1
Spatial Analysis of Bureaucrats’ Attempts to Resist Political Capture in a Developing
Democracy: The Distribution of Solar Panels in Ghana
Jennifer N. Brass
Associate Professor, School of Public & Environmental Affairs, Indiana University
Justin Schon (corresponding author): jschon@ufl.edu
Postdoctoral Associate, Department of Anthropology, University of Florida
Elizabeth Baldwin
Assistant Professor, School of Government & Public Policy, University of Arizona
Lauren M. MacLean
Arthur F. Bentley Chair and Professor, Department of Political Science, Indiana University

Accepted in Political Geography

Abstract
Can civil servants in a developing democracy successfully resist political capture in the
distribution of public goods? Existing research tends to highlight political patronage logics of
distribution, and scholarship focuses primarily on politicians’ motivations. But service decisions
are often made by bureaucrats, who sometimes explicitly try to avoid capture and allocate on thebasis of need. Using qualitative interviews and spatial regressions with geo-coded data from asolar panel program in Ghana, we examine such a situation. We show that, despite considerable
efforts to thwart it, national-level civil servants were unable to fully resist political capture.
While solar panel distribution partially corresponded to need for electricity, we find that panels
also went to areas where voter turnout was historically inconsistent, likely to motivate voter
turnout. The direction of these relationships was consistent across subnational space, but themagnitudes varied considerably and were strongest in districts adjacent to Lake Volta.
Qualitative data analysis reveals that this subnational variation reflects the logistics of space andthe historical politics of place. The article contributes to theories of distributive politics and of
voting behavior, and highlights the need for subnational disaggregation across space.
Keywords: civil servants; bureaucrats; distributive politics; political capture; voter mobilization;
geographically weighted regression (GWR)
Manuscript Word Count: 11,000 words, including the main body of text, notes, references, andtables and figures, but excluding the abstract, text within a table or figure, and online appendices.
Declarations of Interest: None
Data Statement: Data is available from the authors upon request.

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Acknowledgements: This paper was made possible (in part) by a grant from Carnegie
Corporation of New York, as well as one from the Faculty Research Support Program of Indiana
University. We thank Mohammed Awal, Gildfred Asiamah, Mildred Adzraku, Jasper MacLean,
Mumin Mutaru, and Esinam Nyador for their work as members of the Ghana research team. We
also thank colleagues at CDD-Ghana for their tremendous support and collaboration, as well as
those who mobilized, accommodated, participated in, and transcribed interviews and focus group
discussions. We are grateful for feedback on early versions of the paper at MGAPE, MPSA, theAfrican Studies Association annual meetings, and from anonymous reviewers. We also thankDave Warren and Bernard Fraga. The statements made and views expressed are solely the
responsibility of the authors.

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Introduction
Political scientists have studied distributive politics – who gets public goods, where, and when –in industrialized democracies for decades.1 A newer literature has begun to address these issues
in emerging democracies.2 These studies generally focus on whether politicians distribute public
goods according to a particular electoral logic – for example, to reward party loyalists, target
swing voters, or mobilize supporters to vote. But much of the existing literature sidelines the roleof public administrators as decision makers,3 even though career civil servants and donor staff
are often given discretion to make practical decisions about how to distribute scarce resources in
aid-dependent countries.4 While theories of political capture suggest that bureaucrats act onbehalf of the ruling political party, some civil servants are protected from political interference,and others eschew electoral politicking, seeing themselves as falling on the more technocraticside of the “politics-administration divide.”5 Bureaucrats’ decisions, moreover, are shaped by thelogistics of space – they may need to decide whether to allocate goods in geographic clusters thatstreamline service delivery or to maximize the number of geographic locations that receive
services. But they are also shaped by the politics of place, with goods distribution guided at least
partially by historic relationships between citizens, politicians, donors, and civil servants. This
paper explores these dynamics by examining, first, whether civil servants are able to avoid
political capture; and second, whether these patterns vary across sub-national units, suggestingthat civil servants’ behavior may be shaped by space and place.
We analyze a solar panel distribution program in Ghana in which program implementersrecognized the risk of political influence and took explicit steps to avoid political capture. In the
program, officials from Ghana’s Ministry of Energy and Petroleum (MOEP) collaborated withrepresentatives from a donor-funded European multinational company (MNC) to install 1,242
solar panels between 2009 and 2012. The program’s stated aim was to provide solar photovoltaic
panels in un-electrified areas of the country for schools, clinics, police stations, streetlights,
community halls, and entertainment spaces, starting with remote and under-developed
communities around Lake Volta and then moving on to other areas with poor electricity access.6
Knowing that distribution would occur during an election period, both the MOEP bureaucrats
and the company representatives strove to avoid political capture by using data and analysis –rather than local politicians’ input – to determine where panels would be installed. This study
seeks to empirically assess whether program officials were successful at keeping electoralpolitics out of distribution, and to assess the subnational political geography of the effort.
As a young but stable democracy, Ghana provides a useful context for studying the logicof public goods distribution. Since 1992, Ghana has had peaceful elections and has witnessed
several transfers of power between the National Democratic Congress (NDC) and New Patriotic
Party (NPP), revealing that Ghanaian voters can and do react to political strategies and policies.
1

Cox and McCubbins 1986; Dixit and Londregan 1996; Dahlberg and Johansson 2002; Hirano 2011; Weingast,
Shepsle, and Johnsen 1981; Rich 1989; Martin 2003.
2
Habyarimana et al. 2007; Calvo and Murillo 2007; Keefer 2005; Kramon & Posner 2013; Grzymala-Busse 2008;
Wibbels 2006; Gottlieb et al. 2019; Franck and Rainer 2012; Eidemyr et al. 2018; Harris and Posner 2019.
3
Some recent studies have focused on bureaucrats and service provision and/or political capture, such as Brierley
forthcoming, Oliveros & Schuster 2017; Borang et al 2018; and Gulzar & Pasquale 2017.
4
See Gailmard & Patty 2007 for the theory behind this statement.
5
For seminal work, see Wilson 1887 and Waldo 1948. A recent empirical example in this line is Dalhstrom &LaPuente 2017. There is no clear reason that either the political capture model or the “politics-administration divide”
model should be universally dominant.
6
Solar systems were small, ranging from 100 to 250w with 150 to 400wh of batteries.

4
Ghana also has enough subnational administrative units to facilitate quantitative analysis of
meaningful variation across the geographic space.
Additionally, Ghana has a rapidly expanding economy and a goal of universal access toelectricity by 2020 (with 10% from renewable sources). Ghana has successfully doubled access in the past
decade, reaching 82.5% access in 2016,7 illustrating that the country’s civil service is capable ofresponding to citizens’ need for basic services. Nonetheless, Ghana’s MOEP has limited resources tomeet its ambitious electrification goals, and it is unlikely that the MOEP will be able to extend thecountry’s national grid to all of the remote communities that need electricity in the medium term, due tothe high costs and low predicted revenues from difficult to reach and impoverished areas. MOEP officials
therefore worked with donors to distribute solar panels and provide electricity access to somecommunities, but not others.
Our study takes a mixed-methods approach that combines statistical spatial analysis to test
hypotheses and qualitative analysis based on fieldwork to generate hypotheses and explore how
distributional logics vary by space and place. Our choice of methods speaks directly to long-standing
debates in this journal and others about how to conduct studies that bridge perspectives from politicalscience and geography. We intentionally employ some methods to explain “average effects” across all of
Ghana’s districts and other methods to theorize subnational variation within the country.
To unpack the “black box” of program implementation, we draw on qualitative interviews withdonors, subcontractors, politicians, bureaucrats, and citizens conducted in four regions of Ghana during
seven one-month trips between 2014 and 2019. This fieldwork helped to generate hypotheses about how
Ministry officials selected districts to receive panels. To test our hypotheses, we first use spatially-lagged
dependent variable (SLDV) models in order to isolate the direct effects of a district’s political and needbased characteristics on public goods distribution. Then, to determine whether these effects apply
uniformly across Ghana’s 170 districts, we analyze subnational spatial variation using geographicallyweighted regression (GWR). Finally, we employ qualitative evidence to explore how political history in
specific places and the logistics of infrastructure across space shape panel distribution within regions.
Although our results show that districts with more infrastructure were significantly less likely toreceive panels, suggesting that MOEP officials were somewhat successful at distributing panels to theremote districts that needed them, results also show that civil servants failed to completely avoid political
capture. Panels were allocated to districts where voter turnout has varied. Following Cammett and Issar,
we interpret this as suggestive evidence that public goods could be used to mobilize potential voters toturn out.8 Specifically, panels may have been distributed in hopes of inducing voters to turn out andsupport the incumbent in the upcoming election. Our results show that political capture does not always
involve straightforward dyadic exchanges of private goods such as cash or jobs from patrons for loyalty
from constituents. We also find that although the basic logic of results holds throughout Ghana, themagnitude of the relationship varies subnationally, reflecting differing histories of space and place.
This analysis contributes to distributive politics research by bridging insights from geography,
political science, and public administration. Our analysis also suggests that more social scientists should
pay attention to decisions made by unelected bureaucrats, in addition to those of politicians.9 Public
administrators often possess both authority and discretion to make allocative decisions,10 and these actors
sometimes grapple with concerns about how to use scarce resources to maximize citizen benefit frompublic goods amidst political interference. Drawing attention to civil servants allows us to see how avariety of actors with agency – and potentially with different preferences and access to information –shape decision-making. Finally, we implement the recommendation from O’Loughlin’s 2018 article in
7

9

Kumi 2017.

Focus on bureaucrats by mainstream political scientists has become more common recently (e.g., Ang 2016;
Brierley forthcoming; Oliveros & Schuster 2017; Borang et al 2018; and Gulzar & Pasquale 2017), but often
assumes political control of bureaucrats.
10
Gailmard & Patty 2007.

5
this journal to seriously consider “space and place” in our analysis. We use analytical methods that
address spatial dependencies and allow us to examine sub-national variation in hypothesized
relationships, as well as qualitative interviews and historical subnational analysis to understand how
political geographic context can shape goods allocation.11
The Solar Program: Implementers’ Efforts to Achieve Need-Based Goals
Before outlining possible logics shaping how services are distributed, we begin with an overview
of the project, noting the emphasis multiple project implementers placed on the deliberate steps the
MOEP and the MNC took to avoid political capture. In interviews, implementers expressed confidence
that these careful steps had been successful, which presented an intriguing empirical puzzle.12
Through a €300,000 grant from a European government, the MOEP jointly implemented a solar
panel distribution program with a donor subcontractor, a private European MNC.13 The program aimed toprovide solar photovoltaic panels in unelectrified areas for schools, clinics, police stations, streetlights,
community halls, and entertainment spaces. The panels ranged in size from 100 to 250 watts, producing
sufficient electricity to power lights, charge mobile phones, and run small appliances like laptops andradios. While Ghana has one of the highest rates of electric grid access in sub-Saharan Africa, solar arrays
improve electricity access in areas that lack access to the grid, as well as areas where the grid is subject tofrequent power outages, which were common in Ghana during the study period.14 Solar panels are
generally considered inferior to the grid;15 once people sense the possibility of grid access, they no longer
want solar as an alternative.16
The program targeted areas that were unlikely to gain national electric grid access within five
years, starting with tiny, remote island communities within Lake Volta, which were deemed costinefficient for future grid expansion.17 When the Memorandum of Understanding between the Ghanaian
government and the company was signed, Minister of Energy Joe Oteng-Adjei emphasized that thefacilities were installed at public institutions such as security outposts, clinics, schools and publicgathering places, “so as to improve on the general wellbeing of our people.”18 Implementers also asserted
that providing electricity services in remote communities would help those places attract or retain highquality teachers and healthcare providers, and that later in the program, border areas were targeted toenhance security in those areas.19
The program’s 1,242 solar panels were distributed by unelected bureaucrats within the MOEP
renewable energy office, working closely with a MNC representative stationed in the country. Officials
knew at the program onset that they did not have sufficient resources to distribute panels everywhere thatthey were needed. To select placement sites, implementers began with internal documentation and MOEP
Rural Electrification Program maps of existing and planned grid extension, which tends to follow major
roadways.20 Based on these maps, they selected districts that contained areas away from planned grid
extension who would need medium-term alternatives to the grid if citizens were to receive basic access toelectricity (See Figure 1). After selecting districts based on current and planned electric grid maps, MOEP
and MNC staff traveled to the districts to consult with District Planning Engineers, Solar Project Officers,
local bureaucrats from the Ministries of Health and Education, area police, and local-level traditional
11

Fotheringham, Brunsdon et al. 2002.
Several implementers independently explained their efforts to avoid political capture without prompting by theinterviewer, and they did so with apparent pride. This presented a set of questions that motivated our analysis.
13Interview GH170209. Interview codes are used to protect human subjects. The code represents the country, year,
month, and day. An FGD prefix indicates a focus group discussion.
14
Maclean et al. 2016.
15
GH1405298.
16
Dugoua & Urpelainen 2014.
17
GH170225; GH180312A.
18
Government of Ghana ND.
19
GH170209.
20
GH170209; GH180312A.
12

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chiefs.21 Following Ghanaian policy, these district-level civil servants had already prepared district
development plans that assessed and documented the needs of communities within each district.22 MOEP
and MNC staff asked these local officials to help them identify communities to receive solar panels, basedon the following criteria: 1) the population size of the community; 2) the number of public facilities suchas schools or clinics in the community; and 3) the existence of rural industry.23
From these conversations, the MOEP and MNC administrators created lists of all possible
recipient communities. The Solar Project Officers then conducted socio-economic impact studies on ashort list of communities, narrowing their assessments to public institutions in places that would be able
to serve 10 to 30 nearby micro-villages, which were clusters of several families. These communities werethen compared to the MOEP data and grid extension master plan before final sites were chosen. MNC and
MOEP officials based in Accra traveled to approximately 80 percent of implementation sites to confirm
firsthand the absence of electricity in the site, to geocode the precise installation of the solar panels, andthen later to participate in the opening ceremony.
Both Ghanaian and European program personnel were emphatic that selection of recipient
districts was apolitical and need-based. Officials were aware that their three-year contract spanned anelection cycle, and avoided engaging MPs in the selection process, as MPs were known to encourage
distribution to their personal strongholds because “your uncle is living in that place or you want votes forthe next election.”24 When asked whether he worked with local politicians to select recipient districts, one
MOEP official reported that “[w]e want to make it free from politics. …We target the district and it isalways free from politics.” However, other civil servants interviewed did acknowledge that MPs had
attempted to shape the selection by directly contacting MOEP officials while in Accra and highlighting
certain communities on their list, but insisted that final lists of recipient communities were vetted multiple
times and cross-checked at the local level to avoid political influence.25

21

In most communities in Ghana, precolonial chieftancy institutions continue to be recognized as legitimate
traditional authorities guaranteed in the Constitution, as they oversee use of customary lands and mediate local
disputes. They remain separate from the central government structure.
22
GH180312B.
23
GH180312A.
24
GH170209.
25
GH180312A.

7
Figure 1. Maps of Solar Project Locations Shown with Other Key Variables

Possible Logics of Public Goods Distribution and Why They Might Vary SubnationallyThis solar distribution project in Ghana therefore represents an extreme case, in that it allows us
to test whether bureaucrats who explicitly attempted to distribute goods apolitically were successful, andhow it may have varied subnationally. Here, we explore these possibilities theoretically.
Political Distribution
Much of the work in political science on the distribution of public goods, both in the US andelsewhere, has focused on the role of politicians in shaping the distribution of resources for electoral gain.
Scholars have suggested that goods are provided by local party officials,26 by members of Congress or
Parliament,27 or by public officials acting on behalf of incumbent party officials.28 A growing literature
also suggests that within-country aid is distributed largely for political reasons, and may not target thepoor.29 Jablonski, for example, finds that donor-funded programs in Kenya followed the same patronage
logic as state-distributed programs, because Kenyan officials took advantage of donors’ lack of
information about the distribution of real need to steer aid toward areas where voters were most likely to26e.g., Dixit & Londregan 1996.
e.g., Weingast, Shepsle, and Johnsen 1981; Weghorst & Lindberg 2013.
28
Dahlberg & Johanssen 2002.
29
Briggs 2017; Masaki 2018; Öhler and Nunnenkamp 2014; Zhang 2004.
27

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help them win elections; Briggs echoes these findings.30 Other scholars have argued ethnic favoritism
affects goods distribution, but this remains open for debate.31
Many studies thus argue that goods are distributed primarily to affect electoral outcomes by
rewarding loyal voters or by gaining the support of swing voters.32 For example, Dixit and Londregan
posit that voters have fixed partisan preferences, but can be induced to vote for the rival party with
sufficient material inducement.33 This suggests that parties can distribute goods strategically to reward
and retain loyal supporters, or alternatively, to induce the support of swing voters. The swing voter
hypothesis in particular requires an electoral system in which two or more parties actively compete forvoters, and also suggests that politicians or parties need a way to identify swing voters or jurisdictions
who might be persuaded to shift their support.34,35
These competing hypotheses, however, tend to overlook an additional way that voters can express
their preferences: by choosing whether or not to vote on election day. Nichter hypothesizes that parties
target resources toward “unmobilized strong supporters,” not to gain their vote but to ensure turnout
among their base.36 And incumbent governments sometimes work to provide services to non-supporters
in an attempt to mobilize new potential groups of voters.37 This suggests that politicians might seek tomobilize not only known supporters, but also to mobilize voters who have not consistently participated inelections in the hope that they might support an incumbent who has brought goods to their area.
While scholars often test the the loyalist, swing voter, and voter mobilization hypotheses as
mutually exclusive rivals, evidence suggests that all are used in sub-Saharan Africa. Many studies findthat incumbent parties use goods to reward loyalists.38 In a recent study of Ghanaian voters, however,Weghorst and Lindberg find evidence that incumbents can effectively gain support of swing voters byproviding local public goods,39 and Baldwin provides support for the notion that national actors may cede
allocation decision-making to local leaders in order to entice the local leaders to mobilize their supporters
in the future.40 While it is clear that politics can affect distribution in emerging democracies like Ghana,no systematic evidence exists in favor of – or opposed to – any of the hypotheses highlighting politicaldrivers.
Moreover, in our particular context, the goods in question were provided by donors, rather than
Ghanaian politicians, and were distributed by MOEP bureaucrats. While our interview respondents
insisted that they had insulated distribution from political considerations, the implementation process doessuggest some opportunity for political capture, particularly if local political officials sought out informal
opportunities to advise and influence MOEP officials’ distribution decisions.41 Based on the above, our
30

Jablonski 2014; Briggs 2014.
e.g., Fearon 1999; Habyarimana et al. 2007; but see Kramon & Posner 2013 and Burgess et al. 2015 forcounterarguments.
32
e.g., Golden and Min 2013.
33
Dixit and Londregan 1996.
34
Dixit and Londregan 1996; Golden and Min 2013; Dahlberg and Johansson 2002; Stokes 2005; 2009; Golden and
Picci 2008.
35
Strictly speaking, these theories focus on the distribution of patronage goods – such as jobs or favors – toindividuals. Nonetheless, the logic of their model is sometimes scaled to the community level, where politicians or
public officials acting on behalf of the incumbent party may strategically distribute goods for political gain
(Dahlberg and Johansson 2002; Fried 2012; Dellmuth and Stoffel 2012).
36
Nichter 2008.
37
Cammet & Issar 2010.
38
Riedl 2014; LeBas 2011; Briggs 2012.
39
Nuancing these findings, Harding (2015) shows that elections allow voters to hold politicians accountable forservice provision when citizens can correctly attribute public goods to them, suggesting that patronage politics cangive way to issue-based politics.
40
Weghorst and Lindberg 2013; Baldwin 2014.
41
Politicians can also use indirect means of control, such as threats of employee reassignment (Brierley
forthcoming).
31

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first hypothesis, H1, posits that panels were distributed to districts for purposes of electoral gain, using
one of three political logics:
H1A: Officials reward loyalists by directing solar panels to districts with historically high support for
the incumbent political party.
H1B: Officials induce swing voters by directing solar panels to districts with historically variablesupport for the incumbent political party.
H1C: Officials mobilize potential supporters by directing solar panels to districts with variable
participation in elections.
Need based Distribution
Despite the electoral focus of most distributive politics research, the fact is that career civil
servants, not politicians, often make the final decisions about where goods should go. Because these
individuals do not face the electoral pressures of politicians, they may be able to avoid political capture,
particularly in countries like Ghana that are striving to establish a professional civil service with thecapacity to meet ambitious policy goals like providing universal electricity access to all citizens by
2020.42 To the degree that bureaucrats in countries like Ghana seek to avoid political capture, they adhere
to a Weberian model of bureaucracy, where officials are appointed on the basis of their qualifications, areexpected to treat citizens equally, are rewarded for their ability to meet policy goals, and are kept
relatively insulated from political pressures.43 Empirical evidence suggests that the Weberian model –rather than the complete political capture model – tends to predominate in industrialized countries, butsome studies have also shown that bureaucracies in developing countries with greater Weberiancharacteristics produce positive outcomes.44 A literature on “pockets of effectiveness” or “islands of
excellence” in developing countries also highlights that bureaucrats given autonomy – expressly shieldedfrom political patronage pressures – are more able to achieve development goals.45
While political scientists have focused on politics as the predominant logic behind goods
distribution, many non-elected decision-makers claim to act based on citizen need, whether or not theysucceed at doing so. At least one study has shown that aid that by-passes government entirely is not
subject to patrimonial distribution.46 There are several different ways that bureaucrats might think about
citizens’ need for electricity. If bureaucrats are primarily concerned with meeting citizens’ current needfor electricity, we might expect them to simply target solar panel distribution in places that currently lackelectric grid access.
But solar panels are considered inferior to access to the national grid in countries like Ghana. And
since Ghana is rapidly expanding the electricity grid, bureaucrats might have a forward-looking
orientation where they provide panels in areas that are also unlikely to receive grid access in the near
future. Here, we draw on interviews in five sub-Saharan African countries to acknowledge that there is aparticularly strong relationship between road infrastructure and the likelihood of getting grid access. The
link between grid expansion and roads may not be initially obvious, but grid extension requires roadinfrastructure to transport the required materials (poles, transformers). Moreover, energy ministries often
prioritize grid extension along major roads, because this strategy allows the government to provide
electricity to hospitals, schools, and population centers to maximize social benefits. Figure 2 shows a2009 map of planned grid extension (left), with a map of trunk roads (right). Thus, if bureaucrats have amore future-oriented conceptualization of citizens’ need for electricity, we might expect to see panels
allocated to roadless areas where grid expansion is unlikely.

42

Per the Ghana Vision 2020 plan.
Walton 2005; Olson 2006.
44
Evans & Rauch 1999; Rauch & Evans 2000; Oliveros & Schuster 2018.
45
Geddes 1994; Leonard 2010; Bersch et al 2017; Dahlstrom & LaPuente 2017.
46
Dietrich 2013.
43

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Our second hypothesis, H2, posits that panels were distributed to districts to meet citizens’ needfor electricity, using one of two need-based logics:
H2A: Officials meet citizens’ current needs by directing panels to districts with limited grid
access.
H2B: Officials meet citizens’ expected future needs by directing solar panels to districts withlimited road networks, which make future grid expansion unlikely.
Figure 2: 2009 MOEP Planned Grid Extension (Left) and Trunk Roads of Ghana (Right)

Spatial Variation and Spatial Dependence in the Logics of Distribution
In addition to the political and need-based logics proffered above, there are reasons to think thatspace and place might affect officials’ decisions about where to distribute goods in ways that are rarely
examined in empirical analyses. Solar panels, for example, are not a good that is simply distributed
among citizens; panels must be installed in a particular place, connected to lighting or other appliances,and care must be taken to maintain panels over time. Previous literature suggests that solar panels inparticular should be installed in places where there are qualified engineers who can ensure that panels are
installed and maintained correctly,47 or alternatively, that solar panel programs should be clustered inareas that faciliate program implementers’ ability to conduct routine maintenance. There is thus anelement of spatial dependence in solar panel distribution, where panels are distributed in proximity to one
another, or in proximity to existing resources that can support installation, operation, and long-term panel
maintenace.

47

Brass et al. 2012.

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Figure 3: Distinction between the Volta Region, an Administrative Unit (Left) and “the Area around
Lake Volta” Referred to in This Paper (Right)

Moreover, while we hypothesize that both political and need-based logics may play a role inpanel distribution, it is not clear that these logics will be uniform across all of Ghana’s districts. The areaaround Lake Volta (which is not the same territory as the “Volta Region” administrative unit; see Figure3), for example, has a unique history in Ghana, where the area was flooded when Akosombo Dam wascreated in the 1960s to create the country’s first large-scale hydroelectric dam. While the dam’s electricity
improved communications and services in Ghana’s large cities, flooding made the communities around
Lake Volta more isolated, turning some communities into islands and forcing others to rely on bridges
and ferries or to construct new roads that would connect them with the country’s cities and industrial
hubs. The Lake Volta area was also seen as an area where grid extension might never be feasible andwhere need-based logics seemed particularly likely to prevail. Given this historical legacy of place andcontemporary reality of space, civil servants might be particularly attuned to the needs of citizens in theLake Volta area. In the next section, we discuss how we empirically test these hypotheses.
Research Design, Methods, and DataAt the time of project site selection, there were 170 administrative districts in the country.48
Although there are some political appointees at the district level, such as the District Chief Executive,
districts are the primary public administration unit around which public agencies, including ministries,
organize their work.49 For example, statistical data is almost always collected and presented at thisadministrative level.
To determine whether bureaucrats were able to avoid political capture in solar panel distribution,
we analyze whether the number of panels allocated to a particular district is significantly correlated withthat district’s historic voting patterns or characteristics that represent the district’s need for electricity
Districts make up Ghana’s second-level administrative unit, after region.
District Chief Executives are directly appointed by the president, but districts do not have national politicalmeaning in the way that parliamentary constituencies do.
48
49

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services. We test this correlation using both OLS and SLDV models, which provide us estimates for thecountry as a whole. We then use geographically-weighted regression (GWR) to examine how our findings
vary spatially across Ghana’s districts. Finally, we provide qualitative analysis of interviews, focus
groups, and primary and secondary source materials that helps to make sense of our findings. We
purposively selected participants for over 65 in-depth interviews and 19 focus group discussions in avariety of rural and urban communities.
Spatially Lagged Dependent Variable Models and Geographically Weighted Regression
As the above section suggests, we have theoretical reasons to expect that our dependent variable,
the number of panels allocated to a given district, may exhibit spatial clustering. Since such clustering
violates the normal OLS assumption that observations are independent, we use spatially-lagged dependent
variable (SLDV) models, which account for the possibility that the values of the dependent variable in
one location are influenced by the values in nearby locations. SLDV models allow us to isolate the effectof explanatory variables on solar panel installations by controlling for the prevalence of other solar panels
in nearby districts.50 The model used in these analyses is:y=(ρ)Wy + X(β) + ε,
where y is the number of solar panels distributed to a district, X is a matrix of district
characteristics, ε is a vector of error terms, and ρ and β are parameters. Wy is a spatially lagged dependent
variable for weights matrix W, representing a geographically weighted measure of other districts’ solar
panel distributions as a function of X characteristics. In the model, ρ represents the indirect effects ofother districts’ characteristics on y as a function of spatial depedence, and β represents the primarycoefficient of interest in this analysis, the direct effects of a districts’ own characteristics on y, controllingfor spatial autocorrelation.51
We also use geographically weighted regression (GWR) to probe for localized patterns of
behavior in Ghana’s subnational political geography.52 GWR allows us to examine whether the
distributional logics that are significant in our primary SLDV models reflect trends that are consistentthroughout Ghana, or whether these effects hold only in some areas of the country, such as thosebordering Lake Volta where the program initiated. Whereas global models produce a single coefficient
representing the average marginal effect across all units for an independent variable, GWR produces
individual coefficient estimates for each unit of the data.53 Our GWR analysis thus estimates a coefficientfor each district in Ghana for each variable of interest, which allows us to examine subnational variation.
Variables and Data
Data for the dependent variable, “solar projects” come from the MOEP and represent 1,242
individual solar panel installations during the 2009-2012 time period.54 The dataset provides geographic
coordinates for each solar project installed, which allowed us to convert the point data into district-levelcounts using ArcGIS. Because the projects were designed to meet community – rather than individual –needs, a single additional solar installation has the potential to reach multiple households, and places withmany installations provide benefits to many people.
Our independent variables of interest focus on our hypothesized logics of distribution. Startingwith political logics of distribution, we operationalize our hypotheses by exploiting changes in the ruling
50

Diagnostic tests revealed that SLDV models were a better fit for our data than an alternative, spatial error models;
see the Appendix for more information.
51
Results are estimated using a package called “spdep” in R, including the “impacts” function for post-estimation.
Direct effect coefficients can be interpreted like OLS coefficients. See Bivand et al. 2005.
52
Darmofal 2008, p. 944.
53
Fotheringham, Charlton, and Brunsdon 1998; Calvo and Escolar 2003; Darmofal 2008, p. 944-945.
54
Government of Ghana ND.

13party in the 2004 and 2008 elections. The NDC party, in power since 1992, lost to the rival NPP party in2000 and again in 2004, but regained the presidency in 2008, just before the panels were distributed. To
operationalize the “reward NDC loyalists” hypothesis, we measure district level vote share for the NDC
party in the 2008 election. If politicians sought to use solar panels to reward supportive districts, wewould expect them to direct panels to areas where NDC support was high in 2008. To operationalize the“induce swing voters” hypothesis, in contrast, we use the absolute value of the difference between the2004 and 2008 NDC vote share to indicate the amount of support that is potentially “up for grabs.”
We also create two measures that operationalize the hypothesis about mobilizing potential
supporters. First, we create a variable, “Mobilize nonvoting incumbent supporters,” that multiplies thepercentage of 2008 nonvoters in a district by the percentage of incumbent supporters. High scores on thismeasure suggest that a district’s voters are likely to support the incumbent but that they did not turn out inthe last election, giving politicians a potential opportunity to use public goods to mobilize potential
supporters. To operationalize the hypothesis that politicians may target goods in districts where voting ishistorically variable, we measure the absolute value of difference in turnout between the 2004 and 2008
elections. We use the absolute value for this measure because whether turnout increased or decreased is
not relevant to our hypothesis. An increase in turnout between 2004 and 2008 suggests that voters weremore motivated to go to the polls in 2008 than 2004, and a decrease in turnout suggests that voters weremore motivated to vote in 2004 than in 2008. In either case, what is critical for our hypothesis is that
voters’ turnout varies, suggesting that voters’ motivation to engage in politics is inconsistent and could
therefore change in response to politicians’ public goods distribution strategies. In districts where thisvalue is low, voters are consistent in their decisions to go to the polls or stay home, and there is little
opportunity for politicians to use public goods to mobilize them.
All political variables are created using replication data from Ichino and Nathan (2013), which
provides measures of NDC support and voter turnout for Ghana’s political constituencies.55 133 of
Ghana’s 170 districts comprise a single constituency, allowing us to use Ichino and Nathan’sconstituency-level measure. For the 37 districts that contain multiple constituencies, we create districtlevel variables by combining constituency levels and then weighting the results by sub-district
constituency population.
Turning next to our need-based logics of distribution, we create two variables that measure theneed for electricity in different ways. To operationalize our hypothesis that panels are distributed where
current grid access is limited, we calculate the amount of electricity grid in each district on a per capita
basis.56 To operationalize our hypothesis that panels are distributed to areas unlikely to receive grid accessin the future, we create a road density measure.57 Districts with limited road infrastructure are unlikely
targets for future grid expansion (see Figure 2), suggesting high need for solar panels.58
In addition to the independent variables of interest, we also include a number of control variables
that might affect goods distribution, including ethnic fractionalization, population density, poverty rates,
inequality, and literacy, the ratio of females to males, and the number of World Bank-funded projects andhealth care facilities.59 We also include the percentage of 2008 nonvoters, which is used in our interaction
term. All variables are measured at the district level using data from several sources (see Table 1). Details
on variable creation and descriptive statistics are in the Appendix.60

55

Ichino and Nathan 2013.
As an alternative variable specification, we examined electric grid density.
57
Two datasets for roads in Ghana exist: CIESIN’s data and a dataset used by Harding (2015) that focuses on trunk
roads. We use the CIESIN data because it includes urban roads and feeder roads, which connect rural areas to eachother, and which provide a better proxy for local-level infrastructure.
58
Foley 1992.
59
Ichino and Nathan (2013) have more detailed ethnic data for the Brong Ahafo region, but it was not available forthe country as a whole.
60
The number of districts in Ghana grew from 170 to 216 during the life of this program’s implementation (Resnick2017). We use the 2008 boundaries for consistency.
56

14

Table 1: Description of Variables

15
Model Results

Results are presented in Table 2.61 Model 3, which presents an SLDV model, is ourprimary model since it accounts for spatial autocorrelation. We also present Models 1 and 2,
OLS models with standard errors clustered by region, for specific reasons: Model 1 allows us toshow how results of SLDV compare to those using OLS. It also shows the results of a basicmodel without an interaction term in it. Model 2 then includes the interaction between NDC voteshare and the percent of nonvoters in a district, in order to test the hypothesis that solar panels
are intended to mobilize supporters of the incumbent political party. We present this model using
OLS analysis, since it is unclear how to interpret an interaction term in an SLDV model.
Turning first to the political variables, results show no support for the reward loyalists or
induce swing voters hypotheses. However, there is mixed support for the voter mobilization
hypothesis. While Model 2 suggests that panels are not directed at districts with potential tomobilize non-voting incumbent supporters, model results show a statistically significant
relationship between turnout volatility and solar panels: a one standard deviation increase in
district turnout volatility is associated with almost five additional solar panels, holding all otherfactors constant. These results suggest that while the program was not used as a vote-buying
exercise, efforts to avoid political influence were not fully effective, and politicians may haveused solar projects in an attempt to mobilize future turnout.62 Interview responses support thisinterpretation.63 Although MOEP and MNC implementers explained all of the measures they
took to avoid such political capture, another civil servant associated with the program at the
MOEP in Accra reported that some politicians tried to exert control over constituency-level solar
panel allocations. This official said that MPs and other politicians believed that if they
distributed solar devices, recipient constituents would come out and vote for them in future
elections.64
Turning next to the need-based hypotheses, the OLS models show a negative andstatistically significant correlation between electricity grid per capita and solar panel allocations,providing modest support for Hypothesis 2A. In the SLDV Model 3, which accounts for spatial
clustering, the electricity grid per capita variable is not significant, suggesting that spatialconsiderations – such as geographic proximity of panels and ease of providing maintenance –may combine with need-based considerations to shape bureaucrats’ allocation decisions. Acrossall models, there is a significant and negative correlation between panel allocation and roaddensity. A one standard deviation increase in road density is associated with roughly seven fewer
panels, suggesting that panels are significantly less likely to be allocated to road-dense areas
where grid expansion is likely.
While these results suggest that civil servants were not able to fully avoid political
capture, they also suggest that distribution is shaped by infrastructural need and the capacity toorganize the logistics of space. Even after controlling for political factors, solar panels were
distributed to remote areas with limited transportation infrastructure – the very areas that areleast likely to receive grid access in the foreseeable future, and where citizens are likely toreceive long-term benefits from their solar panels.

61

See Appendix for a variety of robustness checks.
We also examined a binary indicator of whether turnout increased or decreased from 2004 to 2008, rather than theabsolute value. Results, available in the Appendix, remained consistent.
63
Akee et. al 2018 and Baicker & Finkelstein 2018 also found politicians using goods to mobilize voter turnout.
64
2GH952914.
62

16
Table 2: Results of Two Modeling Strategies for Total Solar Projects

17
Analysis of Distributional Logics at the Local Level Using Geographically Weighted Regression
While the results of OLS and SLDV models are informative, their presentation of“global” average marginal effects across all districts may mask local variation that is of interest
to political geography scholars. To examine local variation, we employ geographically weighted
regression (GWR), which estimates distinct model coefficients for each district. These estimatesallow us to examine whether relationships between variables are similar throughout Ghana;whether they are positive or negative in different parts of the country; and whether themagnitudes of the relationships vary. Identifying which of these scenarios are occurring adds
valuable nuance. We focus interpretation on the hypothesized drivers of panel distribution thatwere significant in SLDV models: road density and turnout volatility. Maps facilitate theassessment of variation in coefficients across these subunits, which we present in Figures 4 and5. Statistical results and technical details are available in the appendix.
Figure 4 shows GWR coefficient ranges, including their clustering, for road density andturnout volatility at the district level. The maps reveal that there are clear subnational geographic
patterns for how these variables affect solar panel placements, a dynamic that gets hidden in the
global models. But the GWR results also suggest that the basic logic of distributing projects
where infrastructure is limited is fairly consistent countrywide. The relationship between roaddensity and solar panel receipt is almost universally negative, save for the northeast corner of thecountry, an area with high road density that received a cluster of panels. The magnitude of therelationship is strongest in the districts surrounding Lake Volta. Likewise, results from GWR
show that turnout volatility is positively associated with panel distribution in most of Ghana,with a particularly strong relationship in the subset of the districts that border Lake Volta. GWR
results thus suggest that the underlying logic of distribution is consistent countrywide, but thatthe strong magnitude of effects in the area bordering Lake Volta may be driving the statisticallysignificant results reported in our global SLDV models.

18
Figure 4: GWR District Coefficient Maps for Key Variables (DV = Total Solar Panels)

Additional context is needed to understand these local patterns.65 The clustering around
Lake Volta might suggest that solar projects were directed towards areas that suffered fromdisplacement from the new Volta Lake when the Akosombo Dam was constructed in the mid1960s. While we cannot rule out the possibility that historic injustice was a factor behind project
implementers’ decision to target the region, our results indicate that the logic of distribution
cannot be explained by dam-related displacement alone. Although displacement was most acutealong the eastern edge of Lake Volta, we see clusters of solar panels on the east, west and southof the lake, as well as elsewhere in Ghana.
The maps thus tend to suggest that projects went to areas where both need and turnout
volatility were high, even within the areas near the lake. Figure 5 shows maps of solar power
projects with road density and voter turnout volatility, respectively, at the district level. These
maps reveal that the area around Lake Volta is the only area that combines high values of voter
turnout volatility with low values of road density.66 This yields a combined motivation to placesolar power projects in this area: high need and high voter mobilization potential.

65

Cho and Gimpel 2010, 87.
See the Appendix for a robustness check including an indicator for a district containing Lake Volta. Results
remain consistent.
66

19
Figure 5: Solar Projects Overlaid with Road Density and Voter Turnout Volatility

Qualitative Analysis Theorizing Subnational Variation: Politics of Place and Logistics of Space
Our SLDV models suggest that both a political logic and a need-based logic shaped the
distribution of solar panels. The GWR then revealed that the magnitude of the relationships
varied across space within Ghana. We turn now to qualitative evidence from fieldwork totheorize two different spatial dynamics at the subnational level – the politics of place and thelogistics of space. This qualitative analysis suggests that it is not simply access to service
provision that matters, but also the history and quality of how that service is delivered indifferent regions of Ghana.
First, we examine the politics of place. In the southeast corner of Ghana, MOEP officials
initially targeted Dangme East as one of the districts to receive solar panel distribution. Dangme
East received a total of 68 panels, far higher than the nationwide district average of 7. The
district is known to be a political stronghold for the NDC party, with 80% of voters in the district
supporting the NDC in the 2008 elections, and posters promoting local NDC candidates pasted
on every corner. But, politics was not the only factor driving distribution in the district. Dangme
East is also a rural district with limited grid infrastructure and unmet needs for electricity. A
former District Chief Executive lamented that many of the communities “were not hooked to thenational grid.”67 Local and central government politicians and civil servants agreed that thecommunities around what is now Lake Volta were particularly deserving of some kind ofelectricity, precisely because so many people had been displaced from their homes or had their67

GH180308.

20
livelihoods in fishing or agriculture negatively impacted by the construction of the Akosombo
Dam in the 1960s.
While the greatest number of solar panels were distributed around Lake Volta, two othernotable clusters could be found in the border towns on the northern frontier with Burkina Faso
and the western boundary with Cote d’Ivoire. One central ministry official explained that the
government was targeting solar panels for public facilities such as clinics, schools, and security
outposts in communities that were not likely to get the grid.68 Hence, these border areas hadgreater political importance for the security of the country than other similar places without
access to the grid.
Qualitative interviews also revealed how the logistics of space differed across regions. In
the areas bordering Lake Volta, it was too expensive to extend the central grid to “hard to reach”
communities with low population density living on small islands in the lake or in the estuary.69
One District Assembly civil servant in Dangme East explained that island communities wereselected to receive solar panels “because the grid will never go there – it is so difficult to reach,and there is so much cost involved in trying to get there.”70 A central government official echoed
this point when he explained how the MOEP targeted certain remote areas with solar panels: “Insome places, by virtue of their location, it is just impossible to extend the national grid, now or
ever.”71
Meanwhile, in the northern border area, the logistics of space differed in important ways.
Low road density and low population density made grid extension even more expensive thanfurther south. One NGO official working in the Northern region justified the use of small-scalesolar: “There are certain areas that electricity cannot go [in the Northern regions]. The roads arebad or don’t exist. You cannot even cross the area with the trucks carrying the poles.”72 One
official from the distribution company in the northern regions known as NEDCO explained how
lower population density further affected the finances: “In the South, you can take one pole andconnect six houses. Up here, you can take ten poles to connect just one house! And that housewill use only 3 bulbs. No fridge, or TV, or anything. The cost of operation is so huge!”73
These logistics problems were aggravated by the technical challenges of transmitting
electricity across long distances. By the time electricity was transmitted from the dams andthermal plants in the southern regions of Ghana over 700 km to the border town of Paga, thecurrent was notoriously low and unreliable. According to a civil servant from Ghana’stransmission company, “By the time the power reaches all the way to Tamale [still four hours
south of the northern border], then there is some loss because it is a long way; the power is lessstrong.”74 One NGO representative from Bolgatanga stated: “The service provided is not good.
The quality is very poor.”75 A district assembly representative concurred: “Our power is notstable. Power will drop (low current) and then go off and then come back on again.”76 The
fundamental problem was not basic access to the central grid, but, instead, the quality of theelectricity delivered across space given the distance from the power generation source.
68

GH140529.
GH180312; GH180308; GH181019; FGD2-181017.
70
GH180312.
71
GH140529.
72
GH190314.
73
GH190313A.
74
GH190312.
75
GH190316.
76
GH190313B.
69

21

Discussion and Conclusions
Our results offer new insights about distributive politics in emerging democracies. One of
the motivations of this paper was to understand whether political capture is inevitable in them, orwhether it can be avoided in certain contexts – such as here, where a relatively capablebureaucracy, working with donor implementers, explicitly sought to avoid political influence intheir public goods distribution. Our results suggest that MOEP officials were not fully successful
in their efforts to keep politics out of distribution. Although panels went to places whose lack ofroad infrastructure pointed to long-term need for non-grid approaches to electric service
provision, they also went to places with political meaning. Here, though, our results provide very
limited support for distributional logics that center on politicians buying votes from either
supporters or swing voters. Instead, our results suggest that politicians may be aware of and try
to incentivize people to go to the polls in areas with inconsistent turnout. And while the use of
public goods to mobilize voters is usually designed to work to the advantage of the incumbent,
these results suggest a nuanced political calculation. Rather than straightforward vote-buying, itappears that political actors could strategically use welfare-enhancing goods to attract the supportof previously un-mobilized voters.
Our finding that panels may be used to mobilize turnout also suggests that scholars may
have paid short shrift to how the nature of the good shapes the logic of distribution. Whereas our
findings suggest that distribution of panels was used to mobilize voters, Briggs finds that politicalparties use the extension of the central electricity grid to either reward supporters or gain swing
voters in Ghana during the same time period.77 One possible explanation for these divergent
findings is that solar panels and grid extension are a fundamentally different type of public
good.78 Much of the existing literature implicitly assumes that different types of public goods
are, in principle, equivalent – that is, if political strategy or ethnic favoritism is the dominant
strategy for one public good, those findings can be generalized to other types of public goods.
Kramon and Posner, however, demonstrate the limits of this assumption by showing that
distributional logics may vary by type of good.79 In a study of multiple goods and services acrossthree countries in Africa, they find evidence that public officials favor some ethnic groups with
one type of public good, but favor another group with a different good.
Here, we extend Kramon and Posner’s logic to argue that public officials may use shortterm and long-term goods in fundamentally different ways, and that these strategies vary by
space and place. The electricity grid is a long-term good: it is an expensive and durable
investment in welfare-enhancing infrastructure that facilitates long-term access to transportationand communication. Grid extension benefits not only the local community, but also provides
benefits to others who travel to or trade with that community. Having access to the electric grid,moreover, has been a core feature of the collective national identity of Ghana ever since the firstpresident, Kwame Nkrumah, vowed to dam the Volta River to provide power for West Africa,and Ghanaian politicians often make electricity access a key campaign promise.80 Politicians
may thus invest in grid extensions to ensure continued support and gain support of swing votersin subsequent elections. Solar panels, however, represent no major infrastructure investment or77

Briggs 2012.
See also Min (2015) regarding electricity as a public good.
79
Kramon and Posner 2013.
80
MacLean et al. 2016; Gore et al. 2018.
78

22
long-term commitment on the part of the state, and are widely seen by citizens as a stopgap
measure until the state brings the electric grid to a community.81 Although they have significant
potential to enhance community welfare, they are fundamentally different than the national grid,and citizens are unlikely to see panels as fulfilling political promises to provide citizens withelectricity access. They may, however, be easily deployed to areas that are unsuitable for grid
extension; and they could be used to buy votes or induce turnout. Our analysis thus highlightsthat short- and long-term goods could have different logics of distribution.
At the same time, this study’s generalizability is limited by the specific context of asingle country case study. Here, Ghana is a fairly well-consolidated democracy with strong
economic growth, but that is highly dependent on foreign aid. Additional studies are needed totest this relationship in other contexts. Meanwhile, given its high degree of aid dependence, thestudy’s findings that bureaucrats and local actors have some autonomy and influence overproject implementation, particularly at the local level, are significant.82
This study therefore suggests that scholars of distributive politics in developing countries
may need to continue to widen the lens beyond elected politicians and donors to consider the
possibility of an emerging democratic politics within the bureaucracy. Although some studies dothis, we argue that scholars still need to pay more attention to the attitudes and behaviors of
bureaucrats, and how they may shape goods distribution. Our interview evidence counters
negative stereotypes about developing country bureaucrats, showing that in some cases – forexample, in countries with relatively high levels of wealth and administrative capacity – civil
servants can be committed to improving citizens’ welfare and capable of deploying sophisticatedstrategies to improve it. In Ghana, civil servants took strategic advantage of donor funds andengaged in a planning process that used data and analysis to identify priority areas that mostneeded solar panels. They were aware that their efforts might be undermined by election-year
politics and took steps to reduce political influence. While they were not fully successful atinsulating goods distribution from politics – and it is even possible their efforts even represent
the upper bound of bureaucratic prevention of political capture – they nonetheless distributed
public goods to areas with significant long-term need. Our case thus highlights civil servants asimportant decision makers who may exercise discretion in ways that improve citizens’ welfare.
Finally, in addition to examining the overall logics that shape goods distribution, thisstudy analyzed subnational variation in the direction and magnitude of these logics. Our study
highlights that spatial factors – like proximity of roads, availability of skilled technicians, or
potential to streamline distribution by clustering goods – may play an important but overlookedrole in distribution decisions. The difference between our OLS and SLDV models illustrate theimportance of spatial factors, as well as the potential errors that might be made if they are
ignored.
Beyond simply showing the utility of accounting for spatial clustering, however, thisstudy also suggests the need for more careful analytic disaggregation of subnational units,
including more careful analysis of subnational space and place. Results from GWR analysis
show considerable spatial variation in the strength of the effects of need and politics acrossGhana, with effects most pronounced in the area around Lake Volta. Our qualitative interviews
with project implementers, moreover, suggests that perceptions of historic need shape theseobserved patterns; and that logistical concerns about how to most efficiently deliver public goods
81

GH1405298.
Although not a core feature of the paper, our analysis highlights the importance of examining the role of donors
within aid-recipient countries on subnational distributional politics (see e.g. Gibson et al. 2005).
82

23
across physical space also shapes implementation strategies. Statistically significant patterns
across an entire country can easily mask divergent causal processes in individual locations.83 In
cases where there is a particularly strong relationship between variables in one area, averagemarginal effects for the country could lead researchers to mistakenly draw conclusions that do
not apply throughout that country.
Our paper thus illustrates how more careful use of mixed methods can help bring theperspectives of geography to bear on questions of importance to political science. Politicalscientists and geographers often have divergent ideas about what questions are important andhow they should be approached methodologically. Like many political scientists, we areinterested in testing generalizable hypotheses about the factors that drive goods distribution.
Here, to isolate the causal relationship of interest, we must control for relevant spatial factors in
our models. But we are also interested in understanding whether these drivers are stronger insome places than others, and why these patterns might vary across Ghana. To do so, we need to
examine spatial patterns and engage in qualitative analysis to understand whether and why thedrivers of distribution might vary. Our study thus highlights that synthesizing across theseperspectives – and including mixed methods that analyze subnational variation qualitatively –can produce a more complete answer to important questions about distributional politics thanwould otherwise be possible.

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