How family size affects children’s schooling and work in Mexico

Emla Fitzsimons

Bansi Malde

27 Feb 08

Introduction• Question of Interest

– Does family size affect children’s school and/or work participation?

• Theory– Quantity-Quality model: bigger families (quantity) reduce

investment in schooling (quality)

• Empirical Issues– To test how well the quantity-quality model fits reality

requires exogenous changes in fertility that are uncorrelated with preferences or budget constraints

– We use Instrumental Variables to identify the causal effect

Instrumental Variables We use the following instruments

– Twin births: increases family size by definition

– First n children are of the same sex: increases family size if parents have preferences for having children of both sexes

• Note, the literature that considers the causal effects of family size basically always uses these instruments

A glance at the literature!• Most of these studies consider developed

countries and outcomes different to ours (female labour supply)

• Literature most comparable to what we do (effects of family size on education in developing countries):– India: Rosenzweig & Wolpin (1980) – China: Qian (2006), Rosenzweig & Zhang (2006)– Korea: Lee (2004)– Brazil: Ponczek & Souza (2007)– Colombia: Baez (2007)

• We will hopefully improve on them thanks to extremely large samples

Methodology

• Basic model

• IV first stage– Using same-sex instrument

– Using twins instrument

Fi = α1Xi + θ1S12,i + ςi

Yi=0+1Xi+2 Fi+ui

Fi = γ1Xi + λ1ti + ξi

Data

• The main source we use is the Mexican ENCASEH survey: cross-sectional census data collected across marginalised rural areas b/w 1996 and 1999

• Info on individual, household and locality characteristics– Restrict sample to children aged 11-17– Drop households with

• both parents not living together/not married• eldest child>18 • >1 household head

So we’re left with a sample of ~600,000 households and~1.1million children aged 11-17

• Note, average # of children per family in our sample is 4.3• We’re also going to merge these data with the PROGRESA

surveys, as there’s v useful info for our purposes in these - we’ll come back to this later

Randomisation check - same-sex of first 2 births

Table 1 Mean characteristics by same-sex of first two children Variable Same-

sex=0 Same-sex=1

p-value difference

Father’s age 39.686 39.678 0.729

Mother’s age 35.515 35.482 0.166

Mother’s age at first birth 21.649 21.625 0.189

Father’s years of education 3.679 3.668 0.188

Mother’s years of education 3.308 3.301 0.328

Birth spacing between first and second births 2.178 2.174 0.388

Number of siblings 4.122 4.185 0.000

N 273,583 277,068

Randomisation check - twins at

second birth Table 1 Mean characteristics by twins at second birth

Variable Twins at second birth=0

Twins at second birth=1

p-value difference

Father’s age 41.013 42.701 0.000

Mother’s age 36.774 38.335 0.000

Mother’s age at first birth 21.935 23.240 0.000

Father’s years of education 3.416 3.437 0.754

Mother’s years of education 3.042 3.180 0.022

Birth spacing between first and second births 2.159 2.438 0.000 Number of siblings 4.525 4.875 0.000 N 357,468 1,647

Notes to table: N refers to the number of first-born children

First stage results – Boys and Girls (1) (2) (3) (4) (5) COEFFICIENT No of siblings No of siblings No of siblings No of siblings No of siblings

samesex 0.069** (0.004) gg 0.122** (0.005) bb 0.020** (0.006) ggg 0.113** (0.009) bbb 0.057** (0.007) Twins at second birth 0.501** (0.025) Twins at third birth 1.000** (0.059) Observations 511304 511304 513935 429295 429937 R-squared 0.21 0.21 0.21 0.16 0.16 F test 305.68 262.24 396.62 109.26 282.43 Prob>F 0.000 0.000 0.000 0.000 0.000 Robust standard errors in parentheses Also control for parental, household and village characteristics (not shown). All columns show the first-stage regressions for the school participation analysis; first-stage regressions for the years of education analysis are very similar and not shown. Robust standard errors in parentheses, clustered at village level. ** p<0.01, * p<0.05, + p<0.1

First Stage Results - Girls (1) (2) (3) (4) COEFFICIENT No of

siblings No of siblings

No of siblings

No of siblings

gg 0.122** (0.005) ggg 0.113** (0.009) Twins at second birth 0.533** (0.036) Twins at third birth 1.047** (0.088) Observations 242094 243323 204246 204546 R-squared 0.21 0.21 0.16 0.16 F test 509.70 218.88 162.52 141.33 Prob>F 0.000 0.000 0.000 0.000 Robust standard errors in parentheses Also control for parental, household and village characteristics (not shown). All columns show the first-stage regressions for the school participation analysis; first-stage regressions for the years of education analysis are very similar and not shown. Robust standard errors in parentheses, clustered at village level. ** p<0.01, * p<0.05, + p<0.1

First Stage Results - Boys (1) (2) (3) (4) COEFFICIENT No of

siblings No of siblings

No of siblings

No of siblings

bb 0.020** (0.006) bbb 0.057** (0.007) Twins at second birth 0.474** (0.035) Twins at third birth 0.960** (0.071) Observations 269210 270612 225049 225391 R-squared 0.21 0.21 0.16 0.16 F test 12.76 179.79 59.45 185.35 Prob>F 0.000 0.000 0.000 0.000 Robust standard errors in parentheses Also control for parental, household and village characteristics (not shown). All columns show the first-stage regressions for the school participation analysis; first-stage regressions for the years of education analysis are very similar and not shown. Robust standard errors in parentheses, clustered at village level. ** p<0.01, * p<0.05, + p<0.1

TSLS estimates for First Born Boys and Girls

(1) (2) (3) (4) (5) (6) COEFF OLS, All

children IV, All children, instrument=samesex

IV, All children, instrument=gg,bb

IV, All children, instrument=twinsec

IV, All children, instrument=ggg,bbb

IV, All children, instrument=twin3

No. of siblings -0.022** 0.050** -0.002 -0.013 0.011 -0.016 (0.001) (0.017) (0.013) (0.017) (0.019) (0.017) Observations 511304 511304 511304 513935 429295 429937 Robust standard errors in parentheses Also control for parental, household and village characteristics (not shown). All columns show the first-stage regressions for the school participation analysis; first-stage regressions for the years of education analysis are very similar and not shown. Robust standard errors in parentheses, clustered at village level. ** p<0.01, * p<0.05, + p<0.1

TSLS estimates for First Born Girls

(1) (2) (3) (4) (5) COEFF OLS,

girls IV, girls, instrument=gg

IV, girls, instrument=twinsec

IV, girls, instrument=ggg

IV, girls, instrument=twin3

No. of siblings -0.023** -0.014 -0.015 0.002 -0.043+ (0.001) (0.013) (0.023) (0.022) (0.023) Observations 242094 242094 243323 204246 204546 Robust standard errors in parentheses Also control for parental, household and village characteristics (not shown). All columns show the first-stage regressions for the school participation analysis; first-stage regressions for the years of education analysis are very similar and not shown. Robust standard errors in parentheses, clustered at village level. ** p<0.01, * p<0.05, + p<0.1

TSLS estimates for First Born Boys

(1) (2) (3) (4) (5) COEFF OLS,

boys IV, boys, instrument=bb

IV, boys, instrument=twinsec

IV, boys, instrument=bbb

IV, boys, instrument=twin3

No. of siblings -0.021** 0.400** -0.011 0.052 0.011 (0.001) (0.134) (0.023) (0.042) (0.025) Observations 269210 269210 270612 225049 225391 Robust standard errors in parentheses Also control for parental, household and village characteristics (not shown). All columns show the first-stage regressions for the school participation analysis; first-stage regressions for the years of education analysis are very similar and not shown. Robust standard errors in parentheses, clustered at village level. ** p<0.01, * p<0.05, + p<0.1

Issues/Next Steps

Validity of Instruments

Economies of scale? (Rosenzweig and Wolpin (2000))

Issues/Next StepsRobustness Checks

– Compare results across different instruments (what we have just shown)

– Use an instrument that should suffer much less from this issue: twins at second birth that are of different sex from first-born sensitivity first stage.doc; sensitivity second stage.doc

– Use PROGRESA consumption data to see if there is any evidence of economies of scale

• we observe expenditure on children’s clothes and shoes, separately by sex: we are going to use these data to see if sex-sameness affects these expenditures

• we also observe value of assets, may be useful

– Assess sensitivity of parameter estimates to different correlations b/w the IV and the error term using method of Ashley (2008); (note refine correlations using info from PROGRESA data)

Issues/Next Steps

• Results obtained from the IV are all LATE and affect only particular types of households– How do we reconcile all these different

LATEs?

Any Comments/Questions?