multilateral energy lending and urban bias in autocracies: promoting fossil fuels

Mitig Adapt Strateg Glob ChangeDOI 10.1007/s11027-014-9587-y

ORIGINAL ARTICLE

Multilateral energy lending and urban biasin autocracies: promoting fossil fuels

Sung Eun Kim · Johannes Urpelainen

Received: 22 March 2014 / Accepted: 1 May 2014© Springer Science+Business Media Dordrecht 2014

Abstract Energy demand is growing rapidly across the world, and international fundingagencies like the World Bank have responded by emphasizing energy in their project port-folios. Some of these projects promote the use of fossil fuels, while others support cleanerforms of energy. For climate change mitigation, it is important to understand how inter-national funders decide on the choice between fossil fuels and cleaner sources of energy.Examining the energy funding portfolios of the nine most important international fundersfor the years 2008-2011, we show that funding for fossil fuels has been concentrated inhighly urbanized autocracies. Due to economies of scale, fossil fuels are suitable for gen-erating heat and electricity for densely populated urban areas. Autocratic rulers are subjectto urban bias in their policy formulation because the support of concentrated urban con-stituencies is key to an autocrat’s political survival, and in democracies environmentalconstituencies can effectively oppose fossil fuel projects.

Keywords Energy policy · Fossil fuels · Renewable energy · Urbanization · Politicalinstitutions · Multilateral development banks

1 Introduction

Energy demand is exploding across the world (International Energy Agency 2011), andinternational funding agencies, such as the World Bank, have responded to this demand byemphasizing energy projects in their portfolios (Nakhooda 2008). In the 2008-2011 period,these agencies offered more than 100 billion dollars in grants, loans, and credit guarantees

We thank Jeremy Wallace, Alice Xu, Sana Ouji, and the anonymous reviewers for comments on aprevious draft.

S. E. Kim · J. Urpelainen (�)Columbia University 420 West 118th Street, 712 International Affairs Building New York,NY 10027 USAe-mail: [email protected]

S. E. Kime-mail: [email protected]

Mitig Adapt Strateg Glob Change

to the energy sector.1 In doing so, they shaped the economic and environmental future ofthe world. However, the characteristics of these projects vary widely. Some projects fundfossil fuels, providing inexpensive electricity but contributing to climate change and localenvironmental problems, such as water and air pollution. Others promote cleaner forms ofenergy, such as solar and wind electricity generation.

To mitigate global warming, it is important to understand how multilateral lending agen-cies decide on funding for fossil fuels and other energy sources. If we can describe andexplain the funding decisions of major international funders, policymakers can identifypolitical and economic barriers to increased funding for clean development. Our goal is tounderstand the relationship between political institutions, the global urbanization trend, andthe decision of major international energy lenders. This understanding reveals obstacles tothe decarbonization of international energy funding and, as discussed in the conclusion,provides insights for policymakers.

While conventional accounts of environmental governance suggests that democratic gov-ernments have stronger incentives to provide the public good of a clean environment thanautocratic governments (Neumayer 2002; Li and Reuveny 2006), a large share of interna-tional energy funding actually goes to autocracies. Consider the energy portfolios of thenine major funding agencies in the 2008-2011 period.2 While autocracies indeed secure rel-atively more funding for fossil fuels than democracies do (55.6 % versus 34.5 % of totalfunding was for fossil fuels, respectively), there is widespread variation among autocracies.Some authoritarian regimes like Pakistan and Vietnam received much less funding for fossilfuels (21.4 % and 0.2 %, respectively) than democracies on average. This is puzzling, giventhat both Pakistan and Vietnam have abundant domestic coal resources.3

Why do some autocracies, such as Pakistan and Vietnam, not exhibit a bias in favorof fossil fuels? Resolving this puzzle could help predict future trajectories of the world’senergy system and contribute to progress toward a general theory of national politicalinstitutions and environmental governance. Moreover, our focus on international fundingagencies could also help clarify the role of relationships between international organizationsand recipient countries in socio-economic development.

To solve the puzzle, we emphasize the political economy of urbanization (Lipton 1977;Bates 1981; Bates and Lien 1985). Throughout, our focus is on the current level of urban-ization, measured as the percentage of people living in urban areas, instead of the rateof its change. According to the urban bias literature, many governments, and autocraticrulers in particular, have incentives to favor the interests of organized and concentratedurban constituencies. Building on this insight, we note that fossil fuels are particularly

1The data we use are from the Shift the Subsidies database, available at http://shiftthesubsidies.org/. AccessedNovember 5, 2012.2These agencies include three institutions that belong to the World Bank Group – International FinanceCorporation, International Development Association, and International Bank for Reconstruction and Devel-opment – and six other international or regional lending institutions: African Development Bank, AsianDevelopment Bank, European Bank for Reconstruction and Development, European Investment Bank,Inter-American Development Bank, and Multilateral Investment Guarantee Agency.3For Pakistan, see “Pakistan to Tap Coal Riches to Avert Energy Crisis.” Reuters April 13, 2012. Availableat http://www.reuters.com/article/2012/04/13/pakistan-coal-idUSL6E8FC45O20120413. Accessed Novem-ber 5, 2012. For Vietnam, see “Vietnam Coal Mining: A Bright Future Ahead”. http://www.vinacomin.vn/en/news/Home-News/Vietnam-Coal-Mining-A-Bright-Future-Ahead-227.html. Vietnam National Coal –Mineral Industries Holding Corporation Limited Press Release. Accessed June11, 2014.


suited for energy generation in the urban context due to economies of scale (Christensenand Greene 1976; Hisnanick and Kymn 1999). While cleaner forms of energy can often bedeployed in a decentralized fashion, thus serving rural areas with low population densities(Kanase-Patil et al. 2010), fossil fuels are subject to clear economies of scale (Unruh 2000).Building large coal plants for rural areas with low population densities simply does notmake much economic sense, whereas decentralized solutions, such as solar photovoltaics,hold promise (Kamalapur and Udaykumar 2011).

In autocratic countries, the urban bias means that high urbanization levels provide thegovernment with strong incentives to propose fossil fuel projects to international fundingagencies, and these agencies have incentives to fund the projects because they can thusserve large groups of people at a low cost. There should be a positive association betweenurbanization and fossil fuels in autocracies.

In democratic countries, the bias toward fossil fuels stemming from urbanization is coun-tered by environmental interests. As levels of urbanization increase in democracies, thegovernment’s incentive to invest in fossil fuels increases. However, this incentive growsless rapidly than in autocracies, because the urban constituencies do not enjoy the sameprivileged position as they do in autocracies. Moreover, pre-existing environmental inter-ests mobilize to prevent the government from increasing fossil fuel generation as a responseto urbanization. The positive association between urbanization and fossil fuels should beweaker in democracies than in autocracies.

To test the theory, we conduct an empirical analysis of 885 internationally funded energyprojects in the 2008-2011 period. We find empirical support for our hypotheses. Amongcountries coded as democratic according to the binary (Cheibub et al. 2010) measure, thosewith an urbanization level below the median spend 53.2 % of their energy funding onfossil fuels; this proportion is only 36.5 % for autocracies. If anything, democracies withlow urban densities use more fossil fuels than do comparable autocracies. Conversely, fordemocracies with urbanization levels above the median, 30.1 % of funding goes to fossilfuels; the corresponding rate for autocracies is as high as 79.6 %. This means that the auto-cratic bias in fossil fuel funding can be attributed to highly urbanized countries. Moreover,our additional tests below allow us to reject alternative explanations, such as Seers (1977)metropolitan bias and the role of economic development. Given Pakistan’s low urbanizationrate (35.3 %), this theory can resolve the puzzle presented above. Indeed, a detailed projectanalysis below shows that a large share of Pakistan’s energy funding focuses on exploitingrural renewable energy potential.

2 Urbanization and fossil fuels: democracies and autocracies

Our theory focuses on the effects of urbanization on international funding for energyprojects in democracies and autocracies. In our theory, the notion of urban bias plays animportant role. Following Bates (1981), we define urban bias in political-economic termsas the ability of urban constituencies to pressure governments to implement their preferredpolicies. Some policies, such as artificially low food prices or large capital investments in theinfrastructure of urban centers, produce targeted benefits for urban dwellers at the expenseof the countryside. The dominance of such policies is the key idea behind the concept ofurban bias.

We assume the urban bias is particularly strong under autocratic rule because, in theabsence of electoral competition, the ability of urban constituencies to engage in collec-tive action, such as riots, distinguishes them from the diffuse rural interests. As Wallace


(2011) explains, “[t]he conventional wisdom on redistribution and urban unrest argues thatsurvival-oriented autocrats should bias policy toward urban residents, because city dwellersare better able to undermine the regime’s political coalition.” Moreover, as Varshney (1993)maintains, there is empirical evidence that democratic political institutions have empoweredrural interests in the cases of India and Costa Rica. Bezemer and Headey (2008) concur,noting that “better democracies seem to be another route to mitigating urban biases.”

We focus on the autocracy-democracy distinction because previous literature emphasizesthe different incentives of democratic and authoritarian rulers. This is not to say that thereare no differences among authoritarian regimes. For example, personalistic regimes areoften more myopic and extractive than single-party authoritarian regimes (Geddes 1999).While our empirical analysis accounts for differences across authoritarian regimes, the moreparsimonious focus on the democracy-autocracy difference is warranted because we are ulti-mately interested in democracy’s modifying role in the relationship between urbanizationand energy funding.

Our focus on international energy funding is motivated both by substantive and practi-cal considerations. Substantively, international funding agencies play an important role inenergy funding, especially in securing capital for large projects that carry high political andeconomic risks. Moreover, analyzing these agencies is interesting because their role in fund-ing fossil fuels has been a controversial subject in recent years, with some commentatorsarguing that their approach to energy funding has to be overhauled to combat the fossil fuelbias (Nakhooda 2008).

From a practical perspective, international energy funding is an appropriate focusbecause hundreds of projects funded by virtually all major agencies have been consis-tently coded for the 2008-2011 period. In contrast, detailed data on private or public energyprojects in specific countries are not available. While our focus on international energyfunding means that our sample is not representative of the totality of energy funding, thesubstantive importance of this subset itself warrants analysis.

The broad contours of energy project funding depend on factors like fuel endowments,the industrial composition of the economy, and the quality of the available energy infrastruc-ture. In the case of international funding, it is also important to account for the characteristicsof the international agencies themselves. While we account for these factors in the empiricalanalysis, our theory focuses on the interactive effects of urbanization and political institu-tions. The goal of this article is to examine how political institutions modify the effects ofan important global trend, increasing urbanization.

2.1 Urbanization and fossil fuels in autocracies

We first consider an autocratic country. As we do so, we compare the logic of internationalenergy funding at different urban concentrations. Recall our basic assumption that the auto-cratic ruler is relatively more concerned about urban interests than the democratic ruler.The autocratic ruler, who is worried about riots and protests as a key threat to her politicalsurvival, favors urban interests because rural constituencies are less able to engage in col-lective action against the regime. As explained above, this is because elections offer ruralconstituencies a relatively expedient means to influence policy, while in autocracies moredirect collective action, such as riots and protests, are needed, and these in turn are difficultto organize in rural areas with low population densities.

Why would the autocrat’s urban bias shape international energy funding? As fundingagencies consider energy projects, they depend on collaboration on part of the recipient gov-ernment. If a funding agency is to improve energy supply in a recipient country, it cannot


do so unless the recipient government collaborates in implementation through supportivepolicies. Country ownership is increasingly recognized as a condition for successful projectimplementation. Indeed, funding agencies rarely, if ever, initiate projects themselves; theinitiative lies with the recipient government. Consequently, international funding agencieshave incentives to fund projects that their recipients value. In autocratic countries, thisimplies an urban bias. As urban concentrations grow, the urban bias grows stronger andstronger, and this shapes the logic of energy funding.

Consider now an autocratic country and suppose urbanization levels increase, all elseconstant. How should the autocrat react to this? As Unruh (2000) has argued, most fossilfuels, and coal in particular, are subject to economies of scale. Empirical evidence from theUnited States from multiple decades suggests considerable economies of scale in electricitygeneration (Christensen and Greene 1976; Hisnanick and Kymn 1999). Since the generationof heat and electricity from fossil fuels is economically expedient in large power plants,fossil fuels are ideal for areas where the supply of a large power plant meets with sufficientdemand around it. All else constant, urban areas meet this criterion more readily than ruralareas do. Since urban areas have much higher population densities and their dwellers aregenerally wealthier, urbanization creates clusters of energy. In turn, this creates profitableopportunities for power generation on a large scale.

Combining urban bias and economies of scale, we argue that in autocracies, urbanizationincreases international funding for fossil fuels. The autocratic ruler has a strong interestto respond to urbanization by generating electricity and heat to meet the needs of urbanconstituencies, and fossil fuels offer an economically efficient solution to growing energydemand in towns and cities with high population densities.

Hypothesis 1 (autocracy, urbanization, and fossil fuels) In autocracies, urbanizationincreases funding for fossil fuels.

Empirical evidence on the relationship between urbanization and energy demand fromdeveloping countries lends prima facie evidence to the claim. Urbanization is largely exoge-nous to energy projects, as migration from rural to urban areas is largely driven by wagedifferences (Harris and Todaro 1970), and these are in turn driven by more fundamen-tal forces of economic development. Jones (1991) finds that urban development results inincreased energy demand. This is not only true of fossil fuels used in urban transportation,but also of industrial heat and electricity production, as “[t]he reduced bulk and easier trans-portability of fossil fuels compared with most renewables will encourage industrial use ...Bulk transportation of coal and oil to large urban markets can reduce transport costs onthose fuels” (Jones 1991, 623, 626). As densely populated urban areas create concentratedmarkets for large amounts of fossil fuels, the bulk transportation of coal and oil in particu-lar becomes increasingly profitable. At the same time, the use of renewable energy does notbecome any easier or less expensive. Many sources of renewable energy can be produced ina decentralized fashion in rural areas. We qualify Jones’s (1991) argument by emphasizingthe importance of political institutions.

2.2 A democratic difference

For democratic countries, there are several reasons to expect that urbanization does notinduce international funding agencies to support fossil fuels to nearly the same extent. First,according to the literature cited above, urban bias is simply weaker in democracies. Evenin less densely populated rural areas, voting allows mass constituencies to express their


opinions. Even as urban concentrations grow, democratic governments continue to heed tothe demands of rural constituencies. This means that urbanization does not induce such astrong bias as it would under autocratic rule.

Second, empirical evidence across the world shows that democracies create opportuni-ties for environmental interest groups to influence policy (Lim and Tang 2002; Assetto et al.2003; Hochstetler and Keck 2007). In democratic societies, the government’s efforts to pro-duce fossil fuels to satisfy the voracious energy demand of urban clusters would be metwith opposition. Even if the democratic government were inclined toward discriminatingagainst rural constituencies, the government might not promote the increased use of fos-sil fuels because it would worry about losing the support of environmental groups. Fossilfuels produce negative environmental externalities, and so the government must prepare foran environmental backlash if it is to produce fossil fuels. The same goes for internationalenergy funders. If they fund fossil fuels in democratic recipient countries, local organiza-tions mobilize against the projects, and this hurts the reputation of the international fundingagency.

To illustrate this second notion, which is less systematically documented than thedemocracy-autocracy difference in urban bias, we offer an illustration from the state ofAndhra Pradesh in the southern part of India. Nagarjuna Construction Company SompetaThermal Plant was a project to construct a 2,640 megawatt coal power plant in the smallcity of Sompeta. On July 14, 2010, two fishermen protesting the project on environmen-tal grounds were killed by police.4 Following the widely publicized killings, the Ministryof Environment and Protests responded by revoking the project permit already in the samemonth.5 Subsequent legal developments have not changed this decision, and the projectremains on hold. The case illustrates how democratic accountability and free media allowedenvironmental protesters to successfully oppose a major fossil fuel project, in spite of India’srapidly growing demand for electricity. This is not to say that such protests could not some-times be successful in autocracies, such as China. We simply maintain that democraticpolitical institutions facilitate such protests.

For international funding agencies, the prospect of protests and riots against fossil fuelsby the local population is highly unattractive. In addition to the direct reputational cost,local protests may allow international activists to more effectively attack the funding agencyin focus for its putative fossil fuel bias, in what Keck and Sikkink (1998) have termed theboomerang effect. Accordingly, the possibility of environmental protest should, in additionto reducing the recipient government’s enthusiasm for proposing fossil fuel projects to inter-national funding agencies, directly reduce the incentives of these agencies to fund fossil fueldevelopment.

Based on these arguments, we hypothesize that urbanization does not increase the useof fossil fuels under democratic political institutions as much as it does in an autocraticcountry.

Hypothesis 2 (democracy, urbanization, and fossil fuels) In democracies, urbanizationincreases funding for fossil fuels less than in autocracies, and possibly not at all.

4“Srikakulam Still Tense After Police Firing.” Siasat Daily July 15, 2010.5“Nagarjuna Construction May Relocate Andhra Power Project.” Economic Times September 10, 2010.


3 Regime type, urbanization, and fossil fuels: a simple test

This section first introduces our dependent and independent variables and then offers a sim-ple, descriptive test our main hypotheses. Our theory expects a positive association betweenthe level of urbanization and fossil fuel funding only in autocracies. We expect no suchassociation in democracies. Our empirical testing is based on international energy projectsfunded by the nine major international organizations mentioned in the introduction. Thedataset covers 885 projects in 128 recipient countries during the years 2008-2011. The datasource is the “Shift the Subsidies” database.6 The dataset tracks the flow of energy subsidiesfrom the World Bank Group and seven other major regional development banks and thuscovers all countries that received funding from at least one of the nine lending agencies. Thedataset allows us to explore the number of fossil fuel, and clean energy projects funded bymajor international organizations, and the amounts of funding received for the projects.

3.1 Dependent variables

As dependent variables, we use the number and the amount of international energy fundingfor fossil fuel projects. In our dataset, projects are classified as fossil fuel, clean energy, andothers. Fossil fuels include oil, gas, or coal projects. Clean energy includes environmentallyfriendly sources such as wind, solar, geothermal or small hydro (less than 10MW). Othersinclude large hydropower (over 10MW), nuclear, biofuels, biomass, and charcoal.7 We clas-sify projects into fossil fuel and non-fossil fuel, combining all projects not relying on fossilfuels. Fossil fuel projects include all types of energy projects involving oil, gas, or coal.

Fossil fuel projects include all types of energy projects involving oil, gas, or coal. Thecategory could include a range of fossil fuel related projects involving energy development,production or supply. For instance, the project Kosmos Energy in Ghana that received theequity of $100 million from the International Finance Corporation is classified as fossil fuelproject as the project mainly deals with the development of the Jubilee oil field. Anotherexample of fossil fuel project is Tajikista’s Energy Emergency Recovery Assistant Projectthat was granted of $1.88 million loan by the International Development Association. Themain goal of project is to increase the reliability of the national energy supply, yet it onlyfocuses on the oil supply. Non-fossil fuel projects include all energy related projects thatdo not involve oil, gas, or coal but involve wind, solar, or other environmentally neutral orfriendly energy. For instance, the Small Hydropower Development Project by the Chinesecompany, Sanchuan Clean Energy Development, is classified into this category. This projectreceived the equity of $14.4 million and the loan amounting $155 millions from the AsianDevelopment Bank for building, expanding, and operating small-scale hydropower plants.8

Each project described above is counted as one project in our dataset. We also collectedinformation on the amount of funding provided for each project. We consider both the num-ber of fossil fuel projects and the amount of funding received for the projects because alarger number of funded fossil fuel projects does not necessarily mean larger amounts offunding for those projects. While 27.3 % of 851 international energy projects in our dataset

6The dataset is available at http://www.shiftthesubsidies.org/subsidies. Oil Change International, in apartnership with CEE Bankwatch Network and Bank Information Center, collected the data.7For a detailed discussion of the coding, see http://shiftthesubsidies.org/methodology.8For more information on each project, see http://www.shiftthesubsidies.org/projects/636, http://www.shiftthesubsidies.org/projects/84, and http://www.shiftthesubsidies.org/projects/191.


are for fossil fuel projects, the amounts of funding spent for those projects account for39.0 % of the total energy funding (103.6 billion USD). Due to the possible discrepancybetween the number of funded projects and the actual amounts of funding, we examinewhether our hypotheses hold in regards to both the number of fossil fuel projects, and theamounts of funding for the projects. The proportion of fossil fuel projects indicates how fre-quently international energy funding have gone to fossil fuel sectors. The values of fundingflows informs if the frequency of funding corresponds to the size of funding for fossil fuelprojects.

3.2 Independent variables

Our main independent variables are the level of urbanization, regime type, and their inter-action. We measure the level of urbanization by the percentage of urban population, whichranges from 9.5 % to 98.34 %, with the average of 56.8 % in our dataset. Since the main pur-pose of this section is to briefly examine how the probability for receiving funding for fossilfuel projects differs depending on regime type and the level of urbanization, we created twocategories of the urbanization level, where the high level of urbanization is defined as beingmore urbanized than the average (56.8 %), and the low level of urbanization for the rest.We also conduct the same analysis with a different classification of urbanization level inthe appendix, where democracies (autocracies) are coded as highly urbanized if their urban-ization level is above 62.0 % (42.6 %), the average urbanization level among democracies(autocracies).

For regime type, we use a dichotomous variable for democracy and autocracy basedon the presence of free and competitive elections. This minimal definition of democracyis ideal for comparing outcomes under the two regimes, because the emphasis is on elec-toral competition, which we expect to reduce the government’s incentives to discriminateagainst rural constituencies. The data are from Cheibub et al. (2010), who classify regimetype based on the existence of a multiparty system, as well as executive and legislativeelections. In addition to these criteria, they also use the alternation rule to exclude thosecases where regimes hold multiparty elections because they know the opposition cannotwin, and those cases where the opposition would not be allowed to assume office evenif they won. One example of this case is South Africa, which is classified as autocracyfor violating the alternation rule. While we follow this classification rule, we also checkwhether our finding still holds if South Africa is coded as a democratic country in theappendix.

The appendix also examines the results when we use the Polity IV score, which capturesmany other dimensions of democracy than electoral competition, with predictably weakerresults. Specifically, including a full set of controls causes the interaction to disappear. How-ever, if we include both Polity IV and the Cheibub et al. (2010) binary measure, as well astheir interactions with urbanization, the interaction between urbanization and the Cheibubet al. (2010) binary measure remains negative and statistically significant, suggesting thatthese two measures capture different phenomena.

3.3 Descriptive findings

The cross-tabulation of urbanization levels and regime type support our argument. InTable 1, the upper table is based on the number of fossil fuel projects in each category,and the lower table is based on the amounts spent on fossil fuel projects in the samecategory.


Table 1 Proportion of fossil fuel projects

Urbanization

Low High Total

Analysis of the number of fossil fuel projects

Democracies 27.8% 24.9% 25.8%

52 (187) 109 (437) 161 (624)

Non-OECD Democracies 27.8% 31.0% 29.1%

52 (187) 40 (129) 92 (316)

Autocracies 22.5% 59.3% 31.3%

39 (173) 32 (54) 71 (227)

Total 25.3% 28.7% 27.3%

91 (360) 141 (491) 232 (851)

Analysis of the funding amounts of fossil fuel projects

Democracies 53.2% 30.1% 34.5%

8.16 (15.34) 19.85 (65.95) 28.02 (81.29)

Non-OECD Democracies 53.2% 36.5% 45.9%

8.16 (15.34) 4.30 (11.78) 12.46 (27.12)

Autocracies 36.5% 79.6% 55.6%

4.53 (12.40) 7.85 (9.87) 12.38 (22.27)

Total 45.8% 36.5% 39.0%

12.69 (27.74) 27.70 (75.82) 40.40 (103.56)

Each cell contains the proportion (%) of fossil fuel projects, the number of fossil fuel projects, and the totalnumber of funded projects in parentheses. Each cell contains the proportion (%) of funding for fossil fuelprojects, the actual amount of funding for fossil fuel projects, and the total funding in parentheses. Unit: 2010USD million

In democracies, the level of urbanization makes little difference in the number of fossilfuel projects funded by the international funding agencies. In terms of funding for fos-sil fuel projects, less funding is given to fossil fuel projects in highly urbanized countries.While 53.2 % of energy funding goes to fossil fuel projects in less urbanized democraticcountries, only 30.1 % of energy funding goes to fossil fuel projects in more urbanizeddemocratic countries. We also find a similar pattern when we restrict our sample to non-OECD democracies. Here, we use 2008 as the year of reference for OECD membership.Chile, Estonia, Israel, and Slovenia joined in 2010. Countries with high level of urbaniza-tion tend to have a somewhat higher number of fossil fuel projects, yet they have receivedless funding for those projects than less urbanized countries. The similar finding for non-OECD countries implies that our result for democracies is not driven by general levels ofdevelopment.

While our theory does not predict the pattern that fossil fuels would decrease with urban-ization, it is important to remember that urbanization is generally correlated with wealth.One reason why fossil fuels may decrease with urbanization in democracies is a simplewealth effect, whereby wealthy people are more willing to pay for clean energy than poorpeople. Our descriptive analysis cannot account for this because control variables are notincluded. Below, our multivariate suggests that the negative association between fossil fuels


and urbanization in democracies is not robust; instead, the evidence suggests no associationwhatsoever, as our theory predicts.

A contrasting pattern is observed in autocracies, where the level of urbanization ispositively associated with the number and the amount of fossil fuel projects funded bythe multilateral agencies. In highly urbanized autocracies, 59.3 % of projects are fossilfuel projects, accounting for 79.6 % of all funding amounts. This suggests the pres-ence of urban bias in autocracies, which creates incentives to implement fossil fuelprojects.

Table 2 displays the main recipients of international energy funding by regime type,with the amount of received total energy funding, the proportion of funding for fossil fuelprojects, and the recipient country’s urbanization level. The urbanization level is the averageratio of urban population during years 2008-11. Since the European Investment Bank isa major source of multilateral energy funding, top democratic recipients of internationalenergy funding are mostly European countries.

For top democratic recipients, no clear pattern is observed between the level of urban-ization and the proportion of funding for fossil fuel projects. Even highly urbanizeddemocracies such as Spain, United Kingdom, Germany, and France have received relativelyfewer amounts of funding for fossil fuel projects, which suggests no urban bias in democ-racies. Restricting the sample of democratic countries to non-OECD democracies does notchange the main finding. Non-OECD democratic recipients tend to have received largerproportions of funding for fossil fuel projects but the level of funding for fossil fuel projectsis not associated with the urbanization level. For instance, most of the population in Brazil(84.6 %) and Venezuela (92.8 %) live in urban area but only 37.5 % of the energy fund-ing for Brazil has gone for fossil fuels, while Venezuela had no funded fossil fuel projectsduring the examined years.

In contrast, most of funded energy projects go to fossil fuel sectors in highly urbanizedautocracies. For instance, South Africa, Russian Federation, Algeria, all of which have morethan 60.0 % of urban population, have received most of their funding for fossil fuel projects.As discussed above, we follow the binary regime type classification by Cheibub et al. (2010)(CGV), who classify South Africa as autocratic due to the violation of the alternation rulein the choice of the executive.

4 Testing urban bias in democracies and autocracies: multivariate analysis

While the descriptive findings in the previous section provide preliminary support for ourargument, they might result from other confounding factors that influence both our indepen-dent and dependent variables. To demonstrate that the suggested relation holds controllingfor other factors, this section presents the result of a multivariate analysis of 851 multilat-erally funded energy projects. For each project, we created a binary variable coded 1 forfossil fuel projects and 0 for non-fossil fuel projects. In our analysis, we examine how theprobability of receiving funding for fossil fuel projects changes depending on the binaryvariable of regime type, the continuous variable of urbanization level, and their interactionterm.

4.1 Control variables for multivariate regression

We include additional variables that may confound the relationship between our main vari-ables of interest and the type of project. First, we include a variable of the total funded


Table 2 Top recipients of international energy funding by regime type

Recipient Funding Amount Fossil Fuel (%) Urbanization (%)

Top Democratic Recipients

Italy 7124.1 22.7 % 67.8 %

Spain 6838.0 27.2 % 76.9 %

United Kingdom 6215.3 22.4 % 89.8 %

Germany 5521.1 13.1 % 73.5 %

India 5455.2 55.1 % 29.0 %

Turkey 4731.8 33.5 % 67.8 %

France 4247.6 28.6 % 77.0 %

Portugal 2918.6 46.3 % 58.2 %

Romania 2429.8 67.9 % 54.0 %

Poland 2243.6 11.1 % 61.4 %

Top Non-OECD (as of 2008) Democratic Recipients

India 5455.2 55.1 % 29.0 %Romania 2429.8 67.9 % 54.0 %Chile 1598.0 95.4 % 87.9 %Estonia 1562.3 0 % 69.4 %Brazil 1455.9 37.5 % 84.6 %Ghana 1273.0 88.4 % 49.1 %Indonesia 1209.7 24.1 % 49.3 %Ukraine 1115.1 17.2 % 67.7 %Slovenia 909.4 79.6 % 49.1 %

Venezuela, RB 902.3 0 % 92.8 %

Top Autocratic Recipients

South Africa 4577.2 91.1 % 60.0 %

Egypt, Arab Rep. 2881.7 85.5 % 42.6 %

Russian Federation 2524.1 62.0 % 72.9 %

China 2353.6 10.5 % 41.2 %

Vietnam 1694.1 0.2 % 27.1 %

Bangladesh 798.3 83.3 % 26.2 %

Pakistan 13.1 21.4 % 35.3 %

Algeria 670.1 100 % 64.6 %

Congo, Dem. Rep. 629.8 0 % 32.8 %

Tunisia 590.9 39.0 % 65.6 %

Funding amounts in 2010 USD million

amount for each project. Since fossil fuel projects tend to require larger capital investments,we control for the amount not to conflate fuel type and project size. Second, we control forthe international funding organizations, the World Bank’s International Finance Corporation(IFC), and International Development Association (IDA). Compared to other funders, theIFC is found to emphasize fossil fuel projects, while the IDA is less likely to invest in fos-sil fuel projects (Kim and Urpelainen 2013). Since the international funding organizationsdo not randomly choose to fund the energy projects, it is important to control for the typeof organizations. Third, we capture each recipient country’s economic wealth by including


GDP per capita because wealthier countries might be more willing to pay for clean energy(Aklin and Urpelainen 2013; Diekmann and Franzen 1999; Gelissen 2007).

Next, we control for a recipient country’s demand for energy by including per capitaelectricity consumption level, three continuous variables for logged per capita oil, coal, anddry natural gas production, and one binary variable for OPEC membership. The higherdemand for energy might pressure governments to have more fossil fuel projects, which areeconomically less expensive than clean energy projects.9 Natural resource endowments area particularly important set of controls, given that some authors have argued that abundantfossil fuels contribute to the emergence and sustenance of autocratic rule (Ross 2012; Cheonet al. 2013). Furthermore, natural resource endowments could reduce the cost of electricitygeneration from fossil fuels, increasing their attractiveness to international energy lends.Finally, we include regional dummies for Europe, Asia, and Americas,10 as well as yearfixed effects. These account for unobservable variation over time and across geographicregions.

4.2 Logistic regression model

Since the dependent variable on the type of project is binary (fossil fuels or not), we estimatethe following logistic regression model:

Logit (Type of Projectij) = β0 + β1Urbanizationij + β2Democracyij

+β3Democracyj · Urbanizationj + γ′Xij + εij

where i represent a project, j represents a country where the funded project takes place, andX is a vector of a control variable. In the model, regime type is interacted with the urban-ization level, so that the effect of the latter can differ across autocracies and democracies.Our primary parameters of interest are β1. We expect β1 to be positive and statistically sig-nificant, meaning that urbanization increases the likelihood that international agencies fundfossil fuels in autocracies. We do not expect such a strong positive impact of urbanizationin democracies. We estimate the random effects models and report robust standard errorclustered by country.

4.3 Results from multivariate analysis

Our primary findings are summarized in Table 3. Model (1) includes project amount andinternational funder type as control variables. Model (2) reestimates the main model withcountry random effects. We add per capital GDP controls and regional dummies in Mod-els (3), and (4) and additional control variables of electricity consumption and energyproduction control in Model (4). Model (5) reestimates the main model excluding OECDmembers.

9The electricity consumption data are available at http://www.eia.gov/electricity/data.cfm. The oil produc-tion, coal production, and dry natural gas production are available at http://www.eia.gov/petroleum/data.cfm,http://www.eia.gov/coal/data.cfm, http://www.eia.gov/naturalgas/data.cfm, respectively.10For regional classification, we follow the United Nations Statistics Division’s composition of geographicregions. See http://unstats.un.org/unsd/methods/m49/m49regin.htm.


Table 3 Empirical analysis of multilaterally funded energy projects 2008-2011

(1) (2) (3) (4) (5)

Main Country RE Non-OECD

Urban Population (%) 0.042*** 0.037*** 0.044*** 0.040*** 0.042***

(0.012) (0.012) (0.014) (0.015) (0.013)

Democracy 1.670** 1.419** 1.520** 1.101 1.350*

(0.712) (0.682)7 (0.744) (0.723) (0.711)

Interaction (Urban × Demo) -0.043*** -0.038*** -0.037*** -0.027* -0.034***

(0.013) (0.013) (0.014) (0.016) (0.013)

Project Amount Control Yes Yes Yes Yes Yes

Funder Controls Yes Yes Yes Yes Yes

Per Capita GDP Control No No Yes Yes No

Regional Controls No No Yes Yes No

Electricity Consumption Control No No No Yes No

Energy Production Controls No No No Yes No

Pseudo R-Squared 0.071 0.077 0.092 0.096

Observations 851 851 844 844 543

Standard errors in parentheses. Robust standard errors clustered by country for Models (1), (3)-(5)Binary Logit ModelRegional controls include dummy variables for Europe, Asia, and AmericasEnergy production controls include logarithmized oil, coal, dry natural gas production, and OPEC member-shipFunder controls include dummy variables for IDA and IFC*p < 0.10, ** p < 0.05, *** p < 0.01

We find that the coefficients for urbanization are positive and statistically significantacross different estimations, as expected. This finding supports our hypothesis that urban-ization increases the likelihood that international agencies fund fossil fuels in autocracies.Since the interpretation of logic coefficients is not straightforward, it is useful to assessthe substantive effect of urbanization and regime type on the probability that internationalagency funds fossil-fuel projects. Using the Clarify estimation software, we estimated prob-abilities of funding fossil-fuel projects varying the level of urbanization, and regime type,and setting all other variables at their median. The estimation is based on Model (4) andshown in Fig. 1.

The figure presents the contrasting impact of urbanization on the probability to receivefunding for fossil fuel projects. While increasing urbanization level appears to have a posi-tive impact on the probability to have fossil fuel projects in both democracy, and autocracy,the impact of urbanization is substantial only in autocracies. The probability of a fossilfuel project is 0.08 (95 % confidence interval ranges from 0.02 to 0.19) for an autocracywith 20 % of urban population but its probability increases to 0.47 (from 0.18 to 0.77in 95% confidence interval) for an autocracy with 80 % of urban population. This showsthat even though autocracies are, on average, implementing more fossil fuel projects thandemocracies, there is considerable variation among autocracies. Going from a low to high


0.2

.4.6

.81

Pr(

Fos

sil F

uel F

undi

ng)

0 20 40 60 80 100

Urbanization

Democracy

0.2

.4.6

.81

Pr(

Fos

sil F

uel F

undi

ng)

0 20 40 60 80 100

Urbanization

Autocracy

Fig. 1 Predicted probability of funding fossil fuels by regime type and urbanization

urbanization level increases the probability of fossil fuel funding by almost sixfold. Con-versely, the impact of urbanization on fossil fuel funding is minimal for democratic regimes,and the confidence intervals at the maximal level of urbanization overlap with the expec-tation in an imaginary democracy with no urban population at all. Thus, the substantiveeffects lend support to both of our hypotheses.

Among other control variables included in the model, project amount and funding agen-cies are found to be statistically significant across different estimations. In line with ourexpectation, the size of project is positively associated with the probability of fossil-fuelprojects. Regarding funding agency, the IDA-funded and IFC-funded project are morelikely to be based on fossil fuels. The finding on the impact of funding agencies is con-sistent with Kim and Urpelainen (2013), who find that the IFC and the IDA have morefossil-fuel projects than other funding organizations but the IDA’s fossil fuel projectstend to be smaller in size, which makes it spend less on fossil fuel projects than otherorganizations.

5 Further evidence and robustness

In this section, we offer several further tests. First, we conduct a quantitative casestudy of international energy projects in India. We show that even in the world’s largestdemocracy, fossil fuel generation is heavily biased in favor of urban areas, whereas such


bias does not exist for other energy projects. Next, we scrutinize the competing hypothe-sis that it is not urban but a large city bias that prevails. We show that the prominence ofa country’s largest city among urban areas shapes fossil fuel fuel funding neither in autoc-racies nor in democracies. Then, we consider the alternative hypothesis that it is economicwealth, not urbanization, that plays the key role. This hypothesis is difficult to test becauseurbanization and economic development are positively correlated. By analyzing subsam-ples of the data, however, we can provide suggestive indicating that urbanization is moreimportant than economic wealth. We also return to the case of Pakistan and show that ruralconsiderations have indeed promoted the funding of renewable energy in the country thatmotivated our research. Finally, we explore the role of regional factors and specific fundingagencies.

5.1 Evidence from India

We analyzed all international energy projects in our dataset located in India. The world’slargest democracy hosted a total of 42 projects. Of these, ten were fossil fuel projects.These projects were larger than clean energy projects, with approximately 55 % of totalfunding provided for fossil fuels. Given India’s low urbanization rate, this suggests theinterpretation that urbanization is not a good predictor of fossil fuel bias in democratic sys-tems. Since the interpretation is consistent with our theory, India is a good case for furtherstudy.

Based on the project documents, we next coded all 42 projects for their location. Wecharacterized them as urban, rural, or other. We adopted a conservative coding rule forurban projects, requiring that they be located within the administrative boundaries of acity with at least 50,000 people. A project was coded as other if there was no phys-ical location (e.g., administrative capacity building), it had components both rural andurban, or the location was undecided or unclear at the present time. All other projectswere coded as rural. The exact details of every project can be found in the supplementaryappendix.

The findings are reported in Table 4. It shows that six out of ten fossil fuel projects werelocated in cities. Notably, this includes an IFC loan for the huge Tata Ultra Mega CoalPower Plant located in the city of Mundra, Gujarat. With a capacity of 4,000 megawatts,this huge power plant is strategically located to supply electricity to industrial consumersand densely populated urban areas around it.11 While the city of Mundra is itself not nearlylarge enough to consume the electricity from the coal power plant, Mundra’s status as acritical port for exports and imports ensures concentrated electricity demand in neighboringareas. Without industrial and urban concentrations in the area, there would not be enoughdemand for electricity from this multi-billion dollar project. Conversely, only three of the32 other energy projects were located in cities, and many of the renewable energy projectsspecifically target rural electrification. For example, the IFC also funds Husk Power’s effortsto electrify rural villages in Bihar and Uttar Pradesh. Fisher’s exact test suggests that thedifference in the proportion of urban versus rural/other projects is significant at the p < 0.01level. Thus, India’s case nicely illustrates how fossil fuel projects are concentrated in urbanareas.

11“Frequently Asked Questions”: Tata Mundra Project. Available at http://www.ifc.org/wps/wcm/connect/region ext content/regions/south+asia/countries/frequently+asked+questions. Assessed June 12, 2014.


Table 4 Energy projects in India

Urban Rural Other

Fossil 6 1 3

Clean 3 12 17

Fisher’s exact test on proportion of urban projects across rows: p < 0.0025

5.2 Case of Pakistan

To illustrate the findings in another context, we next studied our motivating example,Pakistan, in greater detail. Given that Pakistan is both geographically proximate andsocio-economically similar to India, but has an autocratic political system, it is useful toinvestigate whether the country’s large rural population is indeed a plausible explanation forthe lack of fossil fuel projects. To do this, we conducted a qualitative analysis of every inter-national energy project funded in Pakistan during the 2008-2011 period. During this time,a total of 15 projects were implemented in Pakistan.12 As we conducted the analysis, wefound that most of Pakistan’s funding focuses on improving the efficiency of the electricitysystem, which is rational given the country’s electricity woes. However, approximately 1/4of total funding also went to renewable energy, and all these projects had a rural emphasis.This is consistent with the notion that the relatively small urban concentrations reduced fos-sil fuel bias in Pakistan. We predict that if Pakistan becomes significantly more urbanizedin the future, the country’s rulers will increasingly emphasize large power plants fueled bycoal.

5.3 Urban or large city bias?

According to some scholars (Seers 1977), it is not urban concentration per se, but the dom-inance of large metropolitan cities, that induces policy bias. This argument emphasizesthat metropolitan cities, and often the national capitals in particular, enjoy a privileged sta-tus because political and economic power is concentrated in them. Moreover, rulers maybe interested in the prestige of developing the centers of their nations. If this hypothe-sis were valid, our result could be driven by large cities, as opposed to urbanization ingeneral.

Given that our argument focuses on urbanization itself, it is important to evaluate theplausibility of this alternative explanation. To show that our results are driven by urbanbias, not from large city bias, we estimated our main model replacing the urbanizationvariable with the population in the largest city as a percentage of urban population.13 AsTable 5 shows, the results no longer hold with this variable. None of the coefficients aresignificant, suggesting that the prominence of the largest city does not predict fossil fuelfunding.

In other models, we included the urbanization level, the population of the largestcity, and their respective interaction terms with regime type. The estimation results,

12For a list, see http://shiftthesubsidies.org/projects and select Pakistan from the map that appears. AccessedNovember 6, 2012.13The data source for population in the largest city is the World Development Indicators, http://data.worldbank.org/indicator/EN.URB.LCTY.UR.ZS.


Table 5 Urban bias or large city bias?

(1) (2) (3) (4) (5)

Main Non-OECD

Population in the Largest City (%)* 0.002 -0.004 0.004 0.015 0.003

(0.017) (0.014) (0.016) (0.016) (0.016)

Democracy -0.717 -0.755 -0.626 -0.234 -0.547

(0.655) (0.521) (0.639) (0.548) (0.669)

Interaction (Largest City × Demo) 0.019 0.020 0.017 0.016 0.015

(0.018) (0.016) (0.017) (0.015) (0.018)







Pseudo R-Squared 0.071 0.077 0.102 0.092

Observations 768 768 762 762 463

*Population in the largest city as a percentage of urban populationStandard errors in parentheses. Robust standard errors clustered by country for Models (1), (3)-(5)Binary Logit ModelRegional controls include dummy variables for Europe, Asia, and AmericasEnergy production controls include logarithmized oil, coal, dry natural gas production, and OPEC member-shipFunder controls include dummy variables for IDA and IFC*p < 0.10, ** p < 0.05, *** p < 0.01

reported in Table 6, demonstrate that the large city variable has little impact onfossil fuel funding, regardless of political institutions. The coefficients for urban-ization and its interaction term with democracy remain statistically significant, butthe coefficients for the largest city and its interaction term with democracy are notsignificant. This finding suggests that our result is not driven by the largest citybias.

5.4 Urban or income bias?

Since a country’s urbanization level is highly correlated with its income level (r = 0.63 inour sample), our analysis results could be driven by income bias, not by urban bias. Dueto this high correlation, distinguishing between the two causes is difficult. Even thoughwe have included per capita income in the regressions, further empirical analysis is war-ranted. Our goal is to ensure that urbanization remains a good predictor of fossil fuelfunding for autocracies, but not for democracies, even as we properly account for per capitaincome.

To separate the effects of urbanization from the effects of income, we first estimatedour main model separately for low income and high income countries, separated at the


Table 6 Urban bias or large city bias?

(1) (2) (3) (4) (5)

Main Non-OECD

Urban Population (%) 0.050*** 0.045*** 0.056*** 0.045** 0.050***

(0.015) (0.014) (0.017) (0.019) (0.015)

Population in the Largest City (%)* 0.015 0.011 0.019 0.022 0.015

(0.012) (0.014) (0.013) (0.015) (0.011)

Democracy 2.073** 1.825* 1.967* 1.298 1.993**

(0.929) (1.006) (1.027) (1.019) (0.948)

Interaction (Urban × Demo) -0.052*** -0.048*** -0.047*** -0.031 -0.048***

(0.016) (0.015) (0.018) (0.020) (0.016)

Interaction (Largest City × Demo) 0.004 0.005 0.001 0.007 0.001

(0.014) (0.016) (0.014) (0.013) (0.015)







Pseudo R-Squared 0.096 0.103 0.117 0.130

Observations 768 768 762 762 463

* Population in the largest city as a percentage of urban populationStandard errors in parentheses. Robust standard errors clustered by country for Models (1), (3)-(5)Binary Logit ModelRegional controls include dummy variables for Europe, Asia, and AmericasEnergy production controls include logarithmized oil, coal, dry natural gas production, and OPEC member-shipFunder controls include dummy variables for IDA and IFC* p < 0.10, ** p < 0.05, *** p < 0.01

sample median. If our urbanization hypothesis is correct, urbanization should predict fossilfuels in autocracies in these samples, even as we differentiate between wealthier and poorercountries.

Next, we estimated a model including an interaction term between income and democ-racy instead of the interaction between urbanization level and democracy. We did thisseparately for countries with high and low urbanization levels, again separated at the samplemedian. If the alternative income hypothesis prevailed, income should predict fossil fuels inautocracies within these subsamples.

Table 7 summarizes the results. It shows that regardless of the level of urbanization, vari-ation in income never predicts fossil fuel projects. Conversely, urbanization predicts fossilfuel projects in low and high income autocracies, though only the former effect is statisti-cally significant. While suggestive, this evidence is more consistent with the urbanizationthan with the income thesis.


Table 7 Urban bias or income bias?

(1) (2) (3) (4)

Low High Low High

Income Income Urbanization Urbanization

Urban Population (%) 0.046** 0.087 0.017 0.019

(0.021) (0.059) (0.014) (0.014)

GDP p.c (constant 2000 USD) -0.000 -0.000 0.000 0.002*

(0.000) (0.000) (0.000) (0.001)

Interaction (Urban X Demo) -0.029 -0.097

(0.020) (0.061)

Interaction (GDP p.c. X Demo) -0.000 -0.002*

(0.000) (0.001)

Democracy 1.109 5.229 0.405 3.161

(0.897) (4.500) (0.428) (2.827)

Project Amount Control Yes Yes Yes Yes

PseudoR-Squared 0.074 0.050 0.067 0.067

Observations 419 425 422 422

Robust standard error clustered by country in parenthesesBinary Logit Model* p < 0.10, ** p < 0.05, *** p < 0.01

5.5 Regime type or corruption?

We have thus far demonstrated the existence of urban bias in autocracies. We now inves-tigate whether corruption level, instead of regime type, could drive this finding. In fact,regime type is positively correlated with corruption level: the democracy indicator and theInternational Country Risk Guide’s14 (ICRG) corruption level have a negative, statisticallysignificant correlation (−0.33) in our dataset. Also, investors might prefer to implementfossil fuel projects in urban centers, where demand for energy is higher than in ruural areas.At the same time, fossil fuel projects might be more prone to corruption, as investors canbribe government officials for licenses to implement fossil fuel projects.

To empirically examine the validity of this conjecture, we estimate additional modelsadding the ICRG corruption indicator and its interaction with urbanization. For the cor-ruption indicator, we reversed the original scale so that the higher value would indicatethe higher level of corruption. The reversed variable ranges from 0 to 5.5 in our dataset.Our estimation results are presented in Table 8. Clearly, the urban bias is not driven by thecorruption level. Neither the corruption variable nor its interaction with urbanization levelobtains a conventional level of statistical significance. Yet, our main parameter of interestβ1 is positive and statistically significant across all estimations even when we control for the

14See http://www.prsgroup.com/about-us/our-two-methodologies/icrg.


Table 8 Regime type or corruption?

(1) (2) (3) (4) (5)

Main Non-OECD

Urban Population (%) 0.062** 0.064** 0.064** 0.063** 0.068**

(0.025) (0.027) (0.027) (0.026) (0.031)

Democracy 2.153** 1.881** 1.793* 1.328 1.677*

(0.928) (0.832) (0.916) (0.890) (0.911)

Interaction (Urban × Demo) -0.051*** -0.047*** -0.040** -0.031 -0.039**

(0.016) (0.016) (0.017) (0.019) (0.016)

Corruption 0.251 0.361 0.214 0.186 0.570

(0.411) (0.426) (0.412) (0.356) (0.488)

Interaction (Urban × Corruption) -0.003 -0.004 -0.003 -0.003 -0.004

(0.006) (0.006) (0.006) (0.005) (0.007)







Pseudo R-Squared 0.082 0.088 0.105 0.120

Observations 772 772 772 772 464

Standard errors in parentheses. Robust standard errors clustered by country for Models (1), (3)-(5)Binary Logit ModelRegional controls include dummy variables for Europe, Asia, and AmericasEnergy production controls include logarithmized oil, coal, dry natural gas production, and OPEC member-shipFunder controls include dummy variables for IDA and IFC* p < 0.10, ** p < 0.05, *** p < 0.01

level of corruption and its interaction with urbanization. Again, this finding lends supportfor the urban bias in autocracies story.

5.6 Differences among autocracies

Is urban bias universal among autocracies? To answer this question, we separately examinethe impacts of different types of dictatorships on fossil fuel funding. In the dataset providedby CGV, autocracies are subcategorized into civilian, military, and royal dictatorships. Fol-lowing CGV, we generated additional variables on civilian, military, and royal dictatorshipsand their interaction terms with urbanization. We then estimated the main models replacingthe binary democracy variable and its interaction term by a similar structure for each typeof autocracy.

Table 9 summarizes the estimation results. We find that the urban bias is present onlyin civilian autocracies. The coefficient for the interaction term of urbanization and civilian


Table 9 Different types of autocracies

(1) (2) (3)

Civilian Military Royal

Urban Population (%) 0.002 0.006 0.004

(0.005) (0.005) (0.005)

Civilian Dictatorship -1.694*

(0.978)

Interaction (Urban × Civilian Dictatorship) 0.041**

(0.018)

Military Dictatorship -1.222

(1.141)

Interaction (Urban × Military Dictatorship) 0.044

(0.030)

Royal Dictatorship -3.151

(3.255)

Interaction (Urban × Royal Dictatorship) 0.063

(0.046)

Project Amount Control Yes Yes Yes

Funder Controls Yes Yes Yes

Pseudo R-Squared 0.063 0.055 0.054

Observations 853 853 853

Robust standard errors clustered by countries in parenthesesBinary Logit ModelFunder controls include dummy variables for IDA and IFC* p < 0.10, ** p < 0.05, *** p < 0.01

dictatorship is positive and statistically significant, suggesting that urbanization increasesthe probability of fossil fuel funding in civilian dictatorships. The coefficients for otherinteractions are positive but statistically insignificant. These findings may reflect the factthat civilian dictatorships tend to have somewhat larger constituencies than military androyal dictatorships. A civilian dictatorship needs to provide energy to urban constituencies,while military and royal dictatorships can focus on satisfying the needs of an even smallerelite. Consequently, the interactive effect of urbanization and regime type would be thestrongest in the comparison between democracies and civilian dictatorships.

5.7 Regional differences

To examine the robustness of the findings, we next excluded each international fundingagency and geographic region, one by one. We found that the results are mostly driven bypatterns on the Eurasian continent, as the interactive effects of urbanization and democracyare weaker when European or Asian funders/countries are removed from the sample. Recall,though, that our results hold if we exclude OECD recipients. This makes sense, given that


there are few autocracies in Latin America and that Africa’s energy infrastructure and urbangovernance remain particularly underdeveloped.

5.8 Resource endowment

Could this finding be driven by natural resource endowment? Countries endowed with natu-ral resources tend to be autocratic (Barro 1999) and thus, natural resource endowment mightdetermine both the regime type and ability to receive funding for fossil fuel projects. Weconsidered this possibility and controlled for each county’s energy production by includingoil production, coal production, dry natural gas production, and OPEC membership indi-cator in our estimation. Yet, in order to further ensure the robustness of our finding, wecarry out an additional robustness test by estimating our model excluding top ten oil pro-ducing countries and top ten coal producing countries from the dataset, respectively. Theestimation results, presented in the appendix, show that our findings are not driven by afew outlier countries endowed with natural resources. Our main parameter of coefficient onurbanization, β1, still maintains a statistical significance at the 0.90 level.

5.9 Government effectiveness and the cost of electricity provision

We conduct additional robustness tests by controlling for government effectiveness andthe cost of electricity provision. Our finding indicates that urbanized autocracies attractmore subsidies for fossil fuel projects, and we focus on political leaders’ domestic politicalincentive to explain this pattern. However, urbanization and regime type are correlated withother factors that could account for the pattern of multilateral energy lending. Managingthermal power plants require an effectively functioning government. Thus, we control forgovernment effectiveness using the measure from the World Bank’s Worldwide GovernanceIndicators.15 The score runs from -1.5 to 2.4 in our dataset, with higher score meaning moreeffective governance.

Also, it is important to control for the cost of electricity generation. We addressed thisissue by including two measures that capture the cost of electricity provision: first, effi-ciency of electricity generation, using the net energy generation divided by the net energycapacity; second, electricity transmission and distribution (T&D) losses.16 We estimateadditional models including these three variables to address the potential issue of omittedvariable bias in regard to the government effectiveness and the cost of electricity provision.The estimated results, presented in the appendix, show that our findings remain robust.

6 Conclusion

Energy demand is growing rapidly in the developing world, with large environmental andeconomic consequences at the global level. International funding agencies play an importantrole in facilitating energy supply. We have analyzed variation in international energy fund-ing across recipients. We have argued that large urban populations increase the demand forfossil fuels because this form of heat and electricity generation is subject to economies of

15This dataset is available at http://info.worldbank.org/governance/wgi/index.aspx.16The net energy generation and the net energy capacity variables are from the U.S. Energy InformationAdministration’s database available at http://www.eia.gov/countries/data.cfm, and the T&D losses data arefrom the World Bank database available at http://data.worldbank.org/indicator/EG.ELC.LOSS.KH.


scale. However, this bias should only characterize autocratic regimes, because in democra-cies the urban bias is weaker than in autocracies. Evidence from 885 internationally fundedenergy projects during the 2008-2011 period supports this argument. While autocraciessecure more funding for fossil fuels than democracies, this difference is driven by highlyurbanized autocracies, such as Russia.

Given the trend of rapid urbanization in virtually every developing country, there is a riskthat interest in fossil fuels actually increases for simple economic reasons, fueling climatechange and other environmental woes. Politically, this risk is greater in autocratic than indemocratic societies. This underscores the need to support renewable energy systems suit-able for urban areas. Many major emitters, such as China, are both autocratic and rapidlyurbanizing. For such countries, there is a clear need to develop renewable and other cleanenergy solutions that fit the needs of urban communities. In addition to the need for rec-onciling economic development and climate mitigation (Halsnæs and Verhagen 2007), thechallenges of urbanization warrant greater attention by scholars and policymakers. Inter-national efforts to promote clean technology deployment (Urpelainen 2013) would benefitfrom developing new technologies to meet the needs of the growing urban populations of theworld.

At the same time, our results offer some hope that policy could change the associationbetween energy demand, urbanization, and carbon dioxide emissions that previous studieshave identified (Jones 1991; Satterthwaite 2009; Ponce de Leon Barido and Marshall 2014).While autocratic rulers currently secure funding for fossil fuels in urban areas, future poli-cies could change this if international energy lenders put more emphasis on environmentalsustainability in their funding decisions. If the international development community putsmore emphasis on clean development, the ties between urbanization and carbon dioxideemissions can be severed.

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