Skip to main content

Left behind: intergenerational transmission of human capital in the midst of HIV/AIDS

Abstract

This paper provides evidence on how adverse health conditions affect the transfer of human capital from one generation to the next. We explore the differential exposure to HIV/AIDS epidemic in sub-Saharan Africa as a substantial health shock to both household and community environment. We utilize the recent rounds of the Demographic and Health Surveys for 11 countries in sub-Saharan Africa. First, we find that an additional year of maternal education leads to a 0.37-year increase in children’s years of schooling in the developing economies in sub-Saharan Africa. Second, our results show that mother’s HIV status has substantial detrimental effects on inheritability of human capital. We find that the association between infected mothers’ and their children’s human capital is 30 % less than the general population. Finally, focusing only on noninfected mothers and their children, we show that HIV prevalence in the community also impairs the intergenerational human capital transfers even if mother is HIV negative. The findings of this paper are particularly distressing for these already poor, HIV-torn countries as in the future they will have even lower overall level of human capital due to the epidemic.

This is a preview of subscription content, access via your institution.

Notes

  1. See Black and Devereux (2011) and Corak (2004) for a review of this literature.

  2. A notable exception studying part of African countries is Hertz et al. (2007). They estimate the intergenerational persistence of human capital in four countries in Africa including Egypt, South Africa (KwaZulu-Natal), Ghana, and rural Ethiopia.

  3. It would be ideal to analyze the effects of father’s HIV status on intergenerational transmission of human capital too; however, HIV testing for fathers is only available for small fraction of fathers that are married to mothers in our sample and reside in the same household. We present estimation analysis with father’s HIV status in Appendix Table 12. In Appendix Table 12, we find that for every additional year of father’s education, children with HIV-positive fathers experience 0.13 fewer years of increase in their educational attainment. In percentage terms, the persistence coefficient between HIV-positive fathers and their children is 60 % smaller than the general population. Similarly, we find that children of HIV-positive fathers are half as likely to attend school and, if they attend, they show slower progress at school compared to children with HIV-negative fathers.

  4. There is no consensus on the direction or even the existence of a correlation between life expectancy and economic growth. Weil (2007) and Lorentzen et al. (2008) find positive effects of life expectancy on economic growth, whereas Acemoglu and Johnson (2007) find no effects.

  5. In our model, we endogenize children’s human capital formation. See Cigno (1998), Kalemli-Ozcan (2002), and Soares (2005) for models endogenizing both fertility and human capital decisions.

  6. We present the derivation of our model in Appendix.

  7. If we assume that parents and children have different mortality rates, our FOC remains the same.

  8. DHS datasets are available at www.measuredhs.com, MEASURE DHS, Macro International Inc.

  9. Timberg (2006), among others, argues that this method overestimates HIV prevalence because pregnant women have higher risk of HIV infection since they are engaging in unprotected sex.

  10. Although we have a smaller sample when we restrict ourselves to 11 countries, the results change only slightly compared to the analysis using all 17 countries that HIV data are available. Therefore, since this is a more robust identification, we stick to 11-country sample in the empirical analysis.

  11. Mothers who were born before 1980 account for 99 % of all mothers in our sample.

  12. We define these additional measures of educational attainment as follows: We assign a value of 1 to the variable “school attendance” if the child has completed one or more years of schooling, 0 otherwise. Similarly, we create a variable as “progress through school,” which is correct-grade-for-age, computed by dividing the years of schooling by years since age 7.

  13. Estimates of β close to unity imply high persistence and limited mobility, whereas values of β close to 0 suggest low persistence and almost complete intergenerational mobility in outcomes. Presumably, any real number could be obtained from the estimation of Eq. 5; a positive value indicates immobility where higher parental education is associated with higher child education, whereas a negative value of β indicates a generational reversal where higher parental education is associated with lower child education.

  14. We use the matching procedure developed and described by Leuven and Sianesi (2003). Simple t tests on the equality of means between the HIV-positive and HIV-negative mothers show that our matched sample satisfies the balancing requirement. That is, there are no statistically significant differences between control and treatment groups in terms of observable characteristics.

  15. Results are quantitatively similar when we include children with HIV-positive mothers into estimations. But since we find a significant effect of mother’s HIV status in the previous section, in this part of the paper, we prefer to restrict our analysis to children with HIV-negative mothers only. Results including HIV-positive mothers are available upon request.

  16. Community-level OLS analyses using the recent waves of DHS are available upon request.

References

  • Acemoglu D, Johnson S (2007) Disease and development: the effect of life expectancy on economic growth. J Polit Econ 115(6):925–985

    Article  Google Scholar 

  • Aydemir A, Chen W, Corak M (2008) Intergenerational education mobility among the children of Canadian immigrants. IZA DP 3759

  • Becker G S, Tomes N (1986) Human capital and the rise and fall of families. J Labor Econ 4(3):1–39

    Article  Google Scholar 

  • Behrman JR, Rosenzweig MR (2002) Does increasing women’s schooling raise the schooling of the next generation? Am Econ Rev 92(1):323–334

    Article  Google Scholar 

  • Black S, Devereux PJ (2011) Recent developments in intergenerational mobility. In: Orley A, Card D (eds) Handbook of labor economics, vol 4B. Elsevier, Amsterdam, pp 1487–1541

    Chapter  Google Scholar 

  • Case A, Ardington C (2006) The impact of parental death on school outcomes: longitudinal evidence from South Africa. Demography 43(3):401–420

    Article  Google Scholar 

  • Cigno A (1998) Fertility decisions when infant survival is endogenous. J Popul Econ 11(1):21–28

    Article  Google Scholar 

  • Corak M (2004) Generational income mobility in North America and Europe. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Ehrlich I, Lui FT (1991) Intergenerational trade, longevity, and economic growth. J Polit Econ 99(5):1029–1059

    Article  Google Scholar 

  • Evans DK, Miguel E (2007) Orphans and schooling in Africa: a longitudinal analysis. Demography 44(1):35–57

    Article  Google Scholar 

  • Fortson J (2011) Mortality risk and human capital investment: the impact of HIV/AIDS in Sub-Saharan Africa. Rev Econ Stat 93(1):1-15

    Article  Google Scholar 

  • Gang IN, Zimmermann KF (2000) Is child like parent? Educational attainment and ethnic origin. J Hum Resour 35(3):550–569

    Article  Google Scholar 

  • Haveman R, Wolfe B (1994) Succeeding generations: on the effects of investments in children. Russell Sage Foundation, New York

    Google Scholar 

  • Hertz T, Jayasundera T et al (2007) The inheritance of educational inequality: international comparisons and fifty-year trends. The B. E. Journal of Economic Analysis & Policy 7(2), Article 10

  • Kalemli-Ozcan S, Ryder H, et al (2000) Mortality decline, human capital investment and economic growth. J Dev Econ 62(1):1–23

    Article  Google Scholar 

  • Kalemli-Ozcan S (2002) Does mortality decline promote economic growth? J Econ Growth 7(4):411–439

    Article  Google Scholar 

  • Leuven E, Sianesi B (2003) PSMATCH2: stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing

  • Lorentzen P, McMillan J et al (2008) Death and development. J Econ Growth 13(1):81–124

    Article  Google Scholar 

  • Meltzer D (1992) Mortality decline, the demographic transition and economic growth. PhD Dissertation, University of Chicago

  • National Center for Education Statistics (1995) Dropout rates in the United States. NCES Research Paper, no: 97-473

  • Plug E, Vijverberg W (2003) Schooling, family background, and adoption: is it nature or is it nurture? J Polit Econ 111(3):611–641

    Article  Google Scholar 

  • Pronzato C (2012) An examination of paternal and maternal intergenerational transmission of schooling. J Popul Econ 25(2):591–608

    Article  Google Scholar 

  • Sacerdote B (2007) How large are the effects from changes in family environment? A study of Korean American adoptees. Q J Econ 122(1):119–157

    Article  Google Scholar 

  • Soares RR (2005) Mortality reductions, educational attainment, and fertility choice. Am Econ Rev 95(3):580–601

    Article  Google Scholar 

  • Solon GR (1992) Intergenerational income mobility in the United States. Am Econ Rev 82(3):393–408

    Google Scholar 

  • Timaus IM, Jasseh M (2004) Adult mortality in sub-Saharan Africa: evidence from demographic and health surveys. Demography 41(4):757–772

    Article  Google Scholar 

  • Timberg C (2006) How AIDS in Africa was overstated. Washington Post

  • UNAIDS/WHO AIDS (2009) AIDS epidemic updates

  • United Nations Population Division (2008) World population prospects

  • Weil DN (2007) Accounting for the effect of health on economic growth. Q J Econ 122(3):1265–1306

    Article  Google Scholar 

  • Zimmerman DJ (1992) Regression toward mediocrity in economic stature. Am Econ Rev 82(3):409–429

    Google Scholar 

Download references

Acknowledgements

We are especially grateful to Chinhui Juhn for the very useful comments and discussions. We also thank Randall Akee, Abdurrahman Aydemir, Daniel Hamermesh, Melanie Khamis, Gary Solon, Mutlu Yuksel, and seminar participants at the University of Houston, Dalhousie University, IZA, 2008 SOLE, 2008 World Congress of the International Economic Association, and two anonymous referees for their helpful comments and suggestions. Authors bare sole responsibility for any errors that may remain.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mevlude Akbulut-Yuksel.

Additional information

Responsible editor: Alessandro Cigno

Appendix: Derivation of the model

Appendix: Derivation of the model

All assumptions and parameters are explained in the theoretical framework section. The utility-maximizing household maximizes the present discounted value of income of parents and the children as follows:

$$ \mathop\mathrm{max}\nolimits_{s_{c}}\int_0^{T} e^{-\delta t}ye^{\gamma s_\textrm{p}} p(t) dt + \int_{s_\textrm{c}}^T e^{-\delta t}ye^{\gamma s_\textrm{c}} q(t) dt $$

where the present discounted value of parents’ income is

$$ Yp (p(t))= \int_0^{T} e^{-\delta t}ye^{\gamma s_\textrm{p}} p(t) dt $$

and the present discounted value of children’s income is

$$ Yc (q(t))= \int_{s_\textrm{c}}^T e^{-\delta t}ye^{\gamma s_\textrm{c}} q(t) dt. $$

Hence,

$$ q(t)= e^{-\int_{0}^t\mu(z)dz}= e^{-mt}. $$
Table 10 DHS data
Table 11 Importance of paternal education
Table 12 Effect of father’s HIV status on inheritability of human capital

Then, the schooling decision for children is as follows:

$$ \begin{array}{rll} &&{\kern-6pt} \max\nolimits_{s_\textrm{c}}\int_{s_\textrm{c}}^{T}e^{-\delta t}\: y\: e^{\gamma s_\textrm{c}}\: e^{-mt} dt\\ &&\Longrightarrow ye^{\gamma s_\textrm{c}}\left[-\frac{1}{(\delta+m)}e^{-(\delta+m) t}\biggr|_{s_\textrm{c}}^{T}\right]=ye^{\gamma s_\textrm{c}}\left[-\frac{1}{(\delta+m)}\left(e^{-(\delta+m) T}-e^{-(\delta+m) s_\textrm{c}}\right)\right] \end{array} $$
$$ F=\left[\frac{ye^{\gamma s_\textrm{c}}}{(\delta+m)}\left(e^{-(\delta+m) s_\textrm{c}}-e^{-(\delta+m) T}\right)\right]. $$

For the optimal level of children’s schooling,

$$ \frac{\partial F}{\partial s_\textrm{c}}=0 $$
$$ \frac{\partial F}{\partial s_\textrm{c}}=\gamma\left(e^{-(\delta+m)s_\textrm{c}}-e^{-(\delta+m)T}\right)-(\delta+m)e^{-(\delta+m)s_\textrm{c}}=0 $$
$$ (\gamma-\delta-m)e^{-(\delta+m)s_\textrm{c}}=\delta e^{-(\delta+m)T} $$
$$ e^{(\delta+m)s_\textrm{c}}=\left(\frac{\gamma-\delta-m}{\gamma}\right)e^{(\delta+m)T}, $$

when we take the log of both sides, we get the optimal level of schooling for the children’s generation:

$$ s_\textrm{c}^{\ast}=Tln\left(\frac{\gamma-\delta-m}{\gamma}\right) $$

which yields \(\frac {ds_\textrm{c}}{dm} < 0 \) and \(\frac {ds_\textrm{c}}{dq(t)} > 0 \). This suggests that children get more schooling when the probability of survival increases. Hence, children’s survival probability at age t, q(t), decreases both with parents’ HIV infection and HIV prevalence in the community; therefore

$$ \frac {ds_\textrm{c}}{d\textrm{HIVparents}} < 0\; and \; \frac {ds_\textrm{c}}{d\textrm{HIVcommunity}} < 0 $$

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Akbulut-Yuksel, M., Turan, B. Left behind: intergenerational transmission of human capital in the midst of HIV/AIDS. J Popul Econ 26, 1523–1547 (2013). https://doi.org/10.1007/s00148-012-0439-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-012-0439-3

Keywords

  • HIV/AIDS
  • Intergenerational transmission
  • Education
  • Human capital investment

JEL Classification

  • O12
  • I1
  • I2