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PSC Research Report 05-584 Page 1

Family Size, Demographic Change, and Educational Attainment:

The Case of Brazil

Leticia Marteleto

Research Investigator Population Studies Center Survey Research Center University of Michigan

POPULATION STUDIES CENTER RESEARCH REPORT 05-584

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Abstract

Brazilian families have changed dramatically over the past decades, particularly in regard to family size. For example, among 14 year olds, the 1963 cohort had 5.4 siblings on average, while the 1983 cohort had 2.3 siblings. This paper investigates the effects of family size on schooling of cohorts of children born pre- and post- demographic transition. Analyses of nationally representative data show that children benefit from small family sizes in both cohorts. Fertility decline has benefited education through changes in children’s distribution across family sizes, not from a decrease in the negative association between family size and schooling.

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INTRODUCTION

The structure of Brazilian families has changed dramatically over the past three decades, particularly in regard to number of children. For example, the 1963 cohort of 14 year-olds had an average of 5.4 siblings, while the 1983 cohort had 2.3 siblings. The goal of this paper is to address the effects of the changing family sizes on children’s years of schooling and school enrollment rates for cohorts of children born before and after Brazil’s demographic transition. I will examine whether and how Brazil’s rapid fertility transition, and its resulting smaller families, affected children’s education.

Specifically, I investigate whether children with fewer siblings are better off in terms of educational attainment than children with more siblings. While past research has examined the role of number of siblings on children’s education in developing countries, none has focused simultaneously on cohorts of children born before and after a demographic transition. Because demographic processes, intergenerational transmission of human capital, and opportunities jointly affect patterns of educational attainment, fertility decline may be a significant factor in educational outcomes.

Understanding the determinants of children’s educational attainment is a critical concern in developing countries such as Brazil, particularly because investments in children’s education produce a skilled stock of human capital that helps eliminate constraints on national development. Knowing how demographic transitions – in particular, fertility declines – impact investment in children’s education may inform policies designed to promote universal primary school enrollment and higher educational attainment, goals of most developing countries. Finally, understanding how family size affects educational attainment and school enrollment may help shape policies intended to target children in need. In the first section of this paper, I present a theoretical framework for how number of siblings affects children’s education, and examine the theoretical grounds for the impact of the changing demographic trends on children’s welfare. Next, I discuss the methods and data used in this study, present and discuss results, and draw conclusions.

BACKGROUND

Theories regarding siblings rivalry and resource dilution among siblings offer insights into how children’s educational attainment relates to number of siblings. According to these theoretical frameworks children with many siblings are generally worse off than their counterparts in terms of several outcomes related to life chances, including nutrition, educational attainment, and mortality (Becker 1981; Blake 1981). According to the dilution of resources hypothesis developed by Blake (1985), a large number of siblings, or close spacing among siblings, dilutes the amount of parental time, attention, and money available per child, which tends to negatively influence several child outcomes, including educational attainment (Blau and Duncan 1967; Blake 1981). Nonetheless, the sociological and demographic literature consider that socialization, levels of intimacy and communication vary by group size (Coleman 1988). Simply put, parents with more children spend less time with each of them, thus making them less likely to develop tools to acquire greater social capital than children who receive more support. In contrast to the dilution of resources framework, which posits an equal allocation of resources among siblings, the sibling rivalry hypothesis assumes that parents invest in their children to maximize family utility, which often results in investment inequities. This hypothesis assumes that parents make human capital investments in their children based on assessments of their differential ability to contribute to the wealth of the entire family

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(Becker 1981). These parental investments improve the “quality” or the life prospects of children. However, even with inequities in resource allocation among children, the interaction between the quantity and quality of children means that education per child tends to be lower in families with more children. Therefore, given a set pool of family resources, more children mean fewer resources available for all children, and therefore lower life prospects (Becker 1981).

Much of the empirical work that has examined the effects of family size on schooling attainment confirms the resource dilution and sibling rivalry hypotheses – that is, children from larger families exhibit educational disadvantages compared to children from smaller families. For excellent reviews of studies on family size and schooling in developing countries, see Kelley (1996) and Lloyd (1994). In most of these studies, the negative effect persists even after controlling for socioeconomic variables (Ahn, Knodel, Lam, and Friedman 1998; Knodel and Wongsith 1991; Parish and Willis 1993; Patrinos and Psacharopoulos 1997; Shavit and Pierce 1991). However, other studies have yielded conflicting results: they report negative but not statistically significant effects (Kelley 1996; Lloyd 1994), mixed results (Psacharopoulos and Arriagada 1989), or even positive effects (Chernichovsky 1985). These findings provoke questions as to whether the dominant quantity-quality framework for explaining the relationship between family size and schooling holds for Brazil, and, even more interestingly, whether it holds throughout different stages of the demographic transition. In developing countries, the positive association between number of siblings and schooling found in some studies has been attributed to the specialization of roles in the family. In these settings, “a large number of children in the family may lead not to a universal resource dilution but to improved opportunities for the late born” (Parish and Willis 1993:868). That is, children in larger families may specialize in family resource-producing activities that free late-born children for school. This can be particularly true in countries like Brazil, where children commonly work outside the home whether attending school or not. In contexts such as these, the negative effect of a larger family size on schooling may be offset or even reversed by the support given to younger children by their older siblings. A related hypothesis is that older siblings may improve the educational outcomes of younger siblings by providing interpersonal and direct financial resources. Although it is outside the scope of this paper to empirically examine whether siblings’ composition and birth order affect the relationship between family size and schooling, the specialization framework is a plausible explanation for the positive relationships between family size and schooling found in some developing countries (Kelley 1996).

In short, past research does not clarify whether a quality-quantity framework fits the Brazilian case. Moreover, it may be that the effect on schooling of number of siblings differs according to the demographic and social contexts inherent in high and low fertility regimes.

Cohort Analysis Socioeconomic and demographic trends shape the macro conditions in which the educational attainment process takes place. Brazil’s rapid fertility decline occurred during a period of a far-reaching social change, with encompassed times of both rapid economic growth and economic crisis (Martine 1996). In the 1970s, the demographic transition was on its way in Brazil, as fertility drop from a total fertility rate of approximately 6.5 to 4.3 over this decade (Martine 1996). The total fertility rate in Brazil dropped from

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a high level of 6.5 births per women throughout most of the 1940s, 1950s and 1960s to 4.3 births per women by 1980. Brazil’s total fertility rate was 2.5 in 1990.

Conceptualizing birth cohort as an important determinant of educational well-being (Easterlin 1980), this paper addresses the question of whether the advantageous demographic conditions for children born after rather than before demographic transition have contributed to improvements in children’s schooling. Comprehensive analysis based on this framework will inform policy makers as they face unprecedented smaller demands of students in primary school, a window of opportunity for alleviating educational inequality.

The major recent shifts in Brazil’s demographic patterns have also affected the micro conditions in which the schooling of different cohorts of children takes place by changing family size. In particular, the distribution of family sizes within the Brazilian population has changed considerably. For example, 2% of 14 year-olds born in 1963 came from families of one or two children while 33% came from families of seven or more. In contrast, among 14 year-olds born in 1983, after the fertility decline, 5% came from families with one or two children and only 12% from families with seven or more. Put another way, over the decades, growing proportions of Brazilian children came from smaller families.

Does the negative association between number of siblings and educational attainment still hold with the smaller family sizes resulting from the demographic transition? Few studies have examined the effect of family size on educational attainment over time, and, to my knowledge, none have looked at cohorts born before and after the demographic transition. An exception is the work of Parish and Willis (1993), who found an increasing effect of family size on educational attainment in Taiwan, despite declining average family size and increasing average income. Parish and Willis attribute this unexpected finding to the rising opportunity cost of school in recent decades. The more specific question for this study is: Does the negative association between family size and educational attainment hold in Brazil across cohorts of children separated by a period of fertility decline? Especially in countries like Brazil, where school enrollment is not universal, understanding how fertility factors affect attendance is essential in any broad effort to increase school enrollment and levels of educational attainment.

DATA AND ANALYTICAL SAMPLE

For this investigation, I used data from the 1977 and 1997 Pesquisa Nacional por Amostra de Domicílios (PNAD), (National Research of Household Sample), which are annual household surveys conducted by the Instituto Brasileiro de Geografia e Estatística (IBGE), the Brazilian statistical bureau. The PNAD is carried out in September of each year.

The PNAD contains standard demographic and socioeconomic variables such as sex, age, income, and schooling for all members of the household. Data from 1977 and 1997 are comparable, although the 1977 PNAD does not contain information on race and ethnicity, making it impossible to compare cohort racial distributions. Also, neither the 1977 nor the 1997 PNAD covered the rural part of the northern region, which probably results in overestimates of the educational and socioeconomic statistics of that region.

The PNAD is a nationally representative survey with information on 498,679 individuals in 100,039 households for 1977, and information on 365,870 individuals in 89,939 households for 1997. The samples are sufficiently large to permit sub-sampling of specific groups, such as 14 year-olds. The 1977 PNAD provides data on 12,834 14 year-olds born in 1963; the 1997 PNAD provides data on 7,861 14 year-olds

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born in 1983. Children’s educational experiences at different ages are sufficiently diverse that it is sensible that they are analyzed separately. Fourteen year-olds were chosen as the unit of analysis because this is the eldest age for legally required school attendance in Brazil. Moreover, children who have been successful in school should be making a transition from primary to secondary education at 14 years of age.

Because the PNAD is a household survey, it accounts for all family and non-family members who live in the household, but cannot account for family members outside the household. Therefore, PNAD data do not permit analysis of nonresident family members, particularly siblings, who may influence children’s school enrollment and schooling. For the purposes of this investigation, however, both the 1977 and 1997 PNAD do provide the number of children born to all women over the age of 15, and thus the total resident and non-resident number of siblings for the sampled 14 year-olds.

Because this analysis requires variables such as number of siblings and mother’s education, the analytical sample had to be limited to biological children of the head of the family, that is, children whose mothers and therefore their complete parity can be identified. This choice does not create a selection bias. About nine in 10 children in both cohorts live with at least one of their biological parents and these children are not significantly different from the full sample of children on their distribution across rural/urban location, family income, region, or gender (not shown). Moreover, there are no notable differences in school enrollment rates nor in educational attainment among the full and analytical samples.

METHODS

In order to assess the impact of number of siblings on children’s educational attainment in pre- and post- demographic transition Brazil, I estimate models of years of schooling and school enrollment rates for 14 year-olds in the 1963 and 1983 birth cohorts. First I calculate unadjusted means for each cohort, which are simple cross-tabulations of number of siblings by each of the two educational outcomes. Then I calculate adjusted measures as predicted schooling and predicted probabilities of enrollment through regression techniques. I include in my analyses the standard controls used in models of children’s educational outcomes, such as sex, rural versus urban location, family income, mother’s education, and region of residence. I model complete years of schooling by estimating equation (1) using ordinary least square regressions:

jijijiji ecDbFaS +++= (1) where Si equals the years of schooling for 14 year-old i born in cohort j; F i is a vector of a set of dummy variables indicating number of siblings; Di is a vector of demographic, residence, and family characteristics, such as mother’s education and log of family income, and e i is a normally distributed error term.

I then model the probability of children’s school enrollment by estimating equation (2) using logistic regression:

jijiji cMbDaW ++= (2) where Wi equals the probability of school enrollment for 14 year-old i born in cohort j; D i is a vector of demographic, residence and family characteristics; and M i is a set of dummy variables indicating number of siblings.

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Both sets of regressions – ordinary least square regressions for years of schooling and logistic regressions for school enrollment – consist of separate models for the cohorts of 1963 and 1983 for the whole country. To calculate the adjusted means of schooling and school enrollment, the values of the covariates were set as the own value of each child i. Only the value for number of siblings is then forced to range from zero to seven or more.

I next assess whether the educational improvement across cohorts is related to a change in the distribution of children across family sizes or to a change in the effect of number of siblings on years of schooling. To do so, I decompose the total explained inter-cohort change to the differences accounted for by effects and by distribution of number of siblings, using estimates from regressions that have family size as sole predictor of schooling and using Preston’s model of decomposition (Preston 1975). This method separates the part of the schooling change that is due to shifts in the relationship between family size and schooling from the part due to distribution of family size.

This first set of decompositions can be interpreted as estimates of what schooling in the younger cohort would have been if the older cohort’s relationship between family size and schooling was in effect, and vice versa. The difference between this estimate for the younger cohort and the older cohort’s actual schooling indicates the gain in education attributable solely to declines in family size between the 1963 and 1983 cohorts. I repeat the procedure using the older cohort’s family size but the younger cohort’s family-schooling relationship. The difference between this estimate and the older cohort’s schooling evidences the gain in schooling attributable to shifts in the curve or to factors exogenous to family size. When added to the initial estimate, these two differences should come close to yielding the actual schooling for the younger cohort. The results will indicate, for example, the extent to which a decline in the number of siblings in the younger cohort explains improvements in educational attainment for 14 year-olds. Decompositions of the cohort gain are done only for schooling because this educational outcome reflects current and past educational decisions.

I then perform a second set of decompositions of the cohort difference in mean schooling, now using regression coefficients from full models (shown in Table 3). Here the goal is to estimate the percent of the change in schooling due to family size, controlling for other predictors of schooling described above. The cohort difference in average schooling, which is the gap to be explained, is expressed on Equation (3):

)Xba()Xba()YY( ,j_

,j,j_

,j ∑∑ +−+=−−−

19831983198319631963196319631983 (3) Equation (3) can be expanded in several ways to obtain decompositions of the schooling

difference. Equation (4) provides an estimate of schooling for the 1963 cohort assuming children had all characteristics of the 1963 cohort held constant, but the family size distribution of the 1983 cohort.

f,19831963636319638363 X−

++= ∑ ,ffj

,j_

,jf,^

bXbaY (4) where bf,1963 is the coefficient regression for the 1963 cohort and 1983,

_

fX is the family size mean for the 1983 cohort. All other variables contribute with their coefficient and mean of the 1963 cohort to the estimation of 8363 fY ,

^. The sum of each variable’s contribution added to the regression constant of the

1963 cohort produces an estimate of the mean schooling for these children assuming they had the 1983 cohort distribution of variable i and all other characteristics of the 1963 cohort held constant.

Equation (5) shows a similar exercise using the 1983 cohort as base. f,19631983838319836383 X

++= ∑ ,ffj

,j_

,jf,^

bXbaY (5)

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I calculate equations (6) and (7) to estimate the degree to which a change in family size has generated a change in schooling. Equation (6), or specification 1, reflects the change in schooling represented by a change in family size estimated with 1963 as the base cohort. Specification two is defined by equation (7), which is similar to equation (6) but has 1983 as the basis for the estimation.

Specification one is equal to: 6383638363

___f,

^YY/YY −− (6)

Specification two is equal to: 6383836383

___f,

^YY/YY −− (7)

where 8363 fY ,

^ is the schooling mean calculated using equation (4) and 6383 fY ,

^ is the schooling mean

calculated using equation (5); 63

_

Y is the actual 1963 cohort mean schooling and 83

_

Y is the actual 1983 cohort mean schooling. This last estimate is divided by the 1.32 actual cohort difference in mean schooling, 8363 fY ,

^which is equal to the denominator of equation (6).

The contribution of each variable to the schooling difference is thus equal to the change in the average between predicted schooling from 1) replacing the 1963 cohort distribution with the 1983 cohort distribution of that variable while holding the distribution of the other variables constant and, 2) replacing the 1983 cohort distribution with the 1963 cohort distribution of that variable while holding the distribution of the other variables constant.

A caveat should be made at this juncture. It is possible that parents may make joint decisions about the quantity of children to have in relation to a preferred “quality” of children with regard to education (Becker 1981). Family size may be correlated with unmeasured determinants of children’s schooling as parents may choose a combination of low fertility and high schooling for their children. This contributes to a negative correlation between family size and schooling that tends to overstate the extent to which schooling would increase if parents were required to limit their number of children. However, this assumption relies on the belief that parents are rational in their fertility decisions with respect to their educational expectations for their children, even in developing countries. Arguing the contrary, Knodel and colleagues have pointed out that, in some societies such as Thailand, fertility decisions and educational attainment of children are not calculated in a quality-quantity manner and that the way fertility decisions are made may indeed change over time (Knodel et al. 1990).

RESULTS

Table 1 provides comparisons of the distribution of 1963 and 1983 cohorts across socio-economic and family characteristics. The general life conditions of these cohorts of children are different. In regard to residence, about 75% of all children in both cohorts lived in either southeast or northeast Brazil, but 63% of 14 year-olds born in 1963 lived in urban areas compared to78% of 14 year-olds born in 1983. Brazil’s increased urbanization across the 1970s and 1980s may imply in changes in the overall value of children and consequently on their educational outcomes. The distribution of children by mother’s education changed dramatically across cohorts, with nearly five times more children having mothers who attended at least one year of university in the younger than in the older cohort.

Table 2 shows overall enrollment rates and schooling attainment, as well as the distribution of these educational measures by family and socioeconomic characteristics separately by cohort. The proportion of children enrolled in school is higher for the younger than for the older cohort. Similarly,

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educational attainment of young people has increased dramatically in Brazil over the last 20 years. Years of schooling for 14 year-olds grew from 3.4 for the cohort born in 1963 to 4.7 for the 1983 cohort.

Table 2 provides evidence for gender differences in levels of school enrollment and educational attainment across and within cohorts. The table shows that 78% of boys in the older cohort are enrolled in school compared to 72% of girls. This trend of higher school enrollment for boys is reversed in the younger cohort, where 90% of girls are enrolled in school compared to 81% of boys. Although boys in the older cohort are enrolled in school at higher rates than girls, girls have more years of schooling than boys in both cohorts. This may mean that girls go through school much faster than boys do. These gender differences favoring girls are remarkably different from findings in other developing countries (Knodel and Jones 1996).

Table 2 also shows that the proportion of children enrolled in school in female-headed and male-headed families is nearly the same for both cohorts. However, children in male-headed families have on average more years of schooling than children in female-headed families.

The consequences of the profound demographic change in Brazil during the period that separates this study’s cohorts are evident in the average number of siblings and family size distribution for these 14 year-olds. The average number of siblings decreased from 4.3 in the older cohort to 3.3 in the younger cohort. Figure 1 illustrates the considerable change in family size distribution across the 1963 and 1983 cohorts. In the older cohort, 68% of children had four or more siblings and 33% had seven or more siblings, while in the younger cohort, only 33% of children had four or more siblings and 12% had seven or more siblings. Family Size, School Enrollment and Schooling for Pre- and Post-Demographic Transition Cohorts Figure 2 presents unadjusted and adjusted percentages of 14 year-olds enrolled in school by number of siblings and cohort. Adjusted enrollment rates come from the results of logistic regressions presented in Table 3, and include other socioeconomic and demographic covariates, as discussed in the methods section.

Children in the 1983 cohort are enrolled in school at higher levels than children in the 1963 cohort, at all family sizes. Considering unadjusted proportions, Figure 2 shows that the vast majority of 14 year-olds with one sibling in the older cohort (91%) was enrolled in school, while 68% of the ones who had seven or more siblings were attending school. The pattern is similar for the 1983 cohort, except that the levels of enrollment are higher. Overall, children in larger families have significantly lower school enrollment rates than children in smaller families, with the exception of children with no siblings, who have higher rates than children with one or two siblings. This exception may be related to differences between only children and children who have siblings that operate independent of family size. For instance, only children are more likely to be born out of wedlock, or they may be more likely to have parents with fecundity problems – characteristics that may confound the effect of family size on education. Indeed, many other variables may distort the association of number of siblings and school enrollment. To eliminate the effects of some potential confounding factors, I use adjusted probabilities of school enrollment derived from multivariate analyses that control for other predictors of school attendance.

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Adjusted school enrollment estimates from Figure 2 provide evidence that the disadvantage of only children in relation to children with one and two siblings largely disappears, but that family size remains negatively associated with school enrollment, even when socioeconomic and demographic variables are controlled. Results from adjusted probabilities show that this is true for both cohorts: In the 1963 cohort the probability of school attendance is 75% for children with seven or more siblings and 88% for children with one sibling. This quantity-quality tradeoff is evidenced for both cohorts, making it clear that number of siblings, in and of itself, is an important determinant of school enrollment for children born under high and low fertility regimes.

For children born in 1963, before the demographic transition, the relative advantage of coming from a smaller family is particularly substantial. For those born in 1983 the family-size differentials are smaller and overall enrollment is higher: Figure 2 shows that 90% or more with three or fewer siblings are in school. This result is beneficial for overall children’s education as two-thirds of children in this post-demographic transition cohort are in families with three or fewer siblings.

As shown in Figure 3, the pattern for average years of schooling is similar to that for enrollment. To provide perspective for interpreting the mean unadjusted and adjusted years of schooling, it is worth noting that if a 14 year-old had started school at the mandatory age of seven years old, and did not drop out or repeat a grade, she/he would have six or seven years of schooling. Figure 3 indicates that, although the 14 year-olds in both cohorts and in all family sizes are substantially behind in terms of their educational attainment, children with higher numbers of siblings have even greater schooling disadvantages than their counterparts. Nonetheless, the difference in adjusted years of schooling between children with seven or more and no siblings is 0.9 years of schooling in the older cohort and 1.6 in the younger. This differential is reduced when other covariates are controlled, but the degree of family-size disadvantage remains similar across cohorts. Even though the curve of schooling by family size shifts upward from 1963 to 1983, the disadvantage of being in larger families still holds in the younger cohort. In fact, Figure 3 shows that the difference in schooling between children with seven or more siblings versus no siblings is 1.0 year of schooling for the 1983 cohort – a small increase over the 1963 difference. Although Figures 2 and 3 show that, overall, children in larger families are slightly more disadvantaged in the older than in the younger cohort, the adjusted schooling means for children born in 1963 and 1983 demonstrates that the curve of the relationship between number of siblings and schooling has not changed much across cohorts. The effect of family size on children’s schooling and school enrollment has remained statistically significant and negative under high and low fertility regimes. Decompositions

The first set of decompositions of the schooling increase across cohorts addresses whether the cohort difference in schooling is due to different effects of number of siblings on education, or due to simple variation in the distribution of children in smaller and larger families. The change to be explained is the total increase of 1.32 years of schooling across cohorts. The coefficients used here are from models of family size on years of schooling, or simply the mean and distribution of each variable (not shown).

The first estimate uses the family size regression coefficient and children’s mean years of schooling of the older cohort, resulting in the actual 3.43 years of schooling. Repeating the procedure but using mean family size of the 1983 cohort, the second estimate indicates the level of schooling children in

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the older cohort would have if the family size coefficient of the younger cohort were in effect. The observed decline in family size would have produced a mean of 4.10 years of schooling, or a difference of 0.67 year. The next estimates of mean years of schooling are calculated in a similar way, but using the 1983 cohort family size distribution. They yield a schooling gain of 0.79 year. The average of these two estimated gains in education (0.67 and 0.79) is 0.73 year, or the additional schooling children in the older cohort would have assuming they had the same number of siblings as the younger cohort. This is the proportion of the schooling difference explained by a change in the distribution of family size, which accounts for nearly 55% of the total increase of 1.32 years of schooling across cohorts.

The procedure described above is repeated but now subtracting actual means from estimates based on each cohort’s family size distribution matched with the other cohort’s regression coefficient. The contribution estimate indicates that differences in the family size coefficient and exogenous factors account for 0.59 year of schooling. The estimate of the total contribution using the younger cohort’s coefficients is slightly smaller (45%) than the estimate of the shift due to a change in the distribution of family size (55%). The change in the effect of family size on children’s schooling and factors exogenous to family size suggests that the opportunity cost of education has increased in the 20-year period that separates these cohorts. On the other hand, the schooling gain is explained predominantly by the pronounced growth in the proportion of children coming from small families. The results of these analyses confirm that fertility decline has had a direct impact on increasing children’s schooling mainly through a change in the distribution of children across family sizes.

The next set of decompositions of the schooling increase across cohorts addresses whether the contribution of reduced family size to schooling is substantial when compared to other predictors included in the models. The 1963 and 1983 regression coefficients used in the decompositions are presented on Table 3.

Table 4 shows decomposition results. Mothers’ education and family size are the two most important variables in explaining the schooling difference across cohorts. Children in the older cohort would have 4.01 years of schooling instead of 3.43 if their mothers had had the younger cohort mean education, all else held constant – a difference (0.58) that accounts for 43% of the total cohort schooling gap of 1.32 years. Specification two, discussed in the Methods section, shows that the change in mothers’ education explains 23% of the schooling gap. The average of these percentages is 33%, which leads to the interpretation that the increase in mothers’ education accounts for one-third of the schooling difference across cohorts.

The gain in schooling due to decline in family size is nearly the same as the gain due to the increase in mother’s education. Differences in family size explain 36% of the difference in specification one and 24% in specification two, for an average of 30% of the schooling variation being explained by family size. These results suggest that differences in family size levels played an important role in the schooling gain of the younger cohort of children.

For Brazil as a whole, family headship explains 14% of the change in schooling in specification one and only 1% in specification two. Being in a female-headed family had negative consequences for children’s schooling in the older cohort but positive effects for children in the younger cohort. The positive result for the younger cohort is surprising as the literature on the consequences of growing up in female-headed families points mainly to negative effects for children (McLanahan 1985, 1994). As

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expected, correcting for gender has a negligible effect on the schooling gain of the younger cohort. Region of residence does not have a substantial impact on the change in schooling, which is due primarily to the small changes in the distribution of children across the five Brazilian regions.

While regional changes account for relatively little of the schooling gap across cohorts, rural and urban residence accounts for a substantial part of the two-decade gain in schooling. If children in the older cohort resided in urban areas at higher rates than children in the younger cohort, they would have a 24.5% schooling gain. Conversely, children in the younger cohort would lose 4.3% if they had the urban/rural proportions of children in the older cohort. The change in the distribution of children across rural and urban areas reflects Brazil’s increasing urbanization over the last three decades. DISCUSSION ANDCONCLUSION

This paper establishes the importance of number of siblings on schooling and school enrollment in two very distinct demographically periods in Brazil. Children in larger families from both pre- and post-fertility decline cohorts are disadvantaged compared with children in smaller families – that is, even with other factors accounted for, the negative association of family size and educational outcomes persists in the younger cohort. This persistence of the negative effect of family size after fertility decline suggests that children in larger families in the younger cohort are seriously disadvantaged and should be targeted in policies that aim to increase both enrollment rates and educational attainment. Research that focuses on children’s school attainment should examine siblings’ characteristics and labor market participation to expand this paper’s findings on how school decisions are played out among siblings within families.

Findings from decompositions of the cohort difference in schooling show that the fertility decline advantaged the younger cohort by increasing the proportion of smaller families, although the negative effect of family size on children’s education did not decline. The demographic transition has benefited children’s schooling by producing greater proportions of children with smaller number of siblings.

This paper provides evidence that schooling gains due to the transition from larger to smaller family sizes should not be underestimated. Most studies on educational attainment concentrate on examining the role of family’s socioeconomic status on educational outcomes. That in these analyses family size explains nearly as much of the change in the cohort schooling gap as mothers’ education calls attention to the stratification power of other within-family and demographic factors. The family provides the day-to-day care and the most intimate and long-lasting personal relationships for children. The resources and social capital available to children depend largely on the number, characteristics, and activities of family members. This study also demonstrates that much of the inhibition to educational attainment of Brazilians occurs early in life, which may be true for other developing countries. This reinforces the importance of family factors for educational stratification in such settings.

This study has established the significant role of family size for schooling gains in relation to other factors in Brazil. The obvious next question is: What are the mechanisms through which family size affects educational attainment? This question is not directly addressed here but since the sociological and demographic literatures consider that socialization, levels of intimacy, and communication vary by family size, the availability and allocation of family resources such as attention, support, and even finances are likely to operate in the relationship between family size and schooling. Further investigation elucidating these mechanisms would advance research on the educational consequences of fertility decline.

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Evidently, the reduction of family size has had a direct impact on the meaning of children for parents, and on their ability to support them. Thus increasing maternal education, declining family size, and growing urbanization are interconnected parts of Brazil’s rapid fertility decline, a period that encompassed considerable social change and both rapid economic growth and economic crisis. To disentangle directions of causality is out of the scope of this paper. Nonetheless, given the timing of the demographic transition in Brazil, the 1983 post-fertility decline cohort examined in this study is one of the first to feel the educational consequences of such a transition. As the effects of smaller family sizes are felt in more recent cohorts, it is likely that fertility decline will have broader social consequences on the educational attainment of these new generations of Brazilians.

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Knodel, John, N. Havanon and W. Sittitrai. 1990. “Family size and the education of children in the context of rapid fertility decline.” Population and Development Review 16(1).

Knodel, J. and G. Jones. 1996. “Post-Cairo population policy: does promoting girls' schooling miss the mark?” Population and Development Review, 22: 683-702.

Lloyd, C. 1994. “Investing in the next generation: The implications of high fertility at the level of the family.” in Population and Development: Old Debates, New Conclusions, edited by R. Cassen. Washington, DC: Overseas Development Council.

McLanahan, S. 1985. "Family Structure and the Reproduction of Poverty." American Journal of Sociology 90: 873-901.

——— 1994. “The consequences of single motherhood.” American Prospect 18: 48-58. Martine, G. 1996. "Brazil's Fertility Decline, 1965-95: A Fresh Look at Key Factors." Population and

Development Review 22(1):47-75. Parish, W. L. and R. Willis. 1993. Daughters, education, and family budgets: Taiwan experiences. The

Journal of Human Resources 28:863-98. Patrinos H. and G. Psacharopoulos. 1997. “Family size, schooling and child labor in Peru - an empirical

analysis.” Journal of Population Economics 10(4):387-405. Preston, S. 1976. “The changing relation between mortality and level of economic development.”

Population Studies vol. 29(2). Psacharopoulos, G. and A. Arriagada. 1989. “The determinants of early age human capital formation:

Evidence from Brazil.” Economic Development and Cultural Change 37:683-708. Shavit, Y. and J. Pierce. 1991. “Sibship size and educational attainment in nuclear and extended families:

Arabs and Jews in Israel.” American Sociological Review, vol. 56:321-30.

PSC Research Report 05-584 Page 15

Table 1. Socioeconomic and Family Characteristics of 14 Year-Olds [%] 1963 and 1983

Cohorts, Brazil

Cohort of 1963 Cohort of 1983

Family Headship

Female 9.16 18.42 Male 90.84 81.58

Total Number of Siblings 0 0.07 2.40 1 2.68 3.89 2 6.91 21.02 3 10.92 24.98 4 13.24 14.63 5 11.66 8.77 6 11.98 6.89 7+ 42.54 16.84

Rural/Urban Residence Urban 62.71 77.76 Rural 37.29 22.24

Region Southeast = 0 42.79 40.96 North = 1 2.01 5.51 Northeast = 2 31.40 32.13 South = 3 20.38 14.61 Central = 4 3.42 6.78

Gender Male 50.77 50.60 Female 49.23 49.40

Mother’s Education No Education (0) 36.94 19.43 Attended First Primary (1-4) 47.08 38.72 Attended Second Primary (5-8) 11.33 22.81 Attended High School (9-11) 3.26 11.72 Attended University or more (12+) 1.37 7.30

[N] 11,269 7,131 Source: From author calculations of PNADs 1977 and 1997 data.

PSC Research Report 05-584 Page 16

Table 2. School Enrollment and Schooling by Family and Socioeconomic Characteristics of 14 Year-old

Children of the Head of the Family, 1963 and 1983 Cohorts, Brazil

Mean Years of Schooling Enrollment Rates

[%]

Cohort of 1963 Cohort of 1983 Cohort of 1963 Cohort of 1983

Family Headship Female 3.25 4.76 74.64 86.32 Male 3.43 4.52 75.61 89.21

Total Number of Siblings 0 3.71 5.88 74.16 75.57 1 4.81 5.52 81.60 94.76 2 3.66 5.78 89.55 95.81 3 4.07 5.40 87.23 92.74 4 3.61 4.61 81.66 88.62 5 3.16 4.15 76.32 86.34 6 2.67 3.62 72.73 79.32 7+ 2.58 3.42 67.57 82.24

Rural/Urban Residence Urban 4.16 5.09 83.59 90.98 Rural 2.14 3.44 75.75 80.66

Region Southeast 4.14 5.41 75.96 90.68 North 3.42 4.03 94.97 89.69 Northeast 1.98 3.49 83.59 86.12 South 4.01 5.64 58.91 88.45 Central 3.81 4.96 87.80 88.47

Gender Male 3.19 4.39 77.67 86.96 Female 3.62 5.05 72.38 90.45

Mother’s Education No Education (0) 2.13 2.91 66.19 76.35 Attended First Primary (1-4) 3.80 4.55 75.29 87.01 Attended Second Primary (5-8) 5.00 5.31 89.91 94.88 Attended High School (9-11) 5.81 6.13 97.69 98.23 Attended University or more (12+) 6.37 6.58 100.00 99.40

Family Income (Quintiles) First Quintile 1.86 3.24 68.96 81.83 Second Quintile 2.47 3.98 68.05 83.32 Third Quintile 3.28 4.74 70.57 87.59 Fourth Quintile 4.02 5.52 75.65 93.42 Fifth Quintile 5.09 6.20 88.83 97.52

[N] 7,162 6,672 7,162 6,672 Source: From author calculations of PNADs 1977 and 1997 data.

PSC Research Report 05-584 Page 17

Table 3. Coefficients and Standard Deviations of Logistic Regressions of School Enrollment and OLS Regressions of Years of Schooling 14 Year-old Children of the Head of the

Family, 1963 and 1983 Cohorts, Brazil School Enrollment Years of Schooling Cohort of 1963 Cohort of 1983 Cohort of 1963 Cohort of 1983 Coeff SD Coeff SD Coeff SD Coeff SD Family Headship 0.095 0,17 -0,273 0,16 0,093 0,052 -0,274 0,05

(Omitted=female) Number of siblings (Omitted=7+)

0 0,866 0,46 1,032 0,47 0,043 0,160 0,071 0,111 0,907 0,26 1,103 0,25 -0,021 0,155 -0,021 0,112 0,844 0,16 1,011 0,21 -0,275 0,153 -0,325 0,113 0,589 0,14 0,707 0,21 -0,440 0,154 -0,456 0,124 0,424 0,13 0,577 0,23 -0,685 0,154 -0,846 0,125 0,179 0,13 0,186 0,21 -0,680 0,155 -0,860 0,136 0,184 0,12 0,172 0,28 -0,865 0,150 -1,033 0,12

Rural/Urban Residence (Omitted=rural) -0,985 0,09 -0,404 0,15 -0,985 0,040 -0,404 0,05

Region (Omitted=Southeast) North -0,817 0,35 -0,829 0,28 -0,817 0,123 -0,830 0,09Northeast -1,057 0,11 -0,927 0,17 -1,057 0,044 -0,928 0,05South 0,372 0,11 0,265 0,19 0,372 0,046 0,265 0,06Central -0,505 0,17 -0,289 0,25 -0,505 0,095 -0,289 0,08

Gender 0,388 0,638 0,389 0,033 0,638 0,03Mother's Education 0,201 0,02 0,119 0,03 0,201 0,007 0,120 0,00Log Family Income 0,454 0,05 0,387 0,07 0,454 0,022 0,387 0,02Constant -0,594 0,46 1,175 0,53 0,271 0,225 2,212 0,18R Squared - - - - 0,455 0,452 [N] 9.423 6.408 10.417 6.408

Source: From author calculations of PNADs 1977 and 1997 data.

PSC Research Report 05-584 Page 18

Table 4. Average of Decomposition of Schooling Difference between 1963 and 1983 Cohorts of 14 Year-olds, Brazil Schooling estimated

using mean of alternative cohort:

Difference between actual and calculated

mean schooling:

Average Percent of change in schooling

explained by variable: Mother’s Education 4.23 0.43 33.20% Family Size 4.18 0.39 30.09% Log Family Income 4.18 0.03 2.26% Family Headship 4.20 0.08 6.33% Gender 4.09 -0.01 -0.61% Rural/Urban Residence 4.23 0.19 14.43% Region of Residence 4.18 0.02 1.71% 1963 Actual Schooling 3.43 1983 Actual Schooling 4.75 Source: From author calculations of PNADs 1977 and 1997 data

PSC Research Report 05-584 Page 19

FIGURE 1. PROPORTION OF 14 YEAR-OLDS WITH X SIBLINGS OR MORE – CUMMULATIVE: BRAZIL, 1963 & 1983 COHORTS

81%

68%

56%

44%

33%

92%

100% 98%

12%17%

74%

48%

33%24%

95%

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 7Total Number of Siblings

Prop

ortio

n of

14

year

-old

s

1963 Cohort 1983 Cohort

PSC Research Report 05-584 Page 20

FIGURE 2. PROPORTION OF 14 YEAR-OLDS ENROLLED IN SCHOOL BY FAMILY SIZE: BRAZIL, 1963 & 1983 COHORTS

8688

83

7977

75

9491

89 89 89

85

91

76

72

67 68

8986

84

7874

85

94

8588 87

82 79

9396

95

60

70

80

90

100

0 1 2 3 4 5 6 7Total Number of Siblings

Prop

ortio

n of

14

Yea

r-ol

d

Adjusted 1963 Cohort Adjusted 1983 CohortUnadjusted 1963 Cohort Unadjusted 1983 Cohort

PSC Research Report 05-584 Page 21

FIGURE 3. ADJUSTED AND UNADJUSTED SCHOOLING OF 14 YEAR-OLDS BY FAMILY SIZE: BRAZIL, 1963 & 1983 COHORTS

4.0 4.0 3.93.7

3.53.3 3.3

3.1

5.0 5.1 5.04.7 4.6

4.2 4.24.0

4.3

4.7

4.1

2.92.7

5.65.8

5.4

4.2

3.6

3.13.4

4.8

3.2

3.6

5.0

3.4

4.6

2.0

3.0

4.0

5.0

6.0

0 1 2 3 4 5 6 7Total Number of Siblings

Yea

rs o

f Sch

oolin

g

Adjusted 1963 Cohort Adjusted 1983 CohortUnadjusted 1963 Cohort Unadjusted 1983 Cohort

1963 1983