Impact of Free/Subsidized Secondary School Education on the Likelihood of Teenage Motherhood
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Demography (2021) 58(4):1401–1421 Published online: 5 July 2021
DOI 10.1215/00703370-9357498 © 2021 The Author
This is an open access article distributed under the terms of a Creative Commons license (CC BY-NC-ND 4.0).
Impact of Free/Subsidized Secondary School Education
on the Likelihood of Teenage Motherhood
Steve Muchiri
ABSTRACT Several countries in sub-Saharan Africa, including Kenya, have introduced
free/subsidized secondary education. This paper examines the role of these free/sub
sidized education policies on teenage motherhood. To identify the causal effect, I
exploit the timing of a national reform in Kenya that elimin ated/subsidized secondary
school fees using a difference-in-difference estimation design. Using the 2014 Kenya
Demographic and Health Survey (DHS), I estimate that the likelihood of teenage moth
erhood decreased by approxim ately 5 percentage points after the policy’s implemen-
tation. This study reiterates that the teenage period is crucial in terms of developing
human capital through formal schooling. In most developing countries, parents often
determine and fund human capital, which makes household wealth/income a critical
factor in human capital accumulation and its intergenerational process. I also highlight
positive externalities from educational-centered policies, such as long-term economic
growth, poverty reduction, and reduction of social welfare dependency.
KEYWORDS Teenage moth er
hood • Sub-Saharan Africa • Education • Poverty •
Educational policies
Introduction
In 2013, the United Nations Population Fund (UNFPA 2013b; formerly the United
Nations Fund for Population Activities) noted that teenage pregnancy1 remains a
global challenge that requires urgent resolve. Demonstrating the gravity of teenage
pregnancy, the UNFPA (2015) reported that approximately 16 million girls aged 15–
19 become pregnant worldwide. Estimates for 2010 show that about 95% of adoles
cent births worldwide occur in developing countries, with about 36 million women
aged 20–24 reporting having given birth before their 18th birthday and about 5.6
million reporting a birth before their 15th birthday (UNFPA 2013a, 2013b). In 2013,
the highest incidence of teenage pregnancies occurred in sub-Saharan Africa. In this
region, teenage births accounted for more than one-half of allbirths, an estimated 101
1
Teenage pregnancy is defined as pregnancies among girls aged 10–19. Developed countries often use
age to define teenage pregnancy. Although developing countries also use age to define teenage pregnancy,
they consider teenage pregnancy to be an issue only when it involves unmarried girls (Cherry et al. 2009).
In this paper, I use the term teenage motherhood, defined as teenage childbearing regardless of marital sta
tus. Additionally, I prefer the term motherhood as opposed to pregnancy because the data do not provide
information on abortions, those currently pregnant, or miscarriages.
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births per 1,000 women, which was almost double the global average (Odimegwu
and Mkwananzi 2016). Additionally, among the 15 countries worldwide where more
than 30% of 20- to 24-year-olds gave birth before age 18, 14 are in the sub-Saharan
region, including Niger, Uganda, and Malawi. Although Kenya is not among this
group, it is in the top 10 in terms of numbers of women aged 20–24 who gave birth
by their 18th birthday (see Loaiza and Liang 2013).
Ramifications of teenage childbearing are enormous, one of the foremost being
its high risk of adverse health outcomes for the mother and the child (Kennedy et al.
2011; Magadi 2006). For instance, whereas these births account for approximately
11% of births worldwide, they contribute approxim ately 25% of allthe pregnancy- or
childbirth-related mortality, with the mortality rate of 10- to 14-year-olds estimated
to be fivefold that of adult women (Kennedy et al. 2011). Among 15- to 19-year-olds,
childbirth complications are the leading cause of death (Godding 2008). Infants face
similar adverse outcomes, such as preterm delivery, low birth weight, greater than a
50% increased risk of dying within the first month of life, and higher rates of perina
tal morbidity compared with those born to adult women (Kennedy et al. 2011; Patton
et al. 2009; Shah and Åhman 2012).
Most teenage mothers drop out of school and may not return; they are vulnera
ble to a variety of unfavorable outcomes, such as high rates of unemployment, low
labor market earnings, lower prospects of marriage, and high welfare dependency
and poverty rates (Godding 2008; Hao and Cherlin 2004; Micklewright and Stewart
1999). Consequently, these teenage mothers are unprepared for the psychological,
emotional, and financial responsibilities and challenges of being a parent (Hoffman
and Maynard 2008), and they are more likely to perpetuate these unfavorable out
comes to multiple generations. The phenomenon is not limited to developing coun-
tries: developed countries face similar challenges, with studies showing that children
born to teenage mothers are likely to have lower levels of education and are later
likely to become teenage parents themselves (Bonell et al. 2006; Jaffee et al. 2001;
Moffitt and Team 2002; Pevalin 2003). However, the developed countries often have
programs in place to ease teenage mothers’ financial burdens or to provide them with
avenues for self-reliance. Because many governments in developing countries are
already budget-constrained, they may not be a ble to afford such programs.
In developing countries, the likelihood of teenage motherhood is influenced by
myriad issues, such as education level, culture (ethnicity and religion), socioeco
nomic status, and demographic or sexual behavior factors (e.g., early marriage, age
at sexual debut) (Pradhan et al. 2015). The United Nations Population Fund (UNFPA
2015) reported that girls in the poorest regions of the world are four times more likely
to give birth in adolescence than girls in richer regions.
These observations are particularly relevant in sub-Saharan Africa, a region with
the highest teenage pregnancy rates and a high proportion of girls with poor socio
economic status (Odejimi and Young 2014). Therefore, identifying protective factors
against teenage motherhood, especially in sub-Saharan Africa, could inform policy-
making and help abate the adverse health and economic outcomes of teenage mother
hood. Human capital improvement is critical, and reducing barriers to schooling (e.g.,
cost), especially for girls whose school enrollment rate drops during the teenage years
(Lloyd et al. 2000), is especially important (Duflo 2004; Duflo et al. 2015; Keats
2014; Odejimi and Young 2014; Osili and Long 2008).
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The concentration of adolescent girls aged 10–17 is projected to increase world
wide, with the most substantial change expected to occur in sub-Saharan Africa,2
where adolescent pregnancy is most common, the use of contraceptives is the low
est, and income levels are among the lowest in the world. In general, ages 6–18 are
officially primary and secondary school ages. Unfortunately, many girls are out of
school, especially in sub-Saharan Africa and other Asian regions, such as South and
West Asia, where almost one-third of adolescents of secondary school age are out of
school (Loaiza and Liang 2013).
Addressing adolescents’ pregnancies and vulnerabilities parallel one of the United
Nations Millennium Development Goals: to increase the level of formal education for
allschool-age children by 2015 and to provide both boys and girls with equal access to
education at all levels.3 Addressing the initiative of education for all, several countries
in sub-Saharan Africa have taken steps to eliminate or have already eliminated primary
school fees in government-aided (public) schools: Malawi (in 1994); Uganda (in 1997);
Tanzania (in 2000); and Cameroon, Burundi, Ghana, Rwanda, and Kenya (in 2003)
(Grogan 2008). Other countries—such as Uganda (in 2007), Rwanda (in 2007), and
Tanzania (in 2016)—have expanded these efforts to the secondary school level.
For many countries, an immediate outcome of this policy has been a significant
increase in school enrollment, perhaps an indication of its high impact or a grave state
of many households that were previously unable to educate their children. As a point
of reference, the average net enrollment ratio for primary school education in Africa
increased from 56% in 1999 to 73% in 2007. Although the average net enrollment
ratio for secondary education is lower, it showed a 9 percentage point improvement
(from 18% to 27%) in the same period (Ohba 2011). The relatively small response at
the secondary school level could be attributed to two factors. First, in Kenya, parents
are customarily responsible for guiding and selecting a child’s primary and sometimes
secondary school level. Parents’ incentives, however, may not be fully aligned with the
children’s long-term potential earnings (Baland and Robinson 2000), and parents may
opt to enter the child into the labor market. Second, Jensen (2010) noted that misinfor
mation on the financial returns to further schooling may lead individuals (in this case,
parents) to underinvest in schooling. This point is particularly important in the context
of Kenya, where some communities may hold a negative perception of formal educa
tion, especially in areas with high unemployment rates among high school graduates.
Despite evidence of increasing school enrollment resulting from free primary or
secondary education, the full impact of free education is subject to research, and results
from some research are still contested. For example, some studies have found that free
primary education (FPE) increased public primary school enrollment and did not com
promise the quality of education (Lucas and Mbiti 2012a). Some studies have found
that although FPE improved both boys’ and girls’ primary school completion rates, it
had a more substantial impact on boys, thereby increasing the gender gap in graduation
(Lucas and Mbiti 2012b). Other studies have not found evidence of a net change in
public school enrollment (Bold et al. 2011), perhaps because of a substantial increase in
2
It is projected that by 2030, approximately 1 in every 4 adolescent girls will reside in sub-Saharan Africa.
Countries with the highest projected increases are Nigeria (9.2 million), United Republic of Tanzania (3.7
million), Democratic Republic of the Congo (3.3 million), Uganda (2.5 million), and Kenya (2.3 million).
3
See https://www.un.org/sustainabledevelopment/education/.
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private school enrollment, particul arly in urban areas (Dixon and Tooley 2012). Aiming
to add to this literature, I examine the impact of a nationwide elimin ation or subsidizing
of public secondary school fees on the likelihood of teenage motherhood.
I take advantage of the national free secondary education (FSE) program in Kenya
and use a difference-in-difference framework to analyze the question. Although the
mechanism through which the FSE program acted is not straightforward, I find evi
dence of a reduced likelihood of teenage motherhood. Although these results are
irreconcilable with Filmer and Schady’s (2014) findings for Cambodia, for example,
they complement those of Baird et al. (2010) and Ferré (2009) showing that improv
ing secondary school attendance in East Africa had a significant impact on teenage
motherhood.
Education System and Free Secondary Schooling
Before 1985, the Kenyan education system required seven years of primary edu
cation, four years of secondary education, two years of high school education, and
a minimum of three years in the university. To create a more practical curriculum
(vocationally oriented curriculum), the Kenyan government overhauled the education
system in 1985 to require eight years of primary education, four years of secondary
education, and a minimum of four years in the university (Amutabi 2003).4 Students
are required to take the Kenya Certificate of Primary Education (KCPE) exam to com
plete primary education and the Kenya Certificate of Secondary Education (KCSE)
exam to complete secondary education. Both exams are nationally standardized and
are used for high school and university admissions, respectively. Because students are
required to register for these exams in advance, school enrollment is essential for test
taking. Historically, however, many schools denied students with outstanding school
fees the opportunity to register for these exams, putting additional financial pressure
on poor households to pay the outstanding fees or forfeit the students’ opportunity to
take the national exam.5
Attendance Patterns and the Reform
Kenya has historically had fewer high schools than primary schools. When it gained
its independence in 1963, Kenya had approxim ately 40 times more primary schools
than secondary schools (Ohba 2011). This gap, however, shrunk to almost 4 times
by 2011 (Glennerster et al. 2011) mostly due to community-built secondary schools
(Ohba 2011). Because the establishment of these community-built schools depended
4
In Kenya, secondary education is a single stage in the education system, following primary school edu
cation but preceding the tertiary level. In other countries, education may be divided into lower/junior and
upper/senior levels. This study examines a single stage of secondary education designed for students to
begin primary school at age 6 or 7, although some students delay their entry into formal schooling or with
draw and reenroll when they are older (Kramon and Posner 2016).
5
Registration for the exams, taken in October and November, occurs in March. Those failing to register in
time are often denied the opportunity to take the exams and must wait a year for a second chance.
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on a community’s economic development and strength, some areas were unable to
establish their own.
In addition, although the number of secondary schools in Kenya has increased, sig
nificant bottlenecks in the education system remain. For instance, distance to the near-
est secondary school is still an impediment, more so in some rural areas than in others.
In some areas, less than 50% of individuals are within walking distance of a secondary
school (Glennerster et al. 2011). According to Alderman and King (1998), walking dis
tance to school has a more significant influence on females’ schooling because of safety
concerns. Other impediments to Kenya’s education system include low transition rates
to secondary school (Ohba 2009) and high dropout rates in primary schools (Somerset
2007). The cost of education remains an especially significant barrier for many house
holds (Glennerster et al. 2011; Holla and Kremer 2009; Keats 2014).
Kenya introduced a free secondary education (FSE) program in February 2008. In
his FSE introduction speech, President Kibaki clearly articulated that “. . . the main
objective of providing free secondary education is to ensure that children from poor
households acquire quality education that enables them access [to] opportunities for
self-advancement and become productive members of society” (Ohba 2009:5). The
FSE program covered 10,265 KES (approxim ately US$100) per public secondary stu
dent per year in basic tuition expenses.6 Glennerster et al. (2011) estimated that house
hold spending on secondary education amounts to one-half of their expenditures, and
they spend approximately 8 times more on education for secondary school students than
for primary school students: an average of 25,000 vs. 3,000 KES per year. The govern
ment grant covered only approximately one-half of the cost per student, making house
holds responsible for the costs of school uniforms, boarding fees, and infrastructure and
therefore leaving significant financial burden on many families for whom the cost of
school fees remains a significant barrier to schooling. Funds from the grant were distrib
uted from the central government to individual schools based on the number of students.
Although the FSE program was national, regional variation in program intensity
is likely, perhaps stemming from heterogeneity in pre-program transition rates from
primary to secondary school across the country. Regions with low pre-FSE program
transition rates to secondary school could have experienced larger increases in sec
ondary school enrollment. For instance, North Eastern province may have experienced
higher program intensity than Nairobi and thus may have experienced a larger decline
in teenage motherhood. However, the data I use do not allow me to disentangle poten
tial variance in intensity levels by region, and the results I present are a country average.
Possible Link Between Education and Pregnancy
I first offer a simple mechanism on how a household’s wealth can impact schooling
and teenage motherhood. Suppose a teenage population is split between those who
reside in poor households, P, and those that reside in wealthy households, W. Assume
that teenager i is endowed with ability αi and lives in a household with assets Ait at
time t. I assume that αi varies such that students from wealthy households are more
6
This program aid was not available to students attending private schools.
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likely to perform well on the KCPE exam because they can purchase textbooks or
pay for tutors or because they are less likely to miss meals. However, credit con
straint does not affect the ability to reach the required KCPE cutoff mark to prog
ress to secondary education: in the absence of credit constraint, students from poor
and wealthy households would exhibit simil ar transition rates to secondary school. A
teenager’s ability and the household’s accessible assets determine schooling invest
ment. An individual’s achievable schooling level is S P *(α i , Ait ) for poor households
and SW *(α i , Ait ) for wealthy households, where SW * (αi, Ait) > S P* (αi, Ait). Wealthy
households invest in higher levels of schooling for their teenagers; teenagers in poor
households remain trapped at low levels of schooling and consequently maintain a
poor standard of living and, in this case, I will show that teenagers in poor households
face a greater likelihood of teenage motherhood.
A threshold level of assets, Â, exists such that SW*(αi, Ait) = SP*(αi, Ait.). House-
holds with an initial asset endowment lower than  are budget constrained and cannot
invest in higher schooling levels. Policies such as the FSE in Kenya could address
this need by elevating poor households beyond the threshold asset level. The FSE
would also likely affect transition rates to secondary school for teenagers in wealthy
households, but it is more likely to have a significant impact for teenagers in poor
households who achieved the required exam cutoff marks and would, in the absence
of the FSE, otherwise not attend secondary school because of credit constraint.
In this conceptual mechanism, the removal of financial barriers potentially provi
des access to secondary education, especially for the poor, at an age when most girls
would benefit from formal education. Secondary school enrollment provides girls
with opportunities for growth beyond simple domestic life, especially in sub-Saharan
Africa, where early marriage and childbearing are customary (UNFPA 2013b). Sec-
ondary school could provide them with opportunities that may compete with custom
ary norms and limitations, such as early marriage and early childbearing (Caldwell
et al. 1983; Westoff 1992). A Kenya Demographic and Health Survey conducted in
1998 found that women with at least a secondary education marry later than those
with lower than secondary school education (22 vs. 17 years of age) and have fewer
children (National Council for Population and Development et al. 1999). In addition,
studies have shown that education may change individuals’ preferences toward fewer
children with a better quality of life (e.g., Grossman 2006). Other studies have shown
that a supervised environment, such as that provided by schools, contributes to lower
pregnancy rates. For instance, in a study in South Africa, Rosenberg et al. (2015)
found lower pregnancy rates during the school term than during summer holidays.
Educated women are more likely to use contraceptive methods (Ferré 2009), and
secondary school education provides an additional level of education. Contraceptives
are discussed in classes at the teacher’s discretion, in biology classes, and in the cur
riculum on the biology of AIDS and HIV transmission and prevention (Duflo et al.
2015). Contraceptive knowledge and access could lower the likelihood of both school
dropout and early marriages that could have been an outcome of pregnancy (Ferré
2009; LeVine et al. 1996).
Another possible link between education and pregnancy is the increased status that
a secondary education would confer. As Caldwell and Caldwell (1987) noted, edu
cated women in sub-Saharan Africa, regardless of marital status, are more respected
and enjoy a higher status in society. This higher standing based on education diverges
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from earlier, traditional cultural trends in which marriage and childbearing were
highly regarded. Consequently, education may be perceived as a channel through
which girls can elevate their status in society (Caldwell et al. 1983).
Education also influences the timing of reproductive events, such as delayed sex
ual initiation. Pregnancy in sub-Saharan Africa is a frequent explanation for school
dropout, and although pregnant girls are allowed to return to school after birth in
some countries, the fraction actually returning is often low (Lloyd and Mensch 2008).
If having a child precludes a girl from continuing with schooling, girls may delay sex
ual activities to ensure they complete their schooling.
Regardless of the mechanism through which schooling influences pregnancy, reduced
rates of teenage childbearing could have long-term benefi ts for the child, mother, and
society at large.
Estimation
Isolating the Impact of the Reform
Examining the impact of education on teenage motherhood requires acknowledgment
of causality issues—that is, determining whether teenage motherhood reduces girls’
formal education attainment or that their low education levels lead to teenage mother
hood. I address this endogeneity issue by taking advantage of a quasi-experiment
setting (the policy change). Although the provision of this free/subsidized secondary
school education has a societal-level impact, it is more likely to have a direct and
immediate effect on teenage motherhood. Isolating this effect presents methodologi
cal challenges. For example, period effects may be contemporaneous with the intro
duction of the FSE program. In this case, a comparis on group to the target population
is necessary, but then there may be persistent societal factors that could produce dif
ferences between the target group and a comparis on group.
To address these issues, I evaluate first births for women aged 15–18 from two
cohort groups: (1) the treated group, those impacted directly by the FSE (born in
1990–1993); and (2) the con trol group, those not impacted by the FSE (born
in 1985–1988). Both cohorts were surveyed in 2014, when individuals in the treated
group were aged 21–24 (i.e., they were aged 15–18 at policy implementation) and
those in the control group were aged 26–29 (i.e., they were aged 20–23 at policy
implementation). Figure 1 displays a Lexis diagram of the analysis and helps create
cross-sections of the birth cohort in 2008 (when the policy was implemented) and
2014 (when the survey was conducted). Given my focus here on teenage motherhood
and the 2014 survey’s question on respondent’s age at first birth, I consider only those
women who were at least age 20 in 2014.
Another potential issue is that some students will not have achieved a sufficient
score on the KCPE exam to permit their transition to secondary school regardless of
the ability to finance it. I expect this to affect the control group and the treated group
similarly; therefore, although allsecondary school students were eligible for treat
ment after the FSE policy, only those who successfully completed primary school
potentially received the treatment. Therefore, the methodological approach provi
des estimates akin to the average treatment effect on the treated. The FSE exposure
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Fig. 1 Lexis diagram of birth cohorts and cohort age. The treated group is the 1990–1993 birth cohort, and
the control group is the 1985–1988 birth cohort. Age in 2008 (the FSE policy year) is plotted on the left
axis, and age in 2014 (when individuals were observed/interviewed) is indicated on the right axis.
period differs among respondents. For example, women born in 1993 were exposed
longer to the policy than those born in 1990 (i.e., they were in their first year of sec
ondary school at policy implementation, whereas the latter were in their final years of
secondary school). I collapse the birth cohorts into two groups and consider the aver
age exposure, enabling me to compare trends in first births between the two cohorts.
Human capital investment (i.e., formal education) often takes place during the
teenage years. However, as noted earlier, the high cost of education in developing
countries regularly hinders this process. In poor households, many teenagers may
be forced to quit school, and girls’ educational opportunities may be sacrificed in
favor of providing such opportunities for boys. Therefore, children in poor house
holds are less likely to attend school, and girls are often disadvantaged compared
with boys. I use Demographic and Health Survey (DHS) data on household wealth
to categorize women in the sample into those who are most likely to be impacted by
FSE (those from poor households) and those who are less likely to be impacted by
FSE (those from wealthy households). Using this information and a difference-in-
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difference method, I compare trends (1) between treated and control groups and (2)
between poor and wealthy households. Ideally, this method controls for unobserved
factors between the cohorts, which could affect the differences between the treated
and the control cohorts. I attribute these difference-in-difference estimates to the FSE
program after controlling for observed individual and household characteristics.7
Data
I use data from the 2014 Kenya Demographic and Health Survey (DHS) (Kenya
National Bureau of Statistics [Nairobi] and ICF 2015). DHSs are national household
sample surveys with questions on health, socioeconomic, and anthropomorphic indi
cators. The survey interviews allmembers of the selected household, usually men and
women of reproductive age (15–49). The DHS uses a multistage sampling design, first
selecting primary sampling units (clusters) and then randomly sampling listed house
holds within the randomly selected clusters (excluding families living in institutional
facilities, such as boarding schools, hospitals, army barracks, or police camps).
Descriptive Statistics and Underlying Patterns in the Data
Summary statistics of key varia bles are presented in Table 1. The table reveals some
differences in several variables, such as age at first birth (although I consider only
whether a birth occurred) and education. Age at first birth is higher for the youn
ger cohort, suggesting that the young cohort is more likely to delay first birth than
the older cohort. For the younger cohort, education level is approximately one year
lower, perhaps because some in this cohort are still enrolled in school. Overall, most
sampled women identified as Protestant (as opposed to Catholic, Muslim, or having
no religion), reflecting the high concentration of Protestants in the country. Reflect-
ing the country’s major ethnic groups, most of the sampled women also identified as
Kalenjin, Kamba, Kikuyu, Luhya, Luo, and Kisii (as opposed to Maasai or Somali).
On average, the two cohorts have relatively similar characteristics.
Evidence that secondary school admissions increased with the introduction of the
FSE program can be found in Brudevold-Newman (2016: figure 2). Building on this
evidence, I present a visual representation of trends in teenage birth rates in Figure
2. Approximately 35% to 40% of the sampled women were teenage mothers, but the
trend decreased over time such that younger cohorts have lower teenage birth rates.
Importantly, teenage birth rates are, on average, higher for women born before 1990
than for women born later.
Figure 3 plots teenage birth rates by rural/urban status, religion, and province. The
figure shows that rural women were 10 percentage points more likely to be teenage
mothers than urban women. This gap, however, was larger for the older cohort than
7
I am not aware of any other national program that was enacted in 2008 that would have affected these
groups differently. However, if there were public health initiatives passed in 2008 or later, they would
likely affect groups similarly, and they would be accounted for by the difference-in-difference approach
used here.
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Table 1 Sample characteristics
1985–1988 Cohort 1990–1993 Cohort
Proportion/ Proportion/
Mean SD Mean SD Difference
Teenage Birth Rate 0.31 0.46 0.34 0.47 0.03**
Age at First Birth 18.26 2.25 19.49 3.17 1.23**
Education (years) 8.46 3.88 7.84 4.53 −0.62**
Urban 0.42 0.49 0.43 0.50 0.02
Religion
Catholic .20 0.40 .19 0.40 –.01
Protestant .64 0.48 .66 0.47 .02
Muslim .14 0.35 .13 0.33 –.01
None .02 0.13 .02 0.14 .00
Ethnicitya
Kalenjin .13 0.34 .15 0.36 .02*
Kamba .15 0.36 .15 0.35 –.00
Kikuyu .09 0.28 .10 0.29 .01
Kisii .06 0.24 .06 0.24 –.00
Luhya .13 0.34 .11 0.32 –.02*
Luo .11 0.32 .11 0.31 –.00
Provinces
Nairobi .04 0.20 .04 0.21 .00
Central .08 0.27 .09 0.29 .01†
Coast .13 0.34 .12 0.32 –.01†
Eastern .15 0.36 .17 0.37 .02†
Nyanza .14 0.34 .13 0.34 –.01
Rift Valley .32 0.46 .31 0.46 –.00
Western .09 0.29 .08 0.27 –.01†
North Eastern .05 0.22 .05 0.22 .00
Number of Observations 5,366 5,500
a
Less than 5% of respondents identified as Maasai, Somali, Taita Taveta, or Turkana.
†
p < .10; *p < .05; **p < .01
for the younger cohort. The figure is encouraging: it shows a decline in the over
all rate of teenage motherhood and helps to decipher which groups are driving the
results. Women who identify as Muslim were more likely to be teenage mothers than
were those identifying as Catholic or Protestant. This difference is larger in the pre-
FSE period (1985–1988), with Muslim women approximately 10 percentage points
more likely to be teenage mothers. Still, this gap declined remarkably for the younger
cohort. At the tail end of the period covered, Muslim women were less likely to be
teenage mothers than were Protestant women.
Trends in teenage motherhood between provinces could be related to cultural
differences between ethnic groups, which often share similar migration patterns.
Although ethnic groups are not as concentrated within particular provinces as they
used to be, a high degree of this homogeneity remains, especially in the North East-
ern, Nyanza, and Central provinces. Figure 3 shows that, on average, Nyanza had the
highest rate of teenage motherhood (between 42% and 52%), and these high levels
persisted over time; by contrast, Central and Nairobi provinces had the lowest rates
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Fig. 2 Teenage birth rates by year of birth. Data are from the 2014 Kenya Demographic and Health Survey.
of teenage motherhood. The observed significant drop for the North Eastern province
and a corresponding increase in Nairobi around 1989 may be the result of an influx
of Somali people, who are concentrated in the North Eastern province (bordering
Somalia), into some Nairobi suburbs.
Figure 4 presents trends by household wealth status (low, middle, and high). DHS
data divide wealth index into five categories, with higher values indicating wealthier
households. I define households with a wealth index of 0–2 as low income, households
with a wealth index of 3 as middle income, and households with a wealth index of 4 as
high income. Figure 4 shows that women in high-income households were less likely
to be teenage mothers than women from low-income households. For the control group
(the 1985–1988 birth cohort), the trend for low-income households is relatively stable,
but the trend for high-income households declines around the 1987 birth cohort. I do not
expect this difference to impact the results; if it does, then it will bias the results down
ward, making them understated. For the treated group (the 1990–1993 birth cohort),
low-income and high-income households seem to have a similar trend. Trends for those
in high-income households are relatively similar before (1985–1988) and after (1990–
1993) the reform.
Overall, rates of teenage motherhood vary across characteristics, such as place of
residence or religion. What is common, however, is that teenage girls from low-income
households or who live in rural areas are more likely to be teenage mothers than are
their urban or high-income counterparts, respectively.
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by guest1412 S. Muchiri
Fig. 3 Trend in teenage birth rates by urban/rural residence, religion, and province. Data are from the 2014 Kenya
Demographic and Health Survey.
Empirical Strategy
Because the policy reform has a direct impact on a specific age cohort, I isolate its
effect by comparing birth rate changes of women affected by the policy (i.e., younger
cohort) with those of women not affected by it (i.e., older cohort) and by comparing
low-income with high-income households. Subscripts L, H, O, and Y denote women
from low-income households, high-income households, the older age cohort, and the
younger age cohort, respectively. Equation (1) gives the change in first-birth rates,
Ratet, for the treated (the younger cohort) between those from low-income house
holds and those from high-income households, and Eq. (2) gives the change in first-
birth rates, Ratec, for the control (the older cohort) between those from low-income
households and those from high-income households.
∆ Ratet = Rate(Y ,L) − Rate(Y ,H ). (1)
∆ Ratec = Rate(O,L) − Rate(O,H ) . (2)
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by guestFree Secondary Schooling and Teenage Motherhood 1413
Fig. 4 Teenage birth rates by household wealth index. Data are from the 2014 Kenya Demographic and
Health Survey.
The difference between the two then provides the estimated impact of free/subsidized
secondary school education on teenage motherhood. This is the difference-in-difference
(DD) approach.
DD = ∆ Ratet − ∆ Ratec . (3)
Table 2 presents the estimated mean first-birth rates. Teens are defined as 15–18
years old in panel A and as 15–19 years old in panel B. In each panel, the first column
contains mean first-birth rates for the older cohort (women not directly affected by
the policy), the second column contains mean first-birth rates of the younger cohort
(women directly affected by the policy), and the third contains the difference between
the cohorts. Row 1 of each panel in the table considers women from high-income
households only, and row 2 of each panel considers women from low-income house
holds only. Women from high-income households had a 0.7 percentage point increase
(15.5% vs. 16.2%) in first births compared with a 4.6 percentage points decrease
(43.7% vs. 39.1%) for women from low-income households. The DD estimates of
these rates, shown in bold, corresponds to a (statistically significant) 5.3 percentage
point relative decline in first-birth rates after the reform for women from low-income
households compared with women from high-income households.
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by guest1414 S. Muchiri
Table 2 Difference-in-difference (DD) estimates
Pre-Reform Post-Reform Difference in
Birth Cohort Birth Cohort Birth Cohort
Variable (1) (2) (3)
A. Teens Defined as Ages 15–18
1. Individuals from high-income households 0.155 0.162 0.007
(0.362) (0.368) (0.011)
[1,203] [1,125]
2. Individuals from low-income households 0.437 0.391 −0.046
(0.496) (0.488) (0.009)
[3,203] [3,201]
3. Difference between income groups 0.282 0.299
(0.009) (0.009)
DD estimate −0.053
(0.000)
B. Teens Defined as Ages 15–19
1. Individuals from high-income households 0.160 0.161 0.001
(0.366) (0.368) (0.012)
[959] [1,001]
2. Individuals from low-income households 0.445 0.430 −0.042
(0.497) (0.491) (0.010)
[2,467] [2,781]
3. Difference between income groups 0.285 0.242
(0.011) (0.010)
DD estimate −0.043
(0.002)
Notes: Data are from the 2014 Kenya Demographic and Health Survey. Cells contain mean teenage birth
rates for women in high-income households (in row 1 in each panel) and low-income households (in row
2 of each panel). Standard errors are shown in parentheses, and sample sizes are shown in square brack
ets. Column 1 shows mean teenage first-birth rates for birth cohorts not impacted by the free secondary
school (FSE) policy; these are women who were beyond secondary school age when the policy was passed.
Column 2 shows mean teenage first-birth rates for birth cohorts affected by the policy; these are women
who were younger than 19 when the policy was passed. The DD estimate is obtained by calculating the
difference between values in column 3.
In a sensitivity analysis, I conduct a similar exercise but define teens as 15 to
19-year-olds.8 The results, illustrated in panel B of Table 2, show a statistically sig
nificant 4.3 percentage point decrease in first-birth rates for women from low-income
households compared with women from high-income households. Although this DD
estimate indicates a declining trend and is statistically significant, it is smaller in mag
nitude relative to the estimate shown in panel A. Nonetheless, the evidence from the
DD estimates thus far provides preliminary evidence supporting the hypothesis that
teenage motherhood responded to the policy reform.
8
Most students graduate high school at the age of 18, but it is not uncommon for some to do so at age 19.
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by guestFree Secondary Schooling and Teenage Motherhood 1415
Table 3 Estimates on teenage motherhood (ages 15–18)
Low vs. High Income Low vs. Middle Income
1 2 3 4
Post-FSE Cohort × Low-Income Family −0.052** −0.053**
(0.020) (0.019)
Post-FSE Cohort 0.006 0.005 −0.051** −0.057**
(0.015) (0.015) (0.019) (0.018)
Low-Income Family 0.282** 0.252**
(0.014) (0.017)
Post-FSE Cohort × Low-Income Family 0.005 0.008
(0.022) (0.022)
Low-Income Family 0.159** 0.142**
(0.016) (0.017)
N 8,732 8,732 8,521 8,521
Control Variables N Y N Y
Notes: Women in the pre–free secondary education (FSE) cohort were born in 1985–1988, and women in
the post-FSE were born in 1990–1993. Low-income families are those with wealth index between 0–2,
middle-income families have a wealth index of 3 and higher-income families have a wealth index of 4.
Control variables are years of formal education, dummy variable for rural/urban status, religion, province
status, and ethnicity.
**p < .01
Regression Model
I formalize the empirical strategy in a regression framework to account for differ
ences in individual and household characteristics. Moreover, controlling for individ
ual and household characteristics produces more efficient estimates. I estimate a DD
estimator of the following form:
( )
Yi,t = β0 + β1Cohortt + β 2 Lowi + β3 Cohortt × Lowi + β 4 X i,t + ε i,t , (4)
where Yi,t is an indicator of whether a woman was a teenage mother (equal to 1) or
not (equal to 0). Lowi equals 1 for women from low-income households and 0 other
wise; Cohortt equals to 1 for women in the younger cohort and 0 otherwise. Xi,t is a
vector of individual and household characteristics: education (in years), province of
resid ence, rural/urban resid ence, ethnicity, and religion. The primary coefficient of
interest, β3, captures the policy’s impact on teenage motherhood and is interpreted as
the DD estimator. εi,t is an idiosyncratic error term.
Empirical Results
Tables 3 and 4 present the results without individual and household controls (in col
umns 1 and 3) and with these individual and household characteristics (in columns 2
and 4). In Table 3, I define teens as aged 15–18; in Table 4, I relax this age restriction
and consider teens to be aged 15–19, in line with the idea that graduation could be
delayed, either because of delayed school entry or grade repetitions. In addition to
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by guest1416 S. Muchiri
Table 4 Estimates of teenage motherhood (ages 15–19)
Low vs. High-Income Low vs. Middle-Income
1 2 3 4
Post-FSE Cohort × Low-Income Family −0.043* −0.044*
(0.021) (0.021)
Low-Income Family 0.286** 0.266**
(0.015) (0.019)
Post-FSE Cohort × Low-Income Family 0.016 0.019
(0.025) (0.025)
Low-Income Family 0.155** 0.142**
(0.018) (0.019)
Post-FSE Cohort 0.001 −0.001 −0.057** −0.065**
(0.017) (0.017) (0.021) (0.020)
N 7,208 7,208 7,010 7,010
Control Variables N Y N Y
Notes: Women in the pre–free secondary education (FSE) cohort were born in 1985–1988, and women
in the post–FSE cohort were born in 1990–1993. The low-income families are those with wealth indices
between 0–2, middle-income families have a wealth index of 3, and higher-income families have a wealth
index of 4. Control variables are years of formal education, a dummy variable for rural/urban status, reli
gion, province status, and ethnicity.
*p < .05; **p < .01
comparing women from low-income households with those from high-income house
holds, I conduct a sensitivity analysis comparing women from low-income house
holds (columns 1 and 2) with those from middle-income households (columns 3 and
4) for sensitivity analysis.
In column 1 of Table 3, the coefficient on Cohort × Low, which provides the pol
icy’s impact on teenage motherhood, is −0.052 and is statistically different from
zero. This figure changes marginally to −0.053 when I include additional controls
in column 2, but it remains negative and statistically significant at the 1% level of
significance. These results suggest that the FSE policy led to a 5.3 percentage point
reduction in teenage motherhood. If the average rate of teenage motherhood for poor
women before the policy is approxim ately 48%, as shown in Figure 4, then a 5.3 per
centage point decrease is equivalent to approxim ately a 12% drop in teenage moth
erhood. I consider this to be a consequential impact because, as discussed earlier,
reduced teenage motherhood could translate into higher rates of secondary school
completion, lower rates of child marriage, reduced incidence of complications from
childbirth, and reduced intergenerational transmission of poverty. In addition, this
decline in teenage motherhood provides justific ation for a targeted educational pol
icy like FSE, especially given the projected increase of teenagers in developing
countries.
The results shown in Table 3 may be underestimates if 19-year-olds are regarded
as non-school-aged. As mentioned earlier, most students graduate high school at age
18, but graduating at age 19 is not uncommon. I test the sensitivity of the estimates
to the definition of teens by considering those aged 19 or younger. The results are
presented in Table 4. Teenage motherhood declined by approximately 4.3 percentage
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