1. Cally Ardington, Nicola Branson, Murray Leibbrandt, University of Cape Town David Lam, Vimal Ranchhod University of Michigan January, 2009 Assessing the Impacts of Teen Pregnancy on Human Capital in South Africa

2. Relatively high rates of teen childbearing – 24% had a birth by age 18 Teen childbearing in South Africa

3. Teen mothers have less schooling at any given age – But this is without controlling for any other variables – Does not deal with endogeneity of teen childbearing Teen childbearing in South Africa

4. CAPS is funded by U.S. National Institute of Child Health and Human Development, Andrew W. Mellon Foundation and The National Institute on Aging CAPS The Cape Area Panel Study A Study of the Changing Lives of Young Adults in Cape Town A Joint Project of the University of Cape Town, the University of Michigan and Princeton University Integrated Waves 1-2-3-4 data available for free public download: www.caps.uct.ac.za

5. CAPS data: Timeline and Survey area Wave 1 Aug 2002 – Jan 2003 5257 households 4752 young adults (age 14-22) Wave 2A Aug 2003 – Dec 2003 1360 young adults (age 15-23) Wave 2B May 2004 – Dec 2004 2489 young adults (age 16-24) Wave 3 April 2005 – Dec 2005 all young adults (age 17-25), plus household & parent questionnaires Wave 4 2006-07 All young adults (age 18-26), plus older adults (50+) and children of female young adults 440 Census Enumeration areas 10% of metro Cape Town, South Africa

6. CAPS data: Consequences of Teen Childbearing Young adult data: Two Perspectives Young adults as Teen Mothers Youth and young adult consequences of being a teen mother Young adults as Children of teen mothers Youth and young adult consequences of being born to a teen mother Child data: Children of female YA as children of teens Child consequences of being born to a teen mother

7. Advantages of Cape Area Panel Study Fertility data is extensive in every wave: – A retrospective pregnancy and birth history in Wave 1 – Detailed data on pregnancies and births since the last wave in each wave We see women’s schooling and other characteristics before they have a child – Through retrospective histories in Wave 1 – By following women across waves of the panel We follow women as they move into the labor force Wave 4 has detailed family support and social grant information – Including childcare and transfers We have information on many household characteristics such as income, parent’s education, & employment status of household members

8. Advantages of CAPS continued We have extensive information on Children of young adults – Child care, health and living arrangements from mother’s questionnaire in Waves 1, 3 & 4 – Wave 4 Child questionnaire: Current and birth anthropometry Vaccination history Weight-for-age (0-60 months) Other interesting variables in CAPS – Age at menarche – Characteristics of sexual partners – Literacy and numeracy evaluation in Wave 1

9. Effect of using different estimators: dependent var is educational attainment at age 20 Estimatorolsols w/ cov Change in education from age12sib_FEMatchingIV_csg birth18-1.335***-1.076***-1.215***-0.935**-1.083***-2.167 [0.12][0.11][0.10][0.41][0.14][2.32] Observations1452 1211452705 R-squared0.080.210.090.04 0.19 Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

10. Regression results Controls: age dummies, sex, population group (except for fixed effects) * p-value<0.1, **p-value<0.05 ***p-value<0.01 Dependent Variable Coefficient (std error) of teen mother variable Coefficient (std error) of older variable OLS OLS-with mothers education & childhood poverty Sibling fixed effects Siblings fixed effects-full sample Grade Standardised Maths Score-0.12*-0.14*-0.03 0.05 (0.08)(0.07)(0.08) Grade progression rate, age 9 to 18-0.03** 0.00 0.04** (0.01) (0.02)

11. Africa Centre Demographic Information System

12. Jan 2000Data collection begins Continual registration system (twice a year): Information is collected on all household memberships, residencies, pregnancies, births, deaths, marriages etc. Jan 2001 to Jun 2001First round of household socioeconomic data is collected. HSE1 Jan 2003 to Jun 2004First round of household socioeconomic data is collected. HSE2 Jan 2005 to Jun 2005HSE3 Jan 2006 to Jun 2006HSE4 Jul 2007 to Dec 2007HSE5 Approximately 100,000 people (resident and non-resident) in 11,000 households Timeline for Africa Centre data collection

13. Preliminary findings Teen mothers are not behind other teens of the same age prior to the birth. Teens begin to fall behind in the year of the birth. Teen mothers are not less likely to be enrolled that other teens of the same age prior to the birth. Some teen mothers re-enrol following the birth.

14. Preliminary findings Using waves of socioeconomic data we can control for pre-teen household socioeconomic status (SES). Controlling for baseline SES does not remove the deficit in attainment and enrolment for teen moms compared to teens of the same age. We can also use Africa Centre data to look at longer term outcomes for women. Preliminary results show that women who had their first birth when they were teenagers have significantly less education at ages 30 to 40 than – Those whose first birth was after 20 (around 1.6 years) – Their siblings who had their first birth after 20 (around 1.7 years).

15. Future Steps Further analysis of CAPS and Africa Centre Data Analysis of Wave 1 of National Income Dynamics Study – First wave 2008 – First national panel survey for South Africa – Full fertility history, extensive socieconomic details, community variables, Possible instrumental variables for teen childbearing (all imperfect, but potentially informative) – Child Support Grant – Age at menarche – Access to clinics with special youth services – Variation in sex ratios in communities (especially in rural areas)

19. Outcomes for Children of Teen Mothers: The Samples Young adult ‘s as children of Teens Total # of Young Adults 3662 % born to teen mother 14.58% # of groups (pairs/trips) 817 % (#) with variation on teen mother 19.34% (158) Child Sample Total # of Children 920 % born to teen mother 50.11% # of groups (pairs/trips)160 % (#) with variation on teen mother 58.13% (93) % higher as sample selective of women who begin childbearing early Includes all African and Coloured YA’s with mother’s age at their birth Siblings and cousins in the same household Siblings only

20. Most teen childbearing is non-marital, – Only 18% of those with child at age 20 had ever been married Teen childbearing in South Africa

21. Preliminary findings High rates of teen fertility - by 20 years of age, over 50% of women have given birth.

22. Significant fractions return to school after having child – Over 50% of 15-17 year-olds with a child were in school Teen childbearing in South Africa

23. Educational consequences for Teen Mothers: Practical Questions What is the treatment variable? – `Births’ at a specific age or in an age-group? – What defines the appropriate counter-factual? What is the observed outcome measure? – Data limitations  cannot use final realized lifetime education levels in CAPS – Candidates: Change in education levels between birth and some point Attainment at some specific ages (eg. 18, 20, 25) We use multiple estimators and a few outcome variables, which allow us to also compare the robustness of the estimates to alternative estimation methods.

24. Is the Child Support Grant a valid IV? Our concern is that adolescent fertility may be endogenous. One solution is to use the CSG as an instrument. CSG was introduced in 1999, is means tested, and represents a monthly subsidy of 10-15$ per child per month Theoretically, this should provide a valid instrument. But the value is low and so the instrument might be very weak. Finite sample properties of weak instruments are not good. In later work, we plan to try to use this to estimate the effect on a restricted sample of poorer, possibly rural adolescents. NIDS data will have a much bigger sample.