Family Size, Sibling Rivalry and Migration

Family Size, Sibling Rivalry and MigrationEvidence from Mexico

Mariapia Mendola (U Milan-Bicocca)

joint withMassimiliano Bratti (U Milan)

Simona Fiore (U Venice)

Summer School in Development Economics – Prato, June, 2017

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Objective

I Investigate the within-family dimension of internationalmigration choices in Mexico

I Isolate the causal effect of sibship size, birth order and siblingcomposition (by age and gender) on young adults’ migrationoutcomes

I Shed light on (i) the inter-household and (ii) intra-householdallocation of migrants:

I Do migrants come from larger families?I Is there any systematic role for the household structure? I.e.

who migrates among sibilings within the family?

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Motivation

I Migration ia a key human capital investment whose returnsdecline with age (Sjaastad, 1962; Dustmann and Glitz, 2012)

I Workers moving from a poor to a rich country can experiencesignificant increases in earnings and welfare (Ashenfelter, 2012)

I It is typically a household-level decision mostly involving (onlya few) young members

I International migrants aged 15-35 (15-24) account for over30% (13%) of total migrants (UNDESA, 2001)

I In developing contexts this is a household resource allocationproblem, which entails a number of trade-offs (Rosenzweig,1988; Ghatak and Price, 1996)

I Little evidence on the impact of the childhood family(i.e. siblings) on offspring international migration

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This paper

I We add to the literature on the determinants of migration bydocumenting the causal impact of the household enviromenton migration choices

I This is studied for the case of Mexico-US migration in the1990s (when both migration and fertility rates weresubstantial) by using a large national demographic survey(ENADID).

I Identification of family size comes from exploiting exogenousvariation in the number of born children per mother thatoccurs due to miscarriages and infertility shocks.

I We exploit differences across siblings to identify the impact ofbirth order and sibling composition on child’s migrationoutcome

I We find no evidence that family size drives migration, whilewe show that the chances to migration are not equallydistributed across siblings within the household.

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Contribution

I By addressing the causal effect of parental fertility onmigration outcomes, we show that family size per se is notpush factor for migration – first order effects of population size

I We shed light on (intended or unintended) consequences onmigration of family-planning programs in developing countries

I Our findings hint to the fact that migration may matter forfertility decisions– i.e. migration is endogenous. This is so aschildren provide support to the family in developing countries,even through migration.

I By showing that not all children in the household have thesame chance to migrate, our results point to the existence ofan intra-household selection process which may havesignificant implications in terms of migration impact.

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Outline

I Background literature

I The context of Mexican migration

I Data

I Empirical strategy and identification

I Results

I Discussion and Conclusions

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Background— 1

I Migration as an investment strategy for households indeveloping countries:

I High returns for the migrant in terms of higher productivity,income (Gibson and McKenzie 2012)

I Returns for the origin household, in terms of remittances,access to credit, risk-management, social security (Chen et al.,2003; Stark 1991)

I Extensive evidence on the support of migrants to theirhouseholds of origin (e.g. Rapoport and Docquier, 2006;Ratha et a. 2013; Antman, 2012)

I Little evidence, though, on the household-level determinantsof this decision problem

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Background— 2

I The link btw household structure and investment in children’shuman capital has been influenced by the Q-Q trade-offtheory (Becker 1960, Becker and Lewis 1973, Becker andTomes, 1976)

I Resource dilution effects lead to a fall in child migration

I In developing countries though, parents use their children as asubstitute for missing institutions/mkts (children as’investment goods’)- e.g. old-age social security (Cigno1993, Ray 1998, Duflo and Banerjee, 2011).

I Resource-augmentation effect (from children to parents) suchthat more siblings increase the likelihood to migrate

I Diversification effect

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Background— 3

I A related question is how family structure affects migrationoutcomes among children within the same family

I If migration is placed in a household resource allocationframework, birth order and sibling composition may influencethe marginal utility from child migration

I In the human capital literature, birth order has been found tohave a significant influence on health or education(Jayachandran and Pande 2017, Pande 2003)

I By affecting household resources, the composition of siblings(by age and gender) may influence child outcomes unevenly(sibling rivarly effect) (Morduch 1998)

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The context– Mexico

I One of the largest migrant-sending remittance-recipientcountries in the world

I The approximate number of Mexicans moving to the U.S.annually — with or without documents — has increasedsteadily since the 1970s

I Between 1970 and 1980, according to U.S. Census data,Mexican immigrants went from the fourth largest to thelargest immigrant group in the U.S.

I According to Mexican records, more than 5% of Mexicanpopulation moved to the U.S. in 1990 and more than 10% in2005

I A high-fertility country where the number of children perwoman gradually declined from an average of 6 in the 70s toabout 2.5 children in the 2000 (Cabrera, 1994).

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Mexican migrationI Mexican migration patterns differ by age and gender, with a

significant fraction of Mexican males migrating in the agebetween 15 and 30

Source: Hanson and McIntosh, 2010

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Data

I Encuesta Nacional de la Dinamica Demografica (ENADID),pooled cross-section 1992, 1997

I Big samples (50,000 hhs each wave), nationally representative,detailed data on both fertility and migration histories

I Fertility on all women aged 15-54

I Migration of all household members, even absent (undercountof permanent migration)

I In the 1997 (1992) ENADID, 18% (15%) of households reporthaving a member migrated in the US.

I We define individual migration episodes as non-tied migration,i.e. we exclude from the sample individuals migrated jointwith their parents or other household members.

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Figure: Mexican individual migration by age and gender

0.0

2.0

4.0

6.0

8.1

.12

5 20 35 50Age at migration

Male Female

Source: Our computations on ENADID, 1992 and 1997.

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Sample selection

I We focus on children aged 15 to 25

I The number of siblings is tallied as the number of each child’sbiological brothers and sisters currently alive

I In line with the medical definition of infertility, we restrict oursample to children of nonsterilized women who are notcurrently using contraceptives or who never did (about 80% ofthe sample) (Aguero and Marks 2011).

I Our final estimation sample is made of 26,743 Mexicanyoungsters in the age range 15-25

I Average mothers’ age (45) and average birth spacing btw firstand last child (13) ensure we measure completed fertility atthe moment of child’s migration.

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Figure: Individual migration rate by family size

0.0

5.1

.15

2 3 4 5 6 7 8 9 10 11Family size

Male Female

Source: Our computations on ENADID, 1992 and 1997.

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Descriptive stats

Table: Sample characteristics by migration status

Non-migrants Migrants p−values(A) (B) (A)-(B)

Individual-level characteristicsAge 18.878 20.982 0.000Female 0.458 0.250 0.000N. of siblings 5.071 5.869 0.000Birth order 1 0.181 0.192 0.300Birth order 2 0.231 0.225 0.555Birth order 3 0.178 0.178 0.978Birth order 4 0.137 0.154 0.077Birth order 5 0.102 0.102 0.993Birth order 6 0.071 0.073 0.781Birth order 7 0.046 0.041 0.343Birth order 8 0.028 0.021 0.100Birth order 9 0.014 0.009 0.121Birth order 10+ 0.011 0.006 0.107Household-level characteristicsMother’s age 44.612 46.171 0.000Mother’s age at first pregnancy 20.030 19.699 0.182Mother’s years of schooling 4.091 3.452 0.010Mother chronic illness 0.023 0.008 0.131Single mother 0.185 0.188 0.896Mother’s labor income 600 862 0.037Father’s age 48.799 52.207 0.000Father’s years of schooling 4.931 3.789 0.059Father’s labor income 2,112 3,135 0.000

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Empirical Strategy— 1I Two—steps procedure in order to control for both sibship size

and birth order effects— the two variables have littleindependent variation (Black et al. 2005, Bagger et al. 2013)

I I step: estimate birth order effects by exploiting within familyvariation (family FE estimator)

Mij = α0 +K∑

k=1

α1kboijk + α2Xij + uj + εij (1)

Xij is a vector of individual level characteristics (gender, age,age square, birth cohort fe)

I II step: estimate family size effect exploiting cross-familyvariation (OLS and IV estimator) –dependent variable is thenetted–migration (NM), where

NM ij = Mij −K∑

k=1

α1kboijk

(2)17 / 34

Empirical Strategy— 2

I II step: sibship size effect

NM ij = β0 + β1Sij + β2Xij + β3Wj + vij (3)

I Wij is a vector of household level characteristics (mother’sand father’s age, education, maternal health, single mother),plus municipality FE

I we correct standard errors by weighting the estimation withthe inverse of the standard error of NM ij

I Standard errors are clustered at the household level

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Identification Strategy

I The estimation of (3) with OLS may deliver spurious resultsbecause of unobservables generating a bias

I For example, monetary or non-monetary factors that increasefertility choices may also affect the decision to have a childmigrating to the US.

I IVs: we instrument sibship-size with biological fertility(subfecundity) and infertility shocks (miscarriage at firstpregnancy)

I Biological infertility: indicator variable for whether womendeclare they never took contraception or stopped doing thatbecause of infertility (Aguero and Marks, 2011)

I Miscarriages: women report miscarriage episode on their firstpregnancy (Miller, 2011)

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Relevance of IVs- infertility shock

Figure: Average number of children by women’s subfecundity status

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56

No.

of l

ive

birth

s

30 35 40 45 50 55Women's age

Infertile Fertile

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Relevance of IVs- miscarriage at first birth

Figure: Average number of children by women’s miscarriage at firstpregnancy

23

45

6N

o. o

f liv

e bi

rths

30 35 40 45 50 55Women's age

Miscarriage No Miscarriage

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Exogeneity of IVs

I The epidemiological literature has been finding few riskfactors driving secondary infertility (Buck et al. 1997)

I In the socio-economic literature subfecundity has beenassessed as virtually random (uncorrelated with femalebackground characteristics) in both advanced and developingcountries (Wilcox and Mosher, 1993; Aguero and Marks 2011;Markussen and Strom, 2015)

I Yet, in our estimates we do control for a number of key andpredetermined characteristics, in particular mother’s education(which is correlated with the cumulative level of nutrition andhuman capital) and father’s educaiton (which is a proxy forhousehold well-being).

I As RC we run regressions while controlling for municipality byyear FEs and municipality by father’s education (high/low) FEs

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Results– Birth order effectsVariables (1) (2)

female -0.036*** -0.035***(0.003) (0.003)

birth order -0.019***(0.003)

birth order 2 -0.002(0.005)

birth order 3 -0.021***(0.007)

birth order 4 -0.038***(0.010)

birth order 5 -0.068***(0.013)

birth order 6 -0.086***(0.016)

birth order 7 -0.112***(0.019)

birth order 8 -0.136***(0.022)

birth order 9 -0.161***(0.026)

birth order 10+ -0.199***(0.030)

age 0.020** 0.021**(0.009) (0.009)

age squared 0.000 0.000(0.000) (0.000)

Year of birth indicators YES YESFamily fixed effects YES YESObservations 26,743 26,743

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Sibship size effect: WLS estimates

Variables (1) (2) (3) (4) (5) (6) (7)

N. siblings 0.011*** 0.011*** 0.014*** 0.010*** 0.013*** 0.010*** 0.013***(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

N. siblings × female -0.008*** -0.007*** -0.006***(0.001) (0.001) (0.001)

female -0.038*** -0.036*** -0.033*** -0.031*** -0.033*** -0.031***(0.003) (0.006) (0.003) (0.006) (0.003) (0.003)

Individual’s controls YES YES YES YES YES YESMother’s controls YES YES YES YESFather’s controls YES YES YES YESMunicipality indicators YES YESObservations 26,743 26,743 26,743 26,743 26,743 26,743 26,743

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Sibliship size effect: 2SLS estimates

Variables (1) (2) (3)

Second stageN. siblings 0.004 -0.018 -0.005

(0.014) (0.023) (0.012)female -0.033*** -0.033*** -0.033***

(0.003) (0.003) (0.003)

IV: infertility miscarriage overidentifiedAnderson-Rubin F−statistic 0.0734 0.686 0.389

[0.787] [0.407] [0.678]Hansen J−statistic 0.737

[0.391]

First stage — N. siblingsinfertility -0.494*** -0.491***

(0.095) (0.095)miscarriage -0.437*** -0.433***

(0.10) (0.10)Angrist-Pischke F−statistic instrument(s) 26.90 19.13 23.37Individual’s controls YES YES YESMother’s controls YES YES YESFather’s controls YES YES YESMunicipality indicators YES YES YESObservations 26,743 26,743 26,743

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Child gender and sibliship size effect: 2SLS estimatesVariables (1) (2) (3)Second stageN. siblings 0.005 -0.065 -0.007

(0.016) (0.048) (0.015)N. siblings × female -0.005 0.112 0.005

(0.013) (0.079) (0.013)female -0.032*** -0.064*** -0.034***

(0.004) (0.022) (0.005)IV: infertility miscarriage overidentifiedAnderson-Rubin F−statistic 0.074 2.210 1.150


[0.111]First stage — N. siblingsinfertility -0.567*** -0.564***

(0.109) (0.108)infertility × female 0.168 0.169

(0.115) (0.115)miscarriage -0.453*** -0.450***

(0.117) (0.117)miscarriage × female 0.037 0.038

(0.106) (0.105)Angrist-Pischke F−statistic instrument(s) 28.62 11.98 15.68First stage — N. siblings × femaleinfertility 0.125*** 0.125***

(0.038) (0.038)infertility × female -0.694*** -0.691***

(0.131) (0.131)miscarriage -0.067 -0.068

(0.044) (0.043)miscarriage × female -0.261** -0.254*

(0.131) (0.130)Angrist-Pischke F−statistic instrument(s) 26.93 4.27 13.83Observations 26,743 26,743 26,743 26 / 34

Robustness check: Household level analysis

I It allows us to test the robustness to both estimation strategyand estimation sample

I Household-level regressions disregard birth order effects andallow us to include one-child households in the estimationsample

I We estimate a specification as follows:

mj = γ0 + γ1nj + γ2Wj + vj (4)

where mj is the number of migrants and nj , is total fertility inhousehold j .

I We estimate both OLS and 2SLS using the same instrumentsas in the individual-level models

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HH-level results

(1) (2) (3) (4)Variables OLS 2SLS 2SLS 2SLS

Second stageN. children 0.012*** -0.001 -0.014 -0.005

(0.001) (0.013) (0.022) (0.011)

IV: — infertility miscarriage overidentifiedAnderson-Rubin F−statistic 0.006 0.400 0.203


[0.607]

First stage — N. childreninfertility -0.643*** -0.640***

(0.063) (0.063)miscarriage -0.442*** -0.438***

(0.070) (0.070)Angrist-Pischke F−statistic instrument(s) 103.87 39.81 72.40Mother’s controls YES YES YES YESFather’s controls YES YES YES YESMunicipality indicators YES YES YES YESObservations 17,544 17,544 17,544 17,544

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Discussion

I So far we found no causal effect of fertility (family size)on migration outcomes, while controlling for birth order

I The latter result is robust to the use of different IVs, samplesand empirical models.

I All else equal though, the invididual likelihood of migration issistematically higher for boys and decreases with birth order

I The latter results is consistent with an optimal migraitonmodel where family’s migration returns (i) are higher for boysand (ii) decrease with child parity

I It may be also explained by a pure preference for sons or foreldest children– Hence we explore the interplay between childgender, b.o. and sibling composition in order to better pindown the role of household environment on migrationoutcomes.

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Migration, gender and birth orderTable 9: Birth order effects

Variables (1) (2)

female -0.032*** -0.031***(0.006) (0.007)

birth order -0.019***(0.003)

birth order x female -0.001(0.001)

birth order 2 0.002(0.006)

birth order 3 -0.023***(0.008)

birth order 4 -0.034***(0.011)

birth order 5 -0.070***(0.014)

birth order 6 -0.077***(0.017)

birth order 7 -0.103***(0.020)

birth order 8 -0.140***(0.023)

birth order 9 -0.166***(0.028)

birth order 10+ -0.188***(0.033)

birth order 2, female -0.011(0.009)

birth order 3, female 0.005(0.010)







birth order 10+, female -0.022(0.027)

age 0.020** 0.021**(0.009) (0.009)

age squared 0.000 0.000(0.000) (0.000)

Year of birth indicators YES YESFamily fixed effects YES YESObservations 26,743 26,743R-squared 0.050 0.053

Note. The dependent variable is a dichotomous indicator of the child’s migration status. The model is estimatedusing OLS. Sibship size is absorbed by family fixed effects. Standard errors clustered at the household level inparentheses. *,** and *** denote statistical significance at 10, 5 and 1 percent level, respectively.

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4

Discussion

I Low parity children are in general more likely to migrate but afirst-born daughter is significantly less likely to migrate than afirst-born son (by 3 p.p. which translates in a probability lowerthan the average by 60 percent)

I This is consistent with Mexican migration being a maledominated phenomenon due to higher labor market returnsand lower opportunity costs (both in Mexico and the U.S.) forMexican boys than for girls

I In order to better separate whether these results are due to apro-eldest-son bias or to the intrahousehold allocation ofresources, we next estimate the direct effect of sibilingcomposition, while controlling for family size, gender and birthorder.

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Migration as a function of sibling composition

Variables (1) (2) (3) (4)

N. older brothers -0.014*** -0.014*** -0.017*** -0.016***(0.004) (0.004) (0.004) (0.005)

female -0.028*** -0.026*** -0.022*** -0.016***(0.003) (0.005) (0.005) (0.006)

N. older brothers × female -0.002 -0.002 -0.003(0.002) (0.002) (0.002)

Next brother -0.005 0.001(0.004) (0.004)

Next brother × female -0.012**(0.006)

Age, age squared YES YES YES YESBirth order fixed effects YES YES YES YESYear of birth indicators YES YES YES YESFamily fixed effects YES YES YES YES

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Discussion

I We find that sibling composition (by age and gender) mattersfor child’s migration outcomes, due to intrahouseholdallocation of resources.

I In particular, while having an older brother instead of an oldersister decreases the likelhood to migrate for boys and girlsalike, having a next-born brother instead of nex-born sistersignificanlty reduces the chances to migrate for girls only.

I Indeed, a low-parity Mexican boy in the 90s may be morevaluable to send as a household migrant abroad than a girl.Plus, the opportunity cost of sending girls abroad is higherbecause of household productions duties and social norms.

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Conclusions

I By using detailed individual-level data we provide novelevidence on the impact of siblings on migration of Mexicanyoungsters to the US.

I We find no causal effect of fertility- opposite to popular viewbut consistent with the literature on the determinants ofhuman capital investment.

I On the other hand, the chances to migrate are not equallydistributed across children within the same family: Oldersiblings, especially firstborn sons, are more likely to migrate,while having more brothers than sisters systematicallydecreases the chance of migration, especially among girls.

I Results are stable with respect to several robustness checksand are consistent with an optimal household migrationstrategy where private costs and returns of migration areshared among all siblings.

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Family Size, Sibling Rivalry and Migration