metadata: authors: 0: author: Milazzo, Annamaria
count: Nigeria
docna: 0: docna: Son preference, fertility and family
            structure : evidence from reproductive behavior among
            Nigerian women
docty: Policy Research Working Paper
theme: Other social development
majtheme: Social dev/gender/inclusion
subtopic: Health Care Services Industry,Reproductive Health,Educational Sciences,Gender and Development,Family Planning Research,Law and Justice Institutions
teratopic: Health, Nutrition and Population,Gender
abstracts: cdata!: Strong boy-bias and its consequences for
            young and unborn girls have been widely documented for Asia.
            This paper considers a country in Sub-Saharan Africa and
            finds that parental gender preferences do affect fertility
            behavior and shape traditional social institutions with
            negative effects on adult women's health and
            well-being. Using individual-level data for Nigeria, the
            paper shows that, compared to women with first-born sons,
            women with first-born daughters have (and desire) more
            children and are less likely to use contraceptives. Women
            with daughters among earlier-born children are also more
            likely to have shorter birth intervals, a behavior medically
            known to increase the risk of child and maternal mortality.
            Moreover, they are more likely to end up in a polygynous
            union, to be divorced, and to be head of the household. The
            preference for sons is also supported by child fostering
            patterns in which daughters are substitutes for foster
            girls, while the same does not hold for sons and foster
            boys. These results can partly explain excess female
            mortality among adult women in Sub-Saharan Africa.
geo_reg_key: 80475,80473,80474
repnme: repnme: Son preference, fertility and family
            structure : evidence from reproductive behavior among
            Nigerian women
display_title: Son preference, fertility and family
            structure : evidence from reproductive behavior among
            Nigerian women
listing_relative_url: /research/2014/05/19536489/son-preference-fertility-family-structure-evidence-reproductive-behavior-among-nigerian-women
topicv3: Education m1327048 470,Family Planning m1327501 1452,Gender m1327621 615,Mortality m1504365 3661,Health m1504661 3480
publishtoextweb_dt: 2014-05-01T00:00:00Z
historic_topic: Health, Nutrition and Population,Gender
no_of_pages: 46
model_extractions: WPS6869 Policy Research Working Paper 6869 Son Preference, Fertility and Family Structure Evidence from Reproductive Behavior among Nigerian Women _Annamaria Milazzo_ The World Bank Development Research Group Human Development and Public Services Team May 2014
Policy Research Working Paper 6869

# **Abstract**

Strong boy-bias and its consequences for young and unborn girls have been widely documented for Asia. This paper considers a country in Sub-Saharan Africa and finds that parental gender preferences do affect fertility behavior and shape traditional social institutions with negative effects on adult women’s health and well-being. Using individual-level data for Nigeria, the paper shows that, compared to women with first-born sons, women with first-born daughters have (and desire) more children and are less likely to use contraceptives. Women with daughters among earlier-born children are also more likely to have shorter birth intervals, a behavior medically known to increase the risk of child and maternal mortality. Moreover, they are more likely to end up in a polygynous union, to be divorced, and to be head of the household. The preference for sons is also supported by child fostering patterns in which daughters are substitutes for foster girls, while the same does not hold for sons and foster boys. These results can partly explain excess female mortality among adult women in Sub-Saharan Africa.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at amilazzo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Son Preference, Fertility and Family Structure. Evidence from Reproductive Behavior among Nigerian Women<sup>_∗_</sup> Annamaria Milazzo<sup>_†_</sup> JEL classification: D63, J13, J16, I10 Keywords: son preference, fertility, birth spacing, maternal mortality, polygyny, child fostering, Nigeria

> _∗_ I am grateful to Eliana La Ferrara for continued guidance and support, and to Alberto Alesina, Siwan Anderson, JeanMarie Baland, Sonia Bhalotra, Maristella Botticini, Denis Cogneau, James Fenske, Selim Gulesci, Rajshri Jayaraman, Sylvie Lambert, Adriana Lleras-Muney, Andreas Madestam, Massimo Marinacci, Nicola Pavoni, Martin Ravallion, and Dominique van de Walle for very helpful comments. I thank seminar participants at Bocconi University, Paris School of Economics, CRED at the University of Namur, 2012 CEPR/AMID Conference, IFPRI, IAST Toulouse, and the AEL Conference 2012 for comments and suggestions. The findings, interpretations, and conclusions expressed in this paper do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. All errors are my own.

> _†_ The World Bank. Email: `amilazzo14@gmail.com; amilazzo@worldbank.org`

1
# **1 Introduction**

There is an extensive literature on parental preferences over a child’s gender.<sup>1</sup> The preference for male children, or son preference, has been widely documented in South and East Asia where gender bias is severe especially before birth and at young ages. Sen (1990) documents abnormally high sex ratios at birth (the share of male births) in large parts of Asia: this evidence is suggestive of sex-selective abortion, infanticide and neglect of female children. Sub-Saharan Africa is typically regarded as a region with low or absent gender preferences.<sup>2</sup> Although it is well recognized that sons are strongly valued in traditional patrilineal societies in Sub-Saharan Africa, no evidence has been found of sex ratios favoring boys at birth (possibly due to lack of accessible technologies for fetal sex determination). Recent studies find that many women are indeed missing in Sub-Saharan Africa, especially during their reproductive years (Anderson and Ray, 2010; World Bank, 2011).<sup>3</sup> This paper revisits the issue of son preference in Sub-Saharan Africa by examining patterns of fertility behavior and family structure in Nigeria. It shows that son preference is prevalent and that its deleterious effects are reflected not in missing young or unborn girl children but rather in the health and well-being of adult women. Increasing sex ratios at birth may be envisaged following a future diffusion of techniques for sex selection as parents seek ways to meet their demand for sons.

The reasons given for son preference are various and include cultural, kinship, economic and institutional factors that typically differ across different societies (Das Gupta et al., 2003; Almond, Edlund, and Milligan, 2009). In the Sub-Saharan African context, anthropological and demographic evidence emphasizes the dominant role of males in patrilineal societies in which descent and inheritance are transmitted through the male line (Goody, 1973, 1976; Isiugo-Abanihe, 1994). Furthermore, male children strengthen the relationship between the wife and her husband’s kin (i.e., by guaranteeing the continuation of his lineage) and secure (the mother’s) access to residence and inheritance upon the husband’s death.

This paper makes two contributions to the existing literature. Anderson and Ray (2010) use

> 1For a cross-cultural review of literature on parental preferences see, among others, Williamson (1976).

> 2Two notable exceptions are Garg and Morduch (1998) who show that children with relatively more sisters than brothers benefit in the intra-household allocation of resources in resource-constrained households in Ghana; and Klasen (1996) who uses population and mortality data for Sub-Saharan Africa to construct an adjusted aggregate reference ratio and finds a small and increasing anti-female bias.

> 3Anderson and Ray (2010) and World Bank (2011) find that the sources of excess female mortality among adult women in Sub-Saharan Africa are primarily HIV/AIDS and maternal mortality. Klasen and Vollmer (2013) use different reference standards than Anderson and Ray (2010) and confirm their findings for excess mortality among young female adults in Africa (though of a smaller magnitude).

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aggregate mortality data and find evidence of excess mortality among adult women in Sub-Saharan Africa. In contrast, this paper uses individual-level data and analyzes how son preference affects women’s fertility preferences and choices in a context where sex ratios at birth are in the normal range and where little is known about parental gender preferences. In addition, it explores how parental gender preferences can affect the development of traditional social institutions common in the West African context, namely child fostering and polygyny, as well as other marital outcomes and arrangements, including divorce and female headship. These different sets of outcomes are analyzed separately and, taken together, can shed some light on the complex mechanisms through which the desire for own biological sons can be satisfied in Nigeria.

More specifically, the paper puts forward two channels through which adult women might be missing in Sub-Saharan Africa and in other contexts where fertility choices and family structure are found to be strongly affected by son preference. The first is son-preferring fertility behavior (or son-preferring stopping rules) in which women with girls among earlier-born children are more likely to continue having children and to have shorter birth intervals. This behavior is medically known to adversely affect women’s health (as well as children’s health) and increase the risk of maternal mortality and morbidity due to maternal depletion. Maternal mortality is in fact among the highest in the world in Nigeria (second only to India in absolute number of deaths), which alone accounts for 14 percent of all maternal deaths worldwide (WHO, 2010). Women of lower socioeconomic status are more likely to be affected due to their poorer access to maternal care and more vulnerable health. The second channel refers to the association between parental gender preferences and specific marital outcomes and living arrangements in which women are often found to be particularly vulnerable and disadvantaged (i.e., polygyny and female headship).

I use the 2008 Nigeria Demographic and Health Survey (NDHS) and find evidence of son preference in several dimensions. A comparison of women’s characteristics by the sex of the firstborn reveals that while younger women (younger than 30) are very similar in terms of observables, significant differences appear in the subsample of older women, which is suggestive of selective mortality.<sup>4</sup> I check for the exogeneity of the sex of the first-born child and discuss other potential sources of bias in the data. The empirical strategy relies on the identifying assumption that the sex of the first-born is uncorrelated with the error term after conditioning for a set of observable characteristics. Results are as follows. First, compared to women who had a first-born boy, women with a first-born girl exhibit a 2 percent increase in the number of children ever born. While the

> 4Selective mortality is also supported by nonparametric evidence of a lower fraction of women with a first-born girl among women in the older age group.

3
effect on fertility is stronger when considering the first two female births, I focus on the sex of the first-born to maintain a causal interpretation of the results. Second, I find that women’s desired fertility and use of contraceptives are affected by the sex of the first-born. In particular, women with a first-born girl are 2.3 percentage points more likely to report that they desire another child, and 1.1 percentage points less likely to use contraceptives. Third, I investigate whether the pace at which women have children is influenced by the sex of earlier-born children. I exploit the variation in birth spacing for children of the same mother by estimating regressions with mother fixed effects. I find that, among women who had daughters as the first two born children, those who have a third or fourth daughter are 3.6 (1.8) percentage points (significantly) more likely to wait less than 24 (15) months.

Second, the preference for male children is found to be significantly associated with the social institution of polygyny (the practice in which a man has several wives). Women with first-born daughters are in fact 1.2 percentage points more likely to end up in a polygynous union (i.e., the husband takes another wife). As expected, this effect is specific to first-rank wives (while no effect is found for higher-rank wives), as they are the ones who entered the current union as monogamous. Among women aged 30–49, those with first-born daughters are also 1.4 percentage points more likely to have a nonresident husband, 1.2 percentage points more likely to be ever divorced or separated, and 1.1 percentage points more likely to be the head of the household.

Further evidence is drawn from an examination of the institution of child fostering, in which biological children are temporarily sent to live with other families. I focus on the child-labor hypothesis as one of the motivations for fostering according to which children of each gender and age group have specific roles within the household. Households with an imbalance of biological girls or boys might decide to send or receive a child in order to achieve a balanced gender structure, thus maximizing household productivity (Akresh, 2009). If non-biological girls (boys) are substitutes for biological daughters (sons), households should respond symmetrically to the imbalance of daughters or sons in fostering decisions. Instead, I find that households with an excess of sons are one percentage point more likely to foster-in a girl, while those with an excess of daughters are not more likely to foster-in boys. This asymmetric response may suggest that, motivated by the desire to have their own biological son, households with an excess of daughters might continue bearing children instead of fostering-in outside boys. In contrast, households with an excess of sons are significantly more likely to receive a girl, as girls are often needed for domestic chores. This evidence is consistent with the idea that foster girls are considered substitutes for own biological daughters in fostering-in decisions, while foster boys are not substitutes for sons. Interestingly, results do not 4
differ between urban and rural households (or landed and landless households), suggesting that gender preferences may play a role in shaping fostering decisions beyond considerations related to the production system.

This paper is related to several strands of the literature. First, it relates to the literature documenting son preference in different areas of the world. Evidence of son-preferring fertility behavior is found especially for women in Asian countries (Chowdhury and Bairagi, 1990; Clark, 2000; Dreze and Murthi, 2001; Filmer, Friedman, and Schady, 2009). For the United States, in addition to the well-documented preference for sex-balance (Ben-Porath and Welch, 1976; Angrist and Evans, 1998), recent research has documented unusually high sex ratios among Asian mothers: this pattern is found to be related to the increasing availability of technologies to determine the sex of the fetus (Dahl and Moretti, 2008; Abrevaya, 2009). Similar evidence has been found among Asian immigrants in Canada (Almond, Edlund, and Milligan, 2009). In this paper, I quantitatively investigate if fertility decisions are gender-biased in a context where the sex ratio at birth is in the normal range.

Second, it speaks to the literature examining the effects of son preference in generating gender disparities in health and educational outcomes. In the context of India, the diffusion of prenatal sex determination technologies has not only been found to be associated with increasing sex ratios, but also with preferential _prenatal_ treatment for boys (Bharadwaj and Lakdawala, 2013). The ‘try until you have a son’ fertility rule may also ‘passively’ lead to a lower amount of resources being allocated to female children within the household. This may happen either as a consequence of shorter breastfeeding for girls because parents with daughters want to ‘try again’ for a son (Jayachandran and Kuziemko, 2011), or because girls tend to be born in larger households (Jensen, 2003). Recently, Barcellos, Carvalho, and Lleras-Muney (2014) find that, accounting for the effect of son-preferring stopping rules on family size, boys still receive more parental investment (including childcare time, breastfeeding, vaccinations, and vitamin supplementations) than girls in India. While this literature has mainly focused on young (or not yet born) children, this work represents one of the first attempts to understand the effects of the preference for male children on adult women’s health and well-being.

Third, it relates to the economic and sociological literature on the effects of a child’s gender on marital stability, divorce, and parental involvement (see Lundberg, 2005, for a review). Dahl and Moretti (2008) analyze Census and other data sources for the United States and find that women with a first-born daughter are less likely to marry, more likely to be divorced, and that –after 5
divorce– fathers are more likely to obtain custody of sons compared to daughters. In a previous version, Dahl and Moretti (2004) also use Census data for Kenya and find an association between having a girl and the probability of being in a polygynous union. This paper exploits the entire women’s fertility histories (available in the NDHS but not in the Census, along with other variables such as a wife’s rank) and studies the effect of son preference on the probability that the husband becomes polygynous, that he does not co-reside with his wife, that the woman is ever divorced or separated, and that she is the head of the household.

Lastly, this paper contributes to the literature on child fostering (Isiugo-Abanhie, 1985; Bledsoe, 1990; Ainsworth, 1996; Akresh, 2009) by showing evidence that fostering patterns respond asymmetrically to a gender imbalance of daughters or sons, thereby suggesting a new motivation for household fostering decisions that is complementary to the ones already proposed in the literature.

The remainder of the paper is organized as follows. Section 2 provides some insights on the context and cultural background. Section 3 describes the data and some suggestive patterns in the variables of interest. Section 4 introduces the empirical methodology used to analyze fertility outcomes and birth spacing and further explores the identified patterns. Section 5 presents the results. Section 6 focuses on the interactions between son preference, marital outcomes, and child fostering decisions. Section 7 concludes.

# **2 Context**

As in many parts of Sub-Saharan Africa, society is organized around the extended family which still represents the most basic unit of social organization in Nigeria. Family ties are strong and play an important role in shaping individual behavior, even though there are signs that the extended family system is weakening for some ethnic groups (Wusu and Isiugo-Abanihe, 2006). There is an extraordinary ethnic diversity in Nigeria, but all ethnic groups are predominantly characterized by patrilineality and patrilocality.<sup>5</sup> Large progenies are strongly valued because they strengthen a man’s family status (Caldwell and Caldwell, 1987). The interactions between the principles of social organization, fertility preferences and family structure are documented in several demographic studies, such as Isiugo-Abanihe (1994):

> _‘Childlessness is the most dreaded tragedy for a man or a woman to experience in Nigeria’s_

> 5The three largest ethnic groups are geographically concentrated in different areas of Nigeria. They are the HausaFulani (North), Yoruba (South-West), and Igbo (South-East). They represent 28%, 18%, 16% of the population, respectively (NPC and ICF Macro, 2009).

6
_patrilineal society. (...) The majority of the respondents felt that a man without a child, particularly without a son, will not be remembered in the family; his branch of the family will come to an end. For the same reason, a man who has only daughters may acquire a second wife to enhance the chance of having a son. Clearly, in such a patriarchal system, the perpetuation of the family line is a strong motivation for children.’_ Isiugo-Abanihe (1994, p. 154, from a survey of 3,073 Nigerian couples) Indeed, the desire for many children, especially males, is motivated by descent transmission and the urge to provide continuation to the family lineage. More importantly, these societies are characterized by the fact that only males have the control of family landed property, as documented in Wusu and Isiugo-Abanihe (2006).<sup>6</sup> _‘In most cultures only male children are allowed to share in family land holdings within the context of the extended family structure and communal ownership of land. Since farming is central to economic life, the most economically rewarding reproductive goal a couple could pursue is a large family size, ideally with many male children. Against this backdrop couples dread barrenness, and until a ‘good’ number of male children are born, extended family members exert pressure, which may culminate in the man marrying another wife’_ Wusu and Isiugo-Abanihe (2006, pp. 141–142) In this context, sons are highly valued by women as well because they represent the only way through which they can inherit part of their deceased husband’s property (Fapohunda and Todaro, 1988; Ewelukwa, 2002). This consequently generates pressure for bearing male children as a way to protect themselves in the state of widowhood. It may also lead to competition among co-wives as described in Bledsoe, Banja, and Hill (1998).

_‘In their husbands’ compounds, women seek to establish their security and to gain a competitive edge over present and future co-wives and sisters-in-law by bearing a number of children, especially sons, who will retain rights of residence and inheritance in the compound and will eventually take over its leadership roles.’_ Bledsoe, Banja, and Hill (1998, p. 23)

> 6There are several sources of law in Nigeria: Common law (the _default_ Law, applied only when other sources of law are not applicable), Customary Law, and Islamic law (Oba, 2011). Even though there is some variation in inheritance rules across states and ethnic groups, in most cases only male children are allowed to inherit the father’s property or larger part of it. According to Islamic law, female children are entitled to inherit half of the share of their male siblings.

7
A peculiarity of the West-African context is the widespread fostering of children who are temporarily sent away from their biological parents to be raised in another family.<sup>7</sup> The reasons for child fostering have been studied by several researchers (Isiugo-Abanhie, 1985; Bledsoe, 1990; Ainsworth, 1996; Akresh, 2009; Beck et al., 2011). These include insurance against idiosyncratic shocks to family income, education (i.e., children in rural households are sent to live with their urban kin members where there are schools), kinship fostering (i.e., as part of the obligations between members of the same extended family), and child labor. As argued by some demographers, high fertility is also made possible by the social institution of fostering, through which the costs of childrearing are partly shared with the extended family (Isiugo-Abanhie, 1985; Bledsoe, 1990). Fapohunda and Todaro (1988) argue that ‘ _the presence of parental surrogates in the extended family alleviates problems of incompatibility between child care and work and, thereby, lowers the opportunity cost of children._ ’ (p. 572). According to Smith (2004), the traditional high value associated with having many children (that allow larger kinship networks) might partly motivate the slow transition to lower fertility in Nigeria, even though families start lamenting the increasing costs of raising children due to modernization.

# **3 Data and descriptive evidence**

I use the 2008 Nigeria Demographic and Health Survey (NDHS), a representative cross-sectional dataset of women aged 15 to 49 in Nigeria. It contains individual-level information on women’s birth histories, household composition, biological children living in the household or elsewhere, birth intervals, marital status, and other individual and household characteristics.

Summary statistics for the sample of women aged 15 to 49 with at least one child ever born are reported in table 1.

The mean number of children ever born is 4.35.<sup>8</sup> This does not reflect completed fertility especially for younger women in the sample: the total fertility rate is in fact 5.7 births per woman (NPC and ICF Macro, 2009). As noted in the previous section, high fertility is strongly desired. This is reflected both in the high number of births and desired fertility: 63 percent of women express

> 7Child fostering is a widely accepted and practiced social institution in many parts of Sub-Saharan Africa. LLoyd and Desai (1992) analyze data from 16 DHS survey around the world and find that the percentages of children aged 10–14 living away from their mothers ranges from 0.9 in Tunisia, 4.2 in Sri Lanka, 5.7 in Brazil, 24 in Senegal, 29.4 in Ghana, and 40.9 in Liberia. Isiugo-Abanhie (1985) argues that ‘nowhere is it as institutionalized as in parts of West Africa’ (pp. 56).

> 8The number of children ever born includes children who have died.

8
**Table 1:** Summary statistics

||mean|median|st. dev.|min|max|N|
|---|---|---|---|---|---|---|
|# children ever born|4.35|4|2.76|1|18|23751|
|# daughters ever born|2.12|2|1.72|0|12|23751|
|# sons ever born|2.23|2|1.76|0|12|23751|
|# children alive|3.53|3|2.15|0|15|23751|
|# daughters alive|1.74|1|1.42|0|10|23751|
|# sons alive|1.79|2|1.45|0|11|23751|
|frst child dead|0.18|0|0.39|0|1|23751|
|frst daughter dead|0.08|0|0.27|0|1|23751|
|frst son dead|0.10|0|0.31|0|1|23751|
|frst-born girl|0.48|0|0.50|0|1|23751|
|frst-born boy|0.52|1|0.50|0|1|23751|
|currently married|0.90|1|0.30|0|1|23750|
|currently divorced or sep|0.03|0|0.16|0|1|23750|
|ever divorced or sep.|0.12|0|0.32|0|1|23751|
|currently widowed|0.03|0|0.18|0|1|23750|
|ever widowed|0.05|0|0.22|0|1|23751|
|living with husband|0.90|1|0.30|0|1|21652|
|frst union|0.86|1|0.34|0|1|23050|
|polygynous husband|0.34|0|0.47|0|1|21690|
|live in female headed hh|0.14|0|0.34|0|1|23751|
|female head|0.10|0|0.31|0|1|23751|
|age|32.13|31|8.61|15|49|23751|
|age at frst marriage|17.69|17|4.63|7|45|23153|
|age at frst birth|19.36|19|4.44|9|44|23751|
|husband’s age|42.49|40|11.29|15|96|21457|
|contraceptive use|0.16|0|0.36|0|1|23751|
|wants another child|0.63|1|0.48|0|1|23598|
|wants no more children|0.22|0|0.42|0|1|23598|
|urban|0.32|0|0.47|0|1|23751|
|woman’s eduyrs|4.91|4|5.26|0|22|23735|
|husband’s eduyrs|6.16|6|5.79|0|21|22657|
|catholic|0.10|0|0.30|0|1|23575|
|other christian|0.37|0|0.48|0|1|23575|
|muslim|0.51|1|0.50|0|1|23575|
|hausa-fulani|0.34|0|0.47|0|1|23617|
|igbo|0.12|0|0.33|0|1|23617|
|yoruba|0.17|0|0.37|0|1|23617|
|weight-for-height|-0.55|-0.68|1.21|-4|5.92|22986|
|body mass index|23.07|22.14|4.66|12.1|59.81|23115|

_Notes._ 2008 NDHS. Using sample weights.

the desire for another child. Although knowledge of any contraceptive method is widespread, current use among currently married women is much lower but increasing over time: from 6% in 1990, 13% in 2003, to 15% in 2008.<sup>9</sup> Despite relatively low levels of contraceptive use, most pregnancies are ‘wanted’ by the vast majority of women: 88 percent of women with births in the five years

> 968% of all married women and 90% of all married men know of at least one method of contraception, and 29% (45%) of all married women (men) reported ever using a method of contraception. Among women currently using contraception, about two-thirds are using a modern method (mostly male condom, injectables and pills), while one-third use traditional methods (rhythm method and withdrawal are the most common).

9
preceding the survey states that the births were wanted then, 7 percent were wanted later, and only 5 percent were unwanted. Marriage is almost universal for women in Nigeria: 90 percent are currently married, 3 percent are divorced or separated, 3 percent are widowed; and remarriage is not uncommon. About 34 percent of women have a polygynous husband, 10 percent are the head of the household, and 90 percent of women live with their husband.

**Birth spacing** Medical research has shown that short birth intervals are associated with poor child and maternal health outcomes (Setty-Venugopal and Upadhyay, 2002).<sup>10</sup> With respect to maternal health, relative to women who give birth at 9- to 14-month intervals, those who have their children at 27- to 32-month birth intervals are: 1.3 times more likely to avoid anemia; 1.7 times more likely to avoid third-trimester bleeding; and 2.5 times more likely to survive childbirth (Conde-Agudelo, and Belizan, 2000).<sup>11</sup> Several studies also find that short birth intervals and reduced breastfeeding time have deleterious effects on child health and mortality (Palloni and Millman, 1986; Palloni, Pinto Aguirre, and Lastiri, 1994; and Jayachandran, and Kuziemko, 2011). Palloni and Millman (1986) observe that close birth spacing does not allow a woman to regain her physical strength and the nutrients required to have a successful new pregnancy. The effects go through retardation of fetal growth as well as the mother’s inability to properly breastfeed the child, all leading to increased risk of death of the child. Moreover, competition for resources among siblings born in close succession may aggravate these negative effects.<sup>12</sup>

In the 2008 NDHS, of all births reported by each woman, 8 percent are less than 15 months apart, and 33 percent less than two years. The mean birth interval is 32.6 months, and the median is 27. The survival status of the child who opens the birth interval matters: the median interval is 29 months if the previous child is alive, 24 months if dead.

Given the association between birth spacing and child and maternal health outcomes, it is important to understand whether the desire for sons affects the pace at which mothers try to conceive and give birth.

> 10Birth spacing refers to the time interval (number of months) between births.

> 11See Conde-Agudelo et al. (2007) for a review of medical studies on the relationship between birth spacing and maternal health.

> 12There is a striking negative relationship between the length of the birth interval and child mortality. For example, 252 children (out of 1000 live births) die under the age of 5 if the preceding birth interval is shorter than 24 months, while the number of children who die if the interval is 4 years or longer is reduced to 92 (NPC and ICF Macro, 2009).

10
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<!-- Start of picture text --> irs)“ openseeeseeee<br>Raseeer"” tee coessere ee<br>on cere —_—<br>T reel Meee aoe<br>T ~ caneeeeere<br>x wae<br>y re<br>+<br>wTt<br>20 30 40 50<br>Woman's age at the time of the survey<br>~-----=-- 95% Cl === Uneducated ********= Educated<br>Pooled NDHS 1990, 99, 03, 08. Survey weights used. First births from women’s fertility histories.<br><!-- End of picture text -->
**Table 2:** Selected women’s characteristics, by the sex of the first-born and age group

|a) All women||||||
|---|---|---|---|---|---|
||frst-born girl|frst-born boy|dif|st. error dif|n|
|# children ever born|4.344|4.369|-0.025|(0.038)|23751|
|wants another child|0.637|0.629|0.008|(0.007)|23598|
|use contraceptives|0.157|0.157|-0.000|(0.005)|23751|
|age at frst birth|19.423|19.295|0.128**|(0.064)<br>|23751|
|polygynous husband|0.333|0.336|-0.003|(0.007)|21690|
|female headed hh|0.144|0.133|0.011**|(0.005)|23751|
|living with husband|0.891|0.903|-0.012**|(0.005)|21652|
|frst union|0.862|0.863|-0.001<br>|(0.005)<br>|23050|
|woman’s eduyrs|5.026|4.811|0.215***|(0.076)|23735|
|husband’s eduyrs|6.329|5.999|0.330***|(0.083)|22657|
|weight-for-height|-0.536|-0.561|0.025|(0.018)|22986|
|body mass index|23.116|23.031|0.085|(0.068)|23115|
|b) Women aged 15–29||||||
||frst-born girl|frst-born boy|dif|st. error dif|n|
|# children ever born|2.541|2.545|-0.004|(0.032)|10048|
|wants another child|0.867|0.852|0.015**|(0.007)|9988|
|use contraceptives|0.132|0.132|-0.000|(0.008)|10048|
|age at frst birth|18.345|18.236|0.109|(0.074)|10048|
|polygynous husband|0.267|0.275|-0.008|(0.010)|9173|
|female headed hh|0.107|0.109|-0.002|(0.007)|10048|
|living with husband|0.900|0.899|0.001|(0.007)|9157|
|frst union|0.915|0.913|0.002|(0.006)|9492|
|woman’s eduyrs|4.889|4.861|0.028|(0.111)|10042|
|husband’s eduyrs|6.359|6.203|0.156|(0.125)|9308|
|weight-for-height|-0.571|-0.567|-0.004|(0.023)<br>|9711|
|body mass index|22.114|22.136|-0.022|(0.089)|9775|
|c) Women aged 30–49||||||
||frst-born girl|frst-born boy|dif|st. error dif|n|
|# children ever born|5.649|5.639|0.010|(0.050)|13703|
|wants another child|0.471|0.475|-0.004|(0.009)|13610|
|use contraceptives|0.174|0.174|0.000|(0.007)|13703|
|age at frst birth|20.202|20.032|0.170*|(0.094)|13703|
|polygynous husband|0.381|0.379|0.002|(0.009)|12517|
|female headed hh|0.170|0.149|0.021***|(0.007)|13703|
|living with husband|0.884|0.905|-0.021***|(0.006)|12495|
|frst union|0.826|0.83|-0.004|(0.007)|13558|
|woman’s eduyrs|5.125|4.776|0.349***|(0.103)|13693|
|husband’s eduyrs|6.309|5.864|0.445***|(0.111)|13349|
|weight-for-height|-0.511|-0.557|0.046*|(0.025)|13275|
|body mass index|23.839|23.654|0.185*|(0.096)|13340|

_Notes._ Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

If women’s reproductive behavior is affected by the gender of their first-born child, differences in characteristics may develop among women over time. I split the sample into subsamples of women aged 15–29, and 30–49. Panel (b) shows that the differences are much smaller for younger women (other than the desire for more children, which is expected to be affected among women of 14
reproductive ages). Among older women (panel (c)), those with a first-born daughter are ‘better-off’ in terms of their own and their husband’s education, first gave birth at an older age, are more likely to be household heads, and are also characterized by higher weight-for-height and body mass index (BMI).<sup>19</sup> These findings, especially those related to differences in predetermined characteristics among older women, are again consistent with selective mortality.

# **4 Empirical strategy**

## **4.1 Fertility regressions**

Given that the sex of the first-born can reasonably be considered as random, the empirical strategy should be straightforward: unbiased estimates should be obtained by simply regressing the number of children ever born on the sex of the first-born. However, as discussed in the previous section, there are differences in observable characteristics between women with a first-born daughter or son that may have developed over time as a consequence of son-preferring fertility behavior. This would generate a negative correlation between a first-born girl and fertility because surviving women have fewer children on average. In all regressions I include a set of observable covariates in order to reduce the potential (downward) bias. Conditional on the controls, if there is preference for male children, women with first-born daughters should exhibit higher fertility than women with first-born sons.

In order to understand if the sex of the first-born also affects the _desire_ to have more children, I construct a dummy variable based on the NDHS survey question: _‘Would you like to have another child, or would you prefer not to have anymore children?’_ . I also consider the reported use of contraceptives as an outcome.<sup>20</sup> I estimate the following regression: with woman _i_ , born in year _t_ , and resident in region _r_ . _yi,t,r_ is the dependent variable, either the number of children ever born, the desire for more children, and contraceptive use. _firstborngirl_ indicates whether the first child ever born is a girl; _Xi,t,r_ is a set of covariates including: age (of the woman and her husband) and age squared, age at first marriage, age at first birth, number of years

> 19The Body Mass Index (BMI) is a measure of women’s nutritional status. It is the ratio of the weight in kilograms to the square of the height in meters. Weight-for-height is another indicator of women’s nutritional status and it’s defined in terms of standard deviations from the reference median based on the DHS reference standard.

> 20The variable for contraceptive use is defined as equal to one if the woman reports she is currently using _any_ contraceptive method, zero otherwise.

15
of education (of the woman and her husband), a wealth index<sup>21</sup> , a dummy for urban areas, and woman’s ethnicity and religion.<sup>22</sup> Controlling for age at first marriage and husband’s characteristics entails considering the sample of all currently married women. Given that marital status can also be affected by the sex of the first-born (this is analyzed in section 6.1), women with daughters might be more likely to be ever divorced or separated and, as a result, typically have lower fertility than those continuously married. To account for this possible (downward) bias, I first estimate regression 1 using the entire sample of women, and then restrict it to continuously married women (for whom the effect on fertility should be stronger as found in Dahl and Moretti, 2008).<sup>23</sup> _αr_ , _γt_ are region and cohort of birth fixed effects, respectively. I also control for region and ethnic-specific trends, _δrt_ and _χet_ , respectively, to capture region or ethnic-specific trends that may be correlated with the error term. Given that boys are biologically weaker than girls and are more likely to die in infancy, I additionally control for the survival status of the child (a dummy for whether the firstborn child died and its interaction with _firstborngirl_ ).<sup>24</sup> Results with and without controlling for child mortality are shown. As previously noted, since mothers tend to replace a lost child by having more children, omitting to control for differential mortality would lead to a downward bias of the effect of a first-born daughter on fertility. After controlling for the mortality of the first-born child and its interaction with _firstborngirl_ , _β_ 1 is the estimated effect of a first-born _surviving_ daughter on the outcome of interest. Identification relies on the assumption that the sex of the first-born is exogenous (uncorrelated with the error term) after conditioning on observable characteristics. Regression 1 is estimated using OLS and a model for count data (accounting for over-dispersed outcome variables) when the dependent variable is the number of children ever born, and using a probit model for the probability of desiring more children and using contraceptives.

If women practice son-preferring fertility stopping behavior, I expect to find _β_ 1 _>_ 0 for the regressions for realized (and desired) fertility, and _β_ 1 _<_ 0 for the use of contraceptives. Although the effect on fertility should (and is found to) be larger for women who had more subsequent daughters among earlier-born children, the analysis focuses on the sex of the first-born, thus allowing a causal interpretation of the results (Dahl and Moretti, 2008). In fact, while the sex of the first-born can

> 21The DHS wealth index is a measure of a household’s cumulative living standard. It’s generated using principal component analysis based on the households ownership of consumer goods; dwelling characteristics; type of drinking water source; toilet facilities; and other characteristics that are related to a households socio-economic status.

> 22Ethnicity include Hausa-Fulani, Igbo, Yoruba, and other minor ethnicities as the omitted category. Religion include Catholic, other Christians, Muslims, and Traditionalists (omitted).

> 23The rest of the analysis for fertility behavior and birth spacing uses the sample of continuously married women (so that the fertility history at the time of the survey is relevant for the current union).

> 24I also constructed two alternative variables for child death before reaching the age of 1 or 2 years, and results are qualitatively similar to the ones obtained with child death at any age. This is probably because most deaths happen in the first years of life in this context.

16
be considered as random, the decision to have more children after the first represents a choice that may correlate with other household decisions.<sup>25</sup>

# **4.2 Birth spacing regressions**

If there is social pressure for having a son, women who have had daughters among earlier-born children might try to conceive again sooner than women with sons. To study the association between the birth interval and the sex composition of earlier-born children, I use the sample of all children ever born to each woman (the unit of analysis is now the child). I exclude women with twin births and those with more than 12 children to reduce heterogeneity among mothers. I analyze whether the average birth interval is affected by the previous child’s sex.<sup>26</sup> Given information on the interval between each birth for all children, I can use mother fixed effects and exploit the variation in the length of the interval _within_ the fertility history of each woman. This allows me to control for all observable and unobservable characteristics that may correlate with the error term. I estimate the following regression: with child _i_ , mother _j_ , current child _t_ , and preceding child _t −_ 1. _yi,j,t_ is the birth interval in months (the time between the birth of child _t −_ 1 and _t_ ), or dummy variables equal to one if the birth interval is shorter than 24 or 15 months (the critical intervals below which child and maternal health are adversely affected, respectively). _αj_ are the mother fixed effects, _birthorderi,j,t−_ 1 _,k_ is a set of _k_ dummies for birth order, and _girli,j,t−_ 1 is a dummy equal to one if the previous child is a girl. I also add variables for the survival status of the preceding child (and its interaction with _girl_ ) to control for the fact that boys are more likely to die than girls in this context.

Given the high fertility context, I expect to find larger reductions in birth intervals for women with several consecutive daughters. Therefore, I estimate regression 2 on the following subsamples: the subsample of women who had a first-born daughter (and, separately, those with a first-born boy), the subsample of women who had a first and second-born daughter (and those with mixed

> 25As it is possible to observe women that progress to the next parity only if they have reached the previous one, those who have had children after daughters might be different to those who progress after sons. This is a similar empirical issue to that of _dynamic selection bias_ , exposed in Cameron and Heckman (1998), where they study schooling decisions over the life cycle.

> 26Fayehun et al. (2011) find weak evidence of shorter spacing after the birth of _each_ girl in Nigeria. Compared to them, I estimate a model with mother fixed effects to control for unobserved heterogeneity among mothers. Moreover, I consider the subsamples of women with one or more consecutive daughters to see if the increasing pressure for having sons leads to further shortening of the birth intervals.

17
sex composition), and the subsample of women with three girls (and, separately, those with three boys, two boys and one girl, and two girls and one boy) as earlier-born children. As a further check, I also estimate regressions using the full sample of women and including interaction terms between _girl_ and the sex of earlier-born children.

Identification with fixed effects requires that women with at least three, four, or five children ever born are considered.<sup>27</sup> The prediction with son preference is that the birth intervals succeeding the birth of girls should be significantly shorter ( _β_ 1 _<_ 0) than for boys. This effect should be larger the more girls there are among earlier-born children, because of mounting pressure for a son.

# **5 Main results**

## **5.1 Fertility results**

Table 3 shows the estimates of the effect of a first-born girl on the number of children ever born. In column (1), the coefficient on _firstborngirl_ is positive but not significant. In column (2), age at first birth is added among the controls and the coefficient is now larger and significant at the 10% level. The number of years of education is included in column (3) and the coefficient of interest is very similar to column (2), probably because of the positive correlation between age at first birth and education. Column (4) additionally controls for husband’s characteristics, therefore restricting the sample to married women (with information on their husband’s age and education) and yields similar results. As expected, adding the controls for first-born child mortality in column (5) and restricting the sample to continuously married women (column 6) leads to a further increase in the coefficient of interest. Specifically, compared to women with a first-born son, women with a firstborn (surviving) daughter have 0 _._ 07 more children (column 6) (this effect is significant at the 1% level), which correspond to a 1.7 percent increase of the number of children ever born (considering that the average number of children born to women with a first-born son in this sample is 4.32).<sup>28</sup> The effect is 2 percent in the subsample of women age 30–49 (column 8), who have had more children and are closer to completed fertility.<sup>29</sup>

> 27The percentages of women with at least three, four, or five children ever born in this sample are the following: 70%, 55%, and 42%, respectively.

> 28The estimated effect obtained using a negative binomial regression model for over-dispersed count data is shown in appendix table A.1 and is consistent with the OLS estimates (region and ethnic trends are not included due to failed convergence of the model with all the controls). A poisson regression yields similar results (with all controls as in column 6 of table 3 including region and ethnic trends), but it is less appropriate than the negative binomial regression due to over-dispersion of the dependent variable (the Pearson test for goodness of fit of the poisson model rejects that the conditional variance of the variable ‘children ever born’ is equal to its conditional mean).

> 29For comparison, Dahl and Moretti (2008) use Census data for the Unites States and find that women aged 18–40 with a first-born girl have 0.3 percent more children than women with a first-born boy. Using data for India, I find that women aged 15–49 with a first-born girl have 0.295 more births (equivalent to a 9 percent increase).

18
**Table 3:** The effect of a first-born girl on the number of children ever born

|dep var.||||# chil|dren ever bo|rn|||
|---|---|---|---|---|---|---|---|---|
||||Age|15-49|||Age|30-49|
|||all women|||cur|rently married|women||
|Sample||||all m|arried|continuously<br>married|all married|continuously<br>married|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|frst-born girl<br>age|0.025<br>(0.027)<br>0.289***|0.040*<br>(0.022)<br>0.488***|0.041*<br>(0.022)<br>0.491***|0.045*<br>(0.023)<br>0.466***|0.056**<br>(0.025)<br>0.451***|0.073***<br>(0.025)<br>0.441***|0.090**<br>(0.041)<br>0.576***|0.115***<br>(0.043)<br>0.529***|
||(0.025)|(0.020)|(0.020)|(0.023)|(0.022)|(0.023)|(0.117)|(0.126)|
|age sq.<br>age at frst birth<br>eduyrs|-0.002***<br>(0.000)|-0.004***<br>(0.000)<br>-0.256***<br>(0.003)|-0.004***<br>(0.000)<br>-0.251***<br>(0.003)<br>-0.027***<br>(0.003)|-0.004***<br>(0.000)<br>-0.255***<br>(0.005)<br>-0.023***<br>(0.004)|-0.003***<br>(0.000)<br>-0.249***<br>(0.005)<br>-0.023***<br>(0.004)|-0.003***<br>(0.000)<br>-0.246***<br>(0.005)<br>-0.023***<br>(0.004)|-0.005***<br>(0.002)<br>-0.238***<br>(0.006)<br>-0.028***<br>(0.006)|-0.004***<br>(0.002)<br>-0.233***<br>(0.007)<br>-0.029***<br>(0.006)|
|husband age||||0.038***<br>(0.008)|0.037***<br>(0.008)|0.035***<br>(0.008)|0.042**<br>(0.017)|0.042**<br>(0.017)|
|husband age sq.||||-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.000***<br>(0.000)|-0.001***<br>(0.000)|-0.001***<br>(0.000)|
|husband eduyrs||||-0.001|0.001|-0.000|-0.001|-0.002|
|||||(0.003)|(0.003)|(0.003)|(0.005)|(0.005)|
|age at frst marriage||||-0.006|-0.008|-0.012**|-0.003|-0.007|
|||||(0.005)|(0.005)|(0.006)|(0.006)|(0.007)|
|frst-born dead|||||0.531***<br>(0.045)|0.595***<br>(0.048)|0.619***<br>(0.067)|0.715***<br>(0.073)|
|frst-born girl x|||||0.010|-0.062|0.021|-0.079|
|frst-born dead|||||(0.067)|(0.069)|(0.099)|(0.106)|
|Observations|23440|23440|23440|20479|20479|17587|11897|9775|
|R-squared|0.529|0.664|0.665|0.675|0.681|0.705|0.510|0.543|

_Notes._ OLS estimates. Robust standard errors adjusted for clustering at the household level in parentheses. Sample of women indicated at the top of the table. Using survey weights. Includes six region dummies, urban dummy, birth year fixed effects, ethnicity and religion, ethnic and region-specific time trends, and a wealth index.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

The rising coefficient of _firstborngirl_ across specifications in table 3 confirms that it is important to control for observable characteristics. In particular, surviving women with first-born daughters tend to be more educated and to have the first child later. Since education and age at first birth are negatively correlated with fertility, ignoring these controls would lead to underestimating the effect of a first-born girl on fertility. Similarly, not taking into account that boys are biologically weaker than girls at young ages and are therefore more likely to die would generate a downward bias since mortality is associated with higher fertility.

The results so far suggest that the sex composition of earlier-born children affects subsequent _realized_ fertility. The next question is whether it has an effect on mothers’ fertility preferences. Table 4 shows that women with a first-born girl are 1 _._ 8 percentage points (significant at the 5% level) more likely to report that they want another child than those with a first-born son (column 19
**Table 4:** The effect of a first girl on desired fertility and the use of contraception

|dep. var:|=1 wants a|nother child|=1 using c|ontraceptives|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|frst-born girl|0.018**|0.023**|-0.008|-0.011**|
||(0.009)|(0.010)|(0.005)|(0.005)|
|age|0.004|0.003|0.010|0.011|
||(0.014)|(0.014)|(0.013)|(0.013)|
|age sq.|-0.000**|-0.000**|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|age at frst birth|0.018***|0.019***|0.002|0.001|
||(0.002)|(0.002)|(0.001)|(0.001)|
|eduyrs|-0.003**|-0.003**|0.005***|0.005***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|husband age|0.003|0.003|0.002|0.002|
||(0.003)|(0.003)|(0.002)|(0.002)|
|husband age sq.|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|husband eduyrs|0.004***|0.004***|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|age at frst marriage|0.004***|0.004**|-0.002*|-0.002|
||(0.002)|(0.002)|(0.001)|(0.001)|
|frst–born dead||0.109***<br>(0.014)||-0.032***<br>(0.009)|
|frst-born girl x||-0.014||0.015|
|frst-born dead||(0.023)||(0.017)|
|Observations|17460|17460|17587|17587|
|Pseudo R2|0.277|0.281|0.222|0.223|

_Notes._ Probit estimates, marginal effects reported. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights. Includes six region dummies, urban dummy, birth year fixed effects, ethnicity and religion, ethnic and region-specific time trends, # children ever born, and a wealth index.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels.

1).<sup>30</sup> As expected, the effect is larger for women with a surviving first-born daughter (column 2), and equivalent to a 3.6 percent effect (compared to the mean for women with a first-born son).

Columns (3–4) of table 4 show the results for the use of contraception. Women with a first-born girl are 0 _._ 8 percentage points less likely to use any method of contraception, although the coefficient is not statistically significant. Column (2) estimates the same effect for women with a surviving daughter and, as expected, the effect on the use of contraception is larger ( _−_ 1 _._ 1 percentage points, equivalent to 6.6 percent effect), now significant at the 5% level. This is an important effect considering that only a small fraction of women are currently using contraceptives.

Table 5 shows the estimated effects of the birth of two consecutive daughters on fertility (realized and desired) and contraceptive use for the subsample of women with at least two children ever born. As expected, the percent effects are larger: compared to women with two first born sons, women with two first born daughters have 2.6 percent more children, and are 8.6 percent more (less) likely to desire to have more children (to use contraceptives).

> 30The regressions for desired fertility and contraceptive use also control for the number of children ever born.

20
**Table 5:** Sex of the first two-born children, realized (and desired) fertility and the use of contraception

|dep. var:|# children ever|=1 wants another child|=1 currently using contrac.|
|---|---|---|---|
||(1)|(2)|(3)|
|girl,girl|0.127***|0.051***|-0.015**|
||(0.039)|(0.015)|(0.008)|
|one boy one girl|0.038|-0.003|-0.002|
||(0.032)|(0.012)|(0.007)|
|Observations|15065|14975|15027|
|R-squared (or Pseudo)|0.655|0.262|0.230|

_Notes._ OLS estimates in column (1). Probit estimates in columns (2)-(3), marginal effects reported. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights. Includes all the controls as in column (6) of table 3 as well as a dummy for whether the second child is dead and its interaction with second-born girl. The # children ever born is included in columns (2)-(3).<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

Altogether, these results suggest that women with daughters _want_ to have more children to a greater degree than women with sons, and they seem to intentionally continue bearing children until they have the desired number of sons.

# **5.2 Birth spacing results**

I turn now to the impact of the sex composition of earlier-born children on the length of the interval between births. Columns (1) to (3) of table 6 show that the birth of a girl has no effect on the birth interval when considering all births. This is probably due to the fact that in a high fertility context the effect on spacing might become negative (and significant) only after the birth of several girls. The death of a child has a negative and significant effect on birth spacing as mothers probably attempt to conceive again to replace the lost child. It is interesting to note that the omission of the indicators for mortality leads to an overestimation of the effect of the birth of a girl. Consistent with the results found for fertility, women with male children appear to wait less on average than those with daughters due to the higher mortality of sons and the urge to replace the dead child. Columns (4) to (6) show that the birth of each successive girl implies a reduction in the interval for women with a first-born girl. However, this effect is not significant. Columns (7) to (9) focus on the subsample of women who had at least four children, among which the first two are girls. Column (8) shows that, on average, mothers wait 1.32 months less after the birth of a girl than a boy (this effect is significant at the 5% level). The estimates obtained using OLS are similar (column 9). Finally, columns (10) to (12) consider the subsample of women with 5 or more children. Interestingly, for women with girls as her first three born, the length of the interval after the birth of a girl is reduced relative to that of a boy by 3.25 months and this effect is significant at the 1% level.

Table 7 shows additional results for the subsamples of women with a first-born boy (column 2), first and second-born boys (column 4), three first-born boys (column 7), and mixed gender 21
|||(12)|-3.232***|(0.842)|0.936|(1.317)|-7.204***|(1.202)|Yes|No|4808|0.035|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|5+ children|girl, girl, girl|(10)<br>(11)|-2.724***<br>-3.252***|(0.874)<br>(0.980)|2.120|(1.568)|-6.694***|(1.440)|Yes<br>Yes|Yes<br>Yes|4808<br>4808|0.219<br>0.232|
|||(9)|-1.682***|(0.507)|0.619|(0.883)|-7.514***|(0.768)|Yes|No|11086|0.028|
|4+ children|girl, girl|(8)|-1.316**|(0.616)|1.198|(1.034)|-5.650***|(0.938)|Yes|Yes|11086|0.280|
|||(7)|-1.021*|(0.562)|||||Yes|Yes|11086|0.271|
|n|irl|(6)|-0.257|(0.332)|0.276|(0.564)|-7.450***|(0.452)|Yes|No|24082|0.026|
|3+ childre|frst-born g|(5)|-0.171|(0.427)|0.550|(0.689)|-4.993***|(0.579)|Yes|Yes|24082|0.315|
|||(4)|-0.022|(0.377)|||||Yes|Yes|24082|0.308|
|||(3)|0.015|(0.209)|0.706*|(0.396)|7.459***|(0.276)|Yes|No|51081|0.025|
|3+ children|all women|(2)|0.139|(0.258)|0.530|(0.491)|-4.928***<br>-|(0.360)|Yes|Yes|51081|0.320|
|||(1)|0.300|(0.224)|||||Yes|Yes|51081|0.313|
|men with:|||||_∗_dead child||d child||th order dummies|ther FEs|servations|quared|
|Wo|||girl||girl||dea||Bir|Mo|Ob|R-s|

22
**Table 7:** Length of the birth interval (# months), conditional on the gender of earlier-born children

||frstG|frstB|G,G|B,B|mixed|G,G,G|B,B,B|two boys<br>one girl|two girls<br>one boy|
|---|---|---|---|---|---|---|---|---|---|
|Women with:|3+ ch|ildren||4+ children|||5+ ch|ildren||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|girl|-0.171|-0.039|-1.316**|0.437|0.167|-3.252***|1.015|0.267|-0.391|
||(0.427)|(0.380)|(0.616)|(0.568)|(0.364)|(0.980)|(0.904)|(0.425)|(0.438)|
|girl*dead child|0.550|0.531|1.198|-1.612|1.036|2.120|-1.919|0.675|0.840|
||(0.689)|(0.733)|(1.034)|(1.001)|(0.716)|(1.568)|(1.557)|(0.861)|(0.824)|
|dead child|-4.993***|-4.849***|-5.650***|-4.348***|-4.995***|-6.694***|-3.870***|-4.958***|-5.328***|
||(0.579)|(0.458)|(0.938)|(0.574)|(0.520)|(1.440)|(0.762)|(0.551)|(0.715)|
|B.order dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mother FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|24082|26999|11086|12920|21685|4808|5748|14750|13278|
|R-squared|0.315|0.324|0.280|0.277|0.268|0.232|0.250|0.220|0.237|

_Notes._ OLS estimates. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

composition (columns 5, 8, and 9). As expected, the birth of a girl has no effect on spacing for women who had a first-born boy or mixed gender offspring among earlier-born children. As an additional check, appendix table A.2 shows the results when using the full sample of women with at least three, four, and five children, respectively, and including the interaction terms between the sex composition of earlier-born children with the birth of a girl. Again, the results confirm that mothers tend to significantly reduce spacing between births if they have several daughters: these women are in fact those for whom the pressure for having a son is greater.

Next, I estimate the probability that the birth interval is shorter than 24 and 15 months. The results are shown in table 8: columns (1) and (3) show that, after the birth of any girl, mothers who had two girls as the first two born children are 2.1 percentage points more likely to wait less than 24 months, though not significantly so, and 1.5 percentage points more likely to wait less than 15 months (significant at the 10 percent level). In column (2), the sample is restricted to the first 4 children, to make sure that the effect on spacing is not driven by the behavior of women with many children. The results suggest that the pace at which mothers have children tends to accelerate especially among earlier female births (the third and/or the fourth girl born) rather than later born children. Considering that about 32% of women wait less than 24 month in this subsample, the effect is important and equal to a 11.2% increase. Column (4) shows that the likelihood that the next birth is spaced less than 15 months apart increases by 1.8 percentage points after the birth of a girl for mothers who already had two girls. This is equivalent to a 22% increase given that on average 8% of births are spaced less than 15 months apart in this subsample.

Figure 5 plots the distribution of the length of the interval for the subsample of women with two first born girls (and at least three children ever born) by the sex of the third and/or fourth 23
**Table 8:** Probability of a short birth interval (shorter than 24 or 15 months), conditional on the first two born girls

||_<_24 m|onths|_<_15 m|onths|
|---|---|---|---|---|
|Subsample|All<br>children|frst 4<br>children|All<br>children|frst 4<br>children|
||(1)|(2)|(3)|(4)|
|girl|0.021|0.036*|0.015*|0.018*|
||(0.015)|(0.022)|(0.008)|(0.011)|
|girl<sup>_∗_</sup>dead child|-0.046|-0.069|-0.015|-0.015|
||(0.033)|(0.048)|(0.023)|(0.032)|
|dead child|0.184***|0.193***|0.117***|0.109***|
||(0.028)|(0.044)|(0.020)|(0.029)|
|Birth order dummies|Yes|Yes|Yes|Yes|
|Mother FEs|Yes|Yes|Yes|Yes|
|Observations|11086|8149|11086|8149|
|R-squared|0.244|0.319|0.276|0.373|

_Notes._ OLS estimates, linear probability model. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

child. The continuous (dotted) line represents the distribution after the birth of a girl (boy). Given that the length of the interval increases with birth order, I consider the first four births for each woman.<sup>31</sup> to avoid confounding effects. As expected, the distribution of the length of the interval after the birth of a girl lies to the left of the distribution of the interval after a boy. The two distributions are statistically different from each other, as confirmed by the rejection of the null hypothesis of equality of distributions (Kolmogorov-Smirnov test, with a p-value of 0.004). Appendix figure A.1 shows that the distributions in the subsample of women who had two boys or mixed gender composition are not significantly different from each other. Consistently, figure 6 uses the subsample of women with three girls as the first three born children and shows that the whole distribution of the interval after each successive girl lies to the left of the distribution after each boy (and the two distributions are statistically different from each other as shown by the Kolmogorov-Smirnov test with p-value 0.047). This evidence confirms the idea that the pressure to have a son increases with the number of girls born, and leads to a significant reduction in spacing between births, with implications for the health status of mothers and their children.

# **6 Son preference and family structure**

## **6.1 Marital outcomes**

The anthropological and demographic evidence introduced in section 2 suggests that infertility and sex composition of earlier-born children are among the factors affecting the husband’s decision to

> 31This also allows me to exclude the hypothesis that the effect only comes from women who had many children.

24
<!-- Start of picture text --> Subsample of women with first and second born girl (GG)<br>ksmirnov test for equality of distrib: pvalue=0.004<br>&<br>2. fa \<br>=° if<br>= c<br>3iN8 f fe ;<br>3 Ss<br>i a<br>=£03~are Sat35 =e.<br>o<br>10 20 30# of months 40 50 60<br>—— girl ------- boy<br>Conditionaliti on f i rst three born i girls (GGG<br>ksmirnov test for equality of distrib: pvalue=0.047<br>t<br>= was,<br>- ” ss<br>a eo<br>g © i Ny<br>—° ? %<br>2af&<br>cv Zhe.<br>Eo (f Sts,<br>aoc. a eae ~~ =.<br>o<br>10 20 30# of months 40 50 60<br>—— girl ------- boy<br><!-- End of picture text -->
desirability of being in a polygynous union by women, several papers have documented that women and children in polygynous households exhibit poorer health outcomes because of competition for resources (see Bove and Valeggia, 2009, for a review of the empirical evidence on the relationship between polygyny and women’s health in Sub-Saharan Africa).<sup>34</sup> Female headship has often, but not always, been found to be associated with poverty and insecurity.<sup>35</sup> Horrell and Krishnan (2007) find that female-headed households in rural Zimbabwe are no different from male-headed ones in terms of income poverty, but they lack assets for agricultural production and are thus constrained in their capacity to improve productivity.

# **6.1.1 Data and results**

I use the sample of all married (or ever-married) women to explore whether polygyny and other marital outcomes are associated with the sex of their first-born child. Among polygynous married women (with at least one child ever born), 43 percent are ranked first, 47 percent second, 8 percent third, and 1 percent higher rank.<sup>36</sup> The probability of being in their first union is lower for higherrank wives (89 percent for rank one wives, 69 percent for rank two, 56 percent of rank three, while it is 91 percent for women in monogamous marriages).<sup>37</sup> I analyze the effect of a first-born daughter on four separate outcomes: that the husband takes another wife (the husband is polygynous), that he lives with his wife, and that the woman is ever divorced or separated or she is the head of the household. The empirical strategy is similar to that employed in the analysis of fertility outcomes illustrated in section 4.1.<sup>38</sup> Again, the identification assumption is that the sex of the first-born is conditionally exogenous. Each regression is run separately for all married (or ever-married women), and on the subsample of older women aged 30–49, who are more likely to have experienced the outcomes under consideration.

I exploit the information on the rank of each wife in a polygynous union and run a regression in which the dependent variable is equal to one if the woman is the first-rank wife, and zero if she is monogamously married (thus excluding wives of higher order rank). This is because the outcome of interest is whether the husband marries another woman as a consequence of the fact that the first wife had daughters instead of sons. Thus, I compare monogamous women to women who are

> 34Boserup (1970) and Goody (1976) argue that the prevalence of polygyny is positively associated with the degree of female involvement in agriculture. Jacoby (1995) finds a relationship between polygyny and women’s productivity in agriculture in Cˆote d’Ivoire.

> 35see Buvinic and Rao Gupta, 1997, for a review of the literature, and van de Walle, 2013, for a study of the welfare consequences of being a widowed female head in Mali.

> 36The rank is typically based on the duration of marriage, with women ranked first as the ones married the longest.

> 37Levirate marriage (traditional practice in which the wife of a deceased man is obliged to marry the husband’s brother) is not uncommon in Nigeria.

> 38Additional controls include the number of children ever born and household size.

26
currently first-rank wives and so entered the union with their current husband as monogamous. Table 9 shows the results. Column (1) shows that women with a first-born surviving daughter are 1.2 percentage points (significant at 5% level) more likely to be first-rank wives (as opposed to monogamous) compared to women with a first-born son. As a placebo test, I run a regression in which I compare monogamous women and last-rank wives (of rank higher than the first). If husbands choose to marry another woman because the first had a daughter, the effect should be stronger for first-rank wives (compared to monogamous), while no effect (or a smaller effect, since the husband can always marry more women) should be found for higher rank wives. As expected, the probability of being in a polygynous union is not higher for last-rank wives who have had a firstborn daughter rather than a first-born boy (the coefficient of first-born girl is 0.005 with a standard error equal to 0.006). The effect of a first-born girl on the likelihood of having a polygynous husband is larger for the older group of women (column 2). Another interesting result is that child mortality is strongly associated with polygyny, and that this effect is attenuated (but still positive) if the first-born deceased child is a girl. In other words, the death of a first-born son appears to affect the husband’s decision to marry another woman more than the death of a first-born daugther. It is unclear why this is the case and further research is needed to explain this finding.

Finally, column (4) shows that the husband of women with first-born daughters is 1.4 percentage points less likely to live with his wife (considering all marriages, polygynous and not). Columns (6) and (8) show that ever-married women with first-born girls are also 1.2 percentage points more likely to be ever divorced or separated, and 1.1 percentage points more likely to be the head of the household, respectively. This evidence clearly suggests that the sex of children affects marriage outcomes for Nigerian women. Compared to women with a first-born son, women with first-born daughters appear to be more at risk of falling into a category of women who are typically more disadvantaged. This disadvantage stems from the fact that women often have limited property rights in traditional patrilineal societies and need husbands or other male relatives to access resources.

# **6.2 Child fostering**

According to the child labor hypothesis for child fostering, children participate in household production by performing domestic tasks and children of each gender and age group have their specific roles (Akresh, 2009). Therefore, households with an excess of female or male children decide to send or receive a child in order to achieve a balanced gender structure and thus maximize household productivity. I build on this idea and investigate whether households’ fostering decisions differ depending on the sex composition of children. If non-biological girls (boys) are substitutes for 27
**Table 9:** Sex of the first-born child, marital outcomes and living arrangements

|Dep. var.:|=1 polygy|n husband|=1 living w|ith husband|=1 ever div|orced or sep.|=1 is the|female head|
|---|---|---|---|---|---|---|---|---|
|Sample:||currently m|arried wome|n||ever-marrie|d women||
|AGE|all|30-49|all|30-49|all|30-49|all|30-49|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|frst-born girl|0.012**|0.028***|-0.009**|-0.014**|0.003|0.012*|0.003|0.011***|
||(0.006)|(0.010)|(0.004)|(0.006)|(0.005)|(0.007)|(0.002)|(0.004)|
|age|0.004|-0.012|0.014*|0.016|0.029***|0.036*|0.013***|-0.001|
||(0.013)|(0.025)|(0.007)|(0.014)|(0.010)|(0.019)|(0.005)|(0.009)|
|age sq.|0.000|0.000|-0.000**|-0.000|-0.000**|-0.000*|-0.000***|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|age at frst birth|-0.004***|-0.005***|0.001|0.000|-0.002***|-0.002*|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|
|eduyrs|-0.007***|-0.008***|-0.002***|-0.002**|-0.002***|-0.003**|0.001***|0.001***|
||(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|
|husband age|0.007***<br>(0.002)|0.002<br>(0.004)|-0.005***<br>(0.001)|-0.006***<br>(0.002)|||||
|husband age sq.|-0.000***<br>(0.000)|-0.000<br>(0.000)|0.000***<br>(0.000)|0.000***<br>(0.000)|||||
|husband eduyrs|0.002***<br>(0.001)|0.003**<br>(0.001)|-0.005***<br>(0.001)|-0.005***<br>(0.001)|||||
|age at frst marriage|-0.004***|-0.004***|0.001|0.001|-0.006***|-0.006***|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|
|frst-born dead|0.055***|0.070***|-0.002|0.001|0.042***|0.060***|-0.007**|-0.006|
||(0.011)|(0.016)|(0.008)|(0.009)|(0.008)|(0.012)|(0.003)|(0.006)|
|frst-born girl x|-0.023**|-0.043**|0.002|0.001|0.006|-0.009|-0.007|-0.015**|
|frst-born dead|(0.011)|(0.017)|(0.011)|(0.013)|(0.010)|(0.014)|(0.005)|(0.006)|
|Observations|16343|9395|20310|11802|22112|12745|22072|12745|
|Pseudo R2|0.287|0.295|0.088|0.121|0.184|0.167|0.234|0.247|

_Notes._ Probit estimates, marginal effects reported. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively. In Columns (1) and (2) the sample is restricted to currently married women who are monogamous or rank 1 (excluding women of rank 2 or higher); columns (3) and (4) include all currently married women; columns (5)-(8) include all ever-married women and exclude widows. Other controls include region fixed effects, urban dummy, birth year fixed effects, ethnicity and region-specific time trends, religion and ethnicity, a wealth index, # of children ever born, household size. Columns 5-8 include dummies for whether women are currently married.

biological daughters (sons), I should find symmetric responses for the imbalance with more girls or boys.<sup>39</sup> If instead there is preference for _own_ biological sons but not for _own_ daughters, responses should not be symmetric. It is important to note that this analysis is not meant to be a comprehensive analysis of the motivations for child fostering, but is instead a preliminary test of asymmetric patterns of fostering in/out that may be driven by gender preferences.

# **6.2.1 Household fostering decisions, data and empirical strategy**

The analysis of fostering decisions is done at the household level. The number of biological children is based on fertility histories of the spouse/s of the head or of the female head, and includes all daughters and sons alive _before_ fostering (summing up resident children and children living else-

> 39Specifically, if household fostering decisions are influenced by this motivation with no gender bias, the following patterns should be observed. For households with an excess of daughters: (+) foster-in boys; (+) foster-out daughters, and for households with an excess of sons: (+) foster-in girls; (+) foster-out sons.

28
where).<sup>40</sup> The focus is on living children aged 6 through 14, for whom fostering is most common.<sup>41</sup> First, I empirically examine whether the probability of sending or receiving a child is correlated with household demographic variables, such as the number of biological children (and separately, the number of boys and girls). Second, I construct two variables for the household gender imbalance: ‘ _more sons 6–14_ ’, and ‘ _more daughters 6–14_ ’ (similar to Akresh (2009)).<sup>42</sup> These are dummies equal to one if the number of biological sons aged 6–14 (daughters) exceeds the number of daughters (sons) in the same age range, respectively. The omitted category is a dummy equal to one for households in which the number of sons equals the number of daughters. The latter category also includes households with no children in the relevant age group, which are the ones that typically receive more children in Nigeria.<sup>43</sup> After estimating the regressions on the full sample of households, I also examine households with at least one biological child aged 6–14 (for the fostering-in regressions) and separately households with at least one child, daughter or son (for the corresponding fostering-out regression). This allows a study of fostering decisions for households that have a gender imbalance versus those that are actually balanced because they have the same (strictly positive) number of boys and girls. I focus on the child-labor hypothesis, controlling for a set of additional householdlevel observable characteristics.<sup>44</sup> A foster child is defined as being a resident child whose parents are alive and live elsewhere. Accordingly, dummies for whether the household hosts at least one foster child, girl or boy are created (for the fostering-in regressions). In the NDHS, mothers also report whether each biological child is living in the household or elsewhere. Based on this information, I construct three dummies which are equal to one if at least one biological child, son or daughter of the household head lives elsewhere (for the fostering-out regressions).<sup>45</sup>

# Table A.3 shows some summary statistics. The sample excludes households in which there is no

> 40If the household head has more than one wife, the sum of children over all spouses is considered.

> 41In the child fostering literature, it is common to consider children in this age range. The percentage of households sending out (receiving) at least one child 6–14 is 17% (6%), while it is 3.8% and 1.8% for children 0–5 (2008 NDHS).

> 42The issues of simultaneity of household fostering and fertility decisions as well as selection into fostering are not dealt with in this analysis. However, I control for many observable household characteristics and use the variables for the gender imbalance (in addition to the number of children) which is a more exogenous proxy for demographics.

> 43In the sample of households considered, the percentage of households that has at least one foster child is 6%. This percentage is 8% in the subsample of households with no children in that age group.

> 44The set of controls includes: region fixed effects, urban dummy, wife (or woman head if female headed household) religion and ethnicity, years of education and age of the male head and his wife (or female head), a wealth index, household land ownership, wife and husband working in agriculture, a dummy for polygynous household head, a dummy for female headed household, number of children ever born, number of daughters and sons alive age 0–5, number of household members older than 15 (male and female).

> 45There is no data on the survival status of the father or whether the child is living with the father in a separate compound for non-resident children. Therefore, the variables for fostered-out children are only based on mothers’ survival status (i.e., it overestimates the number of fostered-out children, as it includes paternal orphans and children living with father only).

29
woman eligible for the woman questionnaire (i.e., not in the 15–49 age group): for these households the number and sex of biological children is not available.<sup>46</sup> 17 percent of households have at least one child aged 6–14 who lives elsewhere, and 6 percent of households host at least one child aged 6–14.<sup>47</sup> Moreover, there are more female foster children than male. 24 percent of households have an excess of daughters or sons, while 52 percent are balanced (including households with no children aged 6–14, which represent 40 percent of the sample). The bottom panel of table A.3 shows the summary statistics for urban areas only, where fostering-out (-in) seems to be less (more) frequent than in rural areas. This is consistent with the idea that the quality of the network and the distance to urban areas matter for fostering.

In the empirical part, I estimate a probit model for the probability of fostering-in or -out a child on the number of biological sons and daughters as well as on the constructed variables for the gender imbalance (‘more sons 6–14’, and ‘more daughters 6–14’). Predictions are as follows. First, if fostering is motivated by the need for child labor, I expect that relative to balanced households, unbalanced households tend to foster more to achieve balance. Second, if there is preference for own biological sons, household fostering decisions should differ in response to an excess of male and female children.

# **6.2.2 Household fostering decisions, results**

Table 10 shows the results for the probability of fostering-in a child. Column (1) shows that the higher the number of biological sons and daughters, the less likely a household is to receive a child (a Wald test accepts the null of equality of the coefficients on the number of boys and girls). Columns (2) and (3) show the results for the probability of fostering-in a girl or a boy. In columns (4) to (6) the sample is restricted to households with at least one child in the relevant age range. The results are similar to those obtained for the full sample but smaller in magnitude. These results are consistent with the child-labor hypothesis.

This evidence suggests that biological sons and daughters are both substitutes for foster children in household production. However, from this evidence it is not possible to infer household responses to gender imbalances, since the coefficients only indicate that fewer children are fostered in if there

> 46Polygynous households in which the senior wife is older than 49 (thus, not interviewed) are also not considered.

> 47The difference arises mainly from the fact that the sample does not include households in which there are no women eligible for interview and for whom the fertility history is unavailable. However, households with older women are the ones that host more children (i.e., fostering of grandchildren) and at the same time do not have children in the relevant age group (6–14). Among these households with missing information on the number of children, 11% host at least one child (compared to an average of 6% in the main sample). This explains most of the gap between the percentage of households receiving and sending children. Moreover, the definition of foster (in) children requires _both_ parents to be alive and living elsewhere while, as above noted, there is no information on biological fathers of children living elsewhere.

30
|subsamples<br>at least one child 6–14|(10)<br>(11)<br>(12)<br>foster<br>foster<br>foster<br>child<br>girl<br>boy<br>0.013**<br>0.010**<br>0.003<br>(0.005)<br>(0.005)<br>(0.003)<br>0.005<br>0.004<br>-0.000<br>(0.005)<br>(0.005)<br>(0.003)<br>12477<br>12477<br>12477<br>0.037<br>0.038<br>0.044<br>s. _∗∗∗_, _∗∗_, and _∗_indicate signifcance at<br> foster child is defned as being a resident<br>old) religion and ethnicity, years of<br> dummy for polygynous household head and|
|---|---|
||(9)<br>foster<br>boy<br>-0.005*<br>(0.003)<br>-0.008***<br>(0.002)<br>20578<br>0.039<br> survey weight<br> boy, where a <br>eaded househ<br> agriculture, a <br>male).|
|full sample|(8)<br>foster<br>girl<br>-0.013***<br>(0.003)<br>-0.019***<br>(0.003)<br>20578<br>0.046<br>heses. Using <br> child, girl or<br>d if female h<br> working in <br> (male and fe|
||(7)<br>foster<br>child<br>-0.017***<br>(0.004)<br>-0.025***<br>(0.003)<br>20578<br>0.044<br>evel in parent<br>ast one foster <br>or woman hea<br>e and husband<br>older than 15|
|6–14|(6)<br>foster<br>boy<br>-0.001<br>(0.001)<br>-0.004***<br>(0.001)<br>12477<br>0.046<br> the village l<br>d hosts at le<br>mmy, wife (<br>nership, wif<br>d members|
|subsamples<br>ast one child|(5)<br>foster<br>girl<br>-0.005***<br>(0.002)<br>-0.007***<br>(0.002)<br>12477<br>0.042<br> clustering at <br> the househol<br>ects, urban du<br>ehold land ow<br> # of househol|
|at le|(4)<br>foster<br>child<br>-0.007***<br>(0.002)<br>-0.011***<br>(0.002)<br>12477<br>0.042<br> adjusted for <br> for whether<br>ion fxed ef<br> index, hous<br>ive age 0–5,|
||(3)<br>foster<br>boy<br>-0.004***<br>(0.001)<br>-0.006***<br>(0.001)<br>20578<br>0.043<br>ndard errors <br> are dummies<br>ls include reg<br>ead), a wealth<br>rs and sons al|
|full sample|(2)<br>foster<br>girl<br>-0.011***<br>(0.002)<br>-0.014***<br>(0.002)<br>20578<br>0.056<br>ed. Robust sta<br>dent variables<br> Other contro<br> (or woman h<br> # of daughte|
|Households with:|(1)<br>dep. var:<br>foster<br>child<br># biol. sons<br>-0.015***<br>(0.002)<br># biol. daughters<br>-0.020***<br>(0.002)<br>more biol. sons<br>more biol. daughters<br>Observations<br>20578<br>Pseudo R-squared<br>0.054<br>_Notes._ Probit estimates, marginal efects report<br>1%, 5% and 10% levels, respectively. The depen<br>child whose parents are alive and live elsewhere.<br>education and age of the male head and his wife<br>female headed household, # children ever born,|

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are more biological children of either sex (by keeping constant the number of children of the opposite sex). Columns (7) to (12) include indicators for the gender imbalance and reveal an asymmetric pattern. The negative and significant coefficients in columns (7) to (9) suggest that households with an imbalanced gender composition are less likely to foster-in children compared to balanced or childless households (i.e., the omitted category in columns 7, 8, and 9). Specifically, the excess of daughters is associated with a stronger reduction in the probability of receiving a child (column 7) and in particular a girl (columns 8 and 9), than the excess of sons.<sup>48</sup> To focus on the effect of actual gender imbalances on fostering-in decisions I restrict the sample to households with _at least_ one child aged 6–14. In this case the omitted category (‘same number of boys and girls’) does not include _childless_ households (i.e., the ones _more_ involved in fostering-in). Column (11) shows that households in which there are more sons than daughters are 1 percentage point more likely to receive a girl (significant at 5% level and different from the coefficient on the excess of daughters by a Wald test). By contrast, households with an excess of daughters do not foster in more boys (column 12). This asymmetric response suggests that households with an excess of daughters do not foster in boys because of the desire for having their own biological sons. This motivation may lead them to save household resources (by avoiding hosting boys) and continue bearing children. Instead, households that with an excess of sons are significantly more likely to receive a girl, as girls are often needed in the house for performing domestic chores (Isiugo-Abanihe, 1985; Ainsworth 1996). This evidence is consistent with the idea that foster girls are substitutes for own biological daughters in fostering-in decisions, while boys are not substitutes for sons.

Table 11 reports the results for the fostering-out decision. Column (1) shows that the probability of sending away a child increases with the number of children, which is again consistent with the child-labor motivation for fostering. Moreover, having many daughters increases this probability _more_ than having many sons (the Wald test rejects the null of equality of coefficients). This suggests that biological sons are fostered-out less than daughters, probably because parents tend to privilege having sons under their direct control more than daughters. Sons and daughters are not substitutes for each other in fostering-out, as shown in columns 2 and 3. The higher the number of biological daughters (sons), the more daughters (sons) are sent away: this is consistent with the idea of gender-specific roles within the household. Columns 4 to 6 focus on the subsample of households with at least one child, daughter or son, with similar results. Column (7) shows that unbalanced households send out children more than balanced or childless ones, even though a Wald test indicates that the coefficients of the variables for the excess of sons and daughters are not

> 48A Wald test rejects the equality of the coefficients for the excess of sons and daughters separately in each of the regressions in columns 7 and 8, while accepts it in column 9.

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|here|subsamples<br>at least<br>at least<br>one dau<br>one son<br>(11)<br>(12)<br>daughter<br>son<br>elsewhere<br>elsewhere<br>-0.043***<br>0.054***<br>(0.015)<br>(0.011)<br>0.064***<br>-0.044***<br>(0.012)<br>(0.014)<br>9156<br>9246<br>0.061<br>0.058<br>_∗_, and _∗_indicate signifcance at<br>d lives elsewhere. Other controls<br>d his wife (or woman head), a<br>ren ever born, # of daughters and|
|---|---|
|ing elsew|at least<br>one child<br>(10)<br>child<br>elsewhere<br>-0.015<br>(0.012)<br>-0.000<br>(0.012)<br>12477<br>0.068<br>ghts. _∗∗∗_, _∗_<br>ousehold hea<br>male head an<br>old, # child|
|ge 6-14 liv|(9)<br>son<br>elsewhere<br>0.124***<br>(0.008)<br>-0.021***<br>(0.005)<br>20578<br>0.201<br>ng survey wei<br>hter of the h<br>d age of the <br>eaded househ|
|r or son a|full sample<br>(8)<br>daughter<br>elsewhere<br>-0.021***<br>(0.005)<br>0.135***<br>(0.008)<br>20578<br>0.213<br>rentheses. Usi<br>d, son or daug<br> education an<br> and female h|
|, daughte|(7)<br>child<br>elsewhere<br>0.150***<br>(0.009)<br>0.162***<br>(0.009)<br>20578<br>0.193<br>e level in pa<br>iological chil<br>city, years of<br>usehold head|
|gical child|at least<br>one son<br>(6)<br>son<br>elsewhere<br>0.058***<br>(0.006)<br>-0.007<br>(0.005)<br>9246<br>0.063<br>g at the villag<br>at least one b<br>ion and ethni<br>olygynous ho|
|sion: biolo|subsamples<br>at least<br>one dau<br>(5)<br>daughter<br>elsewhere<br>-0.007<br>(0.005)<br>0.078***<br>(0.006)<br>9156<br>0.072<br> for clusterin<br>qual to one if <br>ousehold) relig<br>a dummy for p|
|-out deci|at least<br>one child<br>(4)<br>child<br>elsewhere<br>0.048***<br>(0.005)<br>0.062***<br>(0.005)<br>12477<br>0.083<br>rors adjusted<br> dummies e<br>le headed h<br>agriculture, <br>male).|
|fostering|(3)<br>son<br>elsewhere<br>0.060***<br>(0.002)<br>0.003*<br>(0.002)<br>20578<br>0.233<br>t standard er<br>bles are three<br> head if fema<br>d working in <br> (male and fe|
|Household|full sample<br>(2)<br>daughter<br>elsewhere<br>0.003<br>(0.002)<br>0.068***<br>(0.003)<br>20578<br>0.257<br>ported. Robus<br>ependent varia<br>wife (or woman<br>e and husban<br> older than 15|
|**Table 11:**|Households with:<br>(1)<br>dep. var:<br>child<br>elsewhere<br># biol. sons<br>0.062***<br>(0.003)<br># biol. daughters<br>0.071***<br>(0.003)<br>more biol. sons<br>more biol. daughters<br>Observations<br>20578<br>Pseudo R-squared<br>0.218<br>_Notes._ Probit estimates, marginal efects re<br>1%, 5% and 10% levels, respectively. The d<br>include region fxed efects, urban dummy, <br>wealth index, household land ownership, wif<br>sons alive age 0–5, # of household members|

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significantly different from each other. Columns (8)-(9) and (11)-(12) suggest that households tend to respond symmetrically to the imbalance of children of either gender in fostering-out decisions.

The asymmetries found for the household fostering decisions suggest that biological sons are preferred to non-biological boys, while this is not the case for female children. Alternatively, this is also partly consistent with the possibility that girls are in general more ‘productive’ or have a greater weight in the household production function. Consistent with this idea and considering that in rural areas boys may have a different (possibly greater) weight in the household production function if they work in the fields, the estimates obtained above should differ in urban and rural areas and between landed and landless households. Panel (a) and (b) of appendix table A.4 show that this is not the case, even though the asymmetries appear to be stronger in rural areas (in agreement with stronger son preference in more traditional rural areas). It is important to consider that the motivations for fostering are widely different in urban and rural areas and that these results deserve further examination.

# **7 Concluding remarks**

This paper shows how fertility decisions are affected by the sex composition of children in a highfertility setting where little is known about parental gender preferences and where sex ratios at birth are in the normal range. It is also among the first to explore how son preference interacts with complex social and family structures typical of a West African country, like polygyny and child fostering.

The findings indicate that women make fertility decisions that are motivated by the desire for giving birth to sons in Nigeria. Compared to women with a first-born son, women with a first-born daughter have and desire significantly more children, and use less contraceptives. Women with daughters among earlier-born children are also more likely to reduce the spacing between births, thereby increasing the risk of child and maternal mortality. Results suggest that son preference also has significant impacts on other aspects of women’s well-being. Women with first-born daughters are significantly more likely to end up in a polygynous union, to be divorced, and to be heads of the household. Moreover, the analysis of child fostering patterns reveals that daughters may be substitutes for foster girls, while the same does not hold for sons and foster boys, thus suggesting that the preference for _own_ biological sons also interacts with household child fostering decisions.

This paper puts forward two channels that may contribute to explain excess mortality among adult women in Sub-Saharan Africa. The first is son-preferring fertility behavior which can have negative effects on women’s health by increasing their risk of maternal mortality and morbidity 34
through high fertility and short spacing. Although son-preferring fertility behavior is not found to be as strong as in other contexts (like India, where fertility is also lower), the risk of adverse consequences on women’s health is significant due to strikingly high lifetime risk of maternal death in Nigeria. The paper provides evidence suggesting that adult mortality is in fact higher for women with first-born girls, especially among those of lower socioeconomic status. The second channel refers to the association between parental gender preferences and specific marital outcomes and living arrangements in which women are often found to be particularly vulnerable and disadvantaged.

The unavailability of accessible and affordable technologies for fetal sex detection may partly explain why sex ratios at birth are currently in the normal range in Sub-Saharan Africa. Nonetheless, the evidence of son preference uncovered in this study poses a potential issue of increasing sex ratios in the future.

Although these results cannot be generalized to all of Sub-Saharan Africa or to other contexts, they represent a starting point for further research studying the effects of gender preferences in a setting that has received little attention by the existing literature. This avenue of research would have straightforward policy implications. For instance, improvements in the quality and availability of health care services, especially for pregnant women, would help reduce the risk of maternal mortality. Other types of reforms include those of the inheritance systems giving women stronger inheritance rights and greater control over resources, thus increasing the ‘value’ of daughters in traditional patrilineal societies. While longer term reforms might take time to have a significant effect on women’s lives, in the short-term social programs should be specifically aimed at improving the livelihoods of women with daughters.

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