metadata: authors: 0: author: Dervisevic,Ervin
1: author: Perova,Elizaveta
2: author: Sahay,Abhilasha
count: Philippines
docna: 0: docna: Long-Term Impacts of Short Exposure to
            Conditional Cash Transfers in Adolescence : Evidence from
            the Philippines
docty: Policy Research Working Paper
subtopic: Gender and Development,Access of Poor to Social Services,Economic Assistance,Services & Transfers to Poor,Disability,Educational Sciences,Rural Labor Markets,Labor Markets,Health Care Services Industry
teratopic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Rural Development,Education,Poverty Reduction,Gender
abstracts: cdata!: This paper evaluates the long-term
            impacts of the national conditional cash transfer program in
            the Philippines on beneficiaries who were exposed to it
            during a relatively short but potentially critical period of
            transitioning from adolescence to adulthood. The paper
            estimates the impacts of the Pantawid Pamilyang Pilipino
            Program on men and women who were enrolled in the program
            for up to 1.5 years when they were between ages 12.5 and 14
            and are currently in their early twenties. The analysis
            finds evidence of impacts on marriage and fertility for
            women: participation in the program is associated with delay
            in marriage and the first birth of approximately one year
            and six months, respectively. No impacts are found on
            educational or labor market outcomes or proxies for economic
            welfare. Further, there is no strong and consistent evidence
            of changes in empowerment or gender norms.
geo_reg_key: 517198,517191
repnme: repnme: Long-Term Impacts of Short Exposure to
            Conditional Cash Transfers in Adolescence : Evidence from
            the Philippines
display_title: Long-Term Impacts of Short Exposure
            to Conditional Cash Transfers in Adolescence : Evidence from
            the Philippines
listing_relative_url: /research/2021/04/32986573/long-term-impacts-short-exposure-conditional-cash-transfers-adolescence-evidence-philippines
topicv3: Labor Markets m1326488 855,Education m1327048 470,Cash Transfers m1327437 1608,Gender m1327621 615,Health m1504661 3480
publishtoextweb_dt: 2021-04-09T00:00:00Z
dois: 10.1596/1813-9450-9617
historic_topic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Rural Development,Education,Poverty Reduction,Gender
origu: EAP Chief Economist Unit (EAPCE)
no_of_pages: 49
author_keys: 0: author_key: 1204570
1: author_key: 943628
2: author_key: 1575428
model_extractions: Policy Research Working Paper 9617 Long-Term Impacts of Short Exposure to Conditional Cash Transfers in Adolescence Evidence from the Philippines _Ervin Dervisevic Elizaveta Perova Abhilasha Sahay_ East Asia and the Pacific Region Office of the Chief Economist April 2021
Policy Research Working Paper 9617

# **Abstract**

This paper evaluates the long-term impacts of the national conditional cash transfer program in the Philippines on beneficiaries who were exposed to it during a relatively short but potentially critical period of transitioning from adolescence to adulthood. The paper estimates the impacts of the _Pantawid Pamilyang Pilipino Program_ on men and women who were enrolled in the program for up to 1.5 years when they were between ages 12.5 and 14 and are currently in their early twenties. The analysis finds evidence of impacts on marriage and fertility for women: participation in the program is associated with delay in marriage and the first birth of approximately one year and six months, respectively. No impacts are found on educational or labor market outcomes or proxies for economic welfare. Further, there is no strong and consistent evidence of changes in empowerment or gender norms.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eperova@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Long-Term Impacts of Short Exposure to Conditional Cash Transfers in Adolescence:

# Evidence from the Philippines

Ervin Dervisevic*, Elizaveta Perova*, Abhilasha Sahay*

**Keywords** : cash transfers; long-term impact; gender; randomized intervention; Southeast Asia

**JEL Codes** : C93, I38, J12; J13; J16

We gratefully acknowledge funding from the Umbrella Facility for Gender Equality (UFGE) and Australia’s DFAT to carry out this work. The data collection protocols were approved by Health Media Labs IRB. We thank Berk Özler, Amber Peterman, Gabriel Demombynes, Jorge Araujo, Yoonyoung Cho and participants of East Asia and Pacific Chief Economist Office seminar for insightful comments. IPA Philippines carried out data collection. We are grateful to Jorge Franco, Reinier Guzman, Ma Angelica Cruz and Alana Teman for excellent support during data collection. All errors are ours.

- World Bank
# 1. Introduction

The primary objective of most conditional cash transfer (CCT) programs is breaking inter-generational transmission of poverty through investments in human capital. An extensive body of research suggests that CCTs succeed in increasing investment in human capital (see Saavedra and Garcia, 2012, and Fiszbein and Schady, 2009, for reviews of evidence on impacts of CCTs on education and health outcomes). There is relatively less evidence on whether children whose families benefitted from CCT programs enjoy better labor market and welfare outcomes as adults. The past years have seen impressive increase in the literature on longer-term impacts of CCTs. However, a consensus on CCT’s ability to reduce the intergenerational transmission of poverty has not emerged. Some studies find impacts of large magnitude on education, labor market and welfare outcomes (for example, Parker and Vogl, 2018 in the case of _Progresa_ in Mexico); others suggest zero or very low impacts (for example, Araujo et al., 2016 in the case _Bono de Desarrollo Humano_ in Ecuador or Baird, McIntosh, and Özler (2019) in Malawi).

Moreover, while a significant share of evaluations of short-term impacts of CCTs relied on randomized controlled trials (RCTs), longer-term evaluations are mostly either limited to capturing relative, rather than absolute impacts or face greater threats to rigorous identification of impacts, than in a case of an RCT. In the former group, studies based on randomized rollout of the programs identify long-term impacts from differential length of exposure (Behrman, Parker and Todd, 2009; Barham, Macours and Maluccio, 2016). While they generally suggest that additional length of exposure results in improvements in education and labor market outcomes, they do not capture absolute impacts - differences in outcomes compared to what would have happened in the absence of the program. To our knowledge, the only exception is Baird, McIntosh, and Özler (2019), who evaluate an impact of approximately two years of exposure to CCTs and UCTs two years after the program completion in a cluster randomized study, with pure control group preserved until the end of the data collection. The fact that CCT/UCT evaluated is not a large-scale governmental program also sets this study apart.

In the latter group, several non-experimental studies capture absolute impacts relying on a range of nonexperimental econometric methods, including matching and regression discontinuity (Baez and Camacho, 2011), cohort difference-in-difference (Parker and Vogl, 2018) or OLS weighted by inverse probability of receiving treatment, based on propensity scores (Kugler and Rojas, 2018). While the counterfactuals in these studies are the outcomes in the absence of the program, identification frequently requires several assumptions, validity of which may be violated. Several studies combine both elements: evaluation of 2
relative impacts, based on randomized rollout, and evaluation of absolute impacts based on nonexperimental methods (Behrman, Parker and Todd, 2011; Araujo et al., 2016).

We attempt to contribute to this literature by analyzing the long-term impacts of the _Pantawid Pamilyang Pilipino Program_ (4P), the flagship program of the Government of the Philippines. We take advantage of randomized rollout of the program in 2008 and the fact that children close in age to the eligibility cut-off did not benefit from the program, even when it was rolled out to their _barangays_ . Thus, we can provide experimental evidence on absolute long-term impacts of the 4P program.

Specifically, we exploit two important aspects of the 4P program. First, the program was initially rolled out in 2008 in a few randomly selected barangays. However, after 2.5 years the control barangays were also included in the program. Second, the program initially targeted children in the age group 0-14 years old. However, the age limit was later increased to 18 years in 2015. Therefore, among control barangays, children who were 12.5 years or older during initial rollout never received the program; they were neither eligible when the program first arrived at their barangays (due to age restriction of 14) nor when the age restriction was raised to 18 in 2015 (since this cohort was above 18 by 2015). Thus, they constitute a pure control group. To eliminate spillovers from younger siblings, we focus on families where the youngest child was aged 12.5 years at the moment when eligibility was determined.

This identification strategy differentiates our paper from other studies focused on the long-term impacts of CCTs in two additional dimensions. First, we capture impacts of relatively short-term exposure: treated individuals in our sample benefitted from the program during a year and a half at most. Three studies which capture relative impacts of CCTs find positive impacts of comparably small number of _additional_ years of exposure: 1.5 years in the case of _Progresa_ (Behrman, Parker and Todd, 2009; Behrman, Parker and Todd, 2011) or 2 years in the case of _Red de Proteccion Social_ in Nicaragua (Barham, Macours and Maluccio, 2016). However, the least amount of exposure in the sample (for control group) was 4.5 years in case of _Progresa,_ and 3 years in case of _Red de Proteccion Social._ While current evidence establishes positive impacts of _additional_ 1.5 years of receiving benefits, we will attempt to answer the question whether just 1.5 years of exposure to a CCT program may significantly improve adult outcomes. In this respect, this study is most similar to Baird, McIntosh, and Özler (2019), who focus on impacts of approximately two years of receiving CCT benefits and find no evidence of sustained impacts on average.<sup>1</sup> With the exception of Baird, McIntosh and Özler (2019), current long-term evaluations of CCTs capture

> 1 Baird et al. (2016) find large effects on school attainment, marriage and childbearing among school dropouts at baseline.

3
impacts of at least 5 and up to 10 years of exposure. Second, the 4P beneficiaries in our study participated in the CCT program during a critical juncture, when adolescents and their parents may make important decisions about continuing schooling, which in turn affect marriage and fertility.

We also expand the set of outcomes explored in the current literature of long-term impacts of CCTs. In addition to impacts on schooling, labor market outcomes and welfare, this study relies on data on a rich set of empowerment indicators. Given complexity of the concept of empowerment, and lack of consensus about optimal measurement of empowerment, we collect data on several of its proxies: (i) socioeconomic proxies of empowerment, such as age at marriage, at first birth and prevalence of transactional sex; and (ii) direct measures of empowerment, such as self-efficacy, relative autonomy or participation in intra-household decision making. The only study that includes measures of empowerment is Baird, McIntosh and Özler (2019) in Malawi. Finally, we explore whether benefitting from a CCT program in adolescence could impact exposure to gender-based violence (GBV) in adulthood and gender norms.

Thus, the contributions of this paper to the literature on the long-term impacts of the CCTs is threefold. First, we provide experimental estimates of absolute impacts of short-term exposure to a CCT program during adolescence on adult outcomes, 10 years after receipt of benefits stopped. Second, we contribute to the literature by exploring long-term impact of CCTs on a broad range of empowerment impacts, including GBV and norms. In that way, we build on evidence provided in Baird, McIntosh and Özler (2019) by focusing on longer term impacts (approximately 10 years after exposure) and expanding the set of empowerment indicators. Finally, to our knowledge, current literature on long-term impacts of CCTs is limited to the Americas and one African country. We expand this literature to a different region. The paper is structured as follows: we provide a brief description of the program and its rollout in the next section. Section 3 reviews literature on long-term impacts of CCTs. Section 4 describes the data. Section 5 presents theory of change and lists outcomes of interest. Section 6 describes identification strategy. Section 7 presents the results. Section 8 describes deviations from the pre-analysis plan. Section 9 concludes.

# 2. Program overview

The 4P program was initiated in 2008. In its first year, it registered approximately 300,000 beneficiary households. In succeeding years, the program continuously expanded its coverage and currently more than four million poor households nationwide are benefitting from it. With the expansion in coverage, the budget allocated to this program has also increased; from 50 million pesos in 2008 to 78 billion pesos in 2017. This constituted 0.5 percent of the nation’s GDP in that year. With approximately 60 percent of the 4
poorest quintile of households in the country covered (World Bank, 2018), the program is a core pillar of the government’s social protection strategy.

The 4P program provides three types of transfers: education grant, health grant and rice-subsidy.<sup>2</sup> The education grant is provided to every child who complies with the education conditions of the program: is enrolled in school and attends 85 percent of the school days every month. Children enrolled in daycare/kindergarten or elementary schools receive 300 pesos per month while children enrolled in high school receive 500 pesos per month. These grants are provided for a period of 10 months per year. The program maintains a limit of three child beneficiaries for the education grants.

The health grant amounts to 500 pesos per month and is given to households subject to compliance of all health conditionalities. These conditionalities include, (i) all children under the age of five must regularly visit a health center or rural health unit for growth monitoring, vaccines and preventive health check-ups; (ii) pregnant women must visit their health center monthly for antenatal and postnatal care, and must deliver in a health facility attended by a trained health professional; (iii) all school-aged children (6-14 years old) are to comply with de-worming protocol at schools; and (iv) among households with children 0-14 years old, the household grantee (mother) and/or spouse must attend Family Development Sessions (FDS) at least once a month.

To be eligible for the program, households must be identified as poor by the _Listahanan_ , formerly known as National Household Targeting System for Poverty Reduction Program (NHTS-PR). They also must have a pregnant member or at least one child aged 0-18 years old at the time of selection and must be willing to commit to meeting the program conditionalities. The _Listahanan_ targets beneficiaries through a household assessment and application of a Proxy Means Test (PMT) methodology to predict income of households based on characteristics such as household composition, education, housing conditions, access to basic services, ownership of assets, etc. Predicted incomes are then compared with poverty thresholds at the provincial level to identify households below (poor) or above (non-poor) these thresholds.

The program has undergone two key changes since its inception in 2008. First, during initial rollout of the 4P program, few randomly selected barangays were given treatment. However, 1.5 years after the program was rolled out, the control group was also exposed to the program. Second, initially the program was designed to provide benefits for children 0 – 14 years old. However, this age limit was extended to

> 2 We do not provide a detailed description of the rice subsidy as it was introduced in 2017.

5
18 years old in 2015, with the motivation of supporting beneficiary children to at least finish high school and thereby increase their chances of getting better employment and higher income.

# 3. Previous literature

Recent years have seen emergence of several studies focused on long-term impacts of the CCT programs. These studies aim to establish whether indeed CCT programs achieve the objective of breaking intergenerational cycle of poverty through investments in human capital. They largely focus on impacts on education, labor market outcomes and wealth proxies. We organize the review below by these outcomes, placing stronger emphasis on government-run CCT programs, which are more akin to the 4P program.

## _Education_

All currently available studies find some impacts on education, captured either in the likelihood of high school completion or number of years of education, at least for some subgroups. Kugler and Rojas (2018) find that exposure to _Progresa_ increased the likelihood of completing high school, the number of years of education attained and the likelihood to attend tertiary educational institution. They find that impacts are weaker for girls and for children of illiterate women. In their sample, average exposure to the treatment is around 7 years. Results from Parker and Vogl (2018) as well as Behrman, Parker and Todd (2011) corroborate this conclusion: they find increase in number of grades completed for both sexes.

Baez and Camacho (2011) also find that beneficiary students of _Familias en Accion_ in Colombia are more likely to graduate from high school. In their study, impacts are stronger for girls and beneficiaries from rural areas. Barham, Macours and Maluccio (2018) find positive impacts of Nicaraguan CCT _Red de Proteccion Social_ on the number of grades attained for men (the study is limited to men). Araujo et al. (2016) also find that _Bono de Desarrollo Humano_ increased schooling attainment for women, but by a small margin – between 1-2 percent above 75 percent counterfactual completion rate. Notably, Ham and Michelson (2018) find impacts of PRAF in Honduras on years of schooling only when transfers were complemented with supply-side incentives to medical facility. Baird, McIntosh and Özler (2019) find, perhaps, the most discouraging results, suggesting that only girls who were dropouts at baseline experienced improvements in passing primary school and increasing the number of completed grades.

The evidence on learning achievement is less conclusive: Barham, Macours and Maluccio (2018) find large and significant improvements in math and language scores in Nicaragua. However, Baez and Camacho (2011), Behrman et al. (2009a) and Araujo et al. (2016) find zero impacts on academic achievement in Colombia, Mexico and Ecuador, respectively.

6
# _Labor market outcomes_

Similar to learning achievement, in the case of labor market outcomes up to date evidence suggests a mix of zero and positive impacts. Rodriguez-Oreggia and Freije (2012) do not find impacts of _Progresa_ on employment, wages or inter-generational occupational mobility. Similarly, Araujo et al. (2016) find zero impact of _Bono de Desarrollo Humano_ in Ecuador on the likelihood of working. Behrman, Parker and Todd (2009a) and Behrman, Parker and Todd (2011) find evidence of negative impact of _Progresa_ on employment for younger boys (15-16) – possibly due to the fact that they are more likely to remain in school longer. Baird, McIntosh and Özler (2019) do not find sustainable impacts of CCTs on labor market outcomes, even for baseline dropouts who experienced gains in education.

However, several studies find evidence of positive impacts of CCTs on extensive (labor force participation, employment) and intensive (quality of employment) margins, at least for one gender. Thus, Behrman, Parker and Todd (2011) find increased likelihood of working for older girls (19-21) due to participation in _Progresa_ . Similarly, Ham and Michelson (2018) find that combination of transfers and supply side incentives under _PRAF_ in Honduras is associated with increased labor force participation and probability of working from home for women only.

Parker and Vogl (2018) find positive impacts of _Progresa_ on labor market outcomes: their results suggest that exposure to _Progresa_ is associated with increased labor force participation and labor earnings for women, and increased labor supply, lower likelihood to work in agriculture and higher likelihood to work in better paid occupations and industries. This result is corroborated in Kugler and Rojas (2018), who use different identification: they find significant increases in employment, hours, income, and employment arrangements secured through contracts and non-wage benefits. They see impacts largely being driven by men and children of literate women.

_Other outcomes: Income, wealth proxies, migration and delayed marriage_ Most long-term evaluations of CCT programs focus on education and labor market outcomes. However, some expand the set of explored outcomes. Parker and Vogl (2018) analyze impact on household income as well as housing and asset indices, which can serve as proxies for wealth. They find positive impacts on housing and asset indices for men and women, and on household income for women only. They also suggest that increase in household income may partially stem from the fact that former _Progresa_ beneficiaries are more likely to marry more educated people who earn better incomes. Similarly, Baird, McIntosh and Özler (2019) find lasting impacts on marriage, fertility and desired lifetime fertility, limited 7
however to baseline dropouts only. Behrman, Parker and Todd (2009) find that _Progresa_ decreases likelihood to migrate for boys and delays the age of marriage.

# 4. Data

To identify causal effects, we rely on an RCT methodology, exploiting the initial randomized rollout of the program at the barangay level. We collect the data on individuals who are currently in the age group of 23-24.5 years old, and compare young adults who were residing in treatment barangays during adolescence (and thus were eligible to receive the 4P transfer) to young adults who were residing in control barangays during adolescence (and were not eligible to receive the transfer at any point in their life).

To identify and track these young adults, we use data from _Listahanan_ (2008-09). Using the _Listahanan_ , we were able to identify households who had children in the age-group 12.5-14 years during the time of initial targeting and whether a household was identified to be eligible to receive the 4P transfers. To ensure that we did not pick up any spillovers across siblings within the same household, we limited the sample to families where the child aged 12.5 years or older was the youngest child in the family when the program was expanded to control barangays. We provided the data collection firm with the list of eligible households for each of the 71 barangays included in analysis.

The firm randomly drew a required subsample of households, stratified by gender in each barangay, and validated that the respondent households were indeed residents of the barangay with local officials. If the household was not recognized by the local official, the household was replaced. Among the replacements, 81% are from the same barangay, and 19% are from a different barangay.

After the validation with barangay officials, the field team visited households and administered a short tracking questionnaire with the objective of (i) confirming that at the time of initial 4P rollout there was a child aged 12.5 to 14 in the household; (ii) collecting information on where this child currently resides. If the household could not confirm that one of its members back in 2008 was in the age group and gender as described by Listahanan, the field team would replace this household. In case the child (who is now a young adult and our respondent of interest) continued to reside in his/her original HH, the field team directly approached him/her and sought permission to conduct the primary IE survey (ideally on the same day). In case the child has moved out, the field team collected information on his/her current location from the parents or older members in the original household. Thereafter, the field team tracked the child who had moved out and sought his/her permission to participate in the study. We instructed the survey 8
firm to track respondents who moved since the time of the initial rollout of the program as long as the household/respondent has moved within the same municipality or if she/he has moved to one of the main economic centers in the Philippines, such as, metro Manila, Cebu, Davao and Cagayan de Oro. About 29% of our sample have moved to major cities. In instances, where the young adult is married or is living with a partner, we also interviewed the partner. Note that the household questionnaire was administered to the current, not original household.

Based on our power calculations, our target was to interview 1,576 young adults: 438 beneficiary women, 438 beneficiary men, and 700 partners in 876 households. Early stages of fieldwork proved that we miscalculated the co-habitation rate in the Philippines. To ensure that we have sufficiently large sample of partners, we increased the number of young adults’ households to 932. However, largely because the co-habitation rate was lower than originally programmed, our final sample is 87% of the target sample: 1,372 respondents, 870 of them are former beneficiaries of the 4P program, 502 partners of the former beneficiaries. This work focuses on the sample of former beneficiaries.

We collected the data between October 2019 and January 2020 from 932 young adult households, spread across approximately 71 barangays. The survey collected extensive data using two questionnaires: household and individual. The household questionnaire included modules on (i) basic demographic characteristics, (ii) economic participation, (iii) housing conditions, (iv) asset ownership, and (v) participation in the 4P program. The individual questionnaire includes modules on (i) decision making, (ii) time use, (iii) self-efficacy, (iv) well-being, (v) social capital, (vi) attitudes towards gender norms, genderbased violence and violence against children, (vii) pregnancy history, (viii) exposure to gender-based violence, and (ix) exposure to transactional sex. The last three modules are only administered to the women in the sample. The sub-sample of female respondents was randomly split in two groups for sections 8 and 9: half of the sample was interviewed using Face-to-Face Direct Questioning (FTFDQ) technique, the other half responded to the same question in Audio Computer Assisted Self-Interview (ACASI). As we do not find differences in prevalence rates reported via FTFDQ vs. ACASI,<sup>3</sup> we pool the responses in the current analysis, but control for the type of the interview.

When collecting data on GBV, we followed WHO recommendations in elaborating data collection protocols, including working with same-sex enumerators, specialized training for enumerators, protocol

> 3 Detailed results are available upon request.

9
for potential interruptions, and provision of information on support services for GBV survivors. We received research ethics review approval from Health Media Labs IRB.

Cumulatively, the survey took about 1-1.5 hours (per person) to administer. We complemented face-toface data collection with a phone survey in April-May 2020 to correct the data on receipt of the 4P benefits: in our initial round of data collection the question about receipt of the 4P benefits was addressed to the respondent of the tracking questionnaire, which resulted in high number of missing values and inconsistencies. The phone survey was administered to target respondents: young women and men who resided in treatment and control barangays at the time of program rollout in 2008.

# 5. Theory of change and outcomes of interest

A key objective of the CCT programs is breaking the cycle of inter-generational transmission of poverty. Thus, we expect that adolescents, who have benefitted from the 4P program at the age of 12.5-14, will enjoy better socio-economic outcomes as adults. we explore this hypothesis by analyzing impacts on education, labor market outcomes and proxies of welfare. For education, we estimate impact on the likelihood to complete schooling, the number of years of education and probability of attending vocational training. Our labor market outcomes include indicator variables for labor force participation, employment, paid employment and underemployment. We collect a rich set of variables on asset ownership and housing conditions and construct three types of indices: an index based on principal component analysis, average z-scores index based on Katz, Kling and Liebman (2007), and an Inverse Covariance Weights (ICW) index obtained by weighting z-scores of variables with inverse of their covariance matrix (Anderson, 2008). The variables used in the construction of the housing conditions index include roof, walls, floor materials, access to piped water and electricity, number of bedrooms, presence of kitchen and type of toilet, type of cooking fuel used. We create asset index based on ownership of residential plot, non-agricultural buildings, farm machinery and implements, livestock and a long list of durables, such as radio, sewing machine, television set, microwave oven, etc.

We expect CCTs to affect empowerment through several channels. Empowerment is a complex construct, with a wide range of measures used to capture empowerment empirically, from socio-economic proxies to more direct measures, such as relative autonomy index (RAI) or self-efficacy index. In this study, we rely on a wide range of measures of empowerment used in previous literature. In the discussion of the theory of change below we link channels to specific measures of empowerment. However, we use these connections as an organizational device, rather than assert dominance of specific channels behind changes in empowerment. The measures we use represent the same latent construct and are 10
interconnected. For example, greater autonomy may lead to higher decision making within the household and higher likelihood to leave/not enter an abusive relationship, resulting in lower exposure to GBV.

_Changes in empowerment through education and better employment prospects_ Better employment prospects may increase opportunity costs of early marriage and childbearing. Consequently, we expect to find positive impacts on two socio-economic proxies of empowerment: marriage and fertility, which we capture in two variables: age at marriage and age at first birth. We also check for impacts on prevalence of transactional sex.

Similarly, better education, health and employment opportunities may affect aspirations and selfperception, leading to increase in direct measures of empowerment, such as generalized self-efficacy index (Jerusalem and Schwarzer, 1995) and RAI (Vaz et al., 2016). We construct a generalized self-efficacy index based on respondent’s agreement or disagreement with eight statements about one’s ability to achieve their goals, such as “I believe I can succeed at most any endeavor to which we set my mind.”<sup>4</sup> We use both: the sum of raw scores, which totals a range of 8 to 32 and increases in self-efficacy, and also construct a z-score of answers, following Katz, Kling and Liebman (2007).

RAI is based on Self-Determination Theory in psychology, which postulates that a person is autonomous when his or her behavior is experienced as willingly enacted and when he or she fully endorses the actions in which he or she is engaged or the values expressed by them (Vaz et al., 2016). The index captures respondent’s ability to act on what she or he values through vignettes which describe people making decisions in different domains. In our survey, we use 6 domains: making small purchases, large purchases, using own earnings, household budget, schooling of children and treatment of sick children.

Each vignette suggests a motivation for action: external (when one’s action is effectively coerced – by another person, or by force of circumstances), introjected (in which the individual acts to please others or to avoid blame – regardless of whether he or she personally values this particular course of action) and internal. External and introjected are relatively controlled forms of motivation. Internal motivation is relatively autonomous and is comprised of intrinsic (when the individual enjoys activity in itself), identified (when the person’s behavior reflects conscious valuing of self-selected goals and activities) and integrated (when the person’s actions are shaped based on his or her own system of values, goals, and identities).

Table 1 shows the vignettes for decision making on small purchases, where the first vignette reflects external, the second – introjected and the third – internal motivation. Please note that vignettes were

> 4 Please refer to the Appendix for the full list of questions.

11
selected depending on the gender of participants: men listened to vignettes with male characters, and women – with female. The respondents are asked if they are completely similar, somewhat similar, somewhat different or completely different from individuals described. The highest score (4) is assigned for being completely similar, the lowest (1) for being completely different. We calculate the average of scores in each motivational category (external, introjected and internal) and construct RAI as the weighted sum of the scores of the three subscales, with the external motivation subscale weighted as (-2), the introjected motivation subscale as (-1) and autonomous motivation subscale weighted as (+3). The RAI ranges from -9 to +9. Higher scores represent a more autonomous sense of motivation whereas negative scores indicate that the respondent’s motivation is more controlled.

**Table 1: Construction of RAI – vignette for decision making about small purchases**

||Now I'm going to read you some descriptions of various people, who make decisions about small<br>purchases. Can you tell me if you're like this person or not like this person, when you are making<br>decisions about small purchases (food, clothing)?|
|---|---|
|RA01A|Maria buys the amount of rice she buys, because she is afraid her husband will scold her for being a<br>spendthrift if she buys more.<br>Albert buys the amount of feed for the farm because he is worried that he wants to avoid a fight with<br>his wife. She would be furious if he got more feed.|
|RA01B|Angela bought a dress because her friends think it is in fashion now. She wants to be seen as<br>fashionable and elegant by her friends.<br>Dodong bought a phone because his friends praised that brand. He cares about the opinion of his<br>friends.|
|RA01C|Jenny buys this amount of rice because she thinks that this is enough to make a nutritious meal for her<br>family.<br>Manny buys this amount of feed because he believes that this is good amount for the animals on the<br>farm to stay strong and healthy.|

_Changes in empowerment through changes in perception of gender norms_ Second, CCTs may have indirect impact on adolescents’ empowerment through changing their perception of gender norms. These impacts are theoretically ambiguous. On the one hand, CCTs may induce gender equitable norms by increasing women’s bargaining power (through channeling cash directly into women’s hands). On the other hand, CCTs may perpetuate unequal gender norms, by imposing conditionalities that generally becomes a purview of women and are aligned with traditional gender roles (such as women taking care of the children).

12
We first check whether exposure to the 4P program in adolescence is associated with differences in adhered to and perceived gender norms. We capture own views on gender norms and acceptability of domestic violence by asking whether respondent agrees with a gender equitable statement, justification of wife-being and justification of child-beating. Then, to capture views on norms by respondent’s community we ask respondent to imagine that we invited 10 men from her or his community and ask how many of them would agree with the same statement. We repeat the same exercise, asking the respondent to imagine 10 women from her/his community. We construct a z-score of agreement with genderequitable statements (or statements condoning wife-beating or child beating) to capture personal attitude. We use the reported fraction of men/women who endorse gender-equitable norms, disagree with wife-being or child-beating to capture perceptions of attitudes in the community.

A change in own views on gender norms, or in perception of gender norms by others may affect such manifestations of empowerment as intra-household bargaining power. We attempt to capture it through questions on intra-household decision making and time-use, focusing on paid and unpaid activities, as well as household chores.

We asked two sets of questions to capture decision-making. First, we ask detailed questions about decision-making process in 7 domains (daily purchases, large household purchases, use of contraception, use of own earnings, use of the household budget, schooling of children and treatment of sick children).<sup>5</sup> We ask the traditional question (who usually makes the decision on a domain) but nuance it by adding questions about decision-making process, asking whether there is a discussion, whether others in the family weigh respondent’s opinion and whose opinion prevails in case of a disagreement. Decision-making in a specific domain may be an expression of empowerment, but may also be a burden (for example, if none in the household members wants to take responsibility). To capture this aspect of decision-making, we are asking respondents if it is important for them to make decisions in specific domain. Based on these questions, we calculate our outcome variables: fraction of domains for which respondent makes the final decision (either independently or jointly with a spouse), fraction of domains in which respondent's opinion is heard, and fraction of domains in which respondent cares about making the decision.

Our second measure of decision-making is based on vignettes about decision-making styles, based on Bernard et al. (2020). We present respondents with vignettes that capture 5 decision-making styles: Dictator, Contributor, Separate Spheres, Norms-Based, and Knowledge-Based. We ask the respondents if their family is like the family in the vignette. For example, for Dictator decision-making style, respondents

> 5 Only people with children are asked about the last two domains.

13
are read the following: “Antonio and Lilibeth are married. Antonio makes all the financial decisions for the family and does not seek Lilibeth’s opinion because he makes all the decisions for the household.”<sup>6</sup> We create a dummy variable which is equal to 1 if a respondent identifies with a specific decision-making style and evaluate the impact of CCTs on increasing the likelihood of identification with a specific decisionmaking style, relative to all other styles.

Finally, we explore the impact on time-use. We check whether exposure to CCTs has affected the fraction of time spent on chores, paid work and unpaid work. The impacts for men and women may be in different directions.

# _Changes in exposure to gender-based violence_

Third, benefitting from CCTs in adolescence may have long-lasting impacts on extreme form of disempowerment, or exposure to GBV through change in behavior/relationship of parents. Buller et al. (2019) demonstrate in a review of literature on the relationship between cash transfers and GBV that CCTs are generally associated with reduction of GBV. Exposure to violence in childhood (either direct exposure or by witnessing violence perpetrated or suffered by parents) is an important predictor of exposure to and/or perpetration of GBV in adulthood (Garcia-Moreno et al. 2006). Thus, the 4P program could reduce incidence of GBV among our sample through lowering GBV their mothers were exposed to a decade ago.

We collect data on intimate partner violence (IPV), domestic violence perpetrated by other household members,<sup>7</sup> and violence outside home.<sup>8,9</sup> For each type of violence, we construct indicators of physical, emotional, sexual and economic violence, with questions largely borrowed from Demographic and Health Surveys (DHS). For IPV, we collect data on prevalence and incidence during the 12 months preceding the survey. For domestic violence and violence outside home, we only administer questions about life-time prevalence.

> 6 The full list of vignettes is included in the Appendix.

> 7 Our questionnaire included the following options for household members: (i) Mother/Step-mother/Mother-inlaw; (ii) Father/step-father/father-in-law; (iii) sister/brother/sister-in-law/brother-in-law; (iv) Other relative (please specify).

> 8Our questionnaire included the following options for non-household members: (i) Employer/someone at workplace; (ii) Community leader; (iii) Health worker; (iv) Teacher; (v) Police/soldier.

> 9 This study’s human subjects’ protection protocols received research ethics review approval in accordance with the requirements of the US Code of Federal Regulations for the Protection of Human Subjects (45 CFR 46 & 45 CFR 46.110) from HML IRB.

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# 6. Estimation

The study design and all analysis methods were pre-registered before data analysis began.<sup>10</sup> We rely on randomized rollout at _baranagay_ level and calculate the intent to treat effect using the following estimating equation: estimating equation: 0 1T T T T (Eq.1) nn ii,bb bb ii jj jj iijj bb ii,bb where _yib_ is the outcome variable measured for individual yy = ββ + ββ TT + XX αα+ ∑θθ XX _i_ who was residing in barangay TT + εε _b_ during adolescence. T T is a dummy variable taking value 1 if the barangay where the respondent resided as an adolescent was randomly assigned to treatment during initial roll-out of the 4P program. bb _Xi_ is the row TT vector of _n_ demeaned control variables: age, gender, dummies for respondent’s religion and ethnicity, dummy variable for treatment LR assignment among women, years of schooling in 2008, and a dummy variable for being enrolled in school in 2008. Following Lin (2013) we also include a full set of control variables interacted with treatment indicator. Table 2 provides summary statistics on the control variables. is the error term. 1 is the main coefficient of interest. It will measure the intent-to-treat (ITT) effect of residing in a barangay that was randomly assigned treatment, compared to the control iibb εε ββ group. We cluster standard errors at barangay of residence level. We carry out estimation of genderspecific impacts by splitting the sample into women and men and running regression (1) separately on these two subsamples. As we are estimating a large-scale governmental program, ITT is our primary estimand of interest. The discussion of results is focused on ITT effects; however, we also estimate the treatment on the treated (ToT) effect by instrumenting participation in the 4P program with the random assignment to the treatment group at barangay level. We present the results in the Appendix.

# 7. Results

## 7.1. Balance

We take advantage of the data collected in 2008 for an impact evaluation of the 4P program to test whether randomization was carried out successfully. We can identify households in our sample in these data, which includes a rich set of characteristics of households where our respondents resided in 2008, including characteristics of the household head, spouse of the household head, access to social services, housing conditions and asset ownership. Of 48 available indicators, we find statistically significant differences in only three. 4P beneficiary households are less likely to be headed by a person with no

> 10 https://ridie.3ieimpact.org/index.php?r=search/detailView&id=923.

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education, less likely to use a community coop and to have shared water source (Table 3). Our results do not qualitatively change if we include these variables in baseline controls.

# 7.2. Attrition

Of the originally selected 886 households, 450 were replaced either because the household barangay officials were not able to identify the household among current barangay residents, or because the field team could not confirm that a child of eligible age and gender was a member of approached household. We do not find difference in the likelihood to be lost to follow-up by treatment status (estimated coefficient is -0.016, with standard error of 0.044).

We also check whether attrition is affected by baseline characteristics in the control group, as well as for evidence of differential attrition by observable characteristics between treatment status. Specifically, we first estimate the probability of receiving the 4P transfer based on the wide range of 2008 characteristics and construct a propensity score. We then check for differential attrition in the following regression: 0 1T T 2 3T T (Eq.2) ii,bb bb ii bb ii ii,bb where is a binary variable equal to 1 if individual AA = ξξ + ξξ TT + ξξ PP _i_ was not tracked, + ξξ TT PP + ςςT T is a barangay level indicator of treatment and ii,bb is the value of propensity score from a regression of treatment status on 2008 bb AA TT household characteristics – we include all variables reported in Table 3. Coefficient ii 2 captures whether PP attrition differs depending on the baseline characteristics which capture the likelihood of participation in ξξ the 4P program among the control group. Coefficient 3 captures whether attrition differs by observable characteristics between our treatment and control group. Table 4 shows that none of the coefficients is ξξ significantly different from 0.

Even though we do not find evidence of differential attrition, given that the fraction of households that we were unable to track is high, we report Lee bounds (Lee, 2009) and Kling-Liebman bounds (Kling and Liebman, 2004) for all the outcomes.

# 7.3. Main effects

## _7.3.1. Impacts on primary outcomes_

As the core objective of CCT programs is to reduce inter-generational transmission of poverty through investment in human capital, we first focus on impacts on education and labor market outcomes. We find no evidence on long-term impacts of the 4P program on completion of compulsory education, years of 16
education, and enrollment in non-compulsory education for women and men<sup>11</sup> (Table 5). Similarly, regressions of labor force participation, employment, paid employment, and underemployment rates yield null results for both genders. Perhaps, not surprisingly, we also do not find impacts on proxies of welfare (Table 6). We use three alternative indices of housing conditions and assets: average of z-scores, following Katz, King and Liebman (2007), the first component of PCA and ICW. None of them yields a significant impact.

# _7.3.2. Impacts on empowerment_

Although we do not find impacts on primary outcomes of the program, our results suggest that it increased age at marriage for women by almost a year and the age at first birth by almost half a year (Table 7). We do not find any impact on the prevalence of transactional sex.

While we can interpret increase in age at marriage and first birth as a manifestation of women’s empowerment, we do not find strong evidence of increase in empowerment when using other measures. We estimate zero impact on generalized self-efficacy index, either raw value or z-score, or RAI for both women and men (Table 7).

We also do not find impact on professed as well as perceived gender norms for either women or men. The estimates in regressions of index of agreement with gender equitable statements, of agreement with child-beating, or estimated percentages of men and women in the community who would agree with gender equitable statements, wife-beating or child beating are very low and insignificant (Table 8).

However, we do find impacts on some measures of empowerment within the household for women (Table 9). The fraction of decision-making domains in which respondent’s opinion is heard and the fraction of domains in which respondent cares about making decisions increases by 3.6 and 4.1 percentage points, respectively. This is a rather small increase from respective averages of 0.728 and 0.762. We do not find evidence of change when using alternative measures of decision-making within households, based on vignettes: exposure to the 4P program in adolescence is not associated with a change in likelihood to identify with a specific decision-making style. Lastly, we find zero impacts on time use (Table 10).

# _7.3.3. Impacts on GBV_

We collected rich data on physical, emotional, social and economic gender-based violence, inflicted by three types of perpetrators: intimate partner, other members of the household or non-household

> 11 We carry out estimation on the pooled sample of men and women; however, for succinctness of the narrative presents them only when they are significant while impacts on either gender is not.

17
member. Our estimates suggest no impacts on physical, sexual or emotional violence perpetrated by intimate partner. However, we find that exposure to the 4P program is associated with 6 percentage points increase in prevalence, and 8 percentage points increase in incidence of economic IPV. These increases are significant at 10 and 5 percent level, respectively, and are large in magnitude: average prevalence of economic IPV is 14 percent in the control group (Table 11).

The indicator of economic intimate partner violence is constructed as a dummy variable equal to 1 if a respondent answered yes to one of the three questions about her partner (in the last 12 months for incidence, and ever for prevalence):

- Did your partner not allow you to engage in legitimate work nor practice your profession?

- Did your partner deprive you or threaten to deprive you of financial resources and the right to use and enjoy, conjugal or commonly owned property?

- Did your partner control your own money or properties or force you to work?

- Did your partner control your personal property, pets or belongings, household property, or threaten to do so?

We checked which of these four behaviors drives increase in economic IPV. Regressions on corresponding binary variables suggest that we find significant impact on economic IPV due to increase in prohibiting to allow respondents to engage in legitimate work or practice profession. It is significant at 5% level at 0.337. Coefficients in other regressions are close to zero and not significant at conventional levels. Combined with weak evidence of some increase in empowerment, the observed change in economic IPV indicator may suggest a story of more empowered women, interested in outside of home employment, facing backlash.

We do not find impacts on prevalence of GBV perpetrated by other household members or someone outside home.

# _7.3.4. Robustness_

Given high attrition rates, we checked robustness of our results to different assumptions about attritors. Specifically, for each estimate we constructed Lee bounds, with and without control variables (Lee 2009), as well as Kling and Liebman bounds at 0.1 and 0.25 standard deviations (Kling and Liebman, 2004). Table 12 presents these estimates for outcomes where we found significant impacts.

Only the impact on age at marriage is highly robust to different assumptions about attrition: the estimates for both Kling-Liebman bounds and Lee bounds remain positive and significant. For the age at 18
first birth, Kling-Liebman bounds remain positive and significant when estimated at 0.1 difference, and for the upper Lee bounds. However, coefficients lose significance in other specifications. Other outcomes are not robust to changes in assumptions about attrition: for each of them, ITT impacts are bounded within an interval of different signs, both of which are sometimes significant.

## _7.3.5. ToT Estimates_

Our paper contributes to the existing literature by providing experimental estimates of absolute longterm impacts of a large-scale governmental program, thus complementing the work of Baird, McIntosh and Ozler (2019), who also provide experimental estimates of absolute impacts, but of a smaller program run by an NGO. Thus, ITT are of primary interest. However, we also carry out ToT estimation by instrumenting household level participation in the 4P program with its barangay level assignment. We present tables with ToT estimates in the Appendix. Generally, our ToT estimates are similar to ITT estimates, and somewhat higher in magnitude. The impacts on age at marriage and age at first birth increase to 2.4 and 1 years, respectively. Similarly, the impacts on incidence and prevalence of economic IPV increase to 17 and 20 percentage points, respectively. Empowerment coefficients (increase in the fractions of decision domains where respondents feel heard and which respondents care about) lose their significance. However, we find small increase in the likelihood to be enrolled in non-formal school for women.

# 8. Deviations from the Pre-Analysis Plan

There are several deviations from the pre-analysis plan in the current analysis. First, we did not include two types of outcomes mentioned in the pre-analysis plan: empowerment within community and subjective well-being. When writing the paper, we felt a lack of strong theoretical basis for inclusion of these outcomes. However, we present them in the Appendix.

We structured the questionnaire to capture impact of the CCT after accounting for his/her partner’s exposure to CCTs during adolescence. By design, this estimation would only include married/partnered individuals. Unfortunately, we have very limited variation in the sample for women: partners of only 25 women were exposed to CCTs in adolescence: less than 10 percent of the sample of partnered women with non-missing information about partner’s exposure to CCTs.

We did not discuss explicitly in the PAP whether we are using only FTFDQ data as our primary specification, or pool FTFDQ and ACASI. We chose to pool outcomes yielded by the two methods, given lack of significant differences across methods, and in order to increase sample size.

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# 9. Conclusions

Despite recent proliferation of studies of long-term impacts of the CCT programs, the jury is still out on their ability to reduce inter-generational transmission of poverty through human capital accumulation. Parker and Vogl (2018), as well as Kugler and Rojas (2018) find strong evidence of impacts of _Progresa_ on labor force participation, as well as quality of employment. Parker and Vogl (2018) also explore changes in household wealth and find impacts driven by increased labor market incomes and higher likelihood of marrying more educated, and hence higher earning, partners. Both studies rely on non-experimental methods. Baird, McIntosh and Özler (2019) and Araujo et al. (2016) find more sobering results for a CCT experiment in Malawi and _Bono de Desarrollo Humano_ in Ecuador, using a non-experimental framework: no impacts on labor market outcomes.

Our study attempts to contribute to this literature by evaluating impacts of approximately 1.5 years exposure to the 4P program in the Philippines during adolescence on adult outcomes in education, labor market, proxies of wealth and empowerment. Notably, there is evidence that additional 1.5-2 years of exposure to a CCT program may significantly improve education and labor market outcomes, compared to beneficiaries enrolled for a shorter time (Behrman, Parker and Todd, 2009; Barham, Macours and Maluccio, 2016). Can just 1.5 years of exposure to a CCT program generate a lasting impact in adulthood? Findings of Baird, McIntosh and Özler (2019) suggest no on average, and only on a limited set of outcomes for most at risk beneficiaries: school dropouts at baseline experience positive impacts on marriage and fertility.

Our results are somewhat more optimistic: we find that the program delayed marriage and fertility for all female beneficiaries, and this finding is robust to attrition bounds. However, similarly to Berk, McIntosh and Özler (2019) and Araujo et al. (2016) we do not find impacts on education or labor market outcomes (school completion, number of years of education, likelihood to be enrolled in non-formal education, labor force participation, employment, and employment in paid work). Nor do we find impacts on the proxies of wealth.

Although we collect a battery of empowerment measures, we do not find robust impacts. Evidence of increase in intra-household decision-making among women does not withstand tests of robustness to differential assumptions about respondents we lost due to attrition: both Lee and Kling and Liebman bounds on estimates are wide, and of different sign.

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Concerningly, we find that participation in the program is associated with the higher likelihood of economic IPV; however, increase in this indicator is fully driven by increase in perception that the respondent’s partner does not allow her to engage in legitimate work or practice her profession. This effect is compatible with increase in women’s willingness to work, without any change in their partner’s attitude to women working. These estimates also are not robust to corrections for attrition using Lee and King and Liebman bounds.

Our work contributes to the literature on long-term impacts of the CCT programs by providing experimental evidence on absolute long-term impacts of short exposure in Asia. We interpret our results as sobering and cautiously optimistic at the same time. On the one hand, while a number of papers demonstrate strong impacts of _additional_ exposure of 1-1.5 years to a CCT program (Behrman, Parker and Todd, 2009; Barham, Macours and Maluccio, 2016), we demonstrate that _just_ 1.5 years of exposure is not sufficient to move the needle on the core objectives of most CCT programs: education, translated into superior labor market outcomes and poverty reduction. At the same time, our results suggest that just 1.5 years of cash transfer support during the critical transition from adolescent to adulthood may trigger delays in marriage and fertility for girls: we find evidence of delay in marriage by one year, and delay in the first birth by approximately half a year. We do not find impacts on any other indicator of women’s empowerment, though.

Overall, we interpret our results as suggestive evidence of the _potential_ of the 4P program to affect longterm outcomes, but recognize the need to complement this work with study of longer exposure to the program, perhaps, employing non-experimental methods as in Parker and Vogl (2018) and Kugler and Rojas (2018).

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<mark>Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(1), 83-119.</mark> <mark>Kugler, A. D., & Rojas, I. (2018).</mark> _<mark>Do CCTs improve employment and earnings in the very long-term? Evidence from Mexico</mark>_ <mark>(No. w24248). National Bureau of Economic Research.</mark> <mark>Lee, D. S. (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects.</mark> _<mark>The Review of Economic Studies</mark>_ <mark>,</mark> _<mark>76</mark>_ <mark>(3), 1071-1102.</mark> <mark>Parker, S. W., & Vogl, T. (2018).</mark> _<mark>Do conditional cash transfers improve economic outcomes in the next generation? Evidence from Mexico</mark>_ <mark>(No. w24303). National Bureau of Economic Research.</mark> Rodríguez-Oreggia, Eduardo and Samuel Freije (2012). Long Term Impact of a Cash- Transfers Program on Labor Outcomes of the Rural Youth in Mexico. CID Working Paper No. 230.

<mark>Saavedra, J. E., & García, S. (2012). Impacts of conditional cash transfer programs on educational outcomes in developing countries: a meta-analysis.</mark> _<mark>RAND Labor and Population Working Paper Series, WR-921-1</mark>_ <mark>.</mark> <mark>Vaz, A., Pratley, P., & Alkire, S. (2016). Measuring women’s autonomy in Chad using the Relative Autonomy Index.</mark> _<mark>Feminist Economics, 22</mark>_ <mark>(1), 264-294</mark> 23
**Table 2: Summary statistics for control variables**

||All<br>mean|All SD|Control<br>mean|Control<br>SD|Treatment<br>mean|Treatment<br>SD|Diff.<br>(Control -<br>Treatment)|P-val|
|---|---|---|---|---|---|---|---|---|
|Age of respondent|24.3858|0.635|24.4419|0.634|24.3287|0.630|0.1132***|0.008|
|Female respondent|0.5017|0.500|0.5011|0.501|0.5023|0.501|-0.0012|0.972|
|Treatment LR assignment among women|0.2560|0.437|0.2437|0.430|0.2685|0.444|-0.0248|0.403|
|Years of schooling in 2008|6.6831|2.097|6.6902|2.082|6.6759|2.114|0.0143|0.920|
|Attending school in 2008|0.7486|0.434|0.7494|0.434|0.7477|0.435|0.0017|0.953|
|Muslim|0.0023|0.048|0.0023|0.048|0.0023|0.048|-0.0000|0.991|
|Protestant|0.0287|0.167|0.0319|0.176|0.0255|0.158|0.0064|0.570|
|Catholic|0.8117|0.391|0.7859|0.411|0.8380|0.369|-0.0521**|0.049|
|Iglesia|0.0080|0.089|0.0068|0.082|0.0093|0.096|-0.0024|0.689|
|Aglipay|0.0161|0.126|0.0296|0.170|0.0023|0.048|0.0273***|0.001|
|Other Christian|0.0631|0.243|0.0752|0.264|0.0509|0.220|0.0242|0.141|
|Adventist|0.0115|0.107|0.0046|0.067|0.0185|0.135|-0.0140*|0.054|
|Tagalog|0.0023|0.048|0.0023|0.048|0.0023|0.048|-0.0000|0.991|
|Cebuano|0.1917|0.394|0.2118|0.409|0.1713|0.377|0.0405|0.129|
|Ilonggo|0.0092|0.095|0.0091|0.095|0.0093|0.096|-0.0001|0.982|
|Bisaya|0.7853|0.411|0.7585|0.428|0.8125|0.391|-0.0540*|0.052|
|Hiligaynon|0.0011|0.034|0.0023|0.048|0.0000|0.000|0.0023|0.318|
|Number of observations|87|1|43|9|43|2|||

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**Table 3: balance tests based on 2008 data**

||Non-<br>4P<br>obs.|Mean<br>for non-<br>4P|4P<br>obs.|Mean<br>for 4P|Diff.<br>(non-4P -<br>4P)|P-val|
|---|---|---|---|---|---|---|
|Household head female|465|0.1097|464|0.0927|0.017|0.391|
|Number of household members|465|6.3441|464|6.3987|-0.055|0.673|
|Number of children in the household (0-14 years)|465|2.9527|464|2.9806|-0.028|0.784|
|Number of adults in the household (15-64 years)|465|3.3011|464|3.3513|-0.050|0.581|
|Number of children in the household (65 years and above)|465|0.0903|464|0.0668|0.024|0.237|
|Household dependency ratio|461|1.0925|461|1.1146|-0.022|0.678|
|Household head no education|465|0.0452|464|0.0237|0.021*|0.073|
|Household head attended elementary school|465|0.4817|464|0.4375|0.044|0.177|
|Household head finished elementary school|465|0.2559|464|0.2823|-0.026|0.365|
|Household head attended high school|465|0.0753|464|0.0948|-0.020|0.286|
|Household head finished high school|465|0.0903|464|0.1056|-0.015|0.434|
|Household head attended college|465|0.0344|464|0.0366|-0.002|0.855|
|Household head finished college|465|0.0194|464|0.0129|0.006|0.438|
|Spouse of household head no education|424|0.0189|417|0.0168|0.002|0.820|
|Spouse of household head attended elementary school|424|0.3726|417|0.3549|0.018|0.594|
|Spouse of household head finished elementary school|424|0.3443|417|0.3237|0.021|0.527|
|Spouse of household head attended high school|424|0.1108|417|0.1439|-0.033|0.151|
|Spouse of household head finished high school|424|0.0920|417|0.1271|-0.035|0.103|
|Spouse of household head attended college|424|0.0401|417|0.0240|0.016|0.185|
|Spouse of household head finished college|424|0.0212|417|0.0168|0.004|0.638|
|Household uses PhilHealth|465|0.3613|464|0.3405|0.021|0.508|
|Household uses HMO|465|0.0043|464|0.0043|-0.000|0.998|
|Household uses PHIC|465|0.0065|464|0.0043|0.002|0.656|
|Household uses community coop|465|0.0086|464|0|0.009**|0.045|
|Roof made of strong materials|465|0.3075|464|0.2909|0.017|0.582|
|Outer walls made of strong materials|465|0.1613|464|0.1789|-0.018|0.476|
|Roof made of light materials|465|0.5204|464|0.5388|-0.018|0.575|
|Outer walls made of light materials|465|0.4624|464|0.4698|-0.007|0.820|

25
|Household owns dwelling|465|0.2817|464|0.2500|0.032|0.274|
|---|---|---|---|---|---|---|
|Household rents dwelling|465|0.0129|464|0.0216|-0.009|0.312|
|Dwelling has toilet|465|0.3742|464|0.3427|0.032|0.317|
|Dwelling has shared water source|465|0.2753|464|0.2004|0.075***|0.007|
|Dwelling has own water source|465|0.0430|464|0.0366|0.006|0.620|
|Dwelling has elctricity|465|0.4344|464|0.4289|0.006|0.865|
|Household has television set|465|0.2366|464|0.2565|-0.020|0.482|
|Household has VHS/VCD/DVD|465|0.1247|464|0.1034|0.021|0.308|
|Household has stereo/CD player|465|0.1763|464|0.1961|-0.020|0.439|
|Household has refrigerator/freezer|465|0.0839|464|0.0776|0.006|0.725|
|Household has washing machine|465|0.0237|464|0.0151|0.009|0.344|
|Household has air conditioner|465|0.0043|464|0.0086|-0.004|0.412|
|Household has living room|465|0.2194|464|0.2306|-0.011|0.682|
|Household has dining set|465|0.3118|464|0.3427|-0.031|0.317|
|Household has car or jeep|465|0.0086|464|0.0043|0.004|0.415|
|Household has telephone/cellphone|465|0.0946|464|0.0884|0.006|0.741|
|Household has a PC|465|0.0043|464|0.0022|0.002|0.565|
|Household has microwave|465|0.0086|464|0.0065|0.002|0.707|
|Household has motorcycle|465|0.0452|464|0.0517|-0.007|0.642|
|*p<0.1 **p<0.05 ***p<0.01|||||||

26
**Table 4: Attrition**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|4P Assignment|-0.07124|-0.17888|-0.09383|-0.22338|
||(0.683)|(0.317)|(0.587)|(0.206)|
|4P Propensity Score|0.07897|-0.12860|0.06994|-0.16800|
||(0.719)|(0.575)|-0.75|(0.451)|
|4P Propensity Score X 4P Assignment|0.16151|0.38872|0.20688|0.47874|
||(0.615)|(0.246)|(0.516)|(0.149)|
|Observations|886|886|886|886|
|Characteristics of spouse of HH head (values for missing spouses<br>coded as zero)|Yes|Yes|No|No|
|Dummyforpresence of spouse of HH head|Yes|No|Yes|No|
|F-test of all vars(p-val)|0.677|0.609|0.590|0.455|
|P-values inparentheses: *p<0.1 **p<0.05 ***p<0.01|||||

27
**Table 5: long term impacts of the 4P program on education and labor market**

||Completed<br>schooling|Years of<br>education|Enrolled in<br>non-<br>formal<br>school|Employment<br>Women|Labor force<br>participation|Paid<br>employment|Under-<br>employment|
|---|---|---|---|---|---|---|---|
|ITT|-0.006|0.266|0.051|-0.050|-0.031|-0.007|-0.068|
||(0.87)|(0.40)|(0.16)|(0.32)|(0.51)|(0.89)|(0.42)|
|Observations|437|436|437|437|437|148|148|
|Adj. R2|0.33|0.39|0.02|0.00|0.01|0.07|-0.02|
|Control mean|0.645|10.714|0.132|0.364|0.418|0.910|0.308|
|||||Men||||
|ITT|0.021|0.238|-0.007|-0.040|-0.029|0.018|-0.073|
||(0.65)|(0.59)|(0.89)|(0.28)|(0.39)|(0.56)|(0.15)|
|Observations|435|435|435|435|435|317|317|
|Adj. R2|0.32|0.47|0.04|0.00|0.01|0.07|0.06|
|Control mean|0.523|9.464|0.241|0.764|0.864|0.889|0.389|
|P-values inpare|ntheses: *p<0.|1 **p<0.05 **|*p<0.01|||||

28
**Table 6: long term impacts of the 4P program on assets**

||Hous<br>|ingconditions in<br>|dices<br>||Asset indices<br>||
|---|---|---|---|---|---|---|
||KKL|PCA|ICW|KKL|PCA|ICW|
||||Wo<br>|men<br>|||
|ITT|-0.062|-0.339|-0.290|0.001|-0.275|0.159|
||(0.21)|(0.13)|(0.46)|(0.98)|(0.31)|(0.81)|
|Observations|437|348|348|437|437|437|
|Adj. R2|0.11|0.08|0.05|0.09|0.12|0.02|
|Control mean|-0.035|0.059|0.951|-0.039|-0.315|-0.653|
||||M|en|||
|ITT|-0.052|-0.182|-0.332|-0.016|-0.361|-0.434|
||(0.30)|(0.44)|(0.43)|(0.57)|(0.15)|(0.51)|
|Observations|435|337|337|435|435|435|
|Adj. R2|0.11|0.15|0.06|0.03|0.12|0.02|
|Control mean|-0.055|-0.138|0.529|-0.066|-0.614|-1.011|

Regression results from equation 1; with demeaned covariates and interaction of full set of covariates with treatment variable. KKL denotes average of z-scores as in Katz, Kling and Liebman (2007); PCA is the first component of PCA; and ICW applies inverse covariate weighting. P-values in parentheses: * p<0.1 ** p<0.05 *** <u>p<0.01</u> 29
**Table 7: long-term impacts of the 4P program on empowerment - socio-economic proxies and direct measures - ITT**

||Age at<br>Marriage<br>|Age at First<br>Birth<br>|Prevalence of<br>transactional sex<br>**W**|Generalized self<br>efficacy (raw<br>values 8-32)<br>**omen**|Generalized self-<br>efficacy (KKL<br>zscore avg)|Relative<br>Autonomy Index|
|---|---|---|---|---|---|---|
|ITT|0.933**|0.457**|-0.004|-0.118|-0.010|-0.044|
||(0.01)|(0.05)|(0.74)|(0.84)|(0.87)|(0.84)|
|Observations|176|299|437|436|436|428|
|Adj. R2|0.04|0.00|-0.00|-0.00|-0.00|0.05|
|Control mean|20.650|20.138|0.023|22.470|0.053|3.072|
|||||**Men**|||
|ITT|0.260|||0.367|0.039|-0.179|
||(0.56)|||(0.54)|(0.54)|(0.52)|
|Observations|89|||435|435|408|
|Adj. R2|0.09|||0.04|0.04|-0.01|
|Control mean|21.694|||22.350|0.039|2.076|
|P-values inparent|heses: *p<0.1 **|p<0.05 ***p<0.01|||||

30
**Table 8: long-term impacts of the 4P program on gender and GBV/DV norms - ITT**

||Agreement<br>|Pc of men|Pc of women|Wo<br>|men<br>||||
|---|---|---|---|---|---|---|---|---|
||with gender<br>equitable<br>statements<br>(KKL z-score<br>average)|<br>who agree<br>with a gender<br>equitable<br>statement|<br>who agree<br>with a gender<br>equitable<br>statement|Pc of men<br>who disagree<br>with wife-<br>beating|Pc of women<br>who disagree<br>with wife-<br>beating|Disagreement<br>with child-<br>beating (KKL z-<br>score average)|Pc of men<br>who disagree<br>with child-<br>beating|Pc of women<br>who disagree<br>with child-<br>beating|
|ITT|-0.034|-0.039|-0.033|0.005|-0.000|-0.013|0.002|-0.016|
||(0.38)|(0.13)|(0.20)|(0.83)|(0.99)|(0.89)|(0.95)|(0.65)|
|Observations|436|436|436|436|436|436|436|436|
|Adj. R2|-0.00|0.00|0.00|-0.00|-0.00|-0.00|-0.00|-0.00|
|Control mean|0.042|0.597|0.689|0.797|0.859|-0.037|0.581|0.638|
|||||M|en||||
||Agreement<br>with gender<br>equitable<br>statements<br>(KKL z-score<br>average)|Pc of men<br>who agree<br>with a gender<br>equitable<br>statement|Pc of women<br>who agree<br>with a gender<br>equitable<br>statement|Pc of men<br>who disagree<br>with wife-<br>beating|Pc of women<br>who disagree<br>with wife-<br>beating|Disagreement<br>with child-<br>beating (KKL z-<br>score average)|Pc of men<br>who disagree<br>with child-<br>beating|Pc of women<br>who disagree<br>with child-<br>beating|
|ITT|-0.048|0.030|-0.019|-0.008|0.006|-0.106|-0.004|-0.024|
||(0.20)|(0.30)|(0.54)|(0.73)|(0.79)|(0.22)|(0.91)|(0.43)|
|Observations|435|435|435|435|435|435|435|435|
|Adj. R2|0.00|0.00|-0.00|-0.00|-0.00|0.00|-0.00|-0.00|
|Control mean|0.003|0.585|0.694|0.790|0.833|0.054|0.593|0.624|
|P-values inparen|theses: *p<0.1 *|*p<0.05 ***p<0.0|1||||||

31
**Table 9: long-term impacts of the 4P program on empowerment - decision-making - ITT**

||Fraction of<br>domains for<br>which<br>respondent<br>makes the<br>decision|Fraction of<br>domains in which<br>respondent's<br>opinion is heard<br>|Fraction of<br>domains in<br>which<br>respondent<br>cares about<br>making the<br>decision<br>|Dummy for<br>dictator<br>decision<br>making<br>Wom|Dummy for<br>contributor<br>decision making<br>en|Dummy for<br>separate<br>spheres<br>decision<br>making|Dummy for<br>norms-based<br>decision<br>making|Dummy for<br>most-<br>knowledge<br>decision making|
|---|---|---|---|---|---|---|---|---|
|ITT|0.019|0.036*|0.041**|-0.034|-0.002|0.024|-0.051|0.011|
||(0.45)|(0.08)|(0.03)|(0.35)|(0.96)|(0.65)|(0.18)|(0.78)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|0.05|0.02|0.04|0.01|0.01|-0.00|0.05|0.06|
|Control mean|0.627|0.728|0.762|0.241|0.286|0.641|0.236|0.327|
|||||Men|||||
|ITT|-0.004|-0.017|-0.007|-0.029|-0.046|-0.075|0.052|0.042|
||(0.90)|(0.52)|(0.74)|(0.50)|(0.24)|(0.19)|(0.22)|(0.39)|
|Observations|435|435|435|435|435|435|435|435|
|Adj. R2|0.03|0.03|0.03|0.02|0.01|-0.00|0.04|0.02|
|Control mean|0.513|0.649|0.703|0.268|0.336|0.668|0.250|0.368|

P-values in parentheses: * p<0.1 ** p<0.05 *** p<0.01 32
**Table 10: long-term impacts of the 4P program on empowerment - time-use**

||Fraction of our<br>t<br>|s spent on acti<br>otal 24 hours<br>|vity; out of<br>|Fraction of ho<br>of to<br>|urs spent on<br>tal awake hou<br>|activity, out<br>rs<br>|
|---|---|---|---|---|---|---|
||Household<br>activity|Paid<br>activity|Unpaid<br>activity|Household<br>activity|Paid<br>activity|Unpaid<br>activity|
||||**Wo**<br>|**men**<br>|||
|ITT|0.015|-0.013|-0.001|-0.025|-0.128|-0.004|
||(0.33)|(0.35)|(0.80)|(0.68)|(0.22)|(0.80)|
|Observations|436|436|436|422|422|422|
|Adj. R2|0.04|0.02|-0.06|-0.00|0.02|-0.06|
|Control mean|0.240|0.059|0.010|0.668|0.261|0.026|
||||**M**|**en**|||
|ITT|0.013|-0.002|0.001|0.082|-0.072|-0.010|
||(0.25)|(0.91)|(0.87)|(0.17)|(0.27)|(0.64)|
|Observations|435|435|435|418|418|418|
|Adj. R2|0.00|0.03|0.06|-0.02|0.02|0.10|
|Control mean|0.095|0.121|0.018|0.257|0.392|0.056|

P-values in parentheses: * p<0.1 ** p<0.05 *** p<0.01 33
**Table 11: long-term impacts of the 4P program on empowerment - exposure to IPV, NPDV, and NDV - ITT**

||Prevalence of<br>physical<br>intimate<br>partner<br>violence|Prevalence of<br>sexual<br>intimate<br>partner<br>violence|Prevalence of<br>emotional<br>intimate<br>partner<br>violence|Prevalence of<br>economic<br>intimate<br>partner<br>violence|Incidence of<br>physical<br>intimate<br>partner<br>violence|Incidence of<br>sexual<br>intimate<br>partner<br>violence|Incidence of<br>emotional<br>intimate<br>partner<br>violence|Incidence of<br>economic<br>intimate<br>partner<br>violence|
|---|---|---|---|---|---|---|---|---|
|ITT|-0.026|-0.027|-0.001|0.066*|-0.003|-0.013|0.026|0.080**|
||(0.48)|(0.15)|(0.97)|(0.06)|(0.91)|(0.39)|(0.56)|(0.02)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|-0.00|0.00|-0.00|0.01|-0.00|-0.00|-0.00|0.01|
|Control mean|0.136|0.045|0.227|0.141|0.095|0.027|0.186|0.105|
||Prevalence of<br>physical non-<br>partner<br>domestic<br>violence|Prevalence of<br>sexual non-<br>partner<br>domestic<br>violence|Prevalence of<br>emotional<br>non-partner<br>domestic<br>violence|Prevalence of<br>economic<br>non-partner<br>domestic<br>violence|Prevalence of<br>physical non-<br>domestic<br>violence|Prevalence of<br>sexual non-<br>domestic<br>violence|Prevalence of<br>emotional<br>non-domestic<br>violence|Prevalence of<br>economic<br>non-domestic<br>violence|
|ITT|0.034|-0.005|-0.011|-0.008|-0.013|-0.004|-0.031|0.000|
||(0.28)|(0.31)|(0.77)|(0.80)|(0.46)|(0.57)|(0.25)|(0.99)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|0.00|-0.00|-0.00|-0.00|-0.00|-0.00|0.00|-0.00|
|Control mean|0.091|0.005|0.205|0.109|0.041|0.009|0.091|0.009|
|P-values inpar|entheses: *p<0.1|**p<0.05 ***p|<0.01||||||

34
**Table 12: robustness**

|||Lower|Kling-Liebm<br>Lower|an bounds<br>|||Lee b<br>|ounds<br>||
|---|---|---|---|---|---|---|---|---|---|
||ITT|<br>with 0.1<br>diff.|<br>with 0.25<br>diff.|Upper with<br>0.1 diff.|Upper with<br>0.25 diff.|Lower w/o<br>controls|Lower w/<br>controls|Upper w/o<br>controls|Upper w/<br>controls|
||0.933**|0.6863***|0.1758**|1.3670***|1.8775***|0.8807**|1.0071***|1.2106***|1.2849***|
|Age at marriage|(0.01)|(0.000)|(0.042)|(0.000)|(0.000)|(0.018)|(0.005)|(0.001)|(0.000)|
||176|583|583|583|583|131|131|130|130|
||0.457**|0.3740***|-0.1049|1.0126***|1.4916***|0.5496|0.5496|0.9122**|0.9122**|
|Age at first birth|(0.05)|(0.000)|(0.291)|(0.000)|(0.000)|(0.188)|(0.188)|(0.016)|-0.02|
||299|600|600|600|600|148|148|147|147|
|Fraction of domains in|0.036*|-0.0518***|-0.0906***|-0.0002|0.0385***|-0.0480**|-0.0466**|-0.0147|-0.0133|
|which respondent's<br>opinion is heard|(0.08)|(0.000)|(0.000)|(0.985)|(0.001)|(0.040)|(0.040)|(0.515)|(0.541)|
||437|883|883|883|883|426|426|425|425|
|Fraction of domains in<br>which respondent cares|0.041**|-0.0420***|-0.0727***|-0.0012|0.0295***|-0.0398**|-0.0387**|-0.0119|-0.0113|
|about making the<br>decision|(0.03)|(0.000)|(0.000)|(0.905)|(0.004)|(0.029)|(0.029)|(0.542)|(0.551)|
||437|883|883|883|883|426|426|425|425|
||0.066*|0.0071|-0.0680***|0.1073***|0.1824***|0.0534|0.0534|0.0895*|0.0895*|
|Prevalence of economic<br>IPV|(0.06)|(0.669)|(0.000)|(0.000)|(0.000)|(0.309)|(0.309)|(0.066)|(0.066)|
||437|665|665|665|665|212|212|211|211|
||0.080**|0.0099|-0.0575***|0.0996***|0.1670***|0.0520|0.0520|0.0884*|0.0884*|
|Incidence of economic<br>IPV|(0.02)|(0.529)|(0.001)|(0.000)|(0.000)|(0.290)|(0.290)|(0.050)|(0.050)|
||437|665|665|665|665|212|212|211|211|

35