On the Political Economy of Urbanization:
Evidence from Africa
Thiemo Fetzer and Amar Shanghavi ∗
June 9, 2015
Abstract
Patterns of urbanization in Africa have been found to be different compared to the
developed world: urbanization in Africa is much more concentrated in a few large
cities. This may be driven by political economy channels, which should weaken
following institutional change. Using a wave of democratic transition during the
1990s we study whether urbanisation has become more evenly spread across cities
in Africa. We find that following democratic transition there is significant catch-up
growth in non-capital cities as measured by night lights data. We also document a
significant improvement in delivery of education services in secondary cities relative to the capital city.
Keywords: Urbanization; Africa; Urban concentration; Democratization; Public Goods
JEL Codes: H1; R1; R12
∗ Both
authors are based at London School of Economics, Houghton Street, WCA2 2AE London.
We want to thank Vernon Henderson for his extensive support; in addition, we would like to thank
Rabah Arezki, Tim Besley, Sam Marden, Oliver Pardo and Gerard Padro-i-Miquel. Financial support
from a World Bank-funded project at LSE and Oxford (8005077) on Policy Research on Urbanization
in Developing Countries is gratefully acknowledged. The findings, interpretations, and conclusions
expressed in this paper are entirely those of the authors. They do not necessarily represent the views of
the International Bank for Reconstruction and Development/World Bank and its affiliated organizations,
or those of the Executive Directors of the World Bank or the governments they represent.
1
When falls the Coliseum, Rome shall fall; And when Rome falls - the World.
- Lord Byron, Childe Harold’s Pilgrimage, Canto IV (1818), Stanza 145.
1
Introduction
Does democracy promote sustainable urbanization? Africa is witnessing an unprecedented projected population growth of more than 40,000 new urban inhabitants per
day between now and 2040.1 Among developing nations, African countries have experienced the fastest rate of urbanization at 3.5% per year over the past two decades
(AFDB 2 ). Yet, urbanization has failed to bring about equal opportunity and poverty
reduction. Rather, proliferation of slums, urban poverty and rising inequality are a
common feature across the continent. 70 percent of urban dwellers in Sub Saharan
Africa (henceforth SSA) live in slums with almost 90% of the inhabitants not having
access to acceptable sanitation (Phillips, 2014). The rural-urban migration has been a
major cause for the increasing urban growth and worsening economic conditions of the
poor; some cities are expected to swell by up to 85 percent of their current size by 2030
(Phillips, 2014). These general figures hide a striking observation: the urbanization
experience in Africa is highly concentrated in few cities. Geographers refer to this as a
primate city centric urbanization process. There is a growing empirical literature that
has documented excessive primacy in SSA cities (see Moomaw and Shatter, 1996; Davis
and Henderson, 2003; Naude and Krugell, 2003; Annez et al., 2010). The cross-sectional
and cross-country empirical evidence found primacy to be particularly pronounced in
non-democratic environments.
This paper is among the first to point out that institutional change away from autocratic regimes towards parliamentary democracy can have a profound effect on the
pattern of urbanization. Specifically, we investigate whether the wave of democratization sweeping the region in the 1990s led to catch-up growth in the hinterland.
To test our hypothesis of increased distribution of wealth post democratization across
urban centers, we use a combination of micro panel datasets covering 38 countries.
Understanding urbanization in SSA is a very important question facing demographers,
geographers and social scientist today. However, there is limited empirical literature on
the impact of democratization on migration patterns, urbanization and development in
1 See
World
Bank,
http://documents.worldbank.org/curated/en/2013/09/18417628/
harnessing-urbanization-end-poverty-boost-prosperity-africa-action-agenda-transformation,
accessed 20.02.2015.
2 See
African
Development
Bank,
http://www.afdb.org/en/blogs/
afdb-championing-inclusive-growth-across-africa/post/urbanization-in-africa-10143/,
accessed 10.03.2015.
2
Africa. This is especially made challenging due to lack of quality data. Further, democracy is likely to be endogenous to socio-economic factors that also affect development
(Lipset, 1959). To isolate the casual effect of democratization on urbanization in Africa,
we need exogenous variation in the implementation of democratization. The empirical challenge is to disentangle the effect of democracy from other confounding factors.
Even though democracy is not randomly assigned across countries, the differential timing of democratization across African countries induces differences in exposure, thus
motivating a difference-in-difference estimator of the treatment effect. The key difficulty in this exercise is finding an adequate control group. We address this concern by
using micro level data within a country, thereby being able to control for a hoard of
demanding fixed effects and also test for common trends.
As a measure of urbanization, we develop a city size measure from the early 1990s
onwards based on nighttime lights emissions. Given that population census are done
every 10 years if not longer, studying urbanization dynamics over time becomes quite
difficult. Thus focusing on nighttime lights has got significant methodological advantages. Henderson et al. (2012) in their seminal work shown that nighttime lights can
be used as a proxy for population density and income, especially in countries where
economic data is scant. Hence, we are able to obtain city level measures of urbanization
over a long period of time from the nighttime lights.
As a measure of development, the paper focuses on education outcomes, defined
as completion of primary and secondary schooling. Low levels of education remain
a huge issue in Africa with many children never even enrolling into schools. Using
Demographic Health Surveys (DHS), we map the geo-coded data of respondents to
the cities identified based on nighttime lights emissions to obtain city level education
outcomes. Using thresholds of 15 years and 21 years of age (by when all primary and
secondary schooling decisions should have been completed respectively), we identify
the year in which the respondent should have completed schooling. This gives us city
level variation in school completion over time. In doing so, we highlight an innovative
way of using DHS data for comparing cross country education outcomes for a longer
time period than other socioeconomic variables.
To circumvent the problems associated with what constitutes a democracy, we use
multiple measures of democracy that have been used in the literature. Our primary
indicator is derived from the Polity IV data set. This data set is heavily used in the academic literature on institutions both by economists and political scientists. The polity-2
variable in the Polity IV dataset aims to capture multiple characteristics of a countries
political institutions in a single indicator. The indicator aims to capture five features of
democratic institutions: the degree to which political participation is competitive, the
3
extent to which participation in political processes is regulated (restricting access), the
competitiveness of executive recruitment, the openness of executive recruitment and
the extent to which there are constraints on the executive, e.g. stemming from a parliament which has oversight. As the measure is subjective and may contain a significant
amount of year to year variation, we process the overall polity-2 variable to obtain a
refined measure that captures the variation in institutional change that is persistent
rather than transitory.
There are two main contributions the paper makes. First, we study institutional
change and urbanization in a panel setting moving from the cross-country framework
to exploiting variation within countries over time. Through this we study the relative
evolution of capital- versus non-capital cities. We document that institutional change
towards democracy robustly predicts catch-up development in the hinterland. By exploiting variation in political institutions within a political leader and over time, we
are able to rule out two competing mechanisms highlighted in the literature, namely
catch up due to regional favoritism (see for example Burgess et al. (2013); and Hodler
and Raschky (2014)) and or due to change in regimes that are associated with changes
in institutions (see for example Kudamatsu (2012)). To address the regional favoritism
channel, we control for city by leader fixed effects, which allows us to absorb time
invariant characteristics such as regional affiliation of the leader. Additionally, by controlling for leader fixed effects, we are able to observe the impact of democratization on
urbanization over time for the same leader, thus eliminating the change of regime channel as a potential mechanism. Our findings suggest that this catch-up growth in the
hinterland is supported by improved public good provision, in particular primary and
secondary schooling. Second, our paper also makes advances on the methodological
front by combining a satellite-based measure of city size (see for example Storeygard
(2012)) with geo-coded demographic health survey data to construct time-varying citylevel measures of socio-economic outcomes. This allows the study of the evolution of
spatial agglomerations over time, which is particularly important given that in many
developing countries micro level data is not or only scarcely available.
The paper contributes to several strands of literature. The first strand of literature
documents patterns of excessive primacy in SSA (see Moomaw and Shatter, 1996; Davis
and Henderson, 2003; Naude and Krugell, 2003; Annez et al., 2010). The primate city
distribution was first proposed by geographer Mark Jefferson in 1939, which states that
a ’primate city distribution’ has one very large city with many smaller cities and towns,
and no intermediate-sized urban centers, in contrast to the linear ’rank-size distribution. Williamson (1965) in his seminal work observed that at early stages of development high urban concentration can be helpful in conserving infrastructure, especially
4
when the economy suffers from a severe scarcity of infrastructure and information. As
the economic activities grow, it becomes easier to spread information and infrastructure to the hinterland, while rising cost of living in the urban areas pushes citizens out
towards the countryside. However, their focus has been mainly on the cross-sectional
dimension as data limitations on population figures are a severe constraint.
The second strand of literature relates political factors and patterns of urbanization
(Henderson and Becker (2000); and Karayalcin and Ulubasoglu (2014)). Taking historical Rome as the archetype of a city that centralizes political power, Karayalcin and
Ulubasoglu (2014) develop a model of rent seeking and economic outcomes, highlighting the link between political competition and urbanization. National governments
favor a capital or central city in terms of investment, granting of loans, licenses, etc.
at the expense of the hinterland, thus, allowing the bureaucrats in the center to compete more effectively against low ranking rivals in the provinces in the extraction of
rents. Related to this hypothesis, Overman and Venables (2010) discuss how externalities and coordination failures can inhibit decentralization of economic activity, leading
to dominance of primate cities. Most importantly, since primate cities are often national
capitals, they are centers of decision-making and opinion forming. This allows them
to dominate their countries both economically and politically (Balchin et al. (2000)).
Consistent with this hypothesis, Michalopoulos and Papaioannou (2013b) show that in
the African context (also using nighttime lights), national institutions only matter for
differences in cross border sub national development so far as the region in question is
close to the capital.
Finally our work also relates to a long standing economic geography literature that
studies rank-size distributions and in particular, estimates Zipf’s power law coefficients
(see Rauch (2013), Rozenfeld et al. (2009)). Methodologically, our measure of city size
derived from night lights emissions is closely related to recent efforts to study Zipf’s
law based on alternative measures of city size for all human agglomerations across
the world (see Jiang et al. (2014)). Jiang et al. (2014) shows that Zipf’s law holds on
average, but there are some exceptions, in particular for Africa. Our paper suggests
that political institutions can help in explaining this finding.
The rest of the paper is structured as follows: section 2 presents the background
and discusses the key data used in the paper; section 3 describes the main empirical
strategy and provides results and robustness tests. Section 4 documents evidence on
the channel, while section 5 concludes.
5
2
2.1
Background and Data
Cross Sectional Evidence of Excessive Primacy and Institutions
Primacy may be a natural feature of the development process as has been discussed
by Williamson (1965). Agglomerations solve coordination problems and network effects for many types of infrastructure, such as roads, naturally reinforce agglomeration
forces. Nevertheless, as economies grow it becomes easier to spread information and
infrastructure to the hinterland, while rising cost of living in the urban areas pushes
citizens out towards the countryside. A growing literature documents that primacy
in Africa is an outlier (in particular, see Ades and Glaeser, 1995; Junius, 1999; Davis
and Henderson, 2003). The measure typically used is a proxy and captures the urban
population share of a country that is captured by the countries largest city. Figure 1
plots this measure of urban primacy around the world for 1992. Africa clearly stands
out with many countries color coded in the reds, suggesting significant primacy. This
paper aims to understand how institutional change affects primacy. Ghana, for example, has seen rapid urbanization since the 1990s. The share of population that lives
in urban areas has increased from 36% in 1990 to 52% in 2012. At the same time, the
share of the population captured by the primate city Accra has gone down from 22%
to 16%. That is, the primate city Accra has captured a significantly smaller share of the
overall increase in the urban population, suggesting that urbanization becomes more
spread out over more cities. The question is whether this reduction in primacy can be
attributed to institutional changes and if so, which are the decisive changes.
Indeed, Ghana has experienced significant institutional change. It was the first
country in Sub-Saharan Africa to achieve independence in 1957. In the period between
1957 and 1990 it had seen only two very brief periods of notionally democratic rule
each lasting less than 30 months, while being under military rule for the remaining
period. Ghana made first changes towards democracy during the military rule of Fleet
Lt. Jerry Rawlings, who had been in power since 1981. Democratization happened
during his rule with the adoption of a multi-party constitution in 1992 and subsequent
multi-party elections. These left Rawlings in power until in 2000, parliamentary and
presidential elections, saw a transition in power to the opposition in both the executive
and legislative branches. Ghana is not an exception. The spread of democracy in Africa
from the early to the late 1990s represents the most significant political change in the
continent since the independence period three decades before. Between 1990 and 1994,
27 out of 49 countries in Africa had their first multiparty elections. Understanding the
role of institutions and underlying mechanisms driving the urbanization patterns in
6
Africa can help policy makers better understand the urbanization crisis in Africa. A
key concern is how to measure democratic change. The next section discusses how we
measure institutional change for this paper.
2.2
Institutional Change in Africa
Discussions on democracy have to contend with the question of what constitutes a
democracy and how to classify countries into democracies- and non-democracies. Political scientist have debated whether one can represent such complex political institutions in form of quantitative scales or indicator variables (see Munck and Verkuilen,
2002). The most prominent and most widely used quantitative indicator is the polity-2
score which is a variable generated as part of the Polity Study. This dataset has been
used in many different research contexts by both, economists and non-economists.3
The Polity dataset with its polity-2 score aims to capture five dimensions of political
institutions, focused on the methods of executive recruitment as well as the extent to
which there are checks- and balances in place in form of executive constraints Marshall
et al. (2013). The five dimensions considered are:
1. Regulation of Chief Executive Recruitment
2. Competitiveness of Executive Recruitment
3. Openness of Executive Recruitment
4. The Independence of Executive Authority
5. Executive Constraints e.g. due to parliamentary oversight
Our primary indicator is a dummy variable derived from the polity-2 score variable,
which ranges between -10 to 10. Values below zero are characteristics of countries considered to be Autocracies or Closed ”Anocracies”; that is regimes with a mix of pseudo
democratic practices without free elections and significant autocratic traits. Countries
with scores above zero are Open Anocracies or Full Democracies. We will focus and
classify countries as democratic if they achieve positive polity-2 score.4 We also use
3 Some
more prominent references using the Polity III and Polity IV democracy indicators include
Fearon and Laitin (2003); Acemoglu et al. (2001); Brückner and Ciccone (2011); Hodler and Raschky
(2014); Bazzi and Blattman (2014); Besley and Persson (2011); Besley and Ghatak (2010); Kudamatsu
(2012); Burke et al. (2010); Blattman and Miguel (2010).
4 Note that the indicator is coded as missing in case of a foreign military intervention, while interregnum or anarchy periods are classified as a polity-2 score of 0. A linear interpolation method is used to
fill polity-2 scores as non-missing in between regimes.
7
two different measures of democracy: a Freedom House Democracy Indicator and the
dummy variable constructed by Cheibub et al. (2009).5
Figure 2 shows the change in three different measures of democracy constructed
from Polity IV and the Freedom House indicators. The left-scale is the share of SubSaharan African countries that are considered to be democratic either based on the
Freedom House dummy or based on having a polity-2 score larger than zero. The right
scale indicates the simple average polity-2 score. Throughout we see an upward trend,
suggesting a sharp rise in democracy in Africa post 1990.
Based on the polity-2 score we construct two derived measures of democracy for a
country c in year t. The first is a simple dummy variable:
Democracyct =

1
if polity 2 scorect > 0
0
else.
(1)
This directly follows the approaches taken in the recent literature (see for example
Besley and Persson, 2009; Kudamatsu, 2012; Nunn and Qian, 2014).
The second measure we construct from the polity-2 scores refines the first measure.
We are interested in capturing persistent transitions, rather than transitory changes. The
concern is that the requirement of a strictly positive polity-2 score creates distinct yearon-year variation that is not informative about systematic institutional change; such
institutional change can be seen as systematic breaks in the polity 2 score.
In order to identify systematic breaks we follow the approach of Bai (1997). We
take the dummy variable defined by a strictly positive polity-2 score as a dependent
variable and then apply the method of Bai (1997) by performing iterative regressions
5 Note
that the Cheibub et al. (2009) indicator is only available up to 2010. The Freedom House indicator is coming from their classification of countries into Free and Unfree from Freedom House’s annual
“Freedom in the World” publication. They consider a country to be free if the following requirements
are met:
1. A competitive, multiparty political system.
2. Universal adult suffrage for all citizens (with exceptions for restrictions that states may legitimately place on citizens as sanctions for criminal offenses).
3. Regularly contested elections conducted in conditions of ballot secrecy, reasonable ballot security,
and the absence of massive voter fraud that yields results that are unrepresentative of the public
will.
4. Significant public access of major political parties to the electorate through the media and through
generally open political campaigning.
This will translate into an indicator variable that is equal to 1, in case a country is considered to be
democratic or not according to these criteria.
8
allowing for an increasing number of systematic breaks.6 The fit is evaluated using a
Bayesian Information Criterion; this, as opposed to a simple R2 requirement, penalizes
increasing the number of breaks, thus allowing for the possibility of there being no
break-point at all. Using such a filtering method for the dummy-variable removes
idiosyncratic jumps in the polity 2 score above the zero-threshold. This is illustrated
for Burundi in figure 3. The left panel shows the non-smoothed democracy indicator
that is based on the polity 2 score to be strictly larger than zero. This would result
in coding the jump in the polity-2 score in 1994 as a democracy year. The right panel
shows the smoothed indicator; the process identified one structural break in 2000.7
The results for the whole set of 38 Sub-Saharan African countries in our sample
are presented in Figure 4. There are 22 countries in the sample between 1990 and
2012 that experienced some form of transition as measured by a strictly positive polity2 score. Of these 22 countries only 15 countries experienced a persistent transition
towards democracy. The countries are: Burundi, Comoros, Cape Verde, Gabon, Ghana,
Guinea, Kenya, Liberia, Mali, Mozambique, Malawi, Senegal, Sierra Leone, Zambia
and Zimbabwe. Filtering transitory from permanent changes in the polity-2 score will
be very useful for a decomposition. We would not expect that transitory changes in the
polity-2 score to trigger persistent changes in the extent of capital primacy.
A key question in this paper is whether the catch-up of non-capital cities can
be attributed to the institutional changes brought about by democratic transitions or
whether, it is simply an effect of “a transition”. Democratic transitions may be correlated with changes in the identity of the executive;8 in addition, elections that start
to really matter in a multi-party democracy could induce further spatial biases in the
public good provision due to “vote buying”, which, without meaningful elections before did not matter (Olson, 1993). This means that ideally we would want to exploit
variation in institutions within political leader or within political regimes. The next
section describes how we aim to control for this.
6 The
idea is that the fit of a regression with one systematic break for a series of random zeroes and
ones, e.g. from coin tosses, is just as bad a fit as a regression fit without a systematic break.
7 An alternative filtering approach is to estimate a Gauss Markov Switching Model with two states,
e.g. applying the Expectation Maximization algorithm. This has been pioneered in the macro-economics
literature to filter time-series to identify recessions in time series (see Hamilton, 1989, 1990).
8 The example of Ghana suggests that institutional change can happen within a specific leader or head
of state over time.
9
2.3
Changes in Identity of Head of State, Cabinet Composition and
Elections
We collected the names of the heads of states across countries from multiple sources.
We construct a panel that is the name of the head of the executive for every countryand year; in case a year was a transition year, we assign the name of the new incoming
head of the executive to that year. We refer to the head of the executive as leader. For
constitutional monarchies, we assign the name of the head of the executive, typically
Prime Minister, following the British model; former French colonies typically have a
Presidential system, with the President being the head of the executive. For the whole
set of 38 Sub-Saharan African countries we work with over the time period from 19922012 there have been 125 distinct leaders. That is, on average, every country had 3.28
distinct leaders in that 20 year period, suggesting that the average tenure of a leader
was 6.08 years.
We use this to define two sets of fixed-effects. First, a set of country-by-leader fixed
effects. We can think of these fixed-effects as capturing distinct country-wide average
effects of specific leaders in power; this allows each leader to have a distinct level effect,
that is, however, equally distributed across cities. This is used to address concerns of
leader-effects as identified in Jones and Olken (2005). Using these fixed effects means
that we remove the element of institutional change that is brought about by transitions
in the political leader, either due to election-caused turnover or due to within autocratic
regime changes in the identity in the leader.
The second set of fixed-effects serves to rule out that catch up growth in the hinterland may be stemming from biased public good provision that is correlated with
changes in the identity of the political leader (see Burgess et al., 2013; Hodler and
Raschky, 2014; Morjaria, 2013). We create a set of city- by leader fixed effects. In the
extreme case where a country has a different political leader in each year, these fixedeffects would be akin to city-by-year fixed effects, which would effectively absorb all
variation in our panel setting. The identifying variation is here coming off solely from
within-leader democratic transitions. Ghana is one of nine countries that experienced
democratic transition within a political leader. The other countries are Burundi, Gabon,
Ghana, Guinea, Kenya, Mozambique, Sierra Leone and Zimbabwe.
A second concern is that in election-years in general, public good provision is
markedly different. Hence, we construct a data set that captures all election years
by country.9 We use this for a triple-interaction to test whether in election years, the
9 This
data is coming from the collection of election results for SSA available on http://
africanelections.tripod.com/.
10
pattern of convergence or divergence between cities in the hinterland is markedly different. A key concern about the relationship between institutional change and city
growth is whether we can treat democratic transitions as exogenous. We discuss the
identification concerns in the next section.
2.4
Identifying Assumption
Democracy is endogenous to socio-economic factors that also affect development. We
address this concern by adopting a difference in difference estimator motivated from
the differential timing of democratization across the continent. By comparing cities
within a country, we argue that our control city (i.e. the national capital) is a valid
counterfactual for the treated cities (i.e. the rest of the urban centres). This is particularly true for addressing macro concerns such as historical and contemporary dynamics
(i,e.colonization), structural and contingent factors (i.e. culture) and economic and political dimensions (i.e. collective action and coordination problems). However, we are
not able to fully exclude the possibility of not picking up the effects of time variant city
level unobservables such as commodity prices and differing cost of living across cities.
Economic factors have also played an important role in democratization in Africa.
The crisis of the 1980s/ 1990s and failure of economic development have been identified as being key determinants of prodemocracy movements. Economic opportunities
could both drive democracy and demand for public goods such as education. Thus,
to control for a potential income bias, we check the robustness of the main result to
controlling for annual regional rainfall, since income mostly depends on rain-fed agriculture in Africa. In addition, commodity prices are another main source of income
- in particular for mineral and oil exporting countries. Country-by-year time effects
absorb most of the variation in these. Finally, to address concerns of external factors
potentially confounding our results (i.e. democracy has been observed to be correlated
with official development assistance (Bratton and Walle, 1997), we control for aid in our
main analysis. The next section describes the construction of the the luminosity data.
2.5
Identifying Cities and Luminosity
A lack of reliable and consistently collected population data is a constraint for empirical research on the dynamics of urbanization in developing countries. Statistical
agencies use different and varying definitions of what comprises a city, in addition to
using different and time-varying spatial resolutions along with population size cut-offs.
Lastly, population figures from censuses are collected infrequently which make panel
11
studies very difficult. We circumvent this problem of data availability by constructing
spatial agglomerations from remote-sensing data. Specifically, we use night light emission data collected from the United States Air Force Defense Meteorological Satellite
Program (DMSP). These satellites have been carrying an Operational Linescan System
(OLS) sensor, which can be used to detect natural light emissions from the earth. The
satellites have been carrying the OLS sensors since the 1970s, a digital archive of the
pictures is only available from 1992 onwards; the data for the years prior to 1992 is resting on magnetic tapes, waiting to be digitized. The DMSP satellites have been orbiting
the earth 14 times per day. This ensures that for each location on the globe there exists
a daily picture taken between 8:30 and 10:00 pm local time. The satellites are regularly
replaced every three to four years. The raw data is processed at the Earth Observatory
Group at the National Oceanic and Atmospheric Administration. The processing consists of removal of ephemeral lights, such as forest fires or gas flares and systematic
distortions due to the varying lunar intensity as well as late sun-sets during summer or
winter for the northern- and southern hemispheres respectively; pictures with significant cloud cover are also removed. The result is supposed to capture light emissions
from human settlements; this is measured in a digital scale between 0 and 63, where
0 stands for no light emissions and 63 is the maximal value, which is top-coded. The
pixel resolution is 30 arc-seconds or about 0.86 square kilometers at the equator.
The data have been shown to correlate extremely well with measures of economic
development and incomes (see Michalopoulos and Papaioannou, 2013b; Henderson
et al., 2012). We identify cities as collections of pixels that emit light. We proceed iteratively. For every satellite-year we define polygons around lit pixels that are connected.
A unique polygon outline is defined by completely dark pixels around it. This gives,
for every satellite-year a set of polygons that can be as small as a single pixel, but
could stretch to including thousands of pixels. We also define a core polygon as the
set of connected pixels that have been continuously lit. Figure 5 illustrates the creation
of the outer envelope polygon. We use various conditions for this ”continuously lit”
requirement, see Appendix A.1. Such an approach has been used to identify cities in
Storeygard (2012), Jiang et al. (2014) and Deichmann et al. (2014).
The unit of analysis we work with is an agglomeration/city, defined by its maximal
outer envelope polygon obtained by studying the spatial union of the most recent five
years. We track the evolution of light emittance from the pixels that fall into this
polygon over time for the 20 year period beginning in 1992. We map each resulting
city-polygon to the country in which the centroid of the polygon falls. In addition, we
map all polygons to the first sub-national administrative division, based on the polygon
centroids. Lastly, we map national and provincial capitals to the set of polygons, details
12
about this are provided in Appendix A.3. This leaves us with three city types per
country: national capital, provincial capitals and other cities. In total in our estimating
sample is a balanced panel where we observe 1865 cities over a 20 year period. Of
these 1865 cities, 38 are classified as capital cities mapping into the 38 countries, 293
are classified as provincial capitals while 1534 are considered to be other cities.
The resulting data set is one in which we have 38 countries. The countries in our
estimation sample, along with the city polygons and the sub-national region boundaries are plotted in Figure 6. There are a few countries that we drop from the analysis
as the identification of cities from night light images is particularly difficult. Currently,
we drop the following sub-Saharan countries: Sudan, Congo, Democratic Republic of
Congo, Nigeria, Benin and South Africa. There are various reasons to drop these countries. For Nigeria and Benin, gas flaring distorts polygons along the coast. For South
Africa, the resulting polygons are extremely large due to the higher level of development making it difficult to identify distinctive cities. In Sudan, the population is
clustered along the Nile river which appears as one big lit polygon without any distinction of cities. For the Congo’s the problem is that the capital cities are opposite one
another along the Congo river. In the resulting polygons they appear as one connected
city.
Our primary outcome measures will be a measure of luminosity at the city level as
defined by the outer envelope. We compute the average light intensity for pixels falling
into the outer envelope and compute the natural logarithm.10 We can think of this
capturing a mixture of both, the extensive and intensive margin of city size. Similarly,
we also look at the size of the city in terms of pixels that emit some light; lastly, we
consider the average luminosity of pixels that fall into the core. The latter measure
serves as a measure of the intensive margin. We ground truth data for two countries
- Kenya and Tanzania - where we obtained census level shape files. The resulting
polygons capture urban population very well. The next section discusses some microdata that we obtain from the Demographic Health Surveys (DHS); this data will allow
us to study socio-economic outcomes at the micro-level and at a fine spatial resolution.
2.6
Micro Data on Educational Outcomes
We use DHS data to construct a novel measures that serve as proxy for socioeconomic
development at a very granular level. Since the 2000’s all respondents surveyed have
been geocoded in the DHS. By matching the geocode of the respondent to the agglom10 We
follow the common approach in the literature by adding 0.01 to the average luminosity before
taking logs, see Hodler and Raschky (2014); Michalopoulos and Papaioannou (2013b,a).
13
eration she falls in, we are able to observe the impact of democratization at the city
level within a country11 . We construct respondent level education outcomes based on
date of birth of the women surveyed. Since all respondents are aged 15 or older in
the year of the survey, we are able to infer their primary school completion at the age
of 15 (by which all women should have completed primary schooling). This gives us
city level variation in the primary school completion based on the year in which the
women surveyed was 15 years old. We match the year in which the respondents were
15 years old to the type of institution in place in that year, thus creating a panel dataset
varying at the city level and over time. For example, a 30 year old woman surveyed
in 2005 would have completed her primary schooling decision 15 years ago, i.e. in
1990. Using this method, we are able to extract historical information on the primary
school completion rates within a particular city and the prevaling institution in that
year. We apply the same technique to identify secondary school completion using 21
years of age as the cut-off point. Further, we restrict the sample to the years 1990 to
2012 so as to have data consistent with years for which we have nighttime light data.
The resulting data is an unbalanced city by year panel with repeated cross sections of
individual level data. Table 7 in appendix B presents simple summary statistics for all
the variables used in the analysis.
We include all DHS surveys carried out post 2000 for a total of 24 countries, with
some countries having multiple rounds of surveys. A list of countries and survey
rounds is given in appendix B table 10. We exclude Nigeria from the analysis to have
consistency with the nighttime lights data. We also ommit city agglomeration on the
border of Ghana’s Volta region as it extends into Togo’s capital Lome on the Eastern
border. A total of 17 out of 24 countries in the sample experience a democratic transition. We remain with 49,835 observations for data on primary school completion and
62,959 observations for data on secondary school completion12 . There is a larger number of observations for secondary school completion analysis as our cutoff year of 1990
excludes all the women who completed primary schooling prior to 1990. The average
number of respondents per city are 48 women for primary school analysis and 60 for
secondary school analysis. Using Ghana as an example, the capital city has 504/819
persons mapped to it for primary/ secondary school analysis. There are a total of 6
regional capitals and 76 other cities, which have on average 68/108 and 23/33 persons
for primary/secondary school analysis respectively. This highlights the sampling bias
11 It
should be noted that the data is not representative at the city level, thus affecting the power of our
results.
12 95,903 respondents for primary school completion analysis and 120,063 for secondary school completion analysis are not matched to a city polygon.
14
between urban and rural areas; unfortunately the DHS sampling is thus only representative at the first administrative level of the country, however this is only a concern as
far as it reduces the power of our estimates.
Another potential concern of DHS data is that there maybe survivor bias in the sampling of women. Since the data sets used are all post democratization in the respective
countries, this may affect the surveyed women in a way that could bias our results. For
example, post democracy, if uneducated women in the non-capital cities die disproportionately when compared to the capital city, we may have an upward bias in education
outcomes for region/ rural cities compared to the national capital. The bias may go
the other way if quality of services in regional and rural cities improves, implying uneducated women in the national capital are more likely to die when compared to the
less educated women in regional and rural cities. Unfortunately, we are not able to use
data for a longer time period as earlier data does not allow us to match respondents
to city agglomerations since respondents were not geo-referenced. However, if indeed
provision of public services improves outside the capital post democratization, then we
would expect there to be a downward bias on our estimates giving us a lower bound.
Keeping these concerns in mind, we present the results in the next section.
2.7
Preliminary Results
The key hypothesis of this paper is that democratic transitions induce catch-up growth
of cities in the hinterland; that is, relative to the year in which democracy sets in, cities
in the hinterland should catch up in terms of their size and night light emissions with
the primate city. This would reduce primacy.
Figure 7 plots out simple summary statistics of a collapse of the data. It plots the
evolution of night light emissions by city-type for Sub-Saharan African countries which
experienced a democratic transitions. The timing of the democratic transition is given
by the year that has been identified in the structural break analysis. The averages
are computed relative to the year to the democratic transition. The simple summary
statistics suggest that, in particular, the luminosity gap for provincial capitals relative
to the national capital is shrinking following democratic transition.
We perform the same exercise for primary and secondary school completion. The
results are consistent with findings from the luminosity data. The gap in education outcomes is shrinking between the capital and the hinterland. The results are presented
in figure 8 and 9. To better understand where this catchup growth is coming from,
we perform a simple difference in difference exercise without any controls using the
micro level data. We look at the education outcomes before and after introduction of
15
democracy across the different types of cities. The results are presented in appendix
B for both the education outcomes. Compared to the capital city, the average primary
school completion is approximately 10 percentage points lower in regional and other
cities, while it is between 12 and 15 percentage points lower for secondary school completion. Based on the difference in difference estimator, this gap in education is almost
fully eliminated for regional cities post democratization. There is also some catch-up in
other cities for primary school completion, but the same does not hold for secondary
school completion. The next section presents the empirical method that we use to
investigate this in a regression framework, along with the main results.
3
Empirical Analysis
We separate the empirical analysis into two steps. First, we look at the implications
of democratization on city growth using night lights. Next, we ask how this growth
translated into provision of public services, namely primary and secondary school completion.
3.1
Method
We begin by studying the evolution of night time light emissions from cities over time.
Our empirical design studies the evolution of capital cities relative to the other agglomerations with- and without democracy. The unit of observation is a city over time. We
categorize cities or lit polygons into three classes indexed with j: national capitals N,
provincial capitals P and other lit polygons O. The cities we classify as national capitals
N, are presented in Table 1.
Our preferred specification takes the following form:
ycijt = αc + δt + φj × Ccij × Dct + b0 Xcit + ecijt
Our dependent variable ycijt measures the log of average luminosity of a city i that
is of type j in country c at time t. We add controls, in particular country- and time
fixed effects, αc and δt respectively. We estimate a separate intercept for every city type
j through the fixed effect η j . The coefficients are the estimates of φj . These capture the
level effect of city size of class j as indicated by the city type dummy Ccij following
democratic change, i.e. when Dct = 1. These coefficients will tell us how the intercept
η j changes following democratic change. A negative coefficient would indicate that
a city has become smaller relative to the evolution of the “average city” as captured
16
by the time-fixed effects and country fixed effects. We perform extensive robustness
checks. In particular, we employ a set of highly demanding fixed effects which will
allow us to rule out a range of mechanisms that have been discussed in the literature.
We also estimate a version of the above specification where we try to highlight
the role of the timing of democratic transition in explaining the catch-up of cities in
the hinterland. The timing of democratic change as indicated by the filtered dummy
variable is fuzzy at best, as democratic transitions may not be seen as clear cut events.
This makes a definition of a pre-treatment relative to a post-treatment period very
tenuous. For countries that experienced multiple transitions going from autocracy to
democracy and back, a definition of a pre- and post- period becomes impossible. For
this reason, we perform the following structural break-type analysis only for the set
of countries that experienced a single transition and compare these countries to the
set of countries that saw no transition. This reduces the set of countries down to the
15 countries that have been identified to have experienced a distinct transition and 18
countries that saw no change in the filtered democracy indicator. We compute the
following simple indicator:
Years to Democracyct =

t − break-year
c
0
if countryc democratic transition
else.
(2)
In the case of Burundi illustrated in Figure 3, which saw a break in the year 2000, this
variable would range between -7 and +10. We can convert this variable into a sequence
of dummies and estimate the coefficient of interest, the capital city size, relative to the
timing of the democratic transition.
3.2
Main Results
Does democratization affect the evolution of cities within a country? We propose that
democratic transition should lead to a more equally distributed urbanization experience. Under autocracies, urbanization is mainly concentrated in the cities that are the
seat of government or concentrate political and economic power. We test whether this is
the case by studying the evolution of light emissions from spatial clusters. We present
these in Table 2. The table presents coefficients for whether a lit polygon is a National
capital city or a Regional capital. For these, we estimate the relative size before and
after democratic change. Note that the variation that we are exploiting in this analysis
is coming from within-city over time. We successively remove different fixed effects to
zoom in and try to exploit solely this variation.
17
The baseline specification in column (1) does not control for any fixed effects. The
coefficient on the National capital indicator suggests that National capital cities emit
roughly one log point more light compared to Regional capital cities. Following democratic change and not controlling for year or country fixed effects suggest that Regional
capital cities become more brightly lit, while capital cities do not experience a change.
We successively add fixed effects. In column (2) we add time fixed effects, which control for general trends in luminosity common to all countries in our estimating sample.
The sign on the National capital by Democracy indicator flips. This suggests that National capital cities, following democratic change, don’t partake in the general trend in
luminosity captured by the time-fixed effects.13 Column (3) takes out country-specific
fixed effects which leave the coefficient pattern unchanged. In column (4) we effectively
control for country-specific non-linear trends in changes in luminosity. The coefficient
patterns suggest that capital cities do not partake in the country-specific upward trend
in luminosity captured by the country-by-year fixed effects: the capital city shrinks. In
column (5) we remove city fixed effects in addition to the country-by-year fixed effects.
Now the variation is solely coming from within-city over time. The negative coefficient
on the Capital city becomes insignificant. Naturally, the intercepts for National and
Regional are perfectly collinear with the city fixed effects. With this set of demanding fixed effects, the Regional city interaction with the Democracy indicator becomes
negative, albeit being insignificant, suggesting that also Regional cities are shrinking.
The emerging theme from this analysis is that, when controlling for very general
time trends, the National capital is robustly shrinking relative to the other cities following democratic change. The size of the coefficient is statistically meaningful. The
capital city shrinks by, on average, 0.4 log points, compared to the general growth performance of cities. Going back to the example of Ghana, urbanisation increased by
44% since 1990. The primate city Accra, grew only by 4.8%, while other cities grew
by 55.5%. This relative contraction of around 50% compares well with the estimated
average effect of 0.4 log points.
Of course, Ghana is just an example and there are many other explanations that
could explain the relatively weaker growth performance of capital cities compared to
cities in the rest of the country following democratic transition. The next section performs an extensive set of robustness checks and rules out a set of alternative mechanism
that could create spatially biased growth of cities.
13 The
estimated time effects are generally positive relative to the base year. This is true for the general
time effects estimated in column (2) and (3) but also for the country-specific time fixed effects estimated
in columns (4) and (5).
18
3.3
Robustness Checks
We divide the checks into two main categories that deal with three issues. First, issues concerning the independent variable. These mainly deal with how we measure
“democracy”. Secondly, we add further controls and trends to ensure that we include
covariates that have been identified in the literature to correlate with growth or institutional change. Lastly, we consider changes in the dependent variable to highlight that
the pattern that emerges is robust to our choice of dependent variable. Lastly, we also
show how the timing of institutional change correlates with the observed changes in
the capital city indicator - that is - we perform a tentative common trends check.
The core robustness checks are presented in Table 3. Our definition of the democracy indicator filtered out transitory jumps above the zero polity-2 threshold commonly
used by political scientist. We would expect that such transitory changes, even though
they may lead to persistent changes, should not drive the observed catch-up effects. We
confirm that this is the case by controlling for the transitory democracy dummy. This
is simply the residual of the unfiltered democracy indicator minus the unfiltered one.14
Not surprisingly, the transitory democracy dummy interacted with city type does not
gain significance in any specification for column (1) and (2).
Typically democratic or institutional change happens over time. Such periods of
transition may have a distinct effect on luminosity in the capital city relative to other
cities. In particular violent transitions through the capture of power in the capital city
may naturally lead to reduced service provision and thus, lower levels of luminosity.
In column (3) we remove periods of transition as identified by the Polity dataset.15 The
estimated coefficient on the National capital interaction hardly changes.
Another concern is that different cities could have simply been on differential trends.
If we believe that there is within-country convergence to respective steady states (see
Mankiw et al., 1992). Since capital cities may be closer to their steady states, they naturally are going to converge slower compared to other cities that are far away. In relative
terms, they shrink. In order to rule this out we control for city fixed effects as well
as city specific linear trends. The result is presented in column (4). Our key result is
robust to the inclusion of these demanding trends.
In light of the work of Brückner and Ciccone (2011), who document that rainfall
shocks may open up a democratic window of opportunity, we control for rainfall shocks
in column (5). We also control for rainfall shocks for rain falling in a 50 km radius
14 In
the case of Burundi, illustrated in Figure 3 this means there is a single transitory jump for the
year 1994.
15 These include cases of foreign intervention (polity score -66), cases of anarchy (polity score -77) or
cases of transition (polity score -88).
19
around the centroid of a city polygon. The results are unchanged.
The next set of regressions focuses on the measurement of democracy. We use three
different indicators. In column (6) we use the unfiltered democracy indicator as being a
strictly positive polity-2 score. In column (7) we use the indicator proposed by Alvarez
et al. (2000), which was later expanded up to 2010 by Cheibub et al. (2009). The results
are robust. In column (8) we use the Freedom House indicator. We treat countries as
treated if they are classified as “free” or “partly free”. The coefficient becomes weaker;
this is due to the inclusion of partially treated countries.
Lastly, in column (9) and (10) we study alternative dependent variables. In column
(9) we look at the log of the number of lit pixels in a city and year, while column (10)
focuses on the log of luminosity of the pixels that fall into the always lit core defining
cities. The results suggest that both the extensive margin of lit pixels as well as the
intensive margin exhibit a contraction in the relative size of the capital city.
3.4
Ruling Out Alternative Explanations
The explanation for the relative catch up of the cities in the hinterland relative to the
capital following democratic transition that we propose is simple. Spending on public
goods by autocrats was biased towards the capital city for a simple reason. The threat
of violent turnover through an organised opposition is increasing in population density,
but decreasing in the distance to the seat of government. Democracy, through regular
elections, has a fundamentally different mechanism for turnover. This takes out the
incentives to provide public goods preferentially in dense locations near the seat of the
executive.
A set of alternative mechanisms could explain region-specific growth that is correlated with political changes. This section aims to rule out alternative mechanisms that
have been prominently discussed in the literature. We rule out vote-buying mechanisms, leader-specific effects and foreign aid. The main tool we use for this purpose is
the estimation of heterogenous effects and the inclusion of powerful fixed effects which
effectively rule out a host of alternative explanations. The results are presented in Table
4.
The first mechanism that could drive catch-up growth in the hinterland following
democratic transition are vote-buying mechanisms (see Dekel et al., 2008; Wantchekon,
2011). In non-democratic environments, elections are of no fundamental consequence.
Hence there are limited incentives to provide public goods to buy votes since voters
effectively have no real choice. Under democracy, elections become important. Given
that, despite primacy, the population in the non-capital city out-numbers the popula20
tion in the capital city, this may create incentives by political agents to provide public
goods in an attempt to buy votes cheaply. This could be particularly pronounced in
election years. We address this by studying the differential effects that election years
have on night-light emissions before and after democratic transition. Simply controlling for whether a year is an election year in column (1) does not yield any results.
However, the interaction effect with National capital is negative. This suggests that in
an election year, the Capital city luminosity is lower relative to the rest of the country.
The triple interaction with the Democracy indicator is also significant but does not gain
statistical significance.
The next host of mechanisms that we aim to rule out is regional favouritism or ethnic
favoritism. In particular, Burgess et al. (2013) and Hodler and Raschky (2014) are related as they study the role of regional favoritism under different political institutions.
Burgess et al. (2013) show that in an autocratic setting in Kenya, road construction is
biased along ethnic dimensions. Parts of Kenya where the dominant ethnic group coincides with the ethnicity of the president, benefit disproportionally from allocation of
public spending towards road construction. While, Hodler and Raschky (2014) describe
the opposite effect, where multiparty democracy leads higher investment of resources
in the region where the president comes from. We rule out such a mechanism by removing the identity of the leader from the equation. In a first exercise in column (3)
we control for leader fixed effects along with year fixed effects. These “leader-effects”
(Jones and Olken, 2005) are essentially a form of country-by-year fixed effects. They
would be exactly identical to country-by-year fixed effects in case a country has a different leader in every year. Unsurprisingly, the estimated coefficient does not change.
The fixed-effects are, however, not perfectly collinear with the Democracy indicator.
This implies that the variation that is left in the Democracy indicator is within leader
and not across leaders. Since democratic transitions are correlated with changes in
the identity of a head of state, these sets of fixed effects narrow down the source of
variation to come from changes in Democracy within a political leader.
In order to address regional favoritism, we need to make these leader effects specific
to a location. We create a set of leader-by-city fixed effects; these effectively allow each
leader to have a distinct level effect on every city. These fixed effects absorb a huge
amount of variation as they are effectively imply that we estimate a separate set of city
fixed effects for different periods of time. Our estimated coefficient on the interaction
between National capital and Democracy hardly moves in column (4). In column (5) we
control for country by year fixed effects in addition to the leader-by-city fixed effects.
This reduces down the amount of variation left to explain the differential effect of
Democracy on the relative size of the capital significantly as is evidence by the R2 of
21
0.81. Nevertheless, the coefficient remains similar, albeit estimated less precisely.
The last set of mechanism relate to the role of Foreign Aid. Foreign Aid could have
affected democratization as discussed by Bratton and Walle (1997). In addition, Foreign
Aid may be biased in favor of the capital city or disproportionate amounts of Foreign
Aid remain in the capital city relative to the hinterland in non-Democratic regimes.
We can not directly control for the geography of aid provision since geo-coded data
is only available from the late 1990s and only for a few countries. We simply control
for the level of Foreign Aid receipts as measured in the Aid Data 2.0 public release
data set presented in Tierney et al., 2011. The results are presented in column (6) and
(7). The coefficient on the Aid interaction with National city and Aid is positive but
insignificant, suggesting that countries that receive a lot of aid saw a smaller drop in
the relative size of the capital city.
We now turn to study the underlying mechanism that we consider as driving the
results. Democracy brings about improved public good provision; before democracy,
leaders put too much weight on the capital city for fear of violent turn-over. We first
document evidence in favor of this mechanism and then document that some indirect
measures of public good provision exhibit similar effects.
4
Mechanisms
In this section we present supporting evidence for devolution of power through the
impact of democracy on the distribution of services across different types of cities. To
test for this hypothesis, we look at an important measures of development, namely
education defined as primary school and secondary school completion16 .
4.1
Method
Education, and in particular universal primary education has been a common intervention across many developing countries (see for example Duflo, 2001, and Osili and
Long, 2008 and Osafo-kwaako, 2012). Providing education services is also an easier
task given that the construction of a school benefits all children in an area and is an
easily verifiable outcome for voters. Harding and Stasavage (2013), show that indeed
democracies have higher rates of school attendance than non-democracies. Thus, one
would expect democratization to have an immediate effect on provision of schooling.
To test this hypothesis, we employ the same empirical design as before, however the
16 We
perform a similar exercise to Kudamatsu (2012) for catch up in infant mortality across different
types of cities, but fail to find any meaningful results.
22
unit of observation is a woman within a city. Our preferred specification takes the
following form:
ymicjt = αi + δt + β 0 + η j + φj Ccij × Dct + b0 Xmicjt + emicjt
Our dependent variable ymicjt is a dummy equal to one for primary/ secondary
school completion for respondent m from city i that is of type j, in country c and in
year t and zero otherwise. We add controls, in particular city- and time fixed effects,
αi and δt respectively. The city fixed effects absorb both the country fixed effects and
city type fixed effect. The coefficients of interest are the estimates of φj . These capture
the level effect of city size of class j as indicated by the city type dummy Ccij following
democratic change, i.e. when Dct = 1. These coefficients will tell us how the intercept
η j changes following democratic change. A negative coefficient would indicate that
school completion rates for women in that particular city are lower relative to the school
completion rates as captured by the time-fixed effects and country fixed effects. Finally,
Xmicjt is a set of time varying characteristics, namely an indicator if the agglomeration
as defined by the night lights is an urban/mixed agglomeration or rural agglomeration
and a measure of rainfall at the sub-national level (to control for economic trends). We
perform the same range of robustness checks as in the previous section.
4.2
Results
Does democratization lead to a more equitable distribution of services across cities
within a country? We test whether this is the case by studying the evolution of primary
and secondary school completion from the DHS. We present these results in Table 5.
In columns (1)-(3) we present results for primary school completion, while in columns
(4)-(6) we present results for secondary school completion. The baseline specification
in column (1) and (4) control for country and year fixed effects. The coefficient on the
National capital indicator suggests that National capital cities have a higher primary
and secondary school completion rate by roughly 10 percentage point compared to
Regional capital cities and other cities. We sequentailly add controls. In columns (2)
and (5), we add city fixed effects while in columns (3) and (6) we add country by year
fixed effects. The coefficient of interest, the interaction terms highlight some intersting findings. The interaction term on national capital and democracy is significant and
negative consistent with luminosity regressions. This implies that national capital cities
seem to be experiencing a decline in primary school completion rates post democratization relative to regional cities and other cities. This implies that the capital cities do
not partake in the country-specific upward trend in primary school completion cap23
tured by the country-by-year fixed effects. A woman in the national capital is 4.5/ 3.6
percentage points less likely to complete primary/ secondary schooling post democratization compared to the general schooling completion rates of cities. Evaluating at the
mean school completion rates, this results in a decline in primary/ secondary school
completion of 6%/ 9%, which are both statistically meaningful estimates. Looking at
the case of Ghana, this would result in a decline in capital cities primary/ secondary
school completion by 5.5%/ 6.3% 17 .
4.3
Robustness checks and Alternative Explainations
To rule out the hoard of alternative mechanisms discussed earlier that could be correlated with regional specific school completion and democratization, we carry out
a similar exercise as table 4. The results are presented in table 6 for both primary
and secondary school completion. Columns (1)-(3) present results for primary school
completion, while columns (5)-(6) present results for secondary school completion. In
columns (1) and (4), we address catch up due to vote buying duirng election years. We
do not find any differential impact of election years on regional city catchup. Next, in
columns (2), (3) and (5), (6), we add demanding fixed effects related to the identify of
the leader of the country to address concerns related to regional favoritism. The coefficient on the interaction of the national capital and democracy remains extremely stable
even when we just consider variation coming from changes in democracy within a city
and political leader. It only becomes insignificant once we control for country specific
non-linear trends.
Since we use individual level data, migration can be of concern. If respondents
change their city location in a way that is correlated to democratic transition, our results maybe biased. For example, educated persons may move back to the hinterland
from capital cities as economic and political climate changes post democratization.
This would change the interpretation of our results from public service delivery being
a mechanism for economic growth to education being simply an outcome of democratization. To address this concern, we restrict our sample of respondents to women
that never migrated from their place of birth. This severely restrict the sample size.
The results are presented in appendix B table 9 for both primary and secondary school
completion. Columns (1) and (6) present results for the non-movers. The coefficients
become statistically insignificant as the sample size is severely restricted. Next, we
control for rainfall in columns (2) and (7). The results remain robust to controlling for
rainfall as a proxy for income. Finally in columns (3)-(5) and (8)-(10) we use differ17 The
mean primary and secondary school completion rates for Ghana are 81.1% and 57%.
24
ent measures of democracy as described in the previous section. Our results remain
qualitatively similar.
5
Conclusion
The emergence of mega-cities and excessive primacy is a stylized fact that has been
presented in the empirical literature. In particular, urbanization has been an experience
that is concentrated in a few locations. The existing literature has attributed this to
excessive transport costs, see Storeygard (2012). In this paper, we provide evidence that
the urbanization gap between the primate city or capital cities and the hinterland may
be attributable to institutional factors.
Autocratic regimes may use public good provision as a strategic device in order to
reduce the threat of a violent turnover. This may create a bias of public good provision
towards a few cities near the seat of government. Following institutional change, these
incentives may be lessened which may lead to a more equal distribution of access to
public goods. We show that, following a wave of democratic transitions in the 1990s,
urbanization in Africa has become more evenly spread.
We document there to be catch up in service provision; gaps in educational attainment become smaller between capital cities and provincial cities. We hypothesize this
to increased regional representation and stronger incentives to provide public goods
across the country due to increased political representation. Further work will study
the underlying mechanism of political power sharing and the implied devolution of
power further. In addition, we aim to study further outcome variables that may proxy
for public good provision and rule out alternative channels, in particular, transport
costs in driving our results.
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29
Main Figures
Population in the largest city as % of Urban Population in 1992
2.67
72.7
Figure 1: Primacy as measured by the share of the urban population that is captured
by the largest city in a country in 1992. Data come from the World Bank World Development Indicators.
30
4
2
.6
-4
-2
0
Polity 2 Score
Share of Democracies
.2
.4
0
1990
1995
2000
Year
2005
Polity 2 > 0
Average Polity2 Score
2010
Democratic Freedom House
10
5
0
-5
-10
-10
-5
0
5
10
Figure 2: Democratization in Sub-Saharan Africa over time. Figure plots out the share
of countries in Sub-Saharan Africa classified as being democratic either by having a
polity-2 score greater than 0 (black circles) or according to Freedom House (blue diamonds) on the left, while it plots the average polity-2 score ranging from -10 to 10 (red
squares) on the right since 1990.
1990
1995
2000
2005
2010
1990
1995
2000
2005
2010
Figure 3: Smoothed Democracy Indicator (right) compared to Non-Smoothed Indicator
(left) derived from Burundi polity 2 scores.
31
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CMR, CAMEROON
10
CIV, COTE D'IVOIRE
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10
10
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CAF, CENTRAL AFRICAN REPUBLIC
10
BWA, BOTSWANA
10
BFA, BURKINA FASO
10
BDI, BURUNDI
10
AGO, ANGOLA
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1995
ERI, ERITREA
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2000
2005
2010
1990
1995
ETH, ETHIOPIA
2000
2005
2010
1990
1995
GAB, GABON
2000
2005
2010
1990
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1995
GHA, GHANA
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2005
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GIN, GUINEA
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1990
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1995
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1990
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CPV, CAPE VERDE
10
COM, COMOROS
2000
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10
1995
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10
1990
10
2010
10
2005
10
2000
−10
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1995
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1990
2000
2005
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10
2000
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1995
GNQ, EQUATORIAL GUINEA
2000
2005
2010
1990
1995
KEN, KENYA
2000
2005
2010
−10
−10
−10
−10
2010
GNB, GUINEA−BISSAU
10
GMB, GAMBIA
1995
1990
1995
LBR, LIBERIA
2000
2005
2010
1990
LSO, LESOTHO
1995
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2005
MDG, MADAGASCAR
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2000
10
2010
10
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10
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10
1995
10
1990
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2000
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2010
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1995
10
MUS, MAURITIUS
10
10
MOZ, MOZAMBIQUE
2000
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−10
1990
MWI, MALAWI
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−10
−10
1990
●
1990
1995
NAM, NAMIBIA
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2005
2010
1990
1995
2000
2005
2010
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RWA, RWANDA
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2000
NER, NIGER
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SWZ, SWAZILAND
10
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1990
SOM, SOMALIA
10
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1990
SLE, SIERRA LEONE
10
SEN, SENEGAL
1995
1990
1995
TCD, CHAD
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2000
2005
2010
1990
TGO, TOGO
1995
2000
2005
2010
TZA, TANZANIA, UNITED REPUBLIC OF
10
2005
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10
UGA, UGANDA
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32
MLI, MALI
1995
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2010
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2005
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2000
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1995
10
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1990
1995
2000
2005
2010
Figure 4: Evolution of Polity-2 Scores, Structural Breaks Identified Following Bai and Perron (2003) for Countries in
Sample
33
Figure 5: Illustrating the Construction of Outer City Envelopes based on Night Light Emissions. Outer Envelope in black
is defined as the polygon that encompasses lit areas in the years 2007-2012. The core in grey is defined as the area that
has been continuously lit in the years 1992-1994 and 2010-2012.
34
Figure 6: Countries in our estimating sample of Sub-Saharan are highlighted with the city envelope polygons identified
from night light emissions outlined; first administrative subnational boundaries are drawn
2.5
Luminosity in log scale
1
1.5
2
.5
35
-11-10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 121314151617
Time to Democratic Transition
National Capital
Other Cities
Provincial Capitals
Figure 7: Natural Log of average luminosity of Sub-Saharan African cities classified as National-, Provincial Capitals or
Other Cities over time relative to the democratic transition as indicated by the vertical line.
1
Primary Completion
.6
.8
.4
36
-11-10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 121314151617
Time to Democratic Transition
National Capital
Other Cities
Provincial Capitals
Figure 8: Primary School Completion in Sub-Saharan African cities classified as National-, Provincial Capitals or Other
Cities over time relative to the democratic transition as indicated by the vertical line.
.6
Secondary Completion
.3
.4
.5
.2
.1
37
-11-10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 121314151617
Time to Democratic Transition
National Capital
Other Cities
Provincial Capitals
Figure 9: Secondary School Completion in Sub-Saharan African cities classified as National-, Provincial Capitals or Other
Cities over time relative to the democratic transition as indicated by the vertical line.
3
2
1
0
38
-16 -14 -12 -10 -8
-6
-4 -2 0 2 4 6 8 10 12 14 16 18 20
Years to Democratic Transition
Figure 10: Capital City size relative to non-capital cities relative to democratic transition. Regression coefficients of the
capital city size in terms of night light luminosity relative to other cities.
Main Tables
Table 1: Countries and Names of Capital Cities
ISO3 Country Code
Capital City Name
Country Name
Type
AGO
BWA
BFA
BDI
CMR
CPV
CAF
TCD
COM
GNQ
ERI
ETH
GAB
GMB
GHA
GIN
GNB
CIV
KEN
LSO
LBR
MDG
MWI
MLI
MUS
MOZ
NAM
NER
RWA
STP
SEN
SYC
SLE
SOM
SWZ
TZA
TGO
UGA
ZMB
ZWE
Luanda
Gaborone
Ouagadougou
Bujumbura
Yaounde
Praia
Bangui
Ndjamena
Moroni
Malabo
Asmara
Addis Ababa
Libreville
Banjul
Accra
Conakry
Bissau
Yamoussoukro
Nairobi
Maseru
Monrovia
Antananarivo
Lilongwe
Bamako
Port Louis
Maputo
Windhoek
Niamey
Kigali
Sao Tome
Dakar
Victoria
Freetown
Mogadishu
Mbabane
Dar es Salaam
Lome
Kampala
Lusaka
Harare
Angola
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Equatorial Guinea
Eritrea
Ethiopia
Gabon
The Gambia
Ghana
Guinea
Guinea-Bissau
Cote d’Ivoire
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritius
Mozambique
Namibia
Niger
Rwanda
Sao Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National capital
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
National and provincial capital
39
Table 2: Effect of Democracy on Luminosity: Catch up
(1)
(2)
(3)
National Capital x Democracy
0.086
(0.109)
-0.179
(0.133)
-0.426**
(0.179)
Regional Capital x Democracy
0.525***
(0.152)
0.320*
(0.165)
0.088
(0.113)
-0.068
(0.135)
Other Cities x Democracy
0.249
(0.222)
0.069
(0.168)
-0.123
(0.129)
-0.096
(0.140)
Capital
1.887***
(0.140)
1.903*** 2.056***
(0.135)
(0.127)
Regional
0.834***
(0.141)
0.813*** 1.003***
(0.135)
(0.077)
Year FE
Country FE
Country x Year FE
City FE
National
Provincal
Other Cities
Observations
R-squared
X
38
293
1534
38630
.0833
38
293
1534
38630
.325
X
X
38
293
1534
38630
.403
(4)
(5)
-0.500*** -0.293**
(0.163)
(0.123)
0.196
(0.148)
X
X
38
293
1534
38630
.681
X
X
38
293
1534
38630
.73
Notes: This table reports the effect democratization on luminosity of cities. The
dependent variable is the log of average luminosity of cities constructed from night
time lights satellite images. National refers to National Capital and Regional refers
to regional or provincial capital. Robust standard errors in parentheses are clustered by country, stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1.
40
Table 3: Effect of Democracy on Rioting: Cross City Comparison
(1)
(2)
(3)
(4)
(5)
National Capital x Democracy
-0.294
(0.240)
-0.294
(0.240)
-0.217
(0.221)
-0.123
(0.185)
-0.310
(.)
Regional Capital x Democracy
-0.205
(0.479)
-0.205
(0.479)
-0.181
(0.446)
-0.619
(0.381)
-0.798
(.)
Other Cities x Democracy
-0.190
(0.452)
-0.190
(0.452)
-0.147
(0.345)
-0.026
(0.321)
-0.082
(.)
Capital
5.584***
(0.243)
5.584*** 5.500***
(0.243)
(0.281)
Regional
2.291***
(0.247)
2.291*** 2.417***
(0.247)
(0.381)
Year FE
Country FE
Country x Year FE
City FE
National
Provincal
Other Cities
Observations
R-squared
X
X
38
293
1534
39148
38
293
1534
39148
X
X
38
293
1534
39148
X
35
114
125
5753
X
X
26
87
97
4905
.887
Notes: This table reports the effect democratization on riot intensity across
city types. Riots is the number of riots that is reported in the SCAD database,
that has been mapped to a city polygon based on the geo-codes reported in
the SCAD database. National refers to National Capital and Regional refers
to regional or provincial capital. Robust standard errors in parentheses are
clustered by country, stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1.
41
Table 4: Robustness of Effect of Democracy on Luminosity in the Hinterland
Transitory vs Persistent
(1)
Controls
Alternative Democracy
Alternative DV
42
(2)
(3)
Anarchy Years
(4)
Trends
(5)
Climate
(6)
Unfiltered
(7)
Cheibub et al
(8)
Freedom House
(9)
Lit Pixels
(10)
Core
National Capital x
Democracy
-0.509***
(0.171)
-0.508***
(0.184)
-0.043
(0.055)
-0.458***
(0.156)
-0.380***
(0.139)
-0.392**
(0.187)
-0.333**
(0.125)
-0.476***
(0.148)
0.589
(0.633)
Regional Capital x
Democracy
-0.088
(0.134)
-0.165
(0.141)
0.093
(0.078)
-0.103
(0.122)
-0.107
(0.105)
-0.167
(0.232)
-0.139
(0.144)
-0.058
(0.147)
0.240
(0.486)
Other Cities x
Democracy
-0.074
(0.157)
-0.129
(0.146)
0.139
(0.133)
-0.085
(0.145)
-0.030
(0.163)
-0.140
(0.120)
-0.051
(0.099)
-0.046
(0.111)
-0.323
(0.355)
38
293
38446
.68
38
293
31170
.708
38
293
38630
.681
38
293
38630
.768
38463
.776
National Capital x
Transitory Democracy
0.107
(0.179)
-0.043
(0.144)
Regional Capital x
Transitory Democracy
-0.136
(0.192)
-0.154
(0.192)
Other City x
Transitory Democracy
0.148
(0.136)
0.128
(0.167)
Rainfall
Capitals
Provincal
Observations
R-squared
0.000
(0.000)
38
293
38446
.68
38
293
38446
.681
35
211
34390
.686
38
293
38630
.629
36
292
34616
.69
Notes: The dependent variable is the log of average luminosity of cities constructed from night time lights satellite images for Columns (1) - (7). All
regressions include country- and year fixed effects. Column (1) studies transitory changes in the polity-2 indicator, column (2) compares the effect
transitory changes relative to persistent ones, column (3) removes years in which the polity score indicates periods of transition. Column (4) controls
for city fixed effects and city- specific linear trends, column (5) controls for precipitation. Column (6)-(8) explore alternative democracy indicators.
Column (6) is the unfiltered polity-2 dummy indicating polity-2 score above zero, column (7) is a Freedom House indicator, while column (8) is the
democracy indicator introduced in Cheibub et al. (2009). Column (9) - (10) study alternative dependent variables: column (9) studies the log of the
number of lit pixels, column (10) looks at luminosity in the always lit core. Robust standard errors in parentheses are clustered by country, stars
indicate *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 5: Ruling Out Alternative Mechanisms Explaining Catch up Effect of Luminosity in the Hinterland
Election Years
(1)
(2)
Regional Favoritism
Foreign Aid
(3)
(6)
(4)
(5)
(7)
National
2.056***
(0.127)
2.096*** 2.062***
(0.137)
(0.131)
2.102*** 2.292***
(0.131)
(0.196)
Regional
1.003***
(0.077)
1.027*** 1.031***
(0.088)
(0.071)
1.029*** 0.998***
(0.079)
(0.117)
National x Democracy
-0.427**
(0.179)
-0.418**
(0.181)
-0.383*
(0.202)
-0.387** -0.298*
(0.177) (0.174)
-0.420**
(0.178)
-0.489*
(0.268)
Regional x Democracy
0.086
(0.113)
0.088
(0.108)
0.078
(0.165)
0.164
(0.248)
0.097
(0.111)
0.242
(0.151)
Election
0.060
(0.053)
National x Election
-0.164*
(0.093)
Regional x Election
-0.049
(0.135)
Regional x Democracy x Election
0.009
(0.188)
National x Democracy x Election
-0.015
(0.132)
0.162
(0.202)
Aid
-0.008
(0.054)
National x Democracy x Aid
0.157
(0.185)
Regional x Democracy x Aid
-0.152
(0.103)
National x Aid
-0.262*
(0.140)
Regional x Aid
0.059
(0.077)
Capitals
Provincal
Other
Observations
R-squared
38
293
1534
38630
.403
38
293
1534
38630
.403
38
293
1534
38509
.418
38
293
1534
38509
.777
38
293
1534
38509
.81
38
293
1534
36519
.387
38
293
1534
34900
.374
Notes: All regressions include country- and year fixed effects. The dependent variable is the log of
average luminosity of cities constructed from night time lights satellite images. Column (1) - (2) rule out
vote-buying mechanisms whereby public good provision improves in election years following democratic changes in cities in the hinterland. Column (3) - (5) rule out Regional Favoritism channels as
discussed in Hodler and Raschky (2014). Column (3) controls for leader fixed effects and year fixed effects; column (4) control for leader-by-city fixed effects and year fixed effects, while column (5) controls
for leader-by-city fixed effects and country-by-year fixed effects. Column (6) - (7) control for foreign aid.
43
Table 6: Effect of Democracy on School Completion: Catch Up
Primary Completion
(1)
(2)
Secondary Completion
(3)
(4)
National x Democracy
-0.017
(0.017)
-0.049*** -0.045***
(0.018)
(0.017)
Regional x Democracy
0.015
(0.015)
-0.004
(0.013)
Other Cities x
Democracy
-0.004
(0.012)
0.027**
(0.012)
Capital
0.080***
(0.013)
0.112***
(0.016)
Regional
0.016
(0.017)
-0.004
(0.020)
Year FE
Country FE
Country x Year FE
City FE
Capital
Provincal
Clusters
Observations
R-squared
Yes
Yes
No
No
16947
13735
1035
49838
.295
Yes
.
No
Yes
16947
13735
1035
49838
.359
0.027
(0.018)
.
.
Yes
Yes
16947
13735
1035
49838
.369
(5)
(6)
-0.039**
(0.016)
-0.039** -0.036**
(0.019) (0.015)
0.053***
(0.016)
0.003
(0.015)
0.003
(0.013)
0.025**
(0.010)
0.013
(0.013)
Yes
.
No
Yes
22027
17101
1041
62861
.251
.
.
Yes
Yes
22027
17101
1041
62861
.263
Yes
Yes
No
No
22027
17101
1041
62861
.203
Notes: This table reports the effect democratization on average school completion of
cities. The dependent variable is a dummy equal to 1 or 0 if the respondent completed
primary/ secondary school or not. National refers to National Capital and Regional
refers to regional or provincial capital. Robust standard errors in parentheses are
clustered by city, stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1.
44
Table 7: Ruling Out Alternative Mechanisms Explaining Catch up Effect
of School Completion in the Hinterland
Election Years
(1)
Primary
Regional Favoritism
(2)
(3)
Secondary Primary
(4)
Secondary
National x Democracy
x Election
0.012
(0.022)
0.018
(0.025)
Regional x Democracy
x Election
0.014
(0.027)
-0.004
(0.031)
National x Democracy
-0.048***
(0.018)
-0.047**
(0.021)
-0.027**
(0.014)
-0.040*
(0.022)
Regional x Democracy
-0.004
(0.013)
0.001
(0.017)
0.014
(0.015)
-0.001
(0.022)
Other Cities x
Democracy
0.031**
(0.013)
0.021**
(0.010)
0.043***
(0.017)
0.012
(0.014)
Capital x Election
0.004
(0.014)
0.003
(0.015)
Regional x Election
-0.001
(0.017)
-0.007
(0.019)
Democracy x Election
-0.008
(0.017)
0.025
(0.016)
Election
-0.012
(0.012)
-0.021*
(0.012)
Capital
Provincal
Other
Clusters
Observations
R-squared
16947
13735
19156
1035
49838
.359
22027
17101
23733
1041
62861
.251
16947
13735
19156
1035
49838
.379
22027
17101
23733
1041
62861
.272
Notes: The dependent variable is a dummy equal to 1 or 0 if the respondent completed primary/ secondary school or not. All regressions include city and year fixed effects. Column (1) and (2) rule
out vote-buying mechanisms whereby public good provision improves
in election years following democratic changes in cities in the hinterland.Column (1) and (2) control for city fixed effects and year fixed
effects. Column (3) and (4) rule out Regional Favoritism channels as discussed in Hodler and Raschky (2014) by including leader-by-city fixed
effects and year fixed effects. Robust standard errors in parentheses are
clustered by city, stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1
45
A
Appendix
A.1
Construction of City-Area Polygons
We construct city size based on the night light series from the DMSP. This assigns a
digital value to each grid pixel on the ground based on the extent of light emittance.
For a given spatial collection of pixels, we consider a sub-collection of pixels to be a
polygon if they cluster together such that there is a unique polygon outline defined
by completely dark pixels around it. This process is performed for each individual
night light picture taken. This implies that we have now, for each year, a collection of
polygons that represent light blobs.
We then compute various measures of the core and the outer envelope of these
polygons. The core is defined as the minimal spatial area that is continuously lit for
a set of consecutive years. The outer envelope is a polygon that wraps around the lit
area from all the individual year on year polygons and thus, presents the maximum
extent.18 The combination of the various core definitions and the definition of the outer
envelope provide a good indication as to where cities and towns are located and, they
allow us to map out key variables on growth.
We consider four possible definitions of the core, however, for the purpose of most
of the analytical exercises, we use a stringent definition requiring core polygons to
be continuously lit for the past 20 years. In case there are multiple satellite x year
observations, we use the data from the newest satellite. The definitions we work with
are as follows:
1. Core defined as the intersection of all polygons pertaining to the night light images
of all rasters. This is the most conservative definition of the core, as it only keeps
parts of polygons that are consistently lit since 1992 and are visible in all satellite
images.
2. Core defined as the intersection of all polygons pertaining to the night light images.
In case there are multiple satellites available per year, the newest satellite was
chosen. This helps to address some issues about satellite deterioration.
3. Core defined as the intersection of the polygons pertaining to the satellites F10
covering 1992-1994 and F18 covering 2010-2012. This definition of the core is less
conservative as the first one, but overcomes the issue that an empty core could be
the result of a bad satellite year at any point between 1994 and 2009.
18 We
refer to the outer envelope also as the union polygon.
46
4. Core defined as the intersection of just the past five years. This represents the
least conservative way of constructing the core, restricting the analysis to areas
that were consistently lit for the past five years.
We define the outer envelope simply as the union of the polygons of the past five
years. By construction, all core polygons are encompassed by the union. However,
the union will have many more polygons as compared to the number of polygons that
define the various cores. This simply arises as the union will create a polygon if there
had been some light in any of the past five years, while the core requires places to be
lit consistently.19
The union polygons are thus - only really useful in combination with the cores and
various definitions of the latter. We will focus in the analysis on those union polygons
that have a non-empty core, i.e. polygons that are - at least for some part always lit.20
The definition of the union polygon will provide a unique identifier for all the lit
areas. We will intersect these with the particular core variables to construct measures of
growth on the intensive margin and the extensive margin. The result is a panel dataset
where the panel identifier is the union polygon id.
We perform two ways of ground truthing of the polygon based city size measures.
These need to address two important issues. First, do the actual polygons capture
towns or the location of human agglomerations sufficiently well? If so, does the size of
the polygons capture urban population sufficiently well?
We ground-truth data for two countries. In order to address this, we obtain city
population data collected from the website citypopulation.de. For Tanzania, the website
provides a cross-section of 141 towns that have some population data reported for any
of the censuses that were performed. Similarly, for Kenya, the website provides a list
of 98 towns that have ever reported some population data in any of the censuses. 21
We geocode these towns using tools such as Google Maps or Open Street Maps
and intersect the resulting list of geo-codes with the shape file for the outer envelope
polygons. This allows us to assign the population data from the various census to the
polygons we had created, furthermore, it allows us to assign names to the polygons.
Again, as in the previous section, it may be the case that there are multiple points
that fall into an outer-level polygon. In this case, we simply add the population to
19 This
could well encompass lights due to wildfires, that have not been removed properly from the
satellite images in the cleaning process.
20 This is why the various definitions of the core come in play in ruling out some, but not all union
polygons.
21 The census years for Tanzania are 1967, 1978, 1988, 2002 and 2012, while the census years for Kenya
are 1969, 1979, 1989, 1999 and 2009.
47
obtain a population measure for the outer envelope polygon. We assign the name of
the town with the highest population figure.
Of the 185 unique core polygons, 111 have some population data in the census
years with most data coverage. 62 places are in Tanzania, where census year 2002 was
identified to provide most population data coverage, while 49 towns are located in
Kenya.
Another step of ground truth we perform, is to assess whether the polygon area size
captures urban population. We use the 2002 population census shape file released by
the National Bureau of Statistics Tanzania, to identify the ward level population falling
within the union polygons defined earlier. Unfortunately, there are no other shape files
available for different census years that could be used to validate the time-variation that
we document in the growth of the lit areas over time. We assign an indicator variable
equal to 1 if a ward falls within an union polygon and 0 otherwise. This is used
to construct a measure for the share of the district population falling within a union
polygon. Tanzanian wards are defined into three categories, namely urban wards, rural
wards and mixed wards. If the nigh light analysis is truly picking up urbanisation in
Tanzania, we would expect the share of population falling within the union polygons
to equal the share of urban population of a district. We define two measures of urban
population, one being the strict measure and the other being less restrictive. The first
is simply based on only urban wards, while the second measure is based on urban and
mixed wards. Below, we present the summary statistics of the exercise described here.
The first three rows display the share of ward level population for district d falling
inside the union polygons (a ward is assigned to a union polygon as long as any part
of the ward falls within the union polygon). The total share of population falling inside
the polygons is on average 34% of the district population. If we restrict wards to only
urban and mixed or urban wards only, then the population falling within polygons falls
to 25% and 12% respectively. Thus, the night lights seem to be picking up mainly urban
population (as defined by the less strict restriction). Panel B, presents the average urban
population for districts in Tanzania based on the two definitions. The share of urban
population based on the strict measure is almost identitical to the respective share of
urban population falling within the polygons. This provides strong evidence in support
of night lights picking up urban activity. The less strict measure based on urban and
mixed wards is off by approximately 10 percentage points when comparing to the same
population group falling within polygons. This discrepancy could potentially be due
to the different degrees of urbanisation of mixed wards and varying levels of rural
electrification across the country. Finally panel C presents the total number of wards
that fall within polygons in a given district. Some districts had no luminosity and thus
48
have a minimum of 0 wards falling inside polygons. On average, there are about 47
wards that intersect with polygons in a given district. This number is 18 and 36, when
considering just urban or urban and mixed wards respectively.
Based on the above exercise, we can conclude that the union polygons as defined
by our measure, indeed capture all major urban activity. They also do a fairly good job
in capturing most of the urban and mixed ward populations. Further, a simple match
of wards to polygons, highlights that more than 75% (35/46) of matches are for urban
or mixed wards.
Figure 11: Example of Provincial Capital Location as Well as City Size Polygons for
Tanzania
In Figure 11, we zoom into Tanzania and take a closer look at how the data looks.
The polygons are the cities that are identified from night light images. The white
circles denote provincial or national capitals. It becomes evident that most regions
have several towns.
The next part describes how we map information about the location of provincial
and regional capitals to the polygons.
49
A.2
Rainfall Data
Recent work suggests that the urbanization process may be driven by climate change.
Weather shocks induce migration into cities. The political economy argument studied
in this paper is not at odds with this, so long as the way that climate change translates into urbanization is not biased towards or against the capital city in a way that is
correlated with institutional change. In order to control for these obvious shifters, we
compute rainfall for the sample period that we study. We rely on the Global Precipitation Climatology Centre gridded dataset available at the 0.5 degree grid resolution.
This reanalysis dataset is constructed from rain gauge measurements. While there are
some concerns about the endogeneity of rainfall reporting (see e.g. Fetzer, 2014), this
dataset is one of the few that is available at the temporal resolution we rely on.
We overlay the centroids of the grid cells for Africa with subnational (first adminstrative level) boundaries for Africa and compute overall average rainfall in a year at
this level. In several cases, there is no grid centroid that falls into the subnational
administrative division. In this case, we identify the nearest grid cell and assign the
rainfall value at that grid cell.
A.3
Capital- and Provincial City Locations
We identify the location of all capital- and provincial capitals based on an ESRI shapefile. The World Cities shapefile is a map layer of the cities for the world. The cities
include all national capitals, provincial capitals, major population centers, and landmark cities. We can think of this layer as providing the location of the cities in which
we would expect the various political economy mechanisms to be operating: catch up
growth due to improved service provision is most likely going to happen in rural and
secondary cities, that is, provincial capitals. For the U.S., the data would give us the
national capital as Washington D.C., while the provincial capitals would be the capitals
of the individual states.
In our fist analysis step we focus on the pattern of growth derived from the night
light luminosity of the provincial capitals relative to the national capital. The following
table provides a tabulation of the resulting cities.
In the majority of cases, the national capital is also a provincial capital. In case of
Senegal, for example, Dakar is also the capital of the Dakar region. For our 40 countries,
the following tabulation provides the set of cities we are working with. In total, there
are 334 cities. So on average, every country has 8.35 cities national and/ or provincial capitals. We take the geo-coordinates to map the national and provincial capital
locations to the polygons that were constructed based on the night lights analysis.
50
City Type
Freq.
Percent
National and provincial capital 42
National capital
8
Other
5
Provincial capital
289
9.58
2.4
1.5
86.53
Total
100
334
In several cases, we assign the de-facto capital city. For Tanzania, the official capital
is Dodoma. However, the seat of government is actually in Dar Es Salaam. Table 1 in
the Appendix provides all countries in our sample and the name of the capital city that
we assigned.
Finally, in the event there is more than one city centroid falling within a polygon,
we assign the polygon the identifier of the politically superior city. Such conflicts occur
14 number of times. Appendix B table 11 presents the list of countries and cities where
we have multiple city centroids in a polygon together with the final city identifier.
In the end, we remain with three types of polygons: capital cities, regional capitals
and other agglomerations. Other agglomerations are defined by all polygons where
there is no intersection of cities based on the ESRI shape files and our definition of an
agglomeration using night lights.
51
B
Additional Tables
Table 8: Summary statistics
Variable
Mean
Primary School Data
Primary
0.756
Democracy Polity Adjusted
0.261
Democracy Polity Unfiltered 0.347
Democracy Freedom House
0.222
Democracy Cheibub et al.
0.196
Urban
0.781
Rainfall Index
102.974
Secondary School Data
Secondary
0.398
Democracy Polity Adjusted
0.337
Democracy Polity Unfiltered 0.428
Democracy Freedom House
0.283
Democracy Cheibub et al.
0.257
Urban
0.781
Rainfall Index
103.009
52
Std. Dev.
Min.
Max.
N
0.429
0.439
0.476
0.415
0.397
0.413
58.177
0
0
0
0
0
0
3.182
1
47670
1
47670
1
47670
1
47670
1
47670
1
47670
432.432 47670
0.49
0.473
0.495
0.451
0.437
0.414
59.094
0
0
0
0
0
0
3.182
1
56911
1
56911
1
56911
1
56911
1
56911
1
56911
432.432 56911
Table 9: Effect of Democracy on Education Outcomes - Simple Difference in Difference
(1)
Primary
(2)
Secondary
Regional x Democracy
0.093***
(0.011)
0.116***
(0.011)
Other Cities x
Democracy
0.036***
(0.010)
-0.041***
(0.009)
Regional
-0.099***
(0.006)
-0.153***
(0.006)
Other Cities
-0.101***
(0.005)
-0.116***
(0.006)
Democracy
-0.039***
(0.008)
-0.089***
(0.007)
16940
13736
19157
49833
.0104
22022
17102
23735
62859
.0249
Capital
Provincal
Other
Observations
R-squared
Notes: This table reports the effect democratization on the primary and secondary school completion for the national capital versus regional
cities and other cities. The panel is unbalanced
and made of 24 countries over the period 19902012. The dependent variable is a dummy equal
to 1 or 0 if the respondent completed primary/
secondary school or not. Regional refers to regional or provincial capital and Other refers to
rural cities. Democracy is is an indicator equal
to 1 for years in which the country is a democracy and 0 otherwise. Stars indicate *** p < 0.01,
** p < 0.05, * p < 0.1.
53
Table 10: Effect of Democracy on School Completion - Robustness
Primary
Secondary
(1)
Non-Movers
(2)
Climate
(3)
Unfiltered
National x Democracy
-0.038
(0.041)
-0.049***
(0.017)
-0.042**
(0.018)
-0.050***
(0.017)
Regional x Democracy
-0.020
(0.035)
-0.003
(0.013)
-0.014
(0.012)
Other Cities x
Democracy
0.058**
(0.024)
0.028**
(0.012)
0.010
(0.012)
Rainfall
54
Capital
Provincal
Other
Clusters
Observations
R-squared
(4)
(5)
Cheibub et al. Freedom House
(6)
Non-Movers
(7)
Climate
(8)
Unfiltered
(9)
Cheibub et al.
(10)
Freedom House
-0.013
(0.018)
0.008
(0.035)
-0.040**
(0.019)
-0.038**
(0.015)
-0.062***
(0.017)
-0.031
(0.019)
-0.010
(0.021)
0.009
(0.011)
-0.050
(0.036)
0.004
(0.015)
0.001
(0.013)
-0.038
(0.024)
-0.025
(0.019)
0.014
(0.012)
0.039***
(0.010)
0.036*
(0.021)
0.024**
(0.010)
0.022**
(0.010)
0.019*
(0.011)
0.010
(0.010)
21757
16709
23554
1041
62020
.252
20964
15927
22200
1041
59091
.252
22027
17101
23733
1041
62861
.251
-0.000**
(0.000)
2621
2009
3255
564
7885
.401
16813
13633
17912
959
48358
.359
-0.000**
(0.000)
16791
13562
19035
1035
49388
.362
16874
13632
19021
1035
49527
.36
16947
13735
19156
1035
49838
.359
3522
2618
4268
605
10408
.336
21931
17030
22275
965
61236
.249
The dependent variable is a dummy equal to 1 or 0 if the respondent completed primary/ secondary school or not. All regressions include city and year fixed effects.
Column (1) and (6) restricts the sample of respondents to non-movers, columns (2) and (7) include rainfall shocks as a control and column (3)-(5) and (8)-(10) explore
alternative democracy indicators. Column (3) and (6) is the unfiltered polity-2 dummy indicating polity-2 score above zero, column (4) and (7) is the democracy indicator
introduced in Cheibub et al. (2009), while column (5) and (10) is a Freedom House indicator. Robust standard errors in parentheses are clustered by city, stars indicate
*** p < 0.01, ** p < 0.05, * p < 0.1.
Table 11: DHS Survey Rounds Used in Analysis
Country
Survey Year
Survey Round
Burkina Faso
Burkina Faso
Burundi
Cameroon
Cameroon
Comoros
Ethiopia
Ethiopia
Gabon
Ghana
Ghana
Guinea
Guinea
Ivory Coast
Kenya
Kenya
Lesotho
Lesotho
Liberia
Madagascar
Malawi
Malawi
Mali
Mali
Mozambique
Rwanda
Rwanda
Senegal
Senegal
Sierra Leone
Swaziland
Tanzania
Uganda
Uganda
Zambia
Zimbabwe
Zimbabwe
2003
2010
2010
2004
2011
2012
2005
2010
2012
2003
2008
2005
2012
2012
2003
2008
2004
2009
2007
2008
2004
2010
2001
2006
2011
2005
2010
2005
2010
2008
2006
2010
2006
2011
2007
2005
2010
BF4
BF6
BU6
CM4
CM6
KM6
ET4
ET6
GA6
GH4
GH5
GN4
GN6
CI6
KE4
KE5
LS4
LS5
LB5
MD5
MW4
MW5
ML4
ML5
MZ6
RW4
RW6
SN4
SN6
SL5
SZ5
TZ5
UG5
UG6
ZM5
ZW5
ZW6
55
Table 12: Multiple Cities Falling into lit polygons
Multiple City Polygon Intersection
Country
hline Ghana
Swaziland
Kenya
The Gambia
Lesotho
Lesotho
Botswana
Angola
Tanzania
Tanzania
Cote d’Ivoire
Cote d’Ivoire
Cote d’Ivoire
Senegal
Major city
Accra
Mbabane
Nairobi
Banjul
Maseru
Hlotse
Gaborone
Luanda
Dar es Salaam
Zanzibar City
Gagnoa
Daloa
Yamoussoukro
Dakar
Minor city 1
Koforidua
Piggs Peak
Nyeri
Bathurst
Teyateyaneng
Butha-Buthe
Mochudi
Caxito
Kibaha
Koani
Lakota
Issia
Toumodi
Thies
56
Minor city 2
Cape Coast
Siteki
Embu
Brikama
Minor city 3
Sekondi
Manzini