1

Fertility, Reproductive Health and Economic

Development:Preliminary results of the

World Bank research program supported by the William and Flora

Hewlett FoundationPresented by Halsey Rogers and Kathleen Beegle World Bank June 4, 2009

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Thematic areas

Fertility and investments in child quality 4 studies

Fertility, poverty, and family welfare in the time of HIV/AIDS 2 studies

Fertility and female labor supply 2 studies

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Thematic Area 1: Fertility and investments in child quality 4 research projects in this category

Family size Family size and early childhood development:

evidence from Ecuador Declining fertility and rising household

investment in education in Vietnam Gender preference

Development, modernization, and childbearing: The role of family gender composition

Financial incentives for female births and parental investments in daughters in North India

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Motivating question: How do fertility choices affect investments in children? A large literature documents associations between family size

and children’s outcomes: In developed countries, many studies have documented a

negative association between family size and educational attainment

Research in developing countries has documented negative associations between children’s health nutritional outcomes and family size

Negative associations between family size and child outcomes could be due to a number of factors: Resource dilution (both financial and parental) produces

“quality-quantity” tradeoffs. Son preference adds another dimension to resource dilution, as parents prefer to invest in sons than in daughters

Changes in family dynamics—larger families may have lower “average maturity” of household members

Omitted variables or selection: characteristics of families that result in larger family size also result in poorer child outcomes

This set of studies documents determinants of family size and tests quantity-quality hypothesis in ways that control for selection/omitted variables

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Family size and early child development: evidence from Ecuador

Christina Paxson (Princeton University) andNorbert Schady (World Bank)

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Questions and data Research questions:

Part 1: What is the association between (1)cognitive and nutrition outcomes in early childhood and (2) family size?

Part 2: Do children in families that grow between baseline and follow-up experience (relative) declines in their cognitive and nutritional outcomes? Use detailed information on maternal characteristics,

including cognitive ability, mental health and parenting behaviors

Information on multiple children in the households permits within-family estimates, and longitudinal information makes it possible to examine how the presence of “new” children influences the outcomes of their older siblings

Sample 4200 low-income families with about 6700 children aged

0-6 at baseline From rural and urban areas of 6 provinces in Ecuador Longitudinal data on families, with two interviews

spaced approximately 18 months apart, with 1,124 births between baseline and the 1st follow-up

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Test of family-size effect

If family size merely reflects family-specific unobservables, we expect that children of families that are going to grow will fare worse

If family size has a negative effect on children, we expect that children in families that grow will experience declines in outcomes relative to children in families that remain the same size

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Result 1: Cognitive and nutritional outcomes are strongly associated with family size (1)

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Result 1: Cognitive and nutritional outcomes are strongly associated with family size (2)

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Result 2

However, there are large differences in the characteristics of large and small families Select one- and two- child families at baseline Examine outcomes that were measured at both

waves: TVIP score, hemoglobin and height Use panel data to:

Examine whether the children in families that grow between baseline and follow-up have worse outcomes at baseline

Examine whether children in families that grow between baseline and follow-up experience declines in their cognitive and nutritional outcomes

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Table 5a – Child outcomes at baseline and changes in outcomes between baseline and follow-upOne-child families

TVIP Hemoglobin Height ∆TVIP ∆Hemoglobin ∆Height

One-child families at baseline; no family controls

Indicator: New child between baseline and follow-up

–0.103 (0.065)

–0.118 (0.048)

–0.198 (0.074)

0.001 (0.066)

0.089 (0.072)

0.069 (0.065)

One-child families at baseline; family controls

Indicator: New child between baseline and follow-up

0.082 (0.065)

0.005 (0.050)

–0.064 (0.076)

–0.012 (0.071)

0.067 (0.076)

0.068 (0.069)

Observations 1029 1829 1924 965 1484 1835

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Table 5a – Child outcomes at baseline and changes in outcomes between baseline and follow-upOne-child families

TVIP Hemoglobin Height ∆TVIP ∆Hemoglobin ∆Height

One-child families at baseline; no family controls

Indicator: New child between baseline and follow-up

–0.103 (0.065)

–0.118 (0.048)

–0.198 (0.074)

0.001 (0.066)

0.089 (0.072)

0.069 (0.065)

One-child families at baseline; family controls

Indicator: New child between baseline and follow-up

0.082 (0.065)

0.005 (0.050)

–0.064 (0.076)

–0.012 (0.071)

0.067 (0.076)

0.068 (0.069)

Observations 1029 1829 1924 965 1484 1835

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Table 5b – Child outcomes at baseline and changes in outcomes between baseline and follow-upTwo-child families

TVIP Hemoglobin Height ∆TVIP ∆Hemoglobin ∆Height Two child families at baseline; no family controls

Indicator: Second child at baseline

0.093 (0.066)

–0.081 (0.052)

–0.180 (0.086)

0.029 (0.071)

0.146 (0.075)

0.080 (0.075)

Indicator: New child between baseline and follow-up

–0.194 (0.050)

–0.102 (0.042)

–0.179 (0.069)

–0.105 (0.057)

0.030 (0.065)

0.012 (0.063)

Two child families at baseline; family controls

Indicator: Second child at baseline

0.058 (0.062)

–0.114 (0.052)

–0.239 (0.086)

0.012 (0.073)

0.112 (0.078)

0.040 (0.078)

Indicator: New child between baseline and follow-up

–0.029 (0.049)

0.005 (0.043)

–0.140 (0.070)

–0.093 (0.059)

0.018 (0.068)

0.012 (0.066)

Observations 1277 2540 2864 1182 2062 2588

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Summary and conclusions

Large negative associations between family size and children’s cognitive and nutritional outcomes These results indicate that associations documented

later in life, for education and earnings, are evident in early life

However, little evidence of deterioration in children’s outcomes or in parenting quality with the addition of a new child Children in families that are going to become larger

have poorer outcomes (Table 5) This evidence is consistent with selection stories--

common factors drive family size and child outcomes Current analysis (still preliminary) analyzes outcomes

using a third round of data Do negative causal effects of family size manifest

themselves after a longer time period? Are they more likely at larger family sizes than the ones

we observe in waves 1 and 2 of our data?

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The decision to invest in child quality over quantity: Declining fertility and rising household investment in education in Vietnam

Hai-Anh Dang and Halsey Rogers (World Bank)

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Methodology and data

Approach: Use data from Vietnam to investigate the hypothesized child quantity-quality tradeoff: Question: Are lower fertility levels making it possible

for households to invest more in their children’s human capital?

IV approach, using instruments from different sources, incl. our own survey

One innovation: Good data on private tutoring expenditures, so we’re not just relying on (e.g.) enrollment or attainment as indicator of parental investment in education

Why Vietnam? Very rapid fertility decline and educational advances

Data sources 2006 household survey (VHLSS) DHS 2002 New survey focused on private tutoring (2008)

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Fig. 1: Share of children attending tutoring classes

0

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Pe

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Correlation between quantity and quality (Tutoring)

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Table 3: Impacts of family size on school enrolment, Vietnam 2007-2008Probit IV Probit IV Probit IV Probit IV Probit IV Probit

No of children age 0-18 -0.167*** -0.544*** -0.999*** -0.867*** -0.945*** -0.923***(-9.48) (-2.61) (-15.55) (-2.79) (-4.86) (-5.50)

Age -0.213*** -0.212*** -0.090** -0.266*** -0.235*** -0.244***(-25.40) (-11.45) (-2.55) (-2.99) (-2.63) (-3.65)

Male -0.133*** -0.209*** -0.161** -0.297*** -0.300*** -0.301***(-3.90) (-4.94) (-2.35) (-3.21) (-3.17) (-3.37)

Head's years of schooling 0.085*** 0.056*** -0.024 0.053 0.033 0.037(13.94) (2.60) (-0.81) (0.82) (0.59) (0.87)

Ethnic major group 0.049 -0.173 -0.316 -0.351 -0.377 -0.360(0.79) (-1.15) (-1.23) (-1.42) (-1.63) (-1.54)

Log of total hh exp. 0.482*** 0.543*** N/A 0.634*** 0.560** 0.585***(11.40) (11.42) (2.75) (2.47) (3.17)

InstrumentsDistance to fam. center YNo of visits per month by mobile fam. team

Y

Government reg. Y YParental siblings Y Y

Overid test (J statistic) 0.04Log likelihood -3467 -16069 -2163 -2195 -2170 -2164N 10797 9052 1259 1371 1350 1350Note 1. Regressions control for regional and urban dummy variables.2. Cluster-robust t statistics in parentheses.3. Overidentification tests are from linear regression.

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Table 3: Impacts of family size on school enrolment, Vietnam 2007-2008Probit IV Probit IV Probit IV Probit IV Probit IV Probit

No of children age 0-18 -0.167*** -0.544*** -0.999*** -0.867*** -0.945*** -0.923***(-9.48) (-2.61) (-15.55) (-2.79) (-4.86) (-5.50)

Age -0.213*** -0.212*** -0.090** -0.266*** -0.235*** -0.244***(-25.40) (-11.45) (-2.55) (-2.99) (-2.63) (-3.65)

Male -0.133*** -0.209*** -0.161** -0.297*** -0.300*** -0.301***(-3.90) (-4.94) (-2.35) (-3.21) (-3.17) (-3.37)

Head's years of schooling 0.085*** 0.056*** -0.024 0.053 0.033 0.037(13.94) (2.60) (-0.81) (0.82) (0.59) (0.87)

Ethnic major group 0.049 -0.173 -0.316 -0.351 -0.377 -0.360(0.79) (-1.15) (-1.23) (-1.42) (-1.63) (-1.54)

Log of total hh exp. 0.482*** 0.543*** N/A 0.634*** 0.560** 0.585***(11.40) (11.42) (2.75) (2.47) (3.17)

InstrumentsDistance to fam. center YNo of visits per month by mobile fam. team

Y

Government reg. Y YParental siblings Y Y

Overid test (J statistic) 0.04Log likelihood -3467 -16069 -2163 -2195 -2170 -2164N 10797 9052 1259 1371 1350 1350Note 1. Regressions control for regional and urban dummy variables.2. Cluster-robust t statistics in parentheses.3. Overidentification tests are from linear regression.

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Table 4: Impacts of family size on children attendance in private tutoring, Vietnam 2007-2008

Probit IV Probit IV Probit IV Probit IV ProbitNo of children age 0-18 -0.131*** -0.932*** -0.286 -0.525 -0.389

(-6.62) (-5.85) (-0.57) (-1.17) (-1.09)Age 0.067*** 0.001 0.060 0.039 0.050

(13.90) (0.04) (1.57) (0.91) (1.62)Male -0.094*** -0.208*** -0.223** -0.235*** -0.220**

(-3.11) (-7.79) (-2.17) (-2.66) (-2.48)Head's years of schooling 0.036*** -0.022 0.022 0.007 0.016

(6.33) (-0.98) (0.65) (0.21) (0.59)Ethnic major group 0.906*** 0.021 0.925** 0.762* 0.853**

(12.73) (0.05) (2.53) (1.78) (2.51)Log of total hh exp. 0.217*** 0.337*** 0.188 0.221* 0.215*

(5.42) (8.44) (1.51) (1.87) (1.79)InstrumentsDistance to fam. center YGovernment reg. Y YParental siblings Y Y

Overid test (J statistic) 0.08Log likelihood -4625 -14547 -2204 -2177 -2172N 8844 7467 1149 1133 1133Note 1. Regressions control for regional and urban dummy variables.2. Cluster-robust t statistics in parentheses.3. Overidentification tests are from linear regression.

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Summary of findings

Larger number of siblings predicts lower educational investment in Vietnam, in un-instrumented regressions Result holds for both school enrolment and use of private

tutoring IV analysis partially confirms this quality-quantity

tradeoff Impact of sibship size on school enrolment is strongly

negative (from -0.5 to -1.0 per sibling) and significant across instruments

Impact on tutoring investment is not robustly significant, though always negative

Distance to family planning center seems the most promising instrument; other instruments yield mixed results, perhaps due to small N and data issues

Coefficients are generally larger in IV than in un-instrumented regressions

Implications Better availability of family planning may increase

investment in education Two-child policy may have led to more education in

Vietnam

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Development, modernization, and childbearing: The role of family gender composition

Deon Filmer, Jed Friedman, and Norbert Schady (all World Bank)

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Research question and methodology Research question: What is the relationship

between continuing fertility and the gender make-up of existing children? Focusing on one indicator of preference for sons

over daughters: son preference in fertility decisions

Note that differential gender-related behavior could be the result of “taste-based” gender discrimination, but also other causes

Focus here is on measuring the extent of son-preferred differential stopping behavior (DSB), regardless of its causes

Methodology Calculate probability of additional birth if zero sons

vs. zero daughters in family already Data: 158 DHS surveys, covering 1.3m women

from 64 countries

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Results: Differential Stopping Behavior largest

in Central Asia (9.4 percentage

points) South Asia (7.8 percentage

points) Middle East/North Africa (5.8

percentage points) No clear evidence of DSB in

Sub-Saharan Africa Latin America and the Caribbean

0 0.05 0.1 0.15

Sub-Saharan Africa

Southeast Asia

South Asia

Central Asia

Middle East/NorthAfrica

LatinAmerica/Caribbean

After zero sons After zero daughters

Probability of an additional birth

DSB

Where and when do we see the greatest differentials in stopping behavior?

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Where and when do we see the greatest differentials in stopping behavior? Son preference increases at higher birth orders

Mean number of children per family is 4.1 in Eastern Europe and Central Asia (ECA), and 4.9 in South Asia.

In such high-fertility settings, the gender composition of lower-parity children is less important in determining future fertility.

But once parents are closer to achieving their total desired number of children, the gender composition of children already born becomes an important determinant of whether parents have another child. For example, families with 4 or 5 children in South Asia

are approximately 14 percentage points more likely to add another child if all of the children up to this point are girls rather than boys.

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Does “modernization” reduce differential stopping behavior?

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

Sub-Saharan Africa

Southeast Asia

South Asia

Central Asia

Middle East/North Africa

Latin America/Caribbean

Six or more years of schooling Less than six years of schooling

-0.02 0 0.02 0.04 0.06 0.08 0.1 0.12

Sub-Saharan Africa

Southeast Asia

South Asia

Central Asia

Middle East/North Africa

Latin America/Caribbean

Urban Rural

Differential Stopping Behavior

Urbanization and female education are often associated with higher, not lower, son preference in continuing fertility

For example, in South Asian countries, son preference is significantly greater for women in urban areas or with more education--and this pattern seems to have increased over time.

It’s possible that latent son preference manifests itself when fertility levels are low—that is, when families are closer to desired fertility at low parity—and indeed fertility has fallen among women in urban areas or with more education.

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Possible implications of DSB for investment in girls Differential stopping behavior driven by son

preference is likely to exacerbate other forms of gender discrimination Mean number of siblings of girls exceeds boys’ in

regions where DSB is high (sons are preferred). Girls in South Asia have about 0.13 more siblings than boys in the Central Asian countries, the comparable number is

0.10 in Sub-Saharan Africa, boys and girls have the same

number of siblings Studies on the association between family size and child

outcomes usually show that more siblings dilute household and parental resources devoted to each child, a “quantity-quality” tradeoff.

If this association is causal, son preference, as manifested in gender-specific fertility choices is likely to have adverse consequences for girls since they will grow up in larger families.

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Long-Term Financial Incentives And Investment In Daughters: Evidence From Conditional Cash Transfers In North India

Nistha Sinha (World Bank) and Joanne Yoong (RAND)

29Sinha and Yoong (2009)

Background and Program Description Gender Bias in Haryana State, North India

One of India’s richest states, but among the worst in terms of female disadvantage 1990s: evidence of consistent gender gap in

sex ratios at birth (Sudha and Rajan,1999) early childhood mortality (Filmer, King and Pritchett,1998) school enrollment for 6-14 year olds (Filmer and Pritchett,

1998)

October 1994: Haryana State Government introduced Apni Beti Apna Dhan (ABAD), a conditional cash transfer program to address these issues Upon the birth of a daughter, families receive

Immediate cash grant of Rs. 500 to cover post-delivery needs

Government savings bond in daughter’s name, redeemable for Rs 25,000 (about $550) only on 18th birthday if still unmarried

Additional bonuses for completed education or claim deferral

Subject to belonging to poor or low caste households

30Sinha and Yoong (2009)

Program Evaluation Strategy and Empirical Challenges

Full evaluation some years away: first beneficiaries turn 18 in 2012

Empirical challenge 1: No systematic data collection; uniformly implemented across Haryana in October 1994 without piloting

Solution: Use data from India’s National Family Health Surveys (NFHS); repeated cross-sections

Empirical challenge 2: No measures of actual participation in NFHS Solution: Program evaluation is limited at best to an intent-to-treat analysis

(measuring effects of being eligible ); use data on eligibility criteria to identify “eligible individuals” among poor households or households belong to certain castes

Empirical method: Basic Difference in Difference Specification (i.e. before and after program, eligible non-eligible girls or households)

1992 NFHS 1 Before program

1998/9 NFHS 2

1994 Program rolled out statewide

2005-06 NFHS 3

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Results: Impact of the program (but recall the challenges of the program evaluation)

Increased girl child survival Positive, significant estimated effects on sex ratio of living children

for individual women Perhaps due to less sex-selective abortion, since insignificant (but

consistently positive) estimated effects on survival rates in early childhood

However, no effects on expressed preferences for girls

Increased health investment in children Positive, significant effects on childhood vaccinations

Effects on education and marriage are limited by time horizon of data, but early results for education suggest positive relationship

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Thematic Area 2: Fertility, Poverty and Family Welfare in the time of HIV/AIDS HIV/AIDS is the leading cause of prime-age death in

Africa. Early sexual initiation, early marriage, risky sexual practices, and commercial sex work have all contributed to the transmission of the pandemic—with consequences for the wellbeing not only of the person who has AIDS, but also for others in their household.

These studies seek to understand the socio-economic consequences of early marriage and non-marital sexual relations, and of efforts to reduce premature adult mortality through the use of anti-retroviral therapy

2 research projects in this category which are entail longitudinal surveys: Marriage Transitions and HIV/AIDS in Malawi HIV/AIDS and the impact of treatment on family and

individual welfare

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Marriage Transitions and HIV/AIDS in Malawi

Kathleen Beegle (World Bank), Berk Ozler (World Bank) and Michelle Poulin (Brown University)

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Research question and methodology Research question: What is the relationship

between socioeconomic characteristics of young people, economic shocks they experience, their partnership choices, sexual behavior, and risk of HIV infection? To explore in detail young people’s transition into

marriage and the effect of these transitions on their subsequent outcomes, such as health, fertility, labor market participation and important outcomes for their young children, such as anthropometrics, nutrition, cognitive ability, etc

Methodology: new data collection effort Surveys integrated over topics not normally

covered in traditional household surveys. Specific sample of young adults. The study is

following an initially never-married sample of 1,185 young Malawians for at least 3 years, using an array of panel data collection methods.

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Details of the data effort

Annual household survey started in summer 2007. Modified LSMS-style household questionnaire, accompanied by a detailed individual component on marital aspirations and sexual behavior .

Interim in-depth partnership interviews (PIs) collected from Feb-March between rounds of the annual household survey.

HIV testing introduced in summer 2008 on a random sub-sample (to address concerns of the influence of testing itself on subsequent behaviors/outcomes).

Tracking individuals who move: This is a highly mobile population.

Last round of data planned for summer 2009.

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Preliminary findings

28 of the 596 young women, aged 14-21 in our sample (5%) have ever given birth and all of them have given birth only once.

The mean (and median) age at birth for these young women is 17 (youngest 14 and oldest 20).

35 of the 583 men, aged 17-25 in our sample (6%) reported than a women has given birth to their child at least once.

The mean (and median) age for these young men when the at the time of the first birth was 19 (youngest 16 and oldest 22).

Approximately, one third of the women and three quarters of the men reported ever having sex.

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HIV/AIDS and the impact of treatment on family and individual welfare

Damien de Walque (World Bank), Harounan Kazianga (World Bank) and Mead Over (CGD)

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Research question and methodology Research question: What is the impact of

HIV treatment on… Lives saved and health outcomes Labor supply of patient and family members Schooling and welfare of children Other welfare indicators

Methodology: new data collection effort Biomedical follow-up including data on treatment

regimen and treatment success (CD4 counts) Household surveys (HIV patients and general

population) including health, schooling, labor force. 7 countries: Burkina Faso, Ghana, Kenya, India,

Mozambique, Rwanda and, South Africa

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Methodological challenges

It is not possible to randomize ARV treatment!

But in some countries, can evaluate some experiments on the conditions of ARV delivery.

Rwanda: performance-based contracting for HIV/AIDS services in health facilities

South Africa: food and counseling intervention as adherence support.

Kenya: text messaging intervention as reminders for adherence

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Preliminary findings from baseline surveys Access to pediatric ART appears limited

(evidence from Mozambique and Ghana) Parents and family might not identify weak or sick

children as suffering from HIV/AIDS HIV/AIDS affects not only the mental health

of persons with AIDS but also affects the mental health of family members in these households (evidence from Ghana)

Compared to other patients, HIV/AIDS patients seem to receive better health services (evidence from Burkina Faso): They wait less They receive higher quality care

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Thematic Area 3: Fertility and female labor supply

Motivating question: What is the relationship between fertility outcomes and women’s labor market participation?

2 research projects using household survey data Fertility and women’s labor force

participation (96 DHS surveys) Fertility and Women’s Labor Supply in A

Low Income Rural Economy (the case of Matlab, Bangladesh)

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Fertility and women’s labor force participation

Elizabeth King (World Bank) and Maria Porter (University of Chicago)

43

Research question and methodology Research question: This study focuses on the

relationship between fertility outcomes and women’s labor market behavior. As fertility declines around the world, childbearing

patterns change in three ways: women may delay their first birth, space their births, or stop having children at an earlier age than previous cohorts. Each of these changes is likely to have a different impact on the ability of women to work outside the home and on the decisions they make regarding work and child-bearing

Methodology: Analysis of 96 Demographic & Health Surveys in 59

countries

44

Methodological challenges

Endogeneity between fertility and labor market behaviors of women. Most previous studies of the relationship between fertility

and labor force participation have relied on cross-sectional data, but with cross-sectional data, it is difficult to correct for both the endogeneity of fertility and the impact of unobserved heterogeneity among women.

In the absence of natural experiments that may affect fertility choice but not otherwise affect other behaviors such as child outcomes, an econometric approach is needed in order to identify and quantify such an effect

Using exogenous shocks to fertility (twins in first birth and sex of first two births), we estimate how fertility affects women’s labor force participation decisions across different regions of the developing world

45

Effect of Sex at 1st Birth on Women’s LFP in Sub-Saharan Africa

Women Ages:15-44 15-24 25-34 35-44

Regression 31st 2 children: same sex -0.003* -0.002 -0.002 -0.004

(0.002) (0.005) (0.003) (0.003)1st child: boy -0.005*** 0.006 -0.007*** -0.008***

(0.002) (0.005) (0.003) (0.003)2nd child: boy -0.003* 0.002 -0.002 -0.005*

(0.002) (0.005) (0.003) (0.003)Regression 4

1st child: boy -0.002 0.005 -0.006 -0.003(0.002) (0.007) (0.004) (0.004)

1st 2 children: both boys -0.006*** -0.001 -0.004 -0.009**(0.002) (0.007) (0.004) (0.004)

1st 2 children: both girls 0.000 -0.004 -0.001 0.001(0.003) (0.007) (0.004) (0.004)

46

Summary of Findings

Women are more likely to have worked in the past year if they have more children in sub-Saharan Africa, and for some older women in South Asia.

Younger women in South Asia face the tradeoff between more children or work in the labor force. These women are less likely to have worked in the past two years as a consequence of having more children.

47

Summary of Findings for Sub-Saharan Africa, where the income effect dominate Women have more children if they had twins in

the first birth, if the first two births were the same sex, or if the first two births were girls.

Women have fewer surviving children if their first or second child was a boy.

Positive effects on the number of surviving children are strongest for women who have completed secondary schooling or higher.

More educated women are also more likely to work when they have twins in the first birth.

Any effect of the sex of first birth(s) does not vary much by education.

48

Fertility and Women’s Labor Supply in A Low Income Rural Economy: the case of Matlab, Bangladesh

Mattias Lundberg (World Bank), Nistha Sinha (World Bank) and Joanne Yoong (RAND)

49

Study Objectives & Context Explore effect of children on women’s labor force

participation using data from rural Bangladesh

Rural labor market characterized by Low female participation in wage labor Cultural practice of female seclusion Home based production by women

Women’s work in rural Bangladesh: Based on a question about primary activity, 58% of women and 82% of men aged 20-55 are “working”: Among those who report earnings (1995),

Men’s mean earnings were 21,370 Takas Women’s mean earnings were 3,005 Takas

Most women’s (87%) location of work is home Women report activities such processing rice, raising

poultry and livestock

50

Data and methodology Data: Matlab Health and Socioeconomic Survey 1996

Survey of 4,363 households in a demographic surveillance area in rural Bangladesh.

Surveillance area is site of a family planning program experiment

Survey covered 142 villages in treatment and control areas Methodology: Identify causal effect of children on

women’s labor supply Unobservables influence both women’s decision to work and

their family size Standard methodology finds a variable that influences

fertility but not labor-force participation (Literature: twins, sex of first-born)

This paper exploits women’s exposure to a family planning program experiment

51

Initial finding Number of children is positively

associated with women’s probability of engaging in home-based work and negatively associated with work outside the home

Finding appears consistent with home-based production technology which allows women to combine child care and work

Next step: Conducting robustness checks

52

Research program summary findings Fertility and investments in child quality

Higher fertility is associated with lower parental investment in children, in some cases reflecting a quantity-quality tradeoff Larger sib-size reduces school enrollment and overall investments

in children in Vietnam Parents who will go on to have larger families invest less in

children today in Ecuador Son preference adds another twist to the problem

Parents stop childbearing earlier if have son(s), so girls are more likely to end up in larger families and therefore suffer more from resource dilution

But this DSB can change: CCTs in North India have some positive effects on improving sex ratios

Fertility, poverty, and family welfare in the time of HIV/AIDS Panel data collection still underway

The link between fertility and female labor supply varies by region Recent births are associated with higher female labor-force

participation in sub-Saharan Africa, but lower in South Asia In Bangladesh, higher fertility is associated with lower labor-force

participation of women outside the home, but higher participation in home-based income-earning activities