Sibling Rivalry, Resource Constraints, and the Health of Children Ashish

Sibling Rivalry, Resource Constraints, and the Health of Children

Ashish Garg and Jonathan Morduch*

Abstract

Low levels of human capital investment in poor countries have important implications for economic growth, distribution, and social conditions -- and competing explanations for the low levels have been suggested. The most prominent explanations cite low returns, parental preferences, cultural barriers, and resource constraints. This paper distinguishes between the explanations by drawing on the economic theory of the household to derive testable hypotheses about the role of family structure on child health. Evidence from a large household-level data set from Ghana suggests that while cultural barriers, parental tastes, and differential labor market returns matter, much of health investment is explained by the presence of resource constraints. The constraints push siblings into competition with each other for scarce resources, and relatively slight initial advantages to boys can make a large difference to the outcome of this rivalry. This explains how parents may gain from having sons while children will gain from having sisters -- with resource constraints and sibling rivalry, the health of both boys and girls increases with the fraction of their siblings that are female. The evidence suggests that labor market discrimination and cultural bias are on their own insufficient to explain the observed disparities in human capital investments. But when exacerbated by sibling rivalry, even small differences in returns can explain patterns similar to what is seen in the data. We predict that removing resources constraints in Ghana -- and thus eliminating sibling rivalry - - can in itself improve health investments by at least 20% to 30% in some households. We show conditions under which removing resource constraints can in itself narrow the gender gap.

JEL Classification: I12 , J16, J24, 012 Keywords: Siblings, Household Models, Human Capital , Gender, Ghana

Ashish Garg is a Ph.D. candidate in Economics at Harvard, and he holds a degree in Philosophy, Politics, and Economics from Oxford University. His recent research includes work on the returns to school quality in Indonesia and the role of tribe and kinship groups in African labor markets.

Jonathan Morduch is Associate Professor of Economics at Harvard and a Research Associate at HIID. His teaching and research focuses on households, markets, and institutional change in low-income economies. Recent research includes work on poverty and economic growth in Bolivia, income inequality in China, risk and credit markets in Asia, and the performance of microfinance institutions.

*We appreciate comments from Paul Gertler, Edward Glaeser, Mark Montgomery, Peter Timmer and participants in the Harvard-HIID-MIT Economic Development and Growth Workshop, Harvard Center for Population Studies, and the Annual Conference on Economic Research at Rutgers University. We are grateful to the World Bank for providing access to the data used here. All views and any errors are our own. The daughter’s job: without a murmur to do the chores piling up around the house until she leaves for work, to pay her younger brother’s fees, to buy her sister ribbons, to get her father’s spectacles changed. To take the others to the movies on holidays, to keep back a little and hand over the rest on pay day.

The son’s job: to get fresh savoury snacks for the whole household to eat, to bring back the clothes from the washerman, to clean and put away the bicycle, to sing out of key while packing his father’s lunch at the last minute, to open the door sulkily whenever someone comes home from the movies, to wrinkle his brow when he puts out his hand for money as is asked instead: ‘How much? For what’

from ‘Household Fires’ by Indira Sant.

translated from Marathi by Vinay Dharwadker, in The Oxford Anthology of Modern Indian Poetry, Vinay Dharwadker and A.K. Ramanujan, editors. Delhi: Oxford University Press, 1994. I. Introduction As in many poor countries, health and education levels in Ghana lag substantially behind levels of richer countries. In 1990, 27% of children under age five were underweight and only 46% of children between age 6 and 23 were in school.1 Girls lag behind especially. Girls have 87% of male primary school enrollment rates, 71% of male secondary school enrollment rates, and just 27% of male tertiary education rates. Contrast these levels with those in Egypt, for example, where purchasing-power-parity adjusted per capita income is 70% higher ($3540 versus $2110 in Ghana). In Egypt, gender gaps are much smaller, and overall levels of health and education are substantially higher than in Ghana: only 10% of children under age 5 are underweight, 67% of children between age 6 and 23 are in school, and girls get 77% of the male secondary school levels and 59% of tertiary levels.

The low levels of human capital investment in countries like Ghana have important implications for economic growth, distribution, and social conditions -- and competing explanations of the low levels have been suggested. The most prominent explanations cite low returns, parental preferences, cultural barriers, and resource constraints.

This paper provides an approach to distinguishing between the explanations, drawing on the economic theory of the household to derive testable hypotheses about the role of family structure on child health. The evidence suggests that while cultural barriers, parental tastes, and differential labor market returns may matter, much of health investment in Ghana is explained by the presence of resource constraints. The constraints mean that parents cannot invest optimally in their children, and this pushes siblings into competition with each other for scarce resources. Relatively slight initial advantages to boys can make a large difference to the outcome of this rivalry. The evidence suggests that labor market discrimination and cultural bias are on their own insufficient to explain the observed disparities in human capital investments. But when exacerbated by sibling rivalry, even small differences in returns can explain patterns similar to what is seen in the data. We predict that removing resources constraints -- and thus eliminating sibling rivalry -- can in itself improve health investments by at least 20% to 30% in some households. Removing resource constraints can, in itself,

1Data are from Human Development Report 1995. Underweight data are from Table 4. Income data are from Table 1 (purchasing-power-parity adjusted dollars for 1992). Education data are from Table A2.1: primary school is ages 6 -11, secondary school is ages 12 - 17, and tertiary education includes ages 18-23.

1 narrow the gender gap. The implications for the gap will depend on the shape of the “health returns” functions for males and females, and we give examples under which gender gaps narrow and widen. The evidence below gives only weak evidence that the gap will narrow as households get richer.

Below we begin by describing competing hypotheses. We then describe the data set and simple bivariate relationships. Because the gender composition of siblings is orthogonal to most other variables that may affect health outcomes, the bivariate analysis tells most of the story. That analysis is followed by a more formal econometric hypothesis test which allows controls for systematic biases due to the use of U.S. standardizations, birth order, cultural factors, and both observed and unobserved family heterogeneity.

The final section provides comparisons of the predicted effects of changing sibling composition. We show that if children had all sisters (the most favorable scenario when boys have intrinsic advantages) they would do roughly 30% better than if they had all brothers (the worst scenario for sibling rivalry). Under the maintained hypotheses, these figures give a lower bound on the improvement in health that would result from lifting resource constraints.

II. Explanations for Low Levels of Investment in Human Capital The most common economic explanation for low human capital investments in countries like Ghana is that returns are low. While the structure of returns may not be sufficient to explain the low levels of investment, there is still much to the argument -- i.e., that the rate of return to investing in child health and education is not as high as many competing investments, so child quality suffers. When returns to the human capital of women are lower than that of men, this also helps explain the gender gap in health and education. In Ghana, the gender gap in returns is due to both labor market forces and, to a large degree, cultural practices. In many households, women move out of the family when they marry, while men stay within the household with their wives. Thus, the full return to investing in sons is more likely to be reaped by parents than the return to investments in daughters.2

2If marriage markets functioned perfectly, parents should be able to recoup the full returns to investments in the human capital of daughters, but in practice, bride prices and dowries value human capital only imperfectly. In cultural groups with matrilineal structures, daughters may retain close connections with their families after marriage and, especially, after divorce. The effects we find here give average effects across cultural groups.

2 This economic logic can be extended to explain why rising income is associated with the increased accumulation of human capital in aggregate and the improvement in its distribution -- even if the pattern of returns remains unchanged. As long as the human capital of children is valued intrinsically, rising income will lead to rising human capital (assuming that human capital is treated like a “normal good”). Gender gaps will close under the common assumption that parents’ aversion to the unequal treatment of their children also increases with income. The evolution of inequality-averse social norms is also likely to follow this pattern.

These two ideas -- the importance of economic returns coupled with inequality aversion -- form the basis for most economic studies of household investment (e.g., Becker 1991; Rosenzweig and Schultz 1982; Behrman, Pollak, and Taubman 1982; Behrman 1988). Empirical evidence repeatedly bears out the predicted positive relationship between income and human capital and the predicted negative relationship between income and gender gaps in health and education (see, e.g.,Strauss and Thomas 1995).

The parallel explanation developed here is that the observed relationships are explained by the presence of resource constraints -- without reference to changing preferences or inequality aversion. The constraints may be in the availability of parental time or in the purchased and non-purchased inputs to child health. When these resources are in scarce supply, child health is likely to fall.

As both explanations are observationally equivalent in terms of how changes in income affect investments, little attention has been accorded to distinguishing whether inequality aversion or resource constraints are driving the results. One likely reason that the former story is more common is that it does not require additional assumptions on the standard household maximization problem -- i.e., it does not require the assumption that there is a resource constraint, nor does it require particular assumptions about health production functions.

3 The Implications of Sibling Sex Composition We show that the two stories can, however, be separated empirically by investigating the role of family structure on human capital investments. We begin with the assumption that males have higher returns than females. The argument is that the inequality aversion story predicts that children with more brothers rather than sisters (holding the total number of siblings constant) will receive more human capital than children with more sisters than brothers. This is because inequality-averse parents will invest more in children with higher returns -- but they will also put additional resources into children with lower returns in order to maintain a degree of fairness.3

In contrast to this scenario, consider the case where parents do not value the human capital of their children intrinsically. Instead, assume that parents make investments in their children solely based on expected economic returns. This is the “pure investment model”. In this model, when there are no resource constraints, investments in children will reflect solely their returns relative to the cost of the funds.

To see this, assume that the parents’ return to the health investments in a male child,

Hm, is given by the concave function R(Hm), where R’ > 0, R’’ < 0. We capture the intrinsic advantage of males by writing the return to health investments in females as αR(Hf), where 0 < α < 1 is a parameter that reflects the degree to which economic and cultural forces hold returns to women below those of men (α = 1 indicates no bias). In this scenario, parents with “pure investment” motives will invest in each child until the marginal value product equals the cost of borrowing: αR’(Hf) = R’(Hm) = (1 + r). Since the investments depend solely on the cost of borrowing and expected returns, the gender composition of siblings will make no difference to investments here -- for boys or for girls.

However, when resource constraints are binding, the story changes sharply: children must compete for scarce resources. In this sibling rivalry, the children with the highest returns win out. This gives the advantage to boys when there is pro-male bias in returns. Both boys

3The model predicts that when there is inequality aversion but not resource constraints (and pro-male bias in returns), boys and girls will be helped by having more brothers. Girls may be helped more, however, as

4 and girls then do worse the more brothers they have -- in contrast to the inequality-aversion story without resource constraints. Correspondingly, all will gain when they have more sisters as a fraction of their siblings. Below we show that under the assumptions above, this effect will be greater for girls than for boys; thus, reducing resource constraints on its own can narrow the gender gap.

Sibling Rivalry and the Gender Gap The assumption above that the returns to investing in human capital are a concave function of investments, coupled with the assumption that female returns are a constant fraction of male returns, yields the result that lifting resource constraints helps narrow the gender gap. (We will abstract here from inequality aversion and continue to assume that households have pure investment motives.)

To see this, assume that net returns to the parents for investments in their sons take 2 the quadratic form: R(Hm) = a Hm - b Hm , where a and b are positive numbers which satisfy concavity of the function. The returns to investing in daughters are then αR(Hf) = α (a Hf - b 2 Hf ), so that with α<1 the returns to females are below those of males for every level of investment, but the returns decline at a slower rate. This is depicted in Figure 1. Figure 2 gives the corresponding marginal returns to health investments. Both relationships are negative and linear, given the quadratic functions. Because α<1, the slope of the marginal returns for females is less than that of males. When resource constraints do not bind, marginal returns are set equal to the cost of borrowing, (1+r), and this leads to investment levels of Hf and Hm. There is a gender gap because α<1, but it is relatively small. However, when resource constraints bind, marginal returns are set at levels higher than the cost of borrowing, depicted in Figures

the introduction of more boys raises the permanent income of the household and induces greater inequality aversion; this especially helps girls.

Slope = 1 + r Male

Total Returns Female Slope = 1+r*

Hf ' Hm' Hf Hm Health Investments Figure 1

Marginal Returns

slope = -2b

slope = -2αb

1 + r *

Female Male

1 + r

Hf ' Hm' Hf Hm Health Investments

Figure 2

Optimal health investments and a narrowing gender gap. Gender gap in investments is ( Hm’ - Hf’ ) when resource constraints bind and ( Hm - Hf ) when they do not. ( Hm’ - Hf ’ ) > ( Hm - Hf ) for α < 1.

6 Male Slope = 1 + r

Total Returns

Slope = 1 + r * Female

Health Investments Hf ' Hm‘ Hf Hm

Figure 3

Marginal Returns

1 + r *

Male Female

slope = -2b slope= -2βb 1 + r

Hf ' Hm ' Hf Hm Health Investments

Figure 4

Optimal health investments and a widening gender gap. Gender gap in investments is (Hm’ - Hf’ ) when resource constraints bind and (Hm - Hf ) when they do not. (Hm’ - Hf ’ ) < (Hm - Hf) for β > 1.

7 * 1 and 2 at level 1+r . With tightening resource constraints, the gender gap widens from (Hm.-

Hf) to (Hm’ - Hf’). Thus, resource constraints exacerbate initial differences due to labor market and cultural discrimination. The positive link between the relaxing of constraints and the narrowing of the gender gap is reinforced under the plausible assumption that discrimination falls at higher education levels.

A counter example is given under the alternative assumption that, while total returns to males remain higher everywhere, marginal returns to investments in daughters fall more quickly than for investments in sons. This case is depicted in Figures 3 and 4. It can be characterized by the assumption that the returns to females are instead given, for example, by 2 the relationship: (a Hf - β b Hf ), where β > 1. Figures 3 and 4 show that the gender gap gets larger as resource constraints are lifted.

Focusing on the shape of the returns functions illuminates a puzzling phenomenon. It has been observed that in some societies, treatment of boys and girls is relatively egalitarian at low levels of prosperity, but as households get richer, gender gaps emerge (Das Gupta 1987). This has been described as a cultural phenomenon whereby only once households can “afford” to discriminate, they do so -- this goes against the notion that inequality aversion increases with income. By considering constarints as well as preferences, we can see that the assumption is compatible with a standard model of human capital investment. When the structure of returns is like those in Figures 3 and 4, it is possible for gender gaps to worsen with income or to worsen before they improve -- even when inequality aversion increases strictly with income. This happens when the direct effect of relaxing borrowing constraints (widening the gender gap) outweighs the effect of increased income on preferences (closing the gender gap).

Despite its importance here, little knowledge exists on the structure of net returns to investments in males and females. 4 This is an important empirical question, and one aim

4A different story is given by non-convexities in returns (e.g., Glaeser 1992). Imagine that there are two health technologies. One has a modest return (e.g., using local, traditional healers), while the other may have higher returns but a sizeable fixed cost (e.g., visiting a medical doctor). In poor households, the fixed investments may be so great that all children are treated with the traditional methods. When households get richer -- and boys have even a slight advantage over girls -- gender gaps will emerge as boys get the “non-convex” treatment while sisters must suffice with the traditional one. Eventually with increasing wealth,

8 below is to highlight the ways that changing resource constraints map into changing gender gaps.

Testable Implications In reality, both inequality aversion and resource constraints may affect parental choices. Investigating the role of sibling composition on human capital investments then gives a way to determine which of the explanations is quantitatively more important. The competing explanations are presented below in Table I. The question marks in the lower right-hand corner of the table indicate that it is an empirical question as to which effects will be strongest in the data. This scenario allows for both forces described above.

Table Predictions of Alternative Models for Sibling Composition and Income Effects

Predicted Effect of : Increased Income Increased % Sisters Resource Predicted Effect Predicted Effect Model Constraint Boys Girls Boys Girls Pure Investment no 0 0 0 0 Inequality Aversion no + ++ - - - Pure Investment yes + ++ + ++ Inequality Aversion yes + ++ ? ?

A negative effect of having more sisters is consistent with the predominance of the inequality aversion model. A net positive effect, on the other hand, shows the empirical predominance of the resource constraint explanation. This test is the focus of the empirical work below, and we show that it is the resource constraint explanation provides the strongest explanation of observed patterns. The coefficients that correspond to the question marks are consistently positive and they tend to be large and statistically significant.

all children will be treated with the non-convex treatment and the gender gap will narrow. This gives

9 Alternative Explanations and Issues In analyzing the role of sibling structure, we also consider complementary psychological and anthropological explanations for relationships between sibling composition and human capital. At least two mechanisms may help explain the data. First, there may be spillovers in the way that children are socialized, such that having at least one brother may lead parents to instill more “masculine” traits in their daughters. Some researchers include among those traits greater self-confidence and enhanced physical activity. This may then affect the way that girls with brothers are treated -- and expect to be treated -- relative to girls with only sisters. We capture this in the empirical work with a dummy variable that indicates whether a child has at least one brother. For girls, this captures possible spillover effects, while for boys it captures possible “reference group effects” (see below). The dummy variable should be non-negative for both boys and girls.

Secondly, “reference group effects” may exist such that girls with only brothers are treated differently from girls with at least one sister. Without sisters, a single daughter may be treated similarly to the boys in the family, but differences may widen once another girl is added to the family, altering the yardstick for comparison of treatment (the dummy variable for having at least one brother captures these reference group effects for boys).5 This effect has been found by Case and Butcher (1994) to explain completed education in a sample of U.S. women. They find that having at least one sister is associated with a decline in average female educational levels of half a year (and, correspondingly, 9% lower incomes.)

Another mechanism through which sibling composition can matter is that sisters may help other siblings directly -- perhaps by working and bringing extra resources into the household or by taking care of younger siblings. Parish and Willis (1994), find strong evidence that in Taiwan having older sisters is associated with higher educational investments in younger children (see Willis and Parish, 1994, for a survey of the related literature.) In

another reason to see gender gaps increasing at low levels of income and eventually narrowing. 5Both of these stories are stylized; age and birth order may also affect the strength of spillovers and reference group effects. We tried various alternative specifications in the empirical work, and we found the simple spillover/reference effect was most consistent in the evidence. These effects may partly reflect non- linearities in the story we are telling, but we do not find that the effects diminish substantially when higher- order sibling composition variables are used in the econometric work. This suggests that these effects are fairly robust.

10 contrast, Das Gupta (1987) finds that in rural Punjab it is girls with older sisters that suffer most in the face of pro-son bias.

In considering these explanations, we find that spillover/reference effects matter consistently in explaining patterns of child health in Ghana. However, we find little evidence in support of reference group effects for girls, nor of the particular advantage of either having older sisters or of having younger sisters. After controlling for these possibilities, the evidence for the resource constraint hypothesis remains strong.6

III. Data Description The data we analyze come from the 1988-89 Ghana Living Standards Survey (GLSS) which includes an extensive household questionnaire. The survey was completed as part of the World Bank’s Living Standard Measurement Survey program, and a variety of quality assurance features were built into the survey design.7

The survey consists of nearly 3,200 households drawn to form a sample that is representative of the ten regional and four ecological zones in the country. Height and weight measurements are available for most young children, and we have converted them to U.S. standards using the National Center for Health Statistics (NCHS) standards. Following best- practices advocated by the World Health Organization, we focus where feasible just on the health of children under age eleven. This minimizes the risk that the data reflect mostly genetic variation, as is more likely to be the case for health outcomes of older children.8 Since height data is also available for adults, we have an important additional control for genetic endowments passed on to children.

We show the robustness of the results to a range of health measures.9 We focus on three general indicators of child nutrition and health and three indicators of extreme health outcomes. The general indicators are height-for-age, a measure of longer-term health status;

6Below, we refer to “spillover effects” as shorthand for “spillover effects for girls/reference effects for boys”. 7Other studies which use the GLSS data on health include Thomas(1994), Glewwe and Jacoby (1995) and Behrman and Lavy (1994). 8It also minimizes the risk that the sample is systematically selected due to the fostering out of children -- a phenomenon which is much more likely to affect the the composition of older children in the survey. 9Garg and Morduch (1996) show that the results are similar when using a range of education measures as well.

11 and weight-for-height and weight-for-age, two measures of medium-term health status. The indicators of extreme health outcomes are stunting, wasting, and being underweight. These are defined by World Health Organization (1986) as being at least two (U.S.) standard deviations below the reference U.S. population. 10

The data permit us to look at the gender and age composition of children living at home at the time of the survey. This may understate the total number of the siblings, especially older siblings. Our full sibling data set contains basic anthropometric outcomes for 5,203 children. Out of these children, only 3,354 are under age eleven, and only 2,458 have consistent and available anthropometric and parental data. The table in the appendix provides summary statistics of the data .

The raw anthropometric data show strong evidence of malnutrition. The standardized measures for height-for-age, weight-for-age and weight-for-height were well below comparable standards in the United States. The median child was 93% of the U.S. median in terms of height-for-age, which is generally regarded as a robust indicator of long-term nutrition; nearly 33% of the population is stunted and 6% wasted.. Earlier studies on Ghana (e.g., Alderman 1991) have reported very similar levels of malnutrition. It is not surprising that malnutrition is more severe in Ghana than in the U.S., but it is notable that Ghanaian child health levels lag behind similar economies in Africa and South Asia. For example, Ghana has worse nutrition than Ivory Coast and Cameroon, two neighbors with similar levels of income.

III.a. The Role of Siblings: Bivariate Analyses Before turning to the econometric results, we show the broad patterns in bivariate analyses. Because the gender composition of children is not chosen by the household -- and is unlikely to be correlated with determinants of health other than fertility -- the bivariate analyses tell most of the story.11 In the sections below, we corroborate the bivariate analyses with regression analyses, and this provides a way to control for biases due to the

10 Alternative measures of acute and chronic malnutrition have been proposed. The alternative criteria for low nutrition are to use 90 percent of the median as a cutoff point for height-for-age and 80% for weight-for- age. The standard deviation measure we employ is age invariant and thus is preferable to the other criteria. Alderman (1991) provides a discussion in the Ghanaian context. 11Because neither excess female mortality nor son-preference in fertility appear to matter in Ghana, sibling sex composition should also be orthogonal to fertility.

12 inappropriateness of U.S. standardizations and the roles of factors beyond family structure. The regression results provide much the same qualitative and quantitative results we see here.

Table II presents mean values of two anthropometric outcomes, height-for-age and weight-for-height. The sample for height-for-age includes all children 15 and below in order to maintain reasonable sizes in each cell; the sample for weight-for-height are for all children age 10 and below. The average values are displayed by total sibling size and, within each sibling group, by the number of sisters. The negative numbers in the table reflect that levels are below U.S. median standards. Thus, improvements in the measures occur as they become less negative.

The results show that, for the most part, children in larger families are less healthy. This is seen most clearly by reading across the next to last row of each table (which gives mean values for each sibling group). Consistent with the idea of a quality-quantity trade-off (Becker and Tomes 1976), we see that average child quality measured by height-for-age worsens from -1.24 to -1.72 as sibling size increases from

13 Table II

How the Number of Sisters Affects Nutritional Status: Bivariate Analyses

Height-for-Age Number of siblings Sisters one two three four five six seven eight none -1.28 -1.44 -1.32 -1.74 -1.60 -1.85 -2.40 -2.12 one -1.22 -1.49 -1.52 -1.62 -1.58 -1.65 -1.91 -1.75 two -1.31 -1.44 -1.28 -1.50 -1.68 -1.90 -1.50 three -1.39 -1.34 -1.27 -1.59 -1.94 -1.73 four -1.22 -1.25 -1.97 -.664 five -1.09 -1.86 -1.54 six Mean -1.24 -1.43 -1.44 -1.53 -1.49 -1.52 -1.74 -1.72 Total 401 595 681 535 330 237 161 112 Height-for-age is calculated for all children age 15 years and below.

Weight-for-Height Number of siblings Sisters one two three four five six seven none -.681 -.632 -.669 -.669 . -.523 -.546 one -.691 -.636 -.721 -.641 -.773 -.684 -1.02 two -.690 -.661 -.629 -.746 -.654 -.782 three -.570 -.594 -.750 -.640 -.717 four -.593 -.713 -.632 -.677 five -.590 -.601 - six -.350 Mean -.686 -.640 -.662 -.644 -.727 -.645 -.789 Total 239 354 397 269 159 116 87 Weight-for-height is calculated for children age 10 years and below.

14 one to eight. A similar finding holds for weight-for-height. In both cases, increasing sibling size from two to seven worsens average health by over twenty percent.

At least as striking is the variation due to shifting sibling composition. This is seen by comparing measures down any given column. For example, for height-for-age, shifting form having four siblings, all of which are brothers, to having one brother and three sisters leads to a thirty percent improvement in height-for-age. Similar qualitative patterns can be seen in the weight-for-height table.

As predicted, the regression specifications below yield similar conclusions: most importantly, sibling composition alone can account for 20% to 30% of child health outcomes. The regression specification, however, is useful in providing an explicit test of the hypotheses, and it allows comparison of the relative magnitudes of the effects of sibling composition, income, birth order, and socio-cultural variables.

IV. Econometric Model Below we describe a more formal analysis of the hypotheses described above. We estimate a series of models that take the general form:

(1) Hij = α 0 + α1 Xi j + α 2 Z j + α 3 Y j + α 4 N j + α 5 Fi j

+ α 6 Ri j + ΣsexΣage αas Si j Ai j + δj + µ ij,

where Hij is the nutrition status of child i in household j (e.g., height-for-age, weight-for-height, etc.), Xi j is a vector of child-specific variables like birth order, Z j is a vector of household variables like the height, and education of parents, and Y j is total household expenditure per capita. We use total expenditure rather than total income to provide a more accurate gauge of current resources given the possibility of consumption smoothing. The total number of siblings is Nj, and sibling composition (i.e., the number of sisters or the percent of siblings that are female) is Fij. Spillover effects are captured by Ri j, a dummy variable that equals one if the child has at least one brother. The equation is a linear approximation to a fundamentally nonlinear relationship, and we estimate it in levels, in levels with quadratic terms (not reported), and in logarithmic from. The central results are robust to these permutations.

15 The health variables are standardized according to U.S. nutritional standards. However, because U.S. standards may not be appropriate for nutrition in Ghana, we include a full set of dummy variables that allow age-sex intercepts specific to Ghana. This is a conservative approach since it controls for the possibility that the results are influenced by systematic biases in the U.S. standardizations, but this is at the cost of absorbing variation in the dependent variables.

The δi accounts for unobserved family fixed effects common to all siblings. This includes preferences for health, knowledge about health, and access to health providers. We deal with these unobservables explicitly by estimating a random effects generalized least squares (GLS) regression and testing whether the δi term is different from zero and uncorrelated with the µij , the individual-specific errors. In no specification can we reject that the coefficients for the random effects model differ systematically from coefficients estimated using fixed effects (Hausman 1978). Where we cannot use random effects (e.g., in the probits on extreme health outcomes), we use Huber's (1967) heteroscedasticity correction to control for the family effect and cluster-based sampling.

The quality-quantity tradeoff (Becker and Tomes 1976) implies that parents that care more about the health of their child will also have fewer children, thus there may be a negative relation between sibling size, Ni, and the unobserved household effects, δi . Since the focus of this paper is not to estimate the quality-quantity tradeoff, we do not instrument for total siblings.12

We turn now to the predicted findings of the model. Following the discussion in section II, we predict the following results. Consider the inequality aversion model without resource constraints versus the pure investment model with constraints. The inequality aversion model will predict that α3 > 0, and α5 < 0. When the sample is divided into groups of girls and boys separately, we predict that the coefficients α3 and α5 will be greater in absolute value for girls.

12 In future work we consider the this possibility, but explicit instruments are not available in this data set.

16 The resource constraints model will predict that α3 > 0, and α5 > 0. Thus the only difference is the sign of α5 , the coefficient on the sibling composition variable, Fij. When the sample is divided into groups of girls and boys separately, we predict again that the coefficients α3 and α5 will be greater in absolute value for girls, although here α5 will be positive.

Under the resource constraint hypothesis, we also predict that these coefficients (α3 and α5) will be attenuated when the sample is restricted just to richer households. This is because resource constraints will be less likely to bind. It is then an empirical question as to which effect will dominate in the data, and we turn to the results below.

V. Empirical Results Results on the Base Anthropometric Measures While the bivariate analyses above show most of the patterns, we estimate regression functions to control for important parental- and child-specific variables. We estimated using random effects GLS regressions to explicitly control for the error common to all siblings within households. We show results from both linear and log-linear specifications. Dummies to account for age and sex (and their interaction) are included in all regressions to control for any systematic biases that result from the standardizations. Because changes in the standardized variables can be difficult to interpret, in Section VI we use these results to yield predictions of the impact of changing household composition on improvements in child health.

Table III gives the results for height-for-age, a measure of long-term health status. Results below for the two other health indicators are based solely on samples of children below age six, so here we provide results for children under age eleven and under age six for comparability. The results are very similar, and from here we will focus uniformly on the under age six group.

17 Table III

Generalized Least Squares (Random Effects) Estimates: the Effect of Sibling Composition on Height-for-Age

SPECIFICATIONS Logarithmic Logarithmic Boy Girl Levels coefficient age < 11 age < 6 age < 6 age < 11 age < 6 at least one brother .06403 ** .08673 ** .11037. ** .08949 * .34136** .41278 ** (.020) (.027) (.038) (.047) (.084) (.108) log birth order b -.01918 -.01078 -.03479 -.03025 .01124 .05700 (.014) (.026) (.022) (.036) (.024) (.037) log per capita expenda .07409 ** .07584 ** .08992 ** .09247 ** .01124 ** 4.82 ** (.012) (.015) (.016) (.022) (.024) (1.08) b log parental education .00637 .02070 .00526 .01809 + .00664 .01853 * (.005) (.007) (.008) (.011) (.005) (.007) urban dummy .00999 .02059 .02091 .03737 .05957 .10372 (.013) (.017) (.019) (.025) (.064) (.085) traditional religion .01435 .02528 .02495 .02604 .08545 .13387 (.023) (.030) (.033) (.044) (.119) (.152) Christian religion .00642 .00161 -.00230 -.01990 .04704 .02745 (.018) (.024) (.026) (.035) (.094) (.123) Akan tribe -.04186 ** -.04488 ** - .04720 ** -.06111 ** -.19833** -.21725 ** (.012) (.015) (.017) (.022) (.063) (.078) log total siblingsb .00445 -.00391 .01423 .00764 -.07037* -.12314 ** (.016) (.022) (.022) (.033) (.031) (.043) c + % siblings female .05873 .08447 * .06714 .04733 .08160 * .10419 * (.034) (.043) (.058) (.077) (.039) (.049) log parental height b .09762 .02429 .06469 -.07826 .00055 * .00024 (.063) (.082) (.097) (.117) (.00024) (.0003) number of observation 2458 1437 727 710 2458 1437 adjusted R2 0.124 0.167 0.189 0.161 0.204 0.189 χ 2 386.7 312.9 32836 146.46 461.47 371.69 number of households 1147 914 579 562 1147 914

Standard errors in parentheses. Additional variables include all sex and age interactions and a dummy variable where parental height was missing from the sample. Hausman's (1978) test that the random effects coefficients are not systematically different from the fixed effects coefficients could not be rejected at the ten percent confidence level for all the above regressions. ** significant at the 1% level * significant at the 5% level + significant at the 10% level a: Per capita expenditure is in millions of cedis for the level specifications b: Log of total siblings is total siblings for the level specifications and similarly for birth order , parental education and parental height . c: Percentage of siblings female is number of sisters for the level specifications

18 Sibling composition affects child health significantly in all specifications. The coefficient on the percentage of siblings that are sisters is between 0.58 and 0.85. Since the dependent variable is the logarithm of height-for-age, this means that switching one brother for one sister (i.e., a 25% increase in the percentage of siblings that are sisters for the median sibling size of four) can result in a 15% to 20% increase in the height-for-age score. The coefficient on expenditure is consistently positive, while birth order is not a significant determinant here. In fact, birth order matters in only a few specifications -- nor does it show up in (unreported) specifications in which we interact the sibling composition variable with birth order. The specifications in levels yield very similar patterns to those in logarithms.

'Spillover effects' as indicated by the dummy variable for having at least one brother affect both boys and girls. Unlike the Case and Butcher (1994) study we find that the effect is slightly larger for boys than for girls. This is consistent with this being an important reference group effect for boys, as discussed earlier.13 When we disaggregate by boys and girls (in the middle columns of Table III) we find that income effects are larger for girls than for boys, but the effects of sisters counters the prediction. Disaggregation reduces the sample size and increases the standard errors. Thus, though the coefficients are large and positive, sibling effects are not significant at the 10% level.

In Table IV we show results for weight-for-age and weight-for-height. Both measures indicate medium-term health status. The results here reinforce results obtained for height-for- age. The standard errors are much smaller and the absolute size of the coefficients is much larger than the height-for-age specifications, ranging from 0.08 to 0.12. This implies that removing a brother and adding a sister can reduce the gap between average indicators of weight-for-age in Ghana and the U.S. median by one tenth. Exchanging for a sister closes the average weight-for-height gap by one

13The dummy variable may also pick up non-linear effects of inequality aversion . This explanation cannot be distinguished from spillover/reference effects. It is not, however, consistent with the evidence to the extent that the effects here and below tend to be stronger for boys than for girls.

19 Table IV

Generalized Least Squares (Random effects) Estimates: The Effects of Sibling Composition on Medium-Term Anthropometric Outcomes

SPECIFICATIONS

Coefficient Weight-for-Age Weight-for-Height Logarithmic Levels Logarithmic Levels at least one brother .07248 ** .31916 ** .02266 .09463 (.019) ( .088) (.014) ( .072) log birth orderb .00568 .02728 .00824 -.01502 (.017) (.030) (.012) (.025) log per capita expenda .06149 ** 4.34 ** .02599 ** 1.83 ** (.010) (. 871) (.007) (.706) log parental educationb .01868 ** .01968 .00707 * .01127 * (.005) ( .006) (.004) (.005) urban dummy .01009 .05155 .00056 -.00263 (.012) (.069) (.008) (.056) traditional religion .02482 .11596 .01509 .07732 (.022) (.123) (.0158) (.100) Christian religion .00975 .04479 .00958 .05255 (.017) (.099) (.012) (.081) Akan tribe -.03091 ** -.16826 ** -.00587 -.03512 (.011) (.063) (.008) (.051) log total siblingsb -.01380 -.10105 ** -.00747 -.02285 (.016) (.035) (.012) (.029) % siblings femalec .09567 ** .12632 ** .05401 * .08669 ** (.032) (.039) (.023) (.032)

number of observation 1437 1437 1437 1437 adjusted R2 0.176 0.193 0.0886 0.089 household sample 914 914 914 914 χ2 297.48 338.16 129.40 126.51

Standard errors in parentheses. Additional variables include all sex and age interactions, parental height, and a dummy variable where parental height was missing from the sample. Hausman's (1978) test that the random effects coefficients are not systematically different from the fixed effects coefficients could not be rejected at the ten percent confidence level for all the above regressions. ** significant at the 1% level * significant at the 5% level + significant at the 10% level a: Per capita expenditure is in millions of cedis for the level specifications b: Log of total siblings is total siblings for the level specifications and similarly for birth order ,parental education and parental height. c: Percentage of siblings female is number of sisters for the level specifications.

20 eighth.14 Both parental education and per capita expenditure positively influence child anthropometrics, but again birth order does not matter.

The results for weight-for-age are generally stronger than those for weight-for-height. This finding reappears through much of the paper. As we see below in considering extreme health outcomes, this finding may be partly because children do relatively well on weight-for-height relative to the other measures. It may also be partly due to measurement error since both height and weight may be measured with error.

Extreme Health Outcomes Below, we analyze the determinants of stunting, wasting, and being underweight; the measures reflect children that fall two standard deviations below the U.S. medians for height- for-age, weight-for-height, and weight-for-age, respectively. While these are “extreme” health outcomes, they are not uncommon. Of children under age 11, over 30% are stunted, 5% are wasted and 26% are underweight.

Since these outcomes are binary, we estimate a probit equation and correct the standard errors using Huber’s (1967) method for accounting for the existence that multiple siblings from the same household may be in the sample. Unlike the previous measures, progress here is indicated by declining values in the dependent variables. Thus, the expected coefficients uniformly take the opposite sign to those above.

The results on stunting in Table V show that stunting is more likely in households with more children, but having a greater percentage be sisters reduces the likelihood of stunting. The spillover effects still hold -- having a brother reduces the chances of stunting by thirty three percent, which is surprisingly large. Household per capita expenditure enters with the expected negative sign, showing that children from richer households are less likely to be stunted.

14These calculations are based on the observation that the average weight-for-age measure is 1.2 standard deviations below the U.S. median, and the average weight-for-height measure is 0.66 standard deviations below.

21 Table V

Maximum Likelihood Probit Estimates: The Effects of Sibling Composition on Extreme Health Outcomes

SPECIFICATIONS

Stunting Wasting Underweight coefficient pooled pooled pooled at least one brother -.33352 ** -.11194 -.43277 ** (.108) (.196) (.110) birth order -.06493 + -.03984 -.08132 * (.038) (.058) (.041) per capita income -3.96 ** -4.24 * -5.28 ** (1.10) (.1.94) (.134) parental education -.01679 * -.02071 + -.01984 * (.007) (.0124) (.008) urban dummy -.04514 -.14537 -.0611 (.086) (.142) (091) traditional religion -.01619 -.50616 -.07389 (.147) (.236) (.158) Christian religion .02624 -.14228 .01206 (.117) (.158) (.129) Akan tribe .19732 ** -.01537 .22482 ** (.073) (.116) (.081) total siblings .14554 ** .07237 .15870 ** (.040) (.059) (.042) number of sisters -.09905 * -.07554 -.13487 ** (.046) (.074) (.047) observations 1437 1437 1437 pseudo R2 0.0093 0.089 0.0773 log likelihood -813.92 -257.34 -761.16

As suggested by the WHO (1986) document, children below six years are considered. Standard errors in parentheses. Additional variables include all sex and age interactions , parental height and a dummy variable where parental height was missing from the sample. Per capita expenditure is in millions of cedis. ** significant at the 1% level * significant at the 5% level + significant at the 10% level

22 The last two columns in Table V are for wasting and being underweight. Neither the coefficients on total siblings nor on the number of sisters is statistically significant in explaining wasting. This may be because the prevalence of stunting is low in the sample. However, for the more prevalent status of being underweight, we get results which look closer to those for stunting. The spillover effects are again very strong (having at least one brother can reduce the chance of being underweight by nearly 43%), as is the role of expenditure. We note also that this is one place where birth order matters substantially. The negative sign on the birth order variable indicates that younger children do better, which is consistent with the assertion that it is older children that pay the heaviest toll in aiding other children. It is surprising, however, that this affect is clearer for extreme health outcomes but not for health outcomes more generally.

Disaggregation by Gender In Table VII, we extend the analysis above by disaggregating the sample into boys and girls.15 Differences between the coefficients in the male and female sample are not always statistically significant, but where they are significant, they are uniformly consistent with the prediction that the coefficients on expenditure and sibling composition will be larger for girls (in absolute value) than for boys. For example, the only significant difference for the per capita expenditure variable is in explaining being underweight. There, the effects for girls is 50% bigger than that for boys. The effects for wasting go as expected, while those for stunting counter our prediction -- but neither set of coefficients is statistically different from each other.16

Having more sisters helps all children here, but only in the wasting equation is the difference between boys and girls statistically significant. The signs again go in the expected direction, with the effect for girls much larger than that for boys. The difference is not statistically different for stunting or being underweight.

15The results here pertain just to extreme health outcomes. Results (to be added) for other health measures are similar. 16The finding of larger income effects for girls versus boys has also been found, e.g., by Alderman and Gertler (1994) for health in Pakistan. Morduch and Stern find no significant differences for child health in Bangladesh. In Garg and Morduch (1996), we find similar patterns to those here for education, but the opposite is found for education by Deolalikar (1994) in Indonesia and Gertler and Glewwe (1992) in Peru, for example.

23 Table VI

Maximum Likelihood Probit Estimates: The Effect of Sibling Composition on Extreme Health Outcomes, By Sex

SPECIFICATIONS

Stunting Wasting Under weight coefficient boys girls boys girls boys girls at least one brother -.17294- -.47856 ** -.4923 + .20537 -.47067 ** -.3775 (.152) (.158) (.280) (.246) (.158) (.162) birth order -.10270 * -.03269 -.11633 .02104 -.15236 -.0037 (.052) (.058) (.083) (.083) (.050) (.062) per capita expend -4.18 ** - 3.83 * -.3.77 -4.83 -4.30 ** -6.65 ** (.1.45) (.1.73) (2.50) (3.04) (1.56) (2.13) + parental education -.01855 + -.01719 -.03422 -.01354 -.02151 * -.01707 (.010) (.011) (.020) (.0162) (.012) (.011) urban dummy -.00378 -.08201 -.19652 -.19183 -.03652 -.09603 (.117) (.128) (.219) (.192) (.123) (.135) traditional religion -.15542 .11397 -.62820 -.50431 .01012 -.13734 (.208) (.211) (.325) (.345) (.219) (.221) Christian religion -.11223 .16362 -.39224 -.00142 .05088 -.01433 (.167) (.167) (.237) (.217) (.182) (.167) Akan tribe .13048 .25516 * -.10214 .03032 .17487 .28099 ** (.103) (.106) (.186) (.153) (.113) (.110) total siblings .17547 ** .12240 * .13230 + .02827 .22667 ** .08559 (.054) (.063) (.072) (.095) (.055) (.068) + + number of sisters -.08108 -.12544 + -.00393 -.13707 -.15562 * -.10893 (.062) (.069) (.130) (.085) (.066) (.070) observations 727 710 727 710 727 710 pseudo R2 0.079 0.099 0.023 0.0784 0.084 0.0778 log likelihood -423.62 -386.04 -97.38 -152.05 -382.89 -.374.63

As suggested by the WHO (1986) document, children below six years are considered. Standard errors in parentheses. Additional variables include all sex and age interactions, parental height, and a dummy variable where parental height was missing from the sample. Per capita expenditure is in millions of cedis. ** significant at the 1% level * significant at the 5% level + significant at the 10% level

24 Disaggregation by Household Expenditure In order to gauge the strength of the resource constraint hypothesis, we divide the sample between richer and poorer groups. The richer group is defined as households spending more than 60,000 cedis per capita (roughly $240 using the 1989 exchange rate); this is slightly above mean expenditure per capita. If resource constraints do not bind for the richer group, we expect that neither the coefficient on income nor on sibling composition should be close to zero. If the richer group remains constrained, the point estimates of the coefficients should at least be smaller than for the poorer groups. Table VII (log specification) and Table VIII (linear specification) display the results when we split the group by richer and poorer households.

In both specifications, the coefficients on expenditure are larger for poorer households than for richer households. While none of the differences are statistically significant in the logarithmic specifications, they are all significant in the linear specifications. There, coefficients are all at least three times larger for poorer households than for richer households.

Again, when the differences between coefficients on the sibling composition variables are statistically significant, they are uniformly much larger for poorer households than for richer households -- by at least three times. For example, in height-for-age, the coefficient is large and significant for poorer households -- four times larger than the coefficient for richer households in the linear specification, while the coefficient for richer households is essentially zero in the logarithmic specification. Where the coefficients from the poorer and richer samples are not significantly different, they take the expected pattern for weight-for-age and the opposite for weight-for-height.

These results add further weight to the argument that resource constraints are central in explaining patterns of child health in Ghana. As with the disaggregation by gender, we find that coefficients have the predicted pattern wherever differences are statistically significant.

25 Table VII

GLS - Random Effects Estimates : Effects of Sibling Composition on Health Outcomes in Richer and Poorer Households

Logarithmic Specification

Height-for-Age Weight - for-Age Weight-for-Height poorer richer poorer richer poorer richer coefficient households households households households households households at least one brother .13339 ** .01136 .09987 ** .02583 .02877 .01224 (.038) (.039) (.027) (.030) (.018) (.023) log birth order -.03297 .03720 -.00064 .01953 .01511 .00086 (.032) (.038) (.022) (.029) (.015) (.022) log per capita expend .08047 ** .07548 * .06822 ** .05488 * .02854 * .01138 (.029) (.034) (.020) (.027) (.014) (.020) log parental education .02720 ** .00981 .01906 ** .01748 * .00271 .01371 * (.010) (.010) (.007) (.008) (.005) (.006) urban dummy .03335 .00356 .01703 -.0055 .00057 .00082 (.024) (.023) (.017) (.018) (.012) (.013) traditional religion .03752 .01602 .06244 * -.02446 .04843 -.02975 (.042) (.044) (.029) (.036) (.020) (.026) Christian religion .01299 -.01444 .02240 -.01081 .01720 -.00101 (.033) (.037) (.023) (.028) (.016) (.021) Akan tribe -.03697 + -.05253 -.01279 -.05415 ** .00882 -.02648 * (.021) (.022) (.015) (.017) (.010) (.013) log total siblings -.00657 -.00339 -.02742 .00716 -.01995 .00923 (.030) (.034) (.021) (.027) (.015) (.020) % siblings female .13471 * -.01316 .11147 ** .04574 .04375+ .05201 (.058) (.068) (.041) (.053) (.027) (.040) log parental height b .41084 .17948 .55716 -.09649 .04776 -.10274 (.836) (.889) (.629) (.093) (.055) (.070) number of observation 865 572 865 572 865 572 adjusted R2 0.183 0.143 0.198 98.0 98.91 54.92 chi 2 196.91 112.37 203.17 0.153 0.11 0.099 Number of households 532 382 532 382 532 382

26 TABLE VIII

GLS - Random Effects Estimates : Effects of Sibling Composition on Health Outcomes in Richer and Poorer Households

Level Specification

Height-for-Age Weight-for -Age Weight-for-Height poorer richest poorer richest poorer richest coefficient households households households households households households at least one brother .65766 ** .21672 .49736 ** .23734 .17952 .11977 (.176) (.199) (.142) (.165) (.114) (.141) birth order .00866 .14564 * .00040 .05296 -.00587 -.03247 (.045) (.070) (.036) (.058) (.029) (.050) per capita expenda 10.4 ** 3.33 * 9.30 ** 2.85 ** 3.82 0.442 (4.08) (1.60) (3.20) (1.32) (.2.59) (1.08) parental education .02876 ** .00443 .01988 ** .01913 * .00417 .02175 ** (.010) (.012) (.008) (.010) (.006) (.008) urban dummy .15079 .03363 .08188 .00042 -.00651 .01046 (.115) (.129) (.092) (.101) (.074) (.089) traditional religion .23673 .03043 .34591 * -.17791 .27682 -.21510 (.197) (.248) (.156) (.205) (.126) (.170) Christian religion .10707 -.11845 .11623 -.08680 .07881 -.00210 (.157) (.205) (.124) (.169) (.100) (.141) Akan tribe -.16556 -.28624 -.07200 -.30136 ** .04623 -.15947 * (.102) (.125) (.080) (.100) (.065) (.085) total siblings -.05799 -.09244 -.04423 -.016242 -.01062 .04207 (.044) (.069) (.035) (.057) (.028) (.048) + # siblings female .59385 * .15703 .52893 ** .42416 .28179 .37275 (.275) (.361) (.220) (.299) (.177) (.252) parental height .00034 ** .00023 .00036 -.00025 .00026 -.00039 (.0004) (.0005) (.0003) (.00043) (.0002) (.0003)

number of observation 865 572 865 572 865 572 adjusted R2 0.22 0.165 0.222 0.170 0.104 0.1004 chi 2 241.37 134.01 236.81 111.41 94.09 53.42 Number of households 532 382 532 382 532 382

27 The tables also show that spillover/reference effects are consistently smaller for the richer groups then for poorer groups. In the logarithmic specification they are all very small and not statistically significant while in the linear specification they are not statistically significant and roughly half the effects for poorer groups. With the data at hand , we are not able to determine the reasons behind the differences, but they are consistent with a variety of explanations.

V. Predicted Minimum Effects of Relaxing Resource Constraints Below we use the regression results to compare the impact of changing sibling composition on health outcomes. We compare predicted outcomes for children when all their siblings are male versus when all their siblings are female. In the face of pro-male bias , the former gives the worst scenario in terms of sibling rivalry since all competing siblings have intrinsic advantages. The latter, correspondingly, gives the best case. The results under these scenarios are presented in Table IX.17

The predictions for the “all sisters” case can be seen as showing the minimum possible improvements which would occur when resource constraints are lifted. This follows from the argument that sibling rivalry is an increasing function of resource constraints. Thus, the case with least sibling rivalry gives a prediction of the lower bound on the effect of reducing resource constraints.

The results show that for height-for-age and weight-for-age, having all sisters improves outcomes over having all brothers by almost 30%. The difference from the mean is roughly 15%. For weight-for-height, the difference is greater -- 77% improvement for “all sisters” versus “all boys” and a 35% improvement over the mean -- but the result should be tempered by the fact that in the original regression the sibling composition variable is not statistically significant.

17 The predictions fix all variables at the mean, but in the all sisters case we set Fi = Ni and Rij = 0 and in the all brothers case we set Fi = 0 and Rij = 1. The predicted effects are the averages over the sample. Coefficients are taken from the pooled linear regressions and from the probits on extreme health outcomes.

28 Table IX

Predicted Effects: The Effect of Sibling Composition on Health Outcomes

% category sibling composition standardized deviation z score from mean all sisters -1.143 14.76 Height-for-age mean value -1.341 00.00 all brothers -1.515 -12.97 all sisters -1.079 15.42 Weight-for-age mean value -1.252 0.000 all brothers -1.405 -12.22 Weight for- all sisters -.351 35.12 height mean value -.541 0.000 all brother -.663 -22.56

category sibling composition probability of % of mean outcome value all sisters .255 82.3 Stunting mean value .308 100.0 all brothers .425 139.1 all sisters .0430 81.6 Wasting mean value .0524 100.0 all brothers .0546 104.2 all sisters .264 100.0 Underweight mean value .265 100.0 all brothers .301 113.6

29 For the extreme health outcomes , the probability of stunting falls from 43% with all- brother siblings to 25% with all-sister siblings. The differences are smaller for wasting and being underweight (from 55% to 43% for wasting and 30% to 26% for being underweight) and not statistically significant for wasting.

These estimates of minimum bounds for improvements do not take into account spillover/reference effects. Considering these variables as well would boost predicted improvements by another 10% to 20% on average.

VI. Summary and Conclusions

The results above show that sibling sex composition matters importantly in explaining child health outcomes in poor economies. While only a few previous papers in the economics literature have considered sibling composition empirically (and those have considered education in richer economies), the importance of sibling composition is in fact an implication of the standard economic models that lie behind nearly all studies of human capital investment (e.g., Strauss and Thomas, 1995).

While competing explanations exist for the role of sibling composition, we have shown evidence that is most consistent with resource constraints as the driving explanation. The resources constraints may be due to scarce parental time or to tight budgets. When the constraints bind, siblings must compete with each other for scarce resources. Children with initial advantages tend to do better in this rivalry -- and, as in many poor economies, it is thus boys that win most in Ghana. Accordingly, from the point of view of both female and male children, it is best to have more sisters than brothers.

By focusing on the role of sibling composition rather than income, we are able to distinguish between competing hypotheses that explain how human capital investments increase with economic development. A common explanation is that improvements in human capital (and the more equal treatment of males and females) are due to changing tastes and social norms. This explanation centers on the parental “taste” for human capital and inequality aversion. Inequality aversion implies that when resource constraints do not bind, having more brothers is most helpful, since fairness dictates that those children with lower returns should be

30 lifted up as well. The pure resource constraint story above gives the opposite prediction, and it is thus an empirical question as to which effect is strongest in household decision-making.

The difference in health outcomes between children with all brothers versus all sisters is substantial in Ghana -- and the results favor the resource constraint hypothesis. We find differences on the order of 30% explained by sibling composition alone. This suggests that policies that reduce resource constraints -- and the sibling rivalries they induce -- can be effective in improving child health. This is so even when social norms, parental tastes, labor market differences, and cultural barriers remain unchanged.

Sibling rivalry is also shown to exacerbate existing cultural and labor market biases that worsen female health outcomes. This suggests that even relatively small improvements in norms, tastes, market differences, and cultural forces can translate into large changes in the health of girls.

31 APPENDIX

Appendix Table I

Means and Standard Deviations of Variables in Regressions

Full Sample Girls Boys variable mean std dev. mean std dev. mean std dev. sex (male=1 ) .539 .498 - - - - total siblings 3.48 2.32 3.33 2.28 3.60 2.35 number of brothers 1.877 1.66 1.807 1.66 1.93 1.66 number of sisters 1.605 1.32 1.525 1.25 1.67 1.37 percentage sisters .481 .332 .484 .338 .479 .327 total annual expenditure 412,884 315,463 412009.3 316106 413,697 317,564 per capita expenditure 59,717.9 37,621 60,387 36,536 59,146 38,528 parents education 5.98 4.70 6.03 4.61 5.94 4.77 age in years 4.75 2.98 4.36 2.78 5.089 3.112 have at least a brother .815 .387 .8044 .396 .825 .379 urban residence .444 .430 .443 .429 .446 .430 Christian religion .747 .434 .748 .434 .746 .435 traditional religion .1368 .343 .133 .340 .139 .346 Akan tribe .490 .500 .492 .500 .488 .500 height-for-age -1.34 1.39 -1.230 1.421 -1.43 1.36 weight-for-age -1.284 1.106 -1.211 1.131 -1.34 1.08 weight-for-height -.657 .896 -.6108 .909 -.623 .884 stunted .308 .461 .272 .445 .3388 .473 wasted .0523 .222 .0625 .242 .0436 .204 underweight .2647 .441 .243 .429 .283 .450 parental height missing .0299 .010 .0241 .0250 .0349 .0250 parental height (mm) 1478 303 1477.37 303 1478.00 304 Full sample 2458 2458 1133 1133 1325 1325 exchange rate in 1989 $1 =C250

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