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					Fertility, Reproductive Health and
     Economic Development:
          Preliminary results of the
World Bank research program supported by the
   William and Flora Hewlett Foundation

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

                                                      1
                    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



                                                     2
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

                                                                               3
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


                                                                                             4
Family size and early child development:
evidence from Ecuador



      Christina Paxson (Princeton University) and
      Norbert Schady (World Bank)




                                                    5
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


                                                                                          6
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




                                                                               7
Result 1: Cognitive and nutritional outcomes are strongly
associated with family size (1)
                              cognitive outcomes by #siblings
     90




                   long-term memory, p'tile




                                                                                       20
     85




                                                                                       15
     80




                                                                                       10
     75




                                                                                        5
     70




                                                                                        0
          0    1                                                  2    3   4 or more        0   1   2   3   4 or more
     40




                                                                                       10
                                              visual closure, p'tile
     30
     20




                                                                                        5
     10
      0




                                                                                        0




          0    1                                                  2    3   4 or more        0   1   2   3   4 or more



                                                                                                                        8
Result 1: Cognitive and nutritional outcomes are strongly
associated with family size (2)
                             nutrition outcomes by #siblings




                                                                                   80
   11.2 11.4




                                                     anemic, percent




                                                                                   70
                                                                                   60
               11
   10.6 10.8




                                                                                   50
                                                                                   40
                     0   1    2                3                       4 or more        0   1   2   3   4 or more




                                                                                   40
                 0




                                  stunted, percent




                                                                                   30
           -.5




                                                                                   20
               -1




                                                                                   10
   -1.5




                                                                                    0




                     0   1    2                3                       4 or more        0   1   2   3   4 or more


                                                                                                                    9
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


                                                                               10
Table 5a –
Child outcomes at baseline and changes in outcomes between baseline
and follow-up
One-child families

                          TVIP     Hemoglobin Height      ∆TVIP ∆Hemoglobin ∆Height
                    One-child families at baseline; no family controls
Indicator: New child
                        –0.103      –0.118      –0.198 0.001          0.089      0.069
between baseline and
                        (0.065)     (0.048)    (0.074) (0.066)       (0.072)    (0.065)
follow-up
                     One-child families at baseline; family controls
 Indicator: New child
                          0.082       0.005     –0.064 –0.012         0.067      0.068
between baseline and
                         (0.065)     (0.050)    (0.076) (0.071)      (0.076)    (0.069)
follow-up



Observations              1029        1829       1924      965           1484    1835




                                                                                    11
Table 5a –
Child outcomes at baseline and changes in outcomes between baseline
and follow-up
One-child families

                            TVIP     Hemoglobin Height ∆TVIP ∆Hemoglobin ∆Height
                   One-child families at baseline; no family controls
Indicator: New child
                           –0.103     –0.118      –0.198 0.001             0.089     0.069
between baseline
                           (0.065)    (0.048)     (0.074) (0.066)         (0.072)   (0.065)
and follow-up
                        One-child families at baseline; family controls
 Indicator: New child
                            0.082      0.005      –0.064 –0.012            0.067     0.068
between baseline
                           (0.065)    (0.050)     (0.076) (0.071)         (0.076)   (0.069)
and follow-up



Observations                1029       1829        1924      965           1484      1835




                                                                                        12
Table 5b –
Child outcomes at baseline and changes in outcomes between baseline
and follow-up
Two-child families


                        TVIP Hemoglobin Height ∆TVIP ∆Hemoglobin ∆Height
                   Two child families at baseline; no family controls
Indicator: Second child 0.093      –0.081      –0.180 0.029             0.146     0.080
at baseline             (0.066)    (0.052)     (0.086) (0.071)         (0.075)   (0.075)
Indicator: New child
                        –0.194     –0.102      –0.179 –0.105            0.030     0.012
between baseline and
                        (0.050)    (0.042)     (0.069) (0.057)         (0.065)   (0.063)
follow-up
                     Two child families at baseline; family controls
Indicator: Second child 0.058      –0.114      –0.239 0.012             0.112     0.040
at baseline             (0.062)    (0.052)     (0.086) (0.073)         (0.078)   (0.078)
Indicator: New child
                        –0.029      0.005      –0.140 –0.093            0.018     0.012
between baseline and
                        (0.049)    (0.043)     (0.070) (0.059)         (0.068)   (0.066)
follow-up
Observations             1277       2540        2864     1182           2062      2588


                                                                                           13
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?

                                                                                14
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)




                                                      15
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)



                                                                             16
Correlation between quantity and quality (Tutoring)
                            Fig. 1: Share of children attending tutoring classes

                       60

                       50
      Percentage (%)




                       40
                                                                               age 0-11
                       30                                                      age 0-15
                                                                               age 0-18
                       20

                       10

                        0
                                1        2        3         4         5
                                    No of children in the household




                                                                                          17
Table 3: Impacts of family size on school enrolment, Vietnam 2007-2008
                               Probit     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)
Instruments
Distance to fam. center                         Y
No of visits per month by
                                                           Y
mobile fam. team
Government reg.                                                       Y                   Y
Parental siblings                                                               Y         Y

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


                                                                                           18
Table 3: Impacts of family size on school enrolment, Vietnam 2007-2008
                               Probit     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)
Instruments
Distance to fam. center                         Y
No of visits per month by
                                                           Y
mobile fam. team
Government reg.                                                       Y                   Y
Parental siblings                                                               Y         Y

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


                                                                                           19
Table 4: Impacts of family size on children attendance in private tutoring,
Vietnam 2007-2008
                               Probit     IV Probit  IV Probit IV Probit IV Probit
No 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)
Instruments
Distance to fam. center                        Y
Government reg.                                           Y                    Y
Parental siblings                                                     Y        Y

Overid test (J statistic)                                                    0.08
Log likelihood                     -4625        -14547  -2204       -2177   -2172
N                                   8844         7467    1149        1133    1133
Note 1. Regressions control for regional and urban dummy variables.
2. Cluster-robust t statistics in parentheses.
3. Overidentification tests are from linear regression.
                                                                                      20
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

                                                                                      21
Development, modernization, and childbearing:
The role of family gender composition




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



                                                            22
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


                                                                            23
    Where and when do we see the greatest differentials in
    stopping behavior?

                                                        Probability of an additional birth
Results:
  Differential Stopping Behavior largest in             Latin
       Central Asia (9.4 percentage points)       America/Caribbean

       South Asia (7.8 percentage points)          Middle East/North
                                                         Africa
       Middle East/North Africa (5.8 percentage                                    DSB
        points)
                                                         Central Asia
   No clear evidence of DSB in
       Sub-Saharan Africa                                South Asia
       Latin America and the Caribbean
                                                      Southeast Asia


                                                   Sub-Saharan Africa


                                                                        0    0.05         0.1   0.15

                                                               After zero sons                  24
                                                                                 After zero daughters
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.



                                                                                   25
    Does “modernization” reduce differential
    stopping behavior?  Differential Stopping Behavior

Urbanization and female education                 Latin America/Caribbean
   are often associated with higher,               Middle East/North Africa
   not lower, son preference in                               Central Asia
   continuing fertility                                         South Asia
   For example, in South Asian countries, son
                                                           Southeast Asia
    preference is significantly greater for
    women in urban areas or with more                  Sub-Saharan Africa

    education--and this pattern seems to have
                                                                         -0.02   0     0.02 0.04      0.06 0.08    0.1   0.12
    increased over time.
                                                                                       Urban     Rural
   It’s possible that latent son preference
    manifests itself when fertility levels are    Latin America/Caribbean
    low—that is, when families are closer to       Middle East/North Africa
    desired fertility at low parity—and indeed
    fertility has fallen among women in urban                 Central Asia

    areas or with more education.                               South Asia

                                                           Southeast Asia

                                                       Sub-Saharan Africa

                                                                          -0.1 -0.05    0      0.05   0.1   0.15    0.25
                                                                                                                   0.2
                                                                                                                   26
                                                 Six or more years of schooling         Less than six years of schooling
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.

                                                                                     27
Long-Term Financial Incentives And Investment In
Daughters: Evidence From Conditional Cash
Transfers In North India




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



                                                   28
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




       Sinha and Yoong (2009)
                                                                                            29
    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




                    1992 NFHS 1                     1998/9 NFHS 2         2005-06 NFHS 3
                    Before program
                                 1994 Program
                                 rolled out statewide
    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)


        Sinha and Yoong (2009)
                                                                                                               30
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



                                                                                                  31
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




                                                                             32
Marriage Transitions and HIV/AIDS
in Malawi



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



                                                      33
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.

                                                                           34
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.

                                                                    35
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.




                                                                          36
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)



                                                 37
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

                                                                        38
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

                                                      39
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

                                                                         40
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)


                                                                  41
Fertility and women’s labor force
participation


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



                                                   42
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



                                                                                43
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
                                                                                         44
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 3
  1st 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)



                                                                             45
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.




                                                         46
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.

                                                                     47
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)



                                                          48
              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
                                                                             49
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




                                                                                           50
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




                                                         51
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


                                                                                                        52

				
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