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Infrastructure, women’s time allocation, and economic development

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Abstract

This paper develops a gender-based OLG model of endogenous growth to analyze the impact of infrastructure on women’s time allocation between market work, raising children, own health care, and home production, and its implications for education and health outcomes. Women’s health status in adulthood, which affects productivity and wages, depends on their health status in childhood. Threshold effects in health and life expectancy, associated with access to infrastructure, may generate multiple development regimes. Whether an increase in government investment in infrastructure succeeds in shifting the economy to a high-growth equilibrium depends crucially on how women reallocate their time and the strength of congestion effects.

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Notes

  1. See, for instance, World Bank (2008) for access to electricity and Agénor (2012b) for a broader overview. Infrastructure in this paper is broadly defined to include transport, water supply and sanitation, telecommunications, and energy.

  2. See Herz and Sperling (2004) and World Bank (2011) for a broad review of the various channels through which better education for women may help to stimulate growth.

  3. For evidence that parental tutoring is important for children in developing countries, see Glewwe and Kremer (2006).

  4. Specifically, they focus on how greater access to consumer durables (made possible by improved technologies and access to electricity) helped to “ liberate” women in the United States from domestic production activities and led to dramatic increases in participation rates of married females in the labor force. See Albanesi and Olivetti (2009) and Cardia (2010) for mitigating results.

  5. In the working paper version of this article (available upon request), and in line with the evidence reviewed in Agénor (2012b), public infrastructure affects also the production of health services.

  6. Persistence in health is also considered by De la Croix and Licandro (2013). In their analysis, however, it is individual spending on childhood development (rather than mothers’ time or public spending) that determines longevity in adulthood. In addition, their focus is on explaining features of the demographic transition, rather than growth and human development.

  7. We also introduce gender bias, both in the work place and in the home. Our goal here, however, is not to provide a detailed analysis of gender bias; as will be made clear later, our modeling choices are aimed essentially at simplifying the solution of the model, in order to focus on the issues at hand.

  8. These congestion effects operate also through the supply of health services in the extended version of the model considered in the working paper version of this article.

  9. In the working paper version of this article, a preliminary section provides a detailed overview of the literature on the impact of infrastructure on women, particularly with respect to their time allocation.

  10. We abstract from leisure, which can be viewed as a luxury good in poor countries. For evidence on differences in labor supply between men and women in these countries, see Blackden and Wodon (2006) and World Bank (2011).

  11. We therefore abstract from the probability of dying in childhood. In general, the determinants of adult and child mortality are different (Cutler et al. 2006); see the working paper version of this article for a more detailed discussion.

  12. Specifically, we assume that in adulthood husbands do not survive their wives and die of sorrow soon after the passing of their spouse. This simplifies matters considerably by allowing us to use the same survival rate for men and women and to keep the gender composition of the population constant.

  13. Giving birth is also assumed to involve no time cost—or, equivalently, that the time involved is fixed and normalized to zero.

  14. These two types of costs could be separated by introducing different spending shares for the schooling and health components. This, however, would mainly add notational clutter and produce little value added to the analysis.

  15. This assumption can actually be formally derived by replacing the term \(\eta _{N}\ln h_{t+1}^{C}n_{t+1}\) in (2) by the composite term \(\eta _{N}^{B}\ln h_{t+1}^{C}n_{t+1}^{B}+\) \(\eta _{N}^{G}\ln h_{t+1}^{C}n_{t+1}^{G} \) where \(n_{t+1}^{B}\) (\(n_{t+1}^{G}\)) is the number of boys (girls). With \(\eta _{N}^{B}=\eta _{N}^{G}\), the solution then yields \(n_{t+1}^{B}=n_{t+1}^{G}\).

  16. It could be assumed that home production requires also the use of marketed commodities. We abstract from this complication, as it would not bring additional insight for the purpose at hand. We also do not consider child labor, given our assumption that children allocate all their time to schooling.

  17. The assumption of perfect substitutability between time and infrastructure is important for analytical tractability—it allows us to derive explicit analytical solutions. An inverse relationship between time allocated to home production and access to infrastructure services (as derived later) could be established with alternative (nonseparable) production functions; however, this would preclude a full analytical treatment and force recourse to numerical simulations.

  18. Nonexcludability(no agent, individual or firm, can prevent other agents from using it concomitantly) justifies the inclusion of the aggregate stock of public capital in the home production function and, as shown later, in the production technology for the marketed good.

  19. Given the linearity of aggregate output in \(K_{t}^{P}\), as shown later, using output as the congestion factor in (8) and elsewhere would not alter the results in any fundamental way. While population could be used also as an indicator of congestion, it is usually viewed more as an indicator of market size.

  20. The loss in income that women suffer as a result of discrimination could be assumed to accrue to capital owners, who use it to finance current consumption. However, to capture the adverse economic effects of gender discrimination, we assume instead that it is a pure deadweight loss.

  21. See for instance Amin and Chandrasekhar (2009) for evidence for Bangladesh on this issue.

  22. In the working paper version of this article, we also account for the fact that access to infrastructure may affect the efficiency of rearing time.

  23. The term \(H_{t}^{G}/K_{t}^{P}\) in (16) could be replaced by \((H_{t}^{G}/n_{t}N_{t})E_{t}^{t-1,f}\), that is, health spending per child mutiplied by a mother’s average human capital (as defined in (14)), to capture the fact that more educated women are more enclined to use access to public health services to take care of their children—or, alternatively, that the provision of health services is more effective if mothers are more literate. However, from the derivations in the Appendix it can be verified that the results are qualitatively similar to those obtained with the simpler specification in (16).

  24. It can be established that if men’s health and productivity depend on the same variables as women’s health and productivity, and if in addition women’s health generates a positive externality in terms of men’s health—perhaps because a healthier wife is more conscious of the benefits of a healthy diet for her husband and prepares home meals accordingly, as suggested in McMahon (2010) for instance—then what matters fundamentally for the dynamics of the economy is indeed women’s health status. See Agénor (2012a).

  25. This specification could be extended to account for the possibility that consumption of public health services affects health, and that more educated individuals tend to take better care of their health. Indeed, in specifications (16) and (17), “ raw” time could be replaced by “ effective” time, \(\varepsilon _{t}^{f,R}e_{t}^{f}\) and \(\varepsilon _{t+1}^{f,H}e_{t+1}^{f}\), to capture in the first case the idea that the productivity of the time allocated to child rearing by a mother depends on her level of education, and in the second the idea that more educated individuals tend to take better care of their own health.

  26. See the Appendix. To ensure that \(p_{m}^{C}n_{t}\ge 2\) requires imposing an upper bound on \(\theta ^{R}\), that is, spending per child on marketed commodities cannot be too high.

  27. The lower bound of zero on \(\varepsilon _{t}^{f,P}\) is of course a matter of convenience.

  28. Because \(\Lambda _{2}\) is greater than unity, time allocated to market work grows at a slower rate than the (absolute) rate at which time spent on home production falls. The figure assumes that \(\Lambda _{1}>1\), to ensure that the initial value \(\varepsilon _{0}^{f,P}\) (for \(k_{t}^{I}=0\)) is higher than \(\varepsilon _{0}^{f,W}\). This is consistent with the evidence. In turn, \(\Lambda _{1}>1\) requires \(1-\omega >1/[1+\pi ^{Q}(1-\sigma )]\), or equivalently that the home good be sufficiently valued by the family.

  29. A somewhat related idea, proposed by Cavalcanti and Tavares (2011), is that public spending may reduce the per child cost of raising children. However, given their focus on explaining the size of government, they do not pursue in any detail the implications of this assumption for women’s occupational choices and long-term growth.

  30. Nevertheless, the model displays a quantity-quality tradeoff in the tradition of Becker and Lewis (1973) and Barro and Becker (1989), as an increase in the unit cost of child rearing induces a drop in the fertility rate and an increase in the time allocated to each surviving child (\(dn_{t}/d\theta ^{R}<0\), \(d\varepsilon _{t}^{f,R}/d\theta ^{R}>0\)).

  31. In addition, children do not provide direct care to their parents, as in models emphasizing the old age-support hypothesis (see, for instance, Raut 1991). More general assumptions could of course alter this result.

  32. From the perspective of women’s occupational choices, what matters is total time allocated to children, \(n_{t}\varepsilon _{t}^{f,R}\), as implied by (1). However, to assess the growth effects of these decisions, changes in time per child alone \(\varepsilon _{t}^{f,R}\) matter also, because they affect directly health outcomes.

  33. Propositions 1–4 could be derived in an OLG model without gender, as long as the link between time allocation and infrastructure of the (single) individual is accounted for. However, Propositions 5 and 6 obviously require a gender-based framework, because they explicitly refer to parameters reflecting gender bias in the home and in the market place.

  34. In the working paper version of this article, we also consider a mechanism related to changes in women’s health status and their productivity, in line with the literature on nutrition, health, and work effort (see Strauss and Thomas (1998), Schultz (2003), and Kedir (2009)). This is also consistent with the recent evidence suggesting that poor physical health tends to be associated with poor mental health, which in turn tends to affect labor force participation and productivity (see Das et al. 2009).

  35. An alternative and perhaps more natural assumption would be to assume that survival rates are related to the individual’s own health status. However, because these effects must be internalized in solving the family’s optimization problem, explicit analytical solutions become difficult to derive. Our formulation, despite its simplicity, is sufficient to convey the main point of our analysis.

  36. Strictly speaking, only women’s life expectancy should increase; however, if men’s life expectancy remains (implicitly) fixed, this is equivalent to assuming that the average life expectancy, \(p\), rises.

  37. While \(HH\) always shifts upward, this is the case for \(XX\) only if \(\nu _{1}\) is not too large—an assumption that we retain for illustrative purposes.

  38. Budget-neutral increases in one share of productive spending that are offset by changes in another productive share would, of course, create trade-offs between spending allocations.

  39. In a study of Indonesia, for instance, Park (2007) found that, with respect to children’s nutritional status, the resource pooling hypothesis can be rejected and that parental household bargaining has an important impact on outcomes. However, with respect to investment in children’s education, results are mixed. The implication is that the process of intrahousehold resource allocation may differ according to the type of decisions being made; thus, it is possible that no single model can explain all these decisions.

  40. See Echevarria and Merlo (1999), Vermeulen (2002), De la Croix and Vander Donckt (2010) and, for studies that build on the framework developed in this paper, especially Agénor (2012a) and Agénor et al. (2013).

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Correspondence to Pierre-Richard Agénor.

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We are grateful to Ed Amman, Kyriakos Neanidis, participants at various seminars and two anonymous referees for many helpful comments. However, the views expressed in the paper are our own. The Appendix referred to in the text is available upon request.

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Agénor, PR., Agénor, M. Infrastructure, women’s time allocation, and economic development. J Econ 113, 1–30 (2014). https://doi.org/10.1007/s00712-013-0358-0

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