# Birth Order and Transition into Adulthood in Madagascar

Francesca Marchetta, Claire Ricard

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Francesca Marchetta, Claire Ricard. Birth Order and Transition into Adulthood in Madagascar. Journal of Human Capital, 2025, 19 (1), 10.1086/733485. hal-04848991

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Birth Order and Transition into Adulthood
in Madagascar

Francesca Marchetta

Université Clermont Auvergne, Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Centre d'Études et de Recherches sur le Développement International (CERDI), Clermont-Ferrand, France

Claire Ricard

IDinsight, Morocco, and Université Clermont Auvergne, CNRS, IRD, CERDI,
Clermont-Ferrand, France

Using panel data from Madagascar, we investigate how birth order influences the transition to adulthood in a context of poverty. Earlier-born children often achieve lower education levels than their later-born siblings and also marry younger and begin working outside family activities earlier. Parental investment, such as quality time or gender preferences, does not significantly explain these differences. Instead, household liquidity constraints appear to push parents to depend on older children to generate savings that are used to support younger siblings' education. Combining these findings with the existing literature, it seems that the liquidity-constraint channel may play a pivotal role, both across and within countries, in explaining the observed variations in firstborn advantage.

# I. Introduction

By chance, we are born in different birth orders—first, second, third, or last. The order of our birth is purely random. However, it can play a decisive role in explaining the variations in our education level, wealth, career

We are grateful to the editors Flavio Cunha and Isaac Ehrlich, two anonymous referees, and to Martine Audibert, Simone Bertoli, Hugues Champeaux, Julien Bergeot, Flore Gubert, and Isabelle Chort for their valuable comments on a previous version of this paper. We thank Alyssa Heinze for her proofreading and also thank the participants at the International Conference on Development Economics of the Association française d'économie du développement, Journées de microéconomie appliquée conference, and the International Conference on Globalization and Development. We gratefully acknowledge the support received from L'Agence nationale de la recherche of the French government through the program France 2030 (grant no. ANR-16-IDEX-0001); the various disclaimers apply.

Electronically Published February 10, 2025.

Journal of Human Capital, volume 19, number 1, spring 2025.

© 2025 The University of Chicago. All rights reserved. Published by The University of Chicago Press.

https://doi.org/10.1086/733485
choices, and even the age at which we marry. Parents, whether consciously or not, tend to invest differently in their children born at different parities, indirectly influencing key outcomes in their future adult lives. This phenomenon might be particularly evident in contexts where resources are scarce, leading to higher competition among siblings.

The effect of birth order on lifelong outcomes has been extensively studied in developed countries. The literature has consistently shown that firstborns tend to outperform their younger siblings in various aspects of life. They exhibit higher cognitive and noncognitive skills, attain a higher level of education, enjoy better health conditions, and secure better jobs (Black 2017). However, the dynamics may differ in developing countries because of tighter budget constraints faced by households (Tenikue and Verheyden 2010), as well as cultural and legal factors. Variables such as the significant role of extended family in child-rearing (Cox and Fafchamps 2007), prevalent fostering practices (Marazyan 2015), gender preferences, and specific formal or informal inheritance and succession rules can contribute to a different relationship between birth order and child development in less economically advanced countries.

Indeed, when exploring the effects of birth order in these contexts, mixed results have emerged. Some studies have found similar outcomes as in developed countries with respect to educational achievement and cognitive abilities (Calimeris and Peters 2017; Schwefer 2018; Moshoeshoe 2019), while others have identified positive effects of birth order on the same outcomes (Eirnæs and Pörtner 2004; Emerson and Souza 2008; De Haan, Plug, and Rosero 2014; Botzet, Rohrer, and Arslan 2021).

In this paper, we investigate how birth order shapes the transition into adulthood in Madagascar, an extremely poor country where the extended family plays a crucial role, early marriage among girls is not uncommon, and gender and birth-order-based discrimination in inheritance may occur.<fnref n="1" />

We use data from a survey that tracked a cohort of individuals initially interviewed when they were approximately 14 years old in 2004 and then again when they were about 22 years old in 2012. The second wave of the survey was specifically designed to capture the transition from adolescence to young adulthood and provide detailed information on school, work, and marriage histories, as well as cognitive and noncognitive skills of the individuals. Information on the highest level of schooling and on the age at marriage of all surviving siblings in 2012 was also collected, providing us with basic information on the entire sibship.

Using a within-family model, which allows us to account for any unobservable family characteristic that is constant across siblings and which could bias the effect of birth order, we find that firstborns attain lower levels of education than their younger siblings, regardless of their gender.

<footnote n="1">¹ In the paper, we refer to transition into adulthood as the process through which an individual progresses from childhood to adulthood, acquiring the responsibilities and the roles typically associated with adult individuals in a given society. It involves a series of social, practical, and emotional changes that mark the shift from childhood to adulthood.</footnote>
Additionally, we find that the risk of marriage after the age of 17 is higher for both male and female firstborns.

Furthermore, additional results, based on a between-family model with fixed effects for sibship size, indicate that firstborns drop out of school and start working outside family-run activities earlier than later-born children.

Taken together, these findings indicate that firstborns transit earlier into adulthood, suggesting that they could receive a lower parental investment than their younger siblings.

We then delve into potential explanations for the observed results. One possibility is that parents allocate more quality time to their later-born children, enabling them to develop cognitive and noncognitive skills that contribute to better school performance, delayed entry to the labor market, and later marriage. We examine this assumption using a subsample of individuals who underwent cognitive tests in both 2004 and 2012, as well as noncognitive tests in 2012. Our findings indicate that birth order does not directly influence cognitive and noncognitive skills, suggesting that the quality of parental resources received during early childhood is not affected by birth order. In the Malagasy context, it is possible that the significance of quality time spent with parents is relatively diminished because children develop their cognitive and noncognitive skills through interactions within the extended family—as exemplified by the concept of *fihavanana* in Ottino (1998)—rather than solely within their nuclear family.

Another plausible mechanism revolves around gender preferences. If we had observed a higher representation of males (females) among the latest-born children, suggesting the presence of a gender-based stopping rule, the greater investments in later-born children could be indicative of a preference for boys (girls). However, our data reveal an equal distribution of males and females among the latest-born children, suggesting that gender preferences do not account for birth-order heterogeneities.

Liquidity constraints could contribute to explain our results. In an extremely poor context such as Madagascar, parents might accumulate savings over time through their own labor as well as the work of older children, investing these resources in the human capital development of the youngest children. This assumption is indirectly supported by the result showing that firstborns begin working outside family-run activities at a younger age. Moreover, heterogeneity analyses conducted in the within-family framework demonstrate that birth order has no positive effect on education among wealthier households and individuals whose parents have some education. Furthermore, the greater effect of birth order observed in rural areas, where budget constraints are more pronounced, aligns with the notion that parents rely on earlier-born children to provide additional resources to the youngest siblings.

Finally, we consider the management of land legacy as another potential mechanism. When parents divide their land among their children, the size of individual plots decreases. To mitigate this reduction, some parents may prefer to pass on physical capital to one of their children, compensating
the others by investing more in their human capital through extended schooling. Because traditionally it is the firstborn child, in particular the firstborn boy, who inherits more land because of the responsibility to carry on the family name, possessions, and traditions to future generations, later-born children may receive greater investment in human capital to compensate for their comparatively lower physical capital. The finding that the birth-order effect is stronger in rural areas partially supports this assumption.

Our work contributes to the literature on birth order's effects on a diversity of outcomes that are related to the transition into adulthood. In particular, we shed light on the effects of birth order on (i) education and cognition, (ii) marriage timing, and (iii) entry to the labor market. We do this in a developing country, for which these research questions have been less explored, and we also investigate some of the possible mechanisms.

Parental time investment has been shown to have a strong effect on the formation of cognitive (and noncognitive) skills, especially at early stages of life (Hsin 2007; Cunha and Heckman 2008; Carneiro and Rodrigues 2009; Cunha, Heckman, and Schennach 2010; Lehmann, Nuevo-Chiquero, and Vidal-Fernandez 2018; Attanasio et al. 2020). Parental time investment could vary depending on the birth order, because several factors come into play. Parents may have less time available for the second and subsequent children because of increased responsibilities and demands on their attention, leading to a reduction in the amount of quality time spent with later-born children. Conversely, parents might gain experience and knowledge from raising their eldest child, they may apply these insights to subsequent children, and this accumulated experience can enhance quality of time spent with later-born children. Moreover, cultural factors and personal preferences can also shape the allocation of quality time investment. In some cultures, specific expectations or traditions may place greater importance on the boys or on the firstborn child, leading parents to invest more time, attention, and resources in their upbringing. Empirical literature shows that the time spent by parents with earlier-born and later-born children varies across countries. In the United States, later-born children spend less quality time with their mother and are less cognitively stimulated by her than earlier-born children (Monfardini and See 2016; Lehmann, Nuevo-Chiquero, and Vidal-Fernandez 2018). Coherent with that, other studies focused on developed countries show that later-born children have lower cognitive abilities than their older siblings (e.g., Black, Devereux, and Salvanes 2005 for Norway; Barclay 2015 for Sweden). For developing countries, conversely, there is no consensus on the effect of birth order on cognitive skills. In Ecuador, De Haan, Plug, and Rosero (2014) find that later-born children receive more cognitive stimulation and are breastfed longer by their mothers, and this translates into higher cognitive tests. On the other hand, in Indonesia, Calimeris and Peters (2017) find that second- and third-born children perform worse than firstborns on cognitive skills when aged between 7 and 14.
The effect of birth order on cognitive skills is related to the effect of birth order on education. It is generally admitted that in developed economies, later-born children have a lower educational attainment than their older siblings (e.g., Booth and Kee 2009 for Great Britain; Black, Devereux, and Salvanes 2005 for Norway).<fnref n="2" /> In developing economies, results are mixed. Some authors find a negative effect of birth order on educational outcomes, as in developed countries (Schwefer 2018; Moshoeshoe 2019). Others find a completely opposite effect: later-born children are more educated than earlier-born children (Lee, Parish, and Willis 1994; Eirnæs and Pörtner 2004; Emerson and Souza 2008; De Haan, Plug, and Rosero 2014; Botzet, Rohrer, and Arslan 2021).

In Madagascar, we find a positive effect of the order of birth on the highest level of education attained, and we also provide suggestive evidence on the positive effect of birth order on schooling duration and on the indirect effect, through schooling, on cognitive skills at adult age.<fnref n="3" /> We also provide some evidence of the irrelevance of the parental quality-time-investment channel in this context: we suppose this is because most children grow up in extended families, where it is not just interactions with their own parents that matter but, more broadly, interactions with adults of the hamlet in which they live. This result could also be attributed to a significant proportion of parents that have limited education, because the level of parental education can play a crucial role in cognitive development, making interactions with parents who have low literacy levels potentially less influential.

The effect of birth order on the time of marriage was explored in the seminal paper of Vogl (2013), who shows that in South Asian countries, where arranged marriages are prevalent and dowry is customary, parents rush the marriage of earlier-born daughters—choosing a less educated and skilled husband—to have a lower dowry to pay and more time to accumulate subsequent dowries to ultimately marry all their younger daughters. This could also be valid for a context in which dowry is not customary, as in Madagascar, because the bride's value on the marriage market declines with age (Wahhaj 2018). When parents have more daughters to marry, they could feel some pressure to rush the marriage of the oldest ones because they have other marriages to arrange in a short period of time, before their daughters become too old to find a spouse.<fnref n="4" /> Moreover, if siblings differ in their education outcomes according to their order of birth, they could also differ in terms of marriage timing: siblings with higher education levels drop out from school later and are thus likely to marry later. In this case,

<footnote n="2">² See Botzet, Rohrer, and Arslan (2021) for a literature review of findings in developed countries.</footnote>

<footnote n="3">³ Here, as in the rest of the paper, we refer to all results obtained through a model that does not control for family fixed effects as suggestive evidence.</footnote>

<footnote>Note that in additional results presented in app. C, we show that birth order does not affect noncognitive skills development in the Malagasy context.</footnote>

<footnote n="4">⁴ In many societies, sisters get married in the order of birth because an out-of-order marriage is perceived as a bad signal about the quality (i.e., virginity or agency) of the eldest daughter and reduces her chances of ever getting married (Vogl 2013).</footnote>
the order of birth could also affect the risk of marriage of males. In Madagascar, our results show that later-born boys are at lower risk of marriage from 18 or 19 years old, while the risk of marriage is lower for all subsequent girls after the second one starting from 17 or 18 years old. Moreover, our results also suggest that it is the second-born girl who has the highest risk of marriage at a very young age (14 or 15) with respect to her sisters.

Research has widely demonstrated that in countries where child work is widespread—for example, Nepal (Edmonds 2006), Brazil (Emerson and Souza 2008), Guatemala (Dammert 2010), Ethiopia (Alvi and Dendir 2011; Haile and Haile 2012), and Ecuador (De Haan, Plug, and Rosero 2014)—firstborn children are more likely to work than their younger siblings at a given point in time, and they also work longer hours. This is rational because wages increase with the age of child workers (Freije and Lopez-Calva 2001). However, as far as we know, only Bertoni and Brunello (2016) estimated the effect of birth order on the age at entry to the labor market, specifically in European countries.<fnref n="5" /> This remains an open question for developing countries. We provide suggestive evidence on the fact that firstborns are sent to work outside family-run activities at younger ages than their later-born siblings; we suppose that in the context of Malagasy poverty, parents send the earlier-born child to work to provide more resources to the whole sibship, and this is detrimental to the earlier-born child’s education.

The liquidity-constraint channel has been tentatively explored in some studies on birth order that have observed heterogeneities within countries with respect to household wealth. For example, in their study on 12 sub-Saharan countries, Tenikue and Verheyden (2010) observe that in poor households, earlier-born children are less educated than their younger siblings, whereas in wealthier families, they are more educated than their younger siblings. This stems from the trade-off between the liquidity effect and the transfer effect, which arises because firstborns typically leave their parents’ household earlier, consequently sending remittances over a more extended period than their siblings born later. The dominance of either of these effects depends on the financial standing of the household (Tenikue and Verheyden 2010). Similarly, in developed countries, firstborns’ advantage in educational attainment is more pronounced in wealthier households (Black, Devereux, and Salvanes 2005; Booth and Kee 2009; Lafortune and Lee 2014; Esposito, Kumar, and VILLASenor 2020; Nuevo-Chiquero, Vidal-Fernandez, and Lehmann 2022; Hiriscau and Pintea 2023). Globally, wealth seems to favor firstborns’ education.<fnref n="6" />

<footnote n="5">⁵ They found that firstborns start to work 0.7 years later than later-born children. It is mostly due to their longer education.</footnote>

<footnote n="6">⁶ The intertwined relationship between wealth, birth order, education, and cognitive skills can also be explained by the dilution theory (Blake 1981). As sibship grows, children have to share parental resources (either time or economic resources) with their siblings. In developing countries, where budget constraints are more binding, inequalities in resources sharing can be exacerbated. In Indonesia, for example, Calimeris and Peters (2017) found that second- and third-born children eat a smaller variety of food than do firstborns.</footnote>
In this paper, we provide some elements that suggest that the liquidity constraints faced by households could be the main mechanism to explain birth-order effects in Madagascar.

The rest of the paper is structured as follows: section II describes the context and the data used in our study, section III presents the empirical strategy, and the main results and the possible mechanisms are presented in sections IV and V, respectively. Finally, section VI concludes. Additional results on noncognitive skills are presented in appendix C.

## II. Context and Data Description

### A. Context

Madagascar is one of the world's poorest countries. In 2022, an alarming 80.2% of the Malagasy population lived below the international poverty line of 2.15 USD per day (in 2017 purchasing power parity).

As highlighted by Razafindrakoto, Roubaud, and Wachsberger (2017), Madagascar's economic trajectory has been characterized by an enduring downward trend spanning more than 50 years since independence. Despite intermittent fluctuations, the trajectory has shown little capacity for reversal. The situation is further exacerbated by the escalating intensity of extreme weather events, particularly cyclones.<fnref n="7" />

Poverty is mainly concentrated in rural areas and in the southeastern regions (Kubot et al. 2015).<fnref n="8" /> Poverty leads children to transit very early into adulthood in Madagascar, where educational outcomes are low, child work is widespread, and the issue of child marriage for girls has not been fully addressed. According to the last Multiple Indicators Cluster Survey (MICS), in 2018 a fifth of children aged 6–10 and a third of children aged 11–14 were not in school. Also, the repetition rate is very high: in 2018 in Madagascar, 26% of primary school students were repeating their previous grade, whereas in sub-Saharan Africa only 7.8% of primary school students were repeating their previous grade (INSTAT and UNICEF 2019).

High rates of repetition lead to low rates of persistence to the last grade of primary school. In 2018 in Madagascar, only 31.6% of students who had been enrolled in first grade of primary school eventually reached the last grade (56.2% for the sub-Saharan Africa region). Low rates of persistence to last grade of primary school translate into low gross secondary enrollment rates. In 2019 in Madagascar, gross secondary enrollment rate was only 34.6% against 43.3% in sub-Saharan Africa (INSTAT and UNICEF 2019).

<footnote n="7">⁷ In the year 2022 alone, the country experienced the impact of four cyclones, resulting in important infrastructure destruction and in the flooding of more than 200,000 hectares of agricultural land. Direct economic damage caused by these four tropical storms is estimated at approximately 4.8% of Madagascar's national GDP. Source: World Bank, Poverty and Equity Brief—Madagascar, April 2023 (https://www.worldbank.org/en/topic/poverty/publication/poverty-and-equity-briefs).</footnote>

<footnote n="8">⁸ Recently, the COVID-19 crisis had a stronger detrimental effect on household income in urban areas, where 20% of the Malagasy population lives. In rural areas, resilient agricultural productivity helped to reduce the effect (Stocker et al. 2020).</footnote>
Besides poor educational outcomes, the country suffers from a low level of learning. Learning of students in the final year of primary school has been assessed four times between 1998 and 2019 by the Programme d'analyse des systèmes éducatifs de la Confemen (PASEC).<fnref n="9" /> The first two evaluations took place in 1998 and in 2005. At that time, the country scored high in math but very low in French (PASEC 2005). The last two evaluations took place in 2015 and 2019. This time, Madagascar performed worse than the assessed countries' average both in math and language (PASEC 2015, 2020).<fnref n="10" />

According to PASEC (2020), Madagascar's poor performance in learning is partly due to a lack of time devoted to study and reading: 97.1% of students assessed in 2019 took part in domestic chores, and 74.1% took part in agricultural activities. According to the last MICS, girls are more involved than boys in domestic chores: 88% of girls aged 5–11 and 93.3% of girls aged 15–17 are involved in these activities, while the percentages for boys are 82.1% and 86.9%, respectively. On the other hand, 38.3% of boys aged 5–11 and 72% of boys aged 15–17 work on-farm or off-farm, and the percentages for girls are 33.4% and 61.3%, respectively (INSTAT and UNICEF 2019). Child work is thus highly prevalent in Madagascar.

On average, girls perform better than boys in terms of learning and schooling. They repeat less and are more likely to complete primary school and to be enrolled in lower secondary school. However, they face a higher risk of marriage before their eighteenth birthday.

According to the latest MICS, 40.3% of women aged 20–24 in Madagascar were married before the age of 18. In comparison, 11.8% of men in the same age group experienced early marriage. Even more concerning, the survey revealed that 12% of women and 2% of men aged 20–24 were married before the age of 15. Child marriage is more prevalent in rural areas (INSTAT and UNICEF 2019). Once married, the bride usually moves to be with her husband's kin. Bride price transfers are small or symbolic.<fnref n="11" />

The size and composition of households are crucial considerations for our research question, because the effect of birth order on the transition into adulthood may depend on who the individual was living with during childhood. The size of households in Madagascar has remained relatively stable over the past 15 years. In 2003–4, the average household size was 4.6 people (INSTAT and ORC Macro 2005), which decreased slightly to 4.2 in 2018.

The majority of households in Madagascar are nuclear households (72.4%), while around a quarter of them (27.6%) are extended households.

<footnote n="9">⁹ PASEC is a tool developed by the Conférence des ministres de l'Éducation des États et Gouvernements de la francophonie (CONFEMEN), an association of ministries of education from 44 French-speaking countries. PASEC assesses the performance of education systems by evaluating reading and math abilities of students in the second and sixth years of primary school in member states.</footnote>

<footnote n="10">¹⁰ As the instruction language was Malagasy in 2019, the language test assessed Malagasy abilities instead of French ones.</footnote>

<footnote n="11">¹¹ Atlas of Pre-Colonial Societies of the Institute of Social Anthropology, University of Zurich (https://www.worlddevelopment.uzh.ch/en/atlas.html).</footnote>
Nuclear households are more prevalent in rural areas (73.9% of households) than in urban areas (66.7% of households) (INSTAT 2019). Individuals tend to live in hamlets consisting of 20–40 individuals who are often related by blood or marriage (Ottino 1998). They are organized in *fokonolonas*, which represent community assemblies consisting of all adults residing in the same *fokontany*, which is the lowest administrative unit in the country (M’Baye et al. 1968). The *fokonolona* serves as a local administrative management unit, where individuals in small urban agglomerations or rural districts come together to pursue common objectives and defend shared interests (McLain et al. 2021). The *fokonolona* plays a crucial role in conflict resolution, management of community land, and provision of support during significant life events such as births, marriages, and deaths. This highlights the influence and significance of relationships beyond the immediate family, particularly in rural areas. Anthropological research points to the concept of *fihavanana*, which captures the trust and mutual support established among individuals living in the same hamlet, transcending mere biological ties (Ottino 1998). Different types of parenting, such as parenting by identity, ownership, and affinity/alliance, demonstrate that biological relationships with parents are not necessarily the strongest ones.

Traditionally, inheritance practices in Madagascar have been predominantly patriarchal, favoring male heirs over female heirs. Today’s practices concerning inheritance are officially regulated by a national law of 1968, which establishes that every legally recognized child is entitled to a part of the inheritance in absence of a will, but parents can totally disinherit one of their children or all of them if they want to (Paule 2015).<fnref n="12" /> The law thus affords much freedom to parents. This reflects in the heterogeneity of practices across regions and ethnicities. Differences exist in particular with respect to the gender and birth-order discrimination in heritage. In some regions, land is divided among the sons of the father at his death. In others, the eldest son is the only heir. Often, daughters are excluded from inheritance (Lanto 2007). But this is not always true: in the Hauts Plateaux area, for example, girls enjoy the same land rights (Randriamamonjy 2007), while in the southeast they need to return the land to their families once they leave the village to marry. These practices are coherent with the tradition that sees the sons administering the family property with their father, in the general interest of all the members and with the aim of guaranteeing an inheritance for future generations. The eldest son is particularly important because he is the one who assumes decision-making power over land and property as well as other responsibilities in place of the father. He is also the only member of the sibship that is accepted into the group of elders. For all these reasons, firstborns are seen as deserving the obedience and the respect of their siblings (Pronk and Evers 2007; Crenn 2013).

<footnote n="12">¹² Law no. 68-012, July 4, 1968.</footnote>
## B. Data Description

To analyze the effect of birth order on transition into adulthood in Madagascar, we use data from the 2012 Madagascar Youth Transition Survey, which represents a follow-up of the 2004 Madagascar Study on Academic Progress and Academic Performance (EPSPAM).

The 2004 EPSPAM was based on the sample of children enrolled in the second grade of primary school who were surveyed by PASEC in 1998. In 2004, 48 clusters were randomly selected from the original 120 PASEC clusters. An additional 25 “small school” communities were also sampled from the Ministry of Education database after stratifying on province.<fnref n="13" /> In each PASEC cluster, 15 households with PASEC children surveyed in 1998 were resurveyed in 2004. Because the PASEC sample was school based, only children enrolled in school in 1998 were part of the sample. To limit selection bias and to include in the sample children who already had dropped out from school at the time they were supposed to attend the second grade, 15 additional non-PASEC households containing children of the same cohort (i.e., born between 1988 and 1992) were randomly selected within each cluster and added to the 2004 sample.<fnref n="14" /> Although the sample was cohort based, all household members were interviewed. This sampling frame was designed in order to obtain a nationally representative survey.<fnref n="15" />

The 2012 Madagascar Youth Transition Survey was designed to track all cohort members interviewed in 2004, even if they moved to another locality, and 1,736 cohort members from the 2004 EPSPAM—representing 88% of the 2004 sample—were revisited, giving an extremely low attrition rate compared with that of other surveys from sub-Saharan African countries. Because of data inconsistencies on identifiers, the initial sample was reduced to 1,726 individuals after data cleaning.<fnref n="16" />

<footnote n="13">13 Small schools were added to the sample because PASEC schools in rural areas were larger than average rural schools. A public primary school was considered as small when it had fewer students than the national median value of 140 students.</footnote>

<footnote n="14">14 See Glick, Randrianarisoa, and Sahn (2011) for a more detailed description of the sampling method.</footnote>

<footnote n="15">15 Unfortunately, the representativeness of this survey and the one of all the other surveys realized in the same period cannot be assessed because no population census was conducted in Madagascar from 1993 to 2018. Indicators on education and child marriage based on our data are better than the corresponding ones from the aforementioned 2018 MICS data. This might be due to the fact that our data originate from a school-based survey, determining a residual selectivity bias.</footnote>

<footnote n="16">16 As shown in table A1, attrition seems to be correlated with the order of birth: earlier-born children are more likely not to be interviewed in 2012. This is probably due to the fact that a larger proportion of firstborns leave the household of origin earlier, and, because of tracking difficulties, this automatically determines a higher attrition for this group, even in the presence of a nonsystematic correlation between individual characteristics and attrition. We also compared other individual and household characteristics (i.e., gender, education, cognitive skills, place of residence, parents' characteristics, and so forth) of cohort members reinterviewed in 2012 with those of attrited cohort members and observe some significant differences among the two groups. Notably, children from the original PASEC survey are overrepresented among nonattribited cohort members, thus explaining that nonattribited cohort members are more likely to be enrolled in school in 2004, less likely to work, and have better cognitive skills.</footnote>
The 2012 Madagascar Youth Transition Survey was specifically designed to capture the transition from adolescence to young adulthood of the cohort members. It included detailed questions on education, employment, health, marriage, and migration for cohort members who were approximately 22 years old in 2012. A notable aspect of the survey is the inclusion of a sibling module, which collected basic information (i.e., age, gender, highest level of education, marital status, and age at marriage) on all the cohort members' siblings (not on half siblings) who were alive at the time of the survey. The information collected in the sibling module allows us to run our main analysis on education outcomes and on timing of marriage on a sample consisting of the cohort members and all their siblings, employing a model with family fixed effects, as described in section III. For additional analyses on outcomes such as age at school dropout and age at entry to the labor market, we use a between-families model on a sample that is limited to only cohort members because we do not have information on these outcomes for their siblings.

### C. Sample for the Within-Family Analysis

For our primary analysis, we employ a within-family model that compares cohort members with their siblings. To the original sample of 1,726 cohort members, we exclude 347 singletons, and we add 3,964 siblings. Singletons are excluded because they provide no identifying variability in a model that relies on comparing individuals across siblings for identification. The final sample consists of 5,343 individuals.<fnref n="17" />

Table 1 presents the main individual characteristics on the basis of the birth order. It is worth noting that the characteristics of the sixth-born and subsequent children are very similar to those of the fifth-born child. As a result, we decided to group them together as one category.<fnref n="18" /> On average, individuals in the sample are 21.22 years old, and we can observe the expected relationship between age and birth order, with firstborns being older than the others. This age difference may explain why firstborns have achieved higher education levels and are more likely to be married.

Thirty-one percent of individuals are married (35.2% of women and 26.6% of men). Average age at marriage is 21 for men and 19 for women. Early marriage is still quite common for girls: 302 women (11% of our sampled women) have been married when less than 18 years old, and 127 (4.8%) have been married when less than 16 years old.

About three-quarters of the individuals resided in rural areas in 2004, and the percentage is higher among later-born children, confirming that larger sibling groups are more common in rural areas. Approximately 39% of the sample individuals have at least one parent who completed

<footnote n="17">¹⁷ When two cohort members belonging to the same sibship were interviewed, their sibling group was counted once.</footnote>

<footnote n="18">¹⁸ There are 888 individuals who are sixth-born or higher, ranging from the sixth-born to the fifteenth-born.</footnote>
<table><caption>TABLE 1
DESCRIPTIVE STATISTICS BY BIRTH ORDER, COHORT MEMBERS AND ALL SIBLINGS:
WITHIN-FAMILY MODEL SAMPLE</caption><thead><tr><th></th><th>Firstborn</th><th>Second Born</th><th>Third Born</th><th>Fourth Born</th><th>Fifth Born or Subsequent</th><th>Total</th></tr></thead><tbody><tr><td>Outcomes:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Highest education level:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>None</td><td>.05</td><td>.04</td><td>.04</td><td>.04</td><td>.09</td><td>.06</td></tr><tr><td>Primary not complete</td><td>.33</td><td>.28</td><td>.29</td><td>.33</td><td>.40</td><td>.33</td></tr><tr><td>Primary complete</td><td>.10</td><td>.13</td><td>.13</td><td>.11</td><td>.09</td><td>.11</td></tr><tr><td>Lower secondary not complete</td><td>.20</td><td>.23</td><td>.24</td><td>.26</td><td>.23</td><td>.23</td></tr><tr><td>Lower secondary complete</td><td>.10</td><td>.09</td><td>.10</td><td>.09</td><td>.07</td><td>.09</td></tr><tr><td>Higher secondary not complete</td><td>.06</td><td>.10</td><td>.08</td><td>.10</td><td>.06</td><td>.08</td></tr><tr><td>Higher secondary complete</td><td>.09</td><td>.08</td><td>.06</td><td>.05</td><td>.04</td><td>.06</td></tr><tr><td>Postsecondary</td><td>.06</td><td>.05</td><td>.06</td><td>.03</td><td>.02</td><td>.04</td></tr><tr><td>Share of ever married</td><td>.52</td><td>.39</td><td>.28</td><td>.21</td><td>.15</td><td>.31</td></tr><tr><td>Age at first marriage</td><td>21.18</td><td>20.39</td><td>19.52</td><td>19.53</td><td>19.07</td><td>20.26</td></tr><tr><td>Control variables:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age</td><td>27.29</td><td>23.65</td><td>21.20</td><td>19.21</td><td>15.83</td><td>21.22</td></tr><tr><td>Female</td><td>.48</td><td>.49</td><td>.51</td><td>.49</td><td>.49</td><td>.49</td></tr><tr><td>Variables for heterogeneities:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rural (2004)</td><td>.77</td><td>.77</td><td>.77</td><td>.78</td><td>.79</td><td>.78</td></tr><tr><td>4th quartile of wealth (2004)</td><td>.26</td><td>.26</td><td>.26</td><td>.24</td><td>.20</td><td>.24</td></tr><tr><td>At least one parent completed primary school</td><td>.41</td><td>.41</td><td>.41</td><td>.40</td><td>.34</td><td>.39</td></tr><tr><td>Observations</td><td>1,097</td><td>1,097</td><td>946</td><td>754</td><td>1,449</td><td>5,343</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—Presented are mean values for the outcome variables, control variables, and variables used for the heterogeneity analysis by birth order. The calculations are made on cohort members and all their siblings declared in the 2012 wave of the Madagascar Youth Transition Survey. Highest education level indicates the highest level of education completed by the individuals. The share of individuals who reported a specific highest level of education is shown for each birth order. The share of ever married represents the share of individuals who are married. Age at first marriage is available only for individuals already married (1,646 individuals). Age and age at marriage are measured in years. Female is a dummy variable equal to 1 if the individual is a female. Rural is a dummy variable equal to 1 if the household was living in a rural area in 2004. Fourth quartile of wealth is a dummy equal to 1 if the household belonged to the last quartile of wealth (based on assets) in 2004. At least one parent completed primary school is a dummy equal to 1 if one or two parents have completed primary school.

primary school or more, and this percentage decreases with birth order, suggesting that better-educated parents have fewer children. We observe a similar trend in the percentage of individuals in the 4th quartile of the income distribution, which stands at 20% for the fifth-born and subsequent children compared with approximately 26% for earlier-born children.

## D. Sample for the Between-Families Analysis

For the analysis of additional outcomes in section IV.B (i.e., age at school dropout and age at entry to the labor market) and for the analysis of other outcomes in section V, where we explore the mechanisms (i.e., cognitive
skills and time use), we use a sample that is limited to only cohort members because we do not have this information for their siblings. When using the cohort-members-only sample, we have, almost invariably, only one observation per family.

When using only cohort members, we are better able to measure the order of birth owing to the questions that are asked in 2004 to all the adult household members on their biological children, irrespective of coresidence. Adults are in fact asked to identify all their coresident children and also to give basic information, including age, concerning other children younger than 30 who do not live in the household. Because of these questions, we are able to identify all the biological siblings of the cohort member, considering that individuals with the same mother or the same father are siblings. In this case, half brothers and half sisters are also considered to be siblings. We believe that this measure is preferable to use compared with the one that is based on the information given by cohort members about the surviving siblings in 2012 because some of the siblings may have died between 2004 and 2012. As shown by Lehmann, Nuevo-Chiquero, and Vidal-Fernandez (2018), the effects of birth order on cognition, education, and employment outcomes depend on life circumstances during childhood. Thus, it is the order of birth in childhood that matters for most of the outcomes we explore in the paper, in particular for the education and learning-related outcomes. Unfortunately, we are unable to use this measure in the within-family analysis.<fnref n="19" />

In the analysis based on the between-families framework, we thus have an initial sample of 1,726 cohort members, including single children, who were excluded from the sample for the within-family model.<fnref n="20" /> We do not have complete information on all the outcomes of interest or on all the variables used as controls for every individual in the sample. As discussed in detail in section III, to accurately estimate the effects of birth order on our outcomes of interest in a framework where we cannot include family fixed effects, we need to include several covariates that pertain to the parents of the cohort members. Unfortunately, these variables have a significant amount of missing data because they were not the primary focus of the survey. For example, information on the mother's age is missing for 274 individuals, even though this variable is important to include in the model because of the tendency for later-born children to have older mothers (see table A3). Because of the missing information on key covariates, we

<footnote n="19">¹⁹ The definition used in the within-family analysis, which is based on the 2012 siblings module, differs from the one mentioned in this section in the following ways: (i) some siblings may have passed away between 2004 and 2012; (ii) it does not include half siblings; (iii) it does not impose any restriction on the age of the siblings. Additionally, both measures could suffer from measurement errors. The discrepancies between the two measures are presented in table A2. We conducted tests to confirm the reliability of our main findings in the between-families models when using the birth-order measure based on the 2012 sibling module.</footnote>

<footnote n="20">²⁰ As it is not possible to properly identify the siblings of fostered or adopted children, because they do not live with their biological parents, 39 adopted or fostered cohort members are dropped from the estimation sample, which totals 1,687 individuals.</footnote>
are obliged to drop individuals from our sample who have missing data on
these variables. This ensures our analysis is based on a sample with com-
plete information on the covariates necessary to accurately estimate the ef-
fects of birth order on our outcomes of interest.

We end up with an estimation sample of 1,235 individuals (53% of fe-
males), interviewed both in 2004 and in 2012 and having up to 13 siblings
and 6 siblings on average. About 26% of them are firstborn and 22.4% sec-
ond born. By construction, in this sample, the average age (22 years) is the
same across birth order because all individuals belong to the same cohort.
The share of women remains stable across birth order. Other variables
used as covariates are described in table A3.

Table 2 provides a description of the outcomes studied under the
between-family framework, categorized by birth order. All outcomes are
measured in 2012, except for the 2004 cognitive skills.

The variation in sample sizes across outcomes is a result of the availability
of data for each of them. In instances where certain individuals have miss-
ing data for a particular outcome, they are excluded from the analysis for
that specific outcome only. This approach was taken to avoid unnecessarily
restricting the sample size, ensuring that a larger number of individuals
could be included in the analysis for other outcomes. By doing so, we
aimed to maximize the available data while still providing valuable insights
into each outcome of interest.<fnref n="21" />

On average, cohort members dropped out of school at the age of 17 and attended school until grade 8. Eighty percent of them completed primary school, while 49% completed lower secondary school. The small differences in education-related outcomes in favor of later-born children are not significant.

In both the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey, cognitive skills were assessed through oral and written French and math tests administered at home. These tests were designed by specialists from the Ministry of Education to compare cognitive abilities among a diverse population. Each cohort member took the same test, regardless of his or her level of education. The survey provides z-scores for math and French separately, as well as a combined math and French z-score, which is used in our analysis. Table 2 shows that later-born children have lower cognitive abilities in 2004 but higher abilities in 2012 compared with those who have lower birth orders.

Approximately 85% of cohort members were already engaged in some
form of work, which includes any income-generating or non-income-
generating activity that is not domestic work, and 54% of them were work-
ing outside family-run activities. The average age at first work is about 14 years,

<footnote n="21">²¹ When we run the between-family models for education level, age at marriage, age at school dropout, and age at entry to the labor market on the same sample of 1,209 cohort members for which the four outcomes are observed, the results are robust. Results are available from the authors upon request.</footnote>
<table><caption>TABLE 2
DESCRIPTIVE STATISTICS BY BIRTH ORDER: BETWEEN-FAMILIES MODEL SAMPLE</caption><thead><tr><th rowspan="2"></th><th colspan="7">Birth Order (2004 EPSPAM)</th><th rowspan="2">Observations</th></tr><tr><th>Firstborn</th><th>Second Born</th><th>Third Born</th><th>Fourth Born</th><th>Fifth Born or Subsequent</th><th>Total</th></tr></thead><tbody><tr><td>School attainment</td><td>7.96</td><td>8.09</td><td>7.90</td><td>7.79</td><td>8.16</td><td>8.00</td><td>1,233</td></tr><tr><td>Highest education level:</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>None</td><td>.01</td><td>.01</td><td>.02</td><td>.01</td><td>.01</td><td>.01</td><td>1,233</td></tr><tr><td>Primary not complete</td><td>.22</td><td>.20</td><td>.17</td><td>.18</td><td>.16</td><td>.19</td><td>1,233</td></tr><tr><td>Primary complete</td><td>.11</td><td>.13</td><td>.17</td><td>.14</td><td>.14</td><td>.14</td><td>1,233</td></tr><tr><td>Lower secondary not complete</td><td>.16</td><td>.18</td><td>.16</td><td>.19</td><td>.18</td><td>.17</td><td>1,233</td></tr><tr><td>Lower secondary complete</td><td>.13</td><td>.10</td><td>.11</td><td>.17</td><td>.14</td><td>.13</td><td>1,233</td></tr><tr><td>Higher secondary not complete</td><td>.11</td><td>.10</td><td>.13</td><td>.13</td><td>.10</td><td>.11</td><td>1,233</td></tr><tr><td>Higher secondary complete</td><td>.17</td><td>.18</td><td>.14</td><td>.12</td><td>.19</td><td>.16</td><td>1,233</td></tr><tr><td>Postsecondary</td><td>.08</td><td>.09</td><td>.09</td><td>.06</td><td>.09</td><td>.08</td><td>1,233</td></tr><tr><td>Cognitive skills (2012)</td><td>-.01</td><td>-.02</td><td>-.04</td><td>.11</td><td>.09</td><td>.02</td><td>1,122</td></tr><tr><td>Cognitive skills (2004)</td><td>.10</td><td>.06</td><td>.03</td><td>-.12</td><td>-.03</td><td>.03</td><td>750</td></tr><tr><td>Out of school</td><td>85.08</td><td>82.48</td><td>78.24</td><td>85.09</td><td>81.18</td><td>82.47</td><td>1,221</td></tr><tr><td>Age at school dropout</td><td>16.70</td><td>16.58</td><td>16.36</td><td>16.51</td><td>16.82</td><td>16.61</td><td>957</td></tr><tr><td>Ever worked</td><td>84.08</td><td>87.27</td><td>81.31</td><td>87.26</td><td>84.86</td><td>84.89</td><td>1,211</td></tr><tr><td>Age at first work</td><td>13.88</td><td>13.92</td><td>13.89</td><td>13.62</td><td>14.22</td><td>13.93</td><td>1,028</td></tr><tr><td>Ever worked outside family</td><td>59.93</td><td>51.48</td><td>55.37</td><td>50.36</td><td>50.94</td><td>54.08</td><td>1,030</td></tr><tr><td>Age at first work outside family</td><td>17.45</td><td>17.83</td><td>17.32</td><td>17.90</td><td>17.34</td><td>17.54</td><td>552</td></tr><tr><td>Ever married</td><td>39.62</td><td>32.85</td><td>33.79</td><td>34.97</td><td>34.11</td><td>35.30</td><td>1,235</td></tr><tr><td>Age at first marriage</td><td>18.71</td><td>18.43</td><td>18.00</td><td>18.25</td><td>18.86</td><td>18.50</td><td>436</td></tr></tbody></table>

Note.—All variables are constructed by using the 2012 Madagascar Youth Transition Survey except for birth order and the cognitive skills measured by the 2004 EPSPAM. School attainment measures the last grade completed by the cohort member. Highest education level indicates the highest level completed by the cohort member. The table reports for each birth order the share of cohort members who reported a specific highest level of education. Cognitive skills in 2012 and in 2004 were collected with a standardized test and are displayed as z-scores. Out of school is a dummy equal to 1 if the cohort member is still in school in 2012. Ever worked is a dummy equal to 1 if the cohort member has ever worked. Ever worked outside family is a dummy equal to 1 if the cohort member has ever worked outside the family-run activity. Ever married is a dummy equal to 1 if the cohort member is already married. We have information on age at school dropout, age at first work, age at first work outside family-run activities, and age at first marriage for cohort members who already dropped out from school, worked, or got married. Age at school dropout, age at first work, age at first work outside family-run activities, and age at marriage are measured in years.

but those who were already employed outside family-run activities began working at about 18 years.

About 35% of cohort members were married or in cohabitation (45.3% of females and 24% of males). The average age at marriage for those who were already married is 19 (18 for females and 19 for males). No clear
trend regarding the relationship between birth order and the timing of en-
try to the labor market or marriage emerges from table 2.

As noted by Black et al. (2018), such simple descriptive statistics can
be misleading because they compare individuals who do not necessarily
have the same family background. For example, table A3 shows that later-
born children belong to larger sibships and have older mothers than
firstborns. These characteristics could also influence our outcomes of
interest.

In the next section, we present the empirical strategy we adopt to iden-
tify the effect of birth order on the outcomes we have described.

### III. Empirical Strategy

We aim to examine the effect of birth order on several key outcomes that reflect the transition of young individuals into adulthood. In our main analysis, where we can use within-family models, we explore their educational attainment and the timing of marriage. For additional outcomes, we use a between-families framework to explore the timing of dropping out of school and the timing of entry to the labor market. In section V, we also look at cognitive skills.

To accurately establish the causal effect of birth order, we must employ a
methodology that can account for the influence of both observable and
unobservable family and community characteristics on outcomes. The most
suitable approach for accomplishing this is a family fixed-effect model, also
known as a within-family model, which allows for the consideration of all
unobservable family characteristics that are constant across siblings, such
as time-invariant family-specific factors, that may introduce a bias in the es-
timated effect of birth order.

When incorporating family fixed effects into the model, the birth-order
effect is identified by comparing siblings within the same family, thereby
eliminating any relationship between birth order and family background.
Implementing within-family models thus requires gathering information
on the outcomes for all siblings within a sufficiently large sample of fami-
lies. This kind of data is particularly challenging to find, especially in devel-
oping countries. We have the chance to dispose of such information for
two of the outcomes we want to study: the highest level of schooling and the
age at marriage. This allows us to investigate the effect of birth order on
these two outcomes by use of a within-family model.

In this framework, it is crucial to control for the year of birth of the
children (Black, Gronqvist, and Ockert 2018). This is because children
with a higher birth order typically belong to more recent cohorts, and the
birth-order estimates may be confounded by unobserved heterogeneity
across birth cohorts. By incorporating the children’s year of birth as a con-
tinuous variable in our estimates, we isolate the specific effect of birth or-
der from potential confounding factors associated with different cohorts
that change linearly over time.<fnref n="22" /> However, this approach does not fully address the issue of possible confounders that evolve in a nonmonotonic way over time, such as economic growth, which has been highly unstable in Madagascar during the study period.<fnref n="23" /> Ideally, we would have included year-of-birth dummy variables in the specifications. Unfortunately, this is not feasible with our data, which are designed to sample cohort members born around 1990, and thus useful to represent individuals born around this date but not those born before or after, who are siblings of the cohort members.<fnref n="24" />

To alternatively address the potential confounding effect of GDP per capita fluctuations on incentives to accumulate human capital, all specifications include a dummy variable that takes the value of 1 for children who were expected to enter primary school between 1997 and 2008, a period of per capita GDP growth (except for the crisis in 2001).<fnref n="25" />

Finally, in a comprehensive test to rule out the hypothesis that birth-order results are driven by unobserved heterogeneity evolving in a non-monotonic way over time across children born in different years, 3-year cohort dummies are included in a robustness check.<fnref n="26" />

When it is not feasible to use family fixed-effect models, researchers resort to between-family models instead.<fnref n="27" /> These models compare earlier-born with

<footnote n="22">²² As robustness checks, we also include a polynomial in the year of birth, adding square and cubic terms. Results, not presented here, are available from the authors upon request.</footnote>

<footnote n="23">²³ The GDP per capita in Madagascar experienced a decline from 1971 to 1996, followed by a period of growth between 1997 and 2001. The crisis in 2001 significantly affected the country, leading to a historical low in GDP per capita. Subsequently, there was a resurgence in economic growth; however, by 2005, per capita income had not yet surpassed the levels seen in 2001. When comparing GDP per capita (in constant 2011 USD) in 1960 and 2017, Madagascar stands out as one of the few countries globally where the GDP per capita in 2017 was lower than that in 1960 (IMF 2020).</footnote>

<footnote n="24">²⁴ The within-family models in our study involve a sample of 5,343 individuals born in 53 different years, between 1952 and 2012 (i.e., an average of 100 observations per year of birth). However, the distribution by year of birth is highly imbalanced. Specifically, the birth year with the highest number of observations, namely 1991, constitutes 8.2% of the sample. The 26 birth years with the lowest number of observations collectively account for the same share of the sample, also at 8.2%. This, in turn, implies that the average of the dependent variable by year of birth, for years with a low number of observations, is not representative of the population that was born in that year, and, as a consequence, it does not convey information on the time-varying unobserved factors that might pose a threat to our analysis.</footnote>

<footnote n="25">²⁵ We name this variable “GDP growth dummy” in what follows. Results are robust when we replace this dummy with the average per capita GDP growth rate during the years when the individual was between 6 and 12 years old—the age range corresponding to primary school attendance. Results are available from the authors upon request.</footnote>

<footnote n="26">²⁶ More specifically, we have formed seven 3-year groups for individuals that were born between 1978 and 1998, e.g., one group for individuals born between 1978 and 1980, another for individuals born between 1981 and 1983, and so on. Eighty-two percent of the observations in our sample are born between 1978 and 1998. We grouped individuals born in the tails (i.e., before 1978 and after 1998) into two broader groups. When we include the 3-year cohort dummies in the model, we do not include the dummy equal to 1 for individuals who were expected to enter primary school in a period of per capita GDP growth.</footnote>

<footnote n="27">²⁷ In addition to family fixed effect and between-family models, Black, Gronqvist, and Ockert (2018) propose a third approach called family-type fixed effect models. These models assume that families of the same size with children born in the exact same year are sufficiently similar to be used as fixed effects. Black, Gronqvist, and Ockert (2018) demonstrate</footnote>
later-born children from different families. Important identification issues thus emerge. The causal effect of birth order is correctly identified only if the researcher is able to control for family and community characteristics that are correlated with both the order of birth and the outcomes. Important variables to include as controls are sibship size, mother's age, mother's age at first birth, and socioeconomic conditions (Black, Gronqvist, and Ockert 2018).

By including sibship size as a control variable, researchers address the issue that later-born children are more commonly observed in larger families, and so the birth-order effect could be confounded with the family-size effect (Eirnæs and Pörtner 2004; Black, Devereux, and Salvanes 2005; Booth and Kee 2009; De Haan 2010; Lafortune and Lee 2014).

Inclusion of mother's age is extremely important because later-born children have older mothers, so birth order is correlated with mother's age, and, without including this control, the birth-order effect could partially capture the mother's age effect. Mothers from different cohorts might indeed have different preferences on a number of child outcomes. Furthermore, controlling for the mother's age at first birth allows to address the correlation between birth order and early childbearing. First-borns are indeed more likely to be born from very young mothers, with all the problems that this might imply for them (Branson, Ardington, and Leibbrandt 2015; Perez-Alvarez and Favara 2023). It is important to also control for the age difference between siblings, because it can interfere with the effect of birth order (Moshoeshoe 2019). The inclusion of socioeconomic controls should allow to capture the other unobserved family characteristics that might be correlated with both birth order and the outcomes under investigation and which could induce a bias in the estimation of the birth-order effect.

In summary, the between-family model relies on assumptions that are more restrictive, which has led the current economic literature to prefer the within-family estimator for its robustness and reduced bias. However, it is worth noting that the between-family model can still be valuable in situations where data limitations prevent the use of the within-family estimator. In cases such as ours, where researchers have access to an adequate set of control variables that help to effectively account for confounding factors, the between-family model can provide meaningful insights and analysis.<fnref n="28" />

<footnote>that their results align when using within-family or family-type fixed effect specifications. In our attempt to analyze outcomes for which we lacked information on the entire sibship, we also considered employing family-type fixed effect models. However, the extensive data requirements posed challenges given the size of our sample and the prevalence of large sibship sizes in Madagascar.</footnote>

<footnote n="28">²⁸ In sec. IV, we present findings demonstrating that, in our case, the between-family model yields similar qualitative results to the within-family model when examining the effect of birth order on educational attainment and the timing of marriage.</footnote>
The between-family model with fixed effects for sibship size is thus the methodology that we use for the outcomes for which we lacked information on the entire sibship, namely the timing of school dropout and the timing of entry to the labor market. This type of model will also be used in section V (and in app. C), where we analyze the effect of birth order on cognitive (and noncognitive) skills.

We analyze the effect of birth order on several outcomes and thus use different estimation strategies according to the nature of each outcome. An ordered probit model is used to analyze the effect on highest education level attained by the individuals, while hazard models are used to study birth-order effects on age at school dropout, age at entry to the labor market, and age at marriage.<fnref n="29" /> All models are described in the following subsections.

A. Highest Education Level

The variable measuring highest education level is defined as a series of ordered discrete choices. It takes values from 0 (when the individual has never been to school) up to 7 (when she has some college education). Therefore, to estimate the birth-order effects, $\beta_n^L$, on the highest completed level of individual $i$, we rely on an ordered probit model, as it is common in the economics literature after the seminal article of Lillard and King (1984). More specifically, we model individual $i$ from sibship $j$ who has attained $c$ levels of schooling; that is, $L_{ij} = c$ if $\mu_c < L_{ij}^* < \mu_{c+1}$, where $L_{ij}^*$ is the latent continuous variable that generates the observed $L_{ij}$, and $\mu_c$ and $\mu_{c+1}$ are the cutoff points to be estimated. The latent variable $L_{ij}^*$ is described as follows:

$$L_{ij}^* = \alpha + \sum_{n=1}^{5} \beta_n^L \mathbf{Birth\_order}_{ij} + \kappa^{L'} \mathbf{X}_{ij} + \omega^L \mu_j + \epsilon_{ij}^L, \quad (1)$$

where the vector $\mathbf{Birth\_order}_{ij}$ denotes the birth order of individual $i$ owing to five birth-order dummies, with the omitted category being firstborn child. Thus, coefficients on the birth-order dummies are interpreted relative to the firstborn child.<fnref n="30" /> The term $\mathbf{X}_{ij}$ is a vector of an individual's background variables, that is, gender, age of individual $i$, and the GDP growth dummy. The term $\omega^L$ captures sibship fixed effect, and $\epsilon_{ij}^L$ indicates the stochastic component.

The use of an ordered probit model within families introduces the possibility of encountering the incidental parameters problem, which can arise when employing fixed effects in a nonlinear model (Greene, Han, and Schmidt 2002). Ideally, we would prefer to use a linear probability

<footnote n="29">²⁹ Linear regression is used to estimate the effect of the order of birth on the cognitive skills, which are measured through a test score, and on girls' time use, as discussed in sec. V.</footnote>

<footnote n="30">³⁰ We also test the robustness of our results when we change the way we use the birth-order variable, (i) not grouping fifth-born and subsequent individuals in the same dummy and (ii) excluding sixth-born and subsequent individuals from the sample.</footnote>
model. However, our available data include information on only the highest education level attained by all siblings of cohort members rather than their highest grade. To overcome this limitation, we adopted an alternative approach. We calculated the average grade attained by cohort members at each school level and created a new variable called “average grade for level.” This variable represents the mean grades achieved for each education level. For example, individuals whose highest level is “primary school not completed” had an average grade of 3, because they left school after the third grade, while the highest-level variable is coded as 1. We constructed this variable for both cohort members and their siblings, enabling us to employ a linear model to analyze the effect of birth order on educational attainment, even for individuals whose highest completed grade is unknown. By running a linear regression on this variable and comparing the results with those obtained from the ordered probit model using actual highest education level, we can assess the potential biases introduced by use of household fixed effects in a nonlinear model.

When a between-families model is used to estimate the effects of birth order on the highest level of education, equation (1) is modified as follows:

$$L_{ij}^* = \alpha + \sum_{n=1}^{5} \beta_n^L \mathbf{Birth\_order}_{ij} + \kappa^{IJ} \mathbf{X}_{ij} + \lambda^{IJ} \mathbf{S}_{ij} + \epsilon_{ij}^L, \quad (2)$$

where vector **Birth_order**$_{ij}$ is the same as in equation (1), but the vector of individual background variables **X**$_{ij}$ now includes not only gender, year of birth, and GDP growth dummy but also ethnicity, mother's age, mother's age at first birth, parent's activity (wage worker, own account worker, family worker, or housewife), parent's education (no education, primary completed, or college completed), place of residence (urban or rural) in 2004, average age difference between siblings, household's wealth in 2004, total surface harvested by the household in 2004, an interaction between household wealth and total surface harvested, and an index measuring the household's proximity to infrastructure (health centers, banks, post offices, schools, transportation, food markets, electricity, and water).<fnref n="31" /> Sibship-size fixed effects are added as a vector of dummies indicating size of sibship *j* for individual *i*: **S**$_{ij}$.

## B. Risk of Marriage and Other Hazard Models

Among cohort members, 17.53% are still in school, 15.11% have never worked, and 64.7% are still single. We do not know how old they will be

<footnote n="31">³¹ Household wealth in 2004 is measured by an index built from housing conditions and belongings.</footnote>

<footnote>Note that control variables are described in table A3. We use place of residence, household wealth, total surface harvested, and proximity to infrastructure in 2004 as controls because we are interested in the household and community characteristics when the cohort member was a child. Moreover, wealth in 2012 is likely to be influenced by the resources from the cohort member's work.</footnote>
when they will leave school, have their first job, or get married. As our data are right-censored, we cannot rely on linear regressions when estimating the effect of birth order on age at school dropout, age at marriage, and age at first work. Therefore, we use time-to-event analysis to estimate the effect of birth order on the hazard functions of school dropout, marriage, and first work. Hazard functions give the probability that an event occurs at age t, given that the individual has never been subject to the event up to age t.

We rely on an extended Cox proportional hazards model allowing for time-dependent variables to estimate the effect of birth order on the hazard functions of school dropout, marriage, and first work. When estimating the risk of marriage for individual i in sibship j by use of a within-family framework, we estimate the following model:

$$h_{ij}(t, \mathbf{W}_{ij}, \mathbf{Z}_{ij}(t), \mu_j) = h_0(t) \exp[\kappa' \mathbf{W}_{ij} + \gamma' \mathbf{Z}_{ij} + \delta' \ln(t) \times \mathbf{Z}_{ij} + \omega \mu_j], \quad (3)$$

where $h_{ij}(t, \mathbf{W}_{ij}, \mathbf{Z}_{ij}(t), \mu_j)$ is the hazard function of marriage for individual i in sibship j, and $h_0(t)$ is the baseline hazard function.

Sibship fixed effects are replaced with sibship-size fixed effects when we estimate the risk of marriage, of school dropout, and of entry to the labor market using a between-families framework:

$$h_{ij}(t, \mathbf{W}_{ij}, \mathbf{Z}_{ij}(t), \mathbf{S}_j) = h_0(t) \exp[\kappa' \mathbf{W}_{ij} + \gamma' \mathbf{Z}_{ij} + \delta' \ln(t) \times \mathbf{Z}_{ij} + \lambda' \mathbf{S}_j], \quad (4)$$

with $h_{ij}(t, \mathbf{W}_{ij}, \mathbf{Z}_{ij}(t), \mathbf{S}_j)$ indicating the hazard function of marriage, school dropout, or first work for individual i in sibship j. In our sample, cohort members start to be at risk of dropping out from school at age 6, which is also the minimum age at first work. No cohort members have started to work outside family-run activities before age 6 or have gotten married before age 14.<fnref n="32" /> Hence, our models are designed to account for individuals becoming at risk at those specific ages. Additionally, considering the limited number of individuals above the age of 30 in our dataset, we formulated the hazard models in a way that individuals exit the analysis when they reach the age of 30.

Proportional hazard models assume that the hazard for one individual is proportional to the hazard for any other individual and that the proportionality constant is independent of time. A covariate violates the proportionality assumption when its interaction with a function of time gives a coefficient significantly different from zero. For instance, our birth-order variable violates the proportionality assumption because below a given age, firstborns and later-born children have the same probability (almost null) to drop out of school, to be married, or to work (outside family-run activities).

<footnote n="32">³² A small number of individuals in the sample have married before the age of 14. However, for the purpose of easier estimation in the model, we imputed the individual's age at marriage as 14 years. It is important to note that, even with this imputation, the resulting confidence intervals for estimates of the risk of marriage at this age are quite wide, indicating the uncertainty surrounding them.</footnote>
Covariates are thus divided in two vectors, $\mathbf{W}_{ij}$ and $\mathbf{Z}_{ij}$, such as $\mathbf{X}_{ij} = (\mathbf{W}_{ij}, \mathbf{Z}_{ij})$. The term $\mathbf{Z}_{ij}$ is a vector of covariates that violate the proportionality assumption (including birth order). Therefore, we extend the Cox proportional hazards model by adding $\ln(t) \times \mathbf{Z}_{ij}$, which is a vector of time-dependent variables, that is, covariates that violate the proportional-ity assumption interacted with $\ln(t)$.<fnref n="33" />

When using a within-family model to estimate the effects of birth order on age at marriage, vector $\mathbf{W}_{ij}$ contains only age of individual $i$ and the GDP growth dummy. Vector $\mathbf{Z}_{ij}$ contains birth order and gender of individuals $i$ as a time-dependent variable.

In the between-families framework, we need to include more covariates in $\mathbf{X}_{ij}$, as in equation (2). Covariates in vector $\mathbf{Z}_{ij}$ change according to the outcome because they can violate the proportionality assumption for one outcome but not for the others. For instance, children in rural areas are always more at risk of dropout than those in urban areas. Therefore, we do not need to add place of residence in our time-dependent variables when we estimate the hazard function of dropping out of school. On the other hand, individuals who live in rural areas are more at risk of working outside family-run activities than those who live in urban areas until age 20. After age 20, if they have not yet started to work outside family-run activities, urban cohort members are more at risk to enter the labor market than rural cohort members. Thus, place of residence violates the proportional-ity assumption for first work outside family-run activities. We thus need to add it in our time-dependent variables. When we estimate the effect of birth order on age at school dropout, $\mathbf{Z}_{ij}$ includes mother's age, mother's age at first birth, father's activity and education, and mother's activity. In the model where we estimate the effect of birth order on age at first work (outside family-run activities), $\mathbf{Z}_{ij}$ includes parents' education, mother's activity, household's wealth, and place of residence. When we estimate the effect of birth order on age at marriage, only gender and birth orders are included as time-dependent variables.

### C. *Heterogeneities*

When examining gender heterogeneity in the effect of birth order on the different outcomes, we incorporate an interaction term between birth order and a binary variable that takes the value of 1 when the cohort member is female. To explore heterogeneity concerning the locality of residence of the household in childhood, we introduce an interaction between birth order and a binary indicator denoting whether the household resided in a rural area in 2004. When investigating heterogeneity based on parents' education, we interact birth order with a binary variable that equals 1 if one or both parents completed at least primary education. Finally, to examine

<footnote n="33">³³ We use $\ln(t)$ because this is the most common function of time used in time-dependent covariates, but any function of time could be used (Statistical Consulting Group, University of California, Los Angeles; https://stats.idre.ucla.edu/stata/seminars/stata-survival/).</footnote>
<figure><img src="image_3.png" /></figure>

Figure 1.—Birth-order effects on highest level of education and age at marriage. A, Shown are the marginal effects of birth order on the probability to declare each education level as the highest education level attained. Marginal effects are calculated after estimating an ordered probit model using sibship fixed effects. B, Shown for each birth order is the relative risk at age *t* to get married compared with a firstborn. Hazard ratios are calculated by using an extended Cox proportional hazards model with sibship fixed effects introduced as dummies. Both models control for an individual's year of birth, gender, and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008, a period of GDP growth. All models are run on 5,343 individuals. Sources: Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

heterogeneity related to the household’s wealth in 2004, we introduce an interaction between birth order and a binary variable that equals 1 if the household falls within the 4th quartile of the asset distribution.

## IV. Results

### A. Main Results: Birth-Order Effects on Education Level and Age at Marriage

Our main findings reveal that birth order significantly influences educational attainment. Figure 1A shows that all younger siblings exhibit higher levels of education than those of firstborns. Individuals born second or later are less likely than firstborns to report no education or only primary-level education as their highest educational attainment. In contrast, they are more likely than firstborns to have achieved at least lower secondary education or higher as their highest level of education. The positive effect of birth order on school attainment is consistent with what has been previously found in other developing countries such as the Philippines (Eirnæs and Pörtner 2004), Brazil (Emerson and Souza 2008), Ecuador (De Haan, Plug, and Rosero 2014), and Indonesia (Botzet, Rohrer, and Arslan 2021). It is also in line with Lafortune and Lee (2014), who state that in liquidity-constrained households, children can benefit from having more older siblings to support their education.

Table A4 shows the consistency of our findings on education level across different model specifications. These robustness checks include use of the original variable for the birth order without imputation for sixth-born and
<figure><img src="image_4.png" /><figcaption>Figure 2.—Birth-order effects on highest level of education and age at marriage, by gender. A and B, Shown for each gender are the marginal effects of birth order on the probability to declare each education level as the highest level of education attained. Marginal effects are calculated after estimating an ordered probit model using sibship fixed effects. C and D, Shown for each birth order and each gender is the relative risk at age *t* to get married compared with a same-sex firstborn. Hazard ratios are calculated by using an extended Cox proportional hazards model with sibship fixed effects introduced as dummies. All models control for an individual's year of birth and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008, a period of GDP growth. All models are run on 5,343 individuals. Sources: Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.</figcaption></figure>

subsequent individuals (col. 2), exclusion of the latter from the estimates (col. 3), and estimation of a linear model by using the same sample as in the baseline but with the average grade for level (as explained in sec. III) as the outcome (col. 4). Figure A1A shows the robustness of the results when we include as control the 3-year cohort dummies, as described in section III.

The educational advantage of later-born children compared with first-
borns applies to both boys and girls (figs. 2A and 2B and table A6), but, in
the case of girls, the educational advantage in relation to firstborns is con-
centrated among the third- and fourth-born individuals.

Regarding the risk of marriage, we find a more complex pattern. First,
figure 1B shows that if later-born individuals remain unmarried, they be-
come less at risk of marriage compared with firstborns after 17 years of
age. This is confirmed by the results shown in figure A1B, which controls
for the 3-year cohort dummies. Figures 2C and 2D show that the lower risk
of marriage after a certain age for later-born children holds true for both boys and girls, but that for girls the effect begins at a younger age, coherent with their youngest average age at marriage. We also remark that a second-born girl does not have a lower risk of marriage with respect to her older sister.

These findings are consistent with the results of Vogl (2013), who found in South Asia that older sisters marry at a younger age than younger sisters. However, our findings suggest that this pattern holds true only from age 17 onward, and for boys as well.

Marrying a child can alleviate budget constraints for households, because they may receive payments from the spouse's family or the child may leave the household after marriage. In the context of Madagascar, the bride price is generally symbolic, but marrying off a child can alleviate the household's budget constraint by reducing the number of mouths to feed, especially when it is a daughter who marries, because the bride typically moves to be with her husband's kin. It is plausible to assume that younger siblings, and particularly younger sisters, might reap advantages from the financial support provided by their firstborn siblings, not only to support their education but also to delay their own marriages. Additionally, particularly for girls, the higher levels of education they achieve might enable them to pursue more desirable marriages and provide them with greater economic advantage later in life, even in the absence of a marital union.

Figure 1B also shows that second-born children are more likely to experience child marriage (i.e., marriage when aged less than 16 years) than firstborns. This last result should be taken with caution: because child marriage is a relatively rare event in our sample, concerning “only” 3% of individuals, it is challenging to estimate accurately the risk associated with its occurrence. The confidence interval is, in fact, quite large for ages 14 and 15. From figures 2C and 2D, we can see that the higher risk of child marriage is only for second-born girls, not boys.

Potential explanations for the lower likelihood of firstborn daughters getting married before age 16 (with respect to her second-born sister) in Madagascar can be attributed to the significance placed on the eldest child within the culture. The expectations and responsibilities conferred upon the firstborn, particularly in terms of supporting the family, may result in the firstborn's increased involvement in domestic duties, caregiving, or work on the family's agricultural land. An examination of table A5, which presents data on the activities of daughters around the age of 14, reveals that parents rely more on their older daughters for household tasks such as water collection, wood gathering, and caring for children and the elderly.<fnref n="34" /> Consequently, parents might prefer not to marry too early the daughter who contributes the most to household productivity.

<footnote n="34">³⁴ This information comes from the 2004 survey. As specified in the footnotes of table A5, we run a linear regression absorbing sibship-size fixed effects on the sample of the cohort members, the one used in the between-families framework.</footnote>
## B. Additional Results: Birth-Order Effects on Age at School Dropout and Age at First Work

Our baseline results on education and marriage are qualitatively confirmed by the specifications that use the between-family models (figs. 3B and 3C). These models reveal a higher educational attainment for all latest children (albeit the level of significance in the education results is low

<figure><img src="image_5.png" /><figcaption>Figure 3.—Birth-order effects on age at school dropout, highest level of education, and age at marriage by using between-family models. A, Shown for each birth order is the relative risk at age *t* to drop out of school compared with a firstborn. B, Shown are the marginal effects of birth order on the probability to declare each education level as the highest level of education attained. C, Shown for each birth order is the relative risk at age *t* to get married compared with a firstborn. Hazard ratios are calculated by using an extended Cox proportional hazards model. Marginal effects are calculated after estimating an ordered probit model. All models include gender, year of birth, average age difference among sibship, ethnicity, mother's age, mother's age at first birth, parent's activity (wage worker, own account worker, family worker, or housewife), parent's education (no education, primary completed, or college completed), place of residence (urban or rural), household's wealth in 2004, total surface harvested by the household in 2004, an interaction between household wealth and total surface harvested, an index measuring the household's proximity to infrastructure (health centers, banks, post offices, schools, transportation, food markets, electricity, and water), a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth), and sibship-size fixed effects. The model on school dropout includes 1,171 cohort members. The model on highest education level includes 1,233 cohort members. The model on age at marriage includes 1,235 cohort members. Sources: Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.</figcaption></figure>
for second- and third-born children) and a lower risk of marriage after the age of 16 or 17 for later-born individuals compared with firstborns. However, unlike our within-family findings, the between-family models do not provide evidence of a higher risk of child marriage among later-born individuals.<fnref n="35" />

We then explore additional outcomes using the between-family model. We acknowledge the limitations of the between-family models, as mentioned in section III, and therefore consider the results from these models as suggestive. However, the coherence between the within-family and between-family models regarding the baseline results on education and marriage is reassuring, indicating the potential to identify a causal effect of birth order by using the between-family model.

Figure 3A indicates that individuals with higher birth order, particularly from the third-born child onward, tend to stay in school for a longer period before dropping out, suggesting a potential advantage in terms of educational persistence, which is consistent with the advantage observed in terms of education level.

No differences in the age at first work are observed according to birth order (table 3, col. 1). However, when considering first work outside family-run activities, all siblings have a lower risk of working outside family-run activities compared with firstborns. It is thus firstborns who enter the broader labor market at the youngest age. This result seems to reflect the liquidity effect described by Tenikue and Verheyden (2010), whereby firstborns are sent to work outside family-run activities at younger ages compared with their later-born siblings in order to provide extra income for the household.

The lower risk of school dropout and the lower risk of working outside family-run activities for later-born children is observed for both boys and girls.<fnref n="36" />

### C. *An Earlier Transition into Adulthood for Firstborns*

In summary, our analysis demonstrates that firstborn children have lower educational outcomes and marry at a younger age than their siblings, although firstborn girls may be at a lower risk of early marriage with respect to the second daughter. Furthermore, we provide suggestive evidence that firstborns drop out of school and work outside family-run activities at an earlier age. These combined outcomes suggest an earlier transition into adulthood for firstborns. The next section speculates on the possible mechanisms that could explain this quicker transition and tests some of them.

## V. Mechanisms

The different timing and dynamics of transition into adulthood for individuals of different birth order that we observed in the previous section

<footnote n="35">³⁵ Even with the between-families framework, estimating the risk of marriage at the age of 14 remains challenging and imprecise. This is evident from the wide confidence intervals observed, indicating significant uncertainty surrounding the estimates.</footnote>

<footnote n="36">³⁶ Results are not presented here, but they are available from the authors upon request.</footnote>
<table><caption>TABLE 3
BIRTH-ORDER EFFECTS ON AGE AT FIRST WORK (in Reference to Firstborn)</caption><thead><tr><th></th><th>Age at First Work<br>(1)</th><th>Age at First Work Outside<br>the Family-Run Activity<br>(2)</th></tr></thead><tbody><tr><td>Second child</td><td>1.035<br>(.112)</td><td>.678***<br>(.101)</td></tr><tr><td>Third child</td><td>.895<br>(.128)</td><td>.685**<br>(.132)</td></tr><tr><td>Fourth child</td><td>.982<br>(.183)</td><td>.518**<br>(.136)</td></tr><tr><td>Fifth child or subsequent</td><td>.774<br>(.175)</td><td>.499**<br>(.156)</td></tr><tr><td>Observations</td><td>1,211</td><td>1,025</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascan Youth Transition Survey.

Note.—Presented for each birth order is the relative risk to (1) work and (2) work outside of the family-run activity compared with a firstborn. Hazard ratios are calculated by using an extended Cox proportional hazards model with sibship-size fixed effects. Both models control for a cohort member's gender, year of birth, average age difference among sibship, ethnicity, mother's age, mother's age at first birth, parent's activity (wage worker, own account worker, family worker, or housewife), parent's education (no education, primary completed, or college completed), place of residence (urban or rural), household's wealth in 2004, total surface harvested by the household in 2004, an interaction between household wealth and total surface harvested, an index measuring the household's proximity to infrastructure (health centers, banks, post offices, schools, transportation, food markets, electricity, and water), a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth), and sibship-size fixed effects. Standard errors are in parentheses.

could be explained by the fact that parents are likely to make heterogeneous investments in their children according to their order of birth. This could be due to different reasons, which we discuss in the following subsections. Although we have limited ability to directly test these mechanisms, we explore heterogeneities that might support or be inconsistent with the proposed mechanisms.

## A. *Quality Time*

One possible mechanism is that parents allocate more quality time to their later-born children, enabling them to develop cognitive and noncognitive skills that contribute to better academic performance. This, in turn, results in a longer duration of schooling, a later entry to the labor market, and delayed marriage. Parents might indeed gain experience and knowledge from raising their eldest child, and they may apply these insights to subsequent children, thereby potentially providing them with a more enriched and informed upbringing. This accumulated experience can result in an enhanced quality of time spent with later-born children. For example, in Ecuador, De Haan, Plug, and Rosero (2014) find that later-born children receive more cognitive stimulation by their mothers, are breastfed longer, and thus have higher cognitive test results.

<footnote>** *p* < .05.</footnote>

<footnote>*** *p* < .01.</footnote>
<table><caption>TABLE 4
BIRTH-ORDER EFFECTS ON COGNITIVE SKILLS (in Reference to Firstborn): LINEAR MODEL</caption><thead><tr><th rowspan="2"></th><th colspan="1">Cognitive Skills in 2003</th><th colspan="2">Cognitive Skills in 2012</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Second child</td><td>-.060<br>(.101)</td><td>.008<br>(.079)</td><td>-.025<br>(.059)</td></tr><tr><td>Third child</td><td>-.097<br>(.136)</td><td>.054<br>(.104)</td><td>-.004<br>(.078)</td></tr><tr><td>Fourth child</td><td>-.179<br>(.177)</td><td>.315**<br>(.133)</td><td>.147<br>(.100)</td></tr><tr><td>Fifth child or subsequent</td><td>.016<br>(.216)</td><td>.395**<br>(.164)</td><td>.060<br>(.123)</td></tr><tr><td>School attainment</td><td></td><td></td><td>.186***<br>(.006)</td></tr><tr><td>Within-R<sup>2</sup></td><td>.34</td><td>.39</td><td>.66</td></tr><tr><td>Observations</td><td>750</td><td>1,121</td><td>1,119</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—Reported are the effects of birth order on cognitive skills. The dependent variables are cognitive skills measured in 2004 (col. 1) and cognitive skills measured in 2012 (cols. 2 and 3). The effects of birth order on cognitive skills are estimated by using a linear model absorbing sibship-size fixed effects. All models include gender, year of birth, average age difference among sibship, ethnicity, mother's age, mother's age at first birth, parent's activity (wage worker, own account worker, family worker, or housewife), parent's education (no education, primary completed, or college completed), place of residence (urban or rural), household's wealth in 2004, total surface harvested by the household in 2004, an interaction between household wealth and total surface harvested, an index measuring household's proximity to infrastructure (health centers, banks, post offices, schools, transportation, food markets, electricity, and water), a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth), and sibship-size fixed effects. In col. 3, control variables also include the highest grade attained by the cohort member. Standard errors are in parentheses.

To test the quality-time channel, we examined the subsample of cohort members who underwent cognitive tests in both 2004 and 2012, as well as noncognitive tests in 2012. Indeed, if differential quality time exists across birth order, we should observe different cognitive (and noncognitive) abilities, especially at an early age. We thus apply a linear regression absorbing sibship-size fixed effects on this restricted sample under the between-families framework. Column 1 of table 4 indicates that later-born children do not possess higher cognitive abilities compared with their older siblings during their early years (as measured in 2004 when cohort members were on average 14 years old).

This suggests, unlike in developed countries, in Madagascar parental investment in terms of quality time does not play a significant role in explaining the higher educational attainment and earlier transition to adulthood observed for firstborns.<fnref n="37" />

<footnote>\*\* $p < .05$.</footnote>

<footnote>\*\*\* $p < .01$.</footnote>

<footnote n="37">³⁷ To further support the notion that there are no significant variations in early parental investment across birth order, we conducted an analysis using data from the 2008–9 round of the Madagascar Demographic and Health Survey. Our focus was on examining maternal</footnote>
This result is in contrast with what is found by De Haan, Plug, and Rosero (2014) for Ecuador and by Monfardini and See (2016) and Lehmann, Nuevo-Chiquero, and Vidal-Fernandez (2018) for developed countries. We speculate that in the Malagasy context, the quality time parents spend with children is relatively less important because children may develop their cognitive (and noncognitive) skills through interactions with members of the extended family, who are especially important in developing countries (Cox and Fafchamps 2007), and particularly in our study context (Ottino 1998).<fnref n="38" /> Furthermore, it is worth noting that a substantial number of parents possess limited education. Consequently, their interaction with children might have comparatively diminished influence on cognitive development.

Table 4 also shows (col. 2) that fourth- and later-born children have higher cognitive skills at adulthood (as measured in 2012) than firstborns. This positive effect of birth order on cognitive skills at adulthood can be explained by the fact that, at that time, later-born children have attained higher education levels than their oldest siblings. Indeed, column 3 shows that when we account for school attainment, the effect of birth order on cognitive skills in adulthood is no longer statistically significant, indicating that birth order's influence on cognitive skills is mediated by its positive effect on school attainment.

## B. Gender Preferences

A second possible mechanism that could explain differential parental investment based on birth order and subsequent differences in outcomes in adulthood is related to gender preferences. If parents had a preference for boys (girls), and boys (girls) were overrepresented among later-born children (due to a stopping rule based on this preference), the higher investment in later-born children could indicate a preference for boys (girls). However, our data show that, among the latest-born children, 51% of them are female, suggesting that gender preferences do not account for the birth-order disparities.<fnref n="39" />

<footnote>investments in the health of their children under the age of 5. Four health-related outcomes were examined: vaccination status, vitamin A supplementation, stunting probability, and breast-feeding duration. The analysis showed no consistent differences in maternal investments based on birth order. Firstborns were slightly less likely to be vaccinated than second-born children but more likely to receive vitamin A compared with third- and fifth-born children. They had a lower chance of experiencing stunting but a shorter breast-feeding duration. These findings support the assumption that birth order does not have a clear relationship to early investments in child health. Limitations of our analysis include the absence of data on the entire sibling group and missing values for some health variables. Further research is necessary to understand early maternal investments in child health in developing countries such as Madagascar.</footnote>

<footnote n="38">³⁸ This would also explain the lack of significant results on the effects of birth order on noncognitive skills, which we present in app. C.</footnote>

<footnote n="39">³⁹ Here, we assume there will not be any newborns in the observed sibships.</footnote>
### C. Liquidity Constraints

A third possible mechanism relates to liquidity constraints faced by households. Models of investment in children based on the support they provide to parents in their old age predict that parents should invest in the first-born (Nugent 1985). Because of liquidity constraints, however, young, poor parents might be unable to do so, and it could be a second-best solution for them to allocate more financial resources to their youngest children owing to the rise in income they might experience over time. In extreme poverty contexts such as Madagascar, parents are likely to accumulate savings gradually through their own work and the contributions of older children. These savings can then be invested in the human capital development of their younger children.

This mechanism seems common in developing countries. In 12 sub-Saharan countries, Tenikue and Verheyden (2010) find that earlier-born children receive less education in poor households, whereas they complete more years of education in the richest families. In the case of developing countries, the liquidity-constraints mechanism seems also to be at play because the firstborn advantage in terms of education is weaker in poorer families. For instance, Esposito, Kumar, and Villaseñor (2020) found that in Mexico, firstborns are more likely to be enrolled in the expected grade given their age, but the difference between firstborns and later-born children is greater in wealthier households. Similarly, Hiriscau and Pintea (2023) find that firstborns complete more years of education in the United States, but the difference between firstborns' and later-born children's years of education is not significant in the last income tertile.

Unfortunately, we lack data on the evolution of household savings or assets over time; however, we find some indirect indications that support this assumption. First, as presented in section IV.B, there is suggestive evidence that firstborns drop out of school and enter the labor market earlier than their youngest siblings, which allows them to contribute directly to the household income.

Furthermore, using the within-family framework, we are able to study the heterogeneities of our main results on education according to three important variables: family wealth, parents' education, and the locality of residence.

Regarding family wealth, we interacted birth order with a dummy variable that indicates whether the household where the individual grew up belongs to the highest 25% of the distribution in 2004. We used the asset index in 2004 rather than the one in 2012 because the latter is more likely to be influenced by the children's contribution to the household income, which could be correlated with the birth-order measure. Columns 1 and 2 of table 5 show that the positive effect of birth order on education is not present for individuals in the highest 25% of the asset distribution.<fnref n="40" />

<footnote n="40">⁴⁰ The asset index value, ranging from 0 to 100 as per its construction, appears to be relatively low across households in our sample, even within the 4th quartile. In this quartile, the average value stands at 50, with a minimum of 29. Consequently, households in the</footnote>
<table><caption>TABLE 5
HETEROGENEOUS EFFECTS OF BIRTH ORDER ON HIGHEST LEVEL OF EDUCATION
(in Reference to Firstborn; N = 5,343)</caption><thead><tr><th rowspan="2"></th><th colspan="2">Wealth</th><th colspan="2">Parent's Level of Education</th><th colspan="2">Area of Residence</th></tr><tr><th>First 3 Quartiles of Wealth (1)</th><th>4th Quartile of Wealth (2)</th><th>None of Parents (3)</th><th>At Least One Parent (4)</th><th>Urban (5)</th><th>Rural (6)</th></tr></thead><tbody><tr><td>Second child</td><td>.211***<br>(.060)</td><td>.042<br>(.093)</td><td>.242***<br>(.067)</td><td>.066<br>(.075)</td><td>-.019<br>(.099)</td><td>.222***<br>(.059)</td></tr><tr><td>Third child</td><td>.278***<br>(.073)</td><td>.116<br>(.104)</td><td>.366***<br>(.079)</td><td>.060<br>(.087)</td><td>.039<br>(.110)</td><td>.294***<br>(.072)</td></tr><tr><td>Fourth child</td><td>.303***<br>(.088)</td><td>.025<br>(.121)</td><td>.506***<br>(.094)</td><td>-.141<br>(.102)</td><td>-.079<br>(.126)</td><td>.323***<br>(.086)</td></tr><tr><td>Fifth child and subsequent</td><td>.292***<br>(.107)</td><td>-.133<br>(.134)</td><td>.425***<br>(.111)</td><td>-.160<br>(.119)</td><td>-.239*<br>(.137)</td><td>.310***<br>(.106)</td></tr><tr><td>R<sup>2</sup></td><td colspan="2">.29</td><td colspan="2">.30</td><td colspan="2">.29</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascan Youth Transition Survey.

Note.—Presented are the effects of birth order on the probability to declare each education level as the highest education level attained. Marginal effects are calculated after estimating an ordered probit model using sibship fixed effects and including an individual's year of birth, gender, and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth). In cols. 1 and 2, the model includes an interaction between the measure of birth order and a dummy equal to 1 if the household belonged to the last quartile of wealth in 2004. In cols. 3 and 4, the model includes an interaction between the measure of birth order and a dummy equal to 1 if one or both of the sibship's parents completed at least primary school. Finally, in cols. 5 and 6, the model includes an interaction between the measure of birth order and a dummy equal to 1 if the household was living in a rural area in 2004. Standard errors are in parentheses.

* p < .10.

*** p < .01.

A similar dynamic is observed when we interact birth order with a dummy variable that equals 1 when at least one parent has some education (i.e., has completed at least primary school), which can be considered as a proxy for parental income. In cases where both parents are not educated, all subsequent children have a higher education level than the firstborn, whereas this pattern does not hold when at least one parent is educated. These two results suggest that financial constraints are significant factors in explaining the relationship between birth order and education (table 5, cols. 3 and 4).

Finally, in our baseline model, we include an interaction between a dummy variable that equals 1 if the individual's household lived in a rural area during childhood (in 2004) and birth order. The results, reported in columns 5 and 6 of table 5, align with our intuition. The positive effect of birth order is present only for individuals who grew up in rural areas, where

<footnote>4th quartile should not be regarded as the wealthiest but rather as the ones experiencing relatively lower poverty.</footnote>
budget constraints are more pronounced, and the liquidity effect (Tenikue and Verheyden 2010) could lead parents to rely on earlier-born children to provide extra resources to their siblings.<fnref n="41" />

### D. Inheritance

A fourth possible mechanism for the different outcomes observed in individuals of different birth orders in adulthood relates to how parents manage their inheritance. Firstborn children are more likely to inherit more land because they bear the responsibility of preserving the family name, wealth, and traditions for future generations. To compensate for the lower physical capital inherited by later-born children, parents may invest more in their human capital. This argument is reminiscent of the one put forward by Botticini and Siow (2003) to explain the existence of the dowry in patrilocal societies; altruistic parents give a substantial dowry to their daughters and transmit their parcels of land to their sons, who thus have better incentives to exert effort in farming activities even before they inherit the land.

Unfortunately, our dataset does not include information on inheritance to test this assumption. However, as mentioned in section II.A, traditional practices in Madagascar favoring certain children still exist. Coupled with the tradition that assigns important responsibilities related to land and property to the eldest son, the inheritance channel becomes a plausible explanation, at least for boys. Moreover, this explanation is particularly plausible in rural areas, and the finding that birth-order effects on education are stronger in such regions partially supports this assumption.

In conclusion, we have explored four possible channels: parental quality time, gender preferences, financial constraints, and inheritance management. We find suggestive evidence that the first two channels are not relevant, and we provide some elements that suggest household financial constraints, as well as practices in managing inheritance in rural areas, could be relevant mechanisms to explain the birth-order effects we found in Madagascar.<fnref n="42" />

<footnote n="41">⁴¹ We applied a similar heterogeneity analysis to the marriage outcome. Results suggest that marriage at a very young age is related to financial constraint because the higher risk of child marriage for the second-born sister is observed only in rural areas, in the three first quartiles of the income distribution, and for the individuals whose parents are not educated. Conversely, no significant heterogeneities emerge on the marriage timing at adulthood with respect to the locality of residence, parental education, and household assets. Results are available from the authors upon request.</footnote>

<footnote n="42">⁴² We acknowledge that our exploration of mechanisms is limited by the necessity of using the between-families framework to test the quality-time channel and by the data limitations that do not allow us to directly test the financial constraint and inheritance channels.</footnote>
## VI. Conclusion

This study examined the effect of birth order on the transition into adulthood in Madagascar, an extremely poor country. To conduct this research, we used a panel survey that was conducted in 2004 and 2012 on a cohort of Malagasy people born between 1988 and 1992. The 2012 survey round was specifically designed to capture the transition from adolescence to young adulthood and collected basic information on all the siblings of the cohort members. With these comprehensive data, the study used a within-family model to estimate the educational attainment and the age at marriage of all siblings observed in 2012. This approach is widely acknowledged in the literature as the most reliable empirical strategy for estimating birth-order effects, because it fully controls for any family-level unobserved characteristics that are constant within the sibship. The findings regarding birth-order effects on education and age at marriage closely align with those obtained by using between-family models, which were based on the sample of cohort members only (one per family). This consistency reassured us on the reliability of admittedly weaker between-family models and enabled the use of these models to estimate birth-order effects on additional outcomes, specifically age at school dropout and age at entry to the labor market.

In 1833, Jane Austen wrote in *Mansfield Park*: “The younger brother must help to pay for the pleasures of the elder.” Our results suggest that in Madagascar, the opposite may hold true: The eldest sibling must help to pay for the pleasures of the younger. Specifically, we find that both female and male firstborn children transition into adulthood earlier. They receive less education and marry at a younger age, although firstborn girls face a lower risk of child marriage with respect to the second-born sister. Moreover, they leave school and enter the labor market at a younger age compared with their later-born siblings.

No direct effects of birth order on cognitive (and noncognitive) skills were observed. Cognitive development is primarily influenced by interactions with parents and siblings during early childhood. The absence of a direct birth-order effect on cognitive (and noncognitive) skills suggests that parental quality time and peer interactions do not differ significantly on the basis of birth order in our study context. This is likely due to the important role of extended family and kinship networks, as extensively documented in Madagascar. The preference for a particular gender does not appear to be a relevant factor either, because boys and girls are equally represented among the last-born children, which is different from what would have been expected in the case of a son-based stopping rule. Instead, our findings suggest that financial constraints may play a relevant role. Positive birth-order effects on education were observed specifically among individuals growing up in rural areas, where budget constraints are more pronounced. These effects were also prominent among individuals whose parents lack education (indicating lower family income) and those belonging to the lowest 75% of the asset distribution. These results suggest parents
who face significant financial constraints require older children to contrib-
ute through work and sacrifice of their own education, accumulating sav-
ings that can be invested in the human capital development of their youn-
ger siblings. Additionally, we speculate that parents transmit more human
capital to their later-born children to compensate for the lower physical
capital they receive through inheritance. Firstborn children, particularly
boys, are more likely to inherit more land because they bear the responsibility
of preserving the family name, wealth, and traditions for future generations,
something that is again consistent with a stronger effect in rural areas.

To assess the applicability of our findings to other lower-income coun-
tries, we can compare them with three key takeaways from existing litera-
ture highlighted in our study's introduction. First, in developed countries,
firstborns consistently outperform their siblings across various outcomes.
Second, the firstborn advantage is notably more pronounced in wealthier
households. Turning to developing countries, existing evidence suggests
that individuals born earlier tend to be more educated than their younger
siblings in affluent families but less educated than their younger siblings in
poor households (Tenikue and Verheyden 2010). Combining these in-
sights with our own findings, it becomes evident that the liquidity-constraint
channel may play a pivotal role, both across and within countries, in ex-
plaining the observed variations in firstborn advantage. If we envision con-
solidating households from both developing and developed countries and
ranking them on the basis of their wealth, ranging from the poorest to the
richest, the firstborn advantage would be more prominent at the upper
end of this spectrum (i.e., in the wealthiest households of developed coun-
tries). It would be relatively low or even nonexistent in the middle range
(among the poorest households in developed countries and wealthier
households in developing countries), transitioning to a negative—a disad-
vantage—at the lower end of the scale. Returning to the issue of external
validity, we could hypothesize that similar dynamics to those observed in
Madagascar may be found in other countries with very high poverty rates.

A limitation of this paper is that a within-family model could be applied
only to a limited set of outcomes, with some results relying on a between-
family framework that presents important identification challenges. While
we believe that our rich dataset adequately addresses most of these issues, it
would be beneficial to study the effects of birth order on school dropout,
entry to the labor market, and cognitive and noncognitive skills by using a
more extensive dataset that provides complete information on a large
number of sibling groups. Another limitation of this study is that the ob-
served individuals are relatively young, with an average age of 21 in the sec-
ond wave of the survey. Therefore, we can observe only their transition into
adulthood and not other important outcomes at adulthood such as fertil-
ity, employment, wealth, and others.
# Appendix

## A. Descriptive Statistics

<table><caption>TABLE A1
COHORT MEMBERS' ATTRITION</caption><thead><tr><th rowspan="2"></th><th colspan="3">Attribted</th><th colspan="3">Nonattribted</th><th rowspan="2">Difference<br/>p-Value</th></tr><tr><th>Mean</th><th>SD</th><th>Observations</th><th>Mean</th><th>SD</th><th>Observations</th></tr></thead><tbody><tr><td colspan="8"><strong>Individuals' characteristics:</strong></td></tr><tr><td>Female</td><td>.55</td><td>.50</td><td>210</td><td>.52</td><td>.50</td><td>1,726</td><td>.49</td></tr><tr><td>Age</td><td>14.54</td><td>1.86</td><td>210</td><td>14.80</td><td>1.13</td><td>1,726</td><td>.00</td></tr><tr><td>Birth order</td><td>2.20</td><td>1.50</td><td>198</td><td>2.74</td><td>1.85</td><td>1,687</td><td>.00</td></tr><tr><td>Firstborn</td><td>.43</td><td>.50</td><td>210</td><td>.34</td><td>.47</td><td>1,726</td><td>.01</td></tr><tr><td>Second born</td><td>.21</td><td>.41</td><td>210</td><td>.20</td><td>.40</td><td>1,726</td><td>.60</td></tr><tr><td>Third born</td><td>.13</td><td>.34</td><td>210</td><td>.15</td><td>.36</td><td>1,726</td><td>.48</td></tr><tr><td>Fourth born</td><td>.09</td><td>.28</td><td>210</td><td>.11</td><td>.32</td><td>1,726</td><td>.23</td></tr><tr><td>Fifth born and subsequent</td><td>.14</td><td>.35</td><td>210</td><td>.20</td><td>.40</td><td>1,726</td><td>.04</td></tr><tr><td>Sibship size</td><td>4.01</td><td>2.45</td><td>198</td><td>4.83</td><td>2.48</td><td>1,687</td><td>.00</td></tr><tr><td>Average age difference<br/>among the sibship</td><td>2.81</td><td>3.67</td><td>198</td><td>3.04</td><td>2.67</td><td>1,687</td><td>.28</td></tr><tr><td>Cognitive skills</td><td>-.25</td><td>.99</td><td>102</td><td>.02</td><td>1.00</td><td>1,052</td><td>.01</td></tr><tr><td>PASEC child</td><td>.20</td><td>.40</td><td>210</td><td>.26</td><td>.44</td><td>1,726</td><td>.07</td></tr><tr><td>Currently in school</td><td>.69</td><td>.46</td><td>193</td><td>.79</td><td>.41</td><td>1,655</td><td>.00</td></tr><tr><td>Ever worked</td><td>.62</td><td>.49</td><td>205</td><td>.54</td><td>.50</td><td>1,681</td><td>.02</td></tr><tr><td colspan="8"><strong>Parents' characteristics:</strong></td></tr><tr><td colspan="8"><strong>Mother:</strong></td></tr><tr><td>Year of birth</td><td>1964</td><td>7.75</td><td>158</td><td>1962</td><td>7.60</td><td>1,461</td><td>.02</td></tr><tr><td>Age at first birth</td><td>21.70</td><td>4.79</td><td>150</td><td>21.71</td><td>4.60</td><td>1,404</td><td>.98</td></tr><tr><td>No education</td><td>.58</td><td>.50</td><td>210</td><td>.52</td><td>.50</td><td>1,726</td><td>.10</td></tr><tr><td>Primary completed</td><td>.12</td><td>.32</td><td>210</td><td>.16</td><td>.36</td><td>1,726</td><td>.16</td></tr><tr><td>Lower secondary<br/>completed</td><td>.30</td><td>.46</td><td>210</td><td>.32</td><td>.47</td><td>1,726</td><td>.57</td></tr><tr><td>Wage worker</td><td>.06</td><td>.24</td><td>178</td><td>.08</td><td>.27</td><td>1,525</td><td>.46</td></tr><tr><td>Own account worker</td><td>.45</td><td>.50</td><td>178</td><td>.45</td><td>.50</td><td>1,525</td><td>.98</td></tr><tr><td>Family worker</td><td>.44</td><td>.50</td><td>178</td><td>.43</td><td>.50</td><td>1,525</td><td>.89</td></tr><tr><td>Housewife</td><td>.02</td><td>.13</td><td>178</td><td>.02</td><td>.13</td><td>1,525</td><td>.98</td></tr><tr><td colspan="8"><strong>Father:</strong></td></tr><tr><td>No education</td><td>.52</td><td>.50</td><td>210</td><td>.48</td><td>.50</td><td>1,726</td><td>.21</td></tr><tr><td>Primary completed</td><td>.13</td><td>.34</td><td>210</td><td>.14</td><td>.34</td><td>1,726</td><td>.89</td></tr><tr><td>Lower secondary<br/>completed</td><td>.34</td><td>.48</td><td>210</td><td>.38</td><td>.49</td><td>1,726</td><td>.29</td></tr><tr><td>Wage worker</td><td>.24</td><td>.43</td><td>201</td><td>.22</td><td>.42</td><td>1,627</td><td>.55</td></tr><tr><td>Own account worker</td><td>.57</td><td>.50</td><td>201</td><td>.52</td><td>.50</td><td>1,627</td><td>.24</td></tr><tr><td>Family worker</td><td>.16</td><td>.37</td><td>201</td><td>.23</td><td>.42</td><td>1,627</td><td>.04</td></tr><tr><td colspan="8"><strong>Household's characteristics:</strong></td></tr><tr><td>Urban</td><td>.28</td><td>.45</td><td>210</td><td>.26</td><td>.44</td><td>1,726</td><td>.43</td></tr><tr><td>Poor household</td><td>.38</td><td>.49</td><td>210</td><td>.35</td><td>.48</td><td>1,726</td><td>.50</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—Presented are mean values for individuals', parents', and household's characteristics for attrited and reinterviewed cohort members. All characteristics are based on the 2004 EPSPAM. The variable "female" is a dummy equal to 1 if the cohort member is a female. Age, average age difference among the sibship, and mother's age at first birth are measured in years. Birth order is measured as explained in sec. II.D. Sibship size and average age difference among the sibship are calculated by using the same principles as those for birth order. Cognitive skills were measured by using a standardized test in 2004. They are displayed as z-scores. PASEC child is a dummy equal to 1 if the cohort member was part of the PASEC sample in 1998. Currently in school is a dummy equal to 1 if the cohort member was enrolled in school in 2004. Ever worked is a dummy equal to 1 if the cohort member had ever worked in 2004. Parents' education and activities are measured by use of dummies denoting the highest level of education completed by the parent and his or her type of activity. Urban is a dummy equal to 1 if the household was living in a rural area in 2004. A household is considered to be poor if its wealth index is below the sample median.
<table><caption>TABLE A2
DIFFERENCE IN BIRTH-ORDER MEASURES</caption><thead><tr><th rowspan="2"></th><th colspan="5">Birth Order Measured with the 2004 EPSPAM</th></tr><tr><th>Firstborn</th><th>Second Born</th><th>Third Born</th><th>Fourth Born</th><th>Fifth Born and Subsequent</th></tr></thead><tbody><tr><td>Birth order measured with the 2012 Madagascar Youth Transition Survey:</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>First born</td><td>86.79</td><td>32.13</td><td>11.87</td><td>12.88</td><td>8.53</td></tr><tr><td>Second born</td><td>9.43</td><td>50.90</td><td>24.66</td><td>15.95</td><td>9.69</td></tr><tr><td>Third born</td><td>2.20</td><td>8.30</td><td>41.55</td><td>20.86</td><td>18.99</td></tr><tr><td>Fourth born</td><td>.63</td><td>4.69</td><td>11.42</td><td>33.74</td><td>14.73</td></tr><tr><td>Fifth born and subsequent</td><td>.94</td><td>3.97</td><td>10.50</td><td>16.56</td><td>48.06</td></tr><tr><td>Observations</td><td>318</td><td>277</td><td>219</td><td>163</td><td>258</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—The cell at the intersection of the data row *i* and the data column *j* gives the share of cohort members for which the birth order was *j* in 2004 (measured on the basis of questions to parents) and *i* in 2012 (measured on the basis of questions to cohort members about their siblings), with the total for each data column being 100.

<table><caption>TABLE A3
DESCRIPTIVE STATISTICS ON CONTROL VARIABLES: BETWEEN-FAMILY MODEL SAMPLE</caption><thead><tr><th></th><th>Full Sample</th><th>First Child</th><th>Second Child</th><th>Third Child</th><th>Fourth Child</th><th>Fifth Child and Subsequent</th></tr></thead><tbody><tr><td>Sibship size<sup>a</sup></td><td>6.37<br>(4.05)</td><td>4.81<br>(3.36)</td><td>5.56<br>(3.74)</td><td>6.71<br>(3.93)</td><td>7.45<br>(4.09)</td><td>8.17<br>(4.30)</td></tr><tr><td>Age in 2012</td><td>21.96<br>(1.24)</td><td>22.00<br>(1.07)</td><td>21.97<br>(1.22)</td><td>21.92<br>(1.45)</td><td>22.00<br>(1.29)</td><td>21.92<br>(1.25)</td></tr><tr><td>Average age difference among the sibship<sup>a</sup></td><td>3.06<br>(2.03)</td><td>3.04<br>(2.19)</td><td>3.04<br>(2.01)</td><td>3.27<br>(2.23)</td><td>2.97<br>(1.86)</td><td>2.98<br>(1.74)</td></tr><tr><td>Female</td><td>.53<br>(.50)</td><td>.55<br>(.50)</td><td>.52<br>(.50)</td><td>.56<br>(.50)</td><td>.55<br>(.50)</td><td>.49<br>(.50)</td></tr><tr><td>Mother's year of birth</td><td>1962<br>(6.66)</td><td>1967<br>(4.99)</td><td>1964<br>(5.25)</td><td>1960<br>(5.54)</td><td>1959<br>(5.05)</td><td>1956<br>(4.71)</td></tr><tr><td>Mother's age at first birth</td><td>30.25<br>(5.68)</td><td>31.44<br>(6.06)</td><td>30.89<br>(5.32)</td><td>30.86<br>(5.33)</td><td>28.74<br>(5.07)</td><td>26.83<br>(4.34)</td></tr><tr><td>Father's activity:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Father is a wage worker</td><td>.22<br>(.42)</td><td>.20<br>(.40)</td><td>.21<br>(.41)</td><td>.28<br>(.45)</td><td>.20<br>(.40)</td><td>.23<br>(.42)</td></tr><tr><td>Father is an own account worker</td><td>.61<br>(.49)</td><td>.63<br>(.48)</td><td>.62<br>(.49)</td><td>.57<br>(.50)</td><td>.60<br>(.49)</td><td>.59<br>(.49)</td></tr><tr><td>Father is a family worker</td><td>.17<br>(.38)</td><td>.16<br>(.37)</td><td>.17<br>(.37)</td><td>.15<br>(.36)</td><td>.20<br>(.40)</td><td>.18<br>(.39)</td></tr><tr><td>Father's education:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No education</td><td>.48<br>(.50)</td><td>.45<br>(.50)</td><td>.47<br>(.50)</td><td>.51<br>(.50)</td><td>.52<br>(.50)</td><td>.47<br>(.50)</td></tr><tr><td>Primary school completed</td><td>.24<br>(.43)</td><td>.25<br>(.43)</td><td>.26<br>(.44)</td><td>.18<br>(.39)</td><td>.22<br>(.42)</td><td>.25<br>(.43)</td></tr><tr><td>Lower secondary school completed</td><td>.29<br>(.45)</td><td>.30<br>(.46)</td><td>.27<br>(.45)</td><td>.31<br>(.46)</td><td>.26<br>(.44)</td><td>.28<br>(.45)</td></tr></tbody></table>
<table><caption>TABLE A3 (Continued)</caption><thead><tr><th></th><th>Full Sample</th><th>First Child</th><th>Second Child</th><th>Third Child</th><th>Fourth Child</th><th>Fifth Child and Subsequent</th></tr></thead><tbody><tr><td>Mother's activity:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mother is a wage worker</td><td>.09<br>(.29)</td><td>.08<br>(.28)</td><td>.12<br>(.32)</td><td>.11<br>(.31)</td><td>.07<br>(.25)</td><td>.07<br>(.26)</td></tr><tr><td>Mother is an own account worker</td><td>.41<br>(.49)</td><td>.41<br>(.49)</td><td>.39<br>(.49)</td><td>.43<br>(.50)</td><td>.42<br>(.50)</td><td>.43<br>(.50)</td></tr><tr><td>Mother is a family worker</td><td>.41<br>(.49)</td><td>.42<br>(.49)</td><td>.44<br>(.50)</td><td>.36<br>(.48)</td><td>.42<br>(.50)</td><td>.42<br>(.49)</td></tr><tr><td>Mother is a housewife</td><td>.08<br>(.27)</td><td>.09<br>(.28)</td><td>.06<br>(.24)</td><td>.10<br>(.30)</td><td>.09<br>(.28)</td><td>.08<br>(.27)</td></tr><tr><td>Mother's education:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No education</td><td>.51<br>(.50)</td><td>.48<br>(.50)</td><td>.46<br>(.50)</td><td>.53<br>(.50)</td><td>.59<br>(.49)</td><td>.55<br>(.50)</td></tr><tr><td>Primary school completed</td><td>.27<br>(.45)</td><td>.30<br>(.46)</td><td>.27<br>(.45)</td><td>.26<br>(.44)</td><td>.25<br>(.44)</td><td>.28<br>(.45)</td></tr><tr><td>Lower secondary school completed</td><td>.21<br>(.41)</td><td>.22<br>(.41)</td><td>.27<br>(.45)</td><td>.21<br>(.40)</td><td>.16<br>(.37)</td><td>.17<br>(.38)</td></tr><tr><td>Wealth index<sup>a</sup></td><td>21.97<br>(18.72)</td><td>20.95<br>(19.19)</td><td>22.04<br>(19.28)</td><td>22.85<br>(18.95)</td><td>22.98<br>(19.37)</td><td>21.76<br>(16.87)</td></tr><tr><td>Cultivated surface<sup>a</sup></td><td>3.08<br>(7.08)</td><td>3.00<br>(8.42)</td><td>2.80<br>(7.61)</td><td>3.08<br>(5.77)</td><td>3.07<br>(7.37)</td><td>3.48<br>(5.35)</td></tr><tr><td>Urban<sup>a</sup></td><td>.22<br>(.41)</td><td>.21<br>(.41)</td><td>.19<br>(.40)</td><td>.20<br>(.40)</td><td>.24<br>(.43)</td><td>.26<br>(.44)</td></tr><tr><td>Proximity to infrastructure<sup>a</sup></td><td>37.43<br>(28.72)</td><td>36.30<br>(29.73)</td><td>36.98<br>(27.31)</td><td>37.55<br>(27.92)</td><td>37.56<br>(29.83)</td><td>39.11<br>(29.03)</td></tr><tr><td>Ethnicity:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Merina</td><td>.26<br>(.44)</td><td>.26<br>(.44)</td><td>.25<br>(.44)</td><td>.30<br>(.46)</td><td>.28<br>(.45)</td><td>.24<br>(.43)</td></tr><tr><td>Betsileo</td><td>.21<br>(.41)</td><td>.16<br>(.36)</td><td>.22<br>(.42)</td><td>.21<br>(.41)</td><td>.23<br>(.42)</td><td>.25<br>(.43)</td></tr><tr><td>Betsimisakara</td><td>.12<br>(.32)</td><td>.14<br>(.35)</td><td>.13<br>(.34)</td><td>.08<br>(.28)</td><td>.10<br>(.31)</td><td>.11<br>(.32)</td></tr><tr><td>Other</td><td>.41<br>(.49)</td><td>.44<br>(.50)</td><td>.39<br>(.49)</td><td>.41<br>(.49)</td><td>.38<br>(.49)</td><td>.40<br>(.49)</td></tr><tr><td>Observations</td><td>1,235</td><td>318</td><td>277</td><td>219</td><td>163</td><td>258</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—Presented are mean values for the full sample and birth order. Standard deviations are in parentheses. Sibship size is measured as the number of siblings among a sibship. Age, average age difference among the sibship, and mother's age at first birth are measured in years. The variable "female" is a dummy equal to 1 if the cohort member is a female. Parents' activities and education are measured by using dummies denoting the highest level of education completed by the parent and his or her type of activity. Wealth index, cultivated surface, and proximity to infrastructure are transformed to fall into [0;100]. Ethnicity is measured by using dummies equal to 1 for each of the three most important ethnicities in Madagascar. Smaller ethnicities are gathered in the modality "other."

ª Denotes variables measured in 2004.
## B. Additional Results

<table><caption>TABLE A4
BIRTH-ORDER EFFECTS ON HIGHEST LEVEL OF EDUCATION (in Reference to Firstborn): ROBUSTNESS CHECKS</caption><thead><tr><th rowspan="2"></th><th colspan="4">Highest Level (Ordered Probit Model)</th></tr><tr><th>Baseline<br>(1)</th><th>All Birth<br>Order<br>(2)</th><th>Without Sixth-<br>Born and Subse-<br>quent Children<br>(3)</th><th>Highest Grade<br>(Linear Model):<br>Baseline Sample<br>(4)</th></tr></thead><tbody><tr><td>Second child</td><td>.164***<br>(.053)</td><td>.161***<br>(.055)</td><td>.213***<br>(.058)</td><td>.296***<br>(.113)</td></tr><tr><td>Third child</td><td>.232***<br>(.066)</td><td>.227***<br>(.072)</td><td>.309***<br>(.079)</td><td>.402***<br>(.141)</td></tr><tr><td>Fourth child</td><td>.228***<br>(.081)</td><td>.220**<br>(.091)</td><td>.316***<br>(.101)</td><td>.378**<br>(.173)</td></tr><tr><td>Fifth child and<br>subsequent</td><td>.184*<br>(.103)</td><td>.173<br>(.110)</td><td>.262**<br>(.123)</td><td>.268<br>(.218)</td></tr><tr><td>R<sup>2</sup></td><td>.29</td><td>.29</td><td>.31</td><td>.08</td></tr><tr><td>Observations</td><td>5,343</td><td>5,343</td><td>4,455</td><td>5,343</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascan Youth Transition Survey.

Note.—Reported are the effects of birth order on education with different specifications. The dependent variables are highest attained level of education (cols. 1–3) and average grade for level (col. 4; see sec. III for more detail on this variable). Column 1 reports our baseline results as coefficients. Column 2 uses the original variable for the birth order without imputation for sixth-born and subsequent individuals. Column 3 excludes sixth-born and subsequent individuals from the sample. The effects of birth order on the highest level of education attained are estimated by using an ordered probit model with sibship fixed effects added as dummy variables. The effects of birth order on average grade for level (col. 4) are estimated by using a linear model absorbing sibship fixed effects. All models include gender, year of birth, and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth). Standard errors are in parentheses.

* $p < .10$.

** $p < .05$.

*** $p < .01$.
<figure><img src="image_6.png" /></figure>

Figure A1.—Birth-order effects on highest level of education and age at marriage including 3-year cohort dummies. A, Shown are the marginal effects of birth order on the probability to declare each education level as the highest education level attained. Marginal effects are calculated after estimating an ordered probit model using sibship fixed effects. B, Shown for each birth order is the relative risk at age *t* to get married compared with that of a first-born. Hazard ratios are calculated by using an extended Cox proportional hazards model with sibship fixed effects introduced as dummies. Both models control for an individual's year of birth, gender, and 3-year cohort dummies (see more explanations about these dummies in n. 26). The two models are run on 5,343 individuals. Sources: Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.
<table><caption>TABLE A5
BIRTH ORDER (in Reference to First Daughter) AND TIME USE IN 2004 FOR FEMALES</caption><thead><tr><th></th><th>Time Use for<br>Domestic Chores<br>(1)</th><th>Time Use<br>for Water<br>(2)</th><th>Time Use<br>for Wood<br>(3)</th><th>Time Use for Care<br>(Children and Elders)<br>(4)</th></tr></thead><tbody><tr><td>Second daughter</td><td>-2.155**<br>(.936)</td><td>-.822<br>(.549)</td><td>-.731*<br>(.383)</td><td>-1.082**<br>(.476)</td></tr><tr><td>Third daughter</td><td>-1.973*<br>(1.154)</td><td>-1.091<br>(.678)</td><td>-.770<br>(.472)</td><td>-.039<br>(.588)</td></tr><tr><td>Fourth daughter</td><td>-3.846***<br>(1.412)</td><td>-1.188<br>(.830)</td><td>-.869<br>(.578)</td><td>-1.466**<br>(.719)</td></tr><tr><td>Fifth daughter and<br>subsequent</td><td>-4.230**<br>(1.698)</td><td>-2.304**<br>(.997)</td><td>-.793<br>(.695)</td><td>-.215<br>(.865)</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>.17</td><td>.07</td><td>.16</td><td>.06</td></tr><tr><td>Adjusted within-R<sup>2</sup></td><td>.16</td><td>.04</td><td>.16</td><td>.05</td></tr><tr><td>Observations</td><td>1,171</td><td>1,179</td><td>1,179</td><td>1,179</td></tr><tr><td>Number of girls</td><td>621</td><td>625</td><td>625</td><td>625</td></tr></tbody></table>

Sources.—Authors's calculations using data from the 2004 EPSPAM and the 2012 Madagascan Youth Transition Survey.

Note.—Reported are the effects of birth order on time use in 2004 for female cohort members. The dependent variables are hours per week spent doing domestic chores (col. 1), hours per week spent fetching water (col. 2), hours per week spent fetching wood (col. 3), and hours per week spent taking care of the children and the elderly in the household (col. 4). The model is estimated on both male and female cohort members. All models include a dummy equal to 1 for females, the interaction between birth order and the female dummy, year of birth, average age difference among the sibship, ethnicity, mother's age, mother's age at first birth, parent's activity (wage worker, own account worker, family worker, or housewife), parent's education (no education, primary completed, or college completed), place of residence (urban or rural), household's wealth in 2004, total surface harvested by the household in 2004, an interaction between household wealth and total surface harvested, an index measuring the household's proximity to infrastructure (health centers, banks, post offices, schools, transportation, food markets, electricity, and water), a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth), and sibship-size fixed effects. In the table, for each order of birth, we report the sum of the birth-order coefficient and the interaction between birth order and the female dummy. Standard errors are in parentheses.

* $p < .10$.

** $p < .05$.

*** $p < .01$.
<table><caption>TABLE A6
BIRTH-ORDER EFFECTS ON HIGHEST LEVEL OF EDUCATION BY GENDER (in Reference to Firstborn): COEFFICIENTS</caption><thead><tr><th rowspan="2"></th><th colspan="3">Second Child</th><th colspan="3">Third Child</th><th colspan="3">Fourth Child</th><th colspan="3">Fifth Child</th></tr><tr><th>All (1)</th><th>Males (2)</th><th>Females (3)</th><th>All (4)</th><th>Males (5)</th><th>Females (6)</th><th>All (7)</th><th>Males (8)</th><th>Females (9)</th><th>All (10)</th><th>Males (11)</th><th>Females (12)</th></tr></thead><tbody><tr><td>None</td><td>-.011***<br>(.004)</td><td>-.019***<br>(.005)</td><td>-.004<br>(.005)</td><td>-.016***<br>(.005)</td><td>-.019***<br>(.006)</td><td>-.013**<br>(.006)</td><td>-.015***<br>(.006)</td><td>-.012*<br>(.007)</td><td>-.018***<br>(.006)</td><td>-.013*<br>(.007)</td><td>-.018**<br>(.008)</td><td>-.007<br>(.008)</td></tr><tr><td>Primary school</td><td>-.023***<br>(.007)</td><td>-.038***<br>(.010)</td><td>-.007<br>(.011)</td><td>-.032***<br>(.009)</td><td>-.036***<br>(.011)</td><td>-.028**<br>(.012)</td><td>-.032***<br>(.011)</td><td>-.022*<br>(.013)</td><td>-.042***<br>(.014)</td><td>-.025*<br>(.014)</td><td>-.036**<br>(.015)</td><td>-.015<br>(.016)</td></tr><tr><td>Primary school completed</td><td>-.001***<br>(.000)</td><td>-.002**<br>(.001)</td><td>.000<br>(.001)</td><td>-.002***<br>(.001)</td><td>-.002**<br>(.001)</td><td>-.002**<br>(.001)</td><td>-.002***<br>(.001)</td><td>-.001<br>(.001)</td><td>-.004***<br>(.001)</td><td>-.001*<br>(.001)</td><td>-.001*<br>(.001)</td><td>-.001<br>(.001)</td></tr><tr><td>Lower secondary school</td><td>.006***<br>(.002)</td><td>.010***<br>(.003)</td><td>.002<br>(.003)</td><td>.008***<br>(.003)</td><td>.010***<br>(.003)</td><td>.006**<br>(.003)</td><td>.008***<br>(.003)</td><td>.007*<br>(.004)</td><td>.008***<br>(.003)</td><td>.007*<br>(.004)</td><td>.010**<br>(.004)</td><td>.004<br>(.004)</td></tr><tr><td>Lower secondary school completed</td><td>.006***<br>(.002)</td><td>.010***<br>(.003)</td><td>.002<br>(.003)</td><td>.008***<br>(.002)</td><td>.009***<br>(.003)</td><td>.007**<br>(.003)</td><td>.008***<br>(.003)</td><td>.006*<br>(.003)</td><td>.010***<br>(.004)</td><td>.007*<br>(.004)</td><td>.009**<br>(.004)</td><td>.004<br>(.004)</td></tr><tr><td>Higher secondary school</td><td>.007***<br>(.002)</td><td>.012***<br>(.003)</td><td>.002<br>(.004)</td><td>.011***<br>(.003)</td><td>.012***<br>(.004)</td><td>.009**<br>(.004)</td><td>.010***<br>(.004)</td><td>.007*<br>(.004)</td><td>.014***<br>(.005)</td><td>.008*<br>(.005)</td><td>.012**<br>(.005)</td><td>.005<br>(.005)</td></tr><tr><td>Higher secondary school completed</td><td>.008***<br>(.003)</td><td>.014***<br>(.004)</td><td>.003<br>(.004)</td><td>.012***<br>(.003)</td><td>.013***<br>(.004)</td><td>.011**<br>(.005)</td><td>.012***<br>(.004)</td><td>.008*<br>(.005)</td><td>.016***<br>(.006)</td><td>.010*<br>(.005)</td><td>.013**<br>(.006)</td><td>.006<br>(.006)</td></tr><tr><td>Postsecondary</td><td>.008***<br>(.002)</td><td>.013***<br>(.003)</td><td>.002<br>(.003)</td><td>.011***<br>(.003)</td><td>.012***<br>(.004)</td><td>.010**<br>(.004)</td><td>.011***<br>(.004)</td><td>.007*<br>(.004)</td><td>.015***<br>(.005)</td><td>.008*<br>(.005)</td><td>.012**<br>(.005)</td><td>.005<br>(.005)</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—Presented are the effects of birth order on the probability to declare each education level as the highest education level attained. Marginal effects are calculated after estimating an ordered probit model using sibship fixed effects and including an individual's year of birth, gender, and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth). To obtain the marginal effects for males and females, we introduced an interaction between the measure of birth order and a dummy equal to 1 if the individual is a female. All models are run on 5,343 individuals. R-square for the model without the gender interaction is 0.29; R² for the model with the gender interaction is also 0.29. Standard errors are in parentheses.

* *p* < .10.

** *p* < .05.

*** *p* < .01.
<table><caption>TABLE A7
BIRTH-ORDER EFFECTS ON AGE AT MARRIAGE BY GENDER (in Reference to Firstborn): HAZARD RATIOS</caption><thead><tr><th rowspan="2">Age (Years)</th><th colspan="3">Second Child</th><th colspan="3">Third Child</th><th colspan="3">Fourth Child</th><th colspan="3">Fifth Child</th></tr><tr><th>All (1)</th><th>Males (2)</th><th>Females (3)</th><th>All (4)</th><th>Males (5)</th><th>Females (6)</th><th>All (7)</th><th>Males (8)</th><th>Females (9)</th><th>All (10)</th><th>Males (11)</th><th>Females (12)</th></tr></thead><tbody><tr><td>14</td><td>1.732**<br>(.404)</td><td>1.018<br>(.471)</td><td>2.102***<br>(.280)</td><td>1.532<br>(.413)</td><td>1.423<br>(.528)</td><td>1.618<br>(.312)</td><td>1.370<br>(.432)</td><td>1.049<br>(.603)</td><td>1.593<br>(.353)</td><td>1.084<br>(.349)</td><td>1.518<br>(.541)</td><td>1.044<br>(.354)</td></tr><tr><td>15</td><td>1.350*<br>(.215)</td><td>.923<br>(.324)</td><td>1.568**<br>(.190)</td><td>1.173<br>(.222)</td><td>1.133<br>(.363)</td><td>1.190<br>(.216)</td><td>1.004<br>(.228)</td><td>.906<br>(.415)</td><td>1.009<br>(.252)</td><td>.844<br>(.214)</td><td>1.057<br>(.388)</td><td>.814<br>(.271)</td></tr><tr><td>16</td><td>1.167<br>(.141)</td><td>.871<br>(.242)</td><td>1.321*<br>(.147)</td><td>1.004<br>(.150)</td><td>.991<br>(.273)</td><td>.994<br>(.172)</td><td>.837<br>(.155)</td><td>.832<br>(.313)</td><td>.772<br>(.208)</td><td>.729<br>(.162)</td><td>.856<br>(.311)</td><td>.703<br>(.236)</td></tr><tr><td>17</td><td>1.052<br>(.104)</td><td>.836<br>(.189)</td><td>1.170<br>(.127)</td><td>.898<br>(.115)</td><td>.902<br>(.216)</td><td>.875<br>(.152)</td><td>.736*<br>(.120)</td><td>.783<br>(.250)</td><td>.639**<br>(.190)</td><td>.657**<br>(.136)</td><td>.736<br>(.267)</td><td>.634**<br>(.221)</td></tr><tr><td>18</td><td>.971<br>(.085)</td><td>.809<br>(.154)</td><td>1.064<br>(.120)</td><td>.824*<br>(.097)</td><td>.838<br>(.181)</td><td>.793<br>(.146)</td><td>.665***<br>(.102)</td><td>.747<br>(.211)</td><td>.551***<br>(.186)</td><td>.606**<br>(.121)</td><td>.655*<br>(.243)</td><td>.585**<br>(.216)</td></tr><tr><td>19</td><td>.909<br>(.076)</td><td>.789*<br>(.133)</td><td>.985<br>(.121)</td><td>.769**<br>(.087)</td><td>.789<br>(.160)</td><td>.731**<br>(.149)</td><td>.613***<br>(.092)</td><td>.719*<br>(.190)</td><td>.489***<br>(.190)</td><td>.567***<br>(.112)</td><td>.596**<br>(.231)</td><td>.548***<br>(.217)</td></tr><tr><td>20</td><td>.860*<br>(.072)</td><td>.772**<br>(.123)</td><td>.923<br>(.126)</td><td>.724***<br>(.083)</td><td>.750*<br>(.152)</td><td>.683**<br>(.155)</td><td>.572***<br>(.087)</td><td>.696**<br>(.183)</td><td>.442***<br>(.198)</td><td>.537***<br>(.106)</td><td>.550***<br>(.228)</td><td>.518***<br>(.221)</td></tr><tr><td>21</td><td>.820**<br>(.072)</td><td>.757**<br>(.122)</td><td>.873<br>(.134)</td><td>.688***<br>(.082)</td><td>.718**<br>(.153)</td><td>.644***<br>(.164)</td><td>.539***<br>(.084)</td><td>.677**<br>(.186)</td><td>.405***<br>(.208)</td><td>.511***<br>(.102)</td><td>.513***<br>(.231)</td><td>.494***<br>(.227)</td></tr><tr><td>22</td><td>.786***<br>(.073)</td><td>.744**<br>(.126)</td><td>.830<br>(.144)</td><td>.657***<br>(.082)</td><td>.690**<br>(.160)</td><td>.611***<br>(.174)</td><td>.511***<br>(.083)</td><td>.660**<br>(.195)</td><td>.374***<br>(.219)</td><td>.490***<br>(.100)</td><td>.482***<br>(.238)</td><td>.474***<br>(.234)</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascar Youth Transition Survey.

Note.—Presented for each birth order and each gender is the relative risk at age *t* to get married compared with a (same sex) firstborn. Hazard ratios are calculated by using an extended Cox proportional hazards model with sibship fixed effects introduced as dummies. To obtain the hazard ratios for males and females, we introduced an interaction between the measure of birth order and a dummy equal to 1 if the individual is a female. Both models control for an individual's year of birth, gender, and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth). All models are run on 5,343 individuals. Standard errors are in parentheses.

* *p* < .10.

** *p* < .05.

*** *p* < .01.
### C. Noncognitive Skills

The formation of noncognitive skills is strongly influenced by the context in which the individual grows up and in particular by the investment of parents in the first years of life (Cunha, Heckman, and Schennach 2010). The way parents interact with their child varies considerably depending on the number of siblings, on the experience of the parents themselves, and, in some contexts, on the gender of the child. Firstborns can, for example, benefit from more attention from parents, who have more time to dedicate to them, while last children can benefit most from their parents' experience (Zajonc 1976; Markus and Zajonc 1977; De Haan, Plug, and Rosero 2014; Monfardini and See 2016; Lehmann, Nuevo-Chiquero, and Vidal-Fernandez 2018). Moreover, parents are often more strict with their first children, and this could lead them to develop greater persuasive and communication skills (Black, Gronqvist, and Ockert 2018). Conversely, later children are likely to suffer from the comparison with their older siblings, and this could induce them to develop more their creativity skills to differentiate from them (Sulloway 1996). Child development is also influenced by the interactions with siblings: older siblings, for example, are likely to develop more their sense of responsibility (Sulloway 1996; Black, Gronqvist, and Ockert 2018), their patience, as well as their pedagogical skills (Zajonc 1976; Markus and Zajonc 1977). All this implies that a child's birth order can have important effects on her noncognitive development with an indirect effect on her chances of success in her adult professional life.

Most of the literature on the effects of birth order on noncognitive skills focuses on developed countries. It is unclear whether results can be extended to poor countries, where budget constraints are more pronounced, and thus economic motivations are likely to play a more important role for parents when they need to decide how much to invest in their child (Eirnæs and Pörtner 2004; Emerson and Souza 2008; Tenikue and Verheyden 2010; De Haan, Plug, and Rosero 2014; Lafortune and Lee 2014). Also, these phenomena are strongly influenced by cultural and social norms, which are context specific. For instance, given the importance of extended family in developing countries (Cox and Fafchamps 2007), interactions with individuals outside the household could particularly shape children's personality.

Existing literature shows no or very little effect of birth order on personality (Damian and Roberts 2015; Rohrer, Egloff, and Schmukle 2015; Lehmann, Nuevo-Chiquero, and Vidal-Fernandez 2018; Boccio and Beaver 2019; Botzet, Rohrer, and Arslan 2021). Only Black, Gronqvist, and Ockert (2018) find a negative effect of birth order on noncognitive skills in Sweden. Black, Gronqvist, and Ockert (2018) take advantage of the military enlistment data from the Swedish War Archive. This dataset provides results of psychological tests that Swedish men formerly took when they enlisted in the military.<fnref n="43" /> Men were interviewed by a certified psychologist who had to assess their emotional stability, persistence, willingness to assume responsibility, ability to take initiative, and whether they were socially outgoing. Black, Gronqvist, and Ockert (2018) use an overall measure of those personality traits to conduct the analysis. They find that later-born males perform significantly worse on noncognitive skills. The effect is stronger for boys with older brothers than for boys with older sisters. Differences in personality depending on birth order and a sibship's sex composition can be explained by the family niche model of Sulloway (1996). According to Sulloway (1996), later-born children have

<footnote n="43">⁴³ Until 2010, all Swedish men had to enlist in the military.</footnote>
to be creative to differentiate themselves from their older (same-sex) siblings. They become more unconventional to attract parental attention. Those differences in personality seem to influence the occupational choices in Sweden, where male firstborns are more likely to be in occupations, such as top manager, that require positive noncognitive skills (Black, Gronqvist, and Ockert 2018).

The Madagascar Youth Transition Survey includes a module of 116 questions aimed at assessing cohort members' noncognitive skills. We describe them in the following subsection.

## C1. Measurement of Personality

Cohort members' noncognitive skills were assessed by a 116-question test. Respondents had to rate on a 5-point Lickert scale whether they fully disagree (rated 1) or fully agree (rated 5) that a statement describes them. The statements are a mix of commonly used items aimed at measuring noncognitive skills.<fnref n="44" /> For example, respondents had to declare whether they agree or not that the statement "I like to tidy" describes them.

We combined the 116 items to build a commonly used taxonomy of noncognitive abilities: the Big-5 personality traits. According to John and Srivastava (1999), the Big 5 "represent personality at the broadest level of abstraction, and each dimension summarizes a large number of distinct, more specific personality characteristics." Big-5 dimensions are known under the acronym OCEAN: openness, conscientiousness, extraversion, agreeableness, and neuroticism. They are particularly accurate for developed countries. As warned by Laajaj and Macours (2017) and Laajaj et al. (2019), Big-5 taxonomy might not emerge in developing countries. The major issue is that in developing economies, noncognitive skills are assessed via face-to-face surveys while they are mostly assessed via computers in developed economies. Biases in responses might especially arise in face-to-face surveys in developing countries because of interactions between respondents and interviewers, translations of items, and lower level of education, which can make questions more difficult to understand. Specifically, acquiescence bias (tendency to agree with every statement, even when statements are contradictory) might be more common. To check whether the Big-5 taxonomy emerges in our data, we conducted a factorial analysis, after correcting for acquiescence bias, on our respondents' answers to the 116 items aimed at measuring noncognitive skills. The personality traits that emerge from the factorial analysis are slightly different from the Big-5 taxonomy. They are responsibility, initiative, extraversion, emotional stability, and openness.

The internal consistency of our personality-trait constructs is satisfactory. For each personality trait except openness, Cronbach's α is over 0.7.<fnref n="45" /> Three of the personality traits that emerge from our factorial analysis are common to the Big-5 personality traits (extraversion, emotional stability, and openness). To check whether they accurately measure extraversion, emotional stability, or openness, we also constructed a naive measurement of noncognitive skills. We simply averaged answers to items that belong to a determined trait.<fnref n="46" /> The naive and factorial analysis constructs

<footnote n="44">⁴⁴ A detailed list of common scales and items used to assess noncognitive skills is available at https://ipip.ori.org.</footnote>

<footnote n="45">⁴⁵ Cronbach's α assesses the extent to which items are measuring the same underlying construct. A minimum threshold of 0.7 is often applied in the literature (Laajaj and Macours 2017).</footnote>

<footnote n="46">⁴⁶ A detailed naive classification of items within each personality trait is available from the authors upon request</footnote>
of common personality traits are sufficiently correlated with each other (0.46 for openness, 0.6 for extraversion, and 0.7 for emotional stability; see table A8). For the sake of simplicity, we use only personality traits that emerged from factorial analysis when analyzing the effect of birth order on noncognitive skills.<fnref n="47" />

## C2. The Effect of Birth Order on Noncognitive Skills

We use a linear model absorbing sibship-size fixed effects and with the same set of controls as in equation (2) to estimate the effects of birth order on z-score of our measures of noncognitive skills (table A9). We do not find any effect of birth order on noncognitive skills. Some gender and wealth heterogeneities seem to emerge.<fnref n="48" /> Fifth-born boys are more extroverted. In the poorest households, second- and third-born children are less responsible than firstborns, while in less poor households, fourth-born children are more open, and fifth-born children take less initiative than firstborns. However, those results are too weak to be correctly interpreted. The absence of birth-order effects on noncognitive skills is in line with the scarce literature on this topic in developing countries (e.g., Botzet, Rohrer, and Arslan 2021).

<footnote n="47">47 Our results are robust to the use of the “naive” measurement of the personality traits.</footnote>

<footnote n="48">48 Results of the heterogeneities analysis are not shown here but are available from the authors upon request.</footnote>
<table><caption>TABLE A8
CORRELATION MATRIX OF PERSONALITY TRAITS</caption><thead><tr><th rowspan="2"></th><th colspan="4">Constructs from PCA</th><th colspan="5">Naive Constructs</th></tr><tr><th>Responsible</th><th>Initiative</th><th>Extraversion</th><th>Emotional Stability</th><th>Openness</th><th>Openness</th><th>Conscientiousness</th><th>Extraversion</th><th>Agreeableness</th><th>Emotional Stability</th></tr></thead><tbody><tr><td>Constructs from PCA:</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Responsible</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Initiative</td><td>.057***</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extraversion</td><td>-.077***</td><td>-.072***</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Emotional stability</td><td>-.096***</td><td>-.099***</td><td>.134***</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Openness</td><td>-.057**</td><td>-.049*</td><td>.075***</td><td>.094***</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Naive constructs:</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Openness</td><td>.575***</td><td>.240***</td><td>.285***</td><td>.066**</td><td>.458***</td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><td>Conscientiousness</td><td>.917***</td><td>.219***</td><td>.007</td><td>.029</td><td>.089***</td><td>.616***</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>Extraversion</td><td>.607***</td><td>.188***</td><td>.601***</td><td>.073***</td><td>.169***</td><td>.629***</td><td>.633***</td><td>1.000</td><td></td><td></td></tr><tr><td>Agreeableness</td><td>.810***</td><td>-.067***</td><td>-.097***</td><td>-.021</td><td>-.102***</td><td>.409***</td><td>.697***</td><td>.441***</td><td>1.000</td><td></td></tr><tr><td>Emotional stability</td><td>.499***</td><td>.180***</td><td>.007</td><td>.699***</td><td>.052**</td><td>.401***</td><td>.550***</td><td>.439***</td><td>.393***</td><td>1.000</td></tr></tbody></table>

Note.—Reported is the correlation between the different noncognitive skills by use of a naive or principal component analysis (PCA) construction. Noncognitive skills are displayed as z-scores.

* *p* < .10.

** *p* < .05.

*** *p* < .01.
<table><caption>TABLE A9
THE EFFECT OF BIRTH ORDER (in Reference to Firstborns) ON NONCOGNITIVE SKILLS (N = 1,179)</caption><thead><tr><th></th><th>Initiative<br>(1)</th><th>Responsibility<br>(2)</th><th>Extraversion<br>(3)</th><th>Emotional<br>Stability<br>(4)</th><th>Openness<br>(5)</th></tr></thead><tbody><tr><td>Second child</td><td>-.060<br>(.099)</td><td>-.151<br>(.095)</td><td>.154<br>(.098)</td><td>-.120<br>(.097)</td><td>-.054<br>(.098)</td></tr><tr><td>Third child</td><td>-.123<br>(.131)</td><td>-.226*<br>(.125)</td><td>.127<br>(.128)</td><td>-.133<br>(.128)</td><td>.107<br>(.128)</td></tr><tr><td>Fourth child</td><td>-.104<br>(.166)</td><td>-.156<br>(.159)</td><td>.177<br>(.163)</td><td>.055<br>(.163)</td><td>.038<br>(.163)</td></tr><tr><td>Fifth child and subsequent</td><td>-.164<br>(.204)</td><td>-.240<br>(.195)</td><td>.350*<br>(.201)</td><td>-.092<br>(.200)</td><td>.027<br>(.201)</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>.03</td><td>.07</td><td>.03</td><td>.08</td><td>.05</td></tr><tr><td>Adjusted within-R<sup>2</sup></td><td>.06</td><td>.09</td><td>.07</td><td>.10</td><td>.07</td></tr></tbody></table>

Sources.—Authors' calculations using data from the 2004 EPSPAM and the 2012 Madagascan Youth Transition Survey.

Note.—Reported are the effects of birth order on z-scores of measurement for initiative (col. 1), responsibility (col. 2), extraversion (col. 3), emotional stability (col. 4), and openness (col. 5) by using a linear model absorbing sibship-size fixed effects. All models include gender, year of birth, average age difference among sibship, ethnicity, mother's age, mother's age at first birth, parent's activity (wage worker, own account worker, family worker, or housewife), parent's education (no education, primary completed, or college completed), place of residence (urban or rural), household's wealth in 2004, total surface harvested by the household in 2004, an interaction between household wealth and total surface harvested, an index measuring the household's proximity to infrastructure (health centers, banks, post offices, schools, transportation, food markets, electricity, and water), and a dummy equal to 1 for individuals who were expected to enter primary school between 1997 and 2008 (a period of GDP growth). Standard errors are in parentheses.

* *p* < .10.

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