Department of Policy and Planning Sciences
Discussion Paper Series

No.1337

Cost of Introducing Multi-Party Elections: Impact of the
1992 Ethnic Violence on Child Health in Kenya
by
Fredah GUANTAI and Yoko KIJIMA

Dec 2015

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPANCost of Introducing Multi-Party Elections:
Impact of the 1992 Ethnic Violence on Child Health in Kenya

Fredah Guantai and Yoko Kijima*
University of Tsukuba

ABSTRACT
By exploiting ethnic violence resulting from the introduction of Kenya’s first multi-party electoral
system in 1992, we examine the impact of exposure to the violence on child health. The results
suggest that prenatal exposure to the violence increased the probabilities of low birth weight andpremature birth by 18.9% and 5.1%, respectively. In addition, an extra month of in utero exposure
to the violence reduced birth weight by 94 grams. The results of our findings reaffirm thesignificance of the nine months in utero as one of the most critical periods in life shaping future
health, economic and education trajectories.

Keywords: Kenya, election, ethnic violence, child health, in utero, maternal stress, birth weight
JEL classification: I15, J13, O15* Graduate School of Systems and Information Engineering, University of Tsukuba,
Address: 1-1-1 Tennodai, Tsukuba, 305-8573, Japan.
Phone: (+81) 29-853-5092, E-mail: kijima@sk.tsukuba.ac.jp
11. Introduction
After Independence, many African countries adopted single-party elections which were used byauthoritarian regimes to obtain public legitimacy (Golder & Wantchekon, 2004). However, fromthe early 1990s followed by the end of the Cold War, multiparty democracy was introduced in aneffort to end decades of authoritarian single party rule, to encourage constitutional reforms and to
uphold human rights (Dercon & Gutiérrez-Romero, 2012; Klaus & Mitchell 2015). The politicaltransition to democracy in many African countries raised the question whether democracy helps inimproving the well-being of these nations. Existing studies found that being in democratic
countries for a longer time resulted in better health (Besley & Kudamatsu, 2006) and changing anincumbent president in multiparty elections is more likely to decrease child mortality (Kudamatsu,
2012) 1.
It seems that introducing democracy results in better health outcome to some extent. However,multiparty elections have failed to be implemented properly 2 hence have been marked by- violent
attacks (Straus & Taylor, 2009) 3, most of which are ethnically instigated (Bekoe, 2010; Collier,2009; Snyder, 2000). Such election-related ethnic violence is responsible for enormous economic
losses as well as psycho-social consequences. Although previous studies focus more on thepositive impact of democracy, enough attention has not been paid to the cost of introducing                                                                                                                        1The mechanism by which democracy can improve welfare of the people is through better service
delivery and provision of public goods (Persson & Tabellini, 2009). New evidence emerges that
democratic transition does not have the same impact on the rich and the poor and has affected thepoor negatively (Ramos, 2014; Ross, 2006; Blimpo et al., 2013).
2
During the 1975-2011 periods, 80% of African polls were disrupted by violence, bribery,
intimidation or inequitable government interference, compared to 40% in non-African countries(Bishop & Hoeffler, 2014).
3
Out of 213 cases of elections in Africa from 1990 to 2007, 19 percent resulted in violence.
2

democratic elections in general and particularly on ethnic violence associated with introduction ofmultiparty elections in ethnically diverse societies. Thus, this study examines the impact ofelection-related ethnic violence on one of the most important welfare measures; child health.
In recent years, epidemiologists and economists have found empirical evidence suggesting thatshocks in utero have negative effects on child health which in turn can have long-run effects on
education, income and health in adulthood. These studies are informed by the fetal origins
hypothesis proposed by Barker (1995) whereby he provided evidence to support the proposition
that circumstances in utero, particularly nutrition lead to chronic degenerative diseases such as
coronary heart disease, blood pressure, and diabetes in adulthood. 4 Economists have expoundedon this hypothesis by investigating a wider range of fetal shocks out of which they have foundsignificant impacts in later life such as reduced test scores, labor productivity, income, health, andeducation attainment (Aizer et al., 2009; Behrman & Rosenzweig, 2004; Case et al. 2011; Islam etal., 2015; Lee, 2014; Leon, 2012; Mansour & Rees, 2012; Akresh & de Walque, 2008; Akresh etal., 2012; Shemyakina, 2011; Ichino & Winter-Ebmer, 2004; Minoiu & Shemyakina, 2012).
Consequently, if circumstances in pregnancy affect an individual’s outcomes in adulthood,emphasis should be placed on identifying conditions that can affect prenatal health anddevelopment.

                                                                                                                        4From a biological perspective, maternal stress during pregnancy results in changes in adrenal
cortisol, adrenaline and noradrenaline hormones produced in the adrenal glands thus stimulating
the production of Corticotrophin Releasing Hormone (CRH) which in turn reduces the length of
the gestation period, thus contributing to low birth weight and premature birth. CRH levels
increase throughout the second and third trimesters (Wadhwa et al., 1998; Wadhwa et al., 2004;
Hobel et al., 1999; Eskenazi et al., 2007; Torche, 2011).
3

Existing studies examining the impact of maternal stress on early childhood health outcome showthat stress emanating from exposure to conflict or violence situations worsens birth outcomes. 5
Despite decades of ethnically and politically instigated unrests, however, their negative effects onchild health have not been adequately investigated. 6 By exploiting exposure to ethnic violence
resulting from Kenya’s first multi-party elections in 1992 as an exogenous source of maternal
stress, we therefore aim to contribute to a growing literature on the effects of disruption of life inutero through maternal stress on birth outcomes
The first episode of ethnic violence in Kenya coincided with the introduction of multi-party
democracy in the early 1990s. When western donors decided to freeze aid in 1991, Arap Moi, thepresident of Kenya, agreed to constitutional changes allowing multiparty elections in December
1992 (Ajulu, 1998). The 1991/1992 violence lasted the longest time and is particularly significant
due to its sporadic nature and was viewed as a serious threat to the unity of the country and rule oflaw. 7 The violence took place mainly in Kenya’s Rift Valley region, where the opposition had astrong presence and because the objective of the violence was to ensure that communities
supporting the opposition parties did not register as voters and if registered would not be able tovote (Kimenyi & Ndung’u, 2005). The economic consequences of the violence were devastating                                                                                                                        5Prenatal shocks analyzed in relation to birth outcomes are: September 11 terrorist attack in New
York (Eskenazi et al., 2007); terrorist attacks in Colombia (Camacho, 2008); homicides in rural
Brazil (Koppensteiner & Manacorda, 2013); 2007 post-election violence in Kenya (Bell et al.,2012); Eritrea-Ethiopian conflict (Akresh et al., 2012); Rwandan civil war (Akresh et al., 2011);
Burundian civil war (Bundervoet et al., 2009); Guatemalan civil war (Chamarbagwala & Moran,2011).
6
The exception is Bell et al. (2012) which investigated the impact of the 2007 post-election
violence in Kenya on birth weight. There are, however, methodological limitations.
7
The 1992 violence lasted from October 1991 up to the December 1992 elections and beyond
throughout 1993.
4and in areas affected by the ethnic violence, there must be a negative impact on people, especiallyon pregnant women and infants.
By utilizing the exogenous variations of timing of the pregnancy and location of each child, we
identify whether he or she was exposed to ethnic violence in utero. After careful analyses, wefind that prenatal exposure to the violence increased the probabilities of low birth weight andpremature birth by 18.9% and 5.1%, respectively. In addition, an extra month of in utero exposure
to the violence reduced birth weight by 94 grams. The results of our findings reaffirm thesignificance of the nine months in utero as one of the most critical periods in life shaping future
health, economic and education trajectories.
The rest of the paper is organized as follows. Section 2 gives a background, triggers andconsequences of ethnic and political violence in Kenya. Section 3 explains the data source andpresents descriptive statistics. Section 4 explains the estimation strategy while Section 5 discusses
the results and robustness checks. Section 6 provides policy implications and concludes.

2. Background2.1. Triggers of the 1991/1992 Ethnic violenceCentral to ethnic violence in Kenya is the issue of land ownership. The epicenter of the violence;
Rift Valley region, is the most fertile part of Kenya occupying 40% of agricultural land. This area
was referred to as “white highlands” during the British colonial rule and was originally home topastoralists’ ethnic groups of Kalenjin, Maasai, Samburu, and Turkana. The colonial government

5

introduced land regulations between 1899 to 1915 by forcefully confiscating the most productive
farm land in the Rift Valley and reserving it for the white settlers. This led to the dislocation anddisinheritance of thousands of Kenyans who had lived in the Rift Valley. Since the pastoralist
communities did not have agricultural experience, the white settlers migrated thousands ofKikuyu, Kisii, Luhya, and Luo squatters to Rift Valley to provide sharecropper labour in the early
1900s hence overcome the cheap labour shortage for their farms (Nyukuri, 1997; Oyugi, 1997;
Kimenyi and Ndung’u, 2005).
At independence in 1963, land issues were still not resolved. Kenyans who were previously
squatter labourers in Rift Valley were now able to buy land individually or communally throughcooperatives. Unlike the original inhabitants; Kalenjin and Maasai who were pastoralists, thesquatter Kikuyu were experienced farmers and took advantage of the opportunity to purchase land.
With financial assistance from President Kenyatta, many Kikuyu bought land in the Rift Valley inthe 1960s and 1970s and migrated from their crowded Central region which was too small to
support their growing population. 8 Since then, land grievances have always fueled ethnic violence(Oyugi, 1997; Human Rights Watch,1993; Kimenyi & Ndung’u, 2005).
Once multi-party democracy was introduced, attacks on Kikuyu and other immigrant ethnic
groups living in Rift Valley were justified as taking back what was rightfully entitled to theindigenous ethnic groups and a move to end political and economic domination of the Kikuyu.
Residents in areas where there were many supporters of the opposition parties were forced to                                                                                                                        8The Kikuyu lost much of their ancestral land to the settler farmers. They are the largest ethnic
group comprising of 21% of the population yet concentrated in one of the smallest regions.
6relocate so that they could not been registered to vote in the displaced areas (Human Rights
Watch, 1993; Oyugi, 1997; Nyukuri, 1997; Ndegwa, 1997; Kimenyi & Ndung’u, 2005). That is
why violence took place mainly in Rift Valley and in the period before the elections. 9 Although
the first incident of ethnic violence was reported on October 29th 1991, the violence occurred
intensively for five months prior to the general elections from July 1992, to after the elections
until July 1993. The districts with serious violence are Nakuru, Kericho, Nandi, and Uasin Gishu
in Rift Valley region (Kimenyi & Ndung’u, 2005).
2.2. Consequences of the violence
The aftermath of the violence was displacement, injuries, death and loss of property. There washeightened insecurity as civilians took law into their hands by targeting the perceived enemies.
This resulted into indiscriminate loss of lives across all the ethnic groups, physical injuries andpsychological trauma (Human Rights Watch, 1993; Nyukuri, 1997; Kimenyi & Ndung’u, 2005).
It is estimated that 1500 people lost lives while 300,000 others were displaced (Human Rights
Watch, 1993; Nyukuri, 1997; Oyugi, 1997; Ndegwa, 1997). The 1991/1992 violence lasted thelongest time and is particularly significant due to its sporadic nature and was viewed as a serious
threat to the unity of Kenya and rule of law. 10 By the election date on December 29, 1992, many
eligible voters had lost property, title deeds, and national identification cards which would have                                                                                                                        9The 2007 elections resulted in ethnic violence in Kenya. The difference from the 1993 election
violence is the timing and cause of violence. The 2007 violence occurred after the election results
were announced hence was a protestation against the alleged rigging of results.
10
The 1992 violence lasted from October 1991 up to the December 1992 elections and beyond
throughout 1993.
7

enabled them to register as voters. This disruption was a major obstacle to achieving free and fairelections.
The economic consequences of the violence were devastating. The parliamentary committee
established to investigate the causes and impact of the violence reported that by September 1992,
the overall damage was estimated at about $ 5 million 11. These figures were much higher by theend of 1993 (Ndegwa, 1997). While on a fact finding mission in the most affected parts of the
Rift Valley region, Human Rights Watch (1993) reported that a majority of the agricultural farms
were abandoned and attempts by the owners (immigrants to Rift Valley) to resume cultivation
were thwarted by the Kalenjin (native ethnic group) who stole their crops and grazed their cattle
in the farms. This led to severe food shortages not only in Rift Valley but the rest of the country 12.
For instance, maize production in 1992 was 390,000 tons below average. In addition, Food andAgriculture Organization (FAO) reported that the victims in Rift valley alone required 7,200 tonsof cereals and 1,080 tons of others foods in emergency aid for that year (Daily Nation 14th May
1993 and 23rd May 1993 cited in Nyukuri, 1997). In addition to the economic losses, the psychosocial consequences of the 1992 violence were enormous. Approximately 1,500 lives were lostand 300,000 families displaced. The victims of the clashes were abused, injured and left
impoverished (Nyukuri, 1997; Kimenyi & Ndung’u, 2005).

3. Data and Descriptive Statistics                                                                                                                        
11By September 1992, more than 700 lives were lost, 54,000 displaced, 9,400 houses destroyedand 3,000 heads of livestock were stolen.
12
Kenya’s staple food is maize which is largely grown in Rift Valley region.
83.1. Data
To estimate the impact of in utero exposure to the ethnic violence from Kenya’s 1992 elections onbirth outcomes, we use data from the 1993 Kenya Demographic and Health Survey (KDHS)
conducted by the National Council for Population and Development (NCPD) and the Central
Bureau of Statistics, Kenya. The KDHS forms part of the worldwide Demographic and Health
Surveys (DHS) program designed to collect data on maternal and child health, fertility and familyplanning.
The nationally representative survey contains information on 7,540 women between 15-49 years.
Although the DHS sample includes women aged 15-49 years, only those who have children aged
0-5 years are used in this study. This accounts for 72% of the whole sample. For our analysis, wedefine all children born in Rift Valley as affected by the violence, because although not everyone
was a target, one does not have to be directly subjected to conflict to suffer the consequences(Islam et al., 2015). Living in a violent environment or fear of victimization and retaliation froman aggrieved party can be traumatizing, especially if there is loss of loved ones or neighbours. An
example of this is the retaliation by the Kikuyu against the Kalenjin (Human Rights Watch, 1993).
The child sample contains children born between March 1988 and July 1993 (aged 0-5 years
during data collection). For in utero exposure, children born in and before July 1992 wereconsidered not exposed to the violence. Three sets of exposure variables are used in the analyses:
The first exposure variable (born during violence) is a dummy variable taking one if a child wasborn during the violence and zero otherwise. This captures the impact of the shock for children
born during the high intensity period regardless of the length exposed. The second exposure

9variable (in utero) is the number of months in utero during the violence. This estimates thepossible different effect of exposure (1-9 months) among the affected children. The third set
contains three dummy variables namely whether a child was exposed for 1-3 months, for 4-6
months, and for 7-9 months. The third measure allows for estimating the possible different effectsof the violence depending on the timing of the pregnancy. This is an important measure for
capturing the effects of the violence during the early pregnancy period as well. 13
3.2. Descriptive Statistics
Panel A of Table 1 compares child characteristics in the full sample and between those in theviolent region (Rift Valley) and non-violent regions (other regions). There are significant age
differences with Rift Valley children slightly younger than those in other regions. There are
however no gender or birth order differences between Rift Valley and other regions.
In Panel B, we show parental characteristics. On education status, 25.5% of mothers in Rift
Valley have no education compared to 17.4% in other regions. Similar results are reported forhusband or partner’s education: 26.7% of the men in Rift Valley have no education in comparison                                                                                                                        
13An increasing number of studies show that exposure to prenatal stress in the first and second
trimesters have a greater reduction in the gestational period than in the third trimester. Glynn et al.
(2001) found out that first trimester exposure to the 1994 earthquake in Northridge California wasassociated with the greatest reduction in gestational period. Second trimester exposure had smaller
impact while exposure in the third trimester was statistically insignificant. Mansour & Rees
(2011) used the 2004 Palestinian Demographic and Health Survey to show that deaths caused byIsraeli security forces during the first trimester were positively correlated to probability of lowbirth weight. These trimester estimates are however possible when the exposure period is acutesuch as the September 11th terrorist attacks, earthquakes and exposure during the month of
Ramadhan (Almond & Mazumder, 2011). Our analysis focuses on before and after the generalelections. This longer period of one year means that it is difficult to accurately distinguish theimpact of the violence at each period due to overlap between trimesters.
10with 16.7% in other regions. Another notable difference is in the number of months of malaria
exposure in pregnancy. 14 This is particularly important because malaria is one of the leading
causes of infant mortality worldwide and in Kenya accounts for 20% of all deaths in children
under 5 years (Pathania, 2014). In pregnancy, it increases the probability of infant mortality
through low birth weight with higher risks faced by children in malaria epidemic areas who inutero experience worse conditions (Kudamatsu et al., 2012). The epidemiological climate of Rift
Valley is less conducive for the spread of malaria as evidenced by less than a month’s exposure inpregnancy compared to 5.5 months in other regions.
From Panel C on household characteristics, Rift Valley has younger household heads and a lower
proportion of female heads. However, the distance to hospital is further by approximately 10
kilometers. This could have hampered access to prenatal health care services in Rift Valley. On
ethnicity, only Kalenjin (native) have a higher concentration in Rift valley compared to otherethnic groups.

                                                                                                                        
14The number of months a pregnant mother was exposed to malaria one year before birth isconstructed by following Kudamatsu et al. (2012). Using rainfall and temperature data (19871993) from the Kenya Meteorological Department, we make a binary variable for a given monthin a given year for each weather station if they satisfy the following 4 conditions: First, averagemonthly rainfall during the past 3 months is at least 60mm. Second, rainfall in at least one of the 3months is at least 80mm. These 2 conditions ensure the availability of vector breeding site andsurvival. Third, no day in the past 12 months had temperatures below 5 degree Centigrade as thevector dies under cold conditions. Fourth, average temperature in the past 3 months exceeds 19.5
degree Centigrade plus one standard deviation to allow the parasite to be infectious inside thevector body before it dies. For malaria endemic regions such as Western and Nyanza, mothers are
exposed for up to 10 months while in epidemic areas such as the Rift Valley, they are exposed forjust a month.
11

Table 2 presents descriptive statistics of main outcome variables namely; birth weight, whether achild was born with low birth weight (less than and equal to 2500g), 15 prematurely (gestation
period is less than 37 weeks), and infant mortality (whether a child died before reaching one yearof age). Panel A shows birth weight separately for Rift Valley and other regions. There are no
significant differences in birth weight between Rift Valley and other regions for children born
before or during the violence on average.
In Panel B, we replace birth weight with an indicator of low birth weight. The full sample has a
low birth weight incidence of 15.6% with no differences across regions. Low birth weight in Rift
valley increases to 20% during the violence period and 15% in other regions. The difference
becomes significant for those who were exposed to violence for 1-3 months in utero. Thissuggests that the exposure to violence, especially in the first 3 months of the pregnancy, may
contribute to lower birth weight.
Panel C on premature birth shows that there are statistically significant differences between Rift
Valley and other regions as marked by a substantial increase in the proportion of premature births
in Rift Valley from 2.2% before the violence to 7.5% during the violent period. Finally in PanelD, we compare infant mortality rates between Rift Valley and other regions. The results are
somehow unexpected: infant mortality declined in Rift Valley during the violence. This isprobably because regional health risks such as the spread of HID/AIDS and malaria prevalence                                                                                                                        
15Although the World Health Organization (WHO) defines low weight as below 2500g, survey
estimates of low birth weight in developing countries are biased with heaping at multiples of 500g.
DHS in particular records birth weight from mothers by using health records if available and byrecall if records are unavailable. Consequently, many of the children heaped at 2500g probably
weigh less than that and should therefore be classified as low birth weight (Blanc & Wardlaw,
2005; Channon et al., 2011) .
12

are not controlled for when comparing the mean differences. 16 Thus, in following sections, we
conduct regression analyses controlling for these factors affecting infant mortality.

4. Empirical Identification Strategy  Our empirical approach exploits the possible unexpected increase of stress levels among pregnant
women due to exposure to the ethnic violence. Our empirical models are as follows;(1) ‫ݕ‬௜௠௧ௗ = ߚ଴ + ߚଵ (ܴௗ × ‫ܧ‬௧௠ ) + ߚ௫ ܺ௜ௗ௧ + ߙ௧ + ߜ௠ + ߨ௘ + ߱ௗ + ߙ௧ × ߱ௗ + ߝ௜௠௧ௗ
where y is the outcome variable; birth weight, premature birth, or infant mortality of child i bornin month m and year t in region d. The interaction term (ܴௗ × ‫ܧ‬௧௠ ) identifies children born in theaffected region (Rd) during the exposed period July 1992-December 1992 (Etm), ȋidt are parental,
child, and household characteristics, Įt, are year of birth fixed effects, įm, are month of birth fixed
effects, ʌe are ethnicity fixed effects to control for differences across ethnic groups residing in theaffected region, and Ȧd are region fixed effects to control for geographical and development                                                                                                                        
16The indirect and direct costs of AIDS increased from 2-4% in the early 1990s which coincides
with the violence period. The National Aids Control Council reported that there were between
250,000-300,000 AIDS orphans by 1994. Differences in regional HIV prevalence rates of Nyanza(18.3%), Nairobi (11.9%), Central (7.6%) and Rift Valley (6.9%) contributed to higher mortality
rates in those regions (Omariba, 2005; Wafula et al., 2012; National Council for Population andDevelopment, Central Bureau of Statistics, Office of the Vice Presient and Ministry of Panningand National Development, & Macro International Inc, 1994). Infant mortality rates in Central,
Rift Valley and Nairobi are 31, 45 and 44.4 deaths per 1000 live births respectively which are
below the 1993 national average of 61.7 deaths per 1000 births. In addition, 3 out of 4 Kenyans
are at risk of contracting malaria and is responsible for one third of outpatient treatments(Internews, 2014; KEMRI, 2015). Nyanza, Western and Coast regions with high infant mortality
rates also have high malaria prevalence rates with transmission all year round. The Rift Valley
highlands on the other hand only have seasonal transmissions (Internews, 2014).
13differences across regions, ߙ௧ × ߱ௗ controls for differences in time and regions, ȕs are coefficientsto be estimated, and İ is error term.
In the second specification, “month of exposure to the violence” (M) is used instead of an
indicator variable for whether a child was exposed in utero or not. Specification (2) measures theseverity of the violence based on the number of months of in utero exposure.
(2) ‫ݕ‬௜௠௧ௗ = ߚ଴ + ߚଵ (ܴௗ × ‫ܯ‬௧௠ ) + ߚ௫ ܺ௜ௗ௧ + ߙ௧ + ߜ௠ + ߨ௘ + ߱ௗ + ߙ௧ × ߱ௗ + ߝ௜௠௧ௗ
The third specification contains three interaction terms based on the timing of the exposure.
ଷ )
ଵ )
ଶ )
(3) ‫ݕ‬௜௠௧ௗ = ߚ଴ + 	
   ߚଵଵ (ܴௗ × ܶ௧௠+ 	
   ߚଵଶ (ܴௗ × ܶ௧௠+ 	
   ߚଵଷ (ܴௗ × ܶ௧௠+ ߚ௫ ܺ௜ௗ௧ + ߙ௧ + ߜ௠ + ߨ௘ +߱ௗ + ߙ௧ × ߱ௗ + ߝ௜௠௧ௗ ,where T1 is an indicator variable taking one when a child was exposed to violence for 1-3 months;
T2 is an indicator variable taking one when a child was exposed to violence for 4-6 months; and T3
is an indicator variable taking one when a child was exposed to violence for 7-9 months.
Birth weight records are missing for 3,223 children representing 53% of the total sample. This isbecause 55% of all Kenyan children were born at home and only 11.6% of them had birth weight
information. 17 As it is unlikely that birth weight records are missing randomly, estimations of the
impact of the violence on birth weight and low birth weight based only the sample with birthweight information are likely to be biased. Since availability of birth weight records is dependenton whether a child was delivered at a hospital/clinic or at home, using sub-sample with birthweight tends to suffer from selection bias.
                                                                                                                        
17In contrast, 88.3% of the children delivered in clinics have birth weight records.
14

To correct this selection bias associated with the place of delivery, we employ the Heckman
selection model. Our selection variable identifying a child as delivered at home, hospital or clinic
is a dummy variable indicating whether a mother received tetanus toxoid injection during
pregnancy or not. Medically, tetanus toxoid has no direct effect on fetal growth but is given to
reduce the risk of the child contracting tetanus during birth (Mwabu, 2008). We assume thatmothers who take the injection are also likely to be motivated to invest in good nutrition andbehaviors which promote fetal growth and development thus decrease the risk of death. Such amother is also most likely to choose the safer hospital or clinic delivery over home delivery. For
the analyses of premature birth and infant mortality, the dependent variables are dummy variables.
Thus, we rather apply Probit model. 18
5. Estimation Results
5.1.  Violence on Birth weight  Columns (1) - (3) of Table 3 present the estimated impact of exposure to ethnic violence on birthweight by Heckman model. 19 All specifications control for region, month of birth, year of birth,
ethnicity and the interaction of region and year of birth fixed effects. The estimated coefficient incolumn (1) is negative but not statistically significant. However, an additional month of in utero
exposure to the violence in Rift Valley is associated with 94 grams reduction in birth weight(column 2). Column 3 shows that the timing and length of the exposure matter: Although
exposure to violence for 1-3 months had no impact on birth weight, birth weight decreases when                                                                                                                        
18We run Linear Probability Model as well, but the results are qualitatively and quantitatively
similar.
19
The results of first stage regression are provided upon request.
15

the exposure period exceeds 4 months and the impact increases when the exposure period islonger than 6 months (551 grams and 836 grams less than those not exposed in utero for morethan 6 months and more than 9 months, respectively). The results in Table 3 show that forchildren born in Rift Valley, the longer a child was exposed in utero, the worse their birth weightoutcomes.
In Columns (4) to (6), the dependent variable is replaced with an indicator for the probability of
low birth weight. Column (4) shows that children who were exposed to the violence in utero havean 18.9 percentage points likelihood of being born with low birth weight. Column (5) indicatesthat an extra month of in utero exposure to the violence increased the probability of low birthweight by 7.5 percentage points. Column (6) shows that those who were exposed for 1-3 months,
4-6 months, and 7-9 months in utero have higher probability of low birth weight by 19.6, 45.6,
and 58.2 percentage points, respectively.
We thus find evidence that exposure to pre-natal maternal stress has a negative impact on achild’s birth weight. Indeed, our estimates exceed those reported in previous studies. Mansour &Rees (2012) found that prenatal exposure to the 2001-2004 Israeli-Palestinian conflict in the 1st,
2nd, and 3rd trimesters increased probabilities of low birth weight by 0.9%, 0.3% and 0.14%,
respectively. Both Lauderdale (2006) and Eskenazi et al. (2007) report that in utero exposure to
the September 11th 2001 New York attacks were associated with 1.50 and 1.44 increased odds of16

low birth weight, respectively. These findings suggest that exposure to the ethnic violence in
Kenya had worse outcomes. 20
5.2. Violence on Premature Birth and Infant Mortality  The estimation results for premature birth and infant mortality are presented in columns (7) to(12) of Table 3. We find a positive and significant impact of in utero exposure to the violence,
meaning that in-utero exposure increased probability of preterm births. The results from columns(7) and (8) show that children born in Rift Valley during the high intensity period had higher
probability of preterm birth by 5.1% than unexposed children. In addition, an extra month in utero
increased the probability of preterm births by 0.7% although the estimates are not significant. In
column 9, only those who were exposed to the violence for 1-3 months in utero increased theprobability of preterm birth (5.4%). These results are an indication of the impact of stress in
shortening the gestation period, thus increasing the fraction of births most vulnerable to poor
health and slowed physical and cognitive development. Compared with other studies, 21 the 1992
violence in Kenya seemed more serious in terms of premature birth.
In columns (10) – (12), we find that none of the exposure variables increased the probability of
infant mortality. Thus, Kenya’s electoral violence had impact on birth weight and preterm birth,but it did not increase infant mortality. The probability of infant mortality increases when                                                                                                                        
20The results from OLS are similar to Heckman. The OLS results are provided upon request.
The first trimester exposure to stress from the 2005 Chilean earthquake increased theprobability of preterm births by 8% (Torche, 2011). Likewise, preterm births for Arabic named
women after September 11th 2001 terrorist attack in New York increased by 1.1 percentage point.
21

17

children are born as multiple births, as first born, to unmarried mothers, and when parents did not
attain formal education.
5.3. Robustness Checks  5.3.1. Selective Migration due to the Violence
One may argue that some of the affected groups migrated to other areas to minimize the negative
effects. Consequently, the composition of children would be altered resulting in overestimation of
the effects if wealthier and healthier women had migrated from Rift Valley to other regions since
the onset of the clashes, and in underestimation if poorer and less healthy mothers had migrated
from Rift Valley to other regions.
It is documented that the affected population tended to move to temporary camps within Rift
Valley (Nyukuri, 1997). It is known that people in Rift Valley moved to their ethnic ancestral
regions to seek refuge (Human Rights Watch, 1993). In non-Rift Valley regions, those who lived
in the current residence for less than two years might have migrated from Rift Valley due to theviolence. Thus, to obtain conservative estimates of the impact of the violence on child health, we
redefine the affected as those who lived in Rift Valley for more than two years or those who lived
in non-Rift Valley regions for less than two years.

The same regression models are used

(Heckman model for birth weights, and Linear Probability model for low birth weight, prematurebirth, and infant mortality).

18

The estimation results in Appendix Table 1 show that even in this conservative definition of theaffected group, we obtain qualitatively and quantitatively similar results (see Table 3). This
indicates that our results are robust even after redefining the affected group.
5.3.2. Selective Fertility due to the Violence
Our estimation results can be due to the fact that women who had children during the violence
period were systematically different from others. For example, if those who were less healthy andless educated in the violent region and who could not avoid being pregnant had a child during theviolence, the coefficient of the interaction term becomes negative in our regression analyses on
health outcomes. Thus, in our final robustness checks, we compare characteristics of women in
the violent region who had children during the violence and the other women whose children were
included in our main child-level analyses. We regress women’s characteristics such as years ofeducation, fertility choice (number of children ever had, mother’s age at child birth), height (longterm health measure), and household’s characteristics such as household head’s age and female
headedness, on an indicator variable for if they had a child during the violent period. 22
Results in Appendix Table 2 shows that there are not any significant differences between women
who had children in Rift Valley during the violence period and women from non-affected regions,
except in height. This suggests that taller (healthier) women in the violence region tended to havea child during the violence, which causes downward bias of the interaction term. Thus, our                                                                                                                        
22We run OLS. Other controls are violence region dummy, a dummy variable if woman had child
who were born during the violence, current age of woman, ethnic group dummies.
19

estimates are considered to be a lower bound of the impact of the violence and it is likely that theviolence affected pregnant women and their children even more seriously.
5.4. Mechanism
The estimation results so far indicate that the 1992 Kenya’s electoral violence resulted in negative
health outcomes. Now a question is the mechanism. One possibility is the disruption of access tohealth services during the violence, which made it impossible for pregnant mothers to accessprenatal care. Prenatal care in general includes nutritional and health checks which help fetal
growth and development. One important prenatal care recommended to pregnant women istetanus vaccination during the pregnancy since it reduces the risk of child’s death after birth(Mwabu, 2008).
Appendix Table 3 shows the percent of children whose mothers took antenatal care and tetanus
injection during the pregnancy, separately for location (violence and non-violence region) andtiming of birth (exposed or non-exposed to the violence). The percent of children whose mothers
took antenatal care and tetanus injections in the violence region is lower than the other regions(the difference for antenatal care is not significant). Especially, the percent of women who took
the tetanus injection and whose children were born during the violence is much lower than that
before the violence. This declining trend was, however, found also in non-violence regions. Since
the comparison of the means is inconclusive, we examine whether mothers’ behavior of taking
prenatal care and tetanus injection changed due to the violence by applying the same estimation
model as above.

20

The results are shown in Table 4 where a dummy variable if women visited prenatal care during
the pregnancy is a dependent variable in columns (1)-(3) while a dummy variable of taking
tetanus injection during the pregnancy is a dependent variable in column (4)-(6). There is no
significant impact of the violence on behaviour to take prenatal care and tetanus injection,
regardless of exposure variables. This therefore means that worse health outcomes of children
during the violence period are not due to the disruption of access to health care services. Although
we cannot directly rule out the other pathways that influenced children’s health outcomes in thistime period because of data limitation, maternal stress resulting from exposure to ethnic violence(as postulated by Fetal Origin Hypothesis) is a plausible mechanism on how the exposure toviolence resulted in the worse health outcomes.

6. Conclusion  This study investigated the impact of prenatal exposure to maternal psychological stress on birthweight by exploiting a region and time specific population wide stressor; pre-election ethnic andpolitical violence as an exogenous source of stress. We found that exposure to prenatal stressnegatively affected the health of the exposed children. The results of our analysis show thatchildren born during the five months preceding the December 29th 1992 general elections andafter throughout to July 1993 had increased probabilities of low birth weight and premature birthby 18.9 percentage point and 5.1% respectively. In addition, an extra month of in utero exposure
to violence decreased birth weight by 94 grams and increased the probability of low birth weightby 7.5 percentage point. The findings are consistent with the hypothesis that prenatal exposure to21

conflict or violence affects birth outcomes through stress. Comparing our findings with the resultsof the existing studies reveals that exposure to the 1992 ethnic violence had a greater impact.
Our findings reveal important child, parental, and household determinants of birth outcomes. We
find that boys have better birth outcomes than girls with the differences very significant. Althoughmuch has been done to reduce the impact of gender related vulnerabilities, more needs to be done
to ensure that girls are adequately shielded from the negative consequences of all shocks. We also
found that first born children face increased risk of worse birth weight outcomes in the event of
exposure to shocks. We therefore recommend increased attention to care for women andespecially those pregnant with their first child. In addition, health care policies for response
during conflict or emergency situations should incorporate commitment to provision of nutritional
supplements, prenatal care and psychological support for pregnant women as prerequisites for thefunding and support of any program
Ethnically and politically instigated conflicts are unfortunately a common phenomenon in Kenya,
Africa and other developing economies. It is therefore important to understand the health,education and economic consequences of such events which derail human and economic
development efforts. A wide range of observational and experimental evidence strongly supports
the correlation between growth and development during fetal and early life and health in later life.
This therefore reinforces the increasing awareness that investment in the health and education ofpeople especially during pregnancy is of utmost importance.
Consequently, healthcare interventions should be replicated to pregnant mothers especially thosevulnerable to shocks emanating from exposure to other forms of conflicts, discrimination,

22

economic conditions or natural disasters. These efforts can be successful if the outcome of apregnancy is considered in terms of maternal and neonatal health, growth and cognitive
development of a child, productivity and health in adulthood and the health of the future
generations.

23

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28

Table 1. Descriptive Statistics by Affected Region and Period
Full Sample
Mean
SD

Other regions(non-violent)
Mean
SD

Rift Valley(violent)
Mean
SD

Observations

6115

4589

1526

Male child
Single birthFirst born child
Age in years
Mother's age at child's birth

0.499
0.972
0.202
2.000
26.12

0.500
0.164
0.402
1.400
6.734

0.495
0.974
0.206
2.019
26.072

0.489
0.156
0.396
1.364
6.600

0.514
0.966
0.190
1.925
26.290Panel B: Parental Characteristics
Mother's Educational level
No education
Primary level
Secondary level
Higher education

0.192
0.594
0.211
0.004

0.394
0.491
0.408
0.059

0.174
0.602
0.222
0.002

0.371
0.479
0.407
0.045

Father's Educational level
No education
Primary level
Secondary level
Higher education

0.188
0.490
0.309
0.011

0.391
0.500
0.462
0.108

0.167
0.501
0.321
0.011

Mother's Marital Status
Currently married
Never married
Formerly marriedMonths of Malaria risk in Pregnancy

0.850
0.074
0.076
4.390

0.357
0.262
0.265
3.980

40.2610.282
0.862
20.290
0.873
0.962
0.133
0.144
0.160
0.067
0.178
0.127
0.072
0.063
0.002
0.007
0.045

Difference

T value a

0.536
0.195
0.421
1.507
7.190

-0.019
0.008
0.016
0.094
-0.218

-1.08
1.05
1.12
2.32**
-0.77

0.255
0.564
0.172
0.009

0.468
0.532
0.405
0.010

-0.0810.038
0.050-0.007-2.02**
0.96
1.71*
-1.47

0.365
0.489
0.457
0.102

0.267
0.451
0.268
0.014

0.475
0.534
0.475
0.127

-0.1000.050
0.053-0.003-3.75**
1.73*
2.01**
-0.47

0.847
0.068
0.083
5.551

0.353
0.245
0.270
3.654

0.863
0.077
0.050
0.189

0.369
0.290
0.235
0.496

-0.016-0.009
0.033
5.362

-1.05
-0.81
3.20**
49.62**

13.601
0.450
0.345
17.54
0.333
0.191

40.7100.300
0.859
18.1480.865
0.096

13.6720.449
0.340
15.4560.334
0.184

38.6100.216
0.871
28.0110.900
0.959

12.9230.442
0.360
22.548
0.323
0.212

2.100
0.084
-0.012-9.863-0.035-0.086

3.16**
3.56**-0.47-3.86**
-1.19
0.88

0.339
0.351
0.367
0.251
0.382
0.333
0.258
0.244
0.050
0.082
0.206

0.008
0.182
0.161
0.076
0.200
0.152
0.091
0.081
0.003
0.090
0.036

0.085
0.378
0.360
0.260
0.391
0.352
0.282
0.267
0.052
0.090
0.182

0.587
0.007
0.157
0.035
0.100
0.037
0.000
0.000
0.001
0.000
0.076

0.529
0.091
0.391
0.198
0.321
0.201
0.000
0.000
0.037
0.000
0.284

-0.579
0.175
0.004
0.041
0.100
0.115
0.091
0.081
0.002
0.090
-0.040-11.63**
7.83**
0.09
1.9*
3.73**
6.48**
6.38**
7.55**
1.07
5.08**-1.4

Panel A: Child characteristics

Panel C: Household Characteristics
Age of household head
Female household headLand ownershipDistance to hospital
Rural residence
Lived in the same region for many years
Ethnicity
Kalenjin
Kamba
Kikuyu
Kisii
Luhya
Luo
Meru/Embu
Mijikenda/ Swahili
SomaliTaita/Taveta
Others

Notes: Sampling weights are used in calculating all the means. . a The t-value tests whether means for Rift Valley and Other
Regions are statistically different or not. * Significant at 10 percent;** significant at 5 percent.

29

Table 2. Health Outcomes by Affected Region and Period
Characteristics

Full sample
Mean

SD

Other regions(non-violent)
Mean

SD

Rift Valley(Violent)
Mean

Difference(non-violent– Violent)

T ValueSD

Panel A: Birth weight (kg)Observations
2651
2108
543
Birth weight
3.282
0.747
3.281
0.737
3.285
0.791
-0.004-0.06
Born before violence (not exposed in utero)
3.290
0.754
3.29
0.743
3.288
0.802
0.002
0.04
Born during violence
3.241
0.710
3.235
0.704
3.268
0.733
-0.033-0.27
Diff. between before and during violence
0.049 (1.10)
0.055 (1.16)
0.020 (0.18)-0.004-0.04
Not exposed in utero
3.296
0.752
3.295
0.741
3.299
0.799
-0.004-0.08
1-3 months exposure in utero
3.253
0.726
3.291
0.673
3.119
0.884
0.172
0.80
4-6 months exposure in utero
3.119
0.776
3.106
0.790
3.196
0.678
-0.090-0.44
7-9 months exposure in utero
3.210
0.626
3.190
0.646
3.300
0.489
-0.110-0.72
Panel B: Birth weight<=2.5kg
Birth weight<=2.5kgs
0.156
0.363
0.157
0.359
0.151
0.377
0.006
0.26
Born before violence
0.155
0.362
0.158
0.360
0.142
0.369
0.016
0.75
Born during violence
0.159
0.366
0.150
0.355
0.198
0.410
-0.048-0.82
Diff. between before and during violence-0.004 (-0.022)
0.008 (0.32)
0.064
0.89
-0.056 (-1.04)
Not exposed in utero
0.153
0.360
0.156
0.358
0.139
0.365
0.017
0.77
1-3 months exposure in utero
0.168
0.376
0.126
0.330
0.314
0.481
-0.188-1.76*
4-6 months exposure in utero
0.225
0.420
0.218
0.414
0.268
0.45
-0.050-0.37
7-9 months exposure in utero
0.135
0.343
0.148
0.353
0.07
0.271
0.078
0.90
Panel C: Premature birthObservations
6064
4551
1513
Premature birth
0.036
0.187
0.038
0.186
0.032
0.189
0.006
0.96
Born before violence
0.034
0.180
0.037
0.184
0.022
0.158
0.015
2.49**
Born during violence
0.050
0.218
0.043
0.198
0.075
0.278
-0.032-1.64*
Diff. between before and during violence-0.017 (-2.03)**-0.006 (-0.69)-0.053 (-2.75)**
0.047
2.24**
Not exposed in utero
0.034
0.181
0.037
0.185
0.022
0.159
0.015
2.51**
1-3 months exposure in utero
0.06
0.239
0.036
0.183
0.121
0.338
-0.085-2.06**
4-6 months exposure in utero
0.045
0.208
0.043
0.199
0.054
0.240
-0.011-0.33
7-9 months exposure in utero
0.045
0.207
0.046
0.207
0.038
0.205
0.008
0.24
Panel D: Infant Mortality
Observations
6115
4589
1526
Infant mortality
0.058
0.234
0.066
0.243
0.03
0.183
0.036
4.71**
Born before violence
0.057
0.231
0.063
0.238
0.033
0.194
0.030
3.42**
Born during violence
0.064
0.244
0.078
0.264
0.015
0.13
0.063
4.44**
Diff. between before and during violence-0.007 (-0.66)-0.015 (-1.18)
0.018 (1.74)*-0.033-1.38
Not exposed in utero
0.057
0.232
0.063
0.238
0.033
0.193
0.030
3.56**
1-3 months exposure in utero
0.057
0.231
0.073
0.257
0.015
0.125
0.058
3.01**
4-6 months exposure in utero
0.098
0.298
0.116
0.315
0.030
0.181
0.086
2.29**
7-9 months exposure in utero
0.039
0.194
0.049
0.212
0
0
0.049
3.17**
Sampling weights are used in calculating all the means. . a The t-value is for testing whether means of Rift Valley and Other Regions
are statistically different or not. * Significant at 10 percent;** significant at 5 percent.

  30

Table 3. Impact of Ethnic Violence on Birth weight, Premature Birth, and Infant Mortality
Birth weight (kgs)(1)
Rift Valley× Born during violence(2)-0.0939**(-2.03)

Mother's Age at Child's Birth/100
Mother's Age at Child's Birthsquared/1000
Mother’s education: No Education
Primary level
Secondary level
Father’s education: No Education
Primary level
Secondary level

(5)

(6)

Premature birth(7)

0.0736**(2.24)
0.714**(7.65)-0.0964**(-2.14)-0.0875(-0.04)0.113(0.30)
0.0614(0.25)
0.141(0.57)
0.177(0.72)
0.0775(0.58)
0.0502(0.42)

0.0736**(2.24)
0.714**(7.66)-0.0952**(-2.11)-0.112(-0.05)0.119(0.32)
0.0707(0.28)
0.152(0.61)
0.187(0.75)
0.0755(0.57)
0.0456(0.39)

0.06600.06150.0621-0.0260*(-1.70)-0.361**(-5.79)0.0329(1.21)-0.711(-0.63)
0.0937(0.47)-0.0643(-0.48)-0.0984(-0.80)-0.124(-1.06)
0.0679(0.83)
0.0668(0.88)
0.0496(8)

Infant mortality(9)0.0514**(2.71)
0.0748**(2.85)-0.273(-1.44)-0.551*(-1.88)-0.836**(-2.45)0.0737**(2.24)
0.714**(7.67)-0.0948**(-2.10)-0.0603(-0.03)0.109(0.29)
0.0559(0.22)
0.137(0.55)
0.172(0.69)
0.0764(0.58)
0.0475(0.40)

Rift Valley× 7-9 months in uteroFirst born dummy(4)0.189**(2.36)

Rift Valley× 4-6 months in utero

Single birth dummy(3)-0.176(-1.30)

Rift Valley× Months of Exposurein Utero
Rift Valley×1-3 months in utero

Male child dummy

Low birth weight (<=2.5kg)

0.0066(1.34)-0.0260*(-1.71)-0.361**(-5.81)0.0320(1.24)-0.669(-0.60)0.0853(0.43)-0.0731(-0.55)-0.108(-0.87)-0.134(-1.12)0.0701(0.88)
0.0710(0.97)

0.196*(1.89)
0.456**(2.46)
0.582**(2.86)-0.0256*(-1.68)-0.361**(-5.81)0.0326(1.21)-0.715(-0.64)0.0946(0.48)-0.0634(-0.47)-0.0972(-0.79)-0.123(-1.05)0.0682(0.84)
0.0672(0.89)-0.0073(-1.44)-0.067**(-4.59)0.0252**(3.32)-0.391(-1.15)0.000081(1.39)-0.098**(-2.63)-0.090**(-2.47)-0.075**(-2.12)0.021(0.67)
0.0248(0.83)

0.05340.05130.0191

31

(10)

(11)

(12)

0.0165(0.51)
0.00823(0.81)-0.00718(-1.42)-0.0677**(-4.69)0.0251**(3.30)-0.39(-1.14)0.0807(1.38)-0.100**(-2.67)-0.0916**(-2.50)-0.076**(-2.14)0.0202(0.65)
0.024(0.80)

0.0536**(2.75)
0.0476(1.51)
0.0648(1.25)-0.00706(-1.40)-0.0663**(-4.53)0.025**(3.30)-0.384(-1.13)0.0796(1.37)-0.0979**(-2.64)-0.0902**
9-2.49)-0.0746**(-2.13)0.0217(0.70)
0.026(0.88)-0.00426(-0.69)-0.0933**(-6.98)0.0214**(2.09)
0.004(-0.93)0.0884(1.22)
0.404**(13.28)0.425**(15.72)
0.396**(12.55)0.06(1.29)
0.0487(1.08)-0.00423(-0.69)-0.0935**(-6.55)0.0214**(2.08)-0.096(-0.93)0.0883(1.22)
0.404**(10.32)0.425**(11.28)0.397**(10.63)0.0602(1.29)
0.049(1.08)-0.00787(-0.22)0.0529(1.07)
0(0.00)-0.00437(-0.70)-0.0948**(-6.65)0.0216**(2.08)-0.405(-0.94)0.0875(1.24)
0.403**(11.02)0.424**(11.67)0.395**(11.31)0.06(1.27)
0.0482(1.05)

0.01850.02050.04540.04570.045

Mother marital status:
Currently Married
Never MarriedMonths of Malaria Exposurein Pregnancy/100
Female Household HeadAge of Household Head/1000
Land ownership dummy
Distance to Hospital(km)/1000
Constant
Region fixed effects(0.58)(0.54)(0.55)(0.72)(0.78)(0.75)(0.64)(0.62)(0.69)(1.00)(1.01)(0.98)

0.0325(0.39)-0.0958(-0.96)-0.436(-0.46)-0.0246(-0.59)-0.327(-0.24)0.0320(0.39)-0.0994(-1.00)-0.415(-0.44)-0.0248(-0.60)-0.342(-0.25)0.0316(0.38)-0.0981(-0.99)-0.401(-0.42)-0.0248(-0.60)-0.308(-0.22)-0.0210(-0.55)0.0306(0.54)
0.530(1.23)
0.0123(0.61)-0.211(-0.31)-0.0211(-0.55)0.0330(0.59)
0.523(1.21)
0.0123(0.61)-0.195(-0.29)-0.0205(-0.53)0.0308(0.54)
0.518(1.20)
0.0123(0.61)-0.212(-0.31)0.00769(0.66)
0.0109(0.67)-0.104(-0.72)0.0004(0.06)
0.0401(0.19)

0.00755(0.65)
0.0109(0.66)-0.096(-0.66)0.0002(0.00)
0.0513(0.24)

0.00755(0.65)
0.0115(0.70)-0.108(-0.76)0.0002(0.04)
0.0363(0.17)-0.0293**(-2.45)-0.228(-1.23)0.0287(0.31)-0.0069(-0.90)-0.181(-0.79)-0.0292**(-2.42)-0.0226(-1.21)
0.0678(0.31)-0.0069(-0.90)-0.179(-0.78)-0.0294**(-2.42)-0.023(-1.22)0.0706(0.32)-0.0069(-0.89)-0.177(-0.76)0.04920.04960.04980.00333

0.003240.00271-0.011-0.0112-0.0111-0.00447-0.00443-0.00453(1.12)(1.14)(1.14)(0.13)(0.14)(0.11)(-1.40)(-1.42)(-1.41)(-0.45)(-0.45)(-0.45)

0.630

0.666

0.718

-0.292-0.311-0.073

0.139

0.188

0.136

0.287

0.282

0.280

(0.54)(0.57)(0.62)(-0.59)(-0.63)(-0.75)(0.85)(0.87)(0.82)(1.41)(1.42)(1.41)

2.413**

2.410**

2.413**

0.653**

0.654**

0.651**-3.515**-3.509**-3.528**-8.751**-8.761**-8.641**(5.58)(5.57)(5.61)(-4.16)(-4.19)

yes

yes

yes

yes

(-9.51)yes(-9.45)yes(-9.23)yes(3.10)
yes(-4.17)yes(3.06)
yes(3.07)

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Year of birth fixed effects

yes

yes

yes

yes

Month of birth fixed effects

yes

yes

yes

yes

yes
yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Ethnicity fixed effects

yes

yes

yes

yes

Region*year fixed effects

yes

yes

yes

yes

yes

Observations
5922
5922
5922
5922
5922
5922
6009
6009
6009
6025
6025
5962
Notes: t statistics in parentheses. *significant at 10%; **significant at 5%. Columns (1) – (6) are estimated by Heckman Selection model (OLS in the 2nd stage) and figures are coefficients. Columns(7) – (12) are estimated by Probit model and figures are marginal effects.

32

Table 4. Impact of Violence on Antenatal Care and Tetanus Injection (Linear Probability Model)
Antenatal Care
(1)
Rift Valley × Born during violence

Tetanus Injection

(2)

(3)

(4)

0.04120.00659(0.57)(0.08)(5)(6)

Rift Valley × Months of Exposure in Utero
Rift Valley× 1-3 months in utero
Rift Valley× 4-6 months in utero

0.002500.00164(0.24)(0.11)
0.02270.00969(0.44)(0.18)

0.04200.0261

Rift Valley× 7-9 months in utero
5217
6011
6011
5133
5923
5923
Observations
Notes: t statistics in parentheses. *siginificant at 10%; **siginificant at 5%. The sample includes all children 0-5 years. All regressions include controls forchild characteristics such as sex, single birth, birth order and mother's age at child birth. Parental characteristics include mother and father's education level,mother's marital status, and months of malaria exposure in pregnancy. Household characteristics include sex and age of the household head, land ownership,and distance to hospital as well as region, year of birth, month of birth, ethnicity, and region-year fixed effects.

33

Table A1. Impact of Violence on Birth weight, Premature Birth, and Infant Mortality (Conservative Definition of the Affected)
Birth weight (kgs)(1)
Rift Valley × Born during violence(2)

Low birth weight (<=2.5kg)(3)-0.310(-1.51)

Rift Valley × Months of Exposure in Utero(4)

Rift Valley× 4-6 months in utero
Rift Valley× 7-9 months in utero

(6)

(7)

(8)

Infant mortality
(9)

(10)

0.215**

0.035**

0.019(3.61)(2.69)(0.92)-0.0974*(-2.00)

Rift Valley× 1-3 months in utero(5)

Premature birth(11)0.207**

0.0058**0.0019(3.97)(2.04)(0.42)(12)-0.1620.457

0.0423**0.0054(-0.62)(2.50)(2.56)(0.19)-0.4030.06710.0373*0.0508(-1.10)(3.74)(1.73)(1.62)-1.043**0.272

0.044*(-3.48)(2.83)(1.66)
5922
5922
5922
5915
5915
5915
6009
6009
6009
6060
6060
6060
Observations
Notes: Figures are coefficients in columns (1)-(6) while they are marginal effects in columns (7)-(12). z and t statistics in parentheses. *siginificant at 10%; **siginificant at 5%. The sample
includes all children 0-5 years. All regressions include controls for child characteristics such as sex, single birth, birth order and mother's age at child birth. Parental characteristics include
mother and father's education level, mother's marital status, and months of malaria exposure in pregnancy. Household characteristics include sex and age of the household head, landownership, and distance to hospital as well as region, year of birth, month of birth, ethnicity, region-year fixed effects.

  34

Table A2. Characteristics of women with children born during the violence
Mother's Characteristics

Rift Valley* Has a child born during violence
Rift Valley
Has a child born during violence
Constant

Observations

Highest year of
education

Number ofchildren

Height incentimeters

Mother's age

Household’s characteristics
FemaleAge ofHouseholdHousehold
Head
Head

(1)

(2)

(3)

(4)

(5)

(6)

-0.07100.06392.005**

0.729

0.008221.352(-0.18)(0.22)(2.58)(0.93)(0.20)(1.08)-0.558*0.302*

0.480-0.157-0.0801**-2.009**(-1.78)(1.81)(0.82)(-0.43)(-3.21)(-2.61)0.212-0.0807-0.251-1.990**-0.0647**0.0679(1.47)(-0.62)(-0.62)(-5.88)(-2.95)(0.10)

5.605**

4.091**

159.0**

28.96**

0.330**

41.33**(50.73)(58.67)(552.37)(164.21)(22.90)(92.48)

3925

3925

3713

3925

3925

3918

Notes: t statistics in parentheses *significant at 10%; **significant at 5%.

35

Table A3. Antenatal Care and Tetanus Injection by Affected Region and Period
Characteristics

Full sample

Other regions(non-violent)

Rift Valley(Violent)

Difference(non-violent– Violent)

T Value

Mean

SD

Mean

SD

Mean

SD

Panel A: Antenatal Care
Observations
Birth weight
Born before violence (not exposed in utero)

6065
0.945
0.948

0.228
0.222

4551
0.095
0.95

0.218
0.212

1513
0.936
0.939

0.262
0.258

0.011
0.014

0.87
0.92

Born during violence

0.931

0.254

0.933

0.245

0.924

0.281

0.01

0.37

Diff. between before and during violence-0.017 (-1.67)*
0.017 (1.49)
0.015 (0.74)
0.002
1.48
Not exposed in utero
0.947
0.224
0.949
0.214
0.938
0.26
0.011
0.95
1-3 months exposure in utero
0.914
0.281
0.915
0.276
0.911
0.295
0.004
0.08
4-6 months exposure in utero
0.942
0.234
0.941
0.232
0.945
0.241
-0.004-0.1
7-9 months exposure in utero
0.95
0.218
0.954
0.207
0.936
0.262
0.018
0.46
Panel B: Tetanus Injection
Observations
5977
4480
1497
Infant mortality
0.911
0.285
0.916
0.272
0.894
0.331
0.022
1.53
Born before violence
0.919
0.273
0.922
0.262
0.907
0.313
0.015
1.09
Born during violence
0.874
0.332
0.885
0.314
0.838
0.389
0.047
1.65*
Diff. between before and during violence
0.045 (3.86)**
0.037(2.81)**0.069 (2.92)**-0.032-0.38
Not exposed in utero
0.918
0.274
0.922
0.262
0.905
0.315
0.017
1.19
1-3 months exposure in utero
0.871
0.335
0.89
0.309
0.824
0.394
0.066
1.18
4-6 months exposure in utero
0.885
0.319
0.902
0.294
0.82
0.22
0.082
1.45
7-9 months exposure in utero
0.86
0.345
0.856
0.346
0.875
0.351
-0.019-0.38
Sampling weights are used in calculating all the means. . a The t-value is for testing whether means of Rift Valley and Other Regions
are statistically different or not. * Significant at 10 percent;** significant at 5 percent.

36