Reallocating children's time: Coping strategies after the 2010 Haiti earthquake

Novella, Rafael; Zanuso, Claire Article Reallocating children's time: Coping strategies after the 2010 Haiti earthquake IZA Journal of Development and Migration Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Novella, Rafael; Zanuso, Claire (2018) : Reallocating children's time: Coping strategies after the 2010 Haiti earthquake, IZA Journal of Development and Migration, ISSN 2520-1786, Springer, Heidelberg, Vol. 8, Iss. 4, pp. 1-32, https://doi.org/10.1186/s40176-017-0109-z This Version is available at: https://hdl.handle.net/10419/197463 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ IZA Journal of Development and Migration Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 https://doi.org/10.1186/s40176-017-0109-z ORIGINAL ARTICLE Open Access Reallocating children’s time: coping strategies after the 2010 Haiti earthquake Rafael Novella1and Claire Zanuso2,3* *Correspondence: [email protected] 2Agence Française de Développement (AFD), 5 rue Roland Barthes, 75012 Paris, France Full list of author information is available at the end of the article Abstract This paper estimates the impact of the 2010 Haiti earthquake on the household decision about children’s time allocation. Using original data and objective geological measures, we exploit the fact that the earthquake might affect the decision about children’s time through its magnitude and household’s revealed vulnerability. Separated measures of the earthquake magnitude and its damage allow us to estimate the impact of the household’s vulnerability on the decision about the time allocation of children 10 to 17 years old. We are also able to analyze the differentiated effect on market and domestic work. Our results show that vulnerability at the time of the earthquake severely affects investments in children’s human capital almost 3 years after the shock, which increases the household’s risk of staying in poverty. JEL Classification: J13, J22, O54, P46, Q54 Keywords: Child labor, School attendance, Natural disasters, Haiti 1 Introduction Frequency and intensity of natural and environmental disasters are on the rise worldwide. Latin America and the Caribbean (LAC) is one of the more disaster-prone areas of the world (Heger et al. 2008; Rasmussen) and suffers the lowest level of insurance coverage (Grislain-Letrémy 2013; Borensztein et al. 2009). For instance, the financial cost of natural disasters in the region has risen from US$16 billion, in the period between 1985 and 1999, to more than US$26 billion, in the period between 2000 and 2014 (EM-DAT 2015). Although the increasing number of studies investigates both the causes and effects of natural disasters, little is still known about the household’s capacity to protect its human capital investment when confronted by such shocks. The primary objective of this paper is to analyze the medium-term (about 3 years after) impact of the 2010 Haiti earthquake on households’ decision about children’s time allocation. We argue that reallocating children’s time is a household coping strategy against an extreme adverse shock, which could lead to persistent negative effects on human capital (Jacoby and Skoufias 1997). The full impact of natural disasters is not entirely natural. They are triggered by external hazards but also stem from vulnerability. Vulnerability is a multidimensional and dynamic phenomenon, which definition varies among disciplines and research areas.1For instance, the disaster risk literature defines vulnerability as “the capacity to be harmed” (Field 2012)orasinDayton-JohnsonJ(2004) “vulnerability is the expected value of the damage that would occur conditional on the realization of the © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 2 of 32 shock.” In practical terms, however, the different elements in which one should focus the study of expected vulnerability (i.e., injury, loss, disruption of livelihood) are subject to constant change, and hence, it is very difficult to estimate them. Therefore, for practical reasons related to data availability, we take into account damage known to have occurred in the past; in other words, we do not rely on the “expected value” or the risk of damage but on the “revealed vulnerability” (Birkmann 2013). Even before the 2010 earthquake, Haiti was the poorest country in LAC and among the poorest worldwide2and had one of the poorest education systems in the world (e.g., about 60% of Haitians have no more than primary school education). Moreover, since 1900, Haiti has been exposed to more natural disasters than any other country in the Caribbean (Briguglio 1995; Heger et al. 2008). On 12 January 2010, Haiti was hit by an unanticipated magnitude 7.0 earthquake, classified as one of the four greatest killers recorded worldwide since 1990 and a massive destroyer of public and private infrastructure. The earthquake smacked headlong into the metropolitan area of Port-au-Prince, home to over one in five Haitians, and also households far beyond the capital. Despite the immediate response from the international community, with rescue teams and pledges of financial assistance and support for reconstruction and development, recovery in the country is still in process nowadays. Evidence shows that assistance was not particularly targeted to the most affected or vulnerable (Échevin 2011) and that instead it largely overlooked the population outside the conurbation of Port-au-Prince, even though just over six in ten of the households hit were outside the capital. Three years after the earthquake, just over one third of the population barely managed to make ends meet (Herrera et al. 2014). Moreover, in 2012, almost 30% of 10–17-year-old children are not in school or dedicate only few hours to studying because they need to contribute to household income or work in the household (Zanuso et al. 2014). This paper uses original information collected during an extended fieldwork aimed at designing, coordinating, and implementing the first nationwide survey about living conditions and labor market after the 2010 earthquake. The Post Earthquake Living Conditions Survey (ECVMAS) consists of 4951 households, in 500 primary sampling units (PSUs), interviewed in the second half of 2012 and is representative of the entire population of the country (Herrera et al. 2014).3Additionally, we merge, at the PSU level, households and individuals in ECVMAS with the measures of the strength of ground motion made available by the US Geological Survey (USGS). The combined data allow us to distinguish between the natural hazard occurrence, which is the observed physical intensity of the 2010 earthquake, and the natural disaster impact (measured by a damage score of the dwelling), which involve the interaction of the natural hazard and vulnerability. Two additional features of the data make it suitable for the purpose of our paper. First, it includes children’s self-reported information about the allocation of their own time. Given the social stigma associated with child labor and absence from school, parents might tend to underreport them. Therefore, it is expected that asking children directly would reduce this source of measurement error. Second, in contrast to most of the literature on the topic (Edmonds 2007), our data allows us to disentangle the effect on the decision about market and domestic work. Our final sample consists of 3833 children aged between 10 and 17 years, who in 2012 are still living in the same house or community where they lived at the time of the earthquake and who belong to the same household to which they belonged at the time of the earthquake.4 Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 3 of 32 Analyzing the impact of the earthquake on children’s time allocation is not straightforward. First, the direct effect of the natural hazard on households is manifested in their revealed vulnerability, which, in turn, might affect the decision about children’s time use. Second, the earthquake might affect the households’ decision through several other channels, for instance, the damage or destruction of services’ infrastructure (e.g., roads, schools) and staff (e.g., reduction in the number of teachers); changes in the household’s composition (e.g., degree of closeness between the child and the adult decision-maker in the household) and economic circumstances; changes in labor markets, which for instance might affect the opportunity cost of sending children to school; international aid response; and changes in children’s physical and mental health, which for instance might affect school readiness. To identify the effect of the household’s socioeconomic vulnerability at the time of the earthquake on the current decision of children’s time allocation, we use several empirical specifications accounting for the effect of the earthquake on children’s time allocation through these channels. Including a large set of household’s, child’s, and community’s characteristic at the time of the 2010 earthquake attempts to wash away unobserved components that might affect the household’s vulnerability and decision about children’s time use. Moreover, we account for the potential correlation between the error terms of the equations for the different alternatives for children’s time use.5Our dependent variables correspond to the household decision, in 2012, about participation of children into different activities (idle, school only, work only, school and work) and the number of hours studying and in market and domestic work. For our main variable of interest, the household’s vulnerability, we empirically define it as the regression residual of the total destructive effect of the earthquake on its physical impact (i.e., peak ground acceleration) and a household’s asset index in 2010. Finally, the other covariates consist in retrospective questions corresponding to the period just before the 2010 earthquake collected in the 2012 ECVMAS survey. The data quality literature stresses that when a phenomenon of large magnitude happens, the risk of measurement error associated to recall is reduced (De Nicola and Giné X 2014;Dex1995). According to Brown and Kulik (1977)’s “flashbulb” memories theory, a highly surprising and consequential event, like the 2010 earthquake, rises memories that show little forgetting (Winograd and Neisser 2006).6Moreover, we account for the potential correlation between the error terms of both the set of equations related to participation into different activities (i.e., idle, school only, work only, school and work) and the set of hours into each category (i.e., leisure, study, market and domestic work). Finally, we also account for unobservable characteristics at the community level, such as social norms about child labor, services supply, local labor market conditions, and aid received as consequence of the earthquake. Results from both extensive and intensive margins show that the household’s vulnerability is negatively associated with medium-term investments in children’s human capital. It is associated with an increase in the risk of sending children to work rather than schools, in the number of hours of domestic and market work, and with a reduction in the number of hours of study. These results highlight the importance of separating market and domestic work activities to correctly assess child labor. Moreover, we find evidence of different time allocations according to children’s characteristics. For instance, while age increases hours of domestic work for girls work more than for boys, it increases hours of market work for them more than for girls. In addition, the further away a child is biologically from Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 4 of 32 the household head is associated to lower investments in children’s human capital. Being active in the labor market at the time of the earthquake is found to be very important to increase the probability of working and reducing the probability of attending school in 2012. Also, exposure to a larger number of negative economic shocks between 2010 and 2012 is associated to lower investments in children’s human capital. This paper contributes to two strands of literature. First, it contributes to the literature looking at the effect of natural disasters on economic well-being and households’ coping strategies. Our results suggest that more vulnerable households can resort to coping strategies leading to persistent negative effects on human capital, such as taking children out of schools, reducing the time for studying, or increasing their work participation (Baez et al. 2010;DeJanvryetal.2006). Second, this paper contributes to child labor literature as it brings more evidence on the differentiated impact on market and domestic work. The paper is organized in seven sections. Section 2reviews the relevant literature on the effects of natural disasters on human capital and child labor and presents the general framework used to evaluate the situation for children in Haiti. Section 3provides background information on the earthquake and the socioeconomic context in which it took place. Section 4describes the data used in the analysis and provides a discussion of intensity, vulnerability, and damage measures. Section 5presents the empirical strategies to identify the mentioned effects. This is followed by a presentation of the results in Section 6.Finally,Section7concludes the paper and discusses policy options. 2 Previous findings and conceptual framework The questions we address in this paper are at the conjunction of two fields of research: the impact of natural disasters and child labor studies. In both cases, the literature barely cover the specific case of Haiti. In part due to climate change, there has been a recently substantial growth in interest in the impact of natural disasters. This trend is set to grow due to a worrying rise in the impact of natural disasters on economic welfare over the last decade. Research focusing more specifically on countries’ resilience suggests that these disasters have devastating effects on economic growth in developing countries because they prevent physical and human capital accumulation, although the channels behind this phenomenon remain unclear (Skoufias 2003). Most of the existing studies on the economic impact of natural disasters have been driven by the availability of the international Emergency Disasters Database (EM-DAT), which contains information about disasters and their damage worldwide since 1900. Macroeconomics studies have put particular attention on the human and economic costs of these disasters. As expected, these studies conclude that even though developing countries are not more exposed than developed countries to natural hazards (i.e., physical and environmental factors), they are more vulnerable to disasters. The channels through which the disasters produce such different impacts remain as a “black box” in these studies even though some suggest differences in institutional factors (e.g., governance, inequalities) (Cavallo and Noy 2009;Noy2009;Kahn2005). To the best of our knowledge, the only existing study evaluating the 2010 earthquake’s impact in Haiti adopts an indirect and macroeconomic approach (Cavallo et al. 2010). It sets out primarily to put a figure to the total financial impact of the earthquake. The estimates are based on strong assumptions and are not very reliable, as the authors themselves recognize. Herrera et al. (2014)andZanusoetal.(2014) (more specifically focused Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 5 of 32 on youth) based on ECVMAS 2012 data, present the most up-to-date image of the labor market situation in Haiti and a systematic and comparative analysis with the EEEI 2007 data is conducted. They calculate comparable indicators and describe the evolution of the labor market in a 5-year interval (before and after the earthquake), but they highlight that the observed dynamic cannot be attributed to the earthquake only, as so many largescale events have intervened in the meantime (floods, hurricanes, epidemics, etc.). More recently, Saint-Macary and Zanuso (2015) assess the long-lasting effects of the 2010 Haiti earthquake on household well-being. 2.1 Effects of natural disasters on education and child labor Modeling the impacts of natural disasters on human capital in a comprehensive manner is not straightforward. The literature have developed theoretical frameworks to model different pieces of the causal chain linking different shocks—including natural disasters— to proxy determinants of human capital and other dimensions of human welfare, but there is no consensus of whether natural disasters have consequences on the creation and use of human capital (Baez et al. 2010). The existing economic literature strongly suggests that the households’ capacity in poor countries to smooth consumption across time and states of nature is limited. This seems to be especially true in the case of large generalized shocks such as natural disasters, when some of the informal mechanisms of risk-sharing become less widespread (Jacoby and Skoufias 1997;Beegleetal.2002). This section does not aim to review the whole existing findings at the household level on the impacts of natural disasters on human capital, and we cite only those studies which are directly related to the impact on education and child labor.7 The theoretical impact of natural disasters on schooling is ambiguous due the varying and sometimes contradicting effects involved. On the one hand, the damages or destruction of schools and complementary infrastructures and resources (e.g., roads and teachers) can worsen the whole education system. Additionally, if natural shocks worsen, the economic situation of households and the access to credit, insurance, or other coping mechanisms are limited; dropping children out of school may be the last resort to reduce outgoings (Jensen 2000; Jacoby and Skoufias 1997) or to increase household income by putting them to work or increasing their participation to domestic work activities (Basu and Van 1999;Edmonds2007). Huisman and Smits (2009) argue this is specially the case in countries where laws regarding compulsory education are not strictly enforced, such as in Haiti. On the other hand, if a natural disaster changes the opportunity cost of sending children to school, through a reduction in market wages for example, or if international assistance increase the supply of education services (Adelman and Holland 2015), the direction of the overall effect remains unclear (Baez et al. 2010). Empirically, it is complex to disentangle all these mechanisms. To the best of our knowledge, only one research tries to assess the effects of a large earthquake on child labor.8 Santos (2007) finds that children in households most affected by the 2001 earthquake in El Salvador were three times more likely to work after the shock. The intensive margin results show an increase of their labor supply of about 32 h per week relative to children in control areas. Studies assessing the effects of other types of disasters (e.g., droughts, floods, rainfall shocks, hurricanes) find that these shocks are associated with declines in enrollment rates or delays in school enrollment (Alderman et al. 2006;Jensen2000; Jacoby and Skoufias 1997). Many studies show that natural disasters are strongly correlated with Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 6 of 32 an increase of workforce, including children, and more hours devoted to off-farm activities at the expense of lower wages (Jalan and Ravallion 1999;Beegleetal.2003;DeJanvry et al. 2006;Santos2007; Baez and Santos 2007). Baez and Santos (2007) estimate that as a consequence of Hurricane Mitch in Nicaragua, child labor force participation increases in 8.5 percentage points (p.p.) and the proportion of children both enrolled in school and working more than doubled. Most of case studies evidence that the impact can be largely negative, and some of them show the potential detrimental effects can be long-lasting (Aldermanetal.2006). Others studies on human capital and shocks (not only natural disasters) further stress that educational achievement is highly path-dependent (Bustelo et al. 2012; Strauss and Thomas 2007). 2.2 Children’s time allocation in developing countries Literature about children’s time allocation in developing countries point to a number of determinants of the demand for education and the supply of child labor. A detailed overview of recent developments of this literature can be found in Edmonds (2007), and a review with a more theoretical focus in Basu and Van (1999). As Basu and Van (1998) mention, the allocation of children’s time to non-labor activities (education or leisure) represents a luxury good for poor households, which can be consumed only once their income rises beyond a certain threshold. Sending children to work, in contrast to sending them to schools, carries negative consequences both for the children’s future well-being and, through the positive externalities of education on growth, for the growth of the society as a whole (Basu and Van 1999). Theoretical and empirical studies on the main determinants of the household’s decision to send children to work rather than to school can be grouped into two main veins: the demand for education, introduced by Becker (1964), and the one that focuses on the impact of various constraints affecting the supply of child labor, the demand for education, or both simultaneously. According to Becker (1964)’s theory, whether to send their children to school is the result of a trade-off between the expected returns and the cost of education (e.g., tuition, material, compulsory uniform, transport, and opportunity cost). It predicts that children may work because the net returns relative to alternative uses of time such as school attendance are low (compared with its cost) and the returns to work experience are relatively larger. The second vein highlights the effect of various constraints on the allocation of children’s time between school and work (Bhalotra 2007; Grimm 2011). Imperfections in the markets for labor and land (Bhalotra and Heady 2003), poverty (Jensen and Nielsen 1997;BasuandVan1998), and credit market imperfections (Jacoby and Skoufias 1997;Ranjan2001;CignoandRosati2006) are the main set of constraints explaining the emergence of child labor and the concomitant fall-off in school attendance (Dammert 2010;DeVreyeretal.2013; Senne 2014). All these constraints, in addition to the degree of altruism of the decision-taker in the household (Baland and Robinson 2000), drive the final decision about the child’s time allocation. This framework suggests how an external negative shock might affect the households’ decision in developing countries. In fact, even where incentives favor education over work, a household may be obliged by budget constraint to send a child to work. When poverty constraints are binding, the opportunity cost of studying is too high and parents are due to send children to work rather than to school. Moreover, when capital markets are imperfect, this model predicts that even altruistic parents may sacrifice investments in children’s education. Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 7 of 32 The existing empirical research about child labor tend to focus on the effects of factors, either the characteristics of parents and their households (Basu et al. 2010; Buchmann 2000; Patrinos and Psacharopoulos 1997)orcountrylevel(Fan2004;Levy1985). This is problematic, since we know that the decisions of parents regarding work and schooling of their children are influenced by factors at the household level as well as by characteristics of the context in which the household is living (e.g., the local labor market and the available educational facility characteristics) (Huisman and Smits 2009). Webbink et al. (2013) develop a new framework including simultaneously three categories of child labor determinants: resources (Basu and Van 1998;Ranjan2001), family factors (or structural factors) (Edmonds 2006), and cultural explanations (Delap 2001). We aimed at adding vulnerability as an additional dimension. In our ex-post vulnerability assessment, our measure of vulnerability is not an expected degree of loss but is based on the observed (revealed) level of damage on dwellings. 3 Context 3.1 Haitian context Haiti is the poorest country in the Western Hemisphere and ranks 161 among 186 countries in the Human Development Index of the United Nations Development Programme. Three years after the 2010 earthquake, poverty is still high, and particularly in rural areas, just over one third of the population barely managed to make ends meet (Herrera et al. 2014). According to the new national poverty line produced by the government of Haiti and based on the ECVMAS 2012, more than one in two Haitians was poor, living on less than $2.41, and one person in four was living below the national extreme poverty line of $1.23 a day. A comparison of household earnings (with the level of income deemed by households to be the minimum required to live) shows that nearly eight in ten households can be classified as “subjective poor” (Herrera et al. 2014). With a population of 10.4 million people,9Haiti is also one of the most densely populated countries in Latin America. Half of the population is under 21 years old, and nearly 60% of Haitians have no more than primary school education. In 2012, almost 30% of 10–17-year-old children were not in school or dedicate only few hours to studying because they need to contribute to household income, work in the household, or do nothing (Zanuso et al. 2014). Prior to the earthquake, the country had already one of the poorest education systems in the world. Right after its independence (in 1804), the importance of education was recognized and the first Constitution, promulgated in 1805, noted explicitly that “education shall be free. Primary education shall be compulsory. State education shall be free at every level.” These principles were never put into practice. The education offered to Haitian children was, and still is, inadequate in terms of quality, quantity, and accessibility (Lunde 2010). Both the direct costs of attending school (e.g., tuition and other fees, mandatory uniforms, transport, books) and the opportunity costs, particularly in the form of lost labor for the household, remain barriers to achieving universal primary enrollment and completion. The private education system has grown by default and continuously, from 20% in the 1959–1960 to 75% in 1995–1996 (Salmi 2000). In 1997, the Ministry of National Education, Youth and Sport (MENJS) set out a National Education and Training Plan (PNEF) to reform the education system in Haiti. The reformed Haitian education system is used as reference here, even if some parts of the country, especially in rural areas, the traditional educational system still operates (Lunde 2010). In the reformed Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 8 of 32 system, education comprises three core levels: (i) pre-school for 3 to 5 years old, not compulsory; (ii) primary education for 6 to 14 years old; and (iii) secondary education for 15 to 18 years old (see Table 7 in the Appendix). The legal age for admission to school is 6 years, and in the reformed system, children who go through normal progression complete primary at the age of 14. In fact, the last school census before the earthquake (2002–2003) highlighted that the average age of students in grade 6, for instance, was 16 years old, when the corresponding age for that grade should be 11 or 12 years old (Adelman and Holland 2015). Even though the earthquake strongly affected the supply of education services, the number of schools were still larger in 2011–2012 than in 2002–2003. In 2002, Haiti counted with a total of 15,268 schools, of which 92% were nonpublic, accounting for more than 80% of enrolled students. The 2011–2012 census, conducted less than 2 years after the 2010 earthquake, counted 16,072 schools across the country in total, of which more than 88% were private institutions. This persistently large share of nonpublic education provision makes difficult the assurance of education quality in Haiti as nonpublic schools are very heterogeneous and they largely ignore government regulations and accreditation standards (Lunde 2010). In 2012, according to the ECVMAS survey, private education represents about 65% of primary and secondary school enrollment, one of the highest proportion of private school enrollment in the world. In addition, between 2002 and 2011, total social expenditure, including education, in the country was reduced from 2.7 to 1.5% of GDP. Lamaute-Brisson (2013)revealsthat the evolution of government education expenditure was erratic between 2002 (1.65% of GDP) and 2005 and then it collapsed in 2006 (0.57% of GDP) until 2011 (0.59% of GDP), when it picked up. This volatility is explained by the fragile political situation and the volatility of GDP growth itself. 3.2 The 2010 earthquake The magnitude 7.0 earthquake that hit Haiti on 12 January 2010 was one of the four greatest killers recorded worldwide since 1990. Official figures place it as being twice as lethal as any previous earthquake of the same magnitude (Bilham 2010). Although recent surveys by international institutions suggest that the official death count is overestimated (Lundahl 2013; Schuller and Morales P 2012), there is no question about the severity of its repercussions (Kolbe et al. 2010;Schwartzetal.2011; Doocy et al. 2013). The death toll as recorded in EM-DAT (2015) is estimated at 222,600. Haiti is one of the most vulnerable developing countries when it comes to natural disasters and the most exposed country in the region (Heger et al. 2008;Briguglio1995). It has been hit by just over 50 natural disasters since 1900. Another aspect, which might explain why Haiti is even more vulnerable than its neighbors Dominican Republic and Jamaica is the level of development and incomplete insurance markets that increase the impact of the shocks when they occur (Strobl 2012). The earthquake’s repercussions were much more dramatic here than in other countries hit by stronger earthquakes. The context surrounding the earthquake was particularly ill-fated in that Haiti had been victim to either a tropical storm or severe flooding every year of that entire decade. The international EM-DAT database shows that these disasters prior to the earthquake affected over one million people in total and were responsible for nearly 7,000 deaths (EM-DAT 2015). Other countries have been struck by similar or stronger earthquakes, but the repercussions were much more dramatic for Haiti Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 15 of 32 pij =Pr(di=j)=exp(x iβj) m l=1exp(x iβl),j=1, .., 4, i=1, .., N(2) Similarly, to allow for correlation in errors between the equations of hours of study and market and domestic work, we estimate a seemingly unrelated (SUR) model. The error terms are assumed to have zero mean and to be independent across individuals, and to allow them to be heteroskedastic, at estimation, we resample over individuals and calculate bootstrapped standard errors. In this case, the jth equation for child iis: hij =x ijαj+uij (3) Finally, we estimate OLS models for both schooling and work decisions and number of hours, which include PSU-level fixed-effects (FEs) accounting for unobservable characteristics at the PSU level (e.g., aid response, local labor markets condition, social norms): yij =x ijγj+δjc+ij (4) Even though this specification does not account for correlation of error terms between equations, the estimators provide the advantage of having well-understood properties and the results are comparable to previous studies. 6 Results This section starts discussing the estimation of households’ vulnerability and then presents two sets of regressions on children’s time allocation. Being heavily hit by the earthquake (measured through PGA) strongly affects the dwelling damage score, even after comparing households equally wealthy at the time of the earthquake (measured through the asset index) (Table 2).18 In addition, the first set (Table 3) includes the MNL estimates of the household decision about children’s time allocation in terms of idle,school only,work only,andschool and work. The second set (Table 4) corresponds to the SUR estimation of the number of hours for studying and market and domestic work. Moreover, it discusses the relationship of other characteristics of children, households, and communities and the decision about children’s time allocation. On the one hand, the Wald test at the bottom of Table 3shows that the vulnerability index is jointly significant across equations at the 95% level. In addition, it shows that an increase in one unit of vulnerability at the time of the earthquake is associated with a Table 2 Household’s vulnerability Earthquake damage score Peak ground acceleration 6.621*** (0.539) Durable assets index, 2010 −0.279*** (0.064) Constant 0.499*** (0.093) Observations 3833 R-squared 0.132 Note: standard errors clustered at the PSU level are in parentheses *p<0.1, **p<0.05, ***p<0.01 Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 16 of 32 Table 3 MNL model for children’s time allocation Idle Work only School and work (base category = school only) Household vulnerability index 2.334* 6.681*** 1.427 (1.124) (4.579) (0.607) Age 1.205*** 1.090 1.059* (0.054) (0.102) (0.036) Sex (male = 1 and at age = 13.55) 0.668*** 1.877*** 1.470*** (0.092) (0.364) (0.147) Age * sex 0.898 1.028 1.039 (0.062) (0.101) (0.043) Child’s age order within the household 1.202** 0.869 0.831*** (0.105) (0.113) (0.047) Child was economically active, 2010 (yes = 1) 3.810*** 31.606*** 16.003*** (1.121) (9.175) (3.283) Relation to head of household = close relative (yes = 1) 1.526** 0.673 0.866 (0.314) (0.292) (0.157) Relation to head of household = other relative (yes = 1) 1.759*** 1.331 0.641** (0.336) (0.361) (0.115) Relation to head of household = domestic employee (yes = 1) 4.882** 6.570** 1.925 (3.014) (5.554) (0.871) Relation to head of household = other relationship (yes = 1) 1.312 3.417** 1.126 (0.771) (1.887) (0.533) Sex of head of household (male = 1) 0.964 1.735** 1.889*** (0.181) (0.469) (0.245) Head of household education level = primary (yes = 1) 0.795 0.530** 0.774* (0.159) (0.146) (0.105) Head of household education level = secondary or more (yes = 1) 0.357*** 0.153*** 0.670** (0.084) (0.058) (0.114) Live in the same house (yes = 1) 1.290 1.092 0.991 (0.282) (0.424) (0.199) Household has received any type of assistance (yes = 1) 0.711** 0.988 1.078 (0.122) (0.269) (0.155) Number of negative economic shock 0.926 1.327** 1.289*** (0.085) (0.156) (0.087) Members living in a temporary camp (%) 1.419 1.328 0.475 (0.534) (0.982) (0.218) Has any household member suffered physical damage (yes = 1) 0.753 1.001 0.828 (0.226) (0.334) (0.187) Household size, 2010 0.830** 0.987 1.163*** (0.069) (0.096) (0.056) Number of children aged 0 to 10 years in household 1.281** 1.090 0.869** (0.129) (0.132) (0.058) Durable assets index, 2010 0.537*** 0.610 0.736*** (0.104) (0.254) (0.083) House ownership, 2010 (yes = 1) 0.812 1.686 1.698*** (0.166) (0.664) (0.267) Constant 0.008*** 0.002*** 0.022*** (0.006) (0.003) (0.015) Observations 3833 Pseudo R-squared 0.155 Wald test of the significance of the vulnerability index, chi2(3) 8.90 PSU FE No Note: standard errors clustered at the PSU level are in parentheses *p<0.1, **p<0.05, ***p<0.01 substantial increase in the odds of choosing work only rather than school only. Similarly, but in lower magnitude, more vulnerability is associated with an increase in the odds of the child of being idle rather than attending school. Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 17 of 32 Table 4 SUR model for children’s time allocation Hours studying Hours working Hours hh chores Household vulnerability index −3.555*** 1.206* 4.940*** (1.300) (0.621) (0.986) Age −0.016 0.264*** 0.281*** (0.089) (0.055) (0.076) Sex (male = 1 and at age = 13.55) −0.514 1.211*** −4.205*** (0.377) (0.223) (0.317) Child’s age order within the household −0.150 −0.480*** −0.461*** (0.169) (0.125) (0.156) Child was economically active, 2010 (yes = 1) −3.925*** 10.304*** 2.026*** (0.546) (0.872) (0.709) Relation to head of household = close relative (yes = 1) −1.286** −0.233 −0.050 (0.569) (0.383) (0.461) Relation to head of household = other relative (yes = 1) −1.099* −0.564 1.327*** (0.590) (0.347) (0.436) Relation to head of household = domestic employee (yes = 1) −6.624*** 2.087 7.703*** (1.776) (1.615) (2.534) Relation to head of household = other relationship (yes = 1) −4.670*** 0.608 2.049* (1.562) (0.919) (1.082) Sex of head of household (male = 1) −0.750** 1.098*** 1.331*** (0.381) (0.240) (0.305) Head of household education level = primary (yes = 1) 0.758* −0.463 −0.256 (0.425) (0.291) (0.349) Head of household education level=secondary or more (yes=1) 3.822*** −0.736** −1.214*** (0.585) (0.310) (0.405) Live in the same house (yes = 1) −1.240* 0.029 0.522 (0.678) (0.368) (0.445) Household has received any type of assistance (yes = 1) −0.155 −0.190 −0.455 (0.428) (0.246) (0.341) Number of negative economic shock −0.224 0.551*** 1.174*** (0.190) (0.101) (0.138) Members living in a temporary camp (%) 4.974*** −0.296 −1.925*** (1.251) (0.705) (0.682) Has any household member suffered physical damage (yes = 1) 2.862*** −0.451 0.071 (0.686) (0.345) (0.468) Household size, 2010 −0.312** 0.385*** −0.129 (0.145) (0.113) (0.134) Number of children aged 0 to 10 years in household −0.332* −0.315** 0.307** (0.182) (0.130) (0.153) Durable assets index, 2010 2.719*** −0.283** −0.896*** (0.289) (0.140) (0.129) House ownership, 2010 (yes = 1) −2.639*** 1.074*** 1.002*** (0.562) (0.272) (0.351) Constant 17.017*** −3.836*** 4.253*** (1.647) (1.036) (1.343) Observations 3833 3833 3833 R-squared 0.171 0.187 0.135 PSU FE No No No Correlation matrix of residuals Hours studying 1.000 – – Hours working −0.098 1.000 – Hours hh chores −0.085 0.111 1.000 Note: bootstrapped standard errors are in parentheses (400 replications). Breusch-Pagan test of independence: chi2(3)=100.625, Pr =0.000 *p<0.1, **p<0.05, ***p<0.01 Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 18 of 32 On the other hand, the bottom part of Table 4shows that the errors in the three equations of hours are correlated (i.e., the Breusch-Pagan Lagrange multiplier test for error independence is rejected at the 99% level). Moreover, it shows that vulnerability at the time of the earthquake is negatively associated with a substantial reduction in the number of weekly hours children devote to study (3.5 h). In contrast, it is positively associated with a substantial increase in the hours dedicated to domestic (5 h) and market work (1 h). To be able to compare our findings to previous studies and to account for unobserved heterogeneity at the PSU level, we present (Tables 5and 6) the linear probability models for separated outcomes including FE at the PSU level in Eq. 4.19 When the correlation of the error terms is not taken into account, we find that more vulnerability is associated with a reduction in 11 p.p. in the probability of exclusively attending school and with an increase in 6 p.p. in the probability of working only. Similarly, more vulnerability is associated with a reduction in 3 h of studying and an increase in 4 h of household chores. Tables 12 and 13 in the Appendix also show estimations by geographical area. Both the reduction in the probability of exclusively attending school and the increase in the probability of working only seem to be driven by children in rural areas. Similarly, we find that the association of vulnerability and more hours of household chores seems to be driven by children living in rural and other non-metropolitan areas of the country.20 Although our main variable of interest is the household’s vulnerability, it is interesting to explore how children’s individual and other household’s characteristics are related to the decision about children’s time allocation. Overall, we find that an additional year of age is associated to a larger likelihood of working (2 p.p.) and working and studying (2 p.p.) and to a lower likelihood of studying exclusively (0.5 p.p.). In addition, we find gender differences on the age effect (coefficient on the interaction age ∗sex). In contrast to girls, forboys,agereducesboththeprobabilityofbeingidleandthenumberofhoursdevoted to household chores and increases both the probability of working and studying and the number of hours or market work. For all children, birth order (i.e., being younger in the household) increases the probability of being idle and hours of leisure and reduces the probability of studying and working and hours of study. We also find evidence of strong and significant state-dependence in terms of previous working status. Children who were economically active at the time of the earthquake spend considerably more hours working for the market (9 h) and domestically (2 h) and fewer hours studying (2 h). They are also much less likely to attend school (46 p.p.) and more likely to exclusively work (14 p.p.) and combine school and work (33 p.p.). We also find some evidence of Hamilton’s rule. Children who are less related biologically to the household head, in contrast to sons or daughters, spend fewer hours studying and longer hours doing domestic work. After controlling for the whole set of covariates, we do not find much evidence of characteristics of the household head affecting children’s time allocation. We find, however, that children in households where the head of the household has at least secondary education are less likely to work, in contrast to living with a head of the household with no formal education. We find that receiving more negative economic shocks between 2010 and 2012 affect investments in children’s human capital. It apparently makes children to stop attending school exclusively and to start combining it with work activities. It also marginally increases the number of hours for market and domestic work. We also find evidence that Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 19 of 32 Table 5 Fixed-effects model, school/work decision Idle School only Work only School and work Household vulnerability index 0.033 −0.106* 0.064** 0.009 (0.034) (0.060) (0.026) (0.049) Age 0.011*** −0.019*** 0.003 0.004 (0.003) (0.005) (0.003) (0.004) Sex (male = 1 and at age = 13.55) −0.027*** −0.036*** 0.018*** 0.045*** (0.008) (0.014) (0.006) (0.013) Age * sex −0.008** −0.004 0.002 0.011** (0.004) (0.006) (0.003) (0.005) Child’s age order within the household 0.013*** 0.004 0.002 −0.019*** (0.004) (0.008) (0.004) (0.007) Child was economically active, 2010 (yes = 1) −0.005 −0.461*** 0.137*** 0.330*** (0.019) (0.031) (0.028) (0.036) Relation to head of household = close relative (yes = 1) 0.044** −0.032 −0.008 −0.004 (0.017) (0.028) (0.010) (0.026) Relation to head of household = other relative (yes = 1) 0.044*** −0.008 0.012 −0.049** (0.015) (0.022) (0.010) (0.019) Relation to head of household = domestic employee (yes = 1) 0.073 −0.007 0.072 −0.138* (0.057) (0.089) (0.054) (0.082) Relation to head of household = other relationship (yes = 1) 0.034 −0.032 0.032 −0.034 (0.040) (0.073) (0.035) (0.065) Sex of head of household (male = 1) −0.018 −0.012 −0.003 0.032* (0.012) (0.021) (0.009) (0.018) Head of household education level = primary (yes = 1) −0.012 0.016 −0.012 0.009 (0.014) (0.024) (0.010) (0.022) Head of household education level=secondary or more (yes=1) −0.025* 0.022 −0.020** 0.024 (0.015) (0.026) (0.009) (0.024) Live in the same house (yes = 1) 0.009 −0.018 0.003 0.006 (0.016) (0.029) (0.013) (0.026) Household has received any type of assistance (yes = 1) −0.024* 0.025 0.000 −0.002 (0.012) (0.024) (0.009) (0.021) Number of negative economic shock −0.004 −0.023** 0.003 0.024*** (0.006) (0.010) (0.003) (0.009) Members living in a temporary camp (%) −0.085 −0.036 0.042 0.079 (0.059) (0.106) (0.038) (0.086) Has any household member suffered physical damage (yes = 1) −0.026 0.011 0.017 −0.002 (0.017) (0.028) (0.012) (0.027) Household size, 2010 −0.010** −0.004 −0.003 0.017*** (0.004) (0.007) (0.003) (0.006) Number of children aged 0 to 10 years in household 0.014** 0.006 0.002 −0.022** (0.006) (0.010) (0.004) (0.009) Durable assets index, 2010 −0.017*** 0.019* −0.003 0.000 (0.006) (0.010) (0.003) (0.009) House ownership, 2010 (yes = 1) −0.008 0.012 −0.004 0.000 (0.014) (0.023) (0.010) (0.019) Constant −0.062 1.095*** −0.042 0.009 (0.051) (0.091) (0.044) (0.079) Observations 3833 3833 3833 3833 Adjusted R-squared 0.148 0.335 0.144 0.309 PSU FE Yes Yes Yes Yes Note: standard errors clustered at the PSU level are in parentheses *p<0.1, **p<0.05, ***p<0.01 at the one hand, the number of household members might reduce time of household chores and increase the chances of study and work, but at the other hand, these categories are affected by a larger number of younger children. Overall, children living in wealthier households at the time of the earthquake have better chances of study and do it for more hours and work less at home. Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 20 of 32 Table 6 Fixed-effects model, number of hours Hours studying Hours working Hours hh chores Household vulnerability index −2.944* 1.007 3.745*** (1.736) (0.806) (1.297) Age −0.136 0.233*** 0.759*** (0.119) (0.088) (0.117) Sex (male = 1 and at age = 13.55) −0.205 1.164*** −4.492*** (0.287) (0.262) (0.335) Age * sex 0.139 0.269** −0.700*** (0.146) (0.114) (0.126) Child’s age order within the household −0.355* −0.231 −0.189 (0.198) (0.151) (0.181) Child was economically active, 2010 (yes = 1) −1.801*** 9.521*** 2.111** (0.686) (0.969) (0.819) Relation to head of household = close relative (yes = 1) −1.024 0.059 −0.009 (0.654) (0.487) (0.529) Relation to head of household = other relative (yes = 1) −1.446*** −0.377 1.379*** (0.550) (0.359) (0.496) Relation to head of household = domestic employee (yes = 1) −2.823 0.632 7.261*** (1.867) (1.655) (2.332) Relation to head of household = other relationship (yes = 1) −3.744*** −0.052 2.698** (1.319) (1.205) (1.356) Sex of head of household (male = 1) 0.720 0.250 0.598 (0.564) (0.343) (0.428) Head of household education level = primary (yes = 1) −0.470 0.231 −0.054 (0.507) (0.367) (0.528) Headofhouseholdeducationlevel=secondaryormore(yes=1) 0.398 0.575 0.147 (0.855) (0.399) (0.598) Live in the same house (yes = 1) −0.957 −0.013 0.606 (0.827) (0.488) (0.637) Household has received any type of assistance (yes = 1) −0.417 −0.121 0.337 (0.571) (0.394) (0.481) Number of negative economic shock 0.314 0.350** 0.621*** (0.292) (0.160) (0.205) Members living in a temporary camp (%) 6.474 4.073* 0.278 (6.045) (2.186) (3.352) Hasanyhouseholdmembersufferedphysicaldamage(yes=1) 0.791 0.013 0.084 (0.911) (0.454) (0.667) Household size, 2010 0.155 0.169 −0.303** (0.173) (0.133) (0.152) Number of children aged 0 to 10 years in household −0.397 −0.181 0.398** (0.268) (0.160) (0.201) Durable assets index, 2010 1.071*** −0.023 −0.426** (0.404) (0.189) (0.201) House ownership, 2010 (yes = 1) −0.341 0.096 −0.099 (0.708) (0.388) (0.503) Constant 13.825*** −2.501 −0.708 (2.152) (1.536) (2.019) Observations 3833 3833 3833 Adjusted R-squared 0.512 0.287 0.272 PSU FE Yes Yes Yes Note: standard errors clustered at the PSU level are in parentheses *p<0.1, **p<0.05, ***p<0.01 Finally, we do not find any evidence that households receiving aid (assistance or having members living at refugee camps) is associated to the decision about the use of time of children. Living in 2012 at the same house where they live at the time of the earthquake, in contrast to living in a different house in the same community, is not associated to changes in investments in children’s human capital. Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 21 of 32 7Conclusions In January 2010, Haiti suffered a devastating earthquake that caused dramatic economic and personal losses. The total effect of the earthquake in people’s lives happens through a natural factor (intensity), which is exogenous to people’s behavior, and a vulnerability factor, which might be affected by the individual’s behavior, and therefore potentially affected by policy. This paper examines the lasting effect of the household’s vulnerability at the time of the earthquake on the decision about children’s time allocation. It is well known in the literature that investment in children’s human capital has many positive effects on individuals’ and countries’ well-being. In fact, while natural hazards can affect a person’s human capital throughout its entire life, it is at the early stages in life when any such impacts most matter if they are not properly and timely addressed. Our results show that this is at risk for Haiti. The household’s vulnerability, as with many other children’s characteristics, is associated with larger chances of working and lower chances of studying for children that are at school age. These results support us to affirm that social policies should focus on reducing households’ vulnerability and other observable characteristics that might reduce investments in children’s human capital. This paper uses a rich and original dataset including information about the earthquake’s intensity and damage, children’s answers about the use of their time, time devoted to domestic and market work, and characteristics of individuals and households before the 2010 earthquake happened. Despite the richness of our data, we are not able to disentangle whether the household decision about children’s time reflects the household or child unobserved characteristics (e.g., household’s preferences for schooling and child labor, child’s depression, stress, or ability to take advantage of school). However, the importance of the research question from a policy point of view and the lack of empirical evidence for Haiti makes the analysis relevant. We believe that the results of this research are of interest beyond Haiti, as key features of the Haitian system—low state capacity and historical lack of welldeveloped and functioning system of public schools—are common to many low-income countries, while the rapid growth of nonpublic schools in many of these countries makes the Haitian case, with a large and vibrant nonpublic sector, increasingly relevant. The sample used in this paper is restricted to survivors of the 2010 earthquake, and therefore, our results can be interpreted as lower bound estimates of the real effect of vulnerability at the time of the earthquake on investments in children’s human capital. This is potentially reinforced by restricting our sample to only children who did not change household and did not move to another geographical area. Endnotes 1Birkmann (2013) provides an extended review of the different frameworks, techniques, and methodologies for assessing and measuring vulnerability at different levels and with different thematic focuses. 2Human Development Index of the United Nations Development Programme https://data.undp.org/dataset/Human-Development-Index-HDI-value/8ruz-shxu 3ECVMAS was conducted by the Haitian Institute of Statistics (IHSI) as part of a program supported by the French National Research Agency (ANR), DIAL Research Unit, and the World Bank. The methodology was a variation on the 1-2-3 Survey, developed Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 22 of 32 by DIAL to measure the informal economy and poverty. Javier Herrera, IRD Research Director, was the ANR project manager and Claire Zanuso was the ANR project coordinator from 2011 to 2014 and survey coordinator in Haiti. All data in this paper are from ECVMAS 2012, unless otherwise indicated. 4These criteria are imposed to avoid the possibility of unobserved variables related to changes in household composition biasing our estimates. At applying them, we exclude less than 10% of children on this age group. Another source of sample selection bias is only having data of children who survived the earthquake. If surviving is not orthogonal to the household’s vulnerability, we should expect that our results are lower estimates of the real effect. 5Unfortunately, even doing this, we are not able to account for some child’s or household’s characteristics that might determine schooling and working (e.g., school readiness). 6The construct of flashbulb memory was introduced in a seminal paper by (Brown and Kulik 1977) to account for memories of events such as the assassination of John F. Kennedy. 7Baez et al. (2010) offers a recent and synthetic review on consumption, nutrition, and health dimensions. 8Likely due to lack of suitable data, few studies address the impact of high-magnitude earthquakes. For instance, see Doocy et al. (2013)forareview;Yang(2008)forChina; and Halliday (2006) for El Savador. Torche (2011) disentangles the impact of the maternal stress on birth outcomes exploiting the 2005 earthquake in Chile as a natural experiment. 9Based on available population projections of the Haitian Institute of Statistics and Informatics (IHSI), 2012. 10 See Lamaute-Brisson (2013); Lamaute-Brisson (2015) for a more exhaustive and historical view of social protection systems in Haiti. 11 The geographic divisions of Haiti are 10 departments, 41 districts, 133 communes, and 565 sections within the communes. The country can also be divided into 11,967 primary sampling units (PSUs), the smallest statistical division. The ECVMAS sample counts 500 PSUs, including 30 PSUs in refugee camps. 12 This information is consistent with the “color-code” damage classification of the Ministry of Public Works Transport and Communications (MTPTC). Between February 2010 and 2011, MTPTC, with the support of USAID, assessed the habitability and color-coded 382,246 buildings according to the level of damages. Green-tagged buildings were structurally evaluated as safe, the yellow-tagged ones were unsafe to inhabit but reparable, and red-tagged buildings as unsafe to enter or damaged beyond repair. The MTPTC’s evaluation followed the ATC-20 (ATC 1987) rapid assessment methodology, which has been successfully used for evaluation after major earthquakes in the USA, with adaptations to Haitian construction practices. We are able to check the consistency between our indicator of damage score and the one of MTPTC for 40% of ECVMAS sample. The remaining corresponds to buildings located outside Port-au-Prince and rubble buildings, which were not evaluated by MTPTC, and households who were no longer living in the dwelling where they lived at the time of the earthquake. Among households in which comparison of the two indicators is possible, 92% of those who declared none-slight damage on their dwelling were tagged as green, 90% of those who declared light-moderate damage were tagged as green and yellow, and 86% of those who declared a destroyed Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 23 of 32 damage were consistently red-tagged. Only 15% of those who declared heavy-major damage and 10% of those who declared destroyed damage were not consistent with the MTPTC classification. 13 The questions related to household chores correspond to time devoted to the following: cleaning, cooking, taking care of other household member, reconstruction or construction of their own houses, fetching water or wood, and going to the market to buy goods for their households. 14 For each 500 PSUs in the ECVMAS sample, 16 households were randomly selected in the metropolitan area of Port-au-Prince and in the refugee camps and 8 households for the rest of the country. 15 In the absence of seismographic stations across the country, PGA is calculated imputing data from earthquakes’ characteristics and geography of impacted areas, based on attenuation relations created by seismologists and engineers. In the specific case of Haiti, even if the PGA is a more complete measure of earthquake intensity than the distance to the epicenter, it is not a perfect measure of it. Eberhard et al. (2010) mentions that the lack of seismographs and detailed knowledge of the physical conditions of the soils (e.g., lithology, stiffness, density, thickness) limit the precision of USGS assessment of ground motion amplification in the widespread damage. 16 This assistance includes the information campaign aimed at preventing cholera epidemic. The incidence is significantly lower when it comes to assistance other than information campaigns. The long-term economic assistance concerned a small proportion of the impacted population (Herrera et al. 2014) 17 υmight also include measurement error. For instance, if, as indicated, the accuracy of an individual’s memory is affected by the intensity of the earthquake, υwould be systematically different, in an unpredictable way, for those less hit. As it is not either possible to a priori give a sign to the effect of the potential measurement error, we are not able to say whether our estimates of vulnerability represent a lower or an upper bound of the real effect. This reflects in the fact that our results should be taken as conditional associations rather than causal effects. 18 Table 2and Fig. 2 in the Appendix show the OLS estimate of Eq. 1and the distribution of the vulnerability index, respectively. 19 As Tables 9 and 10 in the Appendix show, not including PSU FE does not change much the magnitude and significance in all relevant covariates, which suggests that not including them in our MNL and SUR models would not alter the reported estimates. Moreover, using an OLS or a logit model yields to similar estimates in magnitude and significance (Table 11 in the Appendix). 20 Selection of children into each activity (studying, working, etc.) is likely not to be random. In fact, all specifications dealing with participation into each activity along the paper suggest what characteristics are associated with participation. However, we unfortunately do not count with a valid exclusion restriction for modeling this selection (à la Heckman). To partially account for this issue, our specifications control for a large set of observed characteristics that is likely to affect both the selection into the activity and the number of hours on that activity. Still, this does not count for selection driven by unobserved characteristics, and therefore, our results should be taken as conditional associations rather than causal effects. Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 24 of 32 Appendix Table 7 Haitian education system 19 years and above Superior 18 years Final year—Philo Secondary 17 years 1st year—Rheto 16 years 2nd year 15 years 3rd year 14 years 9stAF 3rd cycle Primary 13 years 8stAF 12 years 7stAF 11 years 6stAF 2nd cycle 10 years 5stAF 9 years 4stAF 1st cycle 8 years 3rdAF 7 years 2ndAF 6 years 1st AF 5 years Upper Pre-school 4 years Middle 3 years Lower Table 8 Asset ownership and weights obtained from MCA Assets Ownership % Households (2010) % Households (2012) Weights Oven 0 94.35 94.56 −0.28 1 5.65 5.44 5.03 Television 0 71.7 71.8 −0.75 1 28.3 28.2 2.01 Radio 0 55.96 57.99 −0.69 1 44.04 42.01 0.88 Mobile phone 0 40.07 24.42 −1.04 1 59.93 75.58 0.56 Fridge 0 90.68 91.24 −0.41 1 9.32 8.76 4.16 Generator 0 98.07 97.75 −0.15 1 1.93 2.25 6.16 Inverter 0 96.42 96.58 −0.22 1 3.58 3.42 5.53 Computer 0 97.16 96.09 −0.19 1 2.84 3.91 6.16 Ventilator 0 86.46 86.95 −0.49 1 13.54 13.05 3.09 Car 0 97.23 97.18 −0.20 1 2.77 2.82 6.03 Motorcycle 0 96.32 95.26 −0.06 1 3.68 4.74 1.55 Sewing machine 0 96.93 96.96 −0.06 1 3.07 3.04 1.59 Novella and Zanuso IZA Journal of Development and Migration (2018) 8:4 Page 31 of 32 Beegle K, Dehejia RH, Gatti R. 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