The Short-Term Impacts of a Schooling Conditional Cash Transfer Programon the Sexual Behavior of Young Women1
Sarah Baird, George Washington University
Ephraim Chirwa, University of Malawi
Craig McIntosh, University of California at San Diego
Berk Özler2, World Bank3
Recent evidence suggests that Conditional Cash Transfer Programs (CCTs) for schoolingare effective in raising school enrolment and attendance. However, there is reason tobelieve that such programs can also affect other outcomes, such as the sexual behavior of
their young beneficiaries. Zomba Cash Transfer Program (ZCTP) is a randomized
ongoing CCT intervention targeting young women in Malawi that provides incentives (inthe form of school fees and cash transfers) to current schoolgirls and recent dropouts tostay in or return to school. An average offer of US$10/month conditional on satisfactoryschool attendance – plus direct payment of secondary school fees – led to significant
declines in early marriage, teenage pregnancy, and self-reported sexual activity amongprogram beneficiaries after just one year of program implementation. For program
beneficiaries who were out of school at baseline, the probability of getting married or
becoming pregnant declined by more than 40% and 30%, respectively. More than a thirdof all program beneficiaries also delayed their onset of sexual activity by a full year.
Overall, these results suggest that CCT programs not only serve as useful tools for
improving school attendance, but may also reduce sexual activity, as well as teen
pregnancy and early marriage.

Keywords: cash transfers; education; sexual behavior; gender; randomized intervention

1

We’d like to extend special thanks to Josefine Durazo, Nicola Hedge, and Jacobus de Hoop for excellent
research assistance. We also thank conference participants at AMERB 2009 in Washington, DC, PACDEV2009 in San Francisco, and CSAE 2009 in Oxford, GDN workshop participants in York, and seminar
participants at the World Bank for helpful comments and suggestions. We gratefully acknowledge theprimary sponsorship of the Global Development Network and the Bill and Melinda Gates Foundation
for this project. The project also received substantial additional funding from a variety of World Bank TrustFunds, including Knowledge for Change Trust Fund (TF090932), World Development Report 2007 Small
Grants Fund (TF055926), and Spanish Impact Evaluation Fund (TF092384). The findings, interpretations,and conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the views of the International Bank for Reconstruction and Development/World Bank andits affiliated organizations, or those of the Executive Directors of the World Bank or the governments they
represent.
2
Correspondence to: Berk Ozler, The World Bank, 1818 H St. NW, Washington, DC 20433. Phone: 202458-5861. Fax: 202-522-1153. E-mail: bozler@worldbank.org.
3
The authors have no conflicts of interest to disclose. The study has been approved by the National Health
Sciences Research Committee in Malawi (protocol number: 569) and by the Human Research Protections
Program Committee at University of California, San Diego (protocol number: 090378).

11. Introduction
Conditional Cash Transfers (CCTs) can be an important component of social
protection policy and there is “…considerable evidence that CCTs have improved thelives of poor people” (World Bank, 2009). Early CCT pilots such as Mexico’s Progresa,
Brazil’s Bolsa Escola, and Nicaragua’s Red de Protección have been popular and became
national programs a few years later. As of 2007, twenty-four developing countries had
some type of a CCT program in place, with many others planning or piloting one. It
seems that CCT programs are here to stay – at least for the foreseeable future.
However, such programs have been largely evaluated on a small set of outcomes,
which have more to do with the behavior that the program is being “conditioned” on(such as school enrolment), rather than, say, learning or labor market outcomes.
Naturally, there is now an increased focus, from policy-makers and researchers alike, on
examining a broader set of outcomes that might be plausibly affected by these programsand that are pertinent for policy design. The contribution of this paper is to extend theanalysis of CCT programs to a new context and a novel set of outcomes; namely theirpotential to prevent risky sexual behavior among school-age girls and young women in
Sub-Saharan Africa.
When available, evidence as to the impact of CCT programs on “final”
educational outcomes such as test scores, shows only modest effects (World Bank, 2009).
More importantly for sub-Saharan Africa (SSA), very little is known about the possible
effect of these programs on the sexual behavior of the target beneficiaries, including age

2

of marriage and fertility decisions.4 Given the close link between sexual behavior andHIV infection, and given the burden HIV poses on these economies, , this is potentially avery important impact to document.
There are good reasons to think that CCT programs for schooling may affect thesexual behavior of young people. Education has been suggested as a “social vaccine” tochange sexual behavior and prevent the spread of HIV (Jukes, Simmons, and Bundy,2008), but almost all of the evidence we have on this comes from cross-sectional studies.
Furthermore, the role of income (especially that of women’s poverty) has often been cited
as a significant factor in the spread of HIV in SSA, but again there is little credible
evidence showing a causal link between income, sexual behavior, and HIV risk. Given
the high prevalence of HIV infection among young women in SSA, the policy importanceof identifying any potentially large impacts of CCT programs for schooling on sexual
behavior cannot be overstated.
This paper aims to provide new causal evidence on the effects of a CCT program(with only school attendance used as a condition to receive the transfers) on the selfreported sexual behavior of the young, female beneficiaries of the program. It does so by
examining the one-year impacts of a two-year ongoing randomized intervention in
Malawi that provides cash transfers to young women to stay in (or return to) school.
Conditional cash transfers blend ‘income’ effects (through the transfers themselves) with‘price’ effects (through the conditionality), and our study estimates the joint effect ofthese two policy parameters. As such, this paper provides the first experimental evidence

4

We know of one other study (Duflo et. al., 2006) that examines the effect of providing school uniforms on
the likelihood of teen marriage and childbearing. The implied transfer size for that study is significantly
lower than the transfers in the program being evaluated here.

3

on the impact of a CCT program on age at first marriage, childbearing, frequency of
sexual activity, and risky sexual behaviors in sub-Saharan Africa.
The remainder of this paper is structured as follows. Section 2 provides a brief
literature review both on conditional cash transfer programs and on the relationshipbetween schooling, sexual behavior and HIV risk. Section 3 describes the survey setting
and why this study is particularly pertinent for Malawi, while Section 4 details theresearch design and the intervention. Section 5 describes the impact of the program andSection 6 concludes.

2. Literature Review
CCT programs are utilized around the world with two main objectives: to provide
poor households with a minimum threshold of income (reduce poverty in the very shortrun) and to improve the accumulation of human capital for the next generation (reducepoverty in the longer-run). There is a large body of evidence supporting the success of
CCTs throughout most of the developing world, particularly in relationship to schooling(de Janvry and Sadoulet, 2004; Schultz, 2004).5 Moreover, several evaluations show thatthese programs are both technically feasible (i.e. the stated goals of the program are
actually met in practice) and are politically acceptable in that successive governments arewilling to continue and even expand program coverage (Das, Do, Özler, 2005).
CCTs targeted at education generally consist of giving cash to poor parents under
the condition that they send their children to school. Households are generally targetedusing means testing based on observable characteristics. In Mexico’s Progresa, forexample, cash transfers were offered to poor mothers in rural communities conditional on
5

See World Bank (2009) for a recent and thorough examination of CCT programs.

4their children using health facilities on a regular basis and attending school between thethird year of primary school and the third year of secondary school (de Janvry andSadoulet, 2004).
The World Bank (2009) finds that CCTs led to large increases in school
enrolment, particularly among those with low enrolment rates to begin with. However,evidence on the impact of educational transfer programs (in kind or in cash) on ‘final
outcomes’ such as test scores, is not as encouraging (Miguel and Kremer, 2004; Glewwe,
Kremer, and Moulin, 2008). Filmer and Schady (2009) argue that the lack of any
discernible effect of such programs on learning (despite large impacts on school
enrolment) may be due to the fact that they draw lower ability students back to school.
To our knowledge, no CCT program for schooling has been evaluated to assess its
possible impact on the sexual behavior of the young people benefiting from the program.
This is the case even though there are good reasons to think that the impacts of suchprograms on the sexual behavior of young people may be substantial. The World Bank
(2009) argues that among the areas that should receive high priority in impact evaluations(and, more generally, research) on CCTs is the role they play in reducing the transmissionof HIV, most likely through changes in sexual behavior. Both schooling and poverty
reduction (especially for women) are seen by many as key components in acomprehensive strategy to combat HIV/AIDS. However, causal evidence that links
increased schooling or income to changes in behaviors associated with HIV is very
limited. Most of what we know about the relationship between schooling (attendance or
attainment) or income and HIV risk factors come from cross-sectional studies.

5

While several studies find a positive, cross-sectional correlation between school
attendance and HIV status (e.g. Hargreaves et. al., 2008; Beegle and Özler, 2007), there
is only one study that points to a possible causal link between school attendance andreduced HIV risk factors. A study in Kenya finds that reducing the cost of schooling (bypaying for uniforms) reduced dropout rates, teen marriage, and childbearing (Duflo et. al.
2006). Commenting on the lack of clear and credible evidence addressing the relationshipbetween education and HIV, Jukes, Simmons, and Bundy (2008) suggest that long-term,
follow-up experimental interventions to improve educational access, such as conditional
cash transfer programs, offer the potential to examine the causal relationship between
educational attainment, sexual behavior and subsequent risk of HIV infection.
Credible causal evidence regarding the effect of increased income on risky sexual
behavior among young people is also practically non-existent. The evidence on whether
poorer individuals are more likely to conduct risky sexual behavior and contract HIV,
virtually all of which is cross-sectional, is mixed. Many are quick to assert that poverty isa determinant of HIV status for women because poor women are more likely to engage inrisky sexual activities, such as commercial or informal sex work (Wojcicki, 2002; World
Bank, 2005; Shelton, Cassell, and Adetunji, 2005), have multiple partners (Wines, 2004;
Halperin and Epstein, 2004; Hallman, 2004), or have riskier types of sex for money(Robinson and Yeh, 2006). On the other hand, Swidler and Watkins (2007) argue that it’s
not women’s poverty but the relative wealth of men that is the cause of transactional sex,
and as such improving women’s economic circumstances are unlikely to decrease
women’s vulnerability to HIV infection.

6However, many of the same sources asserting the plausibility of the relationshipbetween poverty and HIV are puzzled to report evidence to the contrary. For example,
Shelton, Cassell, and Adetunji (2005) report a positive correlation between household
possessions and HIV prevalence in Tanzania. Examining the determinants of HIV in fivecountries with DHS data in sub-Saharan Africa, De Walque (2006) finds that wealth(measured by an asset index) is positively correlated with HIV status in three of the fivecountries, especially for females.6 Finally, using prime-age adult mortality as a proxymeasure for HIV/AIDS affected households; several studies find that higher income
households are more likely to suffer an adult death (Yamano and Jayne, 2004; World
Bank, 2006, among others).
HIV/AIDS is an important problem in sub-Saharan Africa, especially among
young women. CCT programs are now starting to be seriously considered for
implementation in the region, with a number of countries piloting or implementing such
schemes. There are good reasons to think that such programs may play a role in affecting
the incidence of HIV infection among young people through behavioral change, as sexual
behavior might reasonably be linked to both schooling and income. However, credible
empirical evidence is lacking to establish such a causal relationship. This paper, which
reports the findings from a prospective evaluation of a randomized CCT program, is wellsuited to attempt to fill some of this knowledge gap.

3. Study Setting
Malawi, the setting for this research project, is an impoverished small country insouthern Africa. Its population of almost 14 million in 2007 is overwhelmingly rural,
6

De Walque and Corno (2007) report a similar positive conditional correlation in Lesotho.

7with most people living from subsistence farming supplemented by small-scale incomegenerating opportunities that are typically more available to men than they are to women.
The country is poor even by African standards: the GNI per capita (PPP, current
international $) is $750 in 2007, compared to an average of $1,870 for sub-SaharanAfrica (World Bank, 2008).7 Malawi also has the eighth-highest HIV prevalence in theworld with 14 percent of the adult population infected (UNAIDS, 2007).8 The gender gap
in HIV prevalence among young adults, aged 15-24, is startling: prevalence was morethan four times higher for females than males in 2004.
Primary schools in Malawi are free through Standard 8, and upon completing thisgrade, students take the Primary School Leaving Certificate (PSLC) exam. Access tosecondary schools is rationed, meaning that only those who obtain sufficiently highscores on the PSLC will gain access, and only if they can pay the tuition fees. Secondary
students sit one exam after two years (the Junior Certificate Exam, or JCE) and a final
exam on completion of secondary school (the Malawi Schools Certificate Exam, or
MSCE). Each of these exams is nationally recognized and considered an importantqualification on the labor market.
The CCT intervention that is the subject of this paper takes place in one district ofMalawi, which both reduces project costs (lower fixed costs of office infrastructure andtransport) and increases data quality through more careful supervision. Zomba district in
the Southern region of Malawi was chosen as the site for this study for several reasons.
First, it has a large enough population within a small enough geographic area rendering

7

Using the Atlas method, The GNI per capita (in current US$) in Malawi is 250 in 1997, compared with
952 in sub-Saharan Africa as a whole.
8
The UNAIDS HIV estimate of 14.1 percent is close to the Demographic and Health Survey 2004 (NSO,
2005) estimate of 12.7 percent (National Statistical Office and ORC Macro, 2005).

8

field work logistics easier and keeping transport costs lower. Second, characteristic ofSouthern Malawi, Zomba has a high rate of school dropouts and low educational
attainment. Finally, HIV/AIDS rates of women aged 15-49 in Zomba are the highest in
the country at 24.6% (NSO, 2005).
Because of Zomba district’s particular characteristics with respect to its relatively
high poverty and HIV prevalence, one might worry that the findings from this study may
not be relevant for other parts of Malawi or for neighboring countries. We feel thatseveral factors mitigate these concerns. First, while Zomba district may be different than
the rest of the country, it certainly is quite representative of the Southern Region (one of
the three major regions of Malawi), which is home to two of the country’s three biggest
cities (Blantyre and Zomba). As the Southern Region is the poorest one in the country
with low educational outcomes and high HIV rates, it would be a natural place for thegovernment to implement a similar program were it to consider geographic targeting.
Second, unlike many other districts, Zomba has the advantage of having a true urban
center as well as rural areas.9 Finally, Zomba and the Southern region of Malawi are not
atypical of the many other environments in Southern Africa which share high poverty andHIV rates. Therefore, while we study a particular population (never married girls, aged
13-22 at baseline), we feel confident that our results are meaningful for this population inother contexts in Southern Africa.

4. Research Design and Intervention

9

The study sample was stratified to get random representative samples from urban areas (Zomba town),
rural areas near Zomba town, and distant rural areas in the district.

9

This paper is evaluating the impact of a randomized conditional cash transfer
intervention targeting young women in Malawi that provides incentives (in the form ofschool fees and cash transfers) to current schoolgirls and young women who haverecently dropped out of school to stay in or return to school.10 Between October 2007
and January 2008, baseline surveys were conducted with 3,805 girls in 176 Enumeration
Areas (EAs) in Zomba district of Malawi. These EAs were selected from the universe of
EAs produced by the National Statistics Office of Malawi from the 1998 Census. The
sample of EAs was stratified by distance to the nearest township or trading centre. The
random sample of 176 EAs consists of 29 EAs in Zomba town, 8 trading centers in
Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs
more than 16 kilometers from Zomba town.
The 3,805 girls were selected based on information collected during a listing
exercise, which involved going door to door to all households in these 176 EAs. This
listing exercise identified all never-married, 13-22 year-old females living in the area.
This age group was chosen because it represents the period during which school dropout
coincides with the onset of sexual activity, and therefore maximizes the power for acausal study of the relationship between these two phenomena. For the study, we sampled
all dropouts and 75-100% of current school girls, where the exact percentage sampleddepended on the age of the school-girl; regression results are re-weighted to make them
representative of the entire eligible frame.11 We considered a ‘dropout’ as eligible for the
program if they had been out of school for less than two years, but we found virtually no10

We selected only females for the treatment because the expected HIV prevalence among males of thesame age cohort is much smaller, making it unlikely that we could detect program impacts with any kind of
precision.
11
These percentages were lower for urban areas since average population in urban areas is much higher
than that in rural areas.

10one out of school this long who remained unmarried. Therefore, in practice, a baseline
dropout was any girl who was out of school at baseline, regardless of how long she had
been out of school prior to the study as long as she had never been married and was
between the ages of 13-22. This sampling procedure led to an average sample size of 5.1
dropouts and 16.6 current school girls at baseline in each EA.12
Out of these 3,805 young women, 1,230 girls in 88 randomly selected EAs were
sampled to be part of the CCT program.13 Treatment status was assigned at the EA level
and therefore we cluster all standard errors to reflect this design effect. A household
questionnaire (not unlike a Living Standards Measurement Survey, or LSMS) was
administered to our entire core sample – both treatment and control – at baseline andfollow-up, which were conducted 12 months apart. This survey, described in more detailbelow, includes information on household characteristics, school enrolment, sexual
behavior, and social networks.
From December 2007 through January 2008 offers to participate in the CCT
program were made to the selected girls in treatment villages. Of the 944 girls in thebaseline survey to whom we attempted to make conditional offers, 17 could not belocated, 37 were deemed ineligible subsequent to the offer process, and one refused.
Because we continue to code all 63 of these ‘non-compliers’ as treated, we effectively

12

We chose to target these two groups separately to ensure that we had a significant number of dropouts inour sample. Treating all dropouts gives our study the statistical power to focus on a sub-population whose
school enrolment rates are more sensitive to the offer to participate in the program.
13
283 of these girls resided in EAs where the offers for baseline schoolgirls were not conditional on school
attendance, and, as such, are not part of the analysis for this paper. In addition, 629 girls in treatment EAs
were randomly selected to not receive cash transfers in order to examine possible spillover effects of the
program. These girls are also not part of the analysis in this paper. This leaves us with a final sample size of
2,893 girls at baseline, for whom the treatment is always conditional on school attendance.

11

estimate the Intention to Treat Effect of the original intent to offer the treatment14. As
part of the offer, a detailed informational sheet was given to each household that
described the conditions of the contract. This information sheet also informed recipientsthat secondary school fees would be paid directly to their school in full.15 The contract
was then signed by both the recipients (parent/guardian and core respondent) and the
NGO delivering the funds.
The average offer to the households consisted of $10/month – for a total of $100
for the school year transferred in equal amounts for 10 months.16 $10/month representsroughly 15% of total monthly household consumption in our sample households atbaseline, which places this program in the middle-to-high end of the range of relativetransfer sizes for conditional cash transfer programs elsewhere.17 In addition to thetransfers to the household, secondary school fees were paid directly to the schools upon
confirmation of enrolment.18 The transfer was split into a component that went to thestudent’s guardian and a component that went directly to the girl herself, with an averageof 30% of the transfer going to the schoolgirl.
14

Due to uncertainties regarding funding, the initial offers were only made for the 2008 school year.
However, upon receipt of more funds for the intervention in April 2008, all the girls in the program were
informed that the program would be extended to cover the 2009 school year and that they could stay in the
program upon satisfactory school attendance .
15
This was the case only for public schools. An upper limit for school fee payments was established for
those attending private schools, which was set to equal the average public school fees in the program
sample.
16
The intervention being evaluated here is part of a larger experiment with multiple treatment arms,
wherein transfer size was randomly varied across treatment units as well as the transfers randomly split
between parents and the young women. To measure possible spillover effects, the percentage of young
women treated in each EA was also randomly varied. Finally, baseline schoolgirls in a randomly selected
small percentage of the EAs received unconditional offers, meaning that the transfers were not conditionalon school attendance, or any other behavior other than showing up to collect monthly payments, for thesebeneficiaries in those EAs. The analysis of the heterogeneity of the impacts with respect to each of these
design features is beyond the scope of this paper. Here, we aim to establish the average effect of theconditional treatment arms, which may not equal the treatment effect of the average treatment if these
impacts are nonlinear. For a more detailed discussion of this contract variation, see Baird et al (2009).
17
For example, Cambodia transfers as little as 2-3% of total monthly household consumption under its
CESSP Scholarship Program (Filmer and Schady, 2009), while Mexico provides over 20% under Progresa.
18
Students have to pay school fees at the secondary level in Malawi, but not at the primary level.

124.1. Implementation of transfers
The cash payments take place monthly at centrally located and well-known
places, such as churches and schools. The cash transfer points were selected so that norecipient has to travel for more than 5 kilometers to the cash payment point.19 At eachmeeting some basic information is collected for each sample respondent, such as who ispicking up the money (girl or parent/guardian), how far they had to travel, etc. In between
payment dates, the NGO collects attendance records for all the students in the program tomake sure that they are complying with the program requirements and attending school.20
Each household receives the transfer only if the young woman attended school for at least
80% of the days that their school was in session in the previous month.

4.2. Survey Instrument
The annual SIHR Household Survey consists of a multi-topic questionnaire that is
administered to the households in which the selected sample respondents reside.
Although it is described as a household questionnaire, the primary goal of the SIHR
Household Survey is to collect detailed information from the individual respondents
selected for the survey. The survey consists of two parts: Part I is administered to the19Some recipients who still live in locations that are remote are visited door-to-door by the NGO
implementing the transfer scheme.
20
The total cost of the program consists of the cash transfers themselves, as well as the administrative costsof running the program. For every $1 that is transferred to a program beneficiary, approximately $0.50 isspent on administrative costs. The main items under the administrative costs are delivering the cash
payments and monitoring attendance, both of which are underlined by large costs of transportation. We
estimate that a similar program implemented by the government itself would spend significantly less on
administrative costs. This is because the cash transfers could be conducted at schools and the programadministers could rely on school records (with spot checks) to monitor attendance, significantly reducing
transport costs and producing scale economies. Furthermore, the government would benefit from collecting
less research-oriented data during cash transfers, which takes significant time to collect and enter.

13

head of the household, while Part II is administered to the core respondent, i.e. thesampled girl from our target population. Part I collects information on the householdroster, dwelling characteristics, household assets and durables, consumption (food andnon-food), household access to safety nets, and shocks (economic, health, and otherwise)
experienced by the household. In Part II, the core respondent provides further information
about her family background, her education and labor market participation, her health,her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, hersocial networks, as well as her own consumption of girl-specific goods (such as soaps,
mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.). This paper
utilizes baseline and follow-up data to analyze the one-year impact of the program on themarital status, childbearing, and the detailed sexual behavior for the program participants.
The first round of the survey was administered from October 2007 to February 2008, andthe second wave from October 2008 to February 2009.

5. Program Impacts
5.1. Balance and Attrition
Before examining the short-term impacts of the CCT program on sexual behavior,
it is important to first confirm that our randomization, with respect to key outcomes andcontrols, was successful. Table 1a provides some basic summary statistics from baseline
data. Table 1b shows the success of the randomization. As per our research design, wealways compare treatment and control groups for dropouts and schoolgirls at baseline
separately, and hence the equality of means at baseline is also examined within each ofthese two important sub-groups. Across the thirteen variables that are most pertinent for14this paper, there are no significant differences at baseline between the treatment andcontrol groups for those who were dropouts at baseline. Among baseline school girls, theonly variable that is significantly different between treatment and control is whether thegirl resides in a female headed household, where those in treatment are significantly lesslikely to reside in a female headed household. The fact that these variables look very
similar across treatment and control is strong evidence that the randomization procedure
was implemented successfully.21
Table 2 shows that the success rate in tracking our respondents in the study
sample was more than 93% in the one-year follow-up. That the panel data are balanced
across treatment and control groups indicates that this small sample attrition will not
introduce any bias into the estimation of treatment impacts. Given these attrition rates, wemove to the analysis with a panel sample consisting of 396 treatment and 408 control
girls who had dropped out of school as of baseline, and 480 treatment and 1,408 control
girls in school as of baseline. This gives us a total sample size of 2,692.
We now turn to the impacts of the program, using a standard difference-indifference estimation strategy. The specification used for estimation is:
Yidt = α i + δ t + β (Td * δ t ) + ε idt ,where i indexes individuals, d indexes Enumeration Areas, and t indexes each of the two
waves of the survey. Then α i represents a set of individual-level fixed effects, δ t is adummy variable for the second round, and the interaction term (Td * δ t ) is anotherdummy variable that is equal to unity only for units offered the treatment in the second

21

Please also notice the significant differences between schoolgirls and dropouts at baseline in Table 1.
Dropouts at baseline are older, less literate, and more likely to have started childbearing. As described in
Section 4, the intervention is randomly assigned within each of these two strata.

15

round. Standard errors are clustered at the EA (village) level because this is the unit atwhich the treatment is administered (see Bruhn & McKenzie, 2008), and observations areweighted by the inverse of their village-level probability of being sampled. The
coefficient βˆ therefore gives the intention-to-treat effect of the program on the average
girl in our study EAs.22

5.2. School Enrolment
We start by showing the impact of the program on self-reported schooling
outcomes since we would be much less likely to find impacts on early marriage, fertility,
and the sexual behavior of the young beneficiaries of the CCT program in the absence of
any impacts on school attendance and attainment. The simple act of attending school may
be enough to cause sexual behavior change among the study beneficiaries – for example
by raising the opportunity cost of pregnancy (Jukes, Bundy, and Simmons, 2008).23
Table 3 shows that the program led to large increases in school enrolment,
especially among those who were not in school at baseline. Column 2 of Table 3 showsthat the percentage of initial dropouts who returned to school (and were in school at theend of the 2008 school year) was 17.2% among the control group compared with 61.4%

22 While the transfer amounts were randomly varied for the parents (from $4/month to $10/month acrossEAs) and the students (from $1/month and $5/month within each EA), here we present the average impactsof these heterogeneous treatments for the sample that received the transfers conditional on school
attendance. Under a linearity assumption, this average effect will give the intention-to-treat effect (ITE) of
the average CCT amount ($10/month) and the average share of the transfer going to the girl (30%).
23
What is learned at school and schooling attainment can also influence sexual behavior of young people
through a variety of channels. While we find some evidence that baseline dropouts show a significant
improvement in self-reported literacy in English, we don’t find any evidence that their knowledge of
HIV/AIDS or their likelihood of being tested for HIV improved. We conclude that the reduction in selfreported sexual activity in the upcoming sub-sections is likely not a result of what is learned at school, but
the incentives associated with staying in school. This is consistent with Duflo et al. (2006) who suggest thatyoung women want to delay childbearing and marriage until after they complete their desired level ofschooling.

16

among treatment. Thus, program beneficiaries were 3-4 times more likely to be in schoolat the end of the 2008 school year than the control group.24 Since those enrolled inschool actually report attending school over 90% of the time, these treatment effects onenrolment translate quite directly into improvements in actual days of school attended.
For the stratum containing baseline schoolgirls, i.e. those who were still in schoolat baseline, while the absolute numbers are smaller (due to high rates of continued
schooling among this group), the relative impact is still impressive (column 3). Amongthe control group, 89.1% of initial schoolgirls were still enrolled in school at the end of
the 2008 school year, compared with 93% in the treatment group. Thinking of these as
dropout rates, the CCT program reduced the dropout rate among this group by 35% -from 10.9% among controls to 7% among treatments.

5.3. Marriage and Fertility
We now turn to early marriage and teen pregnancy as indicators of sexual
activity.25 Table 4 presents the impact of the program on having never been married.
Early marriage increases coital frequency, significantly decreases condom use, andvirtually eliminates the ability to abstain from sex (Clark, 2004). As described earlier, thestudy sample was selected to be never-married at baseline, so levels of marriage are equal
to the incidence during 2008. We see that 27.7% of initial dropouts in the control group
have gotten married during the past year, compared with only 16.4% of the same group in
24

The school enrolment and attainment data are self-reported by the study respondents. However, theschool enrolment and attendance of program beneficiaries, i.e. the treatment group, was monitored as partof the program and can be confirmed. Full enrolment, attendance, school grades, and performance atnational examinations will become available for the entire study sample after we complete conducting aschool census in Zomba between February and May, 2009.
25
The reader may object to marriages in this study being described as ‘early’ and pregnancies as ‘teenage’.
While it is true that the study sample does include some over the age of 19, this is a small percentage(approximately 7% at baseline and less than 13% at the end of Year 1) of the sample.

17

treatment (column 2). This is a reduction in the marriage rate of more than 40% among
those who were not in school at baseline. However, we also note that the program had noeffect on the propensity to get married among the baseline schoolgirls – 4.7% of whom
got married both among the controls and treatments.
Table 5 describes the impact of the program on the incidence of childbearing – i.e.
the likelihood of ever being pregnant. Column 2 shows that baseline dropouts among thetreatment group are 5.1 percentage points less likely to have become pregnant over the
past year, a reduction of more than 30% that is statistically significant at the 5% level.
Again, as with marriage, the CCT program had no impact on the incidence ofchildbearing at follow-up for baseline schoolgirls.

5.4. Sexual Activity and Risk Behaviors:
Finally, we present impacts on self-reported sexual activity and risky behaviors.
Table 6a examines onset of sexual activity and the number of sexual partners in the past
12 months. At baseline, 29.6% of initial dropouts and 79.4% of initial schoolgirls
reported having never had sex. Columns 2-3 of Table 6a indicate that the reduction in theonset of sexual activity is 5.5 percentage points among initial dropouts and 2.5
percentage points among initial schoolgirls, which represent reductions in the onset of
sexual activity of 46.6% and 31.3%, respectively. Columns 5-6 complement this finding
and show that the change in the number of self-reported number of lifetime partners frombaseline to follow-up is smaller for the program beneficiaries. The increase in the numberof lifetime partners is approximately 25% lower for both initial dropouts and schoolgirls,
although the difference is only statistically significant among baseline dropouts. These18

results suggest that program beneficiaries reduce their (self-reported) sexual activity byboth delaying sexual activity and reducing the number of sexual partners. Table 6b
extends the analysis by examining the sub-sample of young women who haven’t yet
gotten married by Round 2 and finds similar impacts – suggesting that the reduction in
self-reported sexual activity is not due solely to delayed marriage in this population.
Table 7 reports the impact of the program on the sexual behavior of those who aresexually active at both baseline and follow-up: condom use, frequency of sexual activity,and having sex with older partners. As the program has both an effect on the extensive
margin, i.e. on being sexually active in follow-up, and on the intensive margin, i.e. thesafety of the sexual activity conditional on being sexually active, we face anidentification problem for the latter. Hence, we ask the following question: “For the
population of young women who would be active in the absence of the program, what
would the effect of the program have been on their sexual behavior?” However, theyoung women we observe to be sexually active in both rounds include both this group,and the group who would have stopped being sexually active had they received theintervention, which introduces a selection bias that prevents us from interpreting thesimple difference-in-differences estimates that are presented in Table 7 as the marginal
effect of treatment on the population in question.
In columns 1-3 of Table 7, we examine self-reported condom use and find no
discernible impact of the program. In columns 4-6, we present the likelihood of having
sexual intercourse at least once a week. We find that treatment baseline schoolgirls are
significantly less likely to have sexual intercourse on a weekly basis, but we find no
significant impact for baseline dropouts. Similarly, the likelihood of having an older

19

sexual partner is lowered significantly for baseline schoolgirls in treatment (columns 79). If we believe that the treatment girls who stopped having sex had a lower propensity
to engage in risky sexual behaviors, then the protective effects of the program found here
are likely to be stronger, and vice versa.26

6. Conclusions
While there have been several evaluations of the impact CCT programs have onschool attainment, learning, early childhood development, as well as adult health, no one,
to our knowledge, has studied the possible effect of these programs on the sexual
behavior of the young target beneficiaries. This is potentially a very important impact todocument in sub-Saharan Africa, where CCT programs are likely to become morecommon in the near future and the risk of HIV infection is disproportionately high among
young women and school-aged girls.
Causal evidence that links increased schooling or income to changes in sexual
behavior is very limited. While most of what we know about these relationships comesfrom cross-sectional studies, the existing evidence is still at least suggestive of thepossibility that CCT programs for schooling may also affect the sexual behavior of their
young beneficiaries. This paper aims to shed some light on this question by analyzing theshort-term impacts of such a randomized CCT program implemented in Malawi.
The results are promising. After one year, the program led to large increases inself-reported school enrolment, as well as declines in early marriage, teenage pregnancy,
sexual activity, and risky sexual behavior. The evidence presented here suggests that as26

We have also tried to ‘bound’ our estimates using “Lee bounds” (Lee, 2005) but as the effect of thetreatment on the extensive margin is substantial, the bounds are too wide to be useful.

20

girls and young women returned to (or stayed in) school, they significantly delayed theonset (and, for those already sexually active, reduced the frequency) of their sexual
activity. The program also delayed marriage – which is the main alternative for schoolingfor young women in Malawi – and reduced the likelihood of becoming pregnant. As thetreatment/control differences in schooling become starker in year 2, the program impacts
on marriage, fertility, and risky sexual behavior are likely to become stronger.
One should not assume that the changes in self-reported sexual behavior will
result in a decline in HIV incidence among this cohort of program beneficiaries. Futurerounds of household survey and Biomarker data collection will shed light on thesequestions. For now, however, schooling CCTs for young women in the context of poor
sub-Saharan countries with high HIV rates seem like “win-win” programs, as they may
not only increase schooling for young women, but also significantly reduce their risk of
HIV infection. Furthermore, increases in age at first marriage and pregnancy, as well as
improved educational attainment may lead to improved outcomes for the next generation,
as there are a host of negative externalities for children that are associated with earlymarriage, such as higher child mortality or lower educational attainment (Morrison and
Sabarwal, 2008). The evidence presented in this paper provides impetus for the expansionof CCT programs (which already cover much of Latin America) to the African continent.

21

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Beegle Kathleen and Berk Özler. 2007. “Young Women, Rich(er) Men, and the Spreadof HIV.” Unpublished manuscript.
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Das, Jishnu & Quy-Toan Do & Özler, Berk. 2005. "Reassessing Conditional CashTransfer Programs." World Bank Research Observer 20: 57-80.
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Scores.” Unpublished manuscript.
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Textbooks and Test Scores in Kenya.”. American Economic Journal: Applied
Economics. 1(1), pp. 112-135.
Hallman, Kelly. 2004. “Socioeconomic Disadvantage and Unsafe Sexual Behaviors ofYoung Women and Men in South Africa,” Policy Research Division Working Paper
No. 190, Population Council. New York, NY.
Halperin, Daniel, and Helen Epstein. 2004. “The Opportunity to Capitalize on the
Growing Access to HIV Treatment to Expand HIV Prevention.” The Lancet 364: 4-6.

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Hargreaves, JR and others. 2008. “The association between school attendance, HIV
infection and sexual behavior among young people in rural South Africa.” Journal of
Epidemiology and Community Health 62:113-119.
Jukes, Matthew, Stephanie Simmons, and Donald Bundy. 2008. “Education and
Vulnerability: the role of schools in protecting young women and girls from HIV insouthern Africa.” AIDS 22 (4): S41-S46.
Lee, David. 2005. “Training, Wages, and Sample Selection: Estimating Sharp Bounds
on Treatment Effects.” NBER Working paper 11721.
Miguel, Edward, and Michael Kremer. 2004. ‘Worms: Identifying Impacts on Educationand Health in the Presence of Treatment Externalities.” Econometrica, 72(1), pp. 159217.
Morrison, Andrew and Shwetlana Sabarwal. 2008. “The Economic Participation ofAdolescent Girls and Young Women: Why Does It Matter?” Unpublishedmanuscript.
National Statistical Office (NSO) [Malawi] and ORC Macro. 2005. Malawi Demographicand Health Survey 2004. Calverton, Maryland: NSO and ORC Macro.
Robinson, Jonathan and Ethan Yeh. 2006. “Sex Work as a Response to Risk in Western
Kenya.” Unpublished manuscript.
Schultz T., Paul. 2004. "School Subsidies for the Poor: Evaluating the Mexican Progresa
Poverty Program." Journal of Development Economics 74 (1):199–250.
Shelton, James D., Michael M. Cassell, and Jacob Adetunji. 2005. “Is Poverty or Wealthat the Root of HIV?” The Lancet 366: 1057-1058.
Swidler, Ann and Susan Watkins. 2007. “Ties of Dependence: AIDS and Transactional
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UNAIDS. 2007. “AIDS Epidemic Update. UNAIDS, Geneva.
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24

Table 1a: Summary Statistics (N=2893)
Age
Father Alive
Mother Alive
Female Headed
Age Household HeadHousehold Size
MuslimUrban Household
Read English
No Qualification
Ever pregnant
Never had sex
Number of partners

Mean
15.610
0.692
0.808
0.337
47.120
6.330
0.186
0.360
0.740
0.669
0.107
0.694
0.448

Std. Dev.
2.240
0.462
0.394
0.473
13.070
2.300
0.389
0.480
0.438
0.471
0.309
0.461
0.808

Min
12
0
0
0
13
1
0
0
0
0
0
0
0

Max
22
1
1
1
110
15
1
1
1
1
1
1
6

Table 1b: Equality of Means at Baseline
Dropouts (N=889)
School Girl (N=2003)
Control Treatment Control
Treatment
Difference
Mean Difference Mean
17.434Age-0.301
15.249-0.213
Father Alive
0.646
0.004
0.696
0.020
Mother Alive
0.784-0.0380.834-0.042
Female Headed
0.420-0.0040.351-0.097***
Age Household Head 46.855-0.50247.775-1.350Household Size
6.091
0.011
6.433
0.100
Muslim0.225-0.0060.194-0.047
Urban Household
0.194-0.0100.362
0.116
Read English
0.469-0.0650.826-0.026
No Qualification
0.667
0.011
0.656
-0.019Ever pregnant
0.436-0.0200.021
0.008
Never had sex
0.309-0.0170.794
0.010
Number of partners
1.135
0.031
0.270
-0.017
Number of obs.
454
436
1497
506
Notes: The entire sample was never married at baseline, so the control andtreatment means were both zero. Dropout and school girl refer to schoolingstatus at baseline. The sample was split into dropouts (girls not in school) andschool girls at baseline, so the control and treatment means of schooling status
were identical at baseline (dropouts were 100% not in school while school girls
were 100% in school). These means are weighted to make results representativeof all study EAs. Standard errors are clustered at the EA level.
*Denote significance at the 10% level, ** at the 5% level and *** at the 1% level25

Table 2: Determinants of Survey AttritionALL=1 if Treatment Girl

-0.001(0.012)

SchoolDropouts
Girls
0.008
(0.013)=1 if Treatment Dropout
Tracking Success
Number of observations

0.931***

0.941***(0.006)(0.007)

2,893

2,003

0.010
(0.020)0.899***(0.013)890

Note: Each column represents an OLS regression with robust standard
errors. Standard errors in parentheses.
*Denotes significance at the 10% level, ** at the 5% level and *** at the
1% level

26

Table 3: Dependent Variable is Enrolled in School

Post-Treatment Indicator
Round 2 Indicator

All

Dropouts

0.163***

0.442***

SchoolGirls
0.038**(0.027)(0.035)(0.019)-0.062***(0.011)Baseline Mean of Outcome inControl
Number of observations
Number of individuals

0.172*** -0.109***(0.020)(0.013)0.799***-0.0001.000***(0.006)(0.009)(0.005)

5,384

1,608

3,776

2,692

804

1,888

Note: All regressions use individual fixed effects with standard errors
clustered at the EA level, and are weighted to make results
representative of all study EAs. Standard errors in parentheses.
*Denotes significance at the 10% level, ** at the 5% level and *** atthe 1% level27

Table 4: Dependent Variable is Never Married

Post-Treatment Indicator
Round 2 Indicator
Baseline Mean of Outcome inControl
Number of observations
Number of individuals

All

Dropouts

School
Girls

0.009

0.113***-0.000(0.014)(0.027)(0.013)-0.086***-0.277***-0.047***(0.009)(0.019)(0.008)1.000***

1.000***

1.000***(0.004)(0.007)(0.003)

5,384

1,608

3,776

2,692

804

1,888

Note: All regressions use individual fixed effects with standard errors
clustered at the EA level, and are weighted to make results
representative of all study EAs. Standard errors in parentheses.
*Denotes significance at the 10% level, ** at the 5% level and *** atthe 1% level28

Table 5: Dependent Variable is Ever Pregnant
All

Dropouts

SchoolGirls

Post-Treatment Indicator-0.006-0.051**-0.001(0.013)(0.024)(0.015)

Round 2 Indicator

0.085***

0.162***

0.070***(0.008)(0.016)(0.008)0.107***

0.434***

0.025***(0.003)(0.006)(0.003)

5,382

1,608

3,774

2,691

804

1,887

Baseline Mean of Outcome inControl
Number of observations
Number of individuals

Note: All regressions use individual fixed effects with standard errors
clustered at the EA level, and are weighted to make results
representative of all study EAs. Standard errors in parentheses.
*Denotes significance at the 10% level, ** at the 5% level and *** atthe 1% level29

Table 6a: Sexual Activity

Dependent Variable:=1 if Never Had Sex

Number of partners in thepast 12 months

All

Dropouts

SchoolGirls

All

Dropouts

All
SchoolGirls

Number of observations

0.029**(0.013)-0.086***(0.009)0.694***(0.003)
5,382

0.055***(0.020)-0.118***(0.016)
0.296***(0.005)1,606

0.025*(0.015)-0.080***(0.010)0.794***(0.004)
3,776-0.036(0.029)0.213***(0.016)0.442***(0.007)
5,382-0.112**(0.048)0.428***(0.031)1.141***(0.012)
1,606-0.039(0.029)0.170***(0.015)0.266***(0.007)
3,776

Number of individuals

2,691

803

1,888

2,691

803

1,888

Post-Treatment Indicator
Round 2 Indicator
Baseline Mean of Outcomein Control

Table 6b: Sexual Activity for those currently NOT married

Dependent Variable:=1 if Never Had Sex

Number of partners in thepast 12 months

All

Dropouts

SchoolGirls

All

Dropouts

All
SchoolGirls

Number of observations

0.023**(0.011)-0.063***(0.008)0.722***(0.003)
4,882

0.032*(0.017)-0.073***(0.014)0.306***(0.004)
1,320

0.021
(0.013)-0.062***(0.008)0.811***(0.003)3,562-0.027(0.026)0.168***(0.014)0.400***(0.006)
4,882-0.076*(0.045)0.326***(0.029)1.126***(0.011)
1,320-0.034(0.029)0.142***(0.014)0.243***(0.006)
3,562

Number of individuals

2,441

660

1,781

2,441

660

1,781

Post-Treatment Indicator
Round 2 Indicator
Baseline Mean of Outcomein Control

Note: All regressions use individual fixed effects with standard errors clustered at the EA level, and areweighted to make results representative of all study EAs. Standard errors in parentheses.
*Denotes significance at the 10% level, ** at the 5% level and *** at the 1% level30

Table 7: Risky Sexual Activity
Dependent Variable:

Average Condom Use

All

Dropouts

SchoolGirls=1 if Sexually Active at Least
Once a Week

Share of Partners who are atLeast One Year Older

Dropouts

SchoolGirls

Dropouts

All
School
Girls

All

All

-0.064-0.2540.046-0.121-0.048-0.205*-0.071

0.043-0.159*(0.274)(0.266)(0.460)(0.076)(0.088)(0.106)(0.060)(0.079)(0.094)0.153

0.356**

0.031

0.125***

0.178***

0.093

0.022

-0.0350.057(0.156)(0.174)(0.201)(0.045)(0.063)(0.058)(0.035)(0.049)(0.053)Baseline Mean of Outcome inControl

2.849***

2.556***

3.061***

0.181***

0.210***

0.159***

0.808***

0.802***

0.813***(0.066)(0.066)(0.100)(0.018)(0.022)(0.025)(0.015)(0.020)(0.022)

Number of observations

671

351

320

671

351

320

672

352

320

Number of individuals

336

176

160

336

176

160

336

176

160

Post-Treatment Indicator
Round 2 Indicator

Note: All regressions use individual fixed effects with standard errors clustered at the EA level, and are weighted to make results representative of
all study EAs. Standard errors in parentheses.
*Denotes significance at the 10% level, ** at the 5% level and *** at the 1% level31