1. 1 David Lam Department of Economics and Population Studies Center University of Michigan World Bank Workshop on "Tackling Adolescent Reproductive Health: Impacts and Interventions to Address Them" December 1, 2009 Impacts of teen fertility on outcomes of teen mothers and their children in South Africa: Evidence from the Cape Area Panel Study Support for this research was provided by the U.S. National Institute of Child Health and Human Development and the William and Flora Hewlett Foundation.

2. 2 Cally Ardington University of Cape Town Nicola Branson University of Cape Town David Lam University of Michigan Murray Leibbrandt University of Cape Town Letícia Marteleto University of Michigan and University of Texas Vimal Ranchhod University of Michigan and University of Cape Town This presentation draws on a number of papers produced by various combinations of the following project team: This work was produced as part of the “Global Teams of Research Excellence in Population, Reproductive Health, and Economic Development” sponsored by the William and Flora Hewlett Foundation and the Population Reference Bureau

3. 3 Background of the Cape Area Panel Study Study began in 2002 with 4,752 14-22 year-olds –Collaboration of University of Cape Town and University of Michigan –All areas and all population groups in Cape Town are represented –Integrated survey of education, employment, sexual behavior, health Wave 2, 2003 and 2004 Wave 3, 2005 –Successfully reinterviewed about 85% of original young adult sample Wave 4, 2006- UCT, Michigan, Princeton collaboration –Tracked all young adults, plus all members of original CAPS households who were age 50+ in 2006, plus all children of female CAPS young adults Wave 5, 2009 (young adult sample, includes HIV testing) Public access data –Integrated Wave 1-2-3-4 data available at www.caps.uct.ac.za

4. 4 Teen childbearing in South Africa Total Fertility Rate of 2.9 is lowest in Sub- Saharan Africa Relatively high rates of teen childbearing –24% had a birth by age 18; 50% by 20 Significant fractions of teenage mothers return to school after having their child –Over 50% of 15-17 year-olds with a child were in school Most teen childbearing is non-marital –Only 18% of 20 year old mothers had ever been married Source: 2001 South African Census Data

5. 5 Using CAPS to study the impact of teen fertility – three approaches 1.Compare CAPS young adult respondents who were born to teen mothers with those born to non-teen mothers 2.Look at the outcomes of the children of CAPS YA respondents, comparing those with teen versus non-teen mothers 3.Look at the educational outcomes of YA respondents with and without teen births

6. 6 Mother was teen when YA was born CAPS young adults Treatment CAPS young adults (age 14- 22 in 2002) Mothers of CAPS young adults Children of female CAPS young adults Analysis 1 Mother was 20+ when YA was born CAPS young adults Control Child of female CAPS YA Treatment Child of female CAPS YA Control CAPS YA had birth as teen CAPS YA had birth at 20+ Analysis 2 CAPS YA had teen birth Treatment CAPS YA did not have teen birth Control Analysis 3Generation

7. 7 1. Using CAPS young adults (YAs) as children of teen mothers CAPS has the mother’s age at YA’s birth for both resident and non-resident mothers Information on schooling and other characteristics at each age from birth based on retrospective histories Information on household characteristics such as income, parent’s education, and employment status of household members Information on up to three YAs in same household – allows sibling fixed effects

8. 8 Sample and Methods CAPS 2002-2006, young adult sample Young Adults as children of teen mothers OLS, with and without controls, plus sibling/cousin fixed effects Total # of Young Adults 3,662 % born to teen mother 14.58% # of groups (pairs/triplets) 1,045 % (#) with variation on teen mother 21% (221) Includes all African and Coloured YA’s with mother’s age at their birth Siblings and cousins in the same household

9. 9 Estimated impact of being born to a teen mother For coloured sample, those with teen mother have 0.2 standard deviations lower math score than those with non- teen mother. Controlling for parents’ education, childhood poverty status, and mother’s fertility reduces coefficient by 30% Comparing siblings with and without teen mothers reduces coefficent to zero. For African sample there is no unadjusted difference in test scores. With controls for parents’ education, childhood poverty status, and mother’s fertility there is a difference of -.02 standard deviations. Comparing siblings with and without teen mothers the coefficient is similar but has larger standard error.

10. 10 CAPS respondents born to teen mothers have younger mothers – as a result their mothers have higher education. Mean education of teen mothers is 1.4 grades higher than for non-teen mothers. This creates a bias in the opposite direction of most studies of teen childbearing.

11. 11 Estimated impact of being born to a teen mother Adjusted point estimates are larger in magnitude than unadjusted estimates for three outcomes for Africans

12. 12 Sensitivity Checks Birth order effect versus teen mother effect Does teen childbearing affect all the teen mother’s children or only the one born to her as a teen? 12

13. 13 Teen mother versus birth order High correlation between being born to teen mother and being the older sibling/cousin Older siblings may fare better on certain outcomes due to birth order effects We restrict sample to YAs not born to teen mothers and look for birth order effects In African sample, older siblings/cousins progress through school faster and are less likely to drop out by age 16 Older sibling advantage might be masking a negative effect of being born to teen mother 13

14. 14 Are all children born to teens equally affected by the initial teen birth? This would explain the small estimated impacts in the Fixed Effects analysis To test this we restrict sample to YAs born to older mothers We compare YAs who have older siblings/cousins born to a teen mother to YAs who do not –Mostly negative but insignificant results found for the African sample –Evidence of lower math scores in coloured sample Some support for “systematic difference” hypothesis, implying that FE estimates may not be informative 14

15. 15 Conclusions from Analysis 1 Negative effects of having a teen mother found for Coloured young adults Effects decline when we include controls and disappear when we compare siblings/cousins –Suggests that unadjusted differences result from adverse pre-birth factors Effects for Africans become larger when we include controls –Teen mothers have more education because they are younger Effects disappear comparing siblings/cousins –Might be a result of the fact that first-born children do better on certain outcomes 15

16. 16 2. Health outcomes of children of CAPS respondents We compare children born to teen mothers with children born to mothers age 20+ Using propensity score matching, we estimate weighted regressions with “born to teen mother” as key variable: –Step 1: Estimate the probability of being a teen mother given pre-childbirth characteristics –Step 2: Predict the propensity scores –Step 3: Calculate a set of weights based on these scores to construct a counterfactual from the children born to older mothers group –Step 4: Estimate the effect of being born to a teen mother using regressions weighted by the constructed weight

17. 17 CAPS data – Timeline & Sample Wave 1 (2002) 4752 young adults (age 14-22) Wave 2A (2003) 1360 young adults (age 15-23) Wave 2B (2004) 2489 young adults (age 16-24) Wave 3 (2005) all young adults (age 17-25) Wave 4 (2006-07) All young adults (age 18-26) plus children of female young adults 607 children – African and coloured first born children only Sample selective of women who begin childbearing early Majority of teen mothers in their late teens - average age = 17.6 Majority of older mothers in their early 20s - average age = 21.6

18. 18 Teen mothers are generally worse off in coloured sample, but teen mothers are better off in African sample on a number of characteristics (marked in red).

19. 19 Unlike most analyses of teen mothers, we estimate larger effects when we control for characteristics than when we don’t. For some characteristics teen mothers are better off than older mothers.

20. 20 Conclusions of Analysis 2 We find some evidence that children born to teen mothers are at risk of worse health –More likely to be born underweight –Have lower height for age z-scores and more likely to be stunted Unlike previous studies, our results do not suggest that teen mothers are inherently socioeconomically disadvantaged –P-score weighted differences are often larger than unadjusted differences Differences between Africans and coloureds are large –Children born to coloured teen mothers have double the health disadvantage seen for Africans

21. 21 3. Analyzing the impact of teen fertility on educational outcomes of teen mothers This analysis uses only the CAPS young adults, comparing teen mothers to other women As in analysis 2, we estimate propensity score weighted regressions. –Step 1: Estimate probits using covariates, with `treatment’ variable, predict the propensity scores. – Step 2: Match untreated observations to treated observations, based on the pscores. –Step 3: This generates a set of weights for the untreated group. (treated obs have weight= 1.) –Step 4: Estimate regressions by OLS weighted by the (sampling weights x propensity score weights).

22. 22 Table 4: Regression results, coefficients on “teen birth” variable Dependent Variable Matric by age 20 Matric by age 22 Grades by age 18 Grades by age 20 Grades by age 22Dropout Specification 1:-0.303***-0.302***-0.931*** -1.331***-1.130***0.190*** No sample restriction, sampling [0.026][0.035][0.10] [0.12][0.14][0.020] weights only, no covariatesN 173511292224 173511292295 R2R2 0.07 0.05 0.090.070.03 Specification 2:-0.208***-0.227***-0.620*** -0.921***-0.801***0.147*** No sample restriction, sampling [0.025][0.033][0.081] [0.11][0.12][0.019] weights only, with limitedN 171811182193 171811182258 covariatesR2R2 0.310.280.45 0.390.370.16 Specification 3:-0.125***-0.112***-0.382*** -0.568***-0.358***0.102*** Had sex by age 19, sampling [0.029][0.037][0.096] [0.12][0.13][0.024] weights only, with allN 12218101467 12218101486 covariatesR2R2 0.270.260.37 0.40.14 Specification 4:-0.100***-0.0754*-0.348*** -0.510***-0.274*0.0825*** Had sex by age 19, sampling [0.032][0.039][0.11] [0.14][0.16][0.026] and propensity score matchingN 12188071464 12188071483 weights, with covariates andR2R2 0.240.270.36 0.340.370.14 common support restriction Standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1 “Naïve estimate” – no covariates or sample restrictions. Teen mothers are 30 percentage points less likely to have completed grade 12 (matric) by age 20. Coefficient falls by 31% with controls for household and socio-economic characteristics Coefficient falls by 58% when we look at sample that had sex by age 19 and add controls for measures of sexual behavior Coefficient falls by 67%, but is still negative and statistically signifcicant, when we use propensity score matching weights based on the full set of covariates including measures of sexual behavior

23. 23 Table 4: Regression results, coefficients on “teen birth” variable Dependent Variable Matric by age 20 Matric by age 22 Grades by age 18 Grades by age 20 Grades by age 22Dropout Specification 1:-0.303***-0.302***-0.931***-1.331***-1.130***0.190*** No sample restriction, sampling [0.026][0.035][0.10][0.12][0.14][0.020] weights only, no covariatesN 173511292224173511292295 R2R2 0.07 0.050.090.070.03 Specification 2:-0.208***-0.227***-0.620***-0.921***-0.801***0.147*** No sample restriction, sampling [0.025][0.033][0.081][0.11][0.12][0.019] weights only, with limitedN 171811182193171811182258 covariatesR2R2 0.310.280.450.390.370.16 Specification 3:-0.125***-0.112***-0.382***-0.568***-0.358***0.102*** Had sex by age 19, sampling [0.029][0.037][0.096][0.12][0.13][0.024] weights only, with allN 1221810146712218101486 covariatesR2R2 0.270.260.37 0.40.14 Specification 4:-0.100***-0.0754*-0.348***-0.510***-0.274*0.0825*** Had sex by age 19, sampling [0.032][0.039][0.11][0.14][0.16][0.026] and propensity score matchingN 1218807146412188071483 weights, with covariates andR2R2 0.240.270.360.340.370.14 common support restriction Standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1 Similar declines in coefficients using other outcome measures

24. 24 Enrollment by age, women with different ages at first birth 50% of those with a birth at age 15 are enrolled in school 1 and 2 years later Those with teen births already had lower enrollment rates at age 14

25. 25 Enrollment by age, women with different ages at first pregnancy Dropout rate after pregnancy is much higher for coloured teens than for African teens This may partly reflect the high rates of grade repetition in African schools, which reduce stigma of going back to school

26. 26 Grades completed by age, women with different ages at first birth Similar schooling at age 20 for those with birth at age 15, 16, or 17

27. 27 Grades completed by age, women with different ages at first pregnancy Coloured teens gain very little schooling after pregnancy, while Africans continue to pass grades after pregnancy.

28. 28 Conclusions from Analysis 3 A large proportion of the mean difference in schooling disadvantage of teen mothers is accounted for by pre-existing covariates. There remain negative and statistically significant effects of teen births on educational attainment after controlling for observeable characteristics. Some evidence that there may be heterogeneity depending on actual age at first birth. Schooling often does continue after teen birth.