Discussion Paper No. 685

HAS DEMOCRATIZATIONREDUCED INFANT MORTALITYIN SUB-SAHARAN AFRICA?
EVIDENCE FROM MICRO DATA
Masayuki Kudamatsu
The Ninth ISER-Moriguchi Prize (2007)
Awarded Paper

March 2007

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Has Democratization Reduced Infant Mortality in
Sub-Saharan Africa? Evidence from Micro Data∗
Masayuki Kudamatsu†
Department of Economics and STICERD
London School of Economics and Political Science
December 15, 2006

Abstract
Does democracy help babies survive in sub-Saharan Africa? Byusing retrospective fertility surveys conducted in 28 African countries,
I compare the survival of infants born to the same mother before andafter democratization to identify the effect of democracy. In measuring
democracy, I adopt a theoretically motivated definition of democracy:
universal suffrage and contested elections for executive office. I findthat infant mortality falls by 1.8 percentage points, 18 percent of thesample mean, after democratization. The size of the reduction is larger
for babies born to mothers from disadvantaged groups. I also find thatthe replacement of a chief executive by democratization is the driving
force behind these results. Additional evidence suggests that improvements in public health service delivery, not an increase in affluence,
are the key mechanism in which democratization has reduced infantmortality.

∗

I thank Maitreesh Ghatak and Tim Besley for their continuous support and valuablecomments, and Robin Burgess and Oriana Bandiera for helpful comments at various stagesof this research. I am also grateful to Fernando Aragon, Yutaka Arimoto, Dave Donaldson, Ray Fisman, Hiro Ishise, Hisaki Kono, Rocco Macchiavello, Torsten Persson, Steve
Pischke, Jim Snyder, and seminar participants at the 4th Development Economics PhD
Seminar held in University of Namur, at University of Warwick, and at London School of
Economics for helpful suggestions and discussions. Sylvia Meek kindly guided me throughthe public health literature. Data appendices to this paper are available on my website:
http://personal.lse.ac.uk/kudamats/research.htm†
Email address: m.kudamatsu@lse.ac.uk

1

1

Introduction

Does democracy promote development? This question has long attracted
attention from many social scientists. Despite a large number of empiricalstudies on this subject, evidence remains inconclusive because it is difficult
to establish causality running from democracy to development: democracy
is likely to be endogenous to socio-economic factors that also affect development (Lipset 1959). The empirical challenge is to disentangle the effect of
democracy from other confounding factors. This paper revisits this questionin the context of human development in sub-Saharan Africa. Specifically,
I investigate whether democratization sweeping the region in the 1990s has
reduced infant mortality, by using a cross-country micro panel dataset covering 28 countries in the region.
How to confront underdevelopment in sub-Saharan Africa is one of themost important questions in economics today. Economists, however, haveso far ignored one important change that the world’s poorest region recently
experienced: a wave of democratization in the 1990s. By the end of 2000,
among the 48 countries in sub-Saharan Africa “[o]nly Congo-Kinshasa, Eritrea, Rwanda, Somalia, Swaziland, and Uganda held no multiparty elections
whatsoever.”1 Much has been discussed, by political scientists and Africanists, on what caused democratization in Africa and whether new democracies
in the region will be consolidated.2 Very few, however, pay attention to how
this political change has affected the lives of people in sub-Saharan Africa.3
Perhaps because of this, the pessimism on the quality of African governmentis deeply entrenched in any debate on African underdevelopment. The longstanding question of whether democracy promotes development, therefore,
gains additional importance in sub-Saharan Africa.
As a measure of development, this paper focuses on infant mortality,
defined as death within the first year of life. The survival of infants remains
a huge concern in sub-Saharan Africa today. Figure 1 plots infant mortal1

Van de Walle (2002), p.67. See also Bratton (1998) and Lindberg (2003). Rwanda
then held multiparty elections in 2003, and Congo-Kinshasa in 2006. Uganda has been
holding multi-candidate elections since 1996.
2
One exogenous factor that contributes to democratization in Africa in the 1990s is
the end of the Cold War. The news of the collapse of communist dictatorships encouraged
Africans to protest against non-democratic regimes. Western donor countries became
reluctant to provide development assistance to African countries unless democracy was
introduced. These changes often forced African dictators to accept the introduction ofmultiparty elections.
3
An exception to this is Stasavage (2005), who looks at whether democratization in
Africa has improved primary school attendance.

2

ity rates per 1,000 live births by developing region over time. Sub-SaharanAfrica has been lagging behind other regions in reducing infant mortality,
since 1980 in particular, with more than one in ten babies still dying before turning one year old in 2000. In addition, focusing on infant mortality
has a methodological advantage: unlike other socio-economic outcomes suchas personal income, the data at the individual level across many countries
over a long period of time is available from the retrospective fertility survey
component of the Demographic and Health Surveys (DHS), conducted in
28 African countries after the mid-1990s. In these surveys, women of childbearing age report when their children were born and whether, and when
(if applicable), they died. As surveyed women in Africa give birth to fourchildren on average during their lifetime, I observe a sizable number of mothers having babies both before and after democratization in the 1990s. This
characteristics of the data allows me to identify the effect of democratization
by exploiting within-mother variation in the survival of babies, instead ofcross-country or within-country variation. As a result, the estimated effectof democratization on infant mortality is robust to a possibility that changes
in the composition of the population over time (e.g. overall education level)
drive both democratization and changes in infant mortality with no direct
relationship between the two.
The DHS surveys also provide information on socio-economic characteristics of interviewed mothers, including their educational attainment andethnicity. Using this information, I check if the effect of democracy is larger
for babies born to disadvantaged groups of mothers such as those uneducated or those whose ethnicity is different from the former dictator’s. The
fundamental idea of democracy is to give voice to every citizen in a country
whereas typically only a few people can influence policy-making under dictatorship. The effect of democratization will therefore be larger for groupsof people who are otherwise excluded from political process. The use ofindividual-level data allows me to see if this argument holds in reality.
Last but not least, any discussion regarding democracy faces a thorny
issue of what constitutes democracy. In this paper, I define democracy asa political institution satisfying the following two conditions: (1) the chief
executive of the government has been elected in multiparty elections with
universal suffrage, without subsequently banning opposition parties and (2)the first multiparty election brings in a new chief executive to office. Political economy models predict that the combination of contested elections anduniversal suffrage — features of democracy underlying the first condition— will provide an incentive for the government to implement public healthinterventions to combat infant mortality. Several additional arguments sug3

gest that contested elections and universal suffrage do not make a differencein policy-making unless the second condition is also satisfied. To identify
the year of democratization defined this way, I originally collect informationon these requirements for a country to be democratic. I check how results
differ when I drop the second requirement in the definition above or rely on
widely-used democracy indicators to define democracy instead. I also check
whether leadership without democratization reduces infant mortality. By
doing so, I try to make some progress in our understanding of what featuresof democracy, and what combination of them, contribute to development.
My findings are as follows. Democratization has reduced infant mortality
in sub-Saharan Africa by 1.8 percentage point, roughly equivalent to what
the region as a whole has achieved over the past two decades (see Figure1). The effect of democratization emerges immediately and becomes larger
over time. A sizable portion of this reduction comes from a fall in neonatal
mortality, the number of deaths within the first month of life. The effects
of democratization on both infant and neonatal mortality are larger forbabies born to uneducated mothers and mothers who do not share theirethnicity with the dictator who ruled the country until democratization.
There is no such reduction in infant mortality in countries where the dictatorholds multiparty elections and stays in power by winning them. When theyear of democratization is identified from widely-used democracy indicators,
estimation results suggest that such measures of democracy may be subject
to measurement error.
Additional evidence suggests that maternal health care provision to uneducated mothers has expanded since democratization, consistent with thefinding that democratization has reduced neonatal mortality, especially forbabies born to uneducated mothers. The breastfeeding practice has spread
after democratization, which can explain the immediate effect of democratization. Access to better sanitation facilities has also expanded, consistent
with the growing effect of democratization over time. On the other hand,
there is no evidence for an increase in affluence after democratization. Consequently, the key mechanism in which democratization has reduced infantmortality in sub-Saharan Africa is likely to be improvements in public healthservice delivery, not in overall living standards.
This paper contributes to the large empirical literature that tries to
identify the effect of democracy on development or other socio-economic
outcomes. I am not aware of studies using micro panel data to estimate
the effect of democracy. Most studies focus on economic growth as an outcome variable. As the source of identification, early studies reviewed inPrzeworski and Limongi (1993) hinge on cross-country variation while re4

cent studies rely on within-country variation (Papaioannou and Siourounis
2004 and Rodrik and Wacziarg 2005). Others look at the effect of democracy on manufacturing wages (Rodrik 1999), child immunization (Gauri andKhaleghian 2002), socio-economic policies (Mulligan, Gil, and Sala-i-Martin
2004), and life expectancy at birth (Besley and Kudamatsu 2006). Ross
(2006) studies the effect of democracy on infant mortality at the country
level, finding no association between the two. This finding may, however, be
subject to confounding factors at the country level.
As an attempt to disaggregate a blunt concept of democracy in the estimation of its effect, this paper is also related to works summarized in Persson
and Tabellini (2006). Finally, this study contributes to recent debates on
how to improve public service delivery in poor countries (e.g. World Bank
2003; Banerjee and Duflo 2006; Chaudhury, Hammer, Kremer, Muralidharan, and Rogers 2006).
The rest of the paper is organized as follows. The next section discusses how I measure democracy in sub-Saharan Africa and describes theindividual-level data on infant mortality. Section 3 describes empiricalmethod, reports main results, checks their robustness, and investigates what
type of democracy matters for infant mortality. Section 4 provides evidence
for possible pathways from democratization to the reduction of infant mortality. Section 5 concludes.

2

Background and Data

To estimate the effect of democratization on infant mortality at the individual level, we need to decide how to measure democracy and to obtain micro
data on the survival of babies. In this section, I first discuss how I measuredemocracy and, based on this measure, show how democracy has evolved
in African countries in the sample. Then I describe the Demographic and
Health Surveys from which I obtain micro data on infant mortality.

2.1

Measuring Democracy in Africa

Democracy is a multi-faceted political institution. Some features of democracy may affect a certain outcome while other features may not. To identify
the effect of democracy, therefore, we need to choose the appropriate definition of democracy depending on what outcome we study. In this paper,
I define democracy as a political system in which (1) the chief executive of
the government has been elected in multiparty elections with universal suffrage without subsequently banning opposition parties and (2) a new chief
5executive assumed office after the first multiparty elections. Below I explain
theoretical motivations behind these two conditions for democracy.
The key concepts underlying the first condition are contested elections
and universal suffrage. The political economy literature provides at least
two reasons for which public health interventions to reduce infant mortality will be implemented after the introduction of contested elections with
universal suffrage. First, the median voter theorem, combined with the assumption that public health interventions are the policies the majority of all
citizens prefer, predicts that contested elections with universal suffrage makepoliticians propose public health policies to assume office. Under universal
suffrage, candidates proposing policies favored by the majority of the population will win elections.4 Second, under the assumption that public healthinterventions are the provision of public goods, the model of distributive politics indicates that implementing public health interventions is less costly forpoliticians to win the majority of votes than distributing private goods toeach voter, because under universal suffrage politicians need to appease alarge number of people to win elections.5 These two arguments suggest that
contested elections and universal suffrage are complements. If suffrage islimited to a certain segment of the population, contested elections alone do
not ensure that politicians propose policies favored by the majority of the
population or provide public goods to appease a large number of people, because they can still win the elections without doing so. Likewise, universal
suffrage alone does not lead to the implementation of these policies because
politicians will stay in office anyway by “winning” non-contested elections.
There are reasons to believe that public health policy to combat infant mortality was one of the policies the majority of the population preferin sub-Sahara African countries in the early 1990s, when democratization
took place (see below). In 1990, only 33% of the population had accessto improved sanitation facilities in sub-Saharan Africa (World Development
Indicators 2005 ). Only 40% of births in the region in 1990 were attended byskilled health personnel (Table 2 in UNICEF 2006).6 These figures suggestthat the provision of sanitation facilities and skilled child delivery care would
benefit the majority of the population in the region in the early 1990s.
Public health interventions to reduce infant mortality also have a public
good component in sub-Saharan Africa because a large number of child4

Acemoglu and Robinson (2000) develop a model of democratization based on thisidea.
5
See Bueno de Mesquita, Morrow, Siverson, and Smith (2002) and Lizzeri and Persico(2004) for this line of arguments.
6
See section 4 for how these two public health interventions affect infant mortality.

6

deaths in the region are caused by infectious diseases. According to estimatesprovided by Murray and Lopez (1996, Appendix Table 6f), the following
four infectious diseases together account for about 65 percent of 4.03 million
deaths of children aged under 5 years in sub-Saharan Africa in 1990: diarrhea(20%), lower respiratory infections (e.g. pneumonia) (18%), malaria (15%),and measles (12%).7 Providing preventive measures and treatments against
these diseases, therefore, benefits all infants living in the same area.8
The argument has so far indicated that politicians need to be elected incontested elections with universal suffrage so that democratization leads to a
reduction in infant mortality. Further considerations refine this condition inthree ways. First, contested elections must be multiparty elections. It may
be difficult for opposition candidates without the support of political parties
to defeat the incumbent, undermining the incentive for the government toadopt policies favored by the majority or to provide public goods. This isparticularly important in the parliamentary system of government. Second,
the political agency model (Barro 1973; Ferejohn 1986) suggests that, if thechief executive elected in multiparty elections bans opposition parties, heor she loses an incentive to implement policies favoured by the majorityof citizens. Therefore, opposition parties need to be legal to exist after
multiparty elections are held. Finally, the political office that is filled via
multiparty elections must be an effective one in policy-making. Otherwise
multiparty elections do not bring about policy change. The literature on
African politics indicates that such political office is the chief executive.9
The second condition for a country to be democratic — the replacementof a chief executive — is an equilibrium outcome rather than the rule of
the game. There are, however, several reasons to believe that leadershipturnover is necessary for contested elections with universal suffrage to haveany bite.
If a dictator decides to introduce multiparty elections in which he intends
to run as a candidate and actually wins, he does so because he knows hecan win. This may be due to his popularity relative to potential opposition
7

Estimates for infant death (i.e. death during the first year of life) are not available.
In 1990, HIV accounts for only 1.5 percent of child deaths in sub-Saharan Africa.
8
Mani and Mukand (2006) offer another possible explanation for why health policies
may change after democratization. Outcomes caused by health policies may be easier for
voters to observe than, say, economic growth at the country level. This observability ofhealth outcomes for voters creates an incentive for policy-makers who seek re-election toprioritize health issues over others. This effect does not emerge under non-democratic
policy-making.
9
For example, van de Walle (2003, p.310) notes that “power is highly centralized aroundthe president”.

7

candidates or due to his capability of rigging the votes. As a result, he does
not need to change his policy to win multiparty elections. (See Przeworski
et al. 2000, pp. 23-8, for a similar argument.)
The second reason is that the identity of political leaders matters forpolicy-making.10 Democratization may bring about change in government
policy only if it replaces policy-makers with those whose policy preferenceis different from their predecessor.
Yet another reason can be given based on Acemoglu and Robinson(2006). They argue that democratization does not lead to change in economic institutions if the elite can intensify their influence on policy outcomesthrough what they call de facto political power, such as lobbying. This
argument may apply to health policies as well. Change in political leadership after democratization may increase the cost of lobbying for the elite as
they need to cultivate personal connections with new political leaders fromscratch. As a result, the cost of intensifying political influences outweighsits benefit, allowing democratization to bring policy change.
Whether or not these arguments hold true is, however, an empiricalquestion. In section 3.4, I investigate whether the replacement of a chief
executive is necessary for democratization to have an impact on infant mortality.
As none of the existing democracy datasets collect all of the requirements
for democracy discussed above, I need to originally create the measurementof democracy.11
For each of the 28 sub-Sahara African countries for which the DHS surveys were conducted since 1996 (see section 2.2 below) and for the beginningof each year since independence or 1950, I collect information on who thechief executive of the government is, whether the chief executive in office(or national legislature if a country adopted the parliamentary form of gov10

There is a growing number of empirical studies that support this idea. See Pande(2003); Chattopadhyay and Duflo (2004); Lee, Moretti, and Butler (2004); Jones and
Olken (2005); and Besley, Persson, and Sturm (2005).
11
The dataset that is most closely related to the purpose of this study is the one constructed by Przeworski et al. (2000) for the period from 1950 to 1990. There are, however,two differences. First, Przeworski et al. (2000) do not require universal suffrage to qualify
a country as democratic. Second, they require multiparty elections not only for executive
office but also for legislature. If their dataset covered the period up to present, I would only
need to collect information on universal suffrage because Przeworski et al. (2000) provide
information on multiparty elections for executive office and for legislature separately. Boixand Rosato (2001) and Cheibub and Gandhi (2004) update Przeworski et al. (2000)’s data
to more recent years, but they do not provide such disaggregated information as necessary
to fit the purpose of this study.

8ernment) has been elected in multiparty elections with universal suffrage,
and whether opposition parties are legal. I consult Nohlen et al. (1999) and
Africa South of the Sahara (London: Europa Publications, various issues)
for collecting such information.12
This coding procedure reveals the following pattern of the evolution of
democracy in the 28 countries. Among 23 countries that became independent by the 1960s, 13 were democratic at independence. All these countries,
however, experienced the collapse of democracy either by a military coupor by the banning of opposition parties by the early 1970s. Around 1980,
three countries (Ghana, Nigeria, and Uganda) became democratic, but allof them saw military coups toppling democratic governments by the mid1980s. During the 1990s, 11 countries were democratized (see column (1) in
Table 1 for the list of these democratized countries).
I exploit these 11 episodes of democratization during the 1990s to estimate the effect of democracy in the following analysis.13 I create a dummy
variable for democratization which is equal to one for years after the yearof democratization in these 11 democratized countries (listed in column (1)of Table 1). This ensures that the democratization dummy is equal to one
only if all babies born in a given year are exposed to democracy.14
For the consistent estimation of the effect of democratization, the 11
democratized countries must be comparable to the other 17 countries interms of determinants of infant mortality. Figure 2 shows the geographical distribution of democratized countries during the 1990s. It reveals that
democratized countries are not concentrated in a particular region, which
ensures the comparability of democratized and non-democratized countries
12

See the Data Appendices A and B (available on my website) for more details on themeasurement of democracy.
13
Namibia and Zimbabwe became independent in 1990 and 1980, respectively, with thechief executive elected in multiparty elections with universal suffrage. Opposition parties
have been legal in both countries until present. However, it is impossible to disentangle
the effect of democratization from that of independence in these two cases. Therefore, I
treat the two countries as non-democratized ones by dropping the sample before the yearof independence.
14
There are three countries in which democratization was followed by coups within
the sample period (Comoros, Lesotho, and Niger). I do not treat these cases differently
for two reasons. First, in each case, a fresh multiparty election for executive office immediately followed (often due to international pressure). Second, treating these cases
differently could cause the selection bias: the remaining “permanent” democratizations
are permanent because they are successful in bringing about benefits to the population
while “broken-down” democratizations broke down because they failed to do so. Limiting
attention to only those “permanent” democratization may cause an upward bias in theestimation of the beneficial effect of democratization.

9

in terms of geographical factors. Columns (1) and (2) in Appendix Table A1 show how comparable the two groups of countries are in terms ofcountry-level variables that are likely to be associated with infant mortality.
Differences in the means of these variables are never statistically significant
at the conventional level.

2.2

Micro Data on Infant Mortality

The data on infant mortality at the individual level is obtained from the Demographic and Health Surveys (DHS), conducted by ORC Macro in various
developing countries since the late 1980s with funding from the U.S. Agency
for International Development (USAID).15 The DHS questionnaire consistsof standardized components and country-specific ones. Using the standardized part of the questionnaire allows researchers to compile cross-country
micro datasets.
In each DHS survey, a nationally representative sample of women ofchild-bearing age (15 to 49) are interviewed about the survival of almost all
the children they gave birth to in the past, including those who died by thetime of the interview.16 From this recall data, I construct a panel dataset ofmothers where the time dimension is the year of child birth given by eachmother. Therefore, as long as at least one round of survey was conductedin a country, a panel dataset of mothers is available for that country.17
To investigate the effect of democratization on infant mortality in subSaharan Africa, I select 28 DHS surveys, one for each sub-Sahara Africancountry, conducted since 1996.18 If there are more than one surveys available in a country during this period, I select the latest survey to maximize15

See www.measuredhs.com (the DHS survey website) for more details and for downloading data files. The DHS data is widely used by demographers and public healthresearchers. Economic research using the DHS data includes Pitt (1997), Dow, Philipson,and Sala-i-Martin (1999), and, most recently, Young (2005).
16
The maximum number of children for each interviewed mother in the dataset is 20.
In the sample, however, there is only one mother giving birth to more than 20 children.
17
An issue with the recall data is its accuracy. The DHS survey interviewers conduct
a number of probes to ensure the quality of birth history data based on interviewees’
memory. See page 14 of ORC Macro (2006).
18
I thank Bernard Barrere for granting access to Mauritania DHS survey results. The
28 countries in the sample are fairly representative for the whole of sub-Saharan Africa.
Columns (3) and (4) in Appendix Table A1 compare the means of various country-levelvariables that are likely to be associated with infant mortality between the 28 sample
countries and all the countries in the region. There is no systematic difference between
the two.

10the number of post-democratization years covered in the sample.19 See Appendix Table A2 for the list of surveys used in the analysis.20
The 28 DHS surveys provide a sample of 693,495 children born to 171,110
mothers with the year of birth of children spanning from 1958 through 2005.
From this, the following observations are dropped: (i) children born before
the year of independence of their country and (ii) children born within 12
months before their mother’s interview. Children of type (i) are excluded
in order to prevent the effect of colonial rule from muddling the comparisonof dictatorship and democracy. Children of type (ii) might have died before
reaching the age of one even if they were alive at the time of their mother’sinterview, which results in measurement error. Dropping these observations
results in the base sample of 643,846 children born to 161,876 mothers withthe year of birth of children spanning from 1960 and 2004.
To measure individual-level infant mortality, a dummy for whether achild dies before turning the age of one year is constructed. As the literaturesuggests that determinants of infant death within the first month of life
(known as neonatal mortality) differ from those for the rest of the first yearof life,21 a dummy for whether a child dies before turning the age of one
month is also constructed. These two dummy variables are the dependent
variables in the following analysis. Other characteristics of babies used in
the analysis below are their sex, whether or not they are born in multiple
birth (i.e. twins, triplets, etc.), their birth order, the age of their motherat their birth, the preceding birth interval (how many months have passed
when they are born since their mother gave birth to the previous child), theownership of consumer durable goods by their household at the survey date,
how many years their mother has lived in the surveyed community when she
is interviewed, the level of their mother’s education, the area of residence(urban or rural), and their mother’s ethnicity.
Note that, due to the nature of retrospective data, children born to themothers who are dead or not eligible to be surveyed (i.e. 50 years old orover) at the time of the survey are missing in the sample. If the impact of
democratization is systematically different between these missing children
19
One exception is Senegal, for which I use the 1997 survey instead the latest survey in
1999, because the 1999 survey data is not recoded and the codebook is written in French.
20
Five more African countries (Botswana, Burundi, Central African Republic, Liberia,
and Sudan) conducted the DHS survey before 1996. Given that democratization in Africa
took place mostly in the early 1990s, however, these surveys are not useful to investigate
the effect of democratization. In addition, Eritrea conducted the DHS surveys in 1995
and in 2002. However, access to survey results is restricted and I have not managed togain permission.
21
See, for example, Razzaque, Alam, Wai, and Foster (1990).

11and those in the sample, all the results in this paper will suffer from sample
selection bias. However, there is no a priori reason for why the impact of
democratization differs between these two types of children.

2.3

Summary Statistics

Table 2 shows summary statistics for variables used in the analysis. Column (1) provides sample means for all countries in the sample; column (2)for babies born in democratized countries until the year of democratization;column (3) for babies born in democratized countries after the year of democratization; column (4) for babies born in non-democratized countries.
The infant mortality rate is 10 percent of live births on average for all the
28 countries in the sample. The neonatal mortality rate is 4.7 percent, indicating that nearly half of infant deaths occur within the first month after
birth.
Figure 3 plots sample mean infant mortality rates by year for democratized and non-democratized countries. It reveals that non-democratizedcountries consistently have lower infant mortality rates than democratizedcountries, with the gap widening in the early 1980s. This gap, however,dramatically disappears by the mid-1990s, when most of the 11 democratization episodes already took place. Figure 3 also shows that infant mortality
has been on the decline for both groups of countries with non-democratizedcountries having a steeper downward trend. This difference in the trend
between the two groups of countries will bias the democracy coefficient upwards, going against the finding that democratization has reduced infantmortality.
Figures 4 and 5 show sample mean infant mortality rates by year foreach country. While the overall infant mortality has been on the decline asshown in Figure 2, each country exhibits its own trend in infant mortality.

3

Empirical Analysis

3.1

MethodTo investigate whether democratization has reduced infant mortality, I estimate the following equation:yimct = αm + βt + γDct + δc T REN Dct + Ximct θ + εimct ,(1)

where yimct is a dummy equal to one if baby i who is born to mother m incountry c in year t dies before reaching the age of one year (or one month if
12the outcome of concern is neonatal mortality), αm is a mother fixed effect,and βt is a birth-year fixed effect. Dct is a dummy variable equal to one if
country c is democratized by the beginning of year t. The term δc T REN Dct
represents a linear time trend specific to country c.22 Ximct is a vector of
exogenous covariates. In the base regression, Ximct includes dummies forbaby girls and for multiple birth (i.e. twins, triplets, or quadruplets). The
sex of babies may affect their survival if the return to raising children for
their parents is different between boys and girls.23 Multiple birth results inan unexpected reduction in the amount of available household resources per
child, which may increase the likelihood of infant death.24 Although there
is no a priori association between democratization and the likelihood thatfemale babies or twins are born, controlling for these exogenous characteristics of babies reduces the error variance, and thus increases the precisionof estimation of coefficients of interest. Standard errors are clustered at thecountry level to take into account any arbitrary correlations of the error
term εimct across babies born in country c in year t and over time in countryc.25
The parameter of interest, γ in equation (1), measures the average difference in changes in the probability of the death of babies born to the same
mother between those countries that are democratized and those that arenot. Under the assumption that, after controlling for mother fixed effects,
country-specific linear trends, and exogenous covariates, changes in infantmortality in non-democratized countries provide a counterfactual for democratized countries (i.e. changes in infant mortality that would occur if there
were no democratization), γ represents the effect of democratization on infant mortality. Specifically, the error term, εimct , must satisfy the following
equation:E(εimct |Dc , Dt , αmc , βt , δc , Xmc , Xt ) = 0,(2)where Dc and Xmc are the vectors containing Dct and Ximct , respectively, for22

As there are plenty of observations before democratization (compare columns 1 and 9
in Table 1), linear trends are unlikely to pick up the post-democratization trend (Wolfers
2006).
23
There is mixed evidence on gender bias in infant and child mortality in sub-SaharanAfrica. See Klasen (1996) and references therein.
24
Pison (1992) reports that in sub-Saharan Africa twins are 3 to 4 times as likely to die
within the first year of life as singletons are.
25
I specify the linear probability model. Conditional fixed effects logit estimation yields
similar results in terms of the sign and statistical significance of the estimated democracy
coefficient. However, its consistency requires no serial correlation in the error term, whichis unlikely to hold in the present context (see Zenger 1993, for example). In addition, thecoefficient estimates in fixed effects logit models are difficult to interpret.

13

all t in which mother m gives birth, and Dt and Xt the vectors containing Dct
and Ximct for all c. Due to the presence of mother and year fixed effects as
controls, εimct must be uncorrelated not only with the contemporary status
of democracy Dct but also with the past and future status of democracy when
the same mother gives birth to another child, and with all other countries’
status of democracy in year t.
Unobservable “prerequisites for democracy” plague all empirical studies
that try to identify the effect of democracy. Unlike cross-country regression
analysis, however, unobservable time-invariant characteristics of countries
such as geography, history and culture are not the source of violation of
the identifying assumption represented by equation (2) because the set ofmother fixed effects in each country captures such country fixed effects.
Among time-variant prerequisites at the country level, one of the major
factors that may drive both democracy and infant mortality is the level of
education among adults.26 However, change in the stock of education in theadult population does not affect equation (2) because the effect of mothers’
education on infant mortality is captured by mother fixed effects.27 This is
the main advantage of using individual-level data instead of country-level
data in the investigation of the effect of democracy. In the country-level
regression analysis, controlling for a time-variant measure of education suchas the average years of schooling in the adult population does not help identification because it is correlated with the error term by some lags. For
example, an improvement in child health that also boosts educational attainment (e.g. Miguel and Kremer 2004) leads to an increase in the stock of
education some years later. This breaks down the strict exogeneity assumption on the error term for consistency in fixed effects estimation.
Another time-variant factor that may drive both democracy and infantmortality is income.28 Due to the lack of data on earnings of each mother’s
26

See section 3.3 of Strauss and Thomas (1996) for a survey on the effect of parental
education on child health. For a classical account of education as the main drive fordemocracy, see Lipset (1959). For the latest debate on this issue, see Acemoglu, Johnson,
Robinson, and Yared (2005a) and Glaeser, Ponzetto, and Shleifer (2006).
27
Among babies with their mother’s years of schooling available, there are 2,351 babies
who are not the only child and born before the year of completion of their mother’s study(calculated as the number of years of schooling plus 6, assuming that primary school
begins at the age of 6). There are 6,896 babies whose mother’s years of schooling is notavailable. Therefore, the level of maternal education differs across different babies bornto the same mother only for at most 1.4 percent of the sample observations.
28
See section 3.4 of Strauss and Thomas (1996) for a survey on the effect of income onchild health. A recent empirical investigation of the income-democracy link is found in
Acemoglu, Johnson, Robinson, and Yared (2005b).

14

household over time, this remains a major concern for identification in thisanalysis. In section 3.3, I deal with this issue in a couple of ways.

3.2

Main Results

Table 3 reports estimated coefficients on the democratization dummy byadding controls one by one. Column (1) only controls for year fixed effects.
The democracy coefficient is positive but not significantly different from zero.
Column (2) additionally controls for country fixed effects. The democracy
coefficient becomes negative and statistically significant. Given that nondemocratized countries have lower infant mortality rates on average thandemocratized countries (see Table 2 and Figure 3), the coefficient estimate
in column (1) picks up such cross-country variation, offsetting within country
variation shown in column (2).
Column (3) controls for mother fixed effects instead of country fixed effects. The democracy coefficient becomes larger in absolute terms. This
result indicates that the composition of mothers changes after democratization with their characteristics worsening in relation to infant mortality.
A possible explanation is that women becoming mothers after democratization are less healthy because they were born in the 1980s, when economic
recessions may have impoverished their parents, and grew up with insufficient nutrients. Economic recessions may have led to democratization in
the early 1990s in the democratized countries.29 The country fixed effects
estimation, therefore, picks up such omitted factors, yielding the biased estimate for the democracy coefficient.
Column (4) additionally controls for country-specific linear trends. The
democracy coefficient remains almost the same and statistically significant.
This result shows that the coefficient estimate in column (3) does not reflect
a steeper declining trend in infant mortality in democratized countries thanin non-democratized countries.30
Finally, column (5) adds exogenous covariates (dummies for female babies and for multiple births) in the set of control variables. Democratization
is followed by a reduction in the infant mortality rate by 1.8 percentage
points, which is as much as 18 percent of the sample mean. To gauge the29

Bratton and van de Walle (1997) find that democratization in Africa followed frequent
political protests, which were in turn more frequent the more often the government adopted
structural adjustment programmes in the 1980s.
30
Controlling for quadratic or cubic country-specific trends makes the democracy coefficient estimate smaller and insignificant, suggesting that the effect of democratization is
not a sudden change in infant mortality.

15

magnitude of this fall, recall Figure 1. The 1.8 percentage point decline
roughly corresponds to the fall in infant mortality in the whole of SubSaharan Africa between 1980 and 2000.
Column (6) reports the result for neonatal death in the same specification
as in column (5). The probability that a mother sees her baby die within
the first month of life falls after democratization by 1 percentage point, 21
percent of the sample mean. A sizable portion of the fall in mortality within
the first year of life is therefore due to the fall in mortality within the firstmonth of life.
The democracy coefficient estimates in columns (5) and (6), however,
may not reflect the effect of democracy. Given that democratization in
Africa often followed economic stagnation, it may be the case that infantmortality temporarily went up due to impoverishment of their mothers before democratization. If this is the case, the democratization coefficient isestimated to be negative even if democratization has no impact. Alternatively, infant mortality may begin to drop some years before democratization
due to changes in some socio-economic prerequisites for democracy. We thenspuriously attribute such an effect to the one of democratization. To deal
with these concerns, I estimate the dynamics of infant mortality before andafter democratization. I replace Dct in equation (1) with the set of yearwise dummy variables which are equal to 1 if n years have passed since theyear of democratization, where −5 ≤ n ≤ 3, and another dummy variableequal to 1 if 4 years or more have passed since the year of democratization.31 Figure 6 plots the estimated coefficients on these dummies (see also
Appendix Table A3) and shows the 95 percent level confidence intervals.32
These are interpreted as percentage point changes in infant mortality, relative to changes in non-democratized countries, compared to the period until
6 years before democratization. There is no statistically significant change
in infant mortality until the year of democratization. This result supports
the interpretation of estimated democracy coefficients in columns (5) and(6) of Table 3 as the effect of democratization.
Figure 6 also indicates that infant mortality drops immediately after
democratization and continues to fall subsequently. The immediate fall in
31

I lump together 4 or more years after democratization because three of the 11 democratized countries (Madagascar, Nigeria, and South Africa) have observations only until 3
years after democratization. Therefore, estimated change in infant mortality from three
years after democratization to four or more years after can be due to change in the composition of countries in the sample.
32
This type of analysis is increasingly common in labour economics. See Jacobson,
LaLonde, and Sullivan (1993), Autor (2003), and Wolfers (2006).

16

infant mortality may reflect the promotion of awareness among mothers on
healthy behavior such as hand-washing. The subsequent additional decline
in infant mortality may indicate that health infrastructure such as sanitation
facilities and health clinics are now put in place after a couple of years
of preparation. These interpretations assume that health policy changes
immediately after democratization. If democratic policy-making tends tobe slow because mustering support from the majority of legislators takes
time, this assumption is unlikely to hold. However, given that the chief
executives of government have large discretion over policy-making in Africa,
as the literature on African politics suggests, the immediate change in healthpolicy after democratization is not entirely implausible.
The estimation results have so far concerned the aggregate effects. The
effects of democratization, however, may not be homogeneous among the
population. Two characteristics of mothers are likely to yield heterogeneity
in changes in infant mortality after democratization: their level of educationand their ethnicity. The effect of democratization may be stronger for uneducated mothers, if the democratized government provides knowledge on
health to tackle infant mortality and if educated mothers are already awareof it. Mothers who do not share their ethnicity with the dictator who ruled
the country until democratization may also benefit more from democracy.
Under dictatorship, they may have been excluded from policy-making process. Their desire for the survival of their babies, therefore, may not beheard by the government.33 After democratization, these mothers gain theright to vote in contested elections for executive office. As a result, the government has an incentive to help their babies survive. On the other hand,mothers from the dictator’s ethnic group may enjoy public health interventions even under dictatorship. Consequently, the effect of democratizationon the reduction of infant mortality is likely to be stronger for babies bornto mothers from ethnic groups not in power under dictatorship.34
33
African countries are known to be highly heterogeneous in terms of ethnicity (Easterly
and Levine, 1997), and it is often argued that political leaders favour their own ethnic
groups against the others (see, for example, Bates 1983).
34
The same argument may explain Almond, Chay, and Greenstone (2003)’s finding that
infant mortality among black people converged to the level of infant mortality for white
people in the United States from 1965 to 1971 via improved access to hospitals for black
people. Although Almond et al. (2003) attribute this infant mortality convergence to theprohibition of racial discrimination in hospital care by a U.S. Appeals Court decision in
1963 and Title VI of the 1964 Civil Rights Act, the federal Voting Rights Act of 1965,
which eliminated poll taxes and literacy tests that had effectively disenfranchised black
people (see Besley, Persson, and Sturm 2005), may have also contributed by the logic
described here.

17

The use of micro data allows me to investigate these possibilities. Table
4 reports the estimated impacts of democratization on infant and neonatal
mortality by mothers’ education level and ethnicity. In columns (1) and (2),I interact the democratization dummy with indicator variables for uneducated mothers (those who never went to school) and for educated mothers(those who at least attended primary school). For both infant mortality
and neonatal mortality, it is uneducated mothers who benefit the most fromdemocratization. Babies born to uneducated mothers are less likely to die
within the first year (month) of life after democratization than before by
2.3 (1.2) percentage points. The difference in the estimated coefficients ofdemocratization between uneducated and educated mothers (1.4 percentage point for infant mortality and 0.6 for neonatal mortality) is statisticallysignificant at 1 percent level. To gauge the economic significance of theseresults, note that the difference in the sample average infant (neonatal) mortality between babies born to educated and uneducated mothers is 3.6 (1.9)
percentage points (see column (1) of Table 2). Therefore, after democratization, inequality in health in terms of infant mortality between the twogroups of babies declines by 39 percent. For neonatal mortality, it is a 32
percent fall in the gap.35
The finding that uneducated mothers benefit more from democratization
might be driven by the fact that there are more uneducated mothers in rural
areas than in urban areas and that democratization has corrected what isoften called “urban-bias”. Bates (1981) points out that African governmentsin the 1960s and 1970s — the period when they were more or less autocratic— favoured urban residents against rural ones in terms of the allocationof public resources.36 To explore this possibility, I create four dummies foruneducated mothers living in urban areas, educated mothers in urban areas,
uneducated mothers in rural areas, and educated mothers in rural areas.37
35

These results, however, do not suggest that democratization has no impact for educated mothers. The coefficient on the interaction term between democratization andeducated mothers reflects a difference between educated mothers in democratized countries and all mothers in non-democratized countries. If I restrict the sample to babies bornto educated mothers and estimate equation (1), then the democracy coefficient is significantly negative, suggesting that educated mothers also benefit from democratization.
36
Majumdar, Mani, and Mukand (2004) suggest that even a democracy creates urban
bias: voters in urban areas are better at observing policy outcomes, and thus more responsive to policy change, than those in rural areas, creating an incentive for policy-makers tofavour urban people in the allocation of public resources.
37
Note that areas of residence refer to mothers’ residence at the survey date. For mothers
who migrated from rural to urban areas or vice versa, this results in measurement error.
If I drop such mothers as I do for column (8) of Table 5 (see section 3.3 below), I obtain
similar results.

18

These four dummies are interacted with the democratization dummy.
Columns (3) and (4) report the results. Uneducated mothers benefit
from democratization equally between those living in urban and rural areas, though the estimated coefficient is larger in absolute terms and more
precise for rural mothers. The difference between educated mothers living
in urban areas and in rural areas is also small. An F-test cannot reject
the null hypothesis that there is no difference in the democracy coefficient
between urban and rural areas both for uneducated mothers and for educated mothers. These results indicate that uneducated mothers benefit fromdemocratization irrespective of where they live.38
In columns (5) and (6), I interact the democratization dummy with dummies for the dictator’s ethnic group and the other ethnic groups. To create
ethnicity dummies, from various sources I identify the ethnicity of the dictator who ruled the country until the year of democratization and match
it with the list of ethnic groups in the DHS surveys.39 Ethnic groups thatare different from the former dictator’s benefit more from democratization.
The difference in the magnitude of the democratization effect is statisticallysignificant for both infant and neonatal mortality. On the other hand, theformer dictator’s ethnic group does not see a statistically significant change
in infant and neonatal mortality though the point estimates are negative. A
newly elected chief executive, therefore, does not appear to retaliate against
the ethnic group in power until democratization. In other words, democratization in Africa does not worsen ethnic conflict.40

3.3

Robustness Checks

The previous subsection reveals that infant mortality falls after democratization. This finding, however, does not necessarily reflect the effect of
democratization on public health policy-making, as argued in section 2, because other factors may have changed at the same time as democratization.
38
If I include only urban and rural dummies (without interaction with education), thedifference in estimated coefficients on these two dummies interacted with democratization
is not statistically significant.
39
See the Data Appendix A on my website for details. Nine countries are dropped
from the sample in this analysis because the DHS surveys in these countries do not ask
respondents about their ethnicity.
40
To deal with a concern that there are more educated mothers among the dictator’s
ethnic group and that this drives the result, I estimated coefficients on the interaction
terms of ethnic groups and educational status, like what I did in columns (3) and (4) of
Table 4. I cannot reject the null that coefficients for each type of ethnic groups are thesame across educational status.

19One such factor is mothers’ income. Even though democratization does
not affect any government health policy, mothers may have become richer
after democratization, because democratization, for example, brings about
political stability and hence an increased investment. Then they become
healthier and thus give birth to healthier babies, and/or babies can afford
sufficient nutrient intake, leading to a reduction in infant mortality.
As the DHS surveys do not collect information on earnings by mothersor their household members over time, I cannot directly control for income
at the individual level. An indirect way of controlling for change of mothers’ income over time is to control for real GDP per capita available in the
Penn World Table 6.2. Column (1) of Table 5 reports the estimation result
when the logarithm of per capita real GDP and its one-year lag are controlled for. An increase in per capita GDP seems to reduce infant mortality
concurrently but not one year after, though both coefficient estimates are
not significantly different from zero. The democratization coefficient, on theother hand, remains statistically significant and its size is almost the sameas in column (5) of Table 3. This result suggests that the estimated effectof democratization in Table 3 is not driven by the concurrent move in percapita income at the country level.
Another way of checking the robustness to the income hypothesis is torestrict the sample to those babies born to mothers with no assets at thetime of the interview. As these mothers are poor at the time of the survey,
the only possible trajectories of their personal income in the past are either
that they have always been poor since their first child birth or that theyused to be better off but have become poorer over time. Therefore, the bias
to the democracy coefficient due to unobserved personal income change overtime will be, if anything, upward for this subsample of babies. If we stillsee a significantly negative coefficient on the democracy dummy, then theincome hypothesis is less likely to be an alternative explanation of the result
found in Table 3, at least for babies born to asset-poor mothers.
The DHS surveys ask the possession of the following consumer durables:
radio, television set, refrigerator, bicycle, motorcycle, and car. If a baby’s
mother owns none of these items, such a baby is retained in the sample. Column (2) of Table 5 reports coefficient estimates for this subsample of babies
born to asset-poor mothers. Although it slightly loses precision, the estimated coefficient on the democracy dummy does not change substantially.
This result encourages the interpretation of the results in Table 3 as not
picking up the effect of change in personal income after democratization.41
41

In section 4.3 below, the possibility that growth in personal income drives the results

20Another time-variant factor that may confound the effect of democratization on health policy-making is the incidence of a war. Democratization
may just bring about peace. Therefore, babies no longer die due to battlerelated causes, or the government can now deliver health services for thoseliving in areas previously controlled by rebels. To deal with this concern,column (3) of Table 5 controls for a war dummy which is equal to one if there
is at least 1,000 battle-related deaths per year in a country, obtained fromthe Armed Conflict Database Version 3-2005b (Gleditsch et al. 2002). The
democracy coefficient remains almost the same as in column (5) of Table
3, suggesting that the estimated effect of democracy does not pick up theeffect of peace.
Yet another confounding factor may be foreign aid. Donor countries
may become willing to provide more financial resources for democratizedcountries, but not for non-democratized countries. This increased foreign aid
may simply expand the budget set for policy-makers who are always willingto provide public health interventions to reduce infant mortality irrespectiveof political regimes. Column (4) of Table 5 deals with this concern byadditionally controlling for the amount of disbursed official development
assistance (in million US dollars, 2004 prices) each country receives in eachyear, obtained from the OECD Donor Assistance Committee Database 2005.
The democracy coefficient remains almost the same as in column (5) of
Table 3, suggesting that change in foreign aid does not drive the effect of
democratization on infant mortality.42
Democratization in sub-Saharan Africa took place in the 1990s, when
the HIV epidemic began to spread in the region. Although the spread of
HIV/AIDS is unlikely to affect democratization, the coincidence of timing of
the two in Africa may bias the democracy coefficient estimate. Bobat et al.
(1999) report that about one-third of children born to HIV-infected mothers
become infected as well and that about a third of the infected children die
within the first year of life. A crude calculation suggests that a 10 percent
increase in the HIV infection rate among pregnant women leads to a 1.1
percent increase in the infant mortality rate. It may be the case that theestimated coefficients on democratization reported in Table 3 are biased because mothers who gave birth before democratization subsequently become
infected with HIV and give birth to another child after democratization.
Column (5) of Table 5 controls for United States Census Bureau’s estimates
will be revisited.
42
Controlling for the amount of foreign aid committed to the health sector, water supply
and sanitation, developmental food aid, or emergency food aid (obtained from the OECD
Creditor Reporting System Database 2005) does not change the result, either.

21

of annual HIV-infection rates amongst pregnant women at the country level,
used by Young (2005).43 I fill missing values with zero because the estimates
are missing before the outbreak of HIV infection in the late 1970s and theearly 1980s.44 The HIV infection rate among pregnant women at the country
level is positively correlated with infant mortality though it is not statistically significant. The point estimate for the democracy coefficient as wellas its precision does not change much, indicating that the spread of HIV
infection does not significantly bias the estimation results.
Column (6) in Table 5 deals with another concern for the consistency ofthe estimation of the effect of democracy. As the source of identification of
the democracy effect comes from democratization, not from the collapse of
democracy, ignoring the effects of birth order and the mother’s age at birth
may result in estimation bias. Children of higher birth orders may be more
likely to die because he or she needs to compete with many other children
for household resources.45 Alternatively, children of lower birth orders, especially the first child, may be more likely to die because mothers are notexperienced in child-bearing or because labor tends to be prolonged during
the first birth-giving, predisposing to birth injury and respiratory distresssyndrome of babies. In addition, the demographic literature finds that babies born to very young mothers are associated with higher infant mortality(e.g. Da Vanzo et al. 1983). If babies born later are more likely to survive
irrespective of democracy, I will find a spurious association between democracy and infant mortality. To deal with this concern, I extract information
on birth order and the age of mothers at birth from the DHS surveys. I create dummies for each birth order from the second to the ninth as well as thetenth or higher with the first birth as the omitted category. For the age of
mothers, I create dummies for whether the mother is aged at her child birthin their 20s, in their 30s, or in their 40s with giving birth under the age of
20 as the reference category. Column (6) in Table 5 reports the result after
controlling for these dummies. The democracy coefficient becomes slightly
smaller in absolute terms, suggesting that some of the estimated democracy
effect captures the effect of birth order and mothers’ age. But the size of43

I thank Alwyn Young for sharing the HIV data.
Note that the estimates are missing for Comoros, Madagascar, and Mauritania because
the Census Bureau does not consider these countries as generalized epidemics (i.e. HIV
is firmly established in the general population). I assign a series of zeros to these three
countries. The result does not essentially change if I drop these three countries from thesample.
45
Behrman (1988) finds that parents favour older children in the allocation of nutrientsin rural India.
44

22

the coefficient remains sizable and statistically significant.
Another potentially confounding factor is fertility. After democratization, the government may have launched population control campaign, leading to longer birth spacing. The demographic literature finds that a child
who has a sibling born within the preceding two years is associated withhigher infant mortality (e.g. Hobcraft et al. 1985). Column (7) of Table 5
controls for a dummy equal to one if the preceding birth interval obtained
from the DHS surveys is less than 24 months. Consistent with the literature, short birth spacing is associated with higher infant mortality. The
democracy coefficient, however, remains statistically significant though its
size becomes slightly smaller. This result indicates that the effect of democratization on infant mortality is not totally driven by change in population
control policy.
Finally, the estimated effect of democratization in Table 3 may simply
pick up the effect of migration of mothers to places where better health careprovision is available around the time of democratization. If this is the case,then infant mortality can fall without any change in public health intervention after democratization. To deal with this concern, column (8) of Table
5 restricts the sample to babies conceived in the surveyed community, by
dropping those babies who were born before one year had passed since theirmother migrated to the surveyed community and those babies whose mother
does not provide information on her migration.46 Babies born during thefirst year since their mother’s migration are dropped because their survival
may be affected by poor antenatal care provision during their mother’s pregnancy at the previous place of residence. For this subsample of babies, thedemocratization coefficient is almost the same as the one in column (5) of
Table 3, suggesting that the estimated effect of democratization does notpick up the effect of migration to places with better health care.

3.4

What Type of Democracy Matters?

As discussed in section 2, I measure democratization in a specific way. Then
I find that it has reduced infant mortality. However, the measurement of
democracy in this paper may be a noisy measure of the exact features of
democracy that really drive infant mortality down.
In section 2, I mentioned that it is an empirical question whether the
replacement of the chief executive is necessary for the introduction of con46
As the DHS surveys for Chad, Cote d’Ivoire, and Guinea do not ask any respondent
how many years she has lived in the surveyed community, these three countries are dropped
from the sample.

23

tested elections with universal suffrage to reduce infant mortality. Column(1) of Table 1 lists the year of the introduction of multiparty elections with
universal suffrage for executive office, based on the information I collect.
Non-democratized countries except Namibia, Rwanda, Senegal, and Zimbabwe also introduced multiparty elections in the 1990s. To see if the introduction of multiparty elections for executive office alone is sufficient to
reduce infant mortality, I create a dummy variable which is equal to one
for 13 non-democratized countries after the year of the first multiparty election in the 1990s listed in column (1) of Table 1. Then I estimate equation(1) with this dummy variable included as an additional regressor. Column(1) of Table 6 reports that the multiparty election dummy coefficient is notstatistically different from zero while the democracy coefficient remains significant. The equality of coefficients on these two dummies is rejected at 5
percent level. This result implies that the introduction of multiparty elections per se does not affect infant mortality. The chief executive needs tobe replaced for multiparty elections to bite.
On the other hand, the estimated effect of democratization may pick up
the effect of leadership change per se, irrespective of whether the change isdue to the introduction of multiparty elections or not. To deal with thisconcern, I identify seven episodes of non-democratic leadership change inthe late 1980s and the early 1990s among the 17 non-democratized countries. These changes are either due to a military coup or to the death orvoluntary resignation of a dictator. Then the new chief executives stay
in office until the end of the sample period. I create a dummy variablewhich is equal to one for years after these seven non-democratic changes in
the chief executives. Column (2) additionally controls for this dummy toequation (1). It shows that non-democratic leadership change does not significantly affect infant mortality while the estimate of the democratization
effect remains the same. The equality of coefficients on these two dummies isrejected at 10 percent level.47 This result indicates that the estimated effectof democratization does not solely reflect the effect of leadership change.
The introduction of multiparty elections and the replacement of the chief
executive are complementary in reducing infant mortality.
Columns (3) to (5) of Table 6 check if the estimated effect of introducing
multiparty elections with the replacement of the chief executive captures
the effect of other features of democracy. Column (3) controls for the degree
47

The large standard error for the non-democratic leadership change dummy is due toRwanda: infant mortality went up sharply before leadership change in 1994 (see Figure5). If I drop Rwanda from the sample, the equality of coefficients on the democracy andleadership change dummies is rejected at 1 percent level.

24

of executive constraints taken from the POLITY IV dataset. This variabletakes values from 1 to 7 with 7 as the highest degree of constraint. The
coefficient on this variable is not statistically different from zero while thedemocracy coefficient remains significantly negative.48 This result suggeststhat unlike its effect on property rights enforcement (North and Weingast
1989; Acemoglu, Johnson, and Robinson 2001), constraints on the chief
executive do not matter for infant mortality.
Columns (4) and (5) explore the possibility that the measurement of
democracy in this paper may capture the degree of human rights protection.
Column (4) additionally controls for the degree of civil liberty restriction(scaled from 1 to 7) taken from Freedom House’s Freedom in the World, anannual survey of political rights and civil liberties since 1972. Column (5)controls for two dummies which are equal to one if press freedom is rated
as “Free” and as “Partly Free”, respectively, as oppose to “Not Free” byFreedom House’s Freedom of the Press, an annual survey of press freedom
since 1980. Both columns show that what matters for infant mortality is
not human rights protection or press freedom but the way in which the chief
executive of the government is chosen.49
Finally, Table 7 provides estimation results when I measure the year of
democratization based on widely used democracy indicators: the POLITY2score from the POLITY IV dataset and Freedom House’s Political Rights
Index. These two indicators assign a score (-10 to 10 for the POLITY2score and 7 to 1 for the Political Rights Index) for each country-year by
aggregating different dimensions of democracy.50 An implicit assumption
underlying the construction of these democracy indicators is that each element of democracy is a substitute for each other. This assumption may
not hold true depending on which outcome we expect democracy to havean impact on. Section 2 above, for example, discusses the complementarity of contested elections and universal suffrage in relation to public healthpolicy-making.
Columns (2) to (7) of Table 1 show the list of years of democratizationin the 1990s by using either of these two democracy indicators with different
48
As the POLITY IV dataset does not measure the degree of executive constraints
for year 2004 and for country-years in which a country is occupied by foreign powers, thecentral political authority collapses, or a country is undergoing the transition of a politicalregime, about 32,000 observations are dropped from the sample. Change in the size of thedemocracy coefficient is mainly due to this change in the sample.
49
The smaller size (in absolute terms) of the democracy coefficient in column (5) is due
to change in the sample. The press freedom variables are not available before 1980.
50
Note that Freedom House’s Political Rights Index becomes smaller if a country becomes more democratic. The opposite is true for the POLITY2 score.

25

cut-off points.51 I create a democratization dummy equal to one for years
after the year of democratization listed in Table 1 for each cut-off value ofeach indicator. As both indicators suggest that some countries became nondemocratic after they were democratized in the 1990s, I also create a dummy
variable which is equal to one for years after the collapse of democracy in the
1990s, in order to check if the effect of democratization on infant mortality
persists even after democracy collapses.
Columns (1) to (6) of Table 7 show the estimated coefficients on these
democratization dummies for infant mortality. Two main results emerge.
First, the democracy coefficient becomes statistically significant only when
the cut-off point for a country to be democratic is the most generous (5for the Political Rights Index and 0 for the POLITY2 score). This result
suggests that the minimal level of democracy is sufficient to reduce infantmortality, although what constitutes such a minimal level of democracy
is unclear due to the nature of these democracy indicators. Second, thecoefficient on the collapse of democracy is negative and not statisticallydifferent from the democracy coefficient (except for column 1). This second
result may be interpreted in two ways. Health infrastructure and clinics
brought in place by democratization do not disappear even after the collapseof democracy, at least not so quickly. Another interpretation is that the way
African countries were democratized is different from the way some of themexperienced the collapse of democracy. If some elements of democracy havean impact on infant mortality but others do not, it is natural to see thisasymmetric effect between democratization and the collapse of democracy.
Unlike Freedom House, which makes public disaggregated scores of democracy only from 2006, the POLITY IV dataset does provide disaggregatedindicators of democracy for all years available in the dataset, allowing researchers to aggregate them in a way that is consistent with theory in theirmind. I use variables EXREC and PARCOMP to construct a democracy
dummy that is as close as possible to the definition of democracy adopted inthis paper. Specifically, a country-year is treated as democratic if EXREC
is 7 or 8 and if PARCOMP is 3 or higher. The first condition roughly corresponds to the requirement of multiparty elections for executive office while
the second is largely consistent with universal suffrage and the existence oflegal opposition parties.52 Column (8) of Table 1 lists the years of democ51

When choosing the cut-off points for the POLITY2 score, I follow the literature:score 0 is used by works summarized in Persson and Tabellini (2006); score 4 by Glaeser,
Ponzetto, and Shleifer (2005). Political scientists often regard a country as democratic if
its POLITY2 score is seven or higher (e.g. Epstein et al., 2003).
52
See the Data Appendix A (available on my website) for more details.

26

ratization in the 1990s based on the above criteria. It is very similar to theyears of democratization based on the POLITY2 score being positive though
it completely ignores the degree of executive constraints, one component of
the POLITY2 score. I again create a democratization dummy which is equal
to one for years after these years of democratization as well as the dummy
for the collapse of democracy. Column 7 of Table 7 reports the estimation
result when I use these dummies as regressors. The estimated coefficient on
the democracy dummy is significantly negative, indicating that democratization reduces infant mortality by 1.5 percentage points. The point estimates
for the democracy coefficient in columns (1) to (6) are smaller than thisor the one in column (5) of Table 3, suggestive of attenuation bias due tomeasurement error.

4

Pathways

Estimation results in the previous section show that democratization has
reduced infant mortality in sub-Saharan Africa. In this section, I provide
some evidence on the mechanisms in which democratization has affected
the survival of babies. Table 8 provides summary statistics for dependent
variables used in this section.

4.1

Maternal Health CareOne key mechanism is likely to be improvements in maternal health careprovision. Recall that a sizable portion of the reduction in infant mortality
after democratization comes from a fall in the probability of death within
the first month of life (Columns (5) and (6) in Table 3). The public healthliterature finds that, among others, the following two health interventions
affect the survival of babies in their first month of life: tetanus toxoid injections to pregnant mothers and child delivery assistance by skilled healthprofessionals. The injection of tetanus toxoid transfers immunity against
neonatal tetanus from a mother to her baby in her womb, which has proved
to be effective (see Demicheli, Barale, and Rivetti (2005) for a comprehensive review on evidence from randomized trials).53 Having a skilled birth53
In addition to the direct effect of tetanus toxoid injections, Dow, Philipson, and Salai-Martin (1999) empirically show that, in four sub-Sahara African countries during 1986 to
1994, the birth weight of babies born to mothers who received tetanus toxoid is larger thanthose born to mothers who did not. The birth weight is known as a significant predictorof early childhood survival (e.g. Da Vanzo et al. 1983; Black, Devereux, and Salvanes
2006). Dow et al. (1999) argue that this is because mothers who received tetanus toxoid27

attendant present during labor and delivery ensures clean child delivery and,if necessary, resuscitation of newborns. Clean delivery — keeping clean thebirth attendant’s hands and instruments to cut the umbilical cord — avoids
death from neonatal tetanus, and resuscitation prevents death from birthasphyxia (see Bhutta et al. (2005, Table 20) for a review on available evidence).
The literature also finds that more educated mothers are associated witha higher probability of receiving these maternal health care services, suggesting that the valuation of such services is higher for educated mothers thanfor uneducated ones.54 If democratically elected governments lower the costof health care services, either by charging lower fees, by creating more healthclinics to reduce the transportation cost, or by educating mothers about thebenefits of such services, an impact will be larger for uneducated mothers,consistent with earlier findings that a fall in neonatal mortality is larger forbabies born to uneducated mothers (Column (2) of Table 4).
To see if maternal health care provision is one key mechanism in which
democratization has reduced infant mortality in sub-Saharan Africa, I construct a repeated cross-section sample of live birth episodes for 19 sub-Sahara
African countries where the DHS survey was conducted more than once since
the late 1980s (see Appendix Table A2 for the list of surveys used).55 The
DHS survey asks a nationally representative sample of women aged 15 to
49 about their live birth episodes during the last few (usually five) years.
Each respondent provides information on whether she received tetanus toxoid injections during pregnancy and on who assisted her child delivery. By
treating each live birth episode as a single observation, I create the samples of 235,516 and 285,739 live birth episodes for tetanus vaccination anddelivery assistance, respectively.56 From this I drop live birth episodes forinjections expect better access to vaccination for her child, thus increasing complementary
investments in child health during pregnancy.
54
See, for example, Akin, Griffin, Guilkey, and Popkin (1986) for a cross-sectional evidence from the Philippines.
55
Chad and Cote d’Ivoire are dropped from the sample though two rounds of the DHS
surveys are available, because one of the two surveys for each country lacks informationon the migration of mothers, making it impossible to match babies and mothers fromdifferent rounds of survey by region of residence without an error. For the same reason,
I do not use Zimbabwe 1994 survey. If I include these surveys in the sample by ignoring
the lack of information on migration, the result does not change substantially.
56
I drop Namibia from the sample for tetanus vaccination because its 2000 survey does
not ask whether the respondent received tetanus toxoid injections, leaving only one roundof survey for the country. An additional difference in the number of observations between
the two samples results from the fact that recent DHS surveys collect information on
tetanus toxoid injections only for the last live birth while information on delivery assistance28

women who are visitors to the surveyed community or do not provide information on their migration as well as episodes of live birth before womenof concern migrated to the surveyed community. Dropping these observations ensures that estimated change in maternal care provision is not due tomothers’ migration to areas with better health care services.57 The resulting sample consists of 198,567 and 232,809 observations (including those forwhich outcome variables are missing) for tetanus vaccination and delivery
assistance, respectively, with years of live births spanning between 1982 and2005 inclusive.
In the estimation of changes in the take-up of maternal health care services before and after democratization, I control for fixed effects of mothercategories defined by where mothers live (which administrative regions andwhether urban or rural areas), their level of education (whether they attended at least primary school or did not go to school at all), and theirbirth cohort (the year of birth), in order to make this analysis as comparable as possible to the previous infant mortality analysis.58 Specifically, I
estimate the following equations:yjrct = αr + βt + γDc,t−1 + εjrct ,(3)

for tetanus vaccination, andyjrct = αr + βt + γDct + Xjrct θ + εjrct ,(4)

for delivery assistance, where j refers to a live birth episode, r refers to amother category by area-education-cohort (as described above), t refers to
the year of live birth. αr is a mother-category fixed effect and βt is a birthyear fixed effect. Xjrct is a dummy variable for multiple birth. Standard
errors are clustered at the country level. The democracy dummy, definedin the same way as in the infant mortality analysis above, is lagged one
year for tetanus vaccination because mothers receive tetanus toxoid injections during the pregnancy. I control for the multiple birth dummy fordelivery assistance regression because pregnant women may know they will
give birth to twins before delivery and therefore change their valuation foris collected for all live births during the past few years.
57
In addition, observations for Namibia before independence (1990) are dropped for thedelivery assistance sample.
58
For example, I control for a fixed effect for educated mothers, born in 1970, living
in urban areas of the Ashanti region of Ghana. See Data Appendix C (available on my
website) for how I match administrative regions from different rounds of DHS surveys ineach country.

29

delivery assistance.59 Note that this analysis does not aim to estimate thecausal effect of democratization on the take-up of maternal health care. The
objective is to find a correlation that is consistent with the findings in theinfant mortality analysis.
Table 9 shows the estimation results. In columns (1) and (2), I reestimate equation (1) for neonatal mortality by restricting the sample tothe 19 countries where maternal health care information is available. The
finding that democratization has reduced neonatal mortality, especially forbabies born to uneducated mothers, holds for this subsample of countries.
In column (3), I estimate equation (3). In column (4), I interact Dc,t−1 inequation (3) with dummies for educated and uneducated mothers. Although
the take-up for receiving tetanus vaccination did not significantly go up on
average after democratization, uneducated mothers became more likely to
be vaccinated after democratization by 10 percentage points (significant at
10 percent level), 17 percent of the sample mean for uneducated mothers.
Columns (5) and (6) report estimation results for delivery assistance. Similarly, although the probability of having a skilled birth attendant present
during delivery did not increase significantly after democratization on average, uneducated mothers became more likely to be attended by healthprofessionals after democratization by 7.6 percentage points (significant at
5 percent level), 25 percent of the sample mean for uneducated mothers.
These results suggest that one key mechanism in which democratization
has reduced neonatal mortality, especially for babies born to uneducatedmothers, is improvements in maternal health care provision.

4.2

Breastfeeding and Sanitation

Another key mechanism in which democratization has reduced infant mortality in sub-Saharan Africa may be the promotion of breastfeeding. The
public health literature identifies breastfeeding as being associated withlower infant mortality through the prevention of death from diarrhea andacute respiratory infection (WHO Collaborative Study Team 2000; Arifeen
et al. 2001).60 The effect of promoting breastfeeding on infant mortality islikely to be immediate, compared to other public health interventions. This
immediate impact is consistent with the finding that infant mortality starts
dropping immediately after the year of democratization (Figure 6).
59

I thank Miyuki Horiuchi for pointing this out.
According to Murray and Lopez (1996, Appendix Table 6f), diarrhea and lower respiratory infections accounted for 38 percent of 4.03 million deaths of children under 5 years
old in sub-Saharan Africa in 1990.
60

30

The provision of sanitation facilities may also be a mechanism in which
democratization has reduced infant mortality. The public health literaturefinds that access to sanitation facilities is associated with a lower child mortality due to diarrhea.61 Death from diarrhea is estimated to account for 20
percent of under-5 child mortality in sub-Saharan Africa in 1990 (Murrayand Lopez 1996, Appendix Table 6f).
To investigate whether breastfeeding is more likely to be practiced after
democratization, I use the same repeated cross-sectional sample of live births
episodes as the one used for delivery assistance. The DHS surveys ask the
respondent if she ever breastfed each of her children born during the last
few years. From this information, I construct a dummy variable equal to
one if the mother ever breast-feeds her baby. To assess whether sanitaryconditions for infants are improved after democratization, I create a repeatedcross-sectional sample of babies born within the past one year before the
DHS survey was conducted, including those who died before the survey, andmatch each baby with information on their household’s access to toilets.62
The original sample includes 66,244 babies in total. From this, I drop thefollowing observations: babies whose mother is a visitor to the surveyed
community; babies born within the first 12 months after their mother moved
to the surveyed community; and babies whose mother does not provide
information on how many years she has lived in the surveyed community.
Dropping these observations ensures that estimation results reflect change inpublic health service delivery in the same place over time. The final sample
consists of 57,634 observations (including those for which information onaccess to toilets is missing) with survey years spanning between 1987 and2004 inclusive.
For breastfeeding, equation (4) is estimated with Xjrct including not only
the multiple birth dummy but also the girl dummy, because mothers maychange their breastfeeding practice depending on the sex of their babies. For
access to toilets, I estimate the following equation:
yircs = αr + βs + γDcs + εircs ,61(5)

See Esrey et al. (1991) for a survey of available evidence.
By “toilets” I mean the term “flush toilets” in the DHS surveys. According to acommentary on the DHS model questionnaire (Institute for Resource Development andMacro International 1990, p.6), the term flush toilet is defined as “a facility where thetoilet is separated from the refuse disposal system by a water seal.” This definition of
flush toilet “does not distinguish between whether the water seal is maintained by water
dumped from a bucket or a plumbing system or whether the disposal system is a pit, septictank or public sewer system.” To avoid the misinterpretation of the term flush toilet, I
use the word “toilets” in this paper instead.
6231

where i refers to babies born within one year before the survey date, r refers
to mother categories by area-education-cohort (as described above), and srefers to five-year spells (1985-89, 1990-94, 1995-99, 2000-04) in which the
DHS survey was conducted. αr is a mother-category fixed effect and βs isa five-year period fixed effect (the DHS survey is usually conducted every
fifth year with the exact year of survey different from country to country).
The democracy dummy, Dcs , is set to be one if country c is democratized
before the DHS survey was conducted during spell s.63 As each country
has conducted at most four surveys, the standard errors are clustered at thecountry-year level because its underestimation due to serial correlations in
the error term is likely to be negligible. Again note that these regressions
do not intend to establish the causal effect of democratization on outcome
variables. The aim is to see if there is a correlation between democratizationand improvements in health inputs that is consistent with the finding that
democratization has an negative impact on infant mortality.
Table 10 shows estimation results. Columns (1) and (2) re-estimate equation (1) for infant mortality for the subsample of the 19 countries with morethan one DHS surveys conducted. They show that the main results holdfor this subsample. Column (3) estimates equation (4) with the breastfeeding dummy as the dependent variable. The probability that a mother everbreast-feeds her child goes up by 1.1 percentage points after democratization(significant at 5 percent level). The magnitude of this change is not verylarge, however, because the sample mean probability of breastfeeding is 97.5
percent. Therefore, the promotion of breastfeeding alone does not seem toexplain the whole immediate effect of democratization on infant mortality
found in Figure 2. It may be that other health practices beneficial for thesurvival of babies are also promoted along with breastfeeding. Column (5)reports the result for access to toilets. After democratization, the probability that an infant’s household has access to toilets goes up by 1.6 percentage
points (significant at 5 percent). The size of this increase is large relative
to the sample mean access rate of 4.9 percent. Columns (4) and (6) showthat the size of changes in breastfeeding practice and access to toilets is
not significantly larger for uneducated mothers than for educated mothers.
For access to toilets, however, this result can be still consistent with thelarger reduction in infant mortality for babies born to uneducated mothers
because there is some evidence that maternal education and sanitary facil63

Consequently, for example, Dcs is zero for Niger during 1990-94 even though its yearof democratization (1993) falls into the same five-year spell, because the DHS survey was
conducted in 1992.

32

ities are substitutes: Using data collected in Malaysia in the 1970s, Esrey
and Habicht (1988) report that the effect of having toilets on a reduction in
infant mortality is larger for illiterate mothers.64

4.3

Affluence

Finally, I provide evidence that the effect of democratization on infant mortality is unlikely to be due to an increase in affluence of mothers. Using
the repeated cross-sectional sample of infants used for estimating changes
in access to toilets, I check if an infant’s household is more likely to owneither of the six consumer durables (radio, television, refrigerator, bicycle,
motorcycle, and car) or to have access to electricity after democratization.
Table 11 reports results from estimating equation (5) with dummies forowning each of the consumer durables or having access to electricity as thedependent variables. Columns 1 to 6 show that there is no evidence that an
infant’s household is more likely to own any of the six consumer durables
after democratization than before. If anything, an infant who was born toan uneducated mother is less likely to live in a household with a television
set and a refrigerator after democratization (columns 2 and 3 of panel B). If
the ownership of a television set and a refrigerator signifies affluence, uneducated mothers have got poorer since democratization. If infant mortality
is primarily determined by affluence, this finding cannot explain the earlier
result that babies born to uneducated mothers are more likely to survive after democratization. This result also suggests that the estimated increases
in access to toilets and in the take-up of maternal health care services byuneducated mothers after democratization are unlikely to be a result froman increase in affluence. Column 7 reports that there is no change in accessto electricity before and after democratization, suggesting that democratically elected governments prioritized public health interventions over theelectrification of households.
64

Barrera (1990) finds an opposite result for child’s height for age: the effects of maternal education and toilet facilities are complementary. Looking at the result (Table 4,column 5) carefully, however, the type of toilet facilities and its interaction with maternal
education does not enter significantly for height for age of children aged under 2 years —the most relevant health outcome to my analysis. The reason for this is probably because
the absence of excreta and its interaction with maternal education are included as regressors. Coefficients on these two variables show that sanitation and maternal education are
substitutes, a result in line with Esrey and Habicht (1988).

33

5Conclusions

By comparing babies born to the same mother before and after democratization, I find that democratization has reduced infant mortality in sub-SaharanAfrica. Various pieces of evidence, taken together, may suggest the following
story. After a new chief executive assumes office by winning contested elections under universal suffrage, the government immediately starts promoting
practices beneficial for the survival of babies, including breastfeeding. Infantmortality starts dropping. The government also makes maternal health caremore accessible. Uneducated women start using it, resulting in a reduction
in neonatal mortality, especially for babies born to uneducated mothers.
Finally, the government starts investing in the provision of better sanitation facilities. Its effect materializes a couple of years after democratization,
pushing infant mortality further down.
These empirical findings convey a powerful message: democratization
may be an important means to improve the quality of government and topromote development in some of the poorest countries in the world. To
corroborate this implication, future empirical research needs to examine
other outcomes and other developing regions. These studies will benefit
from exploiting cross-country individual-level panel data on outcomes as
this paper does. Otherwise we cannot disentangle the effect of democracy
from country-level confounding factors.
More generally, the use of micro panel data can also be applied to estimate the effects on development of other national political institutions
and country-wide political events, including decolonization and leadershipchange under dictatorship. Such studies promise to be a fruitful directionof empirical research in the political economy of development.

34

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“Sustained Effects of the 1974-5 Famine on Infant and Child Mortality
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[67] Rodrik, Dani. 1999. “Democracies Pay Higher Wages.” Quarterly Journal of Economics, 114:3, pp. 707-38.
[68] Rodrik, Dani and Romain Wacziarg. 2005. “Do Democratic Transitions
Produce Bad Economic Outcomes?” American Economic Review, 95:2,
pp. 50-55.
[69] Ross, Michael. 2006. “Is Democracy Good for the Poor?” American
Journal of Politica Science, 50:4, pp. 860-74.
[70] Stasavage, David. 2005. “Democracy and Primary School Attendance:
Aggregate and Individual Level Evidence from Africa.” Unpublished
Paper.
[71] Strauss, John, and Duncan Thomas. 1996. “Human Resources: Empirical Modeling of Household and Family Decisions.” in J. Behrman and
T. N. Srinivasan. (eds.) Handbook of Development Economics, Volume
3A. (Amsterdam: Elsevier). pp. 1883-2023.
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Deliverywww.childinfo.org/areas/deliverycare (accessed on 20th2006).

Care.”
August[73] van de Walle, Nicolas. 2002. “Africa’s Range of Regimes.” Journal of
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42

Infant Mortality per 1,000 live births

180

Sub-SaharanAfricaMiddle East &North Africa
South Asia

160
140
120
100

East Asia & Pacific8060

Latin America &Caribbean
Europe & Central
Asia

40
20
0
1960 1970 1980 1990 2000

Figure 1: Infant Mortality by Developing Region, 1960-2000
Source: World Development Indicators, April 2006.
Notes: The definition of regions follows the World Bank’s classification.

1990: Comoros

1991: Benin and Zambia

Figure 2: Democratization in the Sample Countries over Time1992: Mali

1993: Lesotho, Madagascar, and Niger

Figure 2 (continued): Democratization in the Sample Countries over Time

1994: Malawi and South Africa

1995: Ethiopia

Figure 2 (continued): Democratization in the Sample Countries over Time1999: Nigeria

Figure 2 (continued): Democratization in the Sample Countries over Time
Notes: Grey-colored countries are the sample countries; Black-colored democratized.
In 1990, Comoros was democratized but due to its small area size it is not visible.

Infant death

DemocratizedNon-democratized

.2
.19
.18
.17
.16
.15
.14
.13
.12
.11
.1
.09
.08
.07
.06
.05
.04
.03
.02
.01
0
1970

1975

1980

1985
1990
Year of birth

1995

2000

2005

Figure 3: Sample mean infant mortality rates by year for democratized and nondemocratized countries
Notes: Plotted are sample mean infant mortality rates by year for democratized and nondemocratized countries. Year 1970 includes children born in the 1960s; year 2003 includes
children born in 2004. See Table 1 for the list of democratized and non-democratized countries.

Comoros

.2
.1
0
1970

1980

1990
Year of birth

.2
.1
0

2000

1970

.1
0
1990
Year of birth

.1
0
1970

0

1980

0
1980

1990
Year of birth

1990
Year of birth

2000

.2
.1
0
1970

1980

1990
Year of birth

2000

Nigeria

.1

1970

2000

.3
.2
.1
0
1970

1980

1990
Year of birth

2000

ZambiaInfant death

Infant death

1980

.3

2000

.2

South Africa
.2
.1
0
1990
Year of birth

1990
Year of birth

.3

2000

.3

1980

1970

Infant death

Infant death

Infant death

.1

1970

0

Niger

.2

1990
Year of birth.1

Malawi

.2

Mali

1980

.2

2000

.3

2000

.3

1970

1990
Year of birth

Infant death

.2

1980

1980

.3

Madagascar
Infant death

Infant death

Lesotho.31970

Ethiopia.3

Infant death

Infant death

Infant death

Benin
.3

2000

.3
.2
.1
0
1970

1980

1990
Year of birth

2000

Figure 4: Sample Mean Infant Mortality Rates Over Time by Country for Democratized Countries
Notes: Plotted are sample mean infant mortality rates by year for each of the 11 democratized countries. Year 1970 includes children born in the 1960s; year 2003
includes children born in 2004.

1980

1990
Year of birth

Infant death

Infant death

Infant death

.3
.2
.1
0
1970

.3
.2
.1
0
1970

2000

1980

1990
Year of birth

1970

2000

1980

1970

2000

2000

.3
.2
.1
0
1970

1980

1990
Year of birth

2000

1980

1990
Year of birth

2000

Mauritania
Infant death

Infant death

Infant death

1990
Year of birth

1990
Year of birth

1990
Year of birth

.3
.2
.1
0

Kenya

.3
.2
.1
0
1980

1980

Ghana

.3
.2
.1
0

Guinea

1970

1970

2000

Infant death

Infant death

Infant death

.3
.2
.1
0
1980

1990
Year of birth

.3
.2
.1
0

Gabon

Cote d'Ivoire

1970

Chad

Cameroon

Burkina Faso

2000

.3
.2
.1
0
1970

1980

1990
Year of birth

2000

Figure 5: Sample Mean Infant Mortality Rates over Time by Country for Non-democratized Countries(Continued)

.1

0
1980

1990
Year of birth.3

Infant death

Infant death

Infant death

.2

1970

.2

.1

0
1970

2000

1980

.1

0
1990
Year of birth

.2

.1

0
1970

1980

Infant death

Infant death

.2

.1

0
1990
Year of birth

0
1970

1980

1990
Year of birth

1990
Year of birth

2000

2000

.3

.2

.1

0
1970

1980

1990
Year of birth

2000

Zimbabwe

Uganda

1980

.1

Togo

.3

2000

.3

1970

.2

2000

Infant death

Infant death

Infant death

.2

1980

1990
Year of birth.3

Tanzania

Senegal.3

1970

RwandaNamibia

Mozambique
.3

2000

.3

.2

.1

0
1970

1980

1990
Year of birth

2000

Figure 5 (continued): Sample Mean Infant Mortality Rates Over Time by Country for Non-democratized Countries
Notes: Plotted are sample mean infant mortality rates by year for each of the 17 non-democratized countries. Year 1970 includes children born in the 1960s; year
2003 includes children born in 2004.

0.02

0.01

0
-5

-4

-3

-2

-1

0

1

2

3

4+

-0.01

-0.02

-0.03

-0.04

-0.05

-0.06

-0.07

Number of Years Since Democratization

Figure 6: Dynamics of Infant Mortality Before and After Democratization
Notes: Plotted are estimated coefficients reported in Appendix Table A3. Vertical bands represent 95 percent level confidence intervals of the estimated coefficients.

Table 1: Years of Democratization by Different Measures of Democracy and the Sample Period for 28 African Countries
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Country
Multiparty
PR<3
PR<4
PR<5
POLITY2>6 POLITY2>4 POLITY2>0 EXREC>=7 & PARCOMP>=3 Sample Period
Democratized Countries
Benin
1991
1991
1991
1991
1991
1991
1991
1964-2000
Comoros
1990
1991
1990
1990
1975-1995
Ethiopia
1995
1995
1993
1995
1963-1999
Lesotho
1993
2002
*1993
1993
*1993
*1993
*1993
*1993
1967,69-2004
Madagascar
1993
1993
1993
1990
1992
1992
1991
1992
1962-1996
Malawi
1994
1994
1994
1994
1994
1994
1994
1994
1964-1999
Mali
1992
*1992
1992
1992
*1992
1992
1992
1992
1964-2000
Niger
1993
*1993
*1993
*1992
*1992
*1991
*1992
1960-1997
Nigeria
1999
1999
1999
1965-2002
South Africa
1994
1994
1994
1994
1993
1990
1994
1961-1997
Zambia
1991
*1991
*1991
*1991
*1991
1991
*1991
1965-2001
Non-democratized Countries
Burkina Faso
1991
1999
1966-2002
Cameroon
1992
1968-2003
Chad
1996
1962,65,67-2003
Cote d'Ivoire
1990
1962-1998
Gabon
1993
*1990
1962-1999
Ghana
1992
2000
1996
1995
2001
1996
2001
1967-2002
Guinea1993
1961-1998
Kenya
1992
*1992
2002
2002
2002
2002
1965-2002
Mauritania
1992
1962-2000
Mozambique
1994
1994
1994
1994
1994
1994
1975-2002
Namibia
1990-1999
Rwanda(2003)
1963-1999
Senegal
1978(2002)*1984
1978(2000)(2000)(2000)
1961-1996
Tanzania
1995
1999
2000
2000
1968-2004
Togo
1993
1960-1997
Uganda1996*1997
1964-2000
Zimbabwe
1980-1998
Notes: Column (1) lists the year of the first multiparty elections for executive office. "-" indicates that the country is democratic for all years in the sample.
Years in parentheses are the years of democratization outside the sample period. The blank cells indicate that the country is never democratic during thesample period.
* Democracy collapses within the sample period.

Table 2: Summary Statistics
Countries:
Baby-level Variables
Infant death(All)(Educated)(Uneducated)(Dictator's ethnicity)(Other ethnicity)
Neonatal death(All)(Educated)(Uneducated)(Dictator's ethnicity)(Other ethnicity)

Girl
Multiple birth
Mother's age at birth
20-29
Mother's age at birth
30-39Mother's age at birth
40-49
Short birth spacing
Mother-level Variable
Number of births

(1)
All

0.100
6438460.080
2873870.116356450
N/A
N/A

0.047
6438460.036
2873870.055
356450
N/A
N/A

0.491
6438460.032
6438460.534
6438460.2006438460.018
6438460.292
483880(2)(3)
Democratized
BeforeAfter(4)Non-democratized

0.116
197891
0.086
81284
0.138
116600
0.121
37800
0.124
89028

0.100
79805
0.086
37267
0.111
42538
0.112
18365
0.100
37987

0.091
3661500.076
1688360.104
197312
N/A

0.054
197891
0.038
81284
0.065
116600
0.121
37800
0.124
89028

0.046
79805
0.039
37267
0.053
42538
0.112
18365
0.100
37987

0.043
3661500.035
1688360.049
197312
N/A

0.487
1978910.029
197891
0.541
197891
0.168
197891
0.008
1978910.333
146213

0.494
79805
0.037
79805
0.506
79805
0.261
79805
0.040
79805
0.237
62589

0.492
3661500.032
3661500.537
3661500.203
3661500.018
3661500.282
275078

N/A

N/A

4.028
4.005
4.059
(2.686)(2.634)(2.754)
16187668602
93274
Notes : In each cell, the sample mean is reported at the top row; the number of observations at
the bottom row. For number of births, which refers to the birth order of the youngest child in thesample, standard deviations are reported in parentheses at the middle row. Column (2) reports
means for children born in democratized countries until the year of democratization; Column (3)for children born in democratized countries after the year of democratization; Column (4) for
children born in non-democratized countries.

Table 3: Infant Mortality Drops After Democratization
(1)
(2)
(3)
Dependent Variable:
InfantInfantInfant
Death
Death
Death
Democratization
0.009-0.011*-0.017**[0.008][0.005][0.006]
Girl(4)
InfantDeath-0.017**[0.006](5)(6)
Neonatal
InfantDeath
Death-0.018** -0.010**[0.006][0.003]-0.014** -0.011**[0.001][0.001]
Born in a multiple birth0.231**
0.163**[0.012][0.009]Year fixed effects
YES
YES
YES
YES
YES
YES
Country fixed effects
NO
YES
NO
NO
NO
NO
Mother fixed effects
NO
NO
YES
YES
YES
YES
Country-specific linear trends
NO
NO
NO
YES
YES
YES
Number of Countries
28
28
28
28
28
28
Number of Mothers
161876161876161876161876161876161876
Observations
643846643846643846643846643846643846
Adjusted R-squared
0.001
0.008
0.060
0.061
0.077
0.080
Notes : Standard errors clustered at the country level are reported in brackets. Infant death is death
before turning the age of 1 year; neonatal death is death before turning the age of 1 month. Adjusted
R-squared refers to variation explained by all regressors including any fixed effects.
* significant at 5%; ** significant at 1%.

Table 4: Impact of Democratization by Mother's Education and Ethnicity
(1)
(2)
(3)
(4)
Dependent variable:
InfantNeonatal
InfantNeonatal
Death
Death
Death
Death
Democratization interacted with
Educated-0.009-0.006[0.006][0.003]
Uneducated-0.023** -0.012**[0.007][0.003]
Educated & Urban-0.005-0.004[0.007][0.005]
Educated & Rural-0.011-0.006[0.007][0.004]
Uneducated & Urban-0.018-0.004[0.012][0.009]Uneducated & Rural-0.024** -0.014**[0.007][0.003]Dictator's ethnic group(5)
InfantDeath(6)Neonatal
Death-0.008-0.004[0.008][0.005]
Other ethnic groups-0.017*-0.012**[0.006][0.004]
F-test
7.94
8.46
0.78
0.81
17.16
6.79
p-value
0.009
0.007
0.469
0.454
0.001
0.018
Girl dummy
YES
YES
YES
YES
YES
YES
Multiple birth dummy
YES
YES
YES
YES
YES
YES
Mother fixed effects
YES
YES
YES
YES
YES
YES
Year fixed effects
YES
YES
YES
YES
YES
YES
Country-specific linear trends
YES
YES
YES
YES
YES
YES
Number of Countries
28
28
28
28
19
19
Number of Mothers
161873161873161873161873114324114324
Observations
643837643837643837643837460952460952Adjusted R-squared
0.077
0.080
0.309
0.311
0.074
0.077
Notes : Standard errors clustered at the country level are reported in brackets. Infant death (thedependent variables in odd-numbered columns) is death before turning the age of 1 year. Neonatal
death (the dependent variables in even-numbered columns) is death before turning the age of 1 month.
Adjusted R-squared refers to variation explained by all the regressors, including any fixed effects. The
nulls for F-tests are: for columns (1) and (2), coefficients on the two interaction terms between
democratization and mothers' education level are the same; for columns (3) and (4), coefficients on thetwo interaction terms of democratization and educated mothers with areas of residence are the sameand coefficients on the two interaction terms of democratization and uneducated mothers with areas ofresidence are the same; for columns (5) and (6), coefficients on the two interaction terms between
democratization and mothers' ethnicity are the same.
* significant at 5%; ** significant at 1%.

Table 5: Robustness Checks(The Dependent Variable: Death before turning the age of one year)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Sample:PWT6.2
Born to
Until
Since
All
All
All but
Without
2003
1961
First-born
Migration
Poor Mothers
Democratization-0.017**-0.018*-0.018**-0.018**-0.018**-0.016**-0.015*-0.017*[0.006][0.007][0.006][0.006][0.006][0.006][0.007][0.006]
Log per capita GDP-0.032[0.017]
Log per capita GDP (1-year lag)
0.004
[0.014]War
0.004[0.004]Foreign Aid
0.000(million US$, 2004 prices)[0.000]Foreign Aid (1-year lag)
0.000
(million US$, 2004 prices)[0.000]HIV infection rates
0.021
among pregnant women[0.069]
Short birthspacing
0.039**(less than 24 months)[0.004]
Girl dummy
YES
YES
YES
YES
YES
YES
YES
YES
Multiple birth dummy
YES
YES
YES
YES
YES
YES
YES
YES
Mother fixed effects
YES
YES
YES
YES
YES
YES
YES
YES
Year fixed effects
YES
YES
YES
YES
YES
YES
YES
YES
Country-specific linear trends
YES
YES
YES
YES
YES
YES
YES
YES
Additional Controls
NO
NO
NO
NO
NO
YES
NO
NO
Number of Countries
28
28
28
28
28
28
28
25
Number of Mothers
16185847910
161864
161876161876161876129343116259Observations
643532203027643791643843643846643846483880414767
Adjusted R-squared
0.077
0.091
0.077
0.077
0.077
0.079
0.072
0.067
Notes : Standard errors clustered at the country level are reported in brackets. In columns (1), (3), (4), and (7), some observations are
dropped due to the unavailability of control variables. Column (2) restricts the sample to babies born to mothers having no consumer durable
good at the survey date; Column (8) drops babies born before one year has passed since their mother migrated to the surveyed communityand babies whose mother's information on migration is not available. In Column (6), birth order dummies and mothers' age-at-birth category
dummies are additionally controlled for. Adjusted R-squared refers to variation explained by all regressors, including any fixed effects.
* significant at 5 percent; ** at 1 percent.

Table 6: Robustness to Other Dimensions of Democracy(The Dependent Variable: Death before turning the age of one year)
(1)
(2)
(3)
(4)
Democratization-0.018** -0.018** -0.021** -0.019**[0.006][0.006][0.006][0.006]
Multiparty Elections-0.002[0.004]
Leadership Change-0.002[0.006]
Executive Constraints
0.002[0.001]
Civil Liberty Restriction-0.001[0.001]
Free Press
Partly Free Press(5)-0.015*[0.007]-0.005[0.009]0.000[0.003]F-test5.76
3.85
p-value
0.024
0.060
Girl dummy
YES
YES
YES
YES
YES
Multiple birth dummy
YES
YES
YES
YES
YES
Mother fixed effects
YES
YES
YES
YES
YES
Year fixed effects
YES
YES
YES
YES
YES
Country-specific linear trends
YES
YES
YES
YES
YES
Number of Countries
28
28
28
28
28
Number of Mothers
161876161876160184
161629157709Observations
643846643846
611866634011552397
Adjusted R-squared
0.077
0.077
0.076
0.077
0.076
Notes : Standard errors clustered at the country level are reported in brackets. Adjusted
R-squared refers to variation explained by all regressors, including any fixed effects.
The nulls for F-test are: for column (1) coefficients on democratization and on
multiparty elections are the same; for column (2) coefficients on democratization andon leadership change are the same.
* significant at 5 percent; ** significant at 1 percent.

Table 7: Different Measures of Democracy(The Dependent Variable: Death before turning the age of one year)(1)(2)
Measure of Democracy:
PR<3
PR<4

(3)
PR<5

(4)
POLITY2>6(5)
POLITY2>4(6)
POLITY2>0(7)
EXREC>=7& PARCOMP>=3
Democratization in the 1990s-0.008-0.011-0.011*-0.012-0.011-0.014**-0.015**[0.008][0.007][0.005][0.009][0.006][0.005][0.005]Collapse of Democracy in the 1990s-0.021**-0.021-0.014-0.025-0.018-0.046-0.022[0.007][0.013][0.011][0.014][0.015][0.026][0.014]
F-test
6.65
1.21
0.15
1.84
0.36
1.46
0.36
p-value
0.016
0.280
0.701
0.187
0.551
0.238
0.554
Girl dummy
YES
YES
YES
YES
YES
YES
YES
Multiple birth dummy
YES
YES
YES
YES
YES
YES
YES
Mother fixed effects
YES
YES
YES
YES
YES
YES
YES
Year fixed effects
YES
YES
YES
YES
YES
YES
YES
Country-specific linear trends
YES
YES
YES
YES
YES
YES
YES
Number of Countries
28
28
28
28
28
28
28
Number of Mothers
161521161521161521161864161864161864161864
Observations
629573629573629573643791643791643791643791
Adjusted R-squared
0.077
0.077
0.077
0.077
0.077
0.077
0.077
Notes : Standard errors clustered at the country level are reported in brackets. In columns (1), (2), and (3), a country-year is regarded as democratic if
Freedom House's Political Rights Index is less than 3, 4, or 5, respectively; in columns (4), (5), and (6), a country-year is democratic if POLITY IV's
POLITY2 score is more than 6, 4, or 0, respectively. In column (7), a counry-year is democratic if POLITY IV's variable EXREC is 7 or higher and if
another POLITY IV variable PARCOMP is 3 or higher. Adjusted R-squared refers to variation explained by all regressors, including any fixed effects. The
null for F-tests is that coefficients on democratization and the collapse of democracy are the same.
* significant at 5 percent; ** at 1 percent.

Table 8: Summary Statistics for Pathway Analysis
(1)
(2)
(3)
Countries:
All
Democratized
BeforeAfter
Tetanus Toxoid
(All)
0.700
0.572
0.615
195564
34196
40053(Educated)
0.812
0.774
0.754
98158
15639
17327
(Uneducated)
0.587
0.401
0.510
97384
18554
22724
Delivery Assistance
(All)
0.445
0.412
0.436
23164532153
54428(Educated)
0.578
0.621
0.552
11778714682
23217(Uneducated)
0.307
0.235
0.349
113836
17468
31209
Ever Breastfed
(All)
0.975
0.976
0.977
22856731551
53507(Educated)
0.974
0.976
0.979
11658414455
23020(Uneducated)
0.977
0.977
0.975
111961
17093
30485
Access to Toilets
(All)
0.049
0.052
0.053
57111
6539
16054
(Educated)
0.082
0.109
0.095
29574
2760
7091
(Uneducated)
0.014
0.011
0.020
27532
3779
8962
Radio
(All)
0.552
0.520
0.573
57121
6527
16048
(Educated)
0.592
0.605
0.570
29578
2755
7090
(Uneducated)
0.509
0.458
0.575
27538
3772
8957
Television
(All)
0.096
0.132
0.099
57121
6527
16048
(Educated)
0.142
0.220
0.151
29578
2755
7090
(Uneducated)
0.047
0.067
0.057
27538
3772
8957
Frige
(All)
0.052
0.079
0.045
54084
5702
13851
(Educated)
0.088
0.150
0.091
27599
2306
5571
(Uneducated)
0.015
0.031
0.014
26480
3396
8279

(4)
Non-democratized

0.764
121315
0.836
65192
0.680
56106
0.456
145064
0.577
79888
0.307
65159
0.975
143509
0.973
79109
0.977
64383
0.047
34518
0.074
19723
0.012
14791
0.548
34546
0.598
19733
0.482
14809
0.088
34546
0.127
19733
0.036
14809
0.051
34531
0.080
19722
0.012
14805
(Continued)

Table 8 (continued): Summary Statistics for Pathway Analysis
(1)
(2)
(3)
(4)
Countries:All
Non-democratized
Democratized
BeforeAfter
Bicycle
(All)
0.355
0.228
0.388
0.364
57097
6518
16054
34525
(Educated)
0.297
0.195
0.331
0.299
29565
2747
7093
19725
(Uneducated)
0.417
0.252
0.432
0.450
27527
3771
8960
14796
Motorcycle
(All)
0.086
0.119
0.117
0.065
55970
6520
16044
33406
(Educated)
0.065
0.126
0.082
0.049
28637
2747
7086
18804
(Uneducated)
0.109
0.114
0.145
0.086
27328
3773
8957
14598
Car
(All)
0.035
0.052
0.030
0.034
55037
6518
16032
32487
(Educated)
0.052
0.080
0.045
0.051
28061
2745
7084
18232
(Uneducated)
0.016
0.032
0.018
0.012
26971
3773
8947
14251
Electricity
(All)
0.137
0.208
0.116
0.134
56081
6531
16031
33519
(Educated)
0.206
0.339
0.186
0.195
29018
2759
7086
19173
(Uneducated)
0.063
0.112
0.062
0.052
27058
3772
8944
14342
Notes : In each cell, the sample mean is reported at the top row; the number of observations at
the bottom row. Column (2) reports means for children born in democratized countries until theyear of democratization; Column (3) for children born in democratized countries after the year of
democratization; Column (4) for children born in non-democratized countries.

Table 9: Maternal Health Care
(5)
(6)
Delivery
Delivery
Assistance Assistance
Democratization
0.049[0.032]
Democratization-0.007+-0.024
0.021
interacted with Educated[0.004][0.036][0.026]
Democratization-0.012**0.100+
0.076*
interacted with Uneducated[0.003][0.048][0.034]Multiple birth0.164**
0.164**
0.071**
0.071**[0.009][0.009][0.007][0.007]
F-test
8.65
17.31
6.20
p-value
0.009
0.001
0.023
Girl dummy
YES
YES
NO
NO
NO
NO
Mother fixed effects
YES
YES
N/A
N/A
N/A
N/A
Mother category fixed effects
N/A
N/A
YES
YES
YES
YES
Year fixed effects
YES
YES
YES
YES
YES
YES
Country-specific linear trends
YES
YES
NO
NO
NO
NO
Number of Countries
19
19
18
18
19
19
Number of Mothers
118010118007
N/A
N/A
N/A
N/A
Number of Mother categories
N/A
N/A
23255
23240
23786
23771
Observations
475995475986
195564
195542231645231623Adjusted R-squared
0.081
0.081
0.237
0.238
0.396
0.396
Notes : Robust standard errors clusterd at the country level are reported in brackets. Neonatal death isdeath before turning the age of 1 month. Adjusted R-squared refers to variation explained by all theregressors, including any fixed effects. The null for F-tests is that coefficients on the two interaction
terms between democratization and mothers' education level are the same.
+ significant at 10%; * significant at 5%; ** significant at 1%.
Dependent Variable:(1)
Neonatal
Death-0.010**[0.003](2)
Neonatal
Death(3)
TetanusToxoid
0.041[0.049](4)
TetanusToxoidTable 10: Breastfeeding and Sanitation
(1)
(2)
(3)
(4)
(5)
(6)
Dependent Variable:
InfantInfantEver
Ever
Access to Access to
Death
Death
Breastfed Breastfed Toilets
Toilets
Democratization-0.020*0.011*
0.016*[0.007][0.004][0.007]
Democratization-0.0120.009
0.022
interacted with Educated[0.008][0.006][0.015]
Democratization-0.025*0.012*
0.013*
interacted with Uneducated[0.009][0.005][0.005]Girl-0.014** -0.014**
0.005**
0.004**[0.002][0.002][0.001][0.001]
Born in a multiple birth0.229**
0.229**-0.067** -0.067**[0.013][0.013][0.006][0.006]
F-test
5.90
0.08
0.42
p-value
0.026
0.776
0.519
Mother fixed effects
YES
YES
N/A
N/A
N/A
N/A
Mother category fixed effects
N/A
N/A
YES
YES
YES
YES
Year fixed effects
YES
YES
YES
YES
NO
NO
5-year period fixed effects
NO
NO
NO
NO
YES
YES
Country-specific linear trends
YES
YES
NO
NO
NO
NO
Number of Clusters
19
19
19
19
50
50
Number of Mothers
118010118007
N/A
N/A
N/A
N/A
Number of Mother categories
N/A
N/A
23730
23715
16212
16207
Observations
47599547598622856722854557111
57106
Adjusted R-sqaured
0.077
0.077
0.035
0.035
0.402
0.402
Notes : Robust standard errors clusterd at the country level (columns (1) to (4)) or at the country-yearlevel (columns (5) and (6)) are reported in brackets. Infant death is death before turning the age of 1
year. The number of clusters refers to the number of countries in columns (1) to (4) and to thenumber of country-year cells in columns (5) and (6). Adjusted R-squared refers to variation explainedby all the regressors, including any fixed effects. The null for F-tests is that coefficients on the twointeraction terms between democratization and mothers' education level are the same.
+ significant at 10%; * significant at 5%; ** significant at 1%

Table 11: Impact of Democratization on Asset Ownership and Access to Electricity
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Car
Electricity
Dependent variable:
Radio
Television Refrigerator Bicycle Motorcycle
Panel A
Democratization-0.027-0.0190.002-0.010-0.0060.001
0.004[0.022][0.014][0.009][0.030][0.008][0.005][0.018]Mother-category fixed effects
YES
YES
YES
YES
YES
YES
YES
5-year period fixed effects
YES
YES
YES
YES
YES
YES
YES
Number of clusters
50
50
48
50
49
48
48
Number of mother categories
16214
16214
14933
16206
16185
15866
15903
Observations
57121
57121
54084
57097
55970
55037
56081
Adjusted R-squared
0.148
0.352
0.313
0.292
0.193
0.156
0.520
Panel B
Democratization interacted with
Educated-0.0310.005
0.030-0.004
0.001
0.008
0.006
[0.025][0.024][0.029][0.033][0.011][0.010][0.023]
Uneducated-0.025-0.033**-0.013*-0.0130-0.010-0.0040.003[0.023][0.012][0.006][0.034][0.010][0.004][0.019]F-test
0.08
3.31
2.08
0.09
0.50
1.96
0.02
p-value
0.773
0.075
0.156
0.771
0.482
0.168
0.887
Mother-category fixed effects
YES
YES
YES
YES
YES
YES
YES
5-year period fixed effects
YES
YES
YES
YES
YES
YES
YES
Number of clusters
50
50
48
50
49
48
48
Number of mother categories
16209
16209
14928
16201
16180
15861
15898
Observations
57116
57116
54079
57092
55965
55032
56076
Adjusted R-squared
0.148
0.353
0.313
0.292
0.193
0.157
0.520
Notes : Reported in brackets are standard errors clustered at the country-year level. Panels A and B correspond todifferent regressions for each column. Mothers are categorised by administrative regions of residence, urban/rural
areas of residence, education level, and age cohort to control for mother-category fixed effects. 5-year period fixed
effects are those for 1985-89, 1990-94, 1995-99, and 2000-04. The number of clusters refers to the number ofcountry-year cells. Adjusted R-squared refers to variation explained by all regressors, including any fixed effects.
The null for F-test in Panel B is that coefficients on the two interaction terms between democratization and mothers'
education level are the same.
+ significant at 10%; * significant at 5%; ** significant at 1%

Appendix Table A1: Comparability of Different Groups of Countries in Sub-Saharan Africa
(1)
(2)
(3)
(4)
Group of countries:
DemocratizedNon-democratizedSampleAll
Per Capita Income in 1990
1657
2020
1877
2182
(1996 constant int'l dollars)(2078)'(2036)(2022)(2353)
11
17
28
42
Average Years of Schoolingin 1990(for those aged 15 or over)

2.81(1.79)
7

2.92'(1.05)11

2.87(1.33)
18

2.93(1.51)
30

Average Years of Schoolingin 1990(for female aged 15 or over)

2.42(1.92)
7

2.19'(.94)
11

2.28(1.36)
18

2.37(1.61)
30

Ethnic Fractionalization Index(0 to 1)0.640
(0.269)11

0.709'(0.159)17

0.682(0.207)28

0.659(0.233)46

British Legal Origin(dummy variable)

0.455(0.522)11

0.353'(0.493)17

0.393(0.497)28

0.383(0.491)47

European Settler Mortality(per 1000 mean strengths)

987(1205)
6369'(213)
11

587
(758)
17

556
(693)
23

Malaria Ecology Index(0 to more than 30)

10.53(10.31)
11

12.96'(8.67)17

12.00(9.24)
28

11.38(7.98)
46

HIV infection rate in 2001(% of adults aged 15-49)

9.71(10.06)10

7.45'(6.58)17

8.29(7.93)
27

9.05(9.86)
38% of years of armed conflicts
from 1951 or independence
to 2004

14.68(23.30)11

16.54'(24.85)17

15.81(23.83)28

21.52(29.65)47

Sources : Penn World Table 6.1 for per capita income; Barro and Lee (2000) for years of schooling;
Alesina et al. (2003) for ethnic fractionalization; La Porta et al. (1999) for British legal origin; Acemoglu
et al. (2001) for European settler mortality; Kiszewski et al. (2004) for malaria ecology; the World
Development Report 2005 for the HIV infection rate; and Armed Conflict Dataset Version 3-2005b(Gleditsch et al. 2002) for armed conflicts.
Notes : In each cell, the mean, the standard deviation (in parentheses), and the number of countries
are reported at the top, the middle, and the bottom row, respectively. Column (1) includes 11
democratized countries; colulmn (2) 17 non-democratized countries; column (3) 28 countries with the
DHS surveys available; column (4) all the countries in Sub-Saharan Africa (excluding Eritrea). Eritrea
is excluded because it became independent in 1993, and data in 1990 is therefore not available.

Appendix Table A2: Years of DHS surveys used in the analysis
Country
Infant Mortality Analysis
Pathway Analysis
Benin
2001
2001, 1996
Burkina Faso
2003
2003, 1998, 1992
Cameroon
2004
2004, 1998, 1991
Chad
2004
Comoros
1996
Cote d'Ivoire
1998
Ethiopia
2000
Gabon
2000
Ghana
2003
2003, 1998, 1993, 1988
Guinea
1999
Kenya
2003
2003, 1998, 1993, 1989
Lesotho2004
Madagascar
1997
1997, 1992
Malawi
2000
2000, 1992
Mali
2001
2001, 1995, 1987
Mauritania
2000
Mozambique
2003
2003, 1997
Namibia
2000
2000*, 1992*
Niger
1998
1998, 1992
Nigeria
2003
2003, 1999, 1990
Rwanda2000
2000, 1992
Senegal1997
1997, 1992
South Africa
1998
Tanzania
2004
2004, 1999, 1996, 1992
Togo
1998
1998, 1988
Uganda2000
2000, 1995, 1988
Zambia2001
2001, 1996, 1992
Zimbabwe
1999
1999, 1988
Notes : If a survey was completed in the following year, the year in the table refers to
the one in which the survey began.
* Namibia surveys are not used for analysis of tetanus toxoid injections because the2000 survey does not collect information on it, leaving only one round of survey
available.

Appendix Table A3: Dynamics of Infant Mortality Before and After Democratization(The Dependent Variable: Death before turning the age of one year)
5 Years Before-0.004[0.006]
4 Years Before-0.001[0.005]
3 Years Before-0.004[0.004]
2 Years Before-0.009[0.008]
1 Year Before-0.008[0.008]Year of Democratization-0.014[0.011]
1 Year After-0.023*[0.010]
2 Years After-0.028*[0.012]
3 Years After-0.030[0.016]
4 Years or More After-0.038**[0.013]F-test0.44
0.817
Controls :Sex of Baby
YES
Multiple Birth
YES
Mother fixed effects
YES
Year fixed effects
YES
Country-specific linear trends
YES
Number of Countries
28
Number of Mothers
161876
Observations
643846
Adjusted R-squared
0.077
Notes : Robust standard errors clustered at thecountry level are reported in brackets. The null for
F-test is that five coefficients on dummies for years
before democratization are all zero. Adjusted Rsquared refers to variation explained by all
regressors, including any fixed effects.
* significant at 5%; ** significant at 1%