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Perception of HIV risk and the quantity and quality of children: the case of rural Malawi

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Abstract

The empirical literature on the impact of HIV on the quality (Q) and quantity (N) of children provides limited and somewhat mixed evidence. This study introduces individual HIV risk perceptions, as a predictor of mortality, into a Q–N investment model. In this model, higher maternal mortality predicts lower N, while higher child mortality predicts lower Q. Thus, the two effects together make likely negative associations between HIV and both Q and N. Based on longitudinal micro-data on mothers and their children in rural Malawi, our results suggest that higher mothers’ reported HIV risk reduces both child quality, as reflected in children’s schooling and health, and child quantity, when the perceived risk is already moderate or high. The effects are sizable and, in the case of Q (schooling and health), are found for children and teenagers, both boys and girls, while in the case of N, they are found for young and mature women.

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Notes

  1. Studies that report on schooling declines include Evans and Miguel (2007), Yamano et al. (2006), and Case and Ardington (2006). Mishra and et al (2007) analyze malnutrition, while Weinhardt et al. (1999) and Oster (2012) study on changes in sexual behavior.

  2. Some examples include Case and Paxson (2009) on health care, Papageorgiou and Stoytcheva (2008), and Young (2005) on income and Whiteside (2002) on poverty.

  3. Other mechanisms include supply-side variables, such as teacher and health worker availability (Kelly 2000; Bennell 2005), funding (Grassly et al. 2003; Chen et al. 2004), and services (Case and Paxson 2009). Among the demand-side factors, the expectations about future resources, for example, the expectations about medical costs might influence the way individuals make decisions about investments in children.

  4. An evolutionary perspective intuitively suggests a positive relationship between fertility and the probability of death of children. The literature reviewed in this article, however, did not find a clear relationship between HIV and fertility, nor between mortality and fertility.

  5. Fortson (2009), Kalemli-Ozcan (2012), Kalemli-Ozcan and Turan (2011), Juhn et al. (2008), Fink and Linnemayr (2008), and Young (2005) analyzed regional and local HIV rates, as well as own HIV status among sub-Saharan African countries, with mixed results.

  6. Durevall and Lindskog (2008), Noël-Miller (2003), and Ueyama and Yamauchi (2009) find that Malawian HIV rates (or self-reported risk) are negatively associated with fertility rates, with the exception of young women with no children, for whom the probability of a first birth increases.

  7. In a concurrent study, Shapira (2011) examines the impact of perceived HIV risks on fertility, but does not consider child quality.

  8. De Lannoy (2005) presents related qualitative evidence.

  9. The empirical literature on links between HIV rates and child quality focuses on schooling. However, another important investment in children’s human capital is in their health, which may have important long-run effects in poor populations (e.g., Hoddinott et al. 2008, Maluccio et al. 2009, Behrman et al. 2009).

  10. If the mother dies, there are C 2 resources available, for example, for children to consume. The model presented in this section, however, does not include children’s consumption explicitly. But the inclusion of children’s consumption would not change the major lessons learned in this exercise.

  11. However, changes in the relative preferences between Q and N could change the total amount to be invested in children if the marginal utility of investments in children, W’, changes.

  12. The first-order conditions with respect to C 1, Q, and N, respectively, are the following:

    $$ U^{\prime} -\lambda =0 $$
    (4’)
    $$f( {H_{m} } )\left[ {( {1-H_{c} } )W^{\prime} \ast({\beta N^{\alpha} Q^{\beta-1}})} \right]-\lambda ( {P_{q} +P_{qn} N} )=0 $$
    (5’)
    $$f( {H_{m} } )\left[ {( {1-H_{c} } )W^{\prime} \ast ( {\alpha N^{\alpha-1} Q^{\beta}} )} \right]-\lambda ( {P_{n} +P_{qn} Q} )=0 $$
    (6’)

    Equations 5 plus 6 imply Eq. 3. Equations 4 plus 5 imply that \(f(H_{m})[(1-H_{c})W^{\prime }(w_{q} N^{wn} Q^{wq-1})]/(P_{q}+NP_{qq})=U(C_{1})^{\prime }\). If H m rises, then the left side decreases and thus U(C 1)′decreases, which implies that C 1 increases. Because of Eq. 3, adjustments in Q and N move in the same direction, so both Q and N decrease. Therefore, the total investment in children decreases. The analysis shows that a rise in H c goes along the same lines.

  13. The 2010 MLSFH data, and in particular the longitudinal linkages of children across waves, were not yet available at the time of writing this paper.

  14. Detailed descriptions of the MLSFH sample selection, data collection, and data quality are provided on the project website http://www.malawi.pop.upenn.edu/, in a Special Collection of the online journal Demographic Research that is devoted to the MLSFH (Watkins et al. 2003) and in a recent follow-up publication that incorporates the 2004 and 2006 MLSFH data (Anglewicz et al. 2009).

  15. Tabulations in De Paula et al. (2010) show that two thirds of individuals receiving HIV-positive test results in 2004 classify themselves in the next round in 2006 as zero or low likelihood of having HIV.

  16. For example, Matovu et al. (2005) and Nyblade et al. (2001) find self-selection in testing in Uganda.

  17. Other studies in this discussion have used “HIV prevalence rate” in the geographical area of residence of the individual. This alternative has advantages and disadvantages compared to ours, so we would say that they complement each other. For example, individuals may have a vague idea of the prevalence in their region, just as they may have a vague idea about the “likelihood of contracting HIV,” as is asked, in different forms, in the surveys. Another example is the role of unobservables given that HIV rates are not distributed randomly in the geographical areas, while our identification strategy is susceptible to endogeneity bias. In general, it is difficult to attribute a measure to specific biases in the methodology, so it is useful to have different methodological approaches to the same problem.

  18. The exact wording of the question depends on the language of the interview. Most of the interviews were conducted in Chitumbuka and Chichewa (District of Rumpi, north Malawi), Chichewa (District of Mchinji, central Malawi), and Chiyao (District of Balaka, south Malawi).

  19. We are not aware of evidence about the better predictive power of these extreme answers; Idler and Benyamini (1997) summarize evidence in relation to self-reports on health.

  20. Recoded as 0, “very good” or “good”; and 1, “poor” or “very poor.”

  21. Indeed, our data do not show significant results for these women.

  22. The significance of the differences in the coefficients of interest between the fixed and the random-effect models are investigated by the Durbin–Wu–Hausman test. The random-error model used to conduct the test also included other regressors: age of the mothers, number of siblings, regional fixed effects (one for each of the three regions included in the survey), and perceived HIV status.

  23. Women with HIV, whether or not they know it, change their behavior for health reasons. In this case, the dynamic does not have an effect on the main idea guiding the article. In this article, the test results are not included as a control because it is not available for all individuals, but the health of the mother and a measure of medical expenses are included as controls. Regardless of the above, of the people who received the test, approximately 5 % tested positive, and excluding them from the statistical analysis does not alter the results.

  24. Q 2 and Q 1, as dependent variables, are taken from MLSFH 2008 and MLSFH 2006. Q 1 and Q 0, as control variables, are taken from MLSFH 2006 and MLSFH 2004. But MLSFH 2004 does not contain the required measure of child health. Thus, the included control for child health ( Q1′ and Q0′) is the answers to “Is your child ill?” as included in MLSFH 2006 and 2004.

  25. Another possible wealth indicator is household crop production. However, given that the survey was conducted in the dry season, crop accounts have to rely on retrospective reports that probably have considerable measurement error as usually is the case for near-subsistence agriculture. Also, crop production is affected considerably by weather fluctuations that may obscure the relation with longer-run wealth. Nevertheless, crop variables were included in exploratory regression analysis (not shown), although they have little significance.

  26. In Table 3, 0.36 is just the simple average of the coefficients in child education.

  27. For the sake of simplicity, the budget constraint in this section does not contain the P n N and the P n q × Q N components.

  28. MLSFH 2006 respondents report, by selecting a number of beans from zero to ten, their likelihood of dying within the next 1, 5, or 10 years. On average, individuals who reported a high perceived risk of HIV reported a 10 % higher likelihood of dying, compared to individuals who reported no perceived risk of HIV. We also assume that this represents the children’s mortality risk.

  29. ∂ Q t / ∂ H t − 1 = − C t − 1 × β s × f(H t − 1; b s ), where f(H t − 1; b s ) = (1 − H t − 1)(H t − 1 + 3) / (b s (1 − H t − 1)2 + (2 − H t − 1)).f is a positive function, and decreases with H.

  30. See a brief summary in Datar et al. (2007) and Jayachandran and Lleras-Muney (2009) respectively.

  31. The association between mortality and investments in children quantity (fertility) and quality (education and health) has been discussed for long. A substantial fraction of this body of research is focused on developed countries, for which empirical analysis found that (a) mortality is linked with fewer investments in child education and health (see a brief summary in Datar et al. 2007 and Jayachandran and Lleras-Muney 2009, respectively) and (b) mortality is not clearly linked with fertility. Preston (1978) identified three conceptual mechanisms for the mortality–fertility link, insurance, replacement, and lactation interruption effects, and Montgomery (2000) included the role of perceptions and social definitions, but the empirical counterpart of such associations remains rather weak and poorly understood (Montgomery 2000; Cleland 2001); Cigno (1998) and Strulik (2003) show that there is a negative relationship between mortality and total fertility when mortality is high and a positive relationship when mortality is low. Albanesi and Olivetti (2010) found mixed results for the adult mortality–fertility association in the USA.

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Acknowledgments

This research was supported by the UK Economic and Social Research Council/Hewlett Foundation Grant (PI Marcos Vera-Hernandez, Institute of Fiscal Studies, University College London; PI on a subcontract to the University of Pennsylvania, Hans-Peter Kohler) on “Effects of Reproductive Health on Poverty in Malawi” and R01-HD-053781-01A1 (PI Hans-Peter Kohler) on “Consequences of High Morbidity and Mortality in a Low-Income Country”. The authors are grateful for insightful comments and suggestions of the three anonymous referees.

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  1. Institute of Public Policy, Universidad Diego Portales, Ejército 260, Santiago de Chile, Chile

    Ruben Castro

  2. University of Pennsylvania, Philadelphia, PA, USA

    Jere R. Behrman & Hans-Peter Kohler

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  1. Ruben Castro
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Castro, R., Behrman, J.R. & Kohler, HP. Perception of HIV risk and the quantity and quality of children: the case of rural Malawi. J Popul Econ 28, 113–132 (2015). https://doi.org/10.1007/s00148-013-0498-0

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