metadata: authors: 0: author: Barrera-Osorio,Felipe
1: author: De Barros,Andreas
2: author: Filmer,Deon P.
count: Cambodia
docna: 0: docna: Long-term impacts of alternative approaches
            to increase schooling : evidence from a scholarship program
            in Cambodia
docty: Policy Research Working Paper
subtopic: Inequality,Educational Sciences,Labor Markets,Rural Labor Markets,Wages, Compensation & Benefits
teratopic: Social Protections and Labor,Education,Poverty Reduction,Rural Development
abstracts: cdata!: This paper reports on a randomized
            experiment to investigate the long-term effects of a primary
            school scholarship program in rural Cambodia. In 2008,
            fourth-grade students in 207 randomly assigned schools (103
            treatment, 104 control) received scholarships based on the
            students' academic performance in math and language or
            their level of poverty. Three years after the program's
            inception, an evaluation showed that both types of
            scholarship recipients had more schooling than
            nonrecipients; however, only merit-based scholarships led to
            improvements in cognitive skills. This new study reports
            impacts, nine years after program inception, on the
            educational attainment, cognitive skills, socioemotional
            outcomes, socioeconomic status and well-being, and labor
            market outcomes of individuals who are, on average, 21 years
            old. The results show that both types of scholarships led to
            higher long-term educational attainment (about 0.21-0.29
            grade level), but only merit-based scholarships led to
            improvements in cognitive skills (0.11 standard deviation),
            greater self-reported well-being (0.18 standard deviation),
            and employment probability (3.4 percentage points). Neither
            type of scholarship increased socioemotional skills. The
            results also suggest that there are labeling effects: the
            impacts of the scholarship types differ even for individuals
            with similar characteristics.
geo_reg_key: 517198,517191
repnme: repnme: Long-term impacts of alternative approaches
            to increase schooling : evidence from a scholarship program
            in Cambodia
display_title: Long-term impacts of alternative
            approaches to increase schooling : evidence from a
            scholarship program in Cambodia
listing_relative_url: /research/2018/08/30367311/long-term-impacts-alternative-approaches-increase-schooling-evidence-scholarship-program-cambodia
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Labor Markets m1326488 855,Poverty m1326687 695,Education m1327048 470,Reservation Wage m1504043 2740
publishtoextweb_dt: 2018-08-23T00:00:00Z
historic_topic: Social Protections and Labor,Education,Poverty Reduction,Rural Development
no_of_pages: 53
author_keys: 0: author_key: 1459837
1: author_key: 97345
model_extractions: WPS8566 Policy Research Working Paper 8566 Long-Term Impacts of Alternative Approaches to Increase Schooling Evidence from a Scholarship Program in Cambodia _Felipe Barrera-Osorio Andreas de Barros Deon Filmer_ Development Economics Development Research Group August 2018
Policy Research Working Paper 8566

# **Abstract**

This paper reports on a randomized experiment to investigate the long-term effects of a primary school scholarship program in rural Cambodia. In 2008, fourth-grade students in 207 randomly assigned schools (103 treatment, 104 control) received scholarships based on the students’ academic performance in math and language or their level of poverty. Three years after the program’s inception, an evaluation showed that both types of scholarship recipients had more schooling than nonrecipients; however, only merit-based scholarships led to improvements in cognitive skills. This new study reports impacts, nine years after program inception, on the educational attainment, cognitive skills, socioemotional outcomes, socioeconomic status and well-being, and labor market outcomes of individuals who are, on average, 21 years old. The results show that both types of scholarships led to higher long-term educational attainment (about 0.21-0.29 grade level), but only merit-based scholarships led to improvements in cognitive skills (0.11 standard deviation), greater self-reported well-being (0.18 standard deviation), and employment probability (3.4 percentage points). Neither type of scholarship increased socioemotional skills. The results also suggest that there are labeling effects: the impacts of the scholarship types differ even for individuals with similar characteristics.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at dfilmer@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 Alternative Approaches to Increase Schooling:

**JEL codes:** C93 Field Experiments; I21 Analysis of Education; I22 Educational Finance; I25 Education and Economic Development; I28 Government Policy; O12 Microeconomic Analyses of Economic Development. **Keywords:** Cambodia; education; long-term effects; merit-based targeting; poverty-based targeting; randomization; scholarships. _∗_ We are grateful for financial support from the World Bank’s Strategic Research Program Trust Fund. We thank Simeth Beng, Tsuyoshi Fukao, and staff of the Ministry of Education of the Royal Government of Cambodia for input and assistance at various stages of this project. Alice Danon provided outstanding research assistance. For helpful comments, we thank Maria Bertling, Theresa Betancourt, Monnica Chan, Olivia Chi, Mark Chin, Jishnu Das, David Deming, Alejandro Ganimian, Sibylla Leon Guerrero, Rema Hanna, Andrew Ho, Whitney Kozakowski, Guilherme Lichand, Sophie Litschwartz, Dana McCoy, Jonathan Mijs, Karthik Muralidharan, Charles Nelson, Gautam Rao, Margaret Sheridan, Abhijeet Singh, and Martin West. The usual disclaimers apply. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Harvard University IRB Protocol Title “From Schooling to Young Adulthood”, Number IRB16-1518.

> _†_ Associate Professor of Education and Economics, Harvard University, Graduate School of Education. E-mail: `felipe_barrera-osorio@gse.harvard.edu` .

> _‡_ PhD Candidate, Harvard University, Graduate School of Education. E-mail: `adebarros@g. harvard.edu` .

> §Lead Economist, World Bank. E-mail: `dfilmer@worldbank.org` .

Evidence from a Experimental Scholarship Program in Cambodia<sup>_∗_</sup> Felipe Barrera-Osorio<sup>_†_</sup> Andreas de Barros<sup>_‡_</sup> Deon Filmer<sup>§</sup> **JEL codes:** C93 Field Experiments; I21 Analysis of Education; I22 Educational I25 Education and Economic Development; I28 Government Policy; Analyses of Economic Development. **Keywords:** Cambodia; education; long-term effects; merit-based targeting; randomization; scholarships. We are grateful for financial support from the World Bank’s Strategic Research Program We thank Simeth Beng, Tsuyoshi Fukao, and staff of the Ministry of Education of Government of Cambodia for input and assistance at various stages of this project. provided outstanding research assistance. For helpful comments, we thank Maria Monnica Chan, Olivia Chi, Mark Chin, Jishnu Das, David Deming, Sibylla Leon Guerrero, Rema Hanna, Andrew Ho, Whitney Kozakowski, Sophie Litschwartz, Dana McCoy, Jonathan Mijs, Karthik Muralidharan, Charles Nelson, Rao, Margaret Sheridan, Abhijeet Singh, and Martin West. The usual disclaimers The findings, interpretations, and conclusions expressed in this paper are those of the authors and do necessarily represent the views of the World Bank, its Executive Directors, or the governments represent. Harvard University IRB Protocol Title “From Schooling to Young Adulthood”, IRB16-1518.
# **1 Introduction**

How does additional schooling impact long-term life outcomes? According to the canonical human capital model, labor markets remunerate the skills acquired during the education process (Becker 2009). According to a signaling model (Arrow 1973; Spence 1973), education provides the market with a signal of individuals’ higher abilities; as a result, the market pays for these skills. Both models predict positive effects from investment in education. At the same time, emerging research is showing that, in many settings, increased schooling has not meant increased learning, which is potentially limiting the market returns to education (Pritchett 2013; The World Bank 2017). There are, however, few studies in low-income settings that can isolate the impacts of schooling on skills accumulation.<sup>1</sup> Our paper aims to contribute to this evidence by presenting the causal long-term effects of a scholarship program which induced more schooling on cognitive, socioemotional, socio-economic status and wellbeing,<sup>2</sup> , and labor outcomes in a group of 21-year-old individuals who received the scholarship nine years earlier, in Cambodia. Our study setup is the following. In 2008, 207 schools in Cambodia were randomly allocated between two treatment arms (103 schools) and a control group (104 schools). In half of the treatment schools, students in grade four received a scholarship based on merit—high-performing students were selected using a baseline test of math and language skills—and fourth-graders in the remaining treatment schools received a scholarship based on poverty—students were selected using a poverty index, based on household and family socio-economic characteristics. Scholarships were given to recipients for three years (i.e. until the completion of primary school), conditional on continued school participation and basic performance standards. A first follow-up study, three years after the inception of the program, showed two main effects: higher school progression for individuals receiving either type of scholarship

> 1Few well-identified studies on the causal impacts of education exist for developing countries; important exceptions are Duflo et al. (2017); Parker and Vogl (2018); Ozier (2016); Jakiela et al. (2015) and Friedman et al. (2011).

> 2From this point on, we will refer to both socio-economic status and self-reported well-being as “well-being” for the sake of brevity.

2
(compared with non-recipients), and impacts on cognitive outcomes (as measured by a math test and a test of working memory) only for those receiving merit-based scholarship (Barrera-Osorio and Filmer 2016). In this paper we report results from data collected in 2016—nine years after the beginning of the program—from a subsample of the original study participants. We present evidence of the effects of the scholarship on a range of long-term outcomes spanning cognitive skills, socioemotional outcomes, socio-economic and well-being outcomes, and labor market outcomes. The analysis presents causal evidence to address three questions. First, what are the long-term effects of the program on cognitive skills and socioemotional outcomes? Specifically, we investigate the impacts of (exogenously induced) additional exposure to schooling on these outcomes. Heckman and Kautz (2014) show that socioemotional skills (which are also sometimes referred to as “non-cognitive” skills) are important determinants of labor outcomes in the long-term. Second, what are the long-term effects of the scholarships on socioemotional outcomes? In particular, we investigate whether socioemotional outcomes are co-produced with (or are complements to) cognitive outcomes. We can pursue the answer to this question because only the merit-based scholarship induced changes in cognitive skills after the first three years of the intervention; therefore, we can test whether we observe effects on socioemotional outcomes for this group only, for both treatment groups, or for neither group. Third, what are the long-term effects of the scholarships on well-being and labor market outcomes? Given that scholarships induced more schooling for all treated individuals, but only cognitive skills for some, we can investigate the channels through which this additional education might affect these outcomes. Based on an intent-to-treat model, the results show that, despite some catch-up by the control group between 2011 and 2016, scholarship recipients have on average 0.21-0.29 more years of schooling. This is in line with programs that attempt to reduce direct costs (for example scholarships; see Kremer et al. (2009), and Duflo Based on an intent-to-treat model, the results show that, despite some catch-up by the control group between 2011 and 2016, scholarship recipients have on average 0.21-0.29 more years of schooling. This is in line with programs that attempt to reduce direct costs (for example scholarships; see Kremer et al. (2009), and Duflo et al. (2017)) and indirect costs of education (for example conditional cash transfers; see Fiszbein and Schady (2009)).

We find positive effects on measures of cognitive skills, but only for the meritbased approach to targeting. Impacts of the merit scholarships on a “family index”— 3
that is an index that standardizes the cognitive skill measures and calculates a weighted average<sup>3</sup> —have an effect size of 0.11 standard deviations (significant at the 10% level); the effects of the poverty-based treatment are close to zero (and not statistically significant). This is consistent with the effects found after the initial three years, suggesting limited fade-out for this outcome.

We do not find any systematic impacts on two measures of socioemotional outcomes: emotional and behavioral difficulties (as measured by the Strengths and Difficulty Questionnaire, “SDQ”) and the “Big 5” personality traits (openness, conscientiousness, extroversion, agreeableness and neuroticism).<sup>4</sup> For a “family index” of these outcomes, we find imprecisely estimated impacts of 0.01 (merit-based scholarships) and 0.10 (poverty-based scholarships) standard deviations. The findings therefore neither support the hypothesis that more schooling (necessarily) produces more socioemotional skills, nor the hypothesis that cognitive and socioemotional skills are (necessarily) co-produced. We find that the probability of working increased by 3.4 percentage points for young adults who had received a merit-based scholarship (significant at the 10% level), but the impact for those who had received a poverty-based scholarship was close to zero (and statistically insignificant). The point estimates for earnings are both negative (but not statistically significant), perhaps because the scholarships induced individuals to delay entry into the labor market. Finally, we find positive overall impacts on various measures of self-reported wellbeing, but again only for those who had received merit-based scholarships. For a “family index” of socio-economic status, the point estimate is 0.17 standard deviations (significant at the 1% level) for the merit-based treatment arm; for the poverty treatment, the point estimate is 0.04 standard deviations (and not statistically significant). Overall, both types of scholarships led to more schooling attainment, but only Overall, both types of scholarships led to more schooling attainment, but only the merit-based scholarships had positive impacts on cognitive, well-being, and labor

> 3We follow Anderson (2008) and calculate inverse covariance matrix-weighted averages.

> 4We collected information on other socioemotional outcomes such as grit and growth-mindset. However, the psychometric and statistical properties of these measures in our context were weak (Danon et al. 2018).

4
market outcomes. Neither of the two types of scholarships induced greater socioemotional skills. Two factors are important for interpreting these results. First, they are the marginal effect of increasing schooling by only about four additional months— although these may be critical months, inasmuch the program induced individuals to finish primary education. But it is possible that some of the key impacts of school-

- were still in school) or later on in adolescence (when, for this population,

- groups would have left school). Second, while attrition is neither

- nor systematically different across the three groups of students, our

- limited sample size may nonetheless have reduced the precision of the estimates. overall results present a complex picture, suggesting that demand-side interventions, as scholarships, and their particular targeting approaches can have effects.

- The paper is organized as follows: in Section 2 we (selectively) review the related in Section 3 we describe in more detail the study setup and context,

- Sections 4 and 5 we describe our estimation strategy and data, in Section 6 we present results, and in Section 7 we provide some concluding comments. **Related Literature**

- Our study builds on three strands of literature. First, we add to previous research on effects of demand-side incentive programs in low- and middle-income

- both in terms of their overall effect and with respect to varying targeting approaches. Second, we contribute to research on whether increased schooling produces outcomes go beyond cognitive skills, in particular socioemotional outcomes. We investigate how these might be co-produced. Third, we contribute to the limited literature on the long-term effects of increased school enrollment

The paper is organized as follows: in Section 2 we (selectively) review the related literature, in Section 3 we describe in more detail the study setup and context, in Sections 4 and 5 we describe our estimation strategy and data, in Section 6 we present the results, and in Section 7 we provide some concluding comments.

# **2 Related Literature**

Our study builds on three strands of literature. First, we add to previous research on the effects of demand-side incentive programs in low- and middle-income countries, both in terms of their overall effect and with respect to varying targeting approaches. Second, we contribute to research on whether increased schooling produces outcomes that go beyond cognitive skills, in particular socioemotional outcomes. We furthermore investigate how these might be co-produced. Third, we contribute to the relatively limited literature on the long-term effects of increased school enrollment on outcomes such as employment status or well-being (scant because these impacts only manifest themselves later in life and require a long-term approach to evaluation).

ing on socioemotional skills happen early on (when both the control and treatment groups were still in school) or later on in adolescence (when, for this population, both groups would have left school). Second, while attrition is neither especially high nor systematically different across the three groups of students, our relatively limited sample size may nonetheless have reduced the precision of the estimates. Our overall results present a complex picture, suggesting that demand-side interventions, such as scholarships, and their particular targeting approaches can have important long-term effects.

5
# **2.1 Demand-side incentives**

There is a growing empirical literature on the impact of conditional cash transfers, of which scholarships are one form, in low- and middle-income countries (Baird et al. 2014; Barham et al. 2013; García and Saavedra 2017; Snilstveit et al. 2015).<sup>5</sup> Three studies are of particular relevance given their similar designs and scope. First, Friedman et al. (2011) and Jakiela et al. (2015) present experimental evidence on the effects of a Kenyan merit-scholarship program for sixth-grade girls, nine years postintervention. The studies find that short-term impacts on educational attainment and cognitive skill (initially reported in Kremer et al. (2009)) result in greater female empowerment, improved political knowledge and attitudes in young adulthood (finding weaker evidence for effects on political behaviors). Second, Barham et al. (2013) evaluate the long-run effects of a conditional cash transfer program targeted to poor families in Nicaragua. They find that boys who were 9-12 at the time of the program attained about half a year more schooling when they were 19-22 than boys in a comparison group, and subsequently had better labor market outcomes (the difference for girls was not statistically different from zero between the treatment and comparison groups). Third, Duflo et al. (2017) evaluate the long-term effects of a secondary school scholarship program, in Ghana. This randomized evaluation finds that the program delayed fertility and marriage, improved educational attainment, cognitive skill, and reproductive and health behaviors, and had heterogeneous impacts on earnings. Our study builds on these: The Kenyan study does not report impacts on earnings, and the Ghana and Nicaragua studies investigate few impacts on socioemotional outcomes. Our paper includes indicators for both types of outcomes, and allows for a contrast of targeting approaches (building on Barrera-Osorio and Filmer (2016)). Together, these evaluations inform the degree to which individual findings from specific contexts might have broader external validity (Vivalt 2017).

> 5Scholarships may also be designed as incentive mechanisms, where payments are made based on future performance. See Fryer Jr (2011) for related evidence from the US. See Berry (2015), Blimpo (2014), and Li et al. (2014) for examples of related research from India, Benin, and China, respectively. We study scholarships whose payout is not (or arguably, only weakly) incentivized. See Section 3, below, for a description of the scholarship program.

6
# **2.2 Socioemotional outcomes**

Most of the literature on CCTs and scholarships focuses on schooling and cognitive skill outcomes, with some exceptions that consider the impact of transfers on political and social factors, household consumption smoothing (Sparrow 2007), labor market outcomes (Araujo et al. 2016; Filmer and Schady 2014; Parker and Vogl 2018; Silva and Sumarto 2015), or health (Cruz et al. 2017). Few studies analyze the impacts on various outcome dimensions simultaneously. In high-income countries, socio-emotional skills have been found to be important predictors of success in school and life in general (see West et al. (2016) for an overview), and the importance of social skills has grown in the U.S. labor market between 1980 and 2012 (Deming 2017). Research from the United States suggests that teachers can have large effects on socioemotional outcomes, although a teacher’s productivity in terms of student cognitive achievement is only a weak predictor for her impact on measures of students’ socioemotional outcomes (Blazar 2017; Blazar and Kraft 2017; Jackson et al. 2014; Kraft 2017; Santorella 2017). At the same time, little is known—especially in low- and middle-income countries—about whether increased educational attainment leads to more socioemotional skills, and how this might interact with the formation of cognitive skills. Some analyses have tried to shed light on these relationships. For example, Kyllonen and Bertling (2013) report how participants’ self-reported confidence in mathematics in the 2003 Programme for International Student Assessment (PISA) study was positively correlated with performance. Claro et al. (2016) use a national data set of all tenth-graders in Chile to show that a student’s “growth mindset” can predict academic performance, offsetting socio-economic achievement gaps. However, these studies cannot identify exogeneous variation in schooling and cognitive skill, making causal inferences difficult.<sup>6</sup>

> 6In a well-identified study, Fabregas (2017) investigates the effect of school quality and peer composition on students’ academic performance, perseverance, aspirations, and time-management, in Mexico. But this and related research on peer effects ( _ibid._ , for a review) does not shed light on the of educational attainment.

7
# **2.3 Long-term effects**

Research from the high-income countries suggests a common characteristic for effects of educational interventions is a lack of persistence (or “fade out”); i.e., initial positive effects that diminish in magnitude or disappear altogether over time (Bailey et al. 2017; Protzko 2015). But at the same time, other studies have shown positive effects on long-term outcomes, such as educational attainment, earnings, health outcomes, and (reduced) criminal behavior (Anderson et al. 2009; Carneiro and Ginja 2014; Chetty et al. 2011, 2014; Currie and Thomas 2000; Deming 2009; Dynarski et al. 2013; Frisvold and Lumeng 2011; Garces et al. 2002; Heckman et al. 2010; Ludwig and Miller 2007). Comparable long-term evidence from low- and middle-income countries is scarce, with the examples from Kenya, Nicaragua and Ghana described above being some of the few. Drawing lessons requires that educational interventions be defined more broadly. Acevedo et al. (2016) exploit a randomized controlled trial to assess the effect of a youth training and internship program in the Dominican Republic, approximately four years after its inception. The authors investigate socioemotional outcomes (including grit and self-esteem), expectations, and labor market outcomes, finding that treatment effects differed substantially by gender. Further, Doyle et al. (2011) use a randomized experiment to evaluate the impact of a health education program in grades five to seven of Tanzanian primary schools (in combination with health services and community engagement). Six years after the program’s implementation, the study documents improvements in sexual and reproductive health attitudes, knowledge, and behaviors. In addition, Walker et al. (2007) and Gertler et al. (2013) assess the long-term effects of a randomized early childhood stimulation program (in combination with food supplementation) for a small sample of adolescents in Jamaica. The authors find positive effects on anxiety, depressive symptoms, self-esteem, anti-social behavior, attention deficit, hyperactivity, and oppositional Comparable long-term evidence from low- and middle-income countries is scarce, with the examples from Kenya, Nicaragua and Ghana described above being some of the few. Drawing lessons requires that educational interventions be defined more broadly. Acevedo et al. (2016) exploit a randomized controlled trial to assess the effect of a youth training and internship program in the Dominican Republic, approximately four years after its inception. The authors investigate socioemotional outcomes (including grit and self-esteem), expectations, and labor market outcomes, finding that treatment effects differed substantially by gender. Further, Doyle et al. (2011) use a randomized experiment to evaluate the impact of a health education program in grades five to seven of Tanzanian primary schools (in combination with health services and community engagement). Six years after the program’s implementation, the study documents improvements in sexual and reproductive health attitudes, knowledge, and behaviors. In addition, Walker et al. (2007) and Gertler et al. (2013) assess the long-term effects of a randomized early childhood stimulation program (in combination with food supplementation) for a small sample of adolescents in Jamaica. The authors find positive effects on anxiety, depressive symptoms, self-esteem, anti-social behavior, attention deficit, hyperactivity, and oppositional behavior, along with impacts on labor market outcomes.<sup>7</sup> Finally, both Ozier (2016)

> 7See Krishnan and Krutikova (2013) for another, less well-identified study on the long-term effects of non-cognitive training in a small sample ( _n_ = 154) of students in Mumbai, India. The authors ( _ibid._ ) find large impacts on self-esteem and self-efficacy, smaller impacts on life evaluation

8
and Brudevold-Newman (2016) find positive effects of additional exposure to secondary education on labor market outcomes, in Kenya; Brudevold-Newman ( _ibid._ ) also demonstrates related delays in childbearing and marriage. A review of the longterm impacts of CCT programs in Latin America (Molina-Millan et al. 2016) concludes that the literature produces very mixed results, with CCTs during the school

- settings, but not in others.

- **Intervention and Experimental Design** the Government of Cambodia began implementing a new pilot scholarship for grade 4 students in 207 public schools. The program’s stated goal

- reduce student drop-out rates and increase primary school completion,

- the government also implicitly sought to improve students’ educational performance. time, the program’s 207 schools represented all public schools in three

- country’s 25 provinces<sup>8</sup> (Mondulkiri, Ratanakiri, and Preah Vihear); the had been selected for having the highest drop-out rates in the upper

- grades (grades four to six), according to Cambodia’s Education Management System (EMIS).<sup>9</sup> The program was phased in as a pilot over two

- a random set of 103 schools starting in 2008/09 and the remaining in the following year (random assignment was stratified by province).

- scholarship program targeted students entering grade 4, using one of approaches. In a randomly selected half of the scholarship schools students were selected based on their combined performance on a test

- and mathematics. This “merit-based” eligibility was determined

- aspirations, as well as positive impacts on educational attainment and initial labor 11 after

# **3 Intervention and Experimental Design**

In 2008, the Government of Cambodia began implementing a new pilot scholarship program for grade 4 students in 207 public schools. The program’s stated goal was to reduce student drop-out rates and increase primary school completion, though the government also implicitly sought to improve students’ educational performance. At the time, the program’s 207 schools represented all public schools in three of the country’s 25 provinces<sup>8</sup> (Mondulkiri, Ratanakiri, and Preah Vihear); the three provinces had been selected for having the highest drop-out rates in the upper primary grades (grades four to six), according to Cambodia’s Education Management Information System (EMIS).<sup>9</sup> The program was phased in as a pilot over two years, with a random set of 103 schools starting in 2008/09 and the remaining schools entering in the following year (random assignment was stratified by province). The scholarship program targeted students entering grade 4, using one of two selection approaches. In a randomly selected half of the scholarship schools (52 schools), students were selected based on their combined performance on a test of Khmer and mathematics. This “merit-based” eligibility was determined through and aspirations, as well as positive impacts on educational attainment and initial labor market outcomes (approximately 11 years after program participation started).

> 8Here, we count the capital as Cambodia’s 25th “province”. More precisely, Phnom Penh is a special administrative district whose administrative characteristics partly resemble those of provinces. 9To limit the program’s geographic scope, in Ratanakiri, only five of seven districts were selected, choosing those districts with the highest dropout rate. In the remaining two provinces, all districts were selected.

years resulting in more cognitive, socioemotional skills and labor market outcomes in some settings, but not in others.

9
a centrally-scored test; the maximum possible score was 25. In the remaining 51 schools, they were selected based on a “poverty-based” approach. A student’s “poverty score” was determined based on their self-reported (but validated) household and socio-economic characteristics; the poverty index ranges from 0 (richest household) to 292 (poorest household).<sup>10</sup> Under both approaches, half of a given school’s fourth-graders qualified (i.e., the top half of performers, or the poorest half of students).<sup>11</sup> Crucially (for our study), students in all 207 schools completed both types of assessments, independent of their school’s assignment status. Scholarships were offered to beneficiaries for three years (i.e. through the end of primary school), conditional on their continued enrollment, passing grades, and regular attendance. These requirements were moderately enforced.<sup>12</sup> Scholarships were disbursed as a lump-sum payment of approximately USD20 in the first year, and two payments of approximately USD10 in each of the following two years. As reported by Barrera-Osorio and Filmer (2016), these amounts represent about 3.3 percent of the yearly per capita expenditure in the study sample. These transfers are small compared to similar programs in other countries (Fiszbein and Schady 2009); even relatively small impacts may therefore be cost-effective. Our experimental design exploits the randomized roll-out of the program over its two phases. In 2008/09, during phase one, fourth-graders in schools that were selected to disburse the program in the second phase did not receive any scholarship and did not become eligible in the years thereafter.<sup>13</sup> Note that a sub-set of these fourth-grade students would have been eligible under one of the two targeting schemes (merit-based or poverty-based), had their school been selected. In expectation, these 10The aptitude test was based on the 2005/06 Grade 3 National Learning Assessment. The poverty assessment asked respondents about household demographics and possession of a list of assets (as provided in Table 2). See Barrera-Osorio and Filmer (2016) for more details on the student assessment and the poverty score. 11Median students also qualified for the scholarship.Median students also qualified for the scholarship. The number of scholarships was determined the enrolment numbers.

> 11Median students also qualified for the scholarship.Median students also qualified for the scholarship. The number of scholarships was determined using the previous year’s official enrolment numbers.

> 12If a student lost her scholarship, its amount could not be re-allocated within the same school and the same year. Instead, the amount would be used for the subsequent cohort of fourth-graders.

> 13Recall that the program required students to maintain passing grades. Thus, a phase-one fourth-grader who attended a control group school could not become eligible in phase two by repeating the grade.

10
two sub-samples are equal to their respective eligible peers from phase-one schools (below, we present supportive evidence that the two groups of students are in fact balanced, across phase-one and phase-two schools). Thus, we can identify the causal intent-to-treat effect of the scholarship program, under either of the two targeting approaches. As phase-one schools were moreover randomly assigned to either the effect across the two targeting schemes. **4 Estimation Framework, Internal Validity** We estimate a generic production function model: _Yt,i_<sup>_j_=</sup><sup>_β_0 +</sup><sup>_β_</sup> 1<sup>_jT_</sup> 0<sup>_j_</sup> _,i_<sup>+</sup><sup>_B_</sup><sup>**_X_**0</sup><sup>_,i_+</sup><sup>_μt,i_for</sup><sup>_j_=</sup><sup>_merit_or</sup><sup>_poverty_</sup> (1) where _Y_ are outcomes such as educational attainment, cognitive skills, socioemotional skills, labor outcomes, or measures of well-being (which include socio-economic status, SES). Vector **_X_** 0 _,i_ includes a rich set of baseline characteristics at the student’s school-, village-, and individual-level (the next section describes these measures in greater detail). All estimations include district-level fixed effects and allow for the clustering of standard errors at the assignment level (i.e., within schools; cf. Abadie et al. (2017)). Equation 1 estimates an intent-to-treat model, with _β_ 1<sup>_j_capturingthe</sup> effect of offering the scholarship on outcomes Y. Our default approach is to estimate Equation 1 as two separated OLS models, for the merit- and poverty-based sub-samples.<sup>14</sup> For annual earnings we use a Tobit model with an inverse hyperbolic sine transformation of the outcome variable because its distribution shows a spike at zero (cf. Duflo et al. 2017).<sup>15</sup> 14 Using simulations, we compared our strategy to others that would (a) use a regressionthe continuous and merit-indices and their strict cut-

> 14 Using simulations, we compared our strategy to others that would (a) use a regressiondiscontinuity approach (exploiting the continuous poverty- and merit-indices and their strict cutoff), (b) a difference-in-difference approach, and (c) a difference-in-discontinuity approach (not shown). All three alternative strategies make additional assumptions and do not lead to increased statistical power.

> 15For respondents’ daily reservation wage, we also calculated a two-part regression or “Tobit II” model, where the second part of the model uses a log transformation of the outcome variable (see

poverty-based or merit-based targeting scheme, we can also compare the scholarship’s effect across the two targeting schemes.

11
For each “family” of outcomes, cognitive skills, socioemotional outcomes, and wellbeing, we present the results from a test that the treatment coefficients are jointly zero (using seemingly unrelated regressions, SUR). Within these sets of outcomes, we also use SUR to test whether the treatment coefficient for the poverty subsample is equal to that for the merit subsample.

The data support the fact that our experimental design is valid. First, we find that both sub-samples are balanced on observables. This holds true for the full set of respondents at baseline, as discussed by Barrera-Osorio and Filmer (2016), and for this paper’s estimation samples (see Tables A1 and A2, in the Appendix). Second, overall attrition is 31 percent for either sub-sample, and we managed to track 88 percent of respondents who were included in the three-year follow-up study Belotti et al. 2015). Results do not lead to substantial changes and are available upon request.

Our sampling frame consists of 5,964 fourth-grade students (in the program’s 207 schools), who participated in the baseline eligibility assessment, in December 2008 and January 2009. Of those, 2,996 respondents were randomly selected for the first three-year follow-up survey, in 2011. For this first follow-up, an additional 658 “replacement” students were randomly selected, in case students from the target group could not to be found. In the 2016 follow-up, we tracked all students who had participated in the 2011 study, a random subset of 140 respondents who had previously been found to be attritors, and all replacement students who were interviewed in 2011. Our 2016 sample includes 2,252 respondents, of which 2,024 had been interviewed in 2011, 86 had not been reached previously, and 142 had served as replacements, in 2011. Table 1 provides the control group means for key demographic characteristics, for the “merit” and “poverty” sub-samples (for the control group, these refer to respondents who would have qualified if their school had been assigned to one or the other scholarship approach). Our analysis sample consists of 890 and 825 respondents for the merit-based and poverty-based sub-samples, respectively. Among those, about half (48% and 51%, respectively) are female. On average, respondents live with an additional six household members. Almost all the respondents were already working at the time of the three-year follow-up survey. The data support the fact that our experimental design is valid. First, we find that both sub-samples are balanced on observables. This holds true for the full set of respondents at baseline, as discussed by Barrera-Osorio and Filmer (2016), 12
(i.e., six years after our last contact with study participants). As shown in Table 2, there are no systematic differences in attrition by treatment group. Column (5) of the Table 2’s “merit scholarship” and “poverty scholarship” panels presents the differencein-difference among attritors and non-attritors, across respondents in the treatment and control groups (computed by OLS regression and including stratification fixed Our analysis combines data from five main sources. First, we collect outcome data through in-person interviews at the respondents’ residence, using handheld tablets. Second, to construct a variable reflecting intention-to-treat, we use the official government declaration (“Prakas”) of scholarship recipients. Third, we match each respondent to baseline data—application forms and baseline tests—as collected in December 2008 and January 2009. We can thus control for baseline test scores, and for students’ initial household characteristics. Fourth, we construct a vector of control variables through administrative data on baseline school characteristics, as provided by the country’s Educational Management Information System (EMIS).<sup>16</sup> Fifth, we take advantage of the fact that Cambodia’s 2008 census was conducted just before the scholarship program started. Using geographic coordinates, we match each school to its closest village and include this village’s demographic characteristics as additional controls.<sup>17</sup>

> 16 We include a binary indicator of whether a school had access to drinking water, a binary indicator of whether the school had a toilet facility, the number of primary school classrooms, the number of newly enrolled fourth-graders, the number of teaching staff, and the school’s income.

> 17Village-level data as published by the Cambodian National Institute of Statistics at the Ministry of Planning (2010). We control for the share of villagers who are literate in Khmer, the share

effects). Only two out of 16 indicators in the merit subsample and only three indicators in the poverty subsample show a statistically significant or marginally significant difference-in-differences; this result is not surprising given multiple comparisons. We also test for the individual coefficients being jointly equal to zero, using seemingly unrelated estimation (SUR); the resulting Chi-square statistics (and corresponding p-values) suggests that we should not reject that the two sub-samples are balanced. **5 Data and Measurement** Our analysis combines data from five main sources. First, we collect outcome data through in-person interviews at the respondents’ residence, using handheld tablets. Second, to construct a variable reflecting intention-to-treat, we use the official government declaration (“Prakas”) of scholarship recipients. Third, we match each respondent to baseline data—application forms and baseline tests—as collected in December 2008 and January 2009. We can thus control for baseline test scores, and for students’ initial household characteristics. Fourth, we construct a vector of control variables through administrative data on baseline school characteristics, as provided by the country’s Educational Management Information System (EMIS).<sup>16</sup> Fifth, we take advantage of the fact that Cambodia’s 2008 census was conducted just before the scholarship program started. Using geographic coordinates, we match each school to its closest village and include this village’s demographic characteristics as additional controls.<sup>17</sup> 13
Data collection for the baseline and three-year follow-up occurred from December 2008 to January 2009, and from May to September 2011, respectively. Data collection for our latest round of follow-up took from December 2016 to May 2017. We guaranteed data-quality by following standard monitoring procedures, as described by Glennerster (2017). First, during the first week of field work, we conducted 30% We also collected data on four measures of cognitive skills. First, we administered a computer-adaptive math-test, in which respondents answered ten questions from a larger pool of 23 items.<sup>18</sup> We used a three-parameter logistic (3PL) item response theory (IRT) model with a single guessing parameter (Birnbaum 1968; Samejima 1969) to analyze responses to math tests from an evaluation of a similar scholarship program in Cambodia that was targeted to secondary school students (Filmer and Schady 2008). Participants in this assessment had been tested in two rounds, with overlapping items, and we follow the common (Stocking and Lord 1983) methodology for IRT-based scale equating.<sup>19</sup> Our test begins with the item of median difficulty.

of villagers with no schooling, the percentage of villagers engaged in crop or animal farming, the village’s population size, and a continuous measure of villagers’ household assets.

> 18To our best knowledge, this assessment constitutes the first computer-adaptive ability test as conducted during a household survey, in a developing country.

> 19We removed one item with low discrimination.

of re-surveys (“back-checks”, usually within three days) and then reduced this number, for an overall back-check rate of 15.7%. Second, we spot-checked approximately 20% of interviews, provided immediate feedback, and offered repeat-trainings to enumerators. These spot-checks were not only conducted by field supervisors but also through additional, independent field-monitoring. Third, we ran daily analytics on newly collected data to spot irregularities, and to identify training needs. Finally, we employed 15% of staff as dedicated quality-control officers, such that steps to improve data quality could be taken immediately, as part of the regular data flow. The following discusses our newly collected outcome measures in greater detail. As education outcomes, we measure educational attainment (highest grade completed), formal and informal training that lasted for at least one week (a binary variable), and whether the respondent received any formal education since the early three-year follow-up (a binary variable). We also collected data on four measures of cognitive skills. First, we administered a computer-adaptive math-test, in which respondents answered ten questions from a larger pool of 23 items.<sup>18</sup> We used a three-parameter logistic (3PL) item response theory (IRT) model with a single guessing parameter (Birnbaum 1968; Samejima 1969) to analyze responses to math tests from an evaluation of a similar scholarship program in Cambodia that was targeted to secondary school students (Filmer and Schady 2008). Participants in this assessment had been tested in two rounds, with overlapping items, and we follow the common (Stocking and Lord 1983) methodology for IRT-based scale equating.<sup>19</sup> Our test begins with the item of median difficulty.

14
As the test is administered and respondents answer correctly or incorrectly, our assessment picks the next item to be displayed based on maximum information, recalculates a respondent’s ability estimate using expected a posteriori, and continues thereafter until ten items are administered for each respondent (cf. Bock and Mislevy 1982; van der Linden and Pashley 2010). The second assessment is a test of shapes and puzzles loosely based on the Raven’s Progressive Matrices. This test is a measure of fluid intelligence; respondents are asked to complete 15 sets of pattern recognition. Our third measure is a “Digit Span” test, which asks respondents to repeat sequences of single-digit numbers, of increasing length. This test is a common measure of respondents’ working memory (Hamoudi and Sheridan 2015). Sequences are presented in sets of two and begin with two integers (asking respondents to repeat 2-1 and 1-3). No additional sequences are asked if a respondent fails to repeat both prompts; the last set of longest sequence presents two strings of eight integers (asking respondents to repeat 6-9-1-7-3-2-5-8 and 3-1-7-9-5-4-8-2). The fourth outcome is a vocabulary test based on picture recognition, similar to a Peabody Picture Vocabulary Test (PPVT). This test asks respondents to identify the picture corresponding to a word which the enumerator reads out loud. For each word the respondent is asked to select from a choice of four pictures. The test is structured such that items become increasingly difficult (examples of easy items include, “citrus,” and “garment”; items of highest difficulty include “vitreous” and “lugubrious”). A maximum of 96 items is presented in sets of 12, and no additional item is displayed if a respondent fails to answer at least five items correctly in a given set. The final skill estimate for each of the math, pattern recognition, and vocabulary recognition tests are calculated with a two-parameter logistic (2PL) IRT model. The Digit Span test score reflects the number of integer sequences a respondent repeated correctly. All four measures are standardized (mean zero and standard deviation of one). We report on two sets of socioemotional outcomes: we screen for emotional and We report on two sets of socioemotional outcomes: we screen for emotional and behavioral difficulties with the Strengths and Difficulty Questionnaire (“SDQ”), and measure the “Big 5” personality traits. The SDQ represents a common screening instrument; we use (the official Khmer translation of) its most frequently used version with 25 items on psychological attributes (Goodman 1997). Following its scoring 15
guidelines and official recommendations ( _ibid._ ), we report on three subscales, separated into ‘internalizing problems’ (emotional and peer symptoms, 10 items), ‘externalizing problems’ (conduct and hyperactivity symptoms, 10 items), and a scale of prosocial behavior (5 items). To capture respondents’ personality traits, The Big Five Scale measures five core dimensions of personality. The five broad personality traits measured are extraversion, agreeableness, openness, conscientiousness, and neuroticism. Evidence of the Big Five as being relevant (and associated with life outcomes) has been growing, beginning with the research of Fiske (1949) and later expanded upon by other researchers including Norman (1967), Smith (1967), Goldberg (1981), and McCrae and Costa (1987). We use the short 15 item Big Five Inventory (BFI-S) (Lang et al. 2011), with three items per personality trait. Like the indicators of cognitive skill, all measures of socioemotional outcomes are standardized.<sup>20</sup> We also collected information on five labor market outcomes. We ask whether a respondent is currently working (yes or no) and the age at which she or he first started working. We moreover construct a binary indicator of whether a respondent’s main work activity is cognitively demanding. We categorize an occupation as such if it requires at least occasional use of reading, writing, mathematics, or a computer (according to the respondent). Our survey also asked for respondents’ income; our analysis reports on (the inverse hyperbolic sine of) yearly earnings and (the inverse hyperbolic sine of) a respondent’s daily reservation wage, i.e., the minimum wage or payment for which a respondent is willing to accept work (both are reported in US dollars, a currency commonly used in Cambodia). Our last set of outcomes includes six indicators of socio-economic status and wellbeing. We assess subjective social status using a “MacArthur community ladder”.<sup>21</sup> Respondents were shown a picture of a ladder with ten rungs and were told that 20For further discussion on these measures, and their psychometric and statistical properties, see Danon et al. (2018). In addition to the measures we report on here, we collected data on respondents’ level of grit (Duckworth and Quinn 2009) and their growth mindset (Dweck 2000). We do not report on results for these measures because of their poor psychometric properties in our data. The inclusion of either of these measures does not substantively change our results. 21For a description and bibliography of papers that use MacArthur ladders, see the MacArthur Foundation’s Network on SES and Health website: http://www.macses.ucsf.edu/Research/Psychosocial/subjective.php.

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Finally, for each set of educational outcomes, cognitive outcomes, socioemotional outcomes, and SES and subjective well-being, we also calculate an overall “family index,” following Anderson (2008).<sup>24</sup> These indices have the benefit of reducing the number of statistical tests (and the temptation to selectively focus on positive results). In constructing the indices, we ensured that the qualitative “direction” of the construct was preserved—higher values point to more desirable outcomes. However, our index construction is atheoretical and may therefore group together measurements with different underlying constructs. We therefore present and discuss results from both individual measurements and the family indices.

> 22Filmer and Scott (2012) show that such an IRT approach produces similar household rankingsFilmer and Scott (2012) show that such an IRT approach produces similar household rankings when compared to other aggregation methods.

> 23We standardize by focusing on the endline measures for control group students (who would have qualified for at least one of the two types of scholarships, had they been in a treatment school instead).

> 24We also considered using an alternative index instead, following Kling et al. (2007). The alternative approach does not lead to qualitatively different conclusions.

higher rungs correspond to higher socio-economic status. They were then asked to place themselves on this ladder in relation to everyone in their community. As a second measure of socio-economic status, we construct an index of respondents’ household assets, asking whether they possess items from a list similar to the one presented in Table 2. To calculate an individual’s latent SES score, we borrow from the psychometric literature and estimate a two-parameter logistic (2PL) IRT model, placing responses from 2009, 2011 and 2016 on the same scale.<sup>22</sup> We also asked respondents to rate their satisfaction with life at present, all things considered, on a scale from one (“completely dissatisfied”) to ten (“completely satisfied”) and to rate their quality of life and health, respectively, on a scale from one (“poor”) to five (“excellent”). The fifth and last measure screens for (minor) mental health disorders, using the General Health Questionnaire (“GHQ”). We use the short form of the questionnaire (GHQ-12) with Likert scoring (Goldberg and Williams 2006; Quek et al. 2001). All six measures are standardized (mean zero and standard deviation of one).<sup>23</sup> Finally, for each set of educational outcomes, cognitive outcomes, socioemotional outcomes, and SES and subjective well-being, we also calculate an overall “family index,” following Anderson (2008).<sup>24</sup> These indices have the benefit of reducing the number of statistical tests (and the temptation to selectively focus on positive results). In constructing the indices, we ensured that the qualitative “direction” of the construct was preserved—higher values point to more desirable outcomes. However, our index construction is atheoretical and may therefore group together measurements with different underlying constructs. We therefore present and discuss results from both individual measurements and the family indices. 22Filmer and Scott (2012) show that such an IRT approach produces similar household rankingsFilmer and Scott (2012) show that such an IRT approach produces similar household rankings when to other methods.

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# **6 Results**

Tables 3 to 7 present results on five main categories of outcomes: education; cognitive skills; socioemotional outcomes; socio-economic and well-being outcomes; and labor outcomes. The tables share a common structure. Each table has two panels; Panel A

- Each panel presents separate regressions for a given dependent variable,

- stated in the column headers. For the treatment variable (1 if assigned to treatment, the table presents regression coefficients and standard errors. All

- control for covariates at baseline and district fixed effects; standard

- clustered at the level of randomization (the school) (Abadie et al. 2017). two panels also presents the unconditional mean, as observed for the Each panel moreover includes the results from a chi-square test on the that all treatment coefficients are jointly zero, using seemingly

- (SUR). Finally, across the two panels and for each of the outcome

- we present results (in the bottom two rows of the tables) for a test of the that the two treatment coefficients (merit and poverty) are equal.

- **Education**

- main stated objective of the program was to increase school progression of individuals. Early dropout from primary school is still a major obstacle in Cambodia, especially in rural areas. At inception of the program,

- to 40% of the poorest quintile of income completed 6th grade (Barrera-Osorio

- Filmer 2016). As such, the first set of outcomes that the program aimed was to induce greater school progression, with an immediate goal of successfully complete primary school (grade 6).

- Table 3 presents results for school progression (highest grade attained),

## **6.1 Education**

The main stated objective of the program was to increase school progression of lowincome individuals. Early dropout from primary school is still a major obstacle in education in Cambodia, especially in rural areas. At inception of the program, only close to 40% of the poorest quintile of income completed 6th grade (Barrera-Osorio and Filmer 2016). As such, the first set of outcomes that the program aimed to change was to induce greater school progression, with an immediate goal of helping students successfully complete primary school (grade 6).

Table 3 presents results for school progression (highest grade attained), primary school graduation (a zero-or-one variable), an indicator of whether the respondent received any formal education since the three-year follow-up study (in 2011), and a “family index” of the previous three measures (measured in standard deviations, SDs). On average, students in the control group completed 5.45-5.57 grade levels. Both reports results for the merit sample, whereas Panel B reports results for the poverty sample. Each panel presents separate regressions for a given dependent variable, as stated in the column headers. For the treatment variable (1 if assigned to treatment, 0 otherwise), the table presents regression coefficients and standard errors. All regressions control for covariates at baseline and district fixed effects; standard errors are clustered at the level of randomization (the school) (Abadie et al. 2017). Each of the two panels also presents the unconditional mean, as observed for the control group. Each panel moreover includes the results from a chi-square test on the null hypotheses that all treatment coefficients are jointly zero, using seemingly unrelated regression (SUR). Finally, across the two panels and for each of the outcome variables, we present results (in the bottom two rows of the tables) for a test of the null hypothesis that the two treatment coefficients (merit and poverty) are equal.

18
types of scholarships increased educational attainment, with similar point estimates (0.213 and 0.291 for merit and poverty scholarships, respectively, equivalent to about four additional months of schooling). The effects on overall attainment, as reported by (Barrera-Osorio and Filmer 2016) after three years of starting the program, were slightly higher for the poverty sample (0.34), and similar for the merit sample (0.23)

- The scholarships increased primary school completion, by 5.0 and

- 11.3 percentage points (pp) for the merit- and poverty-based approaches, respectively significant for poverty-based scholarships).

- The point estimates for impacts on participation in any formal education (between

- 2011 and 2016) are positive and statistically significant for both groups: merit-based increased participation by 4.4pp over a control-group average of scholarship increased it by 10pp over a control-group average of

- suggesting some catch-up with the merit-based scholarship group. The joint test of all being equal to zero is rejected for both treatments (a p-value of 0.06

- 0.04 for the merit and poverty treatments, respectively). Both point estimates of the regression with the “family index” as dependent variable are positive and statistically (at the 5% and 1% levels, respectively), with a point estimate of

- deviations for the merit-based and of 0.264 standard deviations for scholarships. However, we cannot conclude that the two coefficients

- fact different (the p-value corresponding to this test is above 0.10). **Cognitive skills**

- implicit objective of the program was to induce an increase in students’ by encouraging greater attendance and retention—that is, inducing We measure cognitive skills through proxies that relate to an individual’s ability to tackle problems, and fluid intelligence. Unsurprisingly,

# **6.2 Cognitive skills**

An implicit objective of the program was to induce an increase in students’ learning by encouraging greater attendance and retention—that is, inducing additional schooling. We measure cognitive skills through proxies that relate to an individual’s knowledge, ability to tackle problems, and fluid intelligence. Unsurprisingly, the control group for the merit sample has higher average test scores on these measures than the control group for the poverty sample (Table 4).

Table 4 presents the impacts of scholarships on these measures of cognitive skills.

(Barrera-Osorio and Filmer 2016, Table 4, column 3), indicating some catch-up by the control group. The scholarships increased primary school completion, by 5.0 and 11.3 percentage points (pp) for the merit- and poverty-based approaches, respectively (statistically significant for poverty-based scholarships). The point estimates for impacts on participation in any formal education (between 2011 and 2016) are positive and statistically significant for both groups: merit-based scholarships increased participation by 4.4pp over a control-group average of 77%, poverty-based scholarship increased it by 10pp over a control-group average of 71%, suggesting some catch-up with the merit-based scholarship group. The joint test of all coefficients being equal to zero is rejected for both treatments (a p-value of 0.06 and 0.04 for the merit and poverty treatments, respectively). Both point estimates of the regression with the “family index” as dependent variable are positive and statistically significant (at the 5% and 1% levels, respectively), with a point estimate of 0.131 standard deviations for the merit-based and of 0.264 standard deviations for the poverty-based scholarships. However, we cannot conclude that the two coefficients are in fact different (the p-value corresponding to this test is above 0.10).

19
Across the different measures, we find suggestive evidence of positive effects for the merit-based treatment. All coefficients are positive, and two of them are statistically significant (Raven’s and the overall “family index”). The estimation suggests an overall effect of 0.113 standard deviations on these measures (significant at the 10% significance level). In contrast, the results for the poverty-based scholarship are

- of -0.129 SDs, significant at the 10% level, and different from the effect

- merit-based transfer, significant at the 5% level). The “family index” is close in the case of the poverty scholarship (<0.01 standard deviations). findings here, nine years after program inception, are consistent with in the previous three-year follow up study. In that study, merit-based

- recipients scored higher in mathematics and for the Digit Span and Filmer 2016), whereas poverty-based scholarship recipients

- **Socioemotional skills**

- important contribution of our study is the analysis of effects on socioemotional We are not only interested in measuring the effects of scholarships on we are also interested in the relationship between cognitive and socioemo-

- skills. The intuition behind this analysis has two parts. First, if scholarships more schooling for both types of scholarship recipients, then, under the

- sumption that schools also “produce” socioemotional skills, we should observe effects on these skills from both poverty- and merit-based scholarships. In contrast, is a complementary relationship in the accumulation of cognitive and socioemotional we should observe effects on socioemotional skills only for students with scholarship, and not for students in the poverty-based scholarship.

- present these relationships in the next paragraphs.

# **6.3 Socioemotional skills**

An important contribution of our study is the analysis of effects on socioemotional skills. We are not only interested in measuring the effects of scholarships on these measures; we are also interested in the relationship between cognitive and socioemotional skills. The intuition behind this analysis has two parts. First, if scholarships induced more schooling for both types of scholarship recipients, then, under the assumption that schools also “produce” socioemotional skills, we should observe effects on these skills from both poverty- and merit-based scholarships. In contrast, if there is a complementary relationship in the accumulation of cognitive and socioemotional skills, we should observe effects on socioemotional skills only for students with the merit-based scholarship, and not for students in the poverty-based scholarship. We formally present these relationships in the next paragraphs.

Our approach is based on two different conceptual models of the relationships between years of education ( _E_ ), cognitive skills ( _C_ ), and socioemotional skills ( _S_ ). As a starting point, based on the evaluation three years after the program’s inception either close to zero or even negative, in the case of the Forward Digit Span (a point estimate of -0.129 SDs, significant at the 10% level, and different from the effect for the merit-based transfer, significant at the 5% level). The “family index” is close to zero in the case of the poverty scholarship (<0.01 standard deviations). The findings here, nine years after program inception, are consistent with those documented in the previous three-year follow up study. In that study, merit-based scholarship recipients scored higher in mathematics and for the Digit Span test (Barrera-Osorio and Filmer 2016), whereas poverty-based scholarship recipients did not.

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(Barrera-Osorio and Filmer 2016), we know that treatment _T_ 0 (at baseline, _t_ = 0) increased years of education schooling for both merit- and poverty-based scholarships ( _Et_ = _f_ ( _T_ 0; _X_ 0 _, Z_ 0); _∂TEt_ 0<sup>_>_0,forbothtypesofscholarships).</sup> Furthermore, the evaluation showed a causal, positive effect of the intervention on cognitive skills for the merit-based scholarship only ( _Ct_<sup>_M_</sup> = _f_ ( _T_ 0<sup>_M_;</sup><sup>_X_0</sup><sup>_, Z_0),</sup><sup>_∂C_</sup> _∂T_ 0 _<u>t</u>_<sup>_MM_</sup> _>_ 0); and zero effects _<u>t</u>_ for the poverty-based scholarship ( _Ct_<sup>_P_=</sup><sup>_f_(</sup><sup>_T_</sup> 0<sup>_P_;</sup><sup>_X_0</sup><sup>_, Z_0),</sup><sup>_∂C_</sup> _∂T_ 0<sup>_PP_= 0),where</sup><sup>_M_denotes</sup> merit-based treatment and _P_ denotes poverty-based treatment. The first conceptual relationship we explore is that between each type of skill— cognitive and socioemotional—and years of education: _Ct_ = _g_ ( _Et_ ; _X_ 0 _, Z_ 0) _St_ = _g_ ( _Et_ ; _X_ 0 _, Z_ 0) , where _X_ 0 are student characteristics and _Z_ 0 are school inputs (at baseline). These equations state that the effect on either set of skills is a function of the years of education; i.e., exposure to more schooling will induce higher cognitive and socioemotional skills. Therefore, the first set of relationships we investigate are: _∂C_<sup>_∂C_</sup> (2) _∂T_<sup>=</sup> _∂E_<sup>_∗∂E_</sup> _∂T_<sup>_>_0</sup> and _∂S_<sup>_∂S_</sup> (3) _∂T_<sup>=</sup> _∂E_<sup>_∗∂E_</sup> _∂T_<sup>_>_0</sup> If schooling produces cognitive and socioemotional skills, both equations 2 and 3 are positive, independently of the type of treatment (merit or poverty). In contrast, the second conceptual relationship is based on a modification of this setup: for the merit-based scholarship we have an additional equation, relating cognitive skills and treatment: _C_<sup>_M_</sup> = _T_<sup>_M_</sup> i.e., treatment induced higher cognitive skills only for the merit ( _M_ ) treatment. The 21
basic relationship of interest is between socioemotional skills and cognitive skills: Table 5 presents results on the Strengths and Difficulties Questionnaire (SDQ)— separating out the three attributes: prosocial, internalizing, and externalizing—and on the Big 5—separating by its five traits: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism (OCEAN).

Overall, we reject the hypothesis of impacts on any subcomponent of these two 22
groups of socioemotional outcomes. All point estimates are close to zero, with the exception of Neuroticism for the poverty treatment, with a coefficient of 0.186 standard deviations (statistically significant at the 1% level). Nevertheless, the coefficients for the impact on “family indices” for both treatments are close to zero (-0.005 and -0.099 standard deviations for the merit- and poverty-based treatment, respectively), and neither coefficient is statistically significant.

- The broad pattern of the table suggests that the program did not produce effects socioemotional skills, despite the observed impact on school progression and, merit sample, on cognitive outcomes. We cannot rule out competing hypotheses

- such as low marginal exposure to schooling (i.e., the treatment groups increased their attainment by only about four months, on average). We note,

- that this amount of additional schooling was sufficient to produce improved cognitive among the recipients of merit-based scholarships.

- **Labor outcomes**

- Table 6 presents the effects of the program on current labor status (coded as one if the is “currently working”, and zero otherwise); the respondent’s age

- they started working (which captures child labor); whether the recipient participated work-related training that lasted for at least one week (formal or informal, the cognitive demands of the respondent’s main work activity;

- two measures of income: yearly earnings and the daily reservation wage using an inverse hyperbolic sine).

- There is a positive impact on the probability of working for recipients of scholarships (3.4 percentage points, statistically significant, at the

- the point estimate for the poverty arm is lower (1.2 percentage points),

- statistically significant. These effects are from a high baseline level of report to be currently working (the means of both control groups are

# **6.4 Labor outcomes**

Table 6 presents the effects of the program on current labor status (coded as one if the respondent is “currently working”, and zero otherwise); the respondent’s age when they started working (which captures child labor); whether the recipient participated in work-related training that lasted for at least one week (formal or informal, a zero-or-one variable); the cognitive demands of the respondent’s main work activity; and two measures of income: yearly earnings and the daily reservation wage (both transformed using an inverse hyperbolic sine).

There is a positive impact on the probability of working for recipients of the merit-based scholarships (3.4 percentage points, statistically significant, at the 10% level); the point estimate for the poverty arm is lower (1.2 percentage points), and not statistically significant. These effects are from a high baseline level of people who report to be currently working (the means of both control groups are around 92%).<sup>25</sup>

> 25 Of those, approximately 84% report agriculture, fishery, or forestry as their main field of work. Approximately 90% report to engage in agriculture, fishery, or forestry among their overall labor activities. We follow the International Standard Classification of Occupations (ISCO); these

The broad pattern of the table suggests that the program did not produce effects on socioemotional skills, despite the observed impact on school progression and, for the merit sample, on cognitive outcomes. We cannot rule out competing hypotheses such as low marginal exposure to schooling (i.e., the treatment groups increased their educational attainment by only about four months, on average). We note, however, that this amount of additional schooling was sufficient to produce improved cognitive performance among the recipients of merit-based scholarships.

23
Respondents in our two samples started to work very early in life, when they were between 12 and 13 years old. Respondents who were offered a scholarship delayed entering the labor market by 0.074 and by 0.339 years for the merit- and poverty-based program, respectively, in line with the results for school progression. However, these estimates have large standard errors and are not significantly different from zero. In

- informal training since 2011 (which could have improved their work prospects), not see any effects on this outcome. Only a small share of respondents

- 18%) engage in economic activities that are cognitively demanding. There

- no evidence of impact on the cognitive demands of the main work activity, two treatments. The point estimates on yearly earnings are negative for arms (but not statistically significant); one potential explanation is

- the scholarship program delayed entry into the market for recipients and, as a result, have less experience than non-recipients. We observe a positive impact on reservation wage for both groups; however, the estimates are very imprecise. **Well-being outcomes**

- 7 presents effects on various measures of well-being. These include both measures as well as more readily observed indicators such as measures asset ownership. All these outcomes are standardized to have a mean

- and a standard deviation of one. As for some of the previous tables, we results for a standardized “family index” of these measures.

- Both treatments caused a positive impact on perceived status as measured by the ladder, with point estimates of 0.173 and 0.208 standard deviations for and poverty-based scholarships respectively. In addition, merit-based

- resulted in statistically significant positive impacts on respondents’ SES ownership of household assets; 0.186 standard deviations), quality of

# **6.5 Well-being outcomes**

Table 7 presents effects on various measures of well-being. These include both selfassessed measures as well as more readily observed indicators such as measures of household asset ownership. All these outcomes are standardized to have a mean of zero and a standard deviation of one. As for some of the previous tables, we also present results for a standardized “family index” of these measures.

Both treatments caused a positive impact on perceived status as measured by the SES ladder, with point estimates of 0.173 and 0.208 standard deviations for meritbased and poverty-based scholarships respectively. In addition, merit-based scholarships resulted in statistically significant positive impacts on respondents’ SES Index (i.e. ownership of household assets; 0.186 standard deviations), quality of health (0.129 standard deviations) and on the “family index” (0.174 standard deviations). Other than the SES ladder, none of the other impacts for the poverty-based scholindividuals’ occupation falls into ISCO Major Group 6 (“skilled agricultural and fishery workers”).

addition, while about 58% of control group respondents report having received formal or informal training since 2011 (which could have improved their work prospects), we do not see any effects on this outcome. Only a small share of respondents (less than 18%) engage in economic activities that are cognitively demanding. There is no evidence of impact on the cognitive demands of the main work activity, for either of the two treatments. The point estimates on yearly earnings are negative for both treatments arms (but not statistically significant); one potential explanation is that the scholarship program delayed entry into the market for recipients and, as a result, they have less experience than non-recipients. We observe a positive impact on the daily reservation wage for both groups; however, the estimates are very imprecise.

24
arships are statistically significantly different from zero, although most of the point estimates are positive. We reject the null hypothesis, for both targeting approaches, of all estimators being equal to zero (at the 1% level of significance). The point estimate for the impact on the overall “family index” for merit-based scholarships is substantially higher than for the poverty-based ones (and statistically significantly so; p-value = 0.168).

- =

- **Heterogeneity**

- investigate two types of heterogeneous effects. Both sets of analyses use indices” for education, cognition, socioemotional outcomes, and as an indicator of labor outcomes, we use a respondent’s daily reservation

- The first analysis of heterogeneous effects compares the impact of scholarships

- respondents who would have qualified for a scholarship under either of the schemes. For those individuals, the scholarship only differs in terms

- name or “label”. To investigate this, we estimate a regression that includes dummy, an indicator for whether a respondent would not have

- under the other scheme, and the interaction between the treatment and this indicator. interest is a comparison of the two direct treatment coefficients (the first rows

- Panels A and B of Table 8) as these reflect the impact of the scholarships on students who were both high merit and high poverty. A difference in point estimates indicates effect.<sup>26</sup>

- results are consistent with heterogeneous impacts by treatment label,

- the merit-based presentation of scholarships over their poverty-based The key pattern in Table 8 is that for the indices other than and socio-emotional outcomes, the coefficients on “Treatment” for

- scholarships are substantively larger than those for “Treatment” for scholarships. For example, for cognitive skills the impact of

# **6.6 Heterogeneity**

We investigate two types of heterogeneous effects. Both sets of analyses use the “family indices” for education, cognition, socioemotional outcomes, and well-being outcomes; as an indicator of labor outcomes, we use a respondent’s daily reservation wage. The first analysis of heterogeneous effects compares the impact of scholarships for respondents who would have qualified for a scholarship under either of the two targeting schemes. For those individuals, the scholarship only differs in terms of its name or “label”. To investigate this, we estimate a regression that includes the treatment dummy, an indicator for whether a respondent would not have qualified under the other scheme, and the interaction between the treatment and this indicator. Of interest is a comparison of the two direct treatment coefficients (the first rows of Panels A and B of Table 8) as these reflect the impact of the scholarships on students who were both high merit and high poverty. A difference in point estimates indicates a labeling effect.<sup>26</sup> The results are consistent with heterogeneous impacts by treatment label, favoring the merit-based presentation of scholarships over their poverty-based presentation. The key pattern in Table 8 is that for the indices other than education participation and socio-emotional outcomes, the coefficients on “Treatment” for the merit-based scholarships are substantively larger than those for “Treatment” for the poverty-based scholarships. For example, for cognitive skills the impact of meritbased scholarships on high-merit high-poverty students is 0.211 (statistically sig-

> 26This labeling effect was shown to be present for impacts on cognitive skills after three years of program implementation (Barrera-Osorio and Filmer 2016).

25
nificantly different from zero) whereas the impact of poverty-based scholarships on high-merit high-poverty students is 0.056 (not statistically significantly different from zero). The difference between these coefficients is 0.155 for cognitive skills, 0.144 for well-being, and 0.158 for reservation wage. By contrast, the impacts on education participation and socio-emotional outcomes are more similar (the differences are - 0.069 and 0.095, respectively).

We interpret these results as suggesting that the labeling effect that was apparent earlier three-year follow-up study remains and is apparent in dimensions before (well-being and reservation wage). However, we recognize in the estimates makes it hard to be confident about this finding—the only difference in coefficient estimates that is statistically significantly different from is that for reservation wage (p-value of 0.018).<sup>27</sup> second dimension of heterogeneity we investigate is that by gender. In we present results from regressions of the dependent variables on a indicator, a gender indicator (female = 1, and zero otherwise), and their interaction. table also assesses whether the size of the impact is different across types for boys (as indicated by a Chi-square test and its corresponding p-value in the two rows of the table). The results are mixed, and should be interpreted caution since, as in the discussion of Table 8, some of the point estimates suffer from standard errors. We do not find gender-differential impacts on a beneficiary’s attainment, socioemotional outcomes, or well-being (whether within the two programs and samples). In contrast, there are large differences in effect of poverty-based transfers on cognitive skills. While the impact on boys is positive (with an effect-size of 0.168), the impact on girls is negative (with an effect-size -0.141=0.168-0.309)—with the difference being a statistically significantly from zero. Unlike in the average effect, the results reveal a positive impact poverty-based scholarships for male recipients. Finally, Column (5) suggests that the We interpret these results as suggesting that the labeling effect that was apparent in the earlier three-year follow-up study remains and is apparent in dimensions not documented before (well-being and reservation wage). However, we recognize that imprecision in the estimates makes it hard to be confident about this finding—the only difference in coefficient estimates that is statistically significantly different from zero is that for reservation wage (p-value of 0.018).<sup>27</sup> The second dimension of heterogeneity we investigate is that by gender. In Table 9, we present results from regressions of the dependent variables on a treatment indicator, a gender indicator (female = 1, and zero otherwise), and their interaction. The table also assesses whether the size of the impact is different across targeting types for boys (as indicated by a Chi-square test and its corresponding p-value in the last two rows of the table). The results are mixed, and should be interpreted with caution since, as in the discussion of Table 8, some of the point estimates suffer from large standard errors. We do not find gender-differential impacts on a beneficiary’s educational attainment, socioemotional outcomes, or well-being (whether within or across the two programs and samples). In contrast, there are large differences in the effect of poverty-based transfers on cognitive skills. While the impact on boys is positive (with an effect-size of 0.168), the impact on girls is negative (with an effect-size of -0.141=0.168-0.309)—with the difference being a statistically significantly different from zero. Unlike in the average effect, the results reveal a positive impact of poverty-based scholarships for male recipients. Finally, Column (5) suggests that the estimated impact on a recipient’s daily reservation wage comes from the impact for

> 27It is notable that the impacts of the merit-based scholarships on cognitive skills and reservation wage appear to be largely driven by poor individuals (as indicated by the size of the coefficient for the interaction term).

26
male recipients; for females, the point estimate is close to zero for either of the two programs.

# **7 Conclusions** This study has investigated the long-term impacts of increased schooling, with particular focus on potential complementarities across schooling, the development of cognitive skills, and socioemotional and labor market outcomes later in life. To end, we evaluated the long-term effects of a primary school scholarship program rural Cambodia, nine years after the program’s inception, tracking study participants when they were, on average, 21-years-old. Overall, we find that targeting matters for the impact on cognitive skills, socio-economic status, and well-being. merit-based and poverty-based targeting schemes both led to increased schooling, but only the merit-based scholarship led to improvements in cognitive skill and to greater well-being. There is limited evidence of systematic differences across outcomes these long-term impacts by gender. Our study points to potential important avenues for research and policy. work argues that more schooling does not necessarily imply more learning Bank, 2018); in turn, our work highlights that more schooling, even if it learning, may not necessarily translate to noticeable changes in the labor outcomes and may not lead to measurable improvements in socioemotional skills. better understand this puzzle, additional research is needed, in at least three First, our analysis of heterogeneous effects provides suggestive evidence that market effects may be concentrated among poorer beneficiaries who are male. result echoes the findings of Duflo et al. (2017), who find labor market effects for subset of male students, only. It will be important to understand how programs such as these can be designed in a way such that they also fully benefit female recipients.

Our study points to potential important avenues for research and policy. Prior work argues that more schooling does not necessarily imply more learning (World Bank, 2018); in turn, our work highlights that more schooling, even if it enhances learning, may not necessarily translate to noticeable changes in the labor market outcomes and may not lead to measurable improvements in socioemotional skills. To better understand this puzzle, additional research is needed, in at least three areas. First, our analysis of heterogeneous effects provides suggestive evidence that labor market effects may be concentrated among poorer beneficiaries who are male. This result echoes the findings of Duflo et al. (2017), who find labor market effects for a subset of male students, only. It will be important to understand how programs such as these can be designed in a way such that they also fully benefit female recipients. Second, our findings are consistent with research by Jackson (2018), which suggests that the school-based production of cognitive skills may not necessarily go handin-hand with improvements in socioemotional outcomes. However, research on how to purposefully foster socioemotional skill in school settings is only in its infancy, This study has investigated the long-term impacts of increased schooling, with a particular focus on potential complementarities across schooling, the development of cognitive skills, and socioemotional and labor market outcomes later in life. To this end, we evaluated the long-term effects of a primary school scholarship program in rural Cambodia, nine years after the program’s inception, tracking study participants when they were, on average, 21-years-old. Overall, we find that targeting approach matters for the impact on cognitive skills, socio-economic status, and well-being. The merit-based and poverty-based targeting schemes both led to increased schooling, but only the merit-based scholarship led to improvements in cognitive skill and to greater well-being. There is limited evidence of systematic differences across outcomes in these long-term impacts by gender.

27
especially in developing countries (see West et al. 2016). Third, our reported lack of impacts on socioemotional skills may be at least partially driven by a lack of precision; we would encourage other researchers to improve upon our study through continued work on the measurement of socioemotional outcomes in low-income countries (such as Laajaj and Macours 2017) and through similar, long-term evaluations with larger samples.

28
# **Tables**

29
|Diference<br>(5)<br>0.353<br>(0.237)<br>0.098***<br>(0.035)<br>0.081<br>(0.12)<br>-1.2<br>(4.482)<br>1.097<br>(0.672)<br>-0.501*<br>(0.287)<br>0.124***<br>(0.034)<br>-0.213<br>(0.148)<br>-0.294<br>(0.218)<br>0.001<br>(0.031)<br>-0.032<br>(0.032)<br>dent’s house-<br> This variable<br> 0 otherwise;<br>he means for<br>en the treat-<br>ard errors in|
|---|
|**olarship**<br>Control<br>(4)<br>11.799<br>(2.054)<br>0.53<br>(0.5)<br>1.783<br>(1.122)<br>246.239<br>(31.404)<br>17.748<br>(4.717)<br>15.643<br>(3.809)<br>0.5<br>(0.501)<br>2.823<br>(1.77)<br>7.289<br>(2.566)<br>0.073<br>(0.261)<br>0.889<br>(0.315)<br>n the respon<br>r separated. <br> moment and <br> (4) display t<br>erence betwe<br>fects. Stand|
|**verty Sch**<br>Treatment<br>(3)<br>12.048<br>(2.028)<br>0.64<br>(0.481)<br>1.856<br>(1.052)<br>242.447<br>(34.06)<br>18.686<br>(4.729)<br>15.082<br>(2.092)<br>0.633<br>(0.483)<br>2.557<br>(1.539)<br>6.897<br>(2.22)<br>0.068<br>(0.252)<br>0.857<br>(0.35)<br>ople living i<br>, divorced o<br> job at the <br>lums (2) to <br>) is the dif<br>vince fxed e|
|**Po**<br>All<br>(2)<br>11.927<br>(2.043)<br>0.587<br>(0.493)<br>1.821<br>(1.086)<br>244.295<br>(32.826)<br>18.229<br>(4.744)<br>15.355<br>(3.063)<br>0.568<br>(0.496)<br>2.687<br>(1.659)<br>7.088<br>(2.401)<br>0.07<br>(0.256)<br>0.872<br>(0.334)<br>mber of pe<br>ver married<br>ek or has a <br>sample. Co<br> Column (5<br>ing for pro|
|n<br>(1)<br>776<br>786<br>770<br>790<br>790<br>718<br>718<br>718<br>718<br>454<br>760<br> to the nu<br>nd 0 if ne<br>e last we<br> analysis <br>entheses. <br>n, controll|
|Diference<br>(5)<br>0.358<br>(0.24)<br>0.005<br>(0.035)<br>-0.123<br>(0.123)<br>-10.614<br>(8.141)<br>0.194<br>(0.481)<br>-0.208<br>(0.244)<br>0.019<br>(0.034)<br>0.058<br>(0.148)<br>0.284<br>(0.227)<br>0.017<br>(0.031)<br>-0.012<br>(0.027)<br>HH size refers<br>tly married a<br>ked during th<br>ations in the<br>iations in par<br>LS regressio|
|**larship**<br>Control<br>(4)<br>11.732<br>(2.114)<br>0.5<br>(0.501)<br>1.742<br>(1.112)<br>217.557<br>(52.676)<br>19.701<br>(3.321)<br>15.382<br>(2.123)<br>0.487<br>(0.5)<br>2.59<br>(1.743)<br>7.028<br>(2.591)<br>0.071<br>(0.257)<br>0.911<br>(0.285)<br>respondent. <br>dent is curren<br>pondent wor<br>ber of observ<br>tandard dev<br>mputed by O<br>.1.|
|**Merit Scho**<br>Treatment<br>(3)<br>12.034<br>(2.066)<br>0.507<br>(0.501)<br>1.631<br>(1.092)<br>204.417<br>(66.586)<br>19.798<br>(2.869)<br>15.101<br>(2.269)<br>0.506<br>(0.501)<br>2.595<br>(1.547)<br>7.209<br>(2.501)<br>0.076<br>(0.266)<br>0.895<br>(0.307)<br> include the <br>if the respon<br> 1 if the res<br>ents the num<br>espectively. S<br>means are co<br><0.05, * p<0|
|All<br>(2)<br>11.882<br>(2.094)<br>0.504<br>(0.5)<br>1.686<br>(1.103)<br>210.88<br>(60.471)<br>19.751<br>(3.098)<br>15.238<br>(2.202)<br>0.497<br>(0.5)<br>2.592<br>(1.645)<br>7.12<br>(2.545)<br>0.074<br>(0.261)<br>0.903<br>(0.296)<br>; this may<br>qual to 1<br>y equal to<br>n (1) pres<br>l group, r<br>rences in <br>0.01, ** p|
|D<br>n<br>(1)<br>**Panel A. Baseline Characteristics**<br>Age<br>822<br>Female<br>828<br>Number of minors in HH<br>809<br>Poverty Index (0-292)<br>858<br><br>Test score (0-25)<br>858<br>**Panel B. Follow-up Characteristics**<br>Age<br>797<br>Female<br>797<br>Number of minors in HH<br>797<br>HH size<br>797<br>Married<br>503<br>Currently Working<br>826<br>Notes:Minors refers to respondents age 14 and under<br>hold, including the respondent. Married is a dummy e<br>is missing for minors. Currently working is a dumm<br>respondents may work and also be a student. Colum<br>the full sample, the treatment group, and the contro<br>ment group mean and the control group mean. Dife<br>parentheses are clustered at the school level. *** p<|

30
|Dif-in-Difs<br>(5)<br>-.16**<br>(.07)<br>-0.07<br>(.18)<br>0.04<br>(.06)<br>-0.02<br>(.03)<br>-0.07<br>(.08)<br>-0.06<br>(.08)<br>-0.01<br>(.08)<br>.17**<br>(.08)<br>0.05<br>(.08)<br>-0.01<br>(.05)<br>0.02<br>(.02)<br>.1*<br>(.06)<br>0.02<br>(.05)<br>0<br>(.02)<br>-0.51<br>(5.99)<br>0<br>(.83)<br>1056<br>0.31<br>18.59<br>0.29<br>t group surveyed.<br>ment group mean<br>red at the school<br>mingly unrelated|
|---|
|T<br>**Poverty Scholarship**<br>Attritor T<br>Non-attritor C<br>Non-attritor T<br>(2)<br>(3)<br>(4)<br>0.4<br>0.53<br>0.65<br>(.49)<br>(.5)<br>(.48)<br>1.89<br>1.78<br>1.85<br>(1.16)<br>(1.13)<br>(1.06)<br>0.29<br>0.27<br>0.23<br>(.46)<br>(.45)<br>(.42)<br>0.04<br>0.03<br>0.05<br>(.19)<br>(.17)<br>(.21)<br>0.4<br>0.37<br>0.51<br>(.49)<br>(.48)<br>(.5)<br>0.48<br>0.42<br>0.55<br>(.5)<br>(.49)<br>(.5)<br>0.23<br>0.17<br>0.26<br>(.42)<br>(.38)<br>(.44)<br>0.4<br>0.36<br>0.36<br>(.49)<br>(.48)<br>(.48)<br>0.4<br>0.4<br>0.42<br>(.49)<br>(.49)<br>(.49)<br>0.86<br>0.78<br>0.82<br>(.35)<br>(.41)<br>(.38)<br>0.03<br>0.01<br>0<br>(.17)<br>(.12)<br>(.05)<br>0.19<br>0.12<br>0.12<br>(.4)<br>(.32)<br>(.32)<br>0.18<br>0.17<br>0.15<br>(.38)<br>(.38)<br>(.35)<br>0.03<br>0.02<br>0.02<br>(.17)<br>(.15)<br>(.12)<br>243.56<br>246.17<br>242.13<br>(34.38)<br>(31.89)<br>(34.08)<br>18.44<br>17.87<br>18.63<br>(5.08)<br>(4.69)<br>(4.78)<br>135<br>341<br>390<br>ors, the control group surveyed and the treatmen<br>g attritors minus the diference between the treat<br> efects. Standard errors in parentheses are cluste<br>dual coefcients being jointly equal to 0 using see|
|Attritor C<br>(1)<br>0.46<br>(.5)<br>1.88<br>(1.12)<br>0.27<br>(.45)<br>0.05<br>(.23)<br>0.38<br>(.49)<br>0.45<br>(.5)<br>0.18<br>(.39)<br>0.24<br>(.43)<br>0.35<br>(.48)<br>0.85<br>(.36)<br>0.02<br>(.14)<br>0.08<br>(.28)<br>0.18<br>(.39)<br>0.04<br>(.19)<br>245.18<br>(32.17)<br>17.46<br>(4.87)<br>190<br>ent group attrit<br>up mean amon<br> province fxed <br>g for the indivi|
|Dif-in-Difs<br>(5)<br>-0.03<br>(.06)<br>-0.09<br>(.16)<br>0.02<br>(.07)<br>-0.06<br>(.05)<br>-0.03<br>(.08)<br>-.12*<br>(.07)<br>0.01<br>(.06)<br>0.01<br>(.07)<br>.12*<br>(.07)<br>-.11**<br>(.05)<br>0.02<br>(.03)<br>0.02<br>(.05)<br>0.02<br>(.07)<br>0.01<br>(.04)<br>9.93<br>(9.95)<br>0.01<br>(.49)<br>1149<br>0.31<br>19.93<br>0.22<br>itors, the treatm<br>d the control gro<br>n, controlling for <br>t of a test testin|
|RA<br>**Merit Scholarship**<br><br>Non-attritor C<br>Non-attritor T<br>(3)<br>(4)<br>0.5<br>0.51<br>(.5)<br>(.5)<br>1.74<br>1.65<br>(1.12)<br>(1.1)<br>0.43<br>0.42<br>(.5)<br>(.49)<br>0.13<br>0.18<br>(.33)<br>(.38)<br>0.54<br>0.58<br>(.5)<br>(.49)<br>0.56<br>0.6<br>(.5)<br>(.49)<br>0.29<br>0.31<br>(.46)<br>(.46)<br>0.5<br>0.58<br>(.5)<br>(.49)<br>0.57<br>0.56<br>(.5)<br>(.5)<br>0.83<br>0.92<br>(.37)<br>(.27)<br>0.06<br>0.05<br>(.23)<br>(.22)<br>0.14<br>0.14<br>(.35)<br>(.34)<br>0.25<br>0.22<br>(.43)<br>(.42)<br>0.05<br>0.05<br>(.22)<br>(.21)<br>217.84<br>205.24<br>(51.9)<br>(66.18)<br>19.79<br>19.83<br>(3.23)<br>(2.85)<br>378<br>417<br> the means for the control group attr<br>etween the treatment group mean an<br>eans are computed by OLS regressio<br>esponding p-value below) is the resul|
|Attritor T<br>(2)<br>0.38<br>(.49)<br>1.52<br>(1.08)<br>0.37<br>(.48)<br>0.1<br>(.3)<br>0.44<br>(.5)<br>0.42<br>(.5)<br>0.22<br>(.41)<br>0.43<br>(.5)<br>0.59<br>(.49)<br>0.82<br>(.39)<br>0.06<br>(.24)<br>0.11<br>(.32)<br>0.23<br>(.42)<br>0.06<br>(.24)<br>220.91<br>(52.46)<br>19.86<br>(3.3)<br>153<br> to 4 display<br>e diference b<br>erences in m<br>re (and corr|
|Attritor C<br>(1)<br>0.4<br>(.49)<br>1.7<br>(1.15)<br>0.35<br>(.48)<br>0.13<br>(.33)<br>0.45<br>(.5)<br>0.49<br>(.5)<br>0.22<br>(.41)<br>0.34<br>(.48)<br>0.47<br>(.5)<br>0.85<br>(.36)<br>0.05<br>(.22)<br>0.1<br>(.3)<br>0.23<br>(.42)<br>0.05<br>(.22)<br>221.83<br>(53.02)<br>19.72<br>(3.24)<br>201<br>. =0<br>baseline. Columns 1<br>s. Column (5) is th<br>g respondents. Dif<br><0.1. The Chi-squa|
|Female<br>Number of minors<br>Own motorcycle<br>Own car/truck<br>Own oxen/bufalo<br>Own pig<br>Own ox or bufalo cart<br>Hard roof<br>Hard wall<br>Hard foor<br>Have automatic toilet<br>Have pit toilet<br>Electricity<br>Piped water<br>Poverty Index (0-292)<br>Test score (0-25)<br>Observations<br>Attrition rate<br>Joint signifcance: Ho: all coef<br>Chi-square<br>p-value<br>Notes: All variables measured at <br>Standard deviations in parenthese<br>and the control group mean amon<br>level. *** p<0.01, ** p<0.05, * p<br>estimation.<br>31|
Table 3: Education Outcomes

||Highest<br>pleted|grade<br>com-|Completed primary|Received<br>any<br>mal<br>education|for-<br>in<br>Family index|
|---|---|---|---|---|---|
|||||2011-2017||
|||(1)|(2)|(3)|(4)|
|**Panel A. Merit**||||||
|Treatment||0.213*|0.0500|0.044*|0.131**|
|||(0.117)|(0.036)|(0.026)|(0.065)|
|Observations||814|814|814|814|
|R-squared||0.160|0.148|0.129|0.167|
|F-statistic||3.240|3.610|3.420|5.040|
|Covariates||Yes|Yes|Yes|Yes|
|Control mean||5.590|0.610|0.780|0.130|
|Joint signifcance: Ho: all coef. =0||||||
|Chi-square||||3.548||
|p-value||||0.0600||
|**Panel B. Poverty**||||||
|Treatment||0.291*|0.113***|0.100**|0.264***|
|||(0.149)|(0.040)|(0.039)|(0.088)|
|Observations||753|753|753|753|
|R-squared||0.169|0.173|0.124|0.174|
|F-statistic||3.250|4.650|2.890|3.720|
|Covariates||Yes|Yes|Yes|Yes|
|Control mean||5.480|0.570|0.720|-0.0100|
|Joint signifcance: Ho: all coef. =0||||||
|Chi-square||||4.107||
|p-value||||0.0430||
|**Joint test: Poverty vs. Merit**||||||
|Chi-square||0.253|2.049|2.184|2.287|
|p-value||0.615|0.152|0.139|0.131|

(1) (2) (3) (4) **A. Merit** 0.213* 0.0500 0.044* 0.131** (0.117) (0.036) (0.026) (0.065) 814 814 814 814 0.160 0.148 0.129 0.167 3.240 3.610 3.420 5.040 Yes Yes Yes Yes mean 5.590 0.610 0.780 0.130 significance: Ho: all coef. =0 3.548 0.0600 **B. Poverty** 0.291* 0.113*** 0.100** 0.264*** (0.149) (0.040) (0.039) (0.088) 753 753 753 753 0.169 0.173 0.124 0.174 3.250 4.650 2.890 3.720 Yes Yes Yes Yes mean 5.480 0.570 0.720 -0.0100 significance: Ho: all coef. =0 4.107 0.0430 **test: Poverty vs. Merit** 0.253 2.049 2.184 2.287 0.615 0.152 0.139 0.131 Estimated treatment effects. The dependent variable in column (1) is the highest grade the individual completed and is equal to -1 if received no education, 0 if he only went to kindergarten and then ranges from 1 to 11 for Grade 1 to Grade 11. In column (2), the variable is a dummy equal to 1 if the individual completed primary education. In column (3), the dependent variable is equal to 1 if was enrolled in the formal education system during any of the years 2011 to 2016. In column (4), the family index is the inverse matrix-weighted mean of the standardized dependent variables from the three previous columns following Anderson (2008). All for district fixed effects, baseline test score, baseline poverty score, individual-level socio-economic variables from baseline, 6 (EMIS) variables and 5 census village-level variables, measured at baseline. Panel A includes respondents who were eligible for the merit scholarship 0 otherwise) and Panel B respondents who were eligible for the poverty scholarship (Treatment=1, 0 otherwise). Robust in parentheses (clustered at the school level). *** p<0.01, ** p<0.05, * p<0.1. The joint significance Chi-square (and corresponding is a result of testing for the coefficients of individuals regressions being jointly equal to 0, using seemingly unrelated estimation. vs. merit Chi-square (and corresponding p-value below) is a result of testing for the coefficient of the merit sample and the coefficient sample being equal.

Notes: Estimated treatment effects. The dependent variable in column (1) is the highest grade the individual completed and is equal to -1 if the individual received no education, 0 if he only went to kindergarten and then ranges from 1 to 11 for Grade 1 to Grade 11. In column (2), the dependent variable is a dummy equal to 1 if the individual completed primary education. In column (3), the dependent variable is equal to 1 if the individual was enrolled in the formal education system during any of the years 2011 to 2016. In column (4), the family index is the inverse covariance matrix-weighted mean of the standardized dependent variables from the three previous columns following Anderson (2008). All regressions control for district fixed effects, baseline test score, baseline poverty score, individual-level socio-economic variables from baseline, 6 school-level (EMIS) variables and 5 census village-level variables, measured at baseline. Panel A includes respondents who were eligible for the merit scholarship (Treatment=1, 0 otherwise) and Panel B respondents who were eligible for the poverty scholarship (Treatment=1, 0 otherwise). Robust standard errors are in parentheses (clustered at the school level). *** p<0.01, ** p<0.05, * p<0.1. The joint significance Chi-square (and corresponding p- value below) is a result of testing for the coefficients of individuals regressions being jointly equal to 0, using seemingly unrelated estimation. The poverty vs. merit Chi-square (and corresponding p-value below) is a result of testing for the coefficient of the merit sample and the coefficient of the poverty sample being equal.

32
Table 4: Cognitive Outcomes

||Math|Raven’s|Forward|Picture<br>Recog-|Family index|
|---|---|---|---|---|---|
||||Digit Span|nition<br>Vocabu-<br>lary Test||
||(1)|(2)|(3)|(4)|(5)|
|**Panel A. Merit**||||||
|Treatment|0.0670<br>|0.155**<br>|0.0700<br>|0.0200<br>|0.113*<br>|
||(0.075)|(0.066)|(0.065)|(0.073)|(0.068)|
|Observations|814|814|813|814|813|
|R-squared|0.189|0.180|0.0930|0.293|0.225|
|F-statistic|6.130|6.670|2.350|11.02|8.710|
|Covariates|Yes|Yes|Yes|Yes|Yes|
|Control mean|0.0800|0.0700|-0.0100|0.100|0.0700|
|Joint signifcance: Ho: all coef. =0||||||
|Chi-square||||0.861||
|p-value||||0.353||
|**Panel B. Poverty**||||||
|Treatment|0.0860|0.0520|-0.129*|0.0600|0.00500|
||(0.064)|(0.080)|(0.070)|(0.081)|(0.072)|
|Observations|753|753|752|753|752|
|R-squared|0.150|0.156|0.114|0.279|0.196|
|F-statistic|6.610|7.370|3.450|4.780|4.190|
|Covariates|Yes|Yes|Yes|Yes|Yes|
|Control mean|-0.0600|-0.0300|0.0400|-0.0400|-0.0300|
|Joint signifcance: Ho: all coef. =0||||||
|Chi-square||||1.938||
|p-value||||0.164||
|**Joint test: Poverty vs. Merit**||||||
|Chi-square|0.0530|1.304|6.455|0.195|1.812|
|D<br>p-value<br>Notes: Estimated treatment efects. The <br>uted using Item Response Theory (IRT) <br> the score on the Raven’s matrices test <br>he standardized score on the digit span t<br> Picture Recognition Vocabulary Test co<br>ovariance matrix-weighted mean of the st<br>ressions control for district fxed efects, b<br> school-level (EMIS) variables and 5 cens<br>or the merit scholarship (Treatment=1, 0 <br> otherwise). Robust standard errors are i<br>ance Chi-square (and corresponding p-val|0.818<br> dependent variabl<br> with a two param<br>computed using I<br>est using forward <br>mputed using IRT <br>andardized depend<br>aseline test score,<br>us village-level va<br> otherwise) and P<br>n parentheses (clu<br>ue below) is a res|0.254<br>e in column (1) is<br>eter logistic (2PL)<br>RT with a 2PL m<br>items only, standa<br> with a 2PL mode<br>ent variables from<br> baseline poverty <br>riables, measured <br>anel B respondents<br>stered at the scho<br>ult of testing for t|0.0110<br> the score on the m<br> model, standardiz<br>odel, standardized. <br>rdized. In column <br>l, standardized. In <br>the four previous c<br>score, individual-le<br> at baseline. Panel <br> who were eligible <br>ol level). *** p<0.<br>he coefcients of in|0.659<br>athematics computer <br>ed. In column (2), the<br> In column (3), the d<br>(4), the dependent var<br>column (5), the family<br>olumns following And<br>vel socio-economic var<br> A includes responden<br>for the poverty scholar<br>01, ** p<0.05, * p<0<br>dividuals regressions b|0.178<br> adaptive test, co<br> dependent varia<br>ependent variable<br>iable is the score <br> index is the inve<br>erson (2008). All<br>iables from baseli<br>ts who were eligi<br>ship (Treatment=<br>.1. The joint sign<br>eing jointly equal|

Notes: Estimated treatment effects. The dependent variable in column (1) is the score on the mathematics computer adaptive test, computed using Item Response Theory (IRT) with a two parameter logistic (2PL) model, standardized. In column (2), the dependent variable is the score on the Raven’s matrices test computed using IRT with a 2PL model, standardized. In column (3), the dependent variable is the standardized score on the digit span test using forward items only, standardized. In column (4), the dependent variable is the score on a Picture Recognition Vocabulary Test computed using IRT with a 2PL model, standardized. In column (5), the family index is the inverse covariance matrix-weighted mean of the standardized dependent variables from the four previous columns following Anderson (2008). All regressions control for district fixed effects, baseline test score, baseline poverty score, individual-level socio-economic variables from baseline, 6 school-level (EMIS) variables and 5 census village-level variables, measured at baseline. Panel A includes respondents who were eligible for the merit scholarship (Treatment=1, 0 otherwise) and Panel B respondents who were eligible for the poverty scholarship (Treatment=1, 0 otherwise). Robust standard errors are in parentheses (clustered at the school level). *** p<0.01, ** p<0.05, * p<0.1. The joint significance Chi-square (and corresponding p-value below) is a result of testing for the coefficients of individuals regressions being jointly equal to 0, using seemingly unrelated estimation. The poverty vs. merit Chi-square (and corresponding p-value below) is a result of testing for the coefficient of the merit sample and the coefficient of the poverty sample being equal.

33
||Family index<br>Neuroticism<br>(8)<br>(9)|0.0580<br>-0.00500<br>(0.075)<br>(0.065)<br>814<br>807<br>0.0700<br>0.102<br>2.110<br>3.550<br>Yes<br>Yes<br>-0.0400<br>0.0300<br>0.370<br>0.543|0.186***<br>-0.0990<br>(0.071)<br>(0.074)|753<br>749<br>0.0840<br>0.107<br>2.950<br>2.890<br>Yes<br>Yes<br>-0.0300<br>0.0100<br>0.003<br>0.957|2.269<br>1.190<br>0.132<br>0.275<br>ength and Difculty Questionnaire (SDQ),<br>ms) of the SDQ, standardized. In column<br>zed. In columns (4) to (8), the dependent<br> the family index is the inverse covariance<br>rehand). In column (10), the family index<br> baseline test score, baseline poverty score,<br>who were eligible for the merit scholarship<br>tered at the school level). *** p<0.01, **<br>using seemingly unrelated estimation. The|
|---|---|---|---|---|---|
||Agreeableness<br>(7)|-0.0720<br>(0.066)<br>811<br>0.0950<br>2.530<br>Yes<br>0.0300|-0.0780<br>(0.078)|752<br>0.0640<br>1.510<br>Yes<br>0.0100|0.00400<br>0.948<br> pro-social) of the Str<br>lizing behavior proble<br> of the SDQ, standardi<br>rdized. In column (9),<br>have been fipped befo<br>r district fxed efects, <br> includes respondents <br>re in parentheses (clus<br>ng jointly equal to 0, <br>ty sample being equal.|
|omes|Big 5<br>Extraversion<br>(6)|0.00500<br>(0.077)<br>813<br>0.0960<br>2.820<br>Yes<br>0.0100|-0.0600<br>(0.084)|752<br>0.0910<br>2.200<br>Yes<br>0.0100|0.437<br>0.509<br> the score, the more<br>re, the more externa<br> behavior problems) <br>g Five scale, standa<br>mns (2), (3), and (8)<br>egressions control fo<br>t baseline. Panel A <br>st standard errors a<br>duals regressions bei<br>fcient of the pover|
|otional Outc|Conscientiousness<br>(5)|-0.0910<br>(0.062)<br>812<br>0.0600<br>2<br>Yes<br>0|-0.00200<br>(0.077)|752<br>0.0980<br>6.250<br>Yes<br>0.0200|1.103<br>0.294<br>o-social facet (the higher<br>acet (the higher the sco<br> the more externalizing <br>roticism facets of the Bi<br>(2008) (scores from colu<br> variables as in (9). All r<br>el variables, measured a<br>t=1, 0 otherwise). Robu<br> the coefcients of indivi<br>merit sample and the coe|
|Socioem|Openness<br>(4)|0.0300<br>(0.067)<br>813<br>0.0900<br>3.540<br>Yes<br>0.0400|0.0110<br>(0.074)|753<br>0.0720<br>2.350<br>Yes<br>0|0.0490<br>0.824<br>m 0 to 10 on the pr<br>rnalizing behavior f<br>the higher the score,<br>reeableness and Neu<br>following Anderson<br>n, on the same set of<br> 5 census village-lev<br>holarship (Treatmen<br> result of testing for <br>e coefcient of the|
|Table 5:|Externalizing<br>(3)|0.00200<br>(0.070)<br>812<br>0.0950<br>2.430<br>Yes<br>0|0.0530<br>(0.072)|753<br>0.0960<br>2.960<br>Yes<br>-0.0400|0.339<br>0.560<br>1) is the score fro<br> to 20 on the inte<br>g behavior facet (<br>, Extroversion, Ag<br>ight frst columns<br> quartimin rotatio<br>MIS) variables and<br>for the poverty sc<br>-value below) is a <br>lt of testing for th|
||SDQ<br>Internalizing<br>(2)|-0.0770<br>(0.059)<br>812<br>0.130<br>6.140<br>Yes<br>0|-0.0420<br>(0.087)|753<br>0.129<br>5.250<br>Yes<br>0|0.161<br>0.688<br>riable in column (<br>s the score from 0<br>n the externalizin<br>Conscientiousness<br>riables from the e<br>lysis (EFA) with <br> 6 school-level (E<br>ho were eligible <br> corresponding p<br>e below) is a resu|
||Prosocial<br>(1)|**Panel A. Merit**<br>Treatment<br>-0.0390<br>(0.066)<br>Observations<br>813<br>R-squared<br>0.0790<br>F-statistic<br>2.180<br>Covariates<br>Yes<br>Control mean<br>-0.0100<br>Joint signifcance: Ho: all coef. =0<br>Chi-square<br>p-value|**Panel B. Poverty**<br>Treatment<br>-0.00400<br>(0.078)|Observations<br>751<br>R-squared<br>0.0960<br>F-statistic<br>2.520<br>Covariates<br>Yes<br>Control mean<br>-0.0500<br>Joint signifcance: Ho: all coef. =0<br>Chi-square<br>p-value|**Joint test: Poverty vs. Merit**<br>Chi-square<br>0.175<br>p-value<br>0.676<br>Notes: Estimated treatment efects. The dependent va<br>standardized. In column (2), the dependent variables i<br>(3), the dependent variables is the score from 0 to 20 o<br>variables are the scores from 3 to 15 on the Openness, <br>matrix-weighted mean of the standardized dependent va<br>represents the frst factor from an exploratory factor ana<br>individual-level socio-economic variables from baseline, <br>(Treatment=1, 0 otherwise) and Panel B respondents w<br>p<0.05, * p<0.1. The joint signifcance Chi-square (and<br>poverty vs. merit Chi-square (and corresponding p-valu|

34
|Table 6: Labor Outcomes|Currently working<br>Age started working<br>Any training since 2011<br>Cog.<br>demands of main<br>work (1/0)<br>Yearly earnings (inv. hy-<br>perbolic sine, USD)<br>Daily res. wage (inv. hy-<br>perbolic sine, USD)<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)|R<br>**Panel A. Merit**<br>Treatment<br>0.034*<br>0.0740<br>-0.0140<br>-0.0400<br>-0.294<br>0.0640<br>(0.020)<br>(0.225)<br>(0.037)<br>(0.027)<br>(0.188)<br>(0.048)<br>Observations<br>772<br>794<br>814<br>775<br>791<br>805<br>R-squared<br>0.106<br>0.0720<br>0.156<br>0.0990<br>0.102<br>F-statistic<br>1.940<br>1.910<br>4.830<br>2.570<br>3.710<br>3.830<br>Covariates<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Control mean<br>0.923<br>12.72<br>0.595<br>0.174<br>7.620<br>2.250|**Panel B. Poverty**<br>Treatment<br>0.0120<br>0.339<br>-0.0310<br>0.0270<br>-0.382<br>0.0370<br>(0.019)<br>(0.235)<br>(0.036)<br>(0.030)<br>(0.239)<br>(0.054)<br>35|AFT<br>Observations<br>712<br>726<br>753<br>713<br>732<br>746<br>R-squared<br>0.0870<br>0.0910<br>0.165<br>0.0900<br>0.118<br>F-statistic<br>2.850<br>1.980<br>7.100<br>2.080<br>959.6<br>2.170<br>Covariates<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Control mean<br>0.930<br>12.55<br>0.590<br>0.146<br>7.608<br>2.219<br>Notes: Estimated treatment efects. The dependent variable in column (1) is a dummy equal to 1 if the individual is currently working, i.e. she worked for at least 1 hour during the last week or has a job at<br>the moment but did not work during the last week. In column (2), the dependent variable is the age at which the individual started to work. In column (3), the dependent variable is a dummy equal to 1 if the<br>individual participated in any formal or informal training that lasted at least one week, since 2011. In column (4), the dependent variable is a dummy equal to 1 if the main work activity demands cognitive<br>ability (read, write, calculate, or use a computer) and 0 otherwise. In column (5), the dependent variable is the yearly earning expressed in US dollars and transformed using an inverse hyperbolic sine. In<br>column (6), the dependent variable is the daily reservation wage in US dollars and transformed using an inverse hyperbolic sine. In column (4), values for respondents who did not work have been imputed with<br>0, except if they were students. In columns (1) and (4), the sample is restricted to respondents who are not currently students. Column (2) includes everyone who ever worked. Column (4) includes only people<br>who worked over the past 12 months. Columns (3) and (6) include the entire sample. Column (1), (2), (3), (4), and (6) are estimated using OLS regression; Column (5) is estimated using Tobit regression. All<br>regressions control for district fxed efects, baseline test score, baseline poverty score, individual-level socio-economic variables from baseline, 6 school-level (EMIS) variables and 5 census village-level variables,<br>measured at baseline. Panel A includes respondents who were eligible for the merit scholarship (Treatment=1, 0 otherwise) and Panel B respondents who were eligible for the poverty scholarship (Treatment=1,<br>0 otherwise). Robust standard errors are in parentheses (clustered at the school level). *** p<0.01, ** p<0.05, * p<0.1.|
|---|---|---|---|---|
|Health Issue In-<br>dex (GHQ)<br>Family index<br>(6)<br>(7)<br>-0.0260<br>0.174***<br>(0.070)<br>(0.065)<br>804<br>804<br>0.0950<br>0.182<br>3.200<br>6.740<br>Yes<br>Yes<br>-0.0200<br>0<br>7.041<br>0.00800|0.0540<br>0.0410<br>(0.081)<br>(0.085)<br>744<br>743<br>0.114<br>0.120<br>4.280<br>4.620<br>Yes<br>Yes<br>-0.0500<br>-0.0300<br>8.864<br>0.00300<br>0.719<br>1.903<br>0.397<br>0.168<br>ple of the same age in the village, standard-<br>heory with a two parameter logistic model,<br> dependent variable is the score from 1 to 5<br>ed. In column (6), the dependent variable is<br>f the standardized dependent variables from<br>dividual-level socio-economic variables from<br>ble for the merit scholarships (Treatment=1,<br> parentheses (clustered at the school level).<br>individuals regressions being jointly equal to<br>cient of the merit sample and the coefcient|
|---|---|
|Quality of Life<br>(5)<br>0.0410<br>(0.058)<br>814<br>0.0970<br>2.450<br>Yes<br>0|0.0250<br>(0.073)<br>753<br>0.0920<br>4.110<br>Yes<br>0.0200<br>0.0400<br>0.841<br> as compared to peo<br>ng Item Response T<br> In column (4), the <br>question, standardiz<br>rix-weighted mean o<br>line poverty score, in<br>ents who were eligi<br>tandard errors are in<br>r the coefcients of <br> testing for the coef|
|Quality of Health<br>(4)<br>0.129**<br>(0.058)<br>814<br>0.104<br>3.760<br>Yes<br>-0.0200|F<br>0.0620<br>(0.078)<br>753<br>0.0730<br>4.180<br>Yes<br>0.0300<br>0.621<br>0.431<br>n an economic ladder <br>nership computed usi<br>uestion, standardized. <br> to 5 on a life quality <br>nverse covariance mat<br>seline test score, base<br>nel A includes respond<br> otherwise). Robust s<br> is a result of testing fo<br>e below) is a result of|
|Life Satisfaction<br>(3)<br>0.0570<br>(0.067)<br>814<br>0.111<br>2.740<br>Yes<br>-0.0300|0.0380<br>(0.071)<br>752<br>0.104<br>2.450<br>Yes<br>-0.0300<br>0.0480<br>0.827<br> score from 1 to 10 o<br>ed based on asset ow<br> a life satisfaction q<br> is the score from 1 <br>amily index is the i<br>trict fxed efects, ba<br>ured at baseline. Pa<br>ip (Treatment=1, 0 <br>ding p-value below) <br>orresponding p-valu|
|D<br>SES Ladder (vil-<br>lage)<br>SES Index (IRT)<br>(1)<br>(2)<br>**Panel A. Merit**<br>Treatment<br>0.173**<br>0.186**<br>(0.068)<br>(0.073)<br>Observations<br>814<br>814<br>R-squared<br>0.122<br>0.275<br>F-statistic<br>3.880<br>13.06<br>Covariates<br>Yes<br>Yes<br>Control mean<br>0.0100<br>0.0200<br>Joint signifcance: Ho: all coef. =0<br>Chi-square<br>p-value|**Panel B. Poverty**<br>Treatment<br>0.208***<br>-0.0640<br>(0.073)<br>(0.084)<br>Observations<br>753<br>753<br>R-squared<br>0.120<br>0.177<br>F-statistic<br>7.400<br>4.950<br>Covariates<br>Yes<br>Yes<br>Control mean<br>-0.0900<br>-0.0800<br>Joint signifcance: Ho: all coef. =0<br>Chi-square<br>p-value<br>**Joint test: Poverty vs. Merit**<br>Chi-square<br>0.179<br>6.899<br>p-value<br>0.673<br>0.00900<br>Notes: Estimated treatment efects. The dependent variable in column (1) is the <br>ized. In column (2), the dependent variables is a socio-economic index construct<br>standardized. In column (3), the dependent variable is the score from 1 to 10 on<br>on a health quality question, standardized. In column (5), the dependent variable<br>the standardized score on the General Health Questionnaire. In column (7), the f<br>all the previous columns following Anderson (2008). All regressions control for dis<br>baseline, 6 school-level (EMIS) variables and 5 census village-level variables, meas<br>0 otherwise) and Panel B respondents who were eligible for the poverty scholarsh<br>*** p<0.01, ** p<0.05, * p<0.1. The joint signifcance Chi-square (and correspon<br>0, using seemingly unrelated estimation. The poverty vs. merit Chi-square (and c<br>of the poverty sample being equal.<br>36|
|Daily res. wage<br>(inv.<br>hyperbolic sine,<br>USD)<br>(5)<br>0.173***<br>(0.065)<br>0.0350<br>(0.076)<br>-0.240**<br>(0.092)<br>805<br>0.110<br>3.960<br>Yes<br>2.260<br>0.0150<br>(0.062)<br>0.0740<br>(0.084)<br>0.0630<br>(0.110)<br>746<br>0.122<br>2.330<br>Yes<br>2.220<br>5.563<br>0.0180<br>e variable as in Table 6. Treatment<br> the merit-based scheme but would<br>eived a merit-based scholarship. All<br>) variables and 5 census village-level<br>o were eligible for the poverty schol-<br> corresponding p-value below) is the|
|---|
|T<br><br>Family index:<br>SES/Well-being<br>(4)<br>0.139<br>(0.084)<br>0.0420<br>(0.114)<br>0.0810<br>(0.120)<br>804<br>0.183<br>6.880<br>Yes<br>-0.280<br>-0.00500<br>(0.097)<br>0.0430<br>(0.136)<br>0.128<br>(0.165)<br>743<br>0.122<br>4.370<br>Yes<br>-0.310<br>0.0630<br>0.802<br> and 7. Column (4) is the sam<br>individuals who qualify under<br>heme but would not have rec<br>baseline, 6 school-level (EMIS<br> and Panel B respondents wh<br>* p<0.1. The Chi-square (and|
|F<br><br>Family index:<br>Socioemotional<br>(3)<br>0.0770<br>(0.099)<br>0.105<br>(0.126)<br>-0.175<br>(0.157)<br>807<br>0.104<br>3.530<br>Yes<br>0<br>-0.0180<br>(0.105)<br>0.0480<br>(0.146)<br>-0.211<br>(0.179)<br>749<br>0.110<br>2.830<br>Yes<br>-0.0200<br>0.0310<br>0.859<br>ily indices from Tables 3 to 5 <br>he median poverty score are <br>y under the poverty-based sc<br>ocio-economic variables from <br>p (Treatment=1, 0 otherwise)<br>vel). *** p<0.01, ** p<0.05, <br>rom the poverty sample.|
|A<br><br>Family index:<br>Cognition<br>(2)<br>0.211**<br>(0.094)<br>0.0340<br>(0.118)<br>-0.214<br>(0.139)<br>813<br>0.228<br>8.120<br>Yes<br>0.0700<br>0.0560<br>(0.090)<br>-0.0720<br>(0.103)<br>-0.140<br>(0.131)<br>752<br>0.200<br>4.400<br>Yes<br>-0.0300<br>0.213<br>0.644<br>) to (3) and (5) are the fam<br>der either scheme. Below t<br>e are individuals who qualif<br>rty score, individual-level s<br>ible for the merit scholarshi<br>es (clustered at the school le<br>mple and interaction term f|
|DR<br><br>Family index:<br>Education<br>(1)<br>**Panel A. Merit**<br>Treatment<br>0.130<br>(0.094)<br>Below the median poverty score<br>0.0850<br>(0.111)<br>Below the median poverty score and treatment<br>0.00900<br>(0.128)<br>Observations<br>814<br>R-squared<br>0.168<br>F-statistic<br>5.200<br>Covariates<br>Yes<br>Control mean<br>-0.0200<br>**Panel B. Poverty**<br>Treatment<br>0.199*<br>(0.103)<br>Below the median test score<br>-0.285***<br>(0.093)<br>Below the median test score and treatment<br>0.159<br>(0.114)<br>Observations<br>753<br>R-squared<br>0.183<br>F-statistic<br>4.040<br>Covariates<br>Yes<br>Control mean<br>-0.160<br>**Joint test: Poverty vs. Merit**<br>Chi-square<br>1.070<br>p-value<br>0.301<br>Notes: Estimated treatment efects. The dependent variables in columns (1<br>captures efects for students who would have qualifed for a scholarship un<br>not have received a poverty-based scholarship. Below the median test scor<br>regressions control for district fxed efects, baseline test score, baseline pove<br>variables, measured at baseline. Panel A includes respondents who were elig<br>arship (Treatment=1, 0 otherwise). Robust standard errors are in parenthes<br>result of testing the equality between the interaction term from the merit sa<br>37|
||Daily res. wage<br>(inv.<br>hyperbolic<br>sine,<br>USD)<br>(5)<br>0.104<br>(0.077)<br>-0.0200<br>(0.091)<br>-0.0820<br>(0.106)|805<br>0.103<br>3.690<br>Yes<br>2.250<br>0.118<br>(0.081)<br>-0.118<br>(0.089)<br>-0.154<br>(0.101)<br>746<br>0.121<br>2.060<br>Yes<br>2.220<br>0.345<br>0.557<br> in Table 6. All regressions control for<br>age-level variables, measured at base-<br>olarship (Treatment=1, 0 otherwise).<br>lt of testing the equality between the|
|---|---|---|
|s by Gender|Family index:<br>SES/Well-being<br>(4)<br>0.188**<br>(0.084)<br>-0.199*<br>(0.118)<br>-0.0270<br>(0.127)|T<br>804<br>0.182<br>6.710<br>Yes<br>0<br>0.0420<br>(0.119)<br>-0.247<br>(0.163)<br>-0.00100<br>(0.168)<br>743<br>0.120<br>4.510<br>Yes<br>-0.0300<br>0.0220<br>0.882<br>. Column (5) is the same variable as <br>el (EMIS) variables and 5 census vill<br>s who were eligible for the poverty sch<br>rresponding p-value below) is the resu|
|Treatment Efect|Family index:<br>Socioemotional<br>(3)<br>-0.0180<br>(0.096)<br>-0.195<br>(0.127)<br>0.0260<br>(0.146)|AF<br>807<br>0.102<br>3.480<br>Yes<br>0.0300<br>-0.0860<br>(0.097)<br>-0.289**<br>(0.119)<br>-0.0250<br>(0.136)<br>749<br>0.107<br>2.820<br>Yes<br>0.0100<br>0.103<br>0.748<br> indices from Tables 3 to 5 and 7<br>riables from baseline, 6 school-lev<br>herwise) and Panel B respondent<br> * p<0.1. The Chi-square (and co|
|9: Heterogeneous|Family index:<br>Cognition<br>(2)<br>0.133<br>(0.096)<br>-0.337***<br>(0.110)<br>-0.0410<br>(0.131)|813<br>0.225<br>8.520<br>Yes<br>0.0700<br>0.168*<br>(0.098)<br>-0.254**<br>(0.117)<br>-0.309**<br>(0.141)<br>752<br>0.202<br>4.380<br>Yes<br>-0.0300<br>2.891<br>0.0890<br> columns (1) to (4) are the family<br>ndividual-level socio-economic va<br>it scholarship (Treatment=1, 0 ot<br>ol level). *** p<0.01, ** p<0.05, <br>m the poverty sample.|
|Table|D<br>Family index:<br>Education<br>(1)<br>**Panel A. Merit**<br>Treatment<br>0.142*<br>(0.085)<br>Female<br>-0.00700<br>(0.114)<br>Female and treatment<br>-0.0220<br>(0.115)|Observations<br>814<br>R-squared<br>0.167<br>F-statistic<br>4.910<br>Covariates<br>Yes<br>Control mean<br>0.130<br>**Panel B. Poverty**<br>Treatment<br>0.257***<br>(0.096)<br>Female<br>-0.146<br>(0.132)<br>Female and treatment<br>0.0120<br>(0.133)<br>Observations<br>753<br>R-squared<br>0.174<br>F-statistic<br>3.690<br>Covariates<br>Yes<br>Control mean<br>-0.0100<br>**Joint test: Poverty vs. Merit**<br>Chi-square<br>0.0670<br>p-value<br>0.796<br>Notes: Estimated treatment efects. The dependent variables in <br>district fxed efects, baseline test score, baseline poverty score, i<br>line. Panel A includes respondents who were eligible for the mer<br>Robust standard errors are in parentheses (clustered at the scho<br>interaction term from the merit sample and interaction term fro|

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