: Eects of Natural Resources on Polity Score - Comparison of Model Speci cations: 1970 - 2000

6 Conclusion In sum, it appears that while the mechanisms of the resource curse are quite well speci ed, some of the empirical evidence to support related hypotheses rests on tenuous foundations. We have argued that the peculiar measurement of natural resource abundance proposed by Sachs and Warner may be endogenous to the outcomes to be explained. Ratio measures using overall GDP or total exports as denominators appear to identify resource dependent instead of resource abundant countries. If these dependencies are a result of poor institutions, then any inferences from such measures are invalid. Empirically, we nd no evidence for the impact of crude oil production on political institutions as argued in the most recent wave of the resource curse literature. Extensive 'pre-regression' analysis, using graphs and comparison of means, reveals none of the within-country patterns of the eects of natural resources on institutions that we would expect if the resource curse hypothesis was true. Instead, we argue that natural resource abundance translates into natural resource dependence only in the presence of poor institutions. Further research that deals with these criticisms is necessary to corroborate that the phenomenon of the 'resource curse' indeed exists. Analysts interested in studying the eects of natural resources should be aware that the way natural resources are measured, could be a decisive determinant of the results they obtain. If resource abundance leads to resource dependence only in poor institutional environments, then using the resource dependence as an exogenous indicator of natural resources may be an inappropriate research strategy.

22

References Auty, R. M. 1990. Resource-based industrialization : sowing the oil in eight developing countries. Oxford [England]: Clarendon Press. 89026571 R.M. Auty. Includes bibliographical references and index. Auty, R. M. 1993. Sustaining Development in Mineral Economies: The Resource Curse Thesis. Routledge (UK). Auty, R. M. 1994. \The Resource Curse Thesis: Minerals in Bolivian Development, 1970-90." Singapore Journal of Tropical Geography 15(2):95{111. Bravo-Ortega, C. & J. De Gregorio. 2005. \The Relative Richness of the Poor? Natural Resources, Human Capital, and Economic Growth.". Brunnschweiler, C. N. 2006. \Cursing the blessings? Natural resource abundance, institutions, and economic growth.". Bulte, E. H., R. Damania & R. T. Deacon. 2005. \Resource Intensity, Institutions, and Development." World Development 33(7):1029{1044. Collier, P. & A. Hoeer. 2004. \Greed and grievance in civil war." Oxford Economic Papers 56(4):563{595. Company., British Petroleum. 2006. BP statistical review of world energy. Vol. 1981- London: British Petroleum Company. British Petroleum statistical review of world energy Statistical review of world energy Issues for issued . Davis, G. A. 1995. \Learning to Love the Dutch Disease: Evidence from the Mineral Economies." World Development 23(10):1765{1779. DeGolyer & MacNaughton. 2005. Twentieth century petroleum statistics. 61th ed. Dallas: DeGolyer and MacNaughton. 20th century petroleum statistics, 2002 col. ill. ; 28 cm. Includes index. Duncan, R. 2006. \Costs and consequences of the expropriation of FDI by host governments.". Fearon, J. D. 2005. \Primary Commodity Exports and Civil War." Journal of Con ict Resolution 49(4):483. Foundation, Heritage. 2008. \The Index of Economic Freedom.". Gwartney, J. D., R. Lawson & W. Easterly. 2006. Economic Freedom of the World: 2006 Annual Report. Fraser Institute. Gylfason, Thorvaldur. 2001. \Natural resources, education, and economic development." European Economic Review 45(4):847{859. 23

Haber, Stephen & Victor Menaldo. 2007. \Do Natural Resources Fuel Authoritarianism?". Hamilton, K. & M. Clemens. 1999. \Genuine Savings Rates in Developing Countries." World Bank Economic Review 13(2):333. Henisz, W. J. 2000. \The Institutional Environment for Economic Growth." Economics and Politics 12(1):1{31. Henisz, W. J. 2002. \The institutional environment for infrastructure investment." Industrial and Corporate Change 11(2):355{389. Isham, J., M. Woolcock, L. Pritchett & G. Busby. 2003. \The Varieties of Resource Experience: How Natural Resource Export Structures Aect the Political Economy of Economic Growth.". Jensen, N. & L. Wantchekon. 2004. \Resource Wealth and Political Regimes in Africa." Comparative Political Studies 37(7):816{841. Jones-Luong, P. & E. Weinthal. 2006. \Rethinking the Resource Curse: Ownership Structure, Institutional Capacity, and Domestic Constraints." Annual Review of Political Science 9:24163. Karl, T. L. 1997. The Paradox of Plenty: Oil Booms and Petro-States. University of California Press. Kaufmann, D., A. Kraay & M. Mastruzzi. 2007. \Governance Matters VI: Governance Indicators for 1996-2006.". Lederman, D. & W. F. Maloney. 2003. Trade Structure and Growth. World Bank, Latin America and the Caribbean Region, Oce of the Chief Economist, Regional Studies Program. Lujala, P., J. K. Rd & N. Thieme. 2006. Fighting over Oil: Introducing a New Dataset. Technical report Working Paper, NTNU/PRIO. Manzano, O. & R. Rigobon. 2001. \Resource Curse or Debt Overhang?". Marshall, M. G. & K. Jaggers. 2005. \POLITY IV PROJECT-Political Regime Characteristics and Transitions, 1800-1999: Dataset Users Manual.". Mehlum, H., K. Moene & R. Torvik. 2006. \Institutions and the resource curse." The Economic Journal 116(508):120. Papyrakis, E. & R. Gerlagh. 2004. \The resource curse hypothesis and its transmission channels." Journal of Comparative Economics 32(1):181{193. Ramsay, Kristopher W. 2006. \Natural Disasters, the Price of Oil, and Democracy.". 24

Robinson, J. A., R. Torvik & T. Verdier. 2002. Political Foundations of the Resource Curse. Centre for Economic Policy Research. Ross, M. 1999. \The Political Economy of the Resource Curse." World Politics 51(2):297{322. Ross, M. 2001a. Timber booms and institutional breakdown in Southeast Asia. Cambridge: Cambridge University Press. Ross, M. L. 2001. \Does Oil Hinder Democracy?" World Politics 53(3):325{61. Sachs, J. & A. M. Warner. 1997. \Sources of Slow Growth in African Economies." Journal of African Economies 6(3):335{376. Sachs, J. & A. Warner. 1999. \The Big Push, Natural Resource Booms And Growth." Journal of Development Economics 59(1):43{76. Development Discussion Paper, No. 517a, Harvard University. Sachs, J. D. & A. M. Warner. 1995. \Natural Resource Abundance and Economic Growth.". Sokolo, K. L. & E. M. Zolt. 2004. \Inequality and Taxation: Some Evidence from the Americas.". States, Energy Information Administration. United. 2004. \International Energy Annual.". Stijns, J. P. 2001. \Natural Resource Abundance and Human Capital Accumulation.". Thomas, J. & T. Worrall. 1994. \Foreign Direct Investment and the Risk of Expropriation." Review of Economic Studies 61(1):81{108. Tsebelis, G. 2002. Veto Players: How Political Institutions Work. Princeton University Press. Tsebelis, George. 1995. \Decision Making in Political Systems: Veto Players in Presidentialism, Parliamentarism, Multicameralism and Multipartyism." British Journal of Political Science 25(3):289{325. 0007-1234 Article type: Full Length Article / Full publication date: Jul., 1995 (199507). / Copyright 1995 Cambridge University Press.

25

Appendix I - Additional graphs and gures Figure 4: Box and Density Plots of Polity Scores before and after Start of Oil Production

26

Table 7: Average Polity Scores before and after Start of Petroleum Production Country Afghanistan Albania Australia Austria Bangladesh Benin Brazil Bulgaria Cameroon Canada Chile Colombia Congo, Kinshasa Cuba Czechoslovakia Denmark Ecuador Equatorial Guinea France Germany, East Germany, West Ghana Greece Guatemala Iraq Israel Italy Ivory Coast Jordan Kyrgyzstan Libya Lithuania Malaysia Mongolia Myanmar New Zealand Norway Oman Pakistan Papua New Guinea Philippines Saudi Arabia Senegal Spain Sudan Sweden Syria Taiwan Thailand Tunisia Turkey United Kingdom Venezuela Vietnam Yemen Arab Republic Yugoslavia

Year Start Prod 1967 1937 1964 1938 1994 1983 1951 1954 1978 1927 1950 1923 1976 1955 1951 1972 1927 1992 1921 1980 1924 1980 1981 1974 1928 1957 1952 1980 1984 1992 1961 1993 1968 1950 1950 1970 1971 1967 1948 1992 1965 1938 1986 1967 1993 1975 1968 1968 1981 1966 1954 1942 1919 1987 1986 1949

At Start 7 9 10 0 6 7 5 7 8 10 2 5 9 9 7 10 1 7 9 9 6 6 8 3 4 10 10 9 9 3 7 10 10 9 8 10 10 10 2 10 5 10 1 7 7 10 7 8 2 9 4 10 6 7 6 7

27

Polity Scores Dierence Average Scores Bef/Aft 3 years 5 years 10 years 20 years 0.0 1.2 2.1 2.9 6.0 7.2 5.8 0.4 0.0 0.0 0.0 0.0 9.0 7.4 3.2 3.2 0.0 4.4 0.0 0.0 4.6 0.0 0.4 5.4 3.1 0.0 0.0 0.9 0.4 0.0 0.2 0.3 0.5 0.0 0.0 0.3 0.3 0.0 0.6 1.8 3.8 0.0 0.0 4.0 7.0 0.0 0.0 0.0 0.1 6.0 5.6 7.8 8.9 0.0 4.6 6.8 9.4 0.0 0.0 0.0 0.0 0.7 1.2 1.6 1.6 2.0 2.0 0.3 0.3 0.5 1.0 0.0 0.0 0.0 0.0 2.7 8.7 4.7 2.8 3.4 2.1 0.0 0.4 6.4 4.0 5.2 5.5 1.2 0.0 0.0 0.3 0.2 0.0 0.0 0.1 0.6 0.0 0.4 5.4 12.2 0.0 0.0 0.2 2.2 1.0 2.0 4.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 7.2 6.6 6.3 1.3 1.6 1.8 1.5 0.0 0.0 0.0 6.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 8.3 8.6 8.2 5.4 0.0 0.0 0.0 1.2 6.5 8.9 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.0 0.0 2.4 9.5 0.0 2.8 0.0 0.0 0.0 0.0 1.3 1.6 0.0 0.0 0.4 1.3 0.0 2.6 3.3 0.0 0.2 3.0 3.0 2.1 7.7 0.0 0.0 0.0 0.0 3.0 3.0 3.0 2.2 0.0 0.0 0.0 0.7 1.0 2.2 0.0 0.6 4.2 1.3

Figure 5: Selected Country Examples

28

29

Appendix II - Results for Henisz P olCon3 measure Table 8: Impact of Oil Production on PolCon3 Score 3 years

5 years

10 years

20 years

0.17

0.16

0.15

0.14

Average PolCon3 Score before production start Average PolCon3 Score after production start

0.18

0.18

0.19

0.20

Sample Size

53.00

53.00

46.00

34.00

Wilcoxon paired signed-rank test p-value

0.30

0.06

0.02

0.06

Table 9: Impact of Oil Production on Trend of PolCon3 Score 3 years

5 years

10 years

20 years

Avg. Annual Change in PolCon3 score before prod

0.01

0.01

0.01

0.00

Avg. Annual Change in PolCon3 score after prod

0.00

0.00

0.00

0.00

Sample Size

52

51

44

33

Wilcoxon paired signed-rank test p-value

0.70

0.18

0.65

0.98

30

Figure 6: Impact of Oil Production on Level of PolCon3 Score

31

Figure 7: Impact of Oil Production on Trend of PolCon3 Score

32