metadata: authors: 0: author: Fitzsimons, Emla
1: author: Mesnard, Alice
count: Colombia
docna: 0: docna: Can conditional cash transfers compensate
            for a father's absence ?
docty: Policy Research Working Paper
subtopic: Governance Diagnostic Capacity Building,Child Labor Law,Labor Markets,Child Labor,Labor Standards,Disability,Skills Development and Labor Force Training,Educational Sciences,Services & Transfers to Poor,Economic Assistance,Economic Forecasting,Access of Poor to Social Services,Macroeconomic Management,Rural Labor Markets
teratopic: Urban Development,Governance,Social Protections and Labor,Health, Nutrition and Population,Education
abstracts: cdata!: This paper investigates how the
            permanent departure of the father from a household affects
            children's school enrollment and work participation in
            rural Colombia. The results indicate that the permanent
            departure of the father decreases children's school
            enrollment by approximately 5 percentage points and
            increases child labor by 3 percentage points. This paper
            explores the rollout of a conditional-cash-transfer program
            during the period of study and shows that this program
            counteracts these adverse effects. When coupled with other
            evidence, this finding strongly suggests that the channel
            through which the father's departure most affects
            children is by reducing the income of very poor households,
            which tightens their liquidity constraints. This finding
            also highlights the important safety-net role played by
            welfare programs with respect to disadvantaged households,
            particularly because these households are unlikely to have
            formal or informal mechanisms with which to insure
            themselves against such vagaries.
geo_reg_key: 80475,80450,80469
repnme: repnme: Can conditional cash transfers compensate
            for a father's absence ?
display_title: Can conditional cash transfers
            compensate for a father's absence
listing_relative_url: /research/2013/06/17831762/can-conditional-cash-transfers-compensate-fathers-absence
topicv3: Child Labor m1326042 1859,Education m1327048 470,Market Failures m1327149 1068,Cash Transfers m1327437 1608,Human Capital Accumulation m1504139 2816
publishtoextweb_dt: 2013-06-01T00:00:00Z
historic_topic: Urban Development,Governance,Social Protections and Labor,Health, Nutrition and Population,Education
no_of_pages: 52
model_extractions: WPS6476 Policy Research Working Paper 6476 Can Conditional Cash Transfers Compensate for a Father’s Absence?

_Emla Fitzsimons Alice Mesnard_ The World Bank Development Economics Vice Presidency Partnerships, Capacity Building Unit June 2013
Policy Research Working Paper 6476

# **Abstract**

This paper investigates how the permanent departure of the father from a household affects children’s school enrollment and work participation in rural Colombia. The results indicate that the permanent departure of the father decreases children’s school enrollment by approximately 5 percentage points and increases child labor by 3 percentage points. This paper explores the rollout of a conditional-cash-transfer program during the period of study and shows that this program counteracts these adverse effects. When coupled with other evidence, this finding strongly suggests that the channel through which the father’s departure most affects children is by reducing the income of very poor households, which tightens their liquidity constraints. This finding also highlights the important safety-net role played by welfare programs with respect to disadvantaged households, particularly because these households are unlikely to have formal or informal mechanisms with which to insure themselves against such vagaries.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at emla_f@ifs.org.uk and Alice.Mesnard.1@city.ac.uk.

_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
Can Conditional Cash Transfers Compensate for a Father’s Absence?

_Emla Fitzsimons and Alice Mesnard_

**JEL classification codes:** I20, J12, J22, O16

**Keywords:** child labor; schooling; permanent absence; income loss; credit and insurance market failures; conditional cash transfer; safety net. **Sector Board** : Social Protection (SOCPT)

Emla Fitzsimons (corresponding author) is Co-Director of the Centre for the Evaluation of Development Policy (EDePo) at the Institute for Fiscal Studies; her email address is <u>emla_f@ifs.org.uk. Alice Mesnard is Reader at City University, Senior Research</u> Economist at the Institute for Fiscal Studies, and Research Affiliate of the CEPR; her email address is Alice.Mesnard.1@city.ac.uk. The authors are grateful to participants at the IFS, CMPO, World Bank, and 3ie-LIDC Seminar Series and to participants in the NEUDC conference for comments. They thank Orazio Attanasio, Rajeev Dehejia, Ian Preston, Dominique van de Walle, and Marcos Vera-Hernández for useful comments. They also thank the Editor and two anonymous referees for very helpful suggestions. Funding from ESRC, grant number RES-000-22-1742, is gratefully acknowledged. All errors are the responsibility of the authors.
A major disruption to family life can have serious consequences for children. A particularly traumatic event is the father's permanent departure from the household. There are at least three different channels through which this event can affect children’s human capital accumulation, particularly their school and work participation rates (for further discussion of the following points, see Case, Paxson, and Ableidinger [2004] and Gertler, Levine, and Ames [2004]). First, this event is likely to involve substantial income loss that may particularly affect school choices coupled with credit and insurance market failures. Second, the balance of decision-making power in the household may change, with the preferences of the remaining adults gaining increased importance, which may have important consequences for children. Third, the loss of a parent may have significant emotional and psychological consequences for children. The importance of the first and third channels was highlighted in a World Bank Development Outreach report (Bell, Bruhns, and Gersbach 2006): If parents sicken and die while their children are still young, then all the means needed to raise the children so that they can become productive and capable citizens will be greatly reduced. The affected families’ lifetime income will shrink, and hence also the means to finance the children’s education, whether in the form of school fees or taxes. On a parent’s death, moreover, the children will lose the love, knowledge and guidance which complement formal education.

Some countries, particularly in Africa, have established policies to provide education and healthcare support to children who have lost one or both parents. These policies may have been implemented in response to the increase in HIV-associated mortality, which has resulted in millions of children losing parents to AIDS. However, a 2
father who is absent from the household while a child is young is a pervasive global phenomenon, although there is surprisingly little evidence that shows how children are affected by the long-term departure of one or more parents and that recommends policies to protect children against such adversities. In this paper, we first investigate how the father’s departure from the household that results in his permanent absence affects children’s school enrollment and work participation in Colombia.<sup>1</sup> We are interested in the effects on children’s school and work participation because of the importance of these institutions for human capital accumulation. Moreover, child labor affects family income and current poverty, which is the reason we may expect it to increase to compensate for income reductions. We then explore the rollout of a conditional cash transfer (CCT) program, _Familias en Acción_ (the purpose of which is to increase schooling for children from poor backgrounds), and we examine the extent to which the effects of the CCT program mitigate the adverse effects that are caused by the father's permanent absence.

The departure of the father from the family household is a relatively rare occurrence among households in rural Colombia. To focus on more permanent reductions in income, which are more difficult to insure against than transitory departures, we consider only departures due to death and divorce, which we can be confident are permanent. A central concern is that divorce or widowhood is not exogenous with respect to other determinants of child outcomes (see van de Walle [2011] for related selection issues). Previous work has attempted to exploit exogenous variation to overcome this problem, for instance, in divorce laws (Gruber 2004) and child sex composition (Dahl and Moretti 2008). In this paper, we provide several pieces of evidence that, taken together, build confidence in the quasi-random nature of the father’s departure. First, we 3
show that important observable characteristics of households (before the departure occurred) in which the father did and did not subsequently depart are similar. Although reassuring, the concern remains that unobserved heterogeneities might distinguish these types of households. Thus, we address time-invariant unobserved heterogeneity by allowing for household-fixed effects over a three-year period with a panel of households. Consistent with the related literature (see, for example, De Janvry et al. [2006]), our empirical method assumes common trends across both types of households. Note that this is conditional on a set of covariates, including transitory income shocks, which makes our approach more credible. We assess the plausibility of this assumption of common trends by examining predeparture trends in children’s schooling and per capita income across households in which the father does and does not subsequently depart. We are reassured by the fact that these trends do not differ significantly. To address endogeneity concerns caused by potential correlation with time-varying shocks, we determine whether divorce is correlated with recent significant time-varying shocks, including crop losses, business losses, and illnesses. We find that it is not correlated in our sample. Finally, to build more confidence in the quasi-random nature of the departure, we perform a falsification exercise by determining whether current child activities are correlated with future departure of the father (that is, if departure is quasi-random, then future departure should not lead to a significant effect on current activities). Reassuringly, we find no evidence that it does.

In the setting we consider in Colombia, our main finding is that the father’s permanent absence from the household adversely affects the schooling of both boys and girls and increases their participation in paid and unpaid work. These findings are 4
particularly pronounced for poorer households, which are likely to face more severe liquidity constraints, and are consistent with the father’s absence affecting activities directly through the associated income reduction. A second key finding of the paper is that the CCT program _Familias en Acción_ helps protect children against the vagaries of the event by protecting their schooling and offsetting increased child labor after the father’s departure. Because the CCT program effectively acts as a safety net, it appears that the main impact of the father’s departure is felt through its associated income loss.

The paper is structured as follows. In section I, we provide a brief overview of the related literature. Section II describes the data that we use in this research. We discuss identification issues in section III and present the empirical methodology and main results in section IV. Section V considers whether the CCT program introduced into the environment we consider has cushioned poor households in our sample against these effects, and section VI concludes.

# **<<A>>** _<mark>I. RELATED LITERATURE</mark>_

Our work fits into several strands of the literature. First, it is related to the growing literature in developing countries on parental death and children’s education. This literature investigates the importance of different channels in explaining the observed effects (Case, Paxson, and Ableidinger 2004; Gertler, Levine, and Ames 2004; Yamano and Jayne 2005; Beegle, De Weerdt, and Dercon 2006, 2010; Evans and Miguel 2007; van de Walle 2011). The literature generally finds adverse effects on schooling, particularly on primary school participation. However, this literature generally does not consider effects on child labor; clearly, this is an important economic activity among 5
children in developing countries and one that may be particularly responsive to an event that induces a substantial income reduction. We consider the permanent departure of the father through either death or divorce, which are the two events in our data that consistently result in substantial income reduction for households. Although the channels through which these events might affect outcomes may differ,<sup>2</sup> our empirical work suggests that income reduction is the main driver of the observed effects.

More generally, our work is related to the literature that considers the causal effects of family disruption on child outcomes, which has concentrated on the United States and other developed countries. Although this literature has mainly focused on the permanent absence of a parent resulting from death or divorce, recent work has also highlighted the negative effects of parental migration on children’s schooling— particularly the migration of the father because it is males who migrate in most contexts (see Antman 2013 for a survey; Giannelli and Mangiavacchi 2010; Lahaie et al. 2009). A key finding throughout this literature is that the absence of a parent is likely to be correlated with unobserved factors, which may also explain the poorer outcomes of the children. Several methods have been used to account for the influence of such factors. For example, some studies have used sibling-difference (household fixed effects) models (Case, Lin, and McLanahan 2001; Ermisch and Francesconi 2001; Ginther and Pollak 2004; Gennetian 2005) that account for the fixed and unobservable endowments that are shared by siblings from the same family or the same mother, whereas others have compared children’s outcomes before and after the divorce of their parents (Cherlin et al. 1991; Painter and Levine, 2000), which assumes that preexisting disadvantages of the family or the child are captured by child fixed effects. Finally, quasi-experimental studies 6
have either considered parental death as an exogenous source of parental absence (Biblarz and Gottainer 2000; Corak 2001; Lang and Zagorsky, 2001) or exploited exogenous variation in separation rates based on differences in divorce laws across states (Gruber 2004) or over time (Piketty 2003).

Our work also dovetails with the literature that considers the relationship between children’s work participation and negative income shocks in developing countries, such as labor market shocks (Parker and Skoufias 2006) or crop losses (Jacoby and Skoufias 1997; Dehejia and Gatti 2005; Beegle, Dehejia, and Gatti 2006; Dammert 2007; Duryea, Lam, and Levison 2007; Gubert and Robilliard 2008; Guarcello, Mealli, and Rosati 2010). As described in this strand of the literature, our results are consistent with the presence of credit and insurance market failures in rural Colombia.

The second part of the paper, which provides evidence that the CCT programs attenuated the negative income effects entailed by the permanent absence of the father on children’s activities, is consistent with the growing literature on the role of CCTs as safety nets. Indeed, CCT programs are a rapidly growing part of social safety-net policies, and there is evidence that they provide households with protection against both systemic and idiosyncratic short-term shocks. For instance, De Janvry et al. (2006) show that the Mexican PROGRESA program fully protected children’s schooling from shocks caused by unemployment and illness of the head of the household and by natural disasters in the community. Maluccio (2005) shows that the Nicaraguan _Red de Protección Social_ protected families’ total expenses (including food expenses) and children’s school attendance against the effects of the Central American coffee crisis in 2000–2001. More recently, Gitter, Manley, and Barham (2011) offer evidence that CCT programs have 7
mitigated the effects of negative shocks on physical development in early childhood. Our results are consistent with these papers, suggesting that CCT programs do provide a safety net against income losses for poor households. A distinctive feature of our work is that we consider income losses that are likely to be permanent and that are therefore even more difficult to insure against than transitory reductions in income.

# **<<A>>** _<mark>II. DATA</mark>_

In this section, we discuss the data used in the paper and present key descriptive statistics relating to our sample.

# **<<B>>** _<mark>Background</mark>_

We use three years of panel data from a survey of households and individuals in rural Colombia. These data were collected to evaluate the large-scale welfare program _Familias en Acción_ , which has been in place in several rural areas of Colombia since 2002 and has since expanded to cover urban areas. The program aims to alleviate poverty by fostering human capital accumulation among the poorest households through conditional subsidies for investments in education, nutrition, and health.

The first wave of data collection for the evaluation of the program occurred in 2002, when approximately 11,500 households were interviewed. We refer to this as the baseline survey. A year later, after the program began, a second wave of data was collected, and a third wave was collected in 2006. We refer to these as the first and second follow-up surveys, respectively. In this paper, we estimate the effects of the father’s permanent absence on children’s outcomes.<sup>3</sup> The socioeconomic data are rich and reflect face-to-face interviews that lasted 3.5 hours on average.

8
# **<<B>>** _<mark>Descriptive Statistics</mark>_

We follow the school and work status of children in households with at least one child aged 7–14 years at the baseline across the first follow-up survey (1 year later) and the second follow-up (3.5 years after the baseline) until they are 17 years of age, at most. Because we consider the effects of the father’s departure since the baseline, we restrict the sample to households in which both parents are present at the baseline.<sup>4</sup>

**<<C>>** _<mark>Outcomes.</mark>_ We consider two outcomes: school enrollment, which relates to whether the child is enrolled in school at the time of the survey, and work participation, which includes all types of paid and unpaid economic activities and includes looking for work as a main activity.<sup>5</sup> Table 1 shows the proportions of our sample enrolled in school and participating in work by age and gender (table 1). The table indicates that school participation rates are high among children aged 7–11 years, which corresponds to primary school.<sup>6</sup> The first substantial drop in school enrollment is observed at age 12, which is the transition from primary to secondary school. Another point worth noting is that the school enrollment of females is higher than that of males. Engagement in work is approximately twice as high for males as for females and is very low for both before the age of 12 (participation in work is not recorded for individuals under 10 years of age).

**<<C>>** _<mark>Permanent absence of the father.</mark>_ To capture a potentially important disruption to family life and long-term reduction in income, we focus on the departure of the father from the household since the baseline. In particular, we focus on departure that results in the father’s subsequent permanent absence from the household.<sup>7</sup> Divorce and death are the two primary reasons for permanent absences that are identifiable from the data. Because these are relatively rare events, we pool them to improve statistical

9
precision.<sup>8</sup> There may be some concern that these events result in different levels of transfers to the household. However, in our data, we found that the amount of transfers received by the household is very similar in magnitude after death and divorce. This is shown in table S1 in the appendix.

To measure the incidence of divorce, we combine information on the marital status of the child’s mother at times _t_ – 1 and _t_ and the status of the father at time _t_ . In particular, if the mother's marital status at time _t_ is divorced and her status at time _t_ – 1 is married and if the father’s status at time _t_ is “no longer in the household”, we consider this a divorce. Deaths, in contrast, are coded directly into the survey. The departure of the father as the result of death or divorce occurred in 5.6 percent of our sample of households (that is, those with at least one 7- to 14-year-old at baseline). Divorce accounts for 82 percent of such departures, and death accounts for 18 percent.<sup>9</sup> The average age of fathers who leave the household is 43 years at the baseline, which results in a substantial income reduction because 90 percent of fathers were working at the baseline. To provide an idea of the extent of the income loss associated with departure, we compared total household labor earnings across households with and without an absent father. Total labor earnings are approximately 22 percent lower after controlling for household composition (number of male adults, number of female adults, number of children aged 0–6 years, and number of children aged 7–17 years).<sup>10</sup> We also compared total household consumption, a more direct indicator of the welfare of the households in our sample, and found that it was lower by approximately 13 percent in households in which the father subsequently departed than in households in which he did 10
not depart, after controlling for household composition as above. Both differences were statistically significant at the 1 percent level.

Regardless of whether such events can be fully anticipated, it is unlikely that the households in our sample have mechanisms to fully insure against the income losses they entail, particularly because these households are in rural municipalities in which credit and insurance markets are thin (Edmonds 2006). Our data confirm that although monetary transfers and in-kind transfers increase significantly after the departure of the father, the magnitude is small compared to the income loss the departure entails.<sup>11</sup> Under these conditions, we expect paternal absence to affect decisions about sending children to school or work. Furthermore, paternal absence is likely to have a number of other important repercussions (see, for example, Gertler, Levine, and Ames [2004] for a discussion). First, the father is likely to have been one of the key decision makers in the household, and his departure may bring about changes in bargaining power and decision making within the household that may affect decisions about children’s education and work. Second, the father may be an important figurehead for children. Although we cannot disentangle these channels in the available data, in anticipation of our results, we note that the evidence is strongly consistent with income loss as the key factor that affects children’s activities.

# **<<A>>** _<mark>III. IDENTIFICATION</mark>_

Two issues that arise in identification are related to the potential endogeneity of parental absence and attrition from the sample over time. In this section, we discuss each of these issues in turn.

11
# **<<B>>** _<mark>Endogeneity</mark>_

An important concern about paternal absence, and one that has received considerable attention in the related literature (see, for example, Gruber [2004]), is that it may not be exogenous to the outcomes of interest (that is, children’s work and schooling). For instance, couples may divorce because they have different preferences for investment in children, in which case we may be identifying the effects of preferences rather than divorce per se.<sup>12</sup> Although it is reassuring that the predeparture (that is, baseline) observed characteristics of households that do and do not experience the subsequent departure of the father are similar (the chief difference mainly relates to the education level of the head of household, as shown in table 2), it is important to address endogeneity concerns.

In our empirical work, we address these concerns in two ways. First, we control for time-invariant unobserved confounding factors through household fixed effects.

Accordingly, our identification strategy relies on the assumption that the time trends are the same in households in which the father does and does not depart. We examine the plausibility of this assumption in detail below. Second, to address the concern that there may be time-varying factors correlated with the father’s departure and child outcomes, we assess the relationship between divorce and observed time-varying shocks, including crop losses, business losses, and illnesses. We then control for such time-varying shocks in the analysis to improve the conditional exogeneity of paternal departure.

To examine the plausibility of the common trends assumption, we investigate trends in two key variables. First, we examine whether trends in children’s schooling were the same in both types of households before the father departed. We have two 12
periods of school enrollment data before the departure, the baseline (2002) and the year before (collected retrospectively at the baseline). We cannot reject the possibility that schooling trends are the same in both types of households, as indicated by the statistically insignificant coefficient of the interaction between the type of household (“absence”) and the year dummy in the upper panel of table 3. As a second check for common trends, we compare trends in household per capita income in both types of households before the father departed. We have three periods of income data before the departure, all collected retrospectively at the baseline. The evolution of per capita household labor income in the years 1999, 2000, and 2001 is similar across both types of households prior to the father's departure (lower panel of table 3). This finding gives us no reason to believe that they would have differed if departure had not occurred.

It is also notable that the signs of the estimates in table 3 point to a positive selection of families with absent fathers in terms of schooling and income trends (although the selection is not statistically significant).<sup>13</sup> In this worst-case scenario, we would underestimate the magnitude of the “true” impacts of departure. To anticipate our main results, the detrimental impacts of the permanent absence of the father on school enrollment and work would be even more pronounced than what we find in our study.

As an additional exercise to build confidence in the quasi-random nature of the father’s absence, we determine whether current child activities are correlated with the future\e absence of the father; that is, _future_ absence should not lead to a significant effect on current activities if departure is random. To do so, we regress current children’s activities (schooling/work at time _t_ for _t_ = 1,2) on future absence (at time _t_ + 1).

13
Reassuringly, we find extremely small and statistically insignificant correlations between these variables (0.009 for schooling, –0.009 for work, both with P values of 0.5).

Although all of the evidence above is reassuring, it does not address concerns that there may be unobserved time-varying shocks that affect both the father’s permanent departure and the children’s outcomes. For instance, a temporary shock to income, such as a crop/business loss or illness, may affect the quality of the marital relationship and the likelihood of divorce in addition to affecting children’s outcomes. We can gauge the importance of this type of shock to some extent because households report the most important shocks in the year prior to the survey, including crop loss, illness, and business loss. When we check whether such shocks in period _t_ – 1 are correlated with divorce in period _t_ , we find no correlation (table 4), which is reassuring. When we control for the point estimates of the main coefficients of interest with and without such shocks in our empirical work, we note that they are very similar.

Taken together, the above evidence helps to build confidence in the quasi-random nature of the father’s absence. We reiterate that we control for time-invariant unobserved household-level characteristics and time-varying observed characteristics (including shocks) throughout the empirical analysis.

# **<<B>>** _<mark>Attrition</mark>_

Overall, approximately 5 percent of households left the sample between the baseline survey and the first follow-up, and an additional 8.5 percent of households left between the first and second follow-ups (3.5 years after the baseline).<sup>14</sup> Although this attrition rate is relatively low,<sup>15</sup> it is a concern if the reason for leaving the sample is related to the behavior being modeled, as might be the case if households from which the 14
father departs are more likely to drop out of the sample. To address this concern, we compare the baseline characteristics of households that did and did not subsequently leave the sample (table 5). As expected, households that own a house or that do not live at high altitudes are significantly less likely to abandon the study than those who do not own a house or who live at high altitudes. Other than these factors, attrition is not systematically related to any of the variables considered in the table. Although this is reassuring, potential selection biases on the basis of unobserved characteristics cannot be ruled out, for which we account in our empirical work. The methods we use to correct for this are discussed in section IV. All results presented take into account this possible selection problem, although it makes little difference to the effects that we estimate.

# **<<A>>** _<mark>IV. EFFECTS OF PERMANENT ABSENCE OF THE FATHER ON SCHOOLING AND WORK</mark>_

In this section, we present the empirical specification used to estimate the effects of the permanent absence of the father on children’s schooling and work outcomes. We then present our empirical findings.

# **<<B>>** _<mark>Main Specification</mark>_

To estimate the effects of the permanent absence of the father on children’s school and work participation, we estimate the following model: where _i_ denotes the child; _j_ denotes the household; _t_ denotes time, where _t_ = 1 denotes baseline, _t_ = 2 denotes the first follow-up, and _t_ = 3 denotes the second follow-up; _yijt_ is a discrete indicator for participation in school or work; and _Djt_ is an indicator that takes the value one if the father is absent from the household permanently and zero otherwise.

15
Note that _Dj_ 1 = 0, by definition.<sup>16</sup> If the father departed the household between the baseline and the first follow-up, then _Dj_ 2 = 1 and _Dj_ 3 = 1; if the father departed between the first and second follow-ups, then _Dj_ 2 = 0 and D _j_ 3 = 1. _Xijt_ is a vector of observed timevarying child and household characteristics, including a cubic in the age of the child and in the number of siblings in different age categories (0–6, 7–12, 13–17, 18+); _Ijt_ – 1 is a vector of time-varying shocks that occurred in the year prior to the survey, including dummies for crop losses, business losses, and illnesses; _fj_ is a household fixed effect capturing the effects of unobserved time-invariant household characteristics; δ _t_ is a survey-round dummy; and _uijt_ is an error term that we assume to be independent and identically distributed. The coefficient of interest is α 2, which is the effect of the absence of the father on the outcome (school or work participation).

We estimate equation (1) using a linear probability model and cluster the standard errors at the municipality level to adjust for potential correlations of household decisions within the same municipalities. Although the dependent variable is discrete, the main advantage of the linear model over discrete choice models in our case is that it is considerably easier to incorporate fixed effects. Most of the explanatory variables are discrete and take on only a few values in our application, which strengthens the case for the linear probability model (Wooldridge 2002, chapter 15). Although a potential limitation of the linear probability model is that it may yield predicted probabilities outside the unit interval, this is not a concern in our case because less than 3 percent of predictions lie outside the unit interval. We verify the robustness of our results to this linear specification by estimating a fixed-effects logit model (Honoré 2002). Although the estimates are less precisely estimated because they are based on the subset of children 16
who changed their activity over time, they indicate the same patterns of coefficients that are discussed in the main text on the basis of linear probability models. These are shown in table S2 in the appendix.

As discussed in section III, an important issue is that our variable of interest, the father’s permanent absence, may be correlated with unobserved household characteristics that have a direct effect on children’s schooling and work. To determine the effects of unobserved characteristics that are fixed over time and that may lead to spurious correlations between the father’s permanent absence and children’s outcomes, we use a household fixed-effects model. We also control for important time-varying shocks to mitigate concerns that shocks may determine both paternal absence and the child outcomes.

A second issue, which is also discussed in section III, is that if nonrandom attrition is present, it will yield inconsistent parameter estimates. We use a standard correction in a two-step sample selection model (Heckman 1979) and estimate the probability that the individual will not leave the survey by using a probit model: where _Sijt_ takes the value one if child _i_ from household _j_ does not leave the survey between wave _t_ – 1 and wave _t_ and zero otherwise; _Zjt_ – 1 are the instruments used for identification, discussed below; _Xijt_ – 1 are individual and household characteristics at wave _t_ – 1; δ _t_ is a survey round dummy;<sup>η</sup> _i_ is a household-level fixed effect, which may be correlated with _fj_ in equation (1); and _νijt_ is an error term.

17
The instrument set _Zjt_ – 1 includes characteristics of the previous interview, including the date (day of the month) of the interview and whether the survey respondent was the household head or spouse. Both may affect the overall experience of the interview and the respondent’s willingness to be reinterviewed, but they are unlikely to affect the outcomes of interest because they relate to the previous interview, which occurred at least a year earlier.<sup>17</sup> The estimates from equation (2) are shown in table S3 in the appendix. The instruments are jointly statistically significant at the 1 percent level. We use these estimates to construct the inverse Mills ratio, which is appended to the set of control variables in equation (1). The selection correction term is not statistically significant at conventional levels in most cases, and the estimates change very little when it is included in equation (1). Nonetheless, all reported results take into account this selection correction.

# **<<B>>** _<mark>Results</mark>_

We next turn to the estimates from our equation of interest, equation (1), which are shown in table 6. Because we observe work (schooling) for children aged 10 years (7 years) and above (see section II), we include an additional column containing estimates for schooling for the subsample aged 10 years and above to enable meaningful comparisons between the estimates for work and schooling outcomes.<sup>18</sup> We observe in column 3 that the permanent absence of the father from the household significantly increases participation in work by approximately 3 percentage points. Notably, we observe in column 2 that the increase in work comes entirely from schooling (and not leisure) because the absence of the father has a significant negative effect on school enrollment of nearly 5 percentage points.<sup>19</sup> Note that the effects on schooling for the full 18
sample, which are shown in column 1, are similar to those for the restricted sample. The estimated effects are not significantly different by gender (columns 4–6).

An important reason why the negative effects on schooling and positive effects on work may be expected, which is discussed in section II, is that households in which the father leaves permanently incur a substantial income reduction. To investigate the extent to which the income loss associated with the absence of the father underlies the estimated impacts, we interact it with the education of the household head (at the baseline, that is, predeparture), which is a proxy for household income. On the one hand, households with relatively less educated heads have less to lose from a departure through an “income effect.”<sup>20</sup> On the other hand, the relatively less well off are more likely to face credit constraints and insurance market failures and are more likely to have fewer formal ways to mitigate the effects of income loss; thus, they are likely to suffer more from the father’s absence. Accordingly, the interaction effect may run in both directions. We test it empirically in columns 7–9. We observe that the detrimental effects of the father’s absence on schooling and child labor are driven by relatively less educated households. This finding highlights the importance of liquidity constraints for these households, which dominates the effect of the loss of relatively less income with the father’s departure.

Finally, we determine whether the effects vary depending on the reason for the father’s absence by allowing the effects of death and divorce to be different. A caveat is that the incidence of death is very low, affecting just 1 percent of our sample of households (compared with 4.6 percent for divorce), which results in imprecise estimations of its effects. The results (available upon request) show that the effects appear 19
to be driven primarily by divorce, although we cannot reject that the coefficient estimates are statistically the same. In this study, we continue to pool these events because we are interested in events that induce permanent income reductions. Another reason for combining these events is to maintain statistical power given their rarity.

# **<<A>>** _<mark>V. DO CCTS HELP PROTECT CHILDREN?</mark>_

In this section, we investigate whether the effects of the father’s permanent absence on children’s outcomes differ depending on whether a CCT program is in effect. We begin by describing the CCT program. We determine whether the absence of the father due to divorce is affected by the CCTs and find no evidence that it is. We then estimate whether CCTs mitigate the adverse effects of the father’s permanent absence.

# **<<B>>** _<mark>The CCT Program</mark>_

To evaluate the impacts of the _Familias en Acción_ CCT program, a representative stratified sample of municipalities was selected. Strata were defined in terms of region and by using an index of infrastructure relating to health and education. Municipalities from the same strata that were excluded from receiving CCTs (but that were similar to eligible municipalities in terms of population, area, and a quality-of-life index) were chosen as controls.<sup>21</sup> A total of 122 municipalities were chosen for the evaluation. Of these, 70 that were eligible for CCTs received the CCTs, which were phased in during the period that we are considering; 26 received CCTs by the time of the baseline survey (“early-treat”); 31 received CCTs by the first follow-up (“mid-treat”); and 13 received CCTs by the second follow-up (“late-treat”). The final evaluation sample comprised approximately 100 households that were randomly selected in each of the 122 20
municipalities. Attanasio et al. (2010) provide an evaluation of the program’s main impacts.

# **<<B>>** _<mark>The CCT Program and Divorce</mark>_

Before studying the interaction between CCTs and the permanent absence of the father, we address the potential concern that the absence of the father—particularly in the case of divorce—may be affected by CCTs. Indeed, there is direct evidence of positive effects of the PROGRESA CCTs on the divorce rate in Mexico (Bobonis 2011) and indirect evidence that the _Familias en Acción_ CCTs may have increased the bargaining power of women (Attanasio, Battistin, and Mesnard 2012). Therefore, women receiving CCTs might be expected to transit more readily out of relationships.

To estimate the effect of CCTs on divorce, we use data from the first and second follow-ups only (because there is no variation in the outcome of divorce at the baseline; see note 3).<sup>22</sup> We estimate the following regression at the household level on our sample of households: pooling _t_ = 2 and _t_ = 3, where _yjt_ is a dummy variable indicating whether the parents living in household _j_ divorced between periods _t_ – 1 and _t_ and _Tjt_ is an indicator equal to one if household _j_ lives in a municipality that is receiving CCTs at time _t_ and zero otherwise. Note that _Tjt_ = 1 for {(early-treat = 1 or mid-treat = 1) and _t_ = 2,3} and for {late-treat = 1 and _t_ = 3}. _Xjt_ are time-varying measures of the composition of children in the household in period _t_ ; _Ijt_ – 1 is a vector of dummies indicating whether the household 21
experienced a crop loss, business loss, or illness in period _t_ – 1; _fj_ is a household fixed effect; δ _t_ is a survey round dummy; and _ujt_ is an error term.

We cannot reject the hypothesis that the cash transfers have no statistically significant effect on divorce (table 7).<sup>23</sup> It is thus unlikely that such an effect underlies the results we discuss next, which show that receiving CCTs compensates for a father’s absence.

# **<<B>>** _<mark>Interaction Effects</mark>_

There is a growing body of literature on the safety net role played by CCTs in the presence of income shocks. To our knowledge, however, no work has examined the role of CCTs in alleviating the risk entailed by permanent loss of income. To investigate whether the effects of the father’s permanent absence on children’s outcomes differ depending on whether CCTs are in place, we augment equation (1) to include an interaction between our variable of interest, father’s permanent absence, and receiving CCTs. We thus estimate the following model: where _Tjt_ is equal to one if household _j_ lives in a municipality that is receiving CCTs at time _t_ and zero otherwise; all other notations are as defined in equation (1). As before, _Tjt_ reflects the gradual rollout of the program, such that _Tjt_ = 1 for {early-treat = 1 and _t_ = 1,2,3}, {mid-treat = 1 and _t_ = 2,3}, and {late-treat = 1 and _t_ = 3}.

In equation (4), the coefficient of interest, α 2, measures the extent to which receiving CCTs mitigates the effect of the permanent absence of the father, α 1.<sup>24</sup> Note that the above specification also implicitly controls for preprogram differences in 22
outcomes across municipalities that are and are not eligible for CCTs (through fixed effects), which is potentially important because of the quasi-experimental setting.

In municipalities that are not receiving CCTs, the permanent absence of the father reduces the likelihood of school enrollment and increases the likelihood of child labor, particularly among relatively less educated households (the left-hand columns of table 8). This is indicated by the coefficient α 1 (displayed in the first row), which estimates the effect of departure in the absence of CCTs. The second row, α 2, indicates that when CCTs are in place, these adverse effects are offset (as shown by α 1 + α 2, which is close to zero and not significantly different from zero, as indicated by the P values of the test).<sup>25</sup> Finally, as a robustness check, we restrict the comparison to households living in municipalities eligible for the CCTs that fall within the common support (that is, the region in which treated individuals have a counterpart in the group of controls according to the propensity score). Consistent with Attanasio et al. (2010), we do this by matching treatment and control observations using kernel-weighted propensity score matching and by imposing common support by dropping the 10 percent of the treatment observations in which the propensity score density of the control observations is the lowest. The results are qualitatively similar and are shown in table S4 of the appendix.

The fact that the welfare program provides insurance to protect very poor children from the adverse consequences of a father’s permanent absence is perhaps not surprising to the extent that the CCTs received represent a sizeable share of income for these households (more than 20 percent of their monthly total consumption on average; see Mesnard [2009]), and the drop in household labor earnings entailed by the father’s departure is of a similar magnitude. Moreover, the welfare program is in place on a 23
permanent basis, which ensures that the insurance it provides will continue as long as the child is enrolled in school. Notably, this result is somewhat different from that of De Janvry et al. (2006), who show that PROGRESA did not prevent children from working more following shocks related to unemployment and illness of the household head, in addition to natural disasters in the community, although it fully protected their schooling.

Taken together, our results indicate the existence of credit and insurance market imperfections with adverse implications for children, who play an important role in cushioning the household against income losses entailed by the departure of fathers.

Although the psychological effects of a departing parent cannot be ruled out, we believe that they are of secondary importance to the income loss channel. In particular, we have no reason to believe that psychological impacts would be stronger among less educated populations, and they do not lend themselves to explaining why CCTs would help to mitigate such effects.

# **<<A>** **<mark>></mark>** _<mark>VI. CONCLUSION</mark>_

This paper has investigated the link between the permanent absence of the father from the household and the school enrollment and work participation of children in rural Colombia. We find that the absence of the father decreases schooling by approximately 5 percentage points and increases participation in work by approximately 3 percentage points. We provide evidence that these effects are mainly driven by households with relatively less educated heads, which are the very poorest of the indigent households in our sample. We show that receiving CCTs offsets these adverse consequences and offers children a form of insurance when the father leaves the household permanently. This 24
finding also suggests that the income reduction associated with paternal absence, which tightens the liquidity constraints of already very poor households, is the main mechanism at play.

Our results have a number of important policy implications. First, they suggest that credit and insurance market failures are potentially important in the context of rural Colombia and can contribute to reducing the human capital accumulation of children. Second, an event such as the permanent departure of the father has potentially important consequences for the schooling and employment of children. This is particularly true for households with relatively low levels of education that are vulnerable to permanent income losses because of insurance market failures. Third, these adverse effects can be offset by well-designed CCT programs targeted at very poor households, which, in the case of Colombia, represent more than 20 percent of total household consumption on average and are in place as long as the child is enrolled in school.

The last finding is the first of its type and offers an important agenda for future work. An important question is whether the same mechanism holds for investments other than schooling (such as children’s health and nutrition) and in other contexts and environments. Another question is whether this mechanism should be taken into account in the design of safety nets and in targeting them at single parents. Such a policy might have the unintended consequence of promoting single parenthood. A final thought involves the particular relevance of these findings for sub-Saharan Africa, which has witnessed a dramatic rise in orphanhood as the result of the prevalence of HIV/AIDS, with estimates suggesting that 12 percent of all children are orphaned (UNICEF 2006).

25
Families and communities have shared this burden, and it might be time to institute government support to help households cope.

# _REFERENCES_

Antman, Francisca M. 2013. “The Impact of Migration on Family Left Behind.” Forthcoming in _The International Handbook on the Economics of Migration_ , eds. Amelie Constant and Klaus F. Zimmermann, Chapter 16. Edward Elgar. Attanasio, Orazio, Emla Fitzsimons, Ana Gomez, Maria I. Gutiérrez, Costas Meghir, and Alice Mesnard. 2010. “Child Education and Work Choices in the Presence of a Conditional Cash Transfer Program in Rural Colombia.” _Economic Development and Cultural Change_ 58 (2): 181–210.

———, Erich Battistin, and Alice Mesnard. 2012. “Food and Cash Transfers: Evidence from Colombia.” _Economic Journal_ 122 (559): 92–124.

Beegle, Kathleen, Rajeev Dehejia, and Roberta Gatti. 2006. “Child Labor and Agricultural Shocks.” _Journal of Development Economics_ 81 (1): 80–96.

———, Joachim De Weerdt, and Stefan Dercon. 2006. “Orphanhood and the Long-Run Impact on Children.” _American Journal of Agricultural Economics_ 88 (5): 1266– 72.

———, Joachim De Weerdt, and Stefan Dercon. 2010. “Orphanhood and Human Capital Destruction: Is There Persistence into Adulthood?” _Demography_ , 47 (1): 163–80.

26
Bell, Clive, Ramona Bruhns, and Hans Gersbach. 2006. “Economic Growth, Education, and AIDS in Kenya: A Long-Run Analysis.” World Bank Policy Research Working Paper 4025. Washington, DC: World Bank.

Biblarz, Timothy J., and Greg Gottainer. 2000. “Family Structure and Children’s Success: A Comparison of Widowed and Divorced Single-Mother Families.” _Journal of Marriage and Family_ 62 (2): 533–48.

Bobonis, Gustavo J. 2011. “The Impact of Conditional Cash Transfers on Marriage and Divorce.” _Economic Development and Cultural Change_ 59 (2): 281–312.

Case, Anne, I-Fen Lin _,_ and Sara McLanahan. 2001. “Educational Attainment of Siblings in Stepfamilies.” _Evolution and Human Behavior_ 22 (4): 269–89.

———, Christina Paxson, and Joseph Ableidinger. 2004. “Orphans in Africa: Parental Death, Poverty and School Enrolment.” _Demography_ 41 (3): 483–508.

Cherlin, Andrew J., Frank F. Furstenberg, P. Lindsay Chase-Lansdale, Kathleen E. Kiernan, Philip K. Robins, Donna R. Morrison, and Julien O. Teitler. 1991. “Longitudinal Studies of Effects of Divorce on Children in Great Britain and the United States.” _Science_ 252 (5011): 1386–9.

Corak, Miles 2001. “Death and Divorce: The Long-Term Consequences of Parental Loss on Adolescents.” _Journal of Labor Economics_ 19 (3): 682–715.

Dahl, Gordon, and Enrico Moretti. 2008. “The Demand for Sons.” _Review of Economic Studies_ 75 (4): 1085–120.

Dammert, Ana. 2007. “Child Labor and Schooling Response to Changes in Cocoa Production in Rural Peru.” IZA Discussion Paper 2869. Bonn: IZA.

27
Dehejia, Rajeev, and Roberta Gatti. 2005. “Child Labor and Agricultural Shocks.” _Journal of Development Economics_ 81 (1): 80–96.

De Janvry, Alain, Frederico Finan, Elizabeth Sadoulet, and Renos Vakis. 2006. “Can Conditional Cash Transfer Programs Serve as _Safety_ - _Nets_ in Keeping Children at School and from Working when Exposed to Shocks?” _Journal of Development Economics_ 79 (2): 349–73.

Duryea, Suzanne, David Lam, and Deborah Levison. 2007. “Effects of Economic Shocks on Children’s Employment and Schooling in Brazil.” _Journal of Development Economics_ 84 (1): 188–214.

Edmonds, Eric. 2006. “Child Labor and Schooling Responses to Anticipated Income in South Africa.” _Journal of Development Economics_ 81 (2): 386–414.

Ermisch, John F., and Marco Francesconi. 2001. “Family Structure and Children’s Achievements.” _Journal of Population Economics_ 14 (2): 249–70.

Evans, David, and Edward Miguel. 2007. “Orphans and Schooling in Africa: A Longitudinal Analysis.” _Demography_ 44 (1): 35–57.

Fiszbein, Ariel, Norbert Schady, Francesco Ferreira, Margaret Grosh, Niall Keleher, Pedro Olinto, and Emmanuel Skoufias. 2009. _Conditional Cash Transfers: Reducing Present and Future Poverty_ . Washington, DC: World Bank.

Gennetian, Lisa. 2005. “One or Two Parents? Half or Step Siblings? The Effect of Family Composition on Young Children.” _Journal of Population Economics_ 18 (3): 415–36.

28
Gertler, Paul, David Levine, and Minnie Ames. 2004. “Schooling and Parental Death.” _Review of Economics and Statistics_ 86 (1): 211–25.

Giannelli, Gianna, and Lucia Mangiavacchi. 2010. “Children’s Schooling and Parental Migration: Empirical Evidence on the ‘Left-Behind’ Generation in Albania.” _Labour_ 24 (S1): 76–92.

Ginther, Donna, and Robert Pollak. 2004. “Family Structure and Children’s Educational Outcomes: Blended Families, Stylized Facts, and Descriptive Regressions.” _Demography_ 41 (4): 671–96.

Gitter, Seth, James Manley, and Brad Barham. 2011. “The Coffee Crisis, Early Childhood Development, and Conditional Cash Transfers.” IDB Working Paper 245. Washington, D.C.: Inter-American Development Bank.

Gruber, Jonathan. 2004. “Is Making Divorce Easier Bad for Children? The Long-Run Implications of Unilateral Divorce.” _Journal of Labor Economics_ 22 (4): 799– 833.

Guarcello, Lorenzo, Fabrizia Mealli, and Furio Rosati. 2010. “Household Vulnerability and Child Labor: The Effects of Shocks, Credit Rationing, and Insurance.” _Journal of Population Economics_ 23 (1): 169-198.

Gubert, Flore, and Anne Sophie Robilliard. 2008. “Risk and Schooling Decisions in Rural Madagascar: A Panel Data-Analysis.” _Journal of African Economies_ 17 (2): 207–38.

Heckman, James. 1979 “Sample Selection Bias as a Specification Error” _Econometrica: Journal of the Econometric Society_ , 153–161.

29
Honoré, Bo E. 2002. “Nonlinear Models with Panel Data.” _Portuguese Economic Journal_ 1: 163–179.

Jacoby, Hanan, and Emmanuel Skoufias. 1997. “Risk, Financial Markets, and Human Capital in a Developing Country.” _Review of Economic Studies_ 64 (3): 311–35.

Lahaie, Claudia, Jeffrey Hayes, Tinka Piper, and Jody Heymann. 2009. “Work and Family Divided across Borders: The Impact of Parental Migration on Mexican Children in Transnational Families.” _Community, Work and Family_ 12 (3): 299– 312.

Lang, Kevin, and Jay Zagorsky. 2001, “Does Growing Up with a Parent Absent Really Hurt?” _Journal of Human Resources_ 36 (2): 253–73.

Maluccio, John. 2005. “Coping with the ‘Coffee Crisis’ in Central America: The Role of the Nicaraguan _Red de Protección Social_ .” FCND Discussion Paper 188.

Washington, DC: International Food Policy Research Institute (IFPRI).

Mesnard, Alice. 2009. “Migration, Violence and Welfare Programs in Rural Colombia.” IFS Working Paper 09/19. London: Institute for Fiscal Studies.

Painter, Gary, and David Levine. 2000. “Family Structure and Youths’ Outcomes: Which Correlations are Causal?” _Journal of Human Resources_ 35 (3): 524–49.

Parker, Susan W., and Emmanuel Skoufias. 2006. “Job Loss and Family Adjustments in Work and Schooling during the Mexican Peso Crisis.” _Journal of Population Economics_ 19 (1): 163–81.

30
Piketty, Thomas. 2003. “The Impact of Divorce on School Performance: Evidence from France, 1968–2002.” CEPR Discussion Paper 4146. London: Centre for Economic Policy Research.

Ravallion, Martin, and Quentin Wodon. 2000. “Does Child Labour Displace Schooling? Evidence on Behavioural Responses to an Enrollment Subsidy.” _Economic Journal_ 110 (462): C158–75.

UNICEF. 2006. _Africa’s Orphaned and Vulnerable Generations: Children Affected by AIDS_ . New York, NY: UNICEF.

Van de Walle, Dominique. 2011. “Lasting Welfare Effects of Widowhood in a Poor Country.” World Bank Policy Research Working Paper 5734. Washington, DC: World Bank.

Wooldridge, Jeffrey. 2002. _Econometric Analysis of Cross Section and Panel Data_ . Cambridge, MA: MIT Press.

Yamano, Takashi, and Thomas S. Jayne. 2005. “Working-Age Adult Mortality and Primary School Attendance in Rural Kenya.” _Economic Development and Cultural Change_ 53 (3): 619–54.

31
|TABLE|1. School a|nd Work Participation,by<br>Males|Age,Surveyand Gender<br>Females|
|---|---|---|---|
|Age at|Baseline|First<br>Second|Baseline<br>First<br>Second|
|||(+3.5|(+3.5|
|baseline||(+1yr)<br>yrs)|(+1yr)<br>yrs)|
|||School|School|
|||enrolment|enrolment|
|||**%**|**%**|
|7|0.904|0.928<br>0.963|0.922<br>0.953<br>0.970|
|8|0.935|0.951<br>0.933|0.961<br>0.959<br>0.947|
|9|0.952|0.943<br>0.895|0.966<br>0.960<br>0.918|
|10|0.932|0.907<br>0.813|0.958<br>0.950<br>0.867|
|11|0.917|0.884<br>0.764|0.935<br>0.901<br>0.835|
|12|0.856|0.782<br>**_0.675_**|0.897<br>0.859<br>**_0.786_**|
|13|0.791|0.755<br>**_0.577_**|0.832<br>0.791<br>**_0.633_**|
|14|0.660|**_0.620_**<br>**_0.457_**|0.740<br>**_0.728_**<br>**_0.536_**|
|N|6090|5726<br>5033|5589<br>5266<br>4482|
||W|orkparticipation|Workparticipation|
|||**%**|%|
|10|0.021|0.019<br>0.083|0.010<br>0.006<br>0.045|
|11|0.031|0.050<br>0.132|0.012<br>0.025<br>0.057|
|12|0.057|0.093<br>**_0.209_**|0.029<br>0.042<br>**_0.117_**|
|13|0.109|0.148<br>**_0.284_**|0.057<br>0.086<br>**_0.143_**|
|14|0.213|**_0.285_**<br>**_0.371_**|0.091<br>**_0.169_**<br>**_0.203_**|
|N<br>_Notes:_W|3672<br>ork includes|4233<br>5022<br>full-time paid and unpaid|3265<br>3870<br>4480<br>activities and looking for work as a|

main activity. Figures in bold (italics) denote ages corresponding to post-compulsory schooling. Note that +1 yr (3.5 yrs) means 1 yr (3.5 yrs) after the baseline survey. N denotes the number of individuals (aged 7-14 at baseline) present in the survey listed at top of column. Schooling observed for children aged ≥7; work observed for children aged ≥10 years.

32
<mark>TABLE 2. Comparison of Baseline (Predeparture) Characteristics across Households That Do and Do Not Experience Subsequent Departure</mark>

||Permanen<br>|t absence o|f father|
|---|---|---|---|
|Characteristic,Baseline↓|(D)|||
||D = 1|D = 0|P value|
|Age of household head|42.88|42.21|0.182|
|Age of spouse|37.81|37.14|0.110|
|_Education of head_||||
|None|0.282|0.230|**0.015**|
|Some(complete/incompleteprimary)|0.535|0.638|**0.000**|
|High(incomplete secondaryor more)|0.181|0.132|**0.004**|
|_Education of spouse_||||
|None|0.199|0.195|0.846|
|Some (complete/incomplete primary)|0.633|0.661|0.238|
|High(Incomplete secondaryor more)|0.168|0.144|0.171|
|_Household composition_||||
|Avg. # of children ≤ 6years|0.388|0.467|**0.011**|
|Avg. # of boys 7–11years|0.727|0.738|0.775|
|Avg. # ofgirls 7–11years|0.718|0.684|0.366|
|Avg. # of boys 12–17years|0.635|0.641|0.890|
|Avg. # ofgirls 12–17years|0.581|0.590|0.809|
|Avg. # of female adults|1.232|1.244|0.708|
|Avg. # of male adults|1.366|1.396|0.432|
|School enrollment rate of||||
|7- to 14-year-olds in household|0.924|0.899|0.057|
|Household monthlyconsumption|421,286|441,994|0.085|
|Program area|0.700|0.682|0.453|
|Altitude|574.45|601.90|0.451|
|N|426|5,720||

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Sample consists of households in which both parents are present at the baseline and there is at least one 7- to 14-year-old child. N denotes the number of households at baseline. P values are based on standard errors clustered at the municipality level. Figures in bold in column (4) indicate that the correlation of the figures in columns (2) and (3) are significant at the 5 percent level or less.

33
|TABLE 3. Common Trends: Schoolingand Income<br>School enrollment<sup>a</sup>|
|---|
|Year = 2002<br>0.0343**<br>(0.005)|
|Absence * Year = 2002<br>0.0238|
|(0.0143)|
|N<sup>1</sup><br>11,679|
|Per capita income<sup>b</sup>|
|Year = 2000<br>0.498**|
|(0.111)|
|Year = 2001<br>1.1019**|
|(0.1432)|
|Absence * Year = 2000<br>−0.0845|
|(0.4641)|
|Absence * Year = 2001<br>0.6144|
|(0.6028)|
|N<sup>2</sup><br>5,066|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ N<sup>1</sup> is the number of children in the sample at the baseline with nonmissing school enrollment data. N<sup>2</sup> is the number of households in the sample at baseline that report income retrospectively for 1999, 2000, and 2001. Standard errors, clustered at the municipality level, in parentheses.

a Dependent variable is school enrollment. Estimates are from a household fixed effects model, controlled for quadratic in child age and gender (female = 1). Reference year is 2001.

b Dependent variable is per capita household labor income. Estimates are from a household fixed-effects model. Reference year is 1999.

34
<mark>TABLE 4. Correlation between Divorce and Shocks in Previous Period</mark>

||Divorce (t)|
|---|---|
|Croploss(t − 1)|−0.0033<br>(0.0045)|
|Business loss(t − 1)|0.0205<br>(0.0181)|
|Illness(t − 1)|−0.0061<br>(0.0071)|
|P value forjoint significance|0.55|
|N|5,796|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Dependent variable is divorce. Reference year is 2001. Estimates are from a household fixed-effects model pooling first and second follow-ups, controlled for child composition and time dummies. N is the number of households remaining in the sample by first follow-up. Standard errors, clustered at the municipality level, are in parentheses.

35
<mark>TABLE 5. Comparison of Characteristics across Households That Do and Do Not Abandon the Study at Any Time after Baseline</mark>

|Abandon the Studyat Any|Time after Baseline<br>Did|<br>Did||
|---|---|---|---|
|Baseline Characteristics|not abandon|abandon|P value difference|
|Age of head|42.1123|42.3617|0.5057|
|Age of spouse|37.1413|37.4586|0.2987|
|Head no education|0.2309|0.2497|0.2263|
|Spouse no education|0.1927|0.2120|0.1889|
|Head some education|0.6321|0.6208|0.5251|
|Spouse some education|0.6579|0.6655|0.6668|
|Head high education|0.1365|0.1260|0.4047|
|Spouse high education|0.1493|0.1225|**0.0398**|
|Treated area|0.6844|0.6756|0.6064|
|Altitude|577.69|726.43|**0.0000**|
|Crop loss at first survey|0.1339|0.1249|0.4708<br>**0.0000**|
|Owns house|0.6466|0.5321||
|N|5,289|857||

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Sample consists of households in which both parents are present at the baseline and there is at least one 7- to 14-year-old child. N denotes the number of households at baseline. P values are based on standard errors clustered at the municipality level. Figures in bold in column (4) indicate that the correlation of the figures in columns (2) and (3) are significant at the 5 percent level or less.

36
<mark>TABLE 6. Marginal Effects of the Father’s Absence on Children’s Schooling and Work</mark>

||School<br>Overall|School<br>Restri|Work<br>cted|School<br>Overall|School<br>Restri|Work<br>cted|School<br>Overall|School<br>Restri|Work<br>cted|
|---|---|---|---|---|---|---|---|---|---|
|Permanent Absence|−0.0412*|−0.0484*|0.0301+|−0.0422+|−0.0486+|0.0361|−0.0556**|−0.0641**|0.0360+|
||(0.0172)|(0.0216)|(0.0167)|(0.0233)|(0.0289)|(0.0233)|(0.0193)|(0.0234)|(0.0183)|
|Permanent Absence * Girl||||0.0021|0.0005|−0.0122||||
|||||(0.0241)|(0.0275)|(0.0245)||||
|Permanent Absence * Head High Education|||||||0.0742*|0.0851*|−0.0319|
||||||||(0.0313)|(0.0367)|(0.0295)|
|N|32,186|24,531|24,531|32,186|24,531|24,531|32,186|24,531|24,531|
|_Source: Familias en Accion_Evaluation Data.||||||||||

_<mark>Note:</mark>_ Marginal effects are presented from a fixed-effects linear-probability model (equation (1)), controlling for absence of father from household for unknown reason; absence of both parents; time dummies; cubic in child age; sibling composition; dummies for crop losses, illness, and business shocks; and the inverse mills ratio, as computed in equation (2) (see table S3). High education is one for incomplete secondary or more at baseline, zero otherwise. N is the number of children in the sample pooled across three waves. We included 6,146 households in our initial sample, from which 426 fathers have subsequently departed. Schooling is observed for all children in sample ≥7 years (overall sample); work is observed for children ≥10 years (restricted sample). Robust standard errors clustered at the municipality level are in parentheses. + significant at 10 percent; *significant at 5 percent; ** significant at 1 percent.

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|TABLE 7. Marginal Effects of CCTs on<br>Divorce|
|---|
|Pr(Divorce = 1)|
|CCTs<br>0.0024|
|(0.0082)|
|Time = 2<br>−0.0286|
|(0.0039)|
|N<br>5,796|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Marginal effects are presented from equation from a fixed-effects linear-probability model (equation (3)), pooling first and second follow-ups and controlling for child composition; dummies for crop losses, illness, and business shocks; and the inverse mills ratio, as computed in equation (2) (see table S3). N is the number of households that did not abandon the study by the first follow-up. Robust standard errors clustered at the municipality level are in parentheses.

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TABLE 8. Cushioning Effects of CCTs: Marginal Effects on Schooling and Work

||<br>|Low Educati<br>|on<br>||All<br>||
|---|---|---|---|---|---|---|
||School|School|Work|School|School|Work|
||Overall<br>−0.103*|Rest|ricted|Overall|Restr|icted|
|Permanent Absence (α1)|*|−0.114*|0.0782*|−0.0801**|−0.0919*|0.0694*|
||(0.0341)|(0.0437)|(0.0355)|(0.0284)|(0.0376)|(0.0300)|
|Permanent Absence|||||||
|* CCTs (α2)|0.0818*|0.0833+|−0.0687+|0.0566+|0.0615|−0.0542+|
||(0.0381)|(0.0472)|(0.0356)|(0.0330)|(0.0420)|(0.0307)<br>−0.0257*|
|CCTs (α3)|0.0143|0.0057|−0.0262**|0.0121|0.0053|*|
||(0.0123)|(0.0148)|(0.0096)|(0.0112)|(0.0134)|(0.00902)|
|Time = 2|0.1080|0.1520|−0.2010*|0.0783|0.1180|−0.1500+|
||(0.0957)|(0.105)|(0.0847)|(0.0872)|(0.0964)|(0.0773)|
|Time = 3|0.0261|0.0689|−0.148+|−0.0025|0.0347|−0.1010|
||(0.1010)|(0.1100)|(0.0889)|(0.0927)|(0.1020)|(0.0809)|
|P value (α1+α2) = 0|0.3498|0.2636|0.6224|0.2633|0.2379|0.3854|
|N|28,027|21,464|21,464|32,186|24,531|24,531|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Marginal effects are presented from a fixed-effects linear-probability model (equation (4)), controlling for absence of father for unknown reason; absence of both parents; cubic in child age; sibling composition; dummies for crop losses, illness, and business shocks; and the inverse mills ratio, as computed in equation (2) (see table S3). “CCTs” indicates whether the household lived in a municipality that was receiving CCTs at the time of survey. N is the number of children in the sample pooled across three waves. We included 6,146 households in our initial sample, from which 426 fathers have subsequently departed. Schooling is observed for all children in sample ≥7 years ('overall' sample); work is observed for children ≥10 years ('restricted' sample). Robust standard errors clustered at the municipality level are in parentheses. + significant at 10 percent; *significant at 5 percent; ** significant at 1 percent.

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