Fertility and women s labor force participation in a low-income rural economy
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1 Fertility and women s labor force participation in a low-income rural economy Mattias Lundberg, Nistha Sinha, Joanne Yoong Pop/Pov Conference Capetown, January 2010
2 Outline Fertility, growth, and income The Matlab programme Childbearing and labor force participation 2
3 Aggregate links poverty Countries with higher fertility are poorer Total fertility rate per woman Source: World Bank data 3
4 Aggregate links growth Countries with lower fertility grow faster Total fertility rate per woman Source: World Bank data 4
5 Changes in fertility and income Clear link between lower fertility and income, other outcomes. But what are the links to women s labor force participation and earned income? And does it matter how fertility declines? Postpone first birth Increase the interval between births (Ghana, Philippines) Stop having children at an earlier age (India) 5
6 Changes in fertility and income Endogeneity of fertility and labor force participation decisions previous studies find causality running both ways Prior IV strategies Twins (Rosenzweig and Wolpin 1980, 2000) Sex composition (Angrist and Evans 1998; Chun and Oh 2002; Carrasco 2001) Miscarriage (Hotz et al. 2005) Matlab programme randomly, exogenously assigned 6
7 The Matlab programme Demographic surveillance of 149 villages in Matlab since 1966 (ICDDR,B) Contraceptive Distribution and Family Planning and Health Services introduced in 70 villages in 1977 Home visits by trained female outreach workers to all currently married women in treatment villages 7
8 The Matlab programme Remarkable gains in the province: Infant mortality: 110 per 1000 live births in 1983, 65 in Total fertility: more than 6 in 1976, just over 3 in Fertility fell 15 percent more in program villages than in control villages. Also improvements to women s health, incomes, and assets in program villages. 8
9 Data 6068 women interviewed during the 1996 Matlab Health and Socioeconomic Survey (MHSS), which surveyed 4,364 households in 141 villages Sample restricted to 1278 eligible women in program and non-program villages who were eligible to receive the program (married women aged 20-55, etc.). 9
10 Data Table 1. Sample descriptive statistics Variable Control villages Program villages Total T-test of equality N Age (years) (1.04) 1278 Hindu (6.60)** 1278 Husband's education (3.17)** 1278 Education (1.69) Ln(farmland) (0.50) 1278 Currently working (percent) (1.88) In informal sector (2.89)** 1278 In formal sector (2.49)* 1278 In home-based business (0.07) 1278 Outside home (0.24) 1278 Number of live births (4.00)** 1278 Birth spacing (months) (2.73)** 1039 Age at first pregnancy (0.13) 1234 Number of births within past five years (3.82)** 1255 **, *, +: significant at.01,.05,.10 percent, respectively. 10
11 Naïve estimates of program impact On fertility (F-test = 6.41**) Age category Control villages Treatment villages 11
12 Naïve estimates of program impact On birth spacing (F-test = 5.30**) Age category Control villages Treatment villages 12
13 Currently working Naïve estimates of program impact On labor force participation (Χ 2 = 3.85) Age category Control villages Treatment villages 13
14 Years of schooling Naïve estimates of program impact On education (F-test = 0.96) Age category Control villages Treatment villages 14
15 Share of women reporting births Correlation between fertility and labor force participation Number of live births 0 15
16 Main results: childbearing and labor force participation Table 4. Main results: impact of childbearing on working Probit IV Probit IV Probit IV Probit Currently working (marginal effects) Number of live births 0.021** (0.01) (0.12) (0.06) (0.09) Ordered OLS Tobit Number of live births probit Treatment village ** -.731** -.355** (0.09) (0.11) (0.06) Wald Chi-squared 29.88** 23.29** 22.72** 20.62** Log-pseudolikelihood (744.65) ( ) ( ) ( ) N Categorical variable **, *, +: significant at.01,.05,.10 percent, respectively. Both regressions include controls for age, Hindu, own and husband education, land owned, and whether the village has a health clinic. 16
17 Results: sector choice Table 5. Probability of working in formal or informal sectors Currently working Informal Formal Out of the sector sector In home home IV probit (marginal effects) Number of live births (0.12) (0.16) (0.12) (0.17) Age * (0.28) (0.39) (0.29) (0.41) Hindu * ** (0.14) (0.20) (0.12) (0.18) Highest education (0.02) (0.03) (0.02) (0.03) Husband education (0.01) (0.02) (0.01) (0.02) ln(farm land) * (0.08) (0.15) (0.10) (0.12) Clinic in village ** ** (0.09) (0.14) (0.09) (0.13) Wald Chi-squared 44.72** 46.17** 31.86** 4.79 Log-pseudolikelihood ( ) ( ) ( ) ( ) N
18 N Results: different fertility outcomes Table 6. Impact of childbearing on working in formal or informal sectors (alternative fertility outcomes) 1 2 Number of Birth spacing (months) births in past five years Working (0.02) (0.43) Informal sector -.027* 0.718** (0.01) (0.33) Formal sector.032* ** (0.01) (0.31) Working in-home (0.02) (0.45) Working out of the home (0.02) (0.72) Impact of treatment on 4.310** ** fertility outcome 1 (1.44) (0.05) 18
19 N Results: different instruments Table 7. Impact of childbearing on working in formal or informal sectors (alternative instruments) 1 2 First child Twin birth male Working * (0.16) (0.21) Informal sector ** (0.15) (0.19) Formal sector (0.25) (1.58) Working in-home (0.16) (0.93) Working out of the home * (0.19) (0.17) Impact of treatment on 1.422* total fertility 1 (0.29) (0.10) 19
20 Conclusions Fertility not robustly or consistently related to labor force participation. Control over childbearing may affect decisions concerning sector of participation. Women with more children, those with more young children, and those who bear children at shorter intervals, are more likely to find employment in the informal sector. 20
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