CASH OR CONDITION?
EVIDENCE FROM A CASH TRANSFER EXPERIMENT

1

SARAH BAIRDCRAIG MCINTOSHBERK ÖZLER
February 23, 2011
Abstract
This paper assesses the role of conditionality in cash transfer programs using a unique experimenttargeted at adolescent girls in Malawi. The program featured two distinct interventions:
unconditional transfers (UCT arm) and transfers conditional on school attendance (CCT arm).
While there was a modest decline in the dropout rate in the UCT arm in comparison to the control
group, it was only 43% as large as the impact in the CCT arm at the end of the two-year program.
The CCT arm also outperformed the UCT arm in tests of English reading comprehension.
However, teenage pregnancy and marriage rates were substantially lower in the UCT than the CCTarm, entirely due to the impact of UCTs on these outcomes among girls who dropped out of school.
Keywords: Conditional Cash Transfers, Education, Adolescent Girls, Fertility
JEL Codes: C93, I21, I38, J12

1 We are grateful to four anonymous referees for helpful feedback on earlier drafts of this paper, as well as seminar

participants at CEGA, George Washington University, IFPRI, NEUDC, Paris School of Economics, Toulouse Schoolof Economics, UC Berkeley, UC San Diego, University of Namur, University of Pennsylvania, and the World Bank for
useful discussions. We particularly appreciate the numerous discussions we had with Francisco Ferreira on this topic. We
thank everyone who provided this project with great field work and research assistance and are too numerous to list
individually. We gratefully acknowledge funding from the Global Development Network, the Bill and Melinda Gates
Foundation, NBER Africa Project, World Bank Research Support Budget Grant, as well as several trust funds at theWorld Bank: Knowledge for Change Trust Fund (TF090932), World Development Report 2007 Small Grants Fund
(TF055926), Spanish Impact Evaluation Fund (TF092384), and Gender Action Plan Trust Fund (TF092029). The
findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do notnecessarily represent the views of the International Bank for Reconstruction and Development or the World Bank.
Please send correspondence to: sbaird@gwu.edu, ctmcintosh@ucsd.edu, or bozler@worldbank.org.

1. INTRODUCTION
Conditional Cash Transfers (CCTs) are “… targeted to the poor and made conditional on
certain behaviors of recipient households” (World Bank 2009). A large and empirically wellidentified body of evidence has demonstrated the ability of CCTs to improve schooling outcomes inthe developing world (Schultz 2004; de Janvry et al. 2006; Filmer and Schady 2010; among manyothers).2 Due in large part to the high-quality evaluation of Mexico‟s PROGRESA, CCT programs
have become common in Latin America and began to spread to other parts of the world, with CCT
programs now in more than 29 developing countries (World Bank 2009).3
There are also rigorous evaluations of Unconditional Cash Transfers (UCTs), which cover awide range of programs: non-contributory pension schemes, disability benefits, child allowance, andincome support. Whether examining a cash transfer program in Ecuador (Bono de Desarollo
Humano), the old age pension program in South Africa, or the child support grants also in South
Africa, studies find that the UCTs reduce child labor, increase schooling, and improve child healthand nutrition (Edmonds and Schady 2009; Edmonds 2006; Case, Hosegood, and Lund 2005; Duflo2003).4 Hence, UCTs also change the behaviors on which CCTs are typically conditioned.
The debate over the relative merits of these two approaches has intensified as CCTs havebecome more widely implemented. Proponents of CCT programs argue that market failures may
often lead to underinvestment in education or health, which are addressed by the conditions
imposed on recipient households. Another advantage of CCT programs is that the conditions makecash transfers politically palatable to the middle and upper class voters who are not direct

Many CCT programs require households to comply with only schooling conditions, while some also requirecompliance with health conditions, such as regular visits to health clinics for children.
3 CCTs are also implemented in developed countries. For example, a three-year pilot CCT program in New York City
ended in early 2010. For more on Opportunity NYC, see:
http://www.nyc.gov/html/ceo/html/programs/opportunity_nyc.shtml.
4 For a recent review of cash transfer programs, see Adato and Bassett (2009), which gives more examples of
unconditional cash transfer programs in Sub-Saharan Africa improving education, health, and nutrition among children.
2

1beneficiaries of such programs.5 To critics of conditionality, the „theoretical default‟ position should
be to favor UCTs, particularly because the marginal contribution of the conditions to cash transfer
programs remains largely unknown.6 Furthermore, the implementation of CCT programs may strain
administrative capacity as these programs expand to poorer countries outside of Latin America.
The existing knowledge base concerning the marginal impact of attaching conditions to cashtransfer programs remains very limited – especially in sub-Saharan Africa where such evaluations are
relatively rare.7 One strain of relevant literature relies on accidental glitches in program
implementation. Based on the fact that some households in Mexico and Ecuador did not think thatthe cash transfer program in their respective country was conditional on school attendance, deBrauw and Hoddinott (2010) and Schady and Araujo (2008) both find that school enrollment wassignificantly lower among those who thought that the cash transfers were unconditional. There is also
a literature that takes a structural approach, where a model of household behavior is calibrated using
real data, and then the impacts of various policy experiments are simulated. Micro-simulating Brazil‟s
Bolsa Escola program, Bourguignon, Ferreira, and Leite (2003) find that UCTs would have no
impact on school enrollment. Todd and Wolpin (2006), examining PROGRESA in Mexico, reportthat the increase in schooling with unconditional transfers would be only 20% as large as theconditional transfers while the cost per family would be an order of magnitude larger.8 Overall, the

5 For an excellent discussion of the economic rationale for conditional cash transfers, see Chapter 2 in World Bank

(2009).
6
For a discussion of “The Conditionality Dilemma”, see Chapter 8 in Hanlon, Barrientos, and Hulme (2010).
7
A few experiments to improve the design of CCT programs have recently been conducted, most notably in Colombia(Barrera-Osorio et al., forthcoming).
8 There is also some evidence that the condition that pre-school children receive regular check-ups at health clinics(enforced by a social marketing campaign, but not monitoring the condition) had a significant impact on child cognitive
outcomes, physical health, and fine motor control. Two studies in Latin America – Paxson and Schady (2007) andMacours, Schady, and Vakis (2008) – show behavioral changes in the spending patterns of parents and households thatthey argue to be inconsistent with changes in just the household income. These studies, however, cannot isolate theimpact of the social marketing campaign from that of the transfers being made to women.

2existing non-experimental evidence suggests that conditionality plays an important role in the overall
impact of CCTs.9
The ideal experiment to identify the marginal contribution of conditionality in a cash transfer
program – i.e. a randomized controlled trial with one treatment arm receiving conditional cashtransfers, another receiving unconditional transfers, and a control group receiving no transfers – has
not previously been conducted anywhere.10 This paper describes the impacts of such an experimentin Malawi that provided cash transfers to households with school-age girls. In the experiment, 176
enumeration areas (EAs) were randomly assigned treatment or control status.11 A sub-group of the
88 treatment EAs was then randomly assigned to receive offers for monthly cash transfers conditionalon attending school regularly (CCT arm) while another group of EAs received offers for unconditional
cash transfers (UCT arm). Offers included separate transfers to the girls and their parents/guardians.
The transfer amounts offered to the parents were randomized at the village level, and those offeredto the girls were randomized at the individual level.
In this paper, we exploit this experiment to not only examine the impact of each treatment
arm on behaviors on which the CCT intervention was conditioned (school enrollment andattendance), but also on outcomes that are of central importance to the long-term prospects ofschool-age girls: human capital formation (measured by tests of English reading comprehension,
mathematics, and cognitive skills), marriage, and childbearing. At the micro-level, improved testscores are associated with increased wages later in life (Blau and Kahn 2005), while delayed fertility is
9 However, using an experiment that provided in-kind food transfers in one arm and equal-valued unrestricted cashtransfers in another arm in Mexico, Cunha (2010) finds that households receiving the latter consumed equally nutritious
foods as the former and that there were no differences in anthropometric or health outcomes of children between the
two treatment arms. The study concludes that there is little evidence to justify the paternalistic motivation when it comesto this in-kind food transfer program.
10 To our knowledge, there are two other studies that plan to examine the impact of the conditionality in the near future.
“Impact Evaluation of a Randomized Conditional Cash Transfer Program in Rural Education in Morocco” has threetreatment arms: unconditional, conditional with minimal monitoring, and conditional with heavy monitoring (using
finger printing machines at schools). A similar pilot in Burkina Faso has comparative treatment arms for conditional andunconditional transfers.
11 An EA consists of approximately 250 households spanning several villages.

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associated with improved maternal and child health outcomes.12 Increased age at first marriage can
improve the quality of marriage matches and reduce the likelihood of divorce, increase women‟sdecision-making power in the household, reduce their chances of experiencing domestic violence,and improve health care practices among pregnant women (Goldin and Katz 2002; Jensen andThornton 2003; Field and Ambrus 2008). At the macro level, improved cognition may lead to more
growth (Hanushek and Woessmann 2009), while lower fertility rates may also contribute toeconomic growth through increased female labor supply (Bloom et al. 2009) and by allowing greater
investments in the health and education of children.
Starting with schooling outcomes, we find that while dropout rates declined in both
treatment arms, the effect in the UCT arm is 43% as large as that in the CCT arm.13 Evidence fromschool ledgers for students enrolled in school also suggests that the fraction of days attended in the
CCT arm is higher than the UCT arm. Using independently administered tests of cognitive ability,
mathematics, and English reading comprehension, we find that while achievement is significantly
improved in all three tests in the CCT arm compared with the control group, no such gains aredetectable in the UCT arm. The difference in program impacts between the two treatment arms issignificant at the 90% confidence level for English reading comprehension. In summary, the CCT
arm had a significant edge in terms of schooling outcomes over the UCT arm: a large gain in
enrollment and a modest yet significant advantage in learning.

12 Evidence on the causal effects of childbearing as a teen is inconclusive in both the biomedical and the economics

literature. While some argue that gynecological immaturity increases the likelihood of preterm births and competition for
nutrients between the mother and baby can cause low birth weight, the evidence is mixed (Fraser, Brockert, and Ward
1995; Akinbami, Schoendorf, and Kiely 2000; Smith and Pell 2001; Horgan and Kenny 2007). In economics, while Hotz,
McElroy, and Sanders (2005) argue that “much of the „concern‟ that has been registered regarding teenage childbearing
is misplaced,” the debate is ongoing (Ashcraft and Lang 2006; Fletcher and Wolfe 2008; Dahl, 2010).
13 Our preferred measure of enrollment uses enrollment data for each term in 2008 and 2009, which are confirmed by
the teachers during the school surveys. Self-reported measures of enrollment produce divergent results in terms of therelative effectiveness of the CCT and UCT arms. We discuss measurement issues in detail in Section 2 and present
enrollment impacts using self-reported data as well as data collected from the schools in Section 3.

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When we turn to examine marriage and pregnancy rates, however, unconditional transfers
dominate. The likelihood of being „ever pregnant‟ and „ever married‟ were 27% and 44% lower in
the UCT arm at the end of the two-year intervention, respectively, whereas program impacts onthese two outcomes were small and statistically insignificant in the CCT arm. These substantial
delays in marriage and fertility in the UCT arm are found entirely among adolescent girls who
dropped out of school after the start of the two-year intervention; rates of marriage and fertility
among girls still enrolled in school at follow-up were negligible regardless of treatment status.
Hence, the success of the conditionality in promoting the formation of human capital among thecompliers appears to be achieved at the cost of denying transfers to non-compliers who are shown
to be particularly „at risk‟ for early marriage and teenage pregnancy.
By exploiting an experiment featuring a CCT and a UCT arm and by broadening the impact
assessment beyond schooling, our study exposes a trade-off that is inherent in CCT programs. The
existing literature is focused primarily on assessing the desired behavior change in CCT programs,
and may have overlooked the effects of denying benefits to those who fail to satisfy the conditions.
Our findings show that UCTs can improve important outcomes among such households eventhough they might be much less effective than CCTs in achieving the desired behavior change. The
trade-off between improved schooling outcomes and delayed marriage and childbearing among
school-age girls illuminates the importance of carefully considering what exactly transfer programs
are trying to achieve in the target population.
In the next section, we describe the study setting and sample selection; the research design
and the intervention; as well as the multiple sources of data collection undertaken for this study. As
the CCT and UCT interventions took place simultaneously in different communities within the same
district, we include a discussion of the circumstances under which this experiment was conducted
and provide evidence on the program beneficiaries‟ understanding of program rules. Issues
5concerning the measurement of various schooling outcomes are also discussed in this section.
Section 3 describes the estimation strategy, presents the main program impacts on schooling,
fertility, and marriage by treatment arm, as well as the heterogeneity of these impacts by age groupand transfer size. Section 4 concludes.

2. BACKGROUND, STUDY DESIGN, AND DATA
2.1. Study setting
Malawi, the setting for this research project, is a small and poor country in southern Africa.
81% of its population of 15.3 million lived in rural areas in 2009, with most people relying on
subsistence farming. The country is poor even by African standards: Malawi‟s 2008 GNI per capita
figure of $760 (PPP, current international $) is less than 40% of the sub-Saharan African average of$1,973 (World Development Indicators Database, 2010). According to the same data source, net
secondary school enrollment is very low at 24%.
2.2. Sampling
Zomba District in the Southern region was chosen as the site for this study. Zomba District
is divided into 550 enumeration areas (EAs), which are defined by the National Statistical Office ofMalawi and contain an average of 250 households spanning several villages. Fifty of these EAs lie in
Zomba city, while the rest are in seven traditional authorities. Prior to the start of the experiment,
176 EAs were selected from three different strata: Zomba city (urban, 29 EAs), near rural (within a
16 KM radius of Zomba city, 119 EAs), and far rural (28 EAs). The choice of a 16 KM radiusaround Zomba city was arbitrary and based mainly on a consideration of transport costs.
In these 176 EAs, each dwelling was visited to obtain a full listing of never-married females,
aged 13-22.14 The target population was then divided into two main groups: those who were out of

14 The target population of 13-22 year-old, never-married females was selected for a variety of reasons. As the study was

designed with an eye to examine the possible effect of schooling cash transfer programs on the risk of HIV infection, we

6school at baseline (baseline dropouts) and those who were in school at baseline (baseline schoolgirls).
Baseline schoolgirls, who form 87% of the target population within our study EAs and among whomthe „conditionality‟ experiment was carried out, are the subject of this paper.15 In each EA, a
percentage of baseline schoolgirls were randomly selected for the study. These sampling percentages
differed by strata and age-group and varied between 14% and 45% in urban areas and 70% to 100%in rural areas. This procedure led to a total sample size of 2,907 schoolgirls in 176 EAs, or an
average of 16.5 per EA.
2.3. Study Design and Intervention
Treatment status was assigned at the EA level and the sample of 176 EAs was randomly
divided into two groups of equal size: treatment and control. The sample of 88 treatment EAs was
further divided into two arms based on the treatment status of baseline schoolgirls: (i) CCT arm (46EAs), and (ii) UCT arm (27 EAs). In the remaining fifteen treatment EAs, no baseline schoolgirls
were made offers to receive cash transfers.16 Excluding the 623 girls who lived in intervention EAs
but did not receive an offer, we are left with a sample of 2,284 baseline schoolgirls in 161 EAs (1,495 in
88 control EAs, 506 in 46 CCT EAs, and the remaining 283 in 27 UCT EAs). See Figure I for an

focused on females because the HIV rate among boys and young men of schooling age is negligible. The age range wasselected so that the study population was school-age and had a reasonable chance of being or becoming sexually active
during the study period. Finally, a decision was made to not make any offers to girls who were (or had previously been)married, because marriage and schooling are practically mutually exclusive in Malawi – at least for females in our study
district.
15 Many cash transfer programs are school-based, meaning that they do not cover those who have already dropped outof school (see, for example, the discussion of Cambodia‟s CESSP Scholarship Program in Filmer and Schady, 2009).
Other programs, such as PROGRESA in Mexico, covered baseline dropouts, but studies usually exclude this group fromthe evaluation due to the „one-time effect‟ at the onset of the program for this group (de Janvry and Sadoulet, 2006).
While outcomes for baseline dropouts were also evaluated under the broader study, they are not the subject of this paper as
the „conditionality‟ experiment was not conducted among this group. As the sample size for this group is quite small(889 girls in 176 EAs at baseline, i.e. approximately 5 girls per EA), dividing the treatment group into a CCT and a UCTgroup would yield an experiment with low statistical power. Hence, in treatment EAs, this group received CCT offers
only.
16 To measure potential spillover effects of the program, a randomly selected percentage (33%, 66%, or 100%) of
baseline schoolgirls in each treatment EA were randomly selected to participate in the cash transfer program. In the 15
treatment EAs, where no baseline schoolgirls were offered cash transfers, this percentage was equal to zero. In these 15
EAs, the only spillovers on baseline schoolgirls would be from the baseline dropouts who were offered CCTs. We do not
utilize this random variation in treatment intensity in this paper.

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illustration of the sample. No EA in the sample had a similar cash transfer program before or during
the study.
2.3.1. CCT arm
After the random selection of EAs and individuals into the treatment group, the local NGO
retained to implement the cash transfers held meetings in each treatment EA between December
2007 and early January 2008 to invite the selected individuals to participate in the program. At thesemeetings, the program beneficiary and her parents/guardians were made an offer that specified themonthly transfer amounts being offered to the beneficiary and to her parents, the condition toregularly attend school, and the duration of the program.17 An example of the CCT offer letters canbe seen in Appendix A. It was possible for more than one eligible girl from a household toparticipate in the program. Transfer amounts to the parents were varied randomly across EAs
between $4, $6, $8, and $10 per month, so that each parent within an EA received the same offer.
Within each EA, a lottery was held to determine the transfer amount to the young female program
beneficiaries, which was equal to $1, $2, $3, $4, or $5 per month.18 The fact that the lottery was held
publicly ensured that the process was transparent and helped the beneficiaries to view the offers theyreceived as fair. In addition, the offer sheet for CCT recipients eligible to attend secondary schoolstated that their school fees would be paid in full directly to the school.19

17 Due to uncertainties regarding funding, the initial offers were only made for the 2008 school year. However, uponreceipt of more funds for the intervention in April 2008, all the girls in the program were informed that the program
would be extended to continue until the end of 2009.
18 The average total transfer to the household of $10/month for 10 months per year is nearly 10% of the average
household consumption expenditure of $965 in Malawi (calculated using final consumption expenditure for 2009, World
Development Indicators 2010). This falls in the range of cash transfers as a share of household consumption (orincome) in other countries with similar CCT programs. Furthermore, Malawi itself has a Social Cash Transfer Scheme,
which is now under consideration for scale-up at the national level that transferred $12/month plus bonuses for schoolage children during its pilot phase (Miller and Tsoka 2007).
19 Primary schools are free in Malawi, but student have to pay non-negligible school fees at the secondary level. The
program paid these school fees for students in the conditional treatment arm upon confirmation of enrollment for eachterm. Private secondary school fees were also paid up to a maximum equal to the average school fee for public
secondary schools in the study sample.

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Monthly school attendance for all girls in the CCT arm was checked and payment for thefollowing month was withheld for any student whose attendance was below 80% of the number ofdays school was in session for the previous month.20 However, participants were neveradministratively removed from the program for failing to meet the monthly 80% attendance rate,
meaning that if they subsequently had satisfactory attendance, then their payments would resume.
Offers to everyone, identical to the previous one they received and regardless of their schoolingstatus during the first year of the program in 2008, were renewed between December 2008 andJanuary 2009 for the second and final year of the intervention, which ended at the end of 2009.
2.3.2. UCT arm
In the UCT EAs, the offers were identical with one crucial difference: there was norequirement to attend school to receive the monthly cash transfers. An example of the UCT offer
letters can also be seen in Appendix A. Other design aspects of the intervention were kept identicalso as to be able to isolate the effect of imposing a schooling conditionality on primary outcomes ofinterest. For households with girls eligible to attend secondary schools at baseline, the total transfer
amount was adjusted upwards by an amount equal to the average annual secondary school fees paidin the conditional treatment arm.21 This additional amount ensured that the average transfer
amounts offered in the CCT and UCT arms were identical and the only difference between the two

20 We were initially concerned that teachers may falsify attendance records for program beneficiaries – either out of

benevolence for the student or perhaps to extract bribes. To ensure this did not happen, a series of spot checks wereconducted about half way through the first year of the program in 2008. This meant that the program administrators
went to a randomly selected sub-sample of schools attended by girls in the CCT arm and conducted roll calls for thewhole class after attendance for that day had been taken. In all schools but one, the ledger perfectly matched theobserved class attendance for that day. As these spot checks were expensive to conduct, they were discontinued after the
study team was convinced that the school ledgers gave an accurate reflection of real attendance.
21 Because the average school fees paid in the conditional treatment arm could not be calculated until the first term fees
were paid, the adjustment in the unconditional treatment arm was made starting with the second of 10 monthly
payments for the 2008 school year. The average school fees paid for secondary school girls in the conditional treatmentgroup for Term 1 (3,000 Malawian Kwacha, or approximately $20) was multiplied by three (to calculate an estimate of
the mean annual school fees), divided by nine (the number of remaining payments in 2008) and added to the transfers
received by households with girls eligible to attend secondary school in the UCT arm. The NGO implementing the
program was instructed to make no mention of school fees but only explain to these households that they wererandomly selected to receive a „bonus.‟

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groups was the “conditionality” of the transfers on satisfactory school attendance. Attendance wasnever checked for recipients in the UCT arm and they received their payments by simply presenting
at the transfer locations each month.
The UCT experiment was conducted alongside the CCT experiment in the same district. 22
Even though the offer letters were differentiated carefully and treatment status for each individual
was reinforced during the monthly cash transfer meetings by the implementing NGO, it is natural toquestion whether the beneficiaries in the UCT arm understood the program rules correctly. In order
to interpret the differential impacts between the two treatment arms, it is important to know what
was understood by those in the UCT arm as to the nature of their transfers and to understand thecontext in which the cash transfer experiment was conducted.
As summarized in Section 1 above and presented in detail in Section 3, we find statisticallysignificant differences between programs impacts in the CCT and UCT for all the main outcome
indicators examined in this paper: enrollment, test scores, marriage, and pregnancy. These
differences offer prima facie evidence that the two interventions were perceived to be different thaneach other.23
In order to understand the perceptions of study participants more fully, we conducted
qualitative interviews with a random sub-sample in the autumn of 2010 – approximately ninemonths after the two-year intervention ended in December 2009. Of the fifteen girls randomly
selected from the UCT arm, only one of them reported a fear of losing payments if she was notattending school.24 The interviews with those in the UCT arm lead to two clear conclusions. First,
the rules of the program were well understood by the girls in the UCT arm; the interviews make22 The reader will note that there are no „ideal conditions‟ under which to conduct this particular experiment. For

example, we could have run the CCT and UCT experiments in separate districts with little inter-district communication,
but then we would not be able to rule out the possibility of unobserved heterogeneity driving the results.
23 In Appendix E, we provide further evidence on this issue by showing that the exogenous variation in the number of
CCT beneficiaries at the monthly cash transfer meetings had no effect on outcomes in the UCT arm.
24 Similarly, we can find no evidence supporting the notion that girls in the UCT arm thought their transfer payments
were conditional on not getting married.

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clear that UCT girls knew that nothing was required of them to participate in the program and they
were given no rules or regulations tied to the receipt of the transfers other than showing up at the
pre-determined cash transfer locations. Second, girls in the UCT arm were very much aware of the
CCT intervention. Interviews suggest that the girls in the UCT arm not only knew about the CCT
program, and but many actually had friends or acquaintances in the CCT arm. Through thesecontacts they knew that school attendance was strictly monitored in the CCT arm, and that noncompliers were penalized.25
The evidence from the in-depth interviews makes it clear that the UCT experiment did not
happen in a vacuum. Instead, it took place under a rubric of education that naturally led thebeneficiaries to believe that the program aimed to support girls to further their education. The
differential impacts of the UCT and CCT interventions should be interpreted in this context.26

2.4. Data sources and outcomes
2.4.1. Data sources
The data used in this paper were collected in three rounds. Baseline data, or Round 1, werecollected between October 2007 and January 2008, before the offers to participate in the program
took place. First follow-up data collection, or Round 2, was conducted approximately 12 months
later – between October 2008 and February 2009. The second follow-up (Round 3) data collection
was conducted between February and June 2010 – after the completion of the two-year intervention
at the end of 2009 to examine the final impacts of the program. To assess program impacts on
school enrollment, attendance, and achievement, as well as on fertility and marriage, we use multiple
25The following excerpt from an interview (with respondent ID 1461204) is a good example:

Interviewer: Earlier you talked of conditional and unconditional. What did you say about the rules for conditional girls?
Respondent: They had to attend class all the time…not missing more than 3 days of classes in a [month] – like I already explained.
Interviewer: How did you say the program managers knew about the missed school days?
Respondent: They would go to the schools…For example, I have a friend, [name], who was learning at [school name]. They would goeach month to the school to monitor her attendance, and if she was absent for more than three days she would not get her monthly money.
26 It is also possible that the existence of a CCT program could have reduced the motivation of the girls in the UCT arm.

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data sources: household surveys (all rounds), school surveys (Rounds 2 and 3), school ledgers,
independently developed achievement tests, and qualitative interviews (Round 3 only).
The annual household survey consisted of a multi-topic questionnaire administered to thehouseholds in which the sampled respondents resided. It consisted of two parts: one that wasadministered to the head of the household and the other administered to the core respondent, i.e.
the sampled girl from our target population. The former collected information on the householdroster, dwelling characteristics, household assets and durables, shocks and consumption. The survey
administered to the core respondent provides detailed information about her family background,schooling status, health, dating patterns, sexual behavior, fertility, and marriage.
During Round 2, we also conducted a school survey that involved visiting every school attended
by any of the core respondents (according to self-reported data from the household survey) in ourstudy sample in 2008. This procedure was repeated in Round 3 for a randomly selected sub-sample
of core respondents who reported being enrolled in school in 2009.27 Using Round 2 (Round 3)
household survey data, we collected the name of the school, the grade, and the teacher‟s name forthe core respondent if she reported being enrolled in school at any point during the 2008 (2009)school year. These teachers were then located at the named schools and interviewed about each
respondent‟s schooling status (term by term) during the past school year. Furthermore, during
Round 3, school ledgers were sought to check the attendance of respondents for each school termin 2009 and the first term of 2010.
To measure program impacts on student achievement, mathematics and English reading
comprehension tests were developed and administered to all study participants at their homes. The tests
were developed by a team of experts at the Human Sciences Research Council according to the
27 The reason why the school survey in Round 3 was conducted for a randomly selected sub-sample instead of every

student who reported being enrolled in school in 2009 is that school ledgers were also sought to check the attendance of
core respondents. As locating these ledgers, examining them, and recording attendance for each core respondent is time
consuming and costly, the study team decided to reduce the sample size.

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Malawian curricula for these subjects for Standards 5-8 and Forms 1-2.28 In addition, to measurecognitive skills, we utilized a version of Raven‟s Colored Progressive Matrices that was used in theIndonesia Family Life Survey (IFLS-2).29 The mathematics and English tests were piloted for a small,randomly selected sub-sample of the study participants in the control group before being finalized
for administration during Round 3 data collection. These tests were administered by trained proctors
at the residences of the study participants and were always administered after the household survey,
but never on the same day. The order of the math and English tests were randomized at theindividual level and the Raven‟s test was always administered last.
Finally, structured in-depth interviews were conducted with a small sample of study participants,
their parents or guardians, community leaders, program managers, and schools. The sample wasselected randomly using block stratification based on treatment status at baseline, as well as
schooling and marital status at Round 3. The total number of baseline schoolgirls sampled was fortyeight, of whom forty-six were successfully interviewed.30 The main aim of these structured
interviews was to gauge the “understanding of the cash transfer intervention” by study participants.
In addition, topics of discussion included schooling decisions, dating, fertility, and marriage, as wellas empowerment and future aspirations. The interviews usually lasted sixty to ninety minutes andwere conducted by trained enumerators, many of whom had previous experience in qualitative fieldwork. The conversations in Chichewa were taped and transcribed in English immediately after theinterviews.

28 In Malawi, there are eight grades in primary school (Standard 1-8) and four in secondary school (Form 1-4).
29 These three tests are available from the authors upon request.

30 We have fourteen in-depth interviews in the control group, seventeen in the CCT arm, and fifteen in the UCT arm.

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2.4.2. Outcomes
Schooling
We measure enrollment and attendance using three different data sources. The first indicator
is constructed using self-reported data from the household survey on whether the core respondent
was enrolled in school. These questions are asked for each of the seven school terms between Term
1, 2008 and Term 1, 2010. As self-reported data may overstate enrollment, we cross-validated thesedata by visiting the schools the study participants reported attending, and asking the same questions
to the teachers of the core respondents. The enrollment indicators from the school survey are coded„zero‟ if the core respondent reported not being enrolled in school for that term or if the teacher
reported her as not enrolled and „one‟ if her teacher(s) confirmed that she was attending school
during the relevant term. Finally, as enrollment may be a poor proxy for actual school attendance,
we utilize the attendance ledgers for the 2009 school year and the first term of 2010 collected during
the school surveys in Round 3 to construct an indicator for the percentage of days the core
respondent enrolled in school was recorded „present‟ during days the school was in session.
We did not independently monitor the school attendance of study participants throughrandom spot checks. While studies such as Miguel and Kremer (2004), Kremer, Miguel, andThornton (2009), and Barrera-Osorio et al. (forthcoming) have measured attendance directly, wedeliberately chose to forego this method of data collection to protect the validity of the UCT
experiment. Despite having data on enrollment from the teachers and attendance from school
ledgers, direct observation would clearly have produced superior evidence to the alternative
measures of school participation used in this paper. However, as reported above in Section 2.3.2,
girls in the UCT arm were fully aware that the attendance of CCT recipients were being regularly
monitored, which led them to believe that program managers „cared about the education‟ of the girls
in the CCT arm. We were concerned that performing random spot checks of attendance for girls in
14

the UCT arm could have given them the impression that they were also supposed to attend school
regularly to receive their payments.31 As a consequence, we chose to avoid direct monitoring ofattendance in order to retain as sharp a differential test as possible of the relative merits ofconditional and unconditional transfers.
As important as school attendance may be for adolescent girls, perhaps as important islearning achievement and cognitive skills.32 To measure these, we conducted independently
developed tests of mathematics, English reading comprehension, and cognitive ability. Total numberof correct answers in each of these tests is standardized to have a mean equal to „zero‟ and standard
deviation equal to „one‟ in the control group and program impacts are presented as changes instandard deviations (SD).
Marriage and Fertility
Teenage pregnancy in Malawi is common with the adolescent fertility rate at 133 per 1,000
women aged 15-19 (World Development Indicators 2010).33 Many girls cite pregnancy as the main
reason for dropping out of school and getting married at an early age. Each of the core respondents
was asked the following questions in each round: “Have you ever been pregnant or are you currently
pregnant?” and “What is your marital status?” We use the answers to these questions to calculate theprevalence of marriage and pregnancy in Rounds 2 and 3.

31 This is nicely illustrated during an in-depth interview with one of the core respondents (respondent ID 1332203):

After describing the CCT girls being followed to their schools to monitor their attendance, which she explained showed
her that the program was interested in „attracting girls to go to school,‟ she was asked by the interviewer in what way the
program managers cared (about schooling). She answered by saying: “They cared for the conditional group only but on
the other group they didn‟t care.”
32 Other schooling outcomes, such as repetition and reentry rates are also important and can lead to different inferences
regarding program impacts on schooling attainment. See, for example, Behrman, Sengupta, and Todd (2005).
33 For comparison, the same figure is 35 in the U.S.A. and 64 in Mexico (World Development Indicators 2010).

15

3. ESTIMATION STRATEGY AND RESULTS
3.1. Sample Attrition and Balance
Figure I summarizes the study sample and attrition. We began with a sample of 2,284
respondents who were in school at baseline and formed the experimental sample for our study ofconditionality. Of this sample, 2,186 were tracked successfully for the Round 3 household survey
and 2,089 were successfully interviewed in all three rounds, a tracking rate of over 90%. Of the 983
subjects randomly sampled for the school survey in Round 3, enrollment data reported by teachers
are available for 922 of them. We were less successful in locating attendance ledgers; of the 821 girls
who were selected for the Round 3 school survey and reported being enrolled in school in 2009,legible ledgers for at least one term are only available for 384.
Table I examines attrition across the two treatment arms and the control group separately byeach of our data sources: household surveys, achievement tests, and school surveys. The regression
analysis indicates that there is no significant differential attrition between the two treatment arms inany of the six sub-samples used for analysis in this study. Study participants in both treatment arms,
however, were equally more likely to take the achievement tests than the control group. Similarly,
legible ledgers are more likely to be found for treatment girls. Thus, the analysis of the available
samples should give us unbiased estimates of differential program impacts on schooling outcomes,
marriage, and childbearing.34
In Table II, we test the balance of the experiment, using baseline data for the sample used in
the analysis, i.e. those successfully interviewed during all three rounds. Panel A shows balance onhousehold attributes, and Panel B on individual characteristics. Overall, the experiment appears wellbalanced between the treatment and control groups over a broad range of outcomes; Column (4)shows that the two treatment arms only differ in age and highest grade attended at baseline – two
34 To assess the robustness of our findings with respect to attrition from the sample that took the achievement tests, we

perform a bounding exercise (Lee, 2009), described in detail in footnote 40.

16variables that are highly correlated with each other. While the share of female-headed households isbalanced between the two treatment arms, it is significantly higher in the control group.35
3.2. Estimation Strategy
The experimental study design gives us a reliable source of identification. To estimate
intention-to-treat effects of the program in each treatment arm on school enrollment, attendance,
test scores, marriage, and pregnancy, we employ a simple reduced-form linear probability model ofthe following type:Yi  Ti C C  TiU  U  X i    i ,(1)where Yi is an outcome variable for individual i, TiC and TiU are binary indicators for offers to be in
the CCT and the UCT arms, respectively, and Xi is a vector of baseline characteristics. The standard
errors εi are clustered at the EA level which accounts both for the design effect of our EA-level
treatment and for the heteroskedasticity inherent in the linear probability model.
In all regressions, we include baseline values of the following variables as controls: ahousehold asset index, highest grade attended, a dummy variable for having started sexual activity,and dummy variables for age. These variables were chosen because they are strongly predictive of
schooling outcomes and, as a result, improve the precision of the impact estimates. We also include
indicators for the strata used to perform block randomization – Zomba Town, within sixteen
kilometers of the town, and beyond sixteen kilometers (Bruhn and McKenzie 2008). Age- andstratum-specific sampling weights are used to make the results representative of the target
population in the study area.

35 In Appendix Tables B.1 and B.2, we examine balance for the same set of characteristics for the school survey andledger sub-samples analyzed in this paper. Both of these tables look similar to Table II, confirming balance in these subsamples.

17

3.3. Results
3.3.1. Schooling
Enrollment
Table III describes enrollment rates by term, including a cumulative variable for the numberof terms the girl was enrolled in school during the two-year intervention that takes on a value
between 0 and 6. When we examine self-reported enrollment in Panel A, we see that dropout rates
in the control group steadily increase over time with the sharpest change occurring between school
years. Impact estimates suggest that dropout rates were significantly lower in both treatment arms,
and that the UCT arm outperformed the CCT arm: the program impact on the number of terms thegirls were enrolled in school during the two-year intervention is 0.41 terms in the UCT arm,
compared to 0.23 terms in the CCT arm – a difference that is significant at the 95% confidence
level.
Self-reported enrollment data can be subject to reporting bias. For example, comparing
program impacts using self-reports vs. monitored data, Barrera-Osorio et al. (forthcoming) reportthat a significant positive bias in self-reported school enrollment compresses the difference between
the treatment and control groups causing a downward bias in observed program impact. Baird andÖzler (2010) confirm this finding for Malawi and show that differential misreporting can further bias
program impacts. As described in Section 2.4, we tried to confirm self-reported enrollment byvisiting the schools the girls reported to be enrolled in and asking their teachers about theirenrollment statuses. Panel B in Table III reports the same information as Panel A, but as reportedby the teachers.
The evidence in Panel B reverses the finding on the relative effectiveness of CCTs and
UCTs in preventing dropouts. First, we note that the dropout rates in the control group are higher
by approximately 5-6 percentage points (pp), consistent with over-reporting by the core18

respondents. Furthermore, while dropout rates are still lower in both treatment arms than thecontrol group, the impacts in the CCT arm are significantly larger than the UCT arm and thedifference between the two treatment arms in terms of total number of terms enrolled during thetwo-year intervention is significant at the 95% confidence level (p-value=0.011). Finally, the impactof the CCT intervention seems to have persisted after the cash payments stopped at the end of
2009, while the enrollment rate in the UCT arm is identical to that in the control group during Term
1 of 2010.36
Given the divergent results using self-reports and teacher-reports, which set of findings
should we believe? Baird and Özler (2010) use administrative records from the CCT program toestablish that the school ledgers collected independently in Round 3 provide a reliable source tomeasure attendance. Using attendance information from these school ledgers here as ourbenchmark, we can examine the extent of misreporting by the students and the teachers. Table IV
presents this evidence. In column (1), we see that 17.0% of the girls in the control group who
reported being enrolled in school during Term 2 of 2009 were found to have never attended school
during that period according to the school ledgers. This likelihood to over-report enrollment isreduced by more than 50% (9.3 pp) in the CCT arm, but is identical in the UCT arm. Column (2)shows that not only is over-reporting substantially reduced when the information comes from theteachers (5.2% in the control group), but also that the differential misreporting disappears.
The analysis above explains the divergent findings in Table III: girls in both the control
group and the UCT arm are significantly more likely than the CCT arm to report being enrolled inschool when in fact they are not. Thus, self-reported data attenuate program impacts for the CCT
arm and give the impression that UCTs outperform CCTs in reducing dropout rates. Teacher

36 In Appendix C, we conduct a battery of exhaustive robustness checks and conclude that the findings presented inTable III are robust to varying the sub-samples analyzed and the rules by which missing data for teacher-reported
enrollment are treated.

19

reports substantially reduce the bias caused by this differential misreporting and reveal the true
program impacts. As we will see below, the evidence regarding program impacts on school
attendance and test scores are also consistent with the finding that the CCT arm was more effective
in reducing school dropout than the UCT arm.
Attendance (intensive margin)
We now turn to examining the intensity of attendance for those enrolled in school in 2009
and the first term of 2010. The school ledgers from Round 3 provide term by term information on
the number of days the students were present for each day the school was in session.37 Table V
indicates that the attendance rate in the control group among those enrolled in school ranges from ahigh of 85% in Term 2, 2009 to a low of 69% in Term 3, 2009. Attendance on the intensive margin
is uniformly higher in the CCT arm than the control group. The overall attendance rate for 2009 is
8.0 pp higher in the CCT arm than the control group, which translates into approximately four
school days per term or more than ten school days over the entire 2009 school year. In the UCTarm, impact estimates are mostly positive, but none of them are statistically significant. Program
impacts in the CCT arm are higher than the UCT arm during Term 1 in both 2009 (13.9 pp vs. 6.3
pp; p-value=0.129) and 2010 (9.2 pp vs. -3.8 pp; p-value=0.010). Term 1 coincides with the leanseason in Malawi, when food is scarcest and the number of malaria cases reaches its peak.38 Thus,
the condition to attend school regularly seems most effective in keeping attendance rates high whenhouseholds need cash the most.39

37

Appendix Table B.2 confirms balance in this sub-sample of girls for whom attendance data from ledgers are available.

38 In 2001, the prevalence of malaria parasitaemia among non-pregnant females, ages 15-19, was 24% (Dzinjalamala2009). The same figure was 47% in school children. Malaria is a frequent cause of absenteeism in school, resulting inpoor scholastic performance on the part of the student.
39 CCT households may have also taken additional measures to minimize school absence by having the girls sleep under
bed nets: the share of girls who reported sleeping under a bed net the previous night was 10 pp higher (p-value=0.049)
in the CCT arm than the UCT arm in Round 3.

20

Test Scores
In Table VI, we present program impacts on tests of English reading comprehension,
mathematics, and cognitive ability, which were administered to all study participants at their homes.40
We see across-the-board improvements in test scores in the CCT arm, while no significant
improvement can be detected in the UCT arm. The 0.140 SD improvement (p-value=0.010) in
English reading comprehension in the CCT arm is significantly higher than the program impact in
the UCT arm at the 90% confidence level (p-value=0.069). The CCT arm also has a 0.114 SD
advantage over the UCT arm in the TIMMS math score, but this difference is not statisticallysignificant.41 Finally, in terms of cognitive ability, measured by Raven‟s colored progressive matrices,
we see improvements of 0.174 and 0.136 SD in the CCT and UCT arms, respectively, comparedwith the control group. However, while the improvement in the CCT arm is statistically significant
at the 99% confidence level, the impact estimate for the UCT arm is noisy and insignificant.
Summarizing the program impacts on schooling outcomes in the two treatment arms, wefind that the CCT arm had significant gains in enrollment on the extensive margin, some
improvement in attendance on the intensive margin, and modest gains in achievement in tests ofEnglish reading comprehension, mathematics, and cognitive skills.42 Girls in the UCT arm were alsosignificantly more likely to be enrolled in school compared with the control group, but there was nodetectable improvement in their intensity of school attendance or their test scores. The increase in

40 We presented evidence in Table I that there is no differential attrition between the CCT and UCT arms. Moreover, inthe sample used for analysis in this paper, i.e. those who were successfully interviewed in all three rounds, only 30 girls
have missing test scores (22 in the control group, 5 in the CCT arm, and 3 in the UCT arm). Even if all the controls wereassigned the highest score for the English test and all treatment girls were assigned the lowest score, the findings ontreatment impacts would not change.
41 TIMMS stands for Trends in Mathematics and Science Study, which is a cycle of internationally comparative
assessments in mathematics and science carried out at the fourth and eighth grades every four years. We borrowed fivemathematics questions from the 2007 TIMMS (four fourth-grade and one eighth-grade question) and incorporated theminto our independently developed mathematics test.
42 The improvements in test scores is different than what has been previously reported in evaluations of other CCT
programs. Behrman, Parker and Todd (2009) and Filmer and Schady (2009) find no impacts of CCTs on tests ofmathematics and language in Mexico and Cambodia, respectively.

21

enrollment (measured by the total number of terms enrolled in school during the two-year program)in the UCT arm was less than half of that achieved in the CCT arm. We conclude that CCTs
outperformed UCTs in terms of improvements in schooling outcomes.
3.3.2. Marriage and Pregnancy
We now turn to an analysis of the prevalence of marriage and pregnancy in Rounds 2 and 3.
Column (1) of Table VII shows that, by Round 2, 4.3% of the initially never-married sample wasmarried in the control group. Marriage rates were unchanged in the CCT arm, but significantly lower
in the UCT arm. By Round 3, the prevalence of marriage rose to 18.0% in the control group with an
insignificant reduction of 1.2 pp in the CCT arm and a significant reduction of 7.9 pp (44%) in the
UCT arm (column (2)). The differences in program impacts between the two treatment arms in
Rounds 2 and 3 are both statistically significant at the 95% confidence level. Columns (3 & 4) showthat while the program had little effect on the prevalence of pregnancy in either treatment arm after
one year, there is a large decline in the UCT arm by Round 3. While roughly a quarter of the control
group and the CCT arm had been „ever pregnant‟ by the end of the experiment, this likelihood was
reduced by 6.7 pp (or 27%) in the UCT arm – significant at the 99% confidence level. The
difference in program impacts between the two treatment arms is also significant at the 99%
confidence level by Round 3.
How do we reconcile the fact that the CCT arm led to schooling increases with the fact thatthe sharp decreases in marriage and pregnancy are found only among the UCTs? Existing evidence
from sub-Saharan Africa suggests that reducing school dropout should lead to declines in teen
marriage and pregnancy rates (Duflo, Dupas, and Kremer 2010; Ozier 2011; Ferré 2009, Osili andLong 2008). There could also be an income effect on marriage if girls are marrying early due to poor
economic circumstances (Field and Ambrus 2008), and on fertility if monthly transfers allow girls to
reduce transactional sex with „sugar daddies‟ (Dupas 2010; Duflo, Dupas, and Kremer 2010).
22

To understand the differential impacts of CCTs and UCTs on any outcome other thanschooling itself, it is useful to consider three latent strata of baseline schoolgirls. In the first stratum,
UCT offers would be sufficient to keep baseline schoolgirls enrolled in school (UCT compliers).43 This
group would receive transfers under both treatments, so program impacts could only differ due tobehavior changes on the intensive margin arising from the conditionality. In the second stratum aregirls who would be enrolled in school in the CCT arm, but not in the UCT arm (CCT compliers). In
this group, which would also receive transfers under both treatments, conditionality would generatea differential impact on enrollment by lowering the opportunity cost of schooling in the CCT arm.
Finally, in the third stratum are girls who would drop out of school under either treatment arm andwould receive transfers only under the UCT (non-compliers).
The differential impacts of CCTs and UCTs on outcomes other than schooling, such as
marriage and pregnancy, will depend on the relative sizes of these three strata and the extent towhich the relevant outcomes are driven by schooling and income. We would expect CCTs to be
more effective for outcomes such as test scores, which would likely improve with schooling (and the
incentives associated with schooling) and are not likely to be affected by increased income among
dropouts. Conversely, UCTs may be more effective for other outcomes if there is a large group of
non-compliers, a strong income effect among them, and small incentive effects among those enrolled
in school. In the analysis below, we apply this simple framework to our data and show that, in our
setting, marriage is a perfect example of the latter case.
A simple way of presenting the sizes of these three strata and the approximate magnitude ofthe effects within each of them is provided in Table VIII, which gives marriage rates by treatment

43 This group includes those who would be enrolled in school in the absence of either treatment. We assume that UCT

offers have no perverse effects on schooling, meaning that an offer of UCTs would not cause a girl to drop out ofschool if she would be enrolled otherwise. We make a similar „no defiers‟ assumption with respect to the conditionality:
if a girl would be enrolled in school under a UCT, then she would also be enrolled under a CCT offering the sametransfer amount.

23

status and teacher-reported enrollment status in Round 3.44 For each treatment arm, the top row
summarizes the marriage rates by follow-up enrollment status, while the bottom row shows the raw
follow-up enrollment rate in that group.45
The first thing to note in Table VIII is that, on average, only 1.1% of those still enrolled inschool are married (column (1)). This confirms the notion that marriage and school enrollment arepractically mutually exclusive in this setting and implies that CCTs cannot generate a differential
impact on marriage among UCT compliers.46 Second, a comparison of marriage rates in the CCT arm
and the control group among those not enrolled in school confirms the prediction that CCTs areunlikely to have an impact among non-compliers as girls in this group stop receiving transfers when
they drop out of school (column (2)). Approximately half of the girls not enrolled in school were
married in both the control group and the CCT arm in Round 3.
These two simple observations imply that the only way in which CCTs can reduce marriage
rates at follow-up is by averting dropouts. Column (1) in Table IX shows that school dropout was
5.8 pp lower in the CCT arm than the control group. Column (2) shows that the reduction in thelikelihood of being „ever married‟ in the CCT arm is 2.6 pp in this sub-sample of girls for whom dataon teacher-reported enrollment are available in Round 3. This small CCT effect on marriage isexactly what we would expect to find given the roughly 50 pp difference in marriage rates between
girls enrolled in school and those not enrolled (-0.058*0.5 = -0.029). We conclude that the program
impact on dropout in the CCT arm was too small to cause a significant reduction in the likelihood of
being married.
44 The three strata are defined over latent choices, so Table VIII presents instead compliance and marriage rates as

actually observed in the control group and each treatment arm in Round 3. This gives us the unadjusted size of eachstratum (UCT compliers are 61%, CCT compliers are 8%, and non-compliers are 31%), but does not tells us which individuals
belong to which stratum.
45 For expositional purposes, it is easiest to present marriage and enrollment rates at the time of Round 3 data collection,i.e. during Term 1 of 2010. The findings from the analysis that follows remain qualitatively the same if we use enrollment
data for any school term in 2008 or 2009, utilize marriage rates reported in Round 2, or replace marriage with pregnancy.
46 Ozier (2011), summarizing the literature on schooling and fertility, states that they are in practice nearly mutually
exclusive in Kenya and argues that this is true in many other contexts.

24

The UCT arm presents a contrasting case, in which the program impacts on marriage andfertility arise only through changes in behavior among those who dropped out of school. Table VIII
shows that the raw marriage rate in the UCT arm among those not enrolled in school isapproximately 50% lower than the CCT arm and the control group. In Table IX, we see that the
UCT impact on marriage was -8.8 pp, while there was no impact on dropping out of school. Hence,
there can be no „schooling‟ channel operating on marriage and the decline in the UCT arm is entirely
due to the effect of cash transfers among those who dropped out of school. The final two columns
in Table IX present regressions of being „ever married‟ for those enrolled in school and those who
dropped out, respectively, and shows that the marriage rate among those who dropped out issignificantly lower in the UCT arm compared to both the control group and the CCT arm.47
The evidence presented here is consistent with the literature that posits female schooling as ameans of delaying marriage and fertility, but the product of the treatment effect on dropping out in
the CCT arm (5.8 pp) and the sizeable effect of school enrollment on marriage (reduced by nearly 50pp) is too small to be detectable given our sample size.48 The novel finding here is the large impactof unconditional cash transfers on delaying marriage and pregnancy among adolescent girls who
dropped out of school. The effect of UCTs on marriage among this group (reduced by more than 20pp) is smaller than the schooling effect on marriage, but experienced in a large enough sub-group(40% of the UCTs had dropped out by Round 3) to be strongly significant in the whole UCT arm.
The „schooling‟ channel on marriage operates through the dropouts averted by the CCT arm, whilethe „income‟ effect on marriage operates through those who dropped out of school; and the lattergroup is substantially larger than the former in this experiment.
47 It should be noted that these are not proper sub-samples on which experimental program impacts can be estimated asthey are constructed using enrollment status at follow-up, which is endogenous to treatment. We only present these
regressions to give a sense of the magnitude and the statistical significance of the raw differences in Table VIII.
48 To confirm the potential importance of the schooling channel, note that Baird et al. (2010) show that CCTs had asignificant impact on reducing marriage and pregnancy rates among baseline dropouts by 11.3 pp and 5.1 pp,
respectively. In this sample the CCT led to a very large increase in self-reported re-enrollment in comparison to thecontrol group (61% vs. 17%), and hence the impact through the „schooling‟ channel is detectable.

253.3.3. Heterogeneity of program impacts
We now turn to an examination of impact heterogeneity, using only covariates that wereused to stratify our initial study design (age) or were directly randomized (the transfer amounts madeto the girls and their parents).49 Table X examines heterogeneity using an indicator for being sixteen
years or older.50 With respect to enrollment, program impacts do not vary significantly by age group:
CCTs outperform UCTs in raising enrollment among early adolescents as well as older teenagers.
However, when it comes to the other three main outcomes examined in this study, we see that theadvantage in English test scores in the CCT arm among early adolescents disappears among girls
sixteen or older at baseline, while the advantage the UCT arm has in preventing marriages andpregnancies is substantially larger among older teenagers. For each of these three outcomes, thedifference in the coefficients for the interaction terms between the CCT and UCT arms is large andstatistically significant. Our results indicate that, among early adolescents, there is a clear trade-off
between a CCT and a UCT program in terms of improved schooling outcomes versus a reduction inteenage pregnancies. Among older teenage girls, however, this trade-off largely disappears and UCTs
become relatively more attractive.51
We next examine the heterogeneity in program impacts by two important program features:the identity of the transfer recipient within the household and transfer size. Bursztyn and Coffman(2010) argue that policies designed to promote school attendance might be more effective if they

49 The study sample was stratified by age group at baseline as program impacts on enrollment, marriage, and pregnancy

can reasonably vary along this dimension. Transfer amounts made separately to girls and their parents were also
randomized to assess whether transfer size or the identity of the recipient within the household – two key policy
parameters in cash transfer programs – affected the outcomes. We limit ourselves to examining heterogeneity of
program impacts only along these baseline characteristics that were defined ex-ante in order to avoid data-mining.
50 The legal age of marriage stood at 16 in Malawi by late 2009 (Nyasa Times September 22, 2009) and many girls aged
16 or older are attending primary or secondary school. In our study sample, 22% of students eligible to attend primaryschool at baseline were aged 16 or older. This share increases to 56% for those eligible to attend secondary school atbaseline.
51 We have also examined heterogeneity of program impacts using a household asset index to imitate the means-tested
targeting schemes like the ones in Brazil or Mexico. In Appendix Table D.1, we show that enrollment impacts in the
UCT arm may improve somewhat under such a targeting scheme, but otherwise program impacts would not besignificantly altered compared to a universal cash transfer program for never-married adolescent girls.

26

target the child instead of focusing on parents because sub-optimal school attendance may be due toa parent-child conflict, where the parents cannot enforce their desire that their children attend
school. Similarly, World Bank (2009) argues that “…the key parameter in setting benefit levels is thesize of the elasticity of the relevant outcomes to the benefit level.” However, random variation inthese design parameters is rarely observed in cash transfer programs around the world. In thisexperiment, separate transfers were offered to girls and their parents (or guardians), the size of eachof which were randomly determined.
We analyze the impact of transfer amounts by estimating the following linear probability
model:Yi  IiC C  IiU  U  HiC C  HiU  U  Ti C C  TiU  U  X i    i(2)

Ti C and TiU are again dummy variables for the CCT and UCT offers, I iC , I iU and H iC , H iU give theindividual and household transfer amounts for each treatment, defined in differences from thelowest amount offered in the treatment arms ($1 for the individual transfer, $4 for the household
transfer). The estimates of  and  thus give the marginal effect of increasing individual andhousehold amounts by $1 under each treatment arm, while the estimates of  measure the impactof each treatment at the lowest total transfer amount. The standard errors εi are again clustered at
the EA level.
Table XI presents the heterogeneity in program impacts by transfer amounts. Column (1)shows program impacts on enrollment: the minimum total transfer amount offered to the household($5/month) seems to be responsible for the entire program impact on the „total number of terms
enrolled‟ in the CCT arm. Additional transfers offered to either party make little difference. In the
UCT arm, however, the effect of the minimum transfer offer is small and insignificant, but
enrollment increases 0.081 terms with each additional dollar offered to the parents over and above27

$4/month.52 The analysis implies that giving an additional $5/month in transfers to the parents
would barely allow the UCT arm to reach the level of enrollment attained by the minimum transfer
amounts in the CCT arm. As the marginal administrative cost of a CCT program is likely to be onlya small share of each dollar transferred to beneficiary households, a CCT program offering$5/month would be substantially more cost-effective in increasing enrollment than a UCT program
offering the same amount.
For English test scores, the results are similar for the CCT arm: there is no indication that
additional amounts to the girls or their parents would improve test scores over and above theminimum monthly transfer. Here, unlike enrollment, the coefficient in the UCT arm is similar to the
CCT arm at the minimum transfer amounts, although insignificant. For marriage, there is notreatment impact at the minimum transfer amounts in the UCT arm, but each additional dollar
offered to the parents of a girl reduces her likelihood of getting married by Round 3 by 1.7 pp.53 The
marginal effect of a dollar offered to parents in delaying marriages is significantly higher in the UCT
arm than that in the CCT arm (p-value=0.069). The large negative and statistically significant
gradient between transfer size and likelihood of marriage at follow-up is consistent with parents
marrying their daughters early due to economic considerations. Finally, the minimum amounts
transferred in the UCT arm seem to be responsible for almost the entire program effect on
preventing pregnancies in this group.
In summary, increasing transfer amounts or varying the recipient within the household has
no effect on any of the outcomes examined in this paper in the CCT arm; this contract variation

52

The difference between the CCT and UCT impacts on enrollment at the minimum transfer amount ($5/month total
to the household) is significant at the 95% confidence level.
53 Similarly, each additional dollar offered to the girl in the UCT arm reduces her chances of being „ever married‟ atfollow-up by 1.6 pp (p-value=0.148).

28

simply does not seem to matter for CCTs.54 In contrast, we find that outcomes vary with increasedtransfer offers to the parents in the UCT arm: enrollment rates increase and the incidence ofmarriage declines as parents are offered more money, but performance in test scores seems to suffer.
Still, however, replacing a CCT program that offers the minimum transfer amounts of $1 to the girland $4 to her parents with a UCT program that offers the parents larger transfer amounts would not
be cost-effective in improving schooling outcomes, but it would reduce marriage rates among
teenage girls. Furthermore, we find no evidence that increasing the amount of transfers madedirectly to the girl rather than her parents would be effective in improving any of the outcomesstudied here.
Before we move to the concluding section, we briefly discuss the robustness of our findings
with respect to two issues. First, treatment and conditionality status were assigned at the EA level inthis experiment. Due to the proximity of EAs to each other, it is possible that the intermingling ofstudents in the two treatment arms led to a change in behavior in the outcomes of interest, thus
biasing our estimates of the marginal impact of the conditionality. Second, as described in Section 2,
the offers in the CCT arm included a promise to pay secondary school fees directly to the schools
upon confirmation of enrollment by the program administrators. To make the average transfer
offers in the UCT arm equal to that in the CCT arm, the average school fee amount was added tothe cash transfer offers of girls in the UCT arm who were eligible to attend secondary school at the
beginning of the program. In Appendix E, we discuss each of these issues in some detail andconclude that it is unlikely that our estimates of differential program impacts are influenced by either
spillovers due to the proximity of girls with discordant treatment statuses or by the manner in which
school fee compensation was handled.
54 This finding is consistent with the evidence presented in Filmer and Schady (2010), which found substantial impacts

on enrollment of a modest CCT offer in Cambodia, but no additional enrollment gains from a larger transfer offer. On
the other hand, Barrera-Osorio, Linden, and Urquiola (2007) find enrollment responses of students to be sensitive to thesize of the implied subsidies in Bogota‟s Gratuidad school fee reduction initiative.

29

4. Concluding discussion and policy implications
This paper presented experimental evidence on the relative effectiveness of conditional andunconditional cash transfer programs. The analysis focused on two sets of outcomes that are ofcentral importance to the long-term prospects of school-age girls: schooling and human capitalformation on the one hand; marriage and fertility on the other. The results show that CCTs
increased enrollment rates and improved regular attendance for those in school, both of which likely
contributed to a modest but significant improvement in English test scores over the UCT arm.
Teenage pregnancy and marriage rates, on the other hand, were substantially lower in the UCT thanthe CCT arm.
The results on school enrollment differ from previous studies that considered the relative
effectiveness of UCTs vs. CCTs, although the difference is a matter of degree rather than direction.
Analyses of Bolsa Escola in Brazil and PROGRESA in Mexico found that UCTs would have little, ifany, impact on school enrollment – implying that almost all of the impacts of these programs were
due to the schooling condition (Bourguignon, Ferreira, and Leite 2003; Todd and Wolpin 2006). In
the Malawi experiment, on the other hand, we find a modest impact on enrollment in the UCT arm,
which is 43% as large as the impact in the CCT arm. Our study thus confirms that conditions
attached to cash transfer programs are effective in increasing enrollment, but the size of this effect islikely to be smaller than suggested by earlier studies, at least for poorer countries like Malawi.
Not only is school enrollment significantly improved in the CCT arm over the UCT arm, but
the evidence presented shows that CCTs are more cost-effective in raising enrollment than UCTs inthis context. To achieve the same enrollment gain obtained from a $5/month total transfer in the
CCT arm, a transfer of more than $10 to the parents in the UCT arm is needed. This difference ismuch larger than the additional cost of administering a CCT program – possibly by an order of30

magnitude.55 Furthermore, the average number of payments in the CCT arm was approximately 14.1
(out of a possible total of 20 over two years), compared with 17.9 in the UCT arm, implying that theactual amount of transfers made per person was 19% lower in the CCT arm over the two-year
intervention. Savings of this magnitude due to non-compliance with the schooling condition would
more than make up for the additional administrative cost of monitoring in most programs.
While CCTs were more cost-effective than UCTs in increasing school enrollment andattendance, they had little effect on reducing the likelihood of teenage pregnancies or marriages. By
contrast, UCT offers were very effective in delaying marriage and childbearing – by 44% and 27%,
respectively, after two years. These impacts in the UCT arm were experienced almost entirely among
those who dropped out of school after the start of the two-year intervention, while the likelihood of
marriage and pregnancy were negligible among those who stayed in school regardless of treatment
status.
When we examine the random variation in transfer amounts to girls and their parents, wefind that this contract variation did not alter outcomes in the CCT arm. This finding is encouraging
for policy-makers as it implies that the smallest transfer amounts ($4/month to the parents and$1/month to the school-age girl) offered in this experiment were sufficient to attain the averageschooling impacts observed under the CCT arm. Furthermore, our results suggest that reallocatingsome of the transfers from the parents to the girls would not improve program impacts under either
treatment. Hence, while the idea of making part of the transfers directly to adolescent girls may beattractive on its face, we find no evidence that it would be effective.56

55 World Bank (2009) cites Grosh et al. (2008) to report that the median administrative cost is 8% of total program costsin ten CCT programs for which administrative cost data were compiled by that study. Given that monitoring compliancewith the schooling condition is only part of the administrative costs, the additional cost would be a few cents on thedollar. However, monitoring and enforcement of conditionality in many of these programs may not have been as
thorough as they were in our experiment, which could reduce the relative effectiveness of CCTs compared with UCTs.
56 The readers should note, however, that there was no cell in the experiment in which the transfer to the girl (or her
parents) was equal to zero. It is possible that even a $1/month transfer made directly to the girls was more effective than31

The experiment highlights the underlying causes of school dropout, marriage, and pregnancy
in the Malawian context. First, the fact that the UCT has some effect on school enrollment indicatesthat poverty is a cause of school dropout in this population, and that poor parents will invest at least
some of the additional funds from a positive income shock towards the education of theirdaughters. The reduction in marriage and pregnancy rates in the UCT arm, as well as the
responsiveness of marriage to unconditional transfer amounts, seems consistent with the idea thatadolescent girls who drop out of school undergo a rapid transition into adulthood that is alsostrongly influenced by economic circumstances.57
The informal framework we introduced in Section 3.3.2 is helpful in considering thegeneralizability of differential program impacts on marriage and pregnancy found in this study. A
necessary condition for UCTs to delay marriage and pregnancy more effectively than CCTs is thepresence of a large group of non-compliers (relative to the group of CCT compliers), a finding that isconfirmed in most evaluations of CCT programs around the world.58 Given a large stratum of noncompliers, UCTs may be more effective in delaying marriage and pregnancy if there are significant
beneficial income effects among this group. In Southern and Eastern Africa, where these outcomesare more or less mutually exclusive with schooling and decisions regarding sexual behavior andmarriage among adolescent girls are influenced by poverty, UCTs may indeed be more effective thanschooling CCTs in reducing teenage marriage and pregnancy. In countries like Bangladesh, where
adding that small amount to the transfers made to the parents. Our results are clear that marginal effect of reallocating
any amount above the $1/month given to the girl would not improve outcomes.
57 In our study sample, approximately 25% of the young women who were sexually active at baseline reported that they
started their sexual relationships because they “needed his assistance” or “wanted gifts/money.”
58 Most studies examining school dropout in CCT programs find that dropout rates are reduced by less than 50%,
implying that the stratum of non-compliers is usually larger than the stratum of CCT compliers. For example, Behrman,
Sengupta, and Todd (2005) report that, among 15 year-olds enrolled in school before the start of PROGRESA, thedropout rate in the treatment group was 31.3%, while the program impact on reducing dropouts was 6.4 pp. These
numbers are remarkably similar to those presented in Table IX. Even in countries like Cambodia, where the program
impacts on enrollment were very large in absolute terms at approximately 25 pp, the reduction in the dropout rate wasless than 50% as the enrollment rate in the control group was 44% (Filmer and Schady 2010). It should be noted that
program impacts on dropout rates reported in studies of CCT programs are upper bounds for the size of the CCT
complier group as there were no counterfactuals for a UCT intervention.

32

dropout and marriage rates among adolescent girls are also high but, unlike sub-Saharan Africa,
dowry payments are made from the bride‟s family to the groom‟s, UCTs may have no effect, or
perhaps even the opposite effect, on the timing of marriage. In such settings, CCTs for schooling orfor staying unmarried may be more effective than UCTs.59
Our framework also allows us to consider the relative merits of CCT and UCT programson outcomes other than schooling, marriage, and pregnancy. CCTs are likely to be more effective in
improving outcomes that may be strongly affected by compliance with the conditions, such as testscores. UCTs may be preferred if there are many non-compliers who might experience strong andsocially beneficial effects from regular income support. If non-compliers can be thought of as avulnerable group in a given context, UCTs may deserve careful consideration given the possible
trade-offs indicated in this study. While similar experiments would be useful in determining theextent to which our findings generalize to other contexts, in the absence of such an experiment,
policymakers can use evidence from previous evaluations of CCT programs to estimate the sizes of
the relevant strata and use existing household survey data to predict the relative effects of schoolingand income on outcomes of interest within each stratum.
Whatever the overall merits of the two transfer schemes, this study found evidence that
schooling CCTs are a much more cost-effective means of reducing dropouts than are UCTs.
However, in the absence of a market failure, such a distortion is inefficient.60 Policy-makers planningto implement a CCT program should clearly articulate the market failures behind this paternalistic
motivation and, if possible, provide evidence of these externalities – private or social. For example,
we have not found any evidence of incomplete altruism – i.e. a conflict of interest between the girls
59 Such an intervention is being currently evaluated in Bangladesh, where girls between 15 and 17 years old living intreatment areas, are offered cooking oil on the condition that they remain unmarried. The amount of oil offered isdesigned to be larger than the cost of delaying marriage, which comes in the form of increased dowry payments from thebride‟s family to the groom‟s: http://www.povertyactionlab.org/evaluation/empowering-girls-rural-bangladesh.
60 In this context, credit market failures would not be a justification for CCTs as UCTs would be sufficient to relax credit
constraints. Another market failure, such as a lack of information, hyperbolic discounting, or positive spillover effects, isnecessary to justify preferring CCTs over UCTs.

33

and their parents with respect to her education, which is sometimes mentioned as a justification of apreference for CCTs over UCTs.
CCT programs create incentives for individuals to change their behaviors by denying
transfers to those who fail to satisfy the conditions. However, at least some of these individuals
come from vulnerable households and are also in need of income support. Our findings suggest that
UCTs to such households can improve important outcomes even though they are not as successful
in improving schooling outcomes as CCTs. This study makes clear that while CCT programs may be
more effective than UCTs in obtaining the desired behavior change, they can also undermine thesocial protection dimension of cash transfer programs.
GEORGE WASHINGTON UNIVERSITY
UNIVERSITY OF CALIFORNIA, SAN DIEGO
THE WORLD BANK

34

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40

Table I: Analysis of Attrition
Dependent Variable:
=1 if
=1 if
=1 if
information information=1 if=1 if took
=1 if legible
surveyed infound infound insurveyededucational
ledgerall threeRound 2Round 3in Round 3
tests
found
Roundsschoolschoolsurveysurvey
(1)
(2)
(3)
(4)
(5)
(6)
Conditional treatment
Unconditional treatment

0.020

0.021

0.029*

0.033-0.0000.116*(0.015)(0.030)(0.016)(0.024)(0.027)(0.064)

0.021

0.030

0.035*-0.029

0.014

0.061

(0.019)(0.024)(0.020)(0.053)(0.028)(0.077)

Mean in the control group

0.946

0.893

0.929

0.890

0.935

0.378

Number of observations

2,284

2,284

2,284

2,284

983

821

Prob > F(Conditional=Unconditional)
0.965
0.797
0.801
0.246
0.627
0.513
Notes: Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted tomake them representative of the target population in the study EAs. Regressions are restricted to the sub-sample of core
respondents who were in school at baseline and sampled to be part of the conditional, unconditional or control sample.
Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

41

Table II: Baseline Means and Balance
Mean (s.d)
p-value(conditionalConditional Unconditional
unconditional)
group
group
(2)
(3)
(4)

Panel A: Household-level variables

Controlgroup(1)Household size

6.432

6.384

6.662

(2.257)(2.146)(2.075)

0.581

0.984

1.221

(2.562)(2.740)(2.447)0.343

0.252**

0.245**(0.475)(0.434)(0.431)

0.616

0.583

0.605

(0.487)(0.494)(0.490)

N/A

6.991

0.822

(2.319)6.829(2.101)15.25214.952*15.4240.007***(1.903)(1.827)(1.923)

Asset index
Female-headed household
Mobile phone accessHousehold transfer amount

0.202
0.623
0.899
0.799

Panel B: Individual-level variables
Age
Highest grade attended
Mother alive
Father alive
Never had sex
Ever pregnant
Individual transfer amount

7.478

7.246

7.896**(1.634)(1.598)(1.604)

0.842

0.802

0.836

(0.365)(0.399)(0.371)

0.705

0.714

0.759

(0.456)(0.453)(0.428)

0.797

0.797

0.775

(0.402)(0.403)(0.419)

0.023

0.030

0.031

(0.149)(0.171)(0.173)

N/A

3.090

3.033

(1.431)(1.451)0.004***
0.360
0.288
0.582
0.973
0.606

Number of observations
1356
470
261
Notes: Mean differences statistically different than zero at 99% (***), 95% (**), and 90% (*)
confidence. Stars on the coefficients in columns (2) and (3) indicate significantly different thanthe control group, while in column (4) stars indicate a significant difference between theconditional and unconditional treatment arms. Standard errors are clustered at the EA level toaccount for the design effect. Means are weighted to make them representative of the target
population in the study EAs. The number of unique observations vary slightly across variables
due to missing observations, with the total sample size ranging between 2089 and 2084.

42

Table III: Program Impact on School Enrollment
Panel A: Program impacts on self-reported school enrollment
Dependent variable: =1 if enrolled in school during the relevant term
(1)

(2)

(3)

(4)

Term2

Term3

Term1

0.007

0.019*

0.041**

0.049*** 0.056*** 0.061*** 0.233***(8)
Year 3:2010
Term 1,
Postprogram
0.005(0.011)(0.011)(0.017)(0.017)(0.070)(0.025)0.034*** 0.051*** 0.054*** 0.072*** 0.095*** 0.101*** 0.406***

0.074***(0.010)(0.011)(0.018)(0.021)(0.022)(0.021)(0.079)(0.026)

Mean in the control group

0.958

0.934

0.900

0.831

0.800

0.769

5.191

0.641

Number of observations

2,087

2,087

2,086

2,087

2,087

2,087

2,086

2,086

Prob > F(Conditional=Unconditional)

0.006

0.012

0.460

0.299

0.102

0.098

0.038

0.028

Year 1: 2008
Term1
Conditional treatment
Unconditional treatment

(5)

(6)

(7)
Total
Terms

Term3

(6 Terms)

Year 2: 2009
Term2

(0.018)(0.019)Panel B: Program impacts on teacher-reported school enrollment
Dependent variable: =1 if enrolled in school during the relevant term
(1)

(2)

(3)

(4)

Term3

Term1

Term2

0.043*** 0.044*** 0.061***

0.094**

0.132*** 0.113*** 0.535***(8)
Year 3:2010
Term 1,
Postprogram
0.058*(0.015)(0.016)(0.018)(0.041)(0.035)(0.039)(0.129)(0.033)0.020

0.038**

0.018

0.027

0.059

0.033

0.231*

0.001

Year1: 2008
Term1
Conditional treatment
Unconditional treatment

Term2

(5)

(6)

(7)
Total
Terms

Term3

(6 terms)

Year2: 2009(0.015)(0.017)(0.023)(0.038)(0.037)(0.039)(0.136)(0.036)

Mean in the control group

0.906

0.881

0.852

0.764

0.733

0.704

4.793

0.596

Number of observations

2,023

2,023

2,023

852

852

852

852

847

Prob > F(Conditional=Unconditional)

0.173

0.732

0.067

0.076

0.014

0.020

0.011

0.108

Notes: The dependent variable in Panel A is whether the core respondent reported being enrolled in school for the relevant
year/term. The dependent variable in Panel B is whether the teacher reported the core respondent being enrolled in school forthe relevant year/term. Post-program refers to Term 1, 2010, the first term after the program ended. Total terms refers to the total
number of terms enrolled during the program. Columns (4)-(8) in Panel B are restricted to the sub-sample of core respondents
sampled for the Round 3 school survey who are also both part of the panel data set and part of the school survey panel.
Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them
representative of the target population in the study EAs. Baseline values of the following variables are included as controls in
the regression analyses: age dummies, strata dummies, household asset index, highest grade attended, and an indicator fornever had sex. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

43

Table IV: Analysis of Reporting Bias on Enrollment
Dependent Variable:
Core
Teachers
respondents
overoverreporting
reporting(1)(2)
Conditional treatment
Unconditional treatment
Mean in the control group
Number of observations-0.093*-0.021(0.052)(0.035)-0.001-0.014(0.058)(0.038)

0.170

0.052

325

325

Prob > F(Conditional=Unconditional)
0.018
0.794
Notes: Regressions are restricted to the sub-sample of core
respondents who report being enrolled in school during Term 2,
2009, and have legible ledger data. Over-reporting refers todifferences between the student (teacher) reports and theledger. Regressions are OLS models with robust standard errors
clustered at the EA level. All regressions are weighted to makethem representative of the target population in the study EAs.
Parameter estimates statistically different than zero at 99% (***),
95% (**), and 90% (*) confidence.

44

Table V: Program Impacts on Attendance from School Ledgers
Dependent variable: Fraction of days respondent attended school

Conditional treatment
Unconditional treatment
Mean in the control group
Number of observations

Term 1, 2009

Term 2, 2009

Term 3, 2009

Overall 2009 Term 1, 2010

(1)

(2)

(3)

(4)

(5)

0.139***

0.014

0.169**

0.080**

0.092**(0.045)(0.033)(0.085)(0.035)(0.041)

0.063

0.038

0.118

0.058

-0.038(0.056)(0.033)(0.102)(0.037)(0.053)

0.778

0.849

0.688

0.810

0.801

284

285

192

319

211

Prob > F(Conditional=Unconditional)
0.129
0.334
0.358
0.436
0.010
Notes: Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted
to make them representative of the target population in the study EAs. The variable "Overall 2009" is defined for all
core respondents who have ledger information for any of the three terms and is constructed by dividing the number ofdays present by the number of days in session for all terms in which there is information. Baseline values of thefollowing variables are included as controls in the regression analyses: age dummies, strata dummies, household asset
index, highest grade attended, and an indicator for never had sex. Parameter estimates statistically different than zero at
99% (***), 95% (**), and 90% (*) confidence.

45

Table VI: Program Impacts on Test Scores
Dependent Variable:
English test score(standardized)(1)
Conditional treatment

TIMMS mathscore
(standardized)(3)

Non-TIMMS
math score(standardized)(2)

Cognitive testscore
(standardized)(4)0.140***

0.120*

0.086

0.174***(0.054)(0.067)(0.057)(0.048)-0.030

0.006

0.063

0.136

(0.084)(0.098)(0.087)(0.119)Number of observations

2,057

2,057

2,057

2,057

Prob > F(Conditional=Unconditional)

0.069

0.276

0.797

0.756

Unconditional treatment

Notes: The cognitive test score is based on Raven's Colored Progressive Matrices. Math and English reading
comprehension tests were developed based on the Malawian school curricula. Five questions (four from the
Fourth Grade test and one from the Eight Grade test) from Trends in Mathematics and Science Study (TIMMS)
2007, which is a cycle of internationally comparative assessments in mathematics and science carried out at the
fourth and eighth grades every four years, were added to the Math test. All test scores have been standardized
to have a mean of zero and a standard deviation of one in the control group. Regressions are OLS models using
Round 3 data with robust standard errors clustered at the EA level. All regressions are weighted to make theresults representative of the target population in the study EAs. Baseline values of the following variables areincluded as controls in the regression analyses: age dummies, strata dummies, household asset index, highest
grade attended, an indicator for ever had sex, and whether the respondent participated in the pilot phase of thedevelopment of the testing instruments. Parameter estimates statistically different than zero at 99% (***), 95%(**), and 90% (*) confidence.

46

Table VII: Program Impacts on Marriage and Pregnancy
Dependent variable:=1 if ever married

Conditional treatment=1 if ever pregnant

Round 2Round 3Round 2Round 3

(1)

(2)

(3)

(4)

0.007

-0.012

0.013

0.029

(0.012)(0.024)(0.014)(0.027)-0.026**-0.079***-0.009-0.067***(0.012)(0.022)(0.017)(0.024)

Mean in the control group

0.043

0.180

0.089

0.247

Number of observations

2,087

2,084

2,086

2,087

Prob > F(Conditional=Unconditional)

0.024

0.025

0.265

0.003

Unconditional treatment

Notes: The dependent variables are having been ever married or ever pregnant at the time of
the relevant survey. Regressions are OLS models with robust standard errors clustered at the
EA level. All regressions are weighted to make them representative of the target population in
the study EAs. Baseline values of the following variables are included as controls in the
regression analyses: age dummies, strata dummies, household asset index, highest grade
attended, and an indicator for never had sex. Parameter estimates statistically different thanzero at 99% (***), 95% (**), and 90% (*) confidence.

47

Table VIII: Prevalence of Being ‘Ever Married’ by School Enrollment
Status during Term1, 2010
Enrolled(1)

Not enrolled
(2)

Total
(3)

Control

1.7%

46.9%

19.9%

(row %)(59.8%)(40.2%)(100.0%)Conditional treatment

0.5%

50.8%

16.0%

(row %)(69.2%)(30.8%)(100.0%)Unconditional treatment

0.3%

25.2%

10.1%

(row %)(60.5%)(39.5%)(100.0%)

Total

1.1%

44.2%

17.2%

(row %)(62.7%)(37.3%)(100.0%)Notes: This table presents the marriage rates by Round 3 enrollment status inTerm 1, 2010 and treatment status. For each treatment arm, the top row
summarizes the marriage rates by follow-up enrollment status, while thebottom row shows the sample size and indicates the raw follow-up row
percentage in each cell. Means are weighted to make them representative ofthe target population in the study EAs.

48

Table IX: Teacher-Reported School Enrollment and Marital Status in Round 3
Dependent variable:=1 if enrolled
term 1 2010

=1 if evermarried=1 if evermarriedAll

All

Enrolled

(1)

(2)

(3)

=1 if evermarriedNot
Enrolled(4)0.058*-0.026-0.0120.033(0.034)(0.037)(0.015)(0.097)-0.000-0.088***-0.011-0.159**(0.036)(0.030)(0.010)(0.067)

Mean in the control group

0.598

0.199

0.017

0.469

Sample Size

844

844

490

354

Conditional treatment
Unconditional treatment

Prob > F(Conditional=Unconditional)
0.099
0.106
0.857
0.088
Notes: The first two columns are impact regressions on Term 1, 2010 teacher-reported
enrollment and being ever married by Round 3, respectively. Columns (3) and (4) present
regressions of being ever married among those who were enrolled and not enrolledduring Term 1, 2010, respectively. Regressions are OLS models with robust standard
errors clustered at the EA level. All regressions are weighted to make them representativeof the target population in the study EAs. Baseline values of the following variables areincluded as controls in the regression analyses: age dummies, strata dummies, household
asset index, highest grade attended, and an indicator for never had sex. Parameter
estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

49

Table X: Heterogeneity of Program Impacts by Age-Group at Baseline
Dependent Variable:

Conditional treatment
Unconditional treatment=1 if Over 15
Conditional treatment*Over 15
Unconditional treatment*Over 15
Number of unique observations
Prob > F(Conditional=Unconditional)

Total number ofterms enrolled(school survey)(1)Standardized
English test
score
(2)

0.467***

0.141*(0.159)(0.073)(0.017)(0.028)0.257-0.116-0.051**-0.059***(0.157)(0.102)(0.020)(0.020)-0.786***-0.546***0.122***

0.176***(0.244)(0.058)(0.026)(0.027)

0.290

0.017

0.037

0.104*(0.291)(0.089)(0.056)(0.054)0.103

0.245**-0.067-0.032(0.255)(0.110)(0.042)(0.046)

852

2,057

2,084

2,087

0.095

0.031

0.188

0.067

=1 if evermarried=1 if everpregnant(3)(4)-0.023-0.008Prob > F(Conditional*Older=Unconditional*Older)

0.364
0.059
0.097
0.027
Notes: An indicator variable is constructed that takes on a value of one if the core respondent was older thanfifteen at baseline, and is zero otherwise. Regressions are OLS models using Round 3 data with robust standard
errors clustered at the EA level. Baseline values of the following variables are included as controls in the
regression analyses: an indicator for being over fifteen, strata dummies, household asset index, highest grade
attended, and an indicator for ever had sex. All regressions are weighted to make the results representative ofthe target population in the study EAs. Parameter estimates statistically different than zero at 99% (***), 95%(**), and 90% (*) confidence.

50

Table XI: Impacts of Household and Individual Transfer Amounts
Dependent variable:
Total number ofterms enrolled(school survey)(1)
Conditional treatment, individual amount
Unconditional treatment, individual amount
Conditional treatment, household amountStandardized English=1 if ever married
test score=1 if everpregnant

(2)

(3)

(4)

0.024

-0.032-0.0020.006(0.051)(0.029)(0.008)(0.012)-0.048-0.019-0.0160.013(0.064)(0.038)(0.011)(0.013)-0.027-0.000

0.001

0.005

(0.035)(0.016)(0.007)(0.010)Unconditional treatment, household amount0.081***-0.058**-0.017**-0.002(0.031)(0.029)(0.007)(0.009)Conditional treatment, minimum transfer amounts

0.572***

0.202*-0.0110.001(0.213)(0.118)(0.044)(0.052)

0.094

0.175

0.001

-0.089*(0.167)(0.132)(0.040)(0.050)Unconditional treatment, minimum transfer amounts

Number of unique observations
852
2,057
2,084
2,087
Prob > F(Conditional=Unconditional), individual
0.390
0.788
0.300
0.702
amountProb > F(Conditional=Unconditional), household
0.025
0.082
0.069
0.614
amountProb > F(Conditional=Unconditional), minimum
0.046
0.877
0.834
0.203
amountNotes: Regressions are OLS models using Round 3 data with robust standard errors clustered at the EA level. Baseline values ofthe following variables are included as controls in the regression analyses: age dummies, strata dummies, household asset index,
highest grade attended, and an indicator for ever had sex. All regressions are weighted to make the results representative of thetarget population in the study EAs. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*)
confidence.

51

Figure I: Study Sample and Attrition

3,796 Core Respondents Surveyed atBaseline

2,907 Baseline Schoolgirls Surveyed atBaseline

2,284 Baseline Schoolgirls sampled to beconditional, unconditional or control

2,186
Surveyedin Round 3

2,089
Surveyed in
all threerounds2,092Interviewed inRound 2
School Survey(2,025 part ofPanel)

983 Baseline schoolgirls randomly
sampled to be part of Round 3 School
Survey922 Interviewed
in Round 3
School Survey(852 part ofPanel and inRound 2 School

821 Baseline schoolgirls report being inschool as part of Round 3 Household
Survey384 Have
LegibleledgerInformation(371 part ofPanel)