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


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.

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  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, 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.


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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.

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Correspondence to Mevlude Akbulut-Yuksel.

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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

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


$$ q(t)= e^{-\int_{0}^t\mu(z)dz}= e^{-mt}. $$
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

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

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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).

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  • Intergenerational transmission
  • Education
  • Human capital investment

JEL Classification

  • O12
  • I1
  • I2