Nutrition, Health, and Economic Performance

Health and nutrition outcomes are critical to the well-being of households and individuals and their economic productivity and prosperity. Although it seems evident that a debilitated worker will be less productive, there are numerous indirect, subtle, and complex pathways that link poor health and nutrition to economic output, such as sick children, or children of sick adults, being less likely to accumulate other forms of human capital, and disease-ridden societies with high infant and child mortality rates having higher fertility, with the consequent economic burdens associated with the risks of child bearing and a higher population growth rate. Compounding the challenges in understanding the impact of health on economic outcomes is the complexity of the temporal dimensions, as the productivity consequences of poor health extend far beyond the short term and affect outcomes across the life course and from one generation to the next.

The relationship between health and economic outcomes is particularly important in developing countries. First, health problems are most severe in these countries, and the ability to perform hard physical labor most important for employment. Second, self-employment and self-provisioning are of particular importance, and under such circumstances, reduced levels of output, from temporary ailments and disease, for example, can contribute to large consumption shortfalls – an outcome less likely to occur in more market-oriented economies. Third, the propensity for market failures, such as those of credit markets, will also simultaneously contribute to economic inefficiencies as mediated by the underinvestment in health and agricultural capital. This raises the prospect of the poor being caught in a low-level equilibrium with binding constraints in terms of time available to devote to the production of health, home production (e.g., care of children), and farm production.

It is also notable that the link between health and productivity is of special importance for women, who often assume a predominant role in the production of food crops. The greater vulnerability of women also results from the extraordinarily high maternal mortality and morbidity, with one of nine women dying during childbirth in some regions of the world. Additionally, women suffer the acute burden associated with social norms and behaviors that have resulted in them bearing the brunt of the ravages of human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), especially in Africa. Finally, women have unique responsibilities in the home, particularly in terms of care of children. Health and nutrition shocks that adversely affect women not only adversely affect their productive role in labor markets but also impact their joint production role as caregivers for their children, and thus they induce a recurring and intergenerational cycle of crisis and deprivation.

Before turning to a discussion of the evidence on the relationship among health, nutrition, and economic outcomes, the author emphasizes that the imperative of, or justification for, improving health and nutritional status goes beyond their importance in promoting economic growth. Rather, wellbeing is multidimensional, comprising factors such as good health and adequate nutrition. These capabilities are intrinsically important, and merit recognition above and beyond their impact on productivity, output, and money metric measures of poverty. Although this article focuses on the contribution of health and nutritional status to productivity and economic outcomes, this may be of secondary importance relative to the intrinsic value of health.

Impact Of Health On Economic Development

Macro And Cross-Country Evidence

Economic historians have argued persuasively that nutrition and health have contributed in an important way to increases in productivity and economic growth. Among the seminal work in this area are papers by Robert Fogel who showed that inadequacies in diet contributed to disease and early mortality, greatly limiting the possibility for productive work in eighteenth century England and France. His estimates indicate that 50% of Britain’s growth since 1800 was attributable to increases in dietary energy available for work and improvements in the efficiency in the transformation of nutrients, particularly calories, into work.

Numerous other authors have examined cross-country associations among health, economic growth, and poverty. Some work employs general measures of health, such as life expectancy, whereas other studies focus on specific diseases, such as the impact of malaria, tuberculosis, and HIV/AIDS on economic output and growth. Among the numerous studies to directly estimate the impact of aggregate measures of health, particularly life expectancy, on economic output, it has been estimated that an increase in life expectancy of 10% will lead to an increase in economic growth of 0.4% per year. These estimates are consistent with similar research, including work that indicates that an increase in life expectancy of 1 year raises gross domestic product (GDP) per capita by 4%. Using adult survival rates to measure health, it has been reported that if health status were equalized across countries, the variance of log GDP per worker would be reduced by 9.9%. The results also suggest that eliminating health gaps would reduce disparities in country level mean incomes. But overall, the results show relatively small effects of poor health on economic development compared to studies that rely on cross-country regressions. Another article suggests an even more limited impact of health on economic outcomes, using an instrumental variable (IV) approach to tell a surprising story of how improvements in life expectancy led to lower GDP per capita. This unexpected result is explained by the fact that increases in worker productivity were offset by rapid population growth in the face of fixed land and a base of physical capital that was slow to adjust, contributing to declines in income. Despite the rigor and compelling nature of this article, it has been criticized for many underlying assumptions, particularly that lagged health has no effect on economic outcomes, and likewise, that it does not address possibilities such as whether reductions in fertility will offset population increases accompanying lower mortality.

In disease-specific literature, a malaria ecology index has been used as an instrument in estimating cross-country regressions of GDP per capita. The results show a dramatic impact of malaria on growth. A concern with this work, as with other such articles, is that of omitted variables contributing to the malaria index having a greater negative effect than it would otherwise have in a more fully specified model.

More recently, much of the attention on macroeconomic impacts of disease has focused on HIV/AIDS. Early work observed little impact on economic growth. This optimism was in fact based on a Solow-type growth framework where the impact of disease on growth was mitigated by a drop in the supply of labor relative to that of capital, which in turn increases the productivity of labor. It has further been suggested that there is a low impact of HIV/AIDS on growth through a process of the epidemic contributing to reduced fertility and a decline in the dependency ratio that subsequently leads to increases in per capita consumption as well as savings. It is assumed that such changes will not only increase investment but also provide resources for health and related support for those suffering from AIDS. Another article also shows little impact of HIV/AIDS. Using an IV technique that relies on the rate of male circumcision as an instrument, it argues that the differences are attributable to exogenous cultural factors. Although the exclusion restrictions are certainly open to debate, the authors show that the circumcision rate is a strong predictor of HIV prevalence, and that it is uncorrelated with other determinants of growth.

These optimistic assessments stand in contrast with other more sobering findings. One report, for example, finds that a 1% increase in HIV prevalence will contribute to a marginal impact on income per capita of negative 0.59%. It argues that the excess labor arguments that have mitigated the macroeconomic impacts of AIDS are not being realized. Another article estimated that GDP was reduced by 17% and per capita incomes by 8% between 1997 and 2010 as a result of the AIDS epidemic in South Africa. Another group of researchers have published an article discussing the possibly devastating effects of HIV/AIDS if the epidemic in Southern Africa continues unchecked.

Although such estimates are informative, the challenges of arriving at actual details of the impact of these communicable diseases on economic growth are clearly daunting. They depend on the economic structure of each country, the relative importance of agriculture, whether land or labor are greater constraints to growth, and the existence of economic and social infrastructure. Thus, there is a need to better understand the intricacies of how HIV/AIDS (and other diseases) are impacting economic relationships and performance and the role that mediating factors, such as the effects of large number of orphans on education capital, play.

Likewise, the extent to which interventions such as the provision of antiretroviral therapy (ART) are available will have an enormous impact on such estimates, both through mitigating the productivity consequences of the disease and the fiscal costs associated with governments contributing to the treatment costs. Most estimates of the economic costs of HIV/AIDS were made before treatment with antiretroviral drugs was widely available. Recent studies have shown a dramatic reversal in the physical well-being of those being treated, clearly reducing the costs of disease in terms of productivity losses. At the same time, the costs of treatment will be staggering. Although these may be largely born by foreign donors, the ability or willingness of the international community to sustain the financial support for ART is questionable and will likely result in more of the burden falling on patients and local health systems.

As the costs of disease in developing countries is considered, there is an epidemiological transition underway as infectious diseases become less prominently a cause of death and disability; instead, there is an emerging epidemic of chronic disease. For example, in 2000 there were more than 7 million cases of diabetes in Africa alone, and it is estimated that direct treatment costs would exceed purchasing power parity US$1000 per person. Treating diabetes and other noninfectious lifestyle diseases in the future will be a formidable challenge for households and governments, with both incurring large financial costs.

Finally, another channel through which improved health will impact economic outcomes is the so-called ‘demographic dividend.’ The pathway is quite simple: improvements in health services and availability of modern technology will bring about a decline in mortality, and after a considerable lag, fertility will fall in response to the expectation for longer life spans and higher probabilities of survival into adulthood. This will lead to a bulge (which in the case of East Asia has been estimated to last nearly 50 years) in the working age population relative to the rest of the population. This demographic transition will in turn contribute to a large economic dividend. However, it has been suggested that the demographic dividend will not necessarily materialize in sub-Saharan African countries for a range of reasons, including the slow rate of fertility decline and HIV/AIDS. A recent article revisits this issue and concludes that the demographic dividend can be expected to materialize in Africa. However, the article also points out the importance of institutional reform and a transparent political and economic environment as a prerequisite for the bulging number of working individuals to be productively engaged.

In sum, caution is necessary in interpreting literature on the macroeconomics of health, even that which makes efforts to deal with problems of omitted variables and endogeneity. Specifications are often ad hoc, data are often unreliable, and most importantly, even the best attempts to deal with problems of omitted variables and unobservables that may jointly affect health status and income are open to serious criticisms. Perhaps a greater lesson is that there is a need to better understand the (primarily microeconomic) pathways, such as the impact on worker productivity and schooling, through which health impacts economic outcomes. Similarly, to the extent that certain factors have reduced the potential impact of health improvements on economic outcomes, such as population growth, this suggests that policymakers consider emphasizing programs to control fertility, and similarly, consider promoting economic opportunities for the burgeoning labor force through, for example, encouraging foreign investment.

Microeconomic Evidence

Pioneering work on the efficiency wage theory links health and nutrition to labor market outcomes, with the basic idea being: output is a concave function of labor inputs, including the number and level of effort among workers. Higher wages will thus improve nutritional intake of workers, and subsequently effort. From the producer’s perspective, therefore, the optimal wage will minimize the wage bill in terms of the wage rate divided by the effort level.

The test of this theory involves determining whether wages respond to nutritional intake of workers. The correlation between health and wages of individuals has been well established with household survey data, but making a causal argument is far more challenging. For example, healthier workers may also be better educated, and likewise, healthier workers may have parents who make choices that not only contribute to their better health but also instill a greater work ethic.

Several microeconomic studies have made serious attempts to overcome the econometric problems inherent in examining such as relationship. Much of the evidence has been comprehensively reviewed.

Among the research that relies on nonexperimental methods, height, which largely reflects health conditions and investments both in utero and during early childhood, is often employed as an indicator of general healthiness. There is compelling evidence of the productivity effects associated with greater stature in numerous studies from developing countries. There is evidence from the historical literature that height affected the price of slaves, presumably reflecting the expected probability gains associated with greater stature.

Another anthropometric indicator that is widely used is the body mass index (BMI), and results indicate a loss of productivity associated with leanness. Similarly, estimates of a farm production function for Sierra Leone finds that calories per adult equivalent have significant positive effects on the marginal product of agricultural labor. One study from Sri Lanka instruments per capita household calories using prices, and its results indicate that there is a positive effect on market wages for rural men but not women. Many academics, however, emphasize the limitations of relying on household calorie intake to measure the effect on productivity. Research on workers in Ghana and Coˆ te d’Ivoire indicates that wage returns to height and BMI in Ghana were also quite large, with a centimeter increase being associated with an 8–10% increase in wages.

Other studies of the impact of nutrient consumption rely on individual 24 h recalls and the measuring of food prepared and/or consumed in the household. Here, the evidence is more mixed. One article studied the impact of individual calorie consumption on agricultural production functions and wage equations. Employing fixed effects to control for individual heterogeneity, results show no impact of calories on either the marginal product of agricultural labor or agricultural wage rates. Interestingly, an impact of weight-for-height, a measure of leanness like BMI, on these outcomes is found to be consistent with the findings of other work. A further study on rural India finds some interesting seasonal effects: calories have a greater impact on productivity in the peak season for men, but weight-for-height is more important in the preharvest season when work is less demanding. Another study on agricultural workers from the Philippines finds that individual calorie intake from 24 h recall has no significant impact on productivity, unlike BMI where the effects on earning are significant. Noteworthy is that all these findings ignore that there are likely additional labor productivity effects that operate through occupation choice.

The impact of days ill on productivity has also been examined in a number of articles. Researchers found that each extra day of illness in Peru contributed to a 1% decline in hourly earnings among male wage workers and a 3% decline among the self-employed. For females, the comparable figure is a 2% decline. Overall, however, the general picture emerges of reduced labor supply in response to illness, although the impacts on productivity are more mixed. This perhaps reflects that such studies are examining agricultural productivity, and as mentioned previously, there is considerable latitude for substitution of labor, either with other family members or hired labor.

Recently, a great deal of attention has been accorded to examining the micro impact of HIV/AIDS on productivity. A comprehensive review of this issue notes numerous studies that focus on the impact of AIDS illness and death on household incomes and expenditures, largely mediated through declines in labor supply, a fall in farm production, and the burdens associated with spending on health care and funerals. Likewise, there is troubling evidence that these economic stresses often lead to household dissolution, and, of course, a dramatic increase in orphanage, which is shown to have significantly deleterious economic and social consequences.

There is also evidence that declines in labor availability due to illness lead households to change cropping patterns and cultivation practices. One study shows that although Kenyan households afflicted by AIDS protect land under food cultivation, land devoted to cash crop production declines. A similar finding has been reported for Uganda. Other studies, however, have not found such changes in labor supply. An interesting study that focuses on the impact of the provision of ART for AIDS patients in Kenya reports a 20% increase in the probability of being in the labor force 6 months after treatment and that the hours worked increases by 35% among the treated. Ethical consideration naturally precluded randomization of treatment, and instead the authors needed to rely on other survey data collected during the same time on households without treatment to control for time varying factors that could bias the estimate.

Even more compelling evidence on the links between health and productivity comes from experiments designed to isolate the causal impact of health on productivity and labor market outcomes. There has been a considerable amount of experimental research on the impacts of micronutrients on labor market outcomes, with perhaps the greatest attention given to examining the impact of iron deficiency. Two biological pathways have been identified. First, aerobic capacity declines with decreasing levels of hemoglobin. Depletion of iron stores also contributes to reductions in the amount of oxygen available to muscles. As a consequence, endurance suffers, and there are greater demands on the heart in order to achieve the same activity. Iron deficiency also raises susceptibility to disease and is associated with fatigue and impaired cognitive development. Noteworthy among the many studies that examine causal effects of iron supplementation are the impacts on the output of rubber workers in Indonesia, cotton mill workers in China, and tea plantation workers in Sri Lanka. Additionally, several studies have demonstrated how the cognitive development of children is impaired by iron deficiency.

A particularly interesting field experiment is the Work and Iron Status Evaluation study that provides iron supplements to older adults in Central Java, Indonesia. Approximately half the male workers in the study are self-employed (primarily as rice farmers), and the other half are paid a time-wage. There is no evidence that hours of work corresponded to the treatment for time-wage workers, although those receiving treatment reduced the amount of time spent sleeping, and there is evidence that after a year they took on more work in self-employment. Among males who earned a time-wage, there is no evidence of changes in productivity as indicated by their hourly earnings; of course, if their wages are set by an employer, it is not obvious the worker will reap the benefits of greater productivity. This is not true for the self-employed. Males who were self-employed and iron deficient at baseline reported approximately 20% higher hourly earnings after 6 months of supplementation relative to similar controls.

Although the study demonstrates that iron deficiency has a causal impact on time allocation and economic productivity, it also highlights the importance of including behavioral responses to the experiment itself in assessing the impact of treatment.

Experimental evidence of other forms of nutrition interventions is less compelling. One study that randomized food supplementation of sugarcane cutters in Guatemala indicated that those living in treatment villages were not more productive than the control villages. Another study in Kenya found a limited impact of food supplementation on the productivity of road workers. An experiment in Indonesia exploited the application of user fees at randomly selected ‘treatment’ districts while prices were held constant (in real terms) in neighboring ‘control’ districts. Two years after the intervention, relative to control areas, health care utilization and labor force participation had declined in treatment areas (where prices had increased). Reductions in employment were particularly large (and significant) for men and women at the bottom of the education distribution, those whom we would expect to be the most vulnerable. The most plausible interpretation is that the average treatment effects on labor supply indicate a causal role of improved health on the allocation of time to the labor market.

Beyond the issue of worker productivity and labor market outcomes, the impact of health on schooling and cognition has also been widely studied. One study reports that in a randomized control trial, treatment of helminthic infections in schools contributes to a reduction in absentee rates by onequarter, although it does not find an improvement in test score outcomes. Another study uses IVs and a fixed effects estimator and finds that stunted growth among young children will lead to delayed enrollment, but not eventual attainment. A further study from Zimbabwe that employs a quasi-experimental approach indicates a large impact of heights on school attainment. Finally, there is strong evidence from the Philippines that children’s performance in school is enhanced by better nutritional status.

Similar evidence indicates that specific diseases contribute to worse school outcomes. One report finds that in Paraguay and Sri Lanka, reducing the prevalence of malaria by 10 percentage points would increase years of schooling by 0.1 years and raise the probability of being literate by 1–2%. Corroborating results were reported elsewhere in Latin America.

Finally, beyond these impacts of the health of the child on schooling and cognition, there is also evidence that the health of the parents may impact a child’s human capital accumulation, particularly through illness of mothers and fathers contributing to early withdrawal from school. For example, one study reports that the death of a 15-year-old child’s mother raises the probability of the child dropping out of school within 3 years by 15.8% points. Similarly, the death of a 15-year-old’s father raises the probability of dropout by 18.7% points. Illness among parents also has a large impact on the likelihood of dropout. Among 15-year-olds, for example, a child is 13.1 percentage points more likely to dropout if her father has a prolonged illness that interferes with work and other normal activities. The comparable number for the mother is 14.8 percentage points.

Life Course And Intergenerational Issues

Poor health and nutrition will not only limit a worker’s productivity and earnings, but, as discussed in the Section Microeconomic Evidence, will also contribute to a cycle of poverty, poor health, and poor human capital outcomes across generations. Of particular concern is the evidence that traumas in utero or in early childhood, such as exposure to toxins (including alcohol and tobacco), or nutrient deficiencies of folate or iodine will contribute to permanent dysfunction over the entire life course. The Barker hypothesis (Barker et al., 2005) argues that nutritional and other stresses to the fetus contribute to imprinting on the genes and metabolic changes, which in turn contribute to heightened risks of obesity, diabetes, heart disease, and other chronic disease later in life.

There is a growing body of evidence in support of the fetal origins of disease theory. One study suggests that reduced infections during childhood contributed dramatically to adult height and longevity due to lower levels of inflammation. Another observes the importance of the year in which children were born in the business cycle in the Netherlands in the nineteenth and early twentieth-centuries on mortality rates. Likewise, work using data from the US observes longstanding and major consequences for the schooling, productivity, and health of the offspring of mothers afflicted during the flu pandemic. Finally, there exists compelling evidence that raising birth weights will contribute to better labor market outcomes, particularly among low birth weight babies.

One particularly informative set of studies was conducted in Guatemala, where a randomized experiment of children who had been enrolled in an early childhood nutrition supplementation program were followed as young adults. Adult men, who had received the protein-rich nutrition supplement as children, were found to have hourly earnings that were US$0.67 greater than the control group that had received a drink containing no protein. This represented a 46% higher average wage rate. One important finding, however, is that the largest impact of the supplementation resulted from treatment during the first 2 years of life; there was no impact of receiving the supplement from ages 36 to 72 months. These strong positive effects were not observed for women. These results were consistent with other follow-up studies of this cohort that found increased schooling and cognition among the treated.

Another study, using panel data from Brazil, Guatemala, India, the Philippines, and South Africa, addressed the question of the relative importance of low birth weight, and weight gain in the first 2 years of life and between the ages of 2 and 4 years on schooling outcomes. Although not able to examine causality, the researchers report that from 0 to 24 months of age, weight gain had a particularly important impact on schooling, whereas there was no significant effect between 2 and 4 years of life. To get a sense of the magnitude of the effects, their comparative statistics suggested that children whose growth was stunted at 2 years or age were likely to have completed nearly 1 year less of schooling and suffer from a 16% increased risk of failing at least one grade. The authors’ calculations, based on returns to schooling in the population, indicate that child stunting during the first 2 years of life would be associated with a reduction in lifetime income of approximately 10% in the countries studied.

Another avenue through which poor health has implications over the life course arises from the expectations for a short life span, which will in turn reduce savings and thus investment in physical capital. Related to the accumulation of physical capital is the fact that disease and early mortality among the children themselves have adverse intertemporal effects. Illness and malnutrition among children reduces the incentives for parents to invest in their education. The difficulties of identifying the impact of health on investing in human capital, and specifically distinguishing those effects from how health may directly effect human capital through other channels, such as the impact on school attendance and ability of children to learn, has resulted in few studies that causally show this relationship. One notable exception is a study from Sri Lanka that shows that the decline in maternal mortality risk by 4.1% resulted in a 2.5% increase in female literacy. The elasticity of human capital with respect to life expectancy was thus calculated as between 0.6 and 1.0.

Finally, one manifestation of poor maternal health and inadequacy of health care for women is the prospect of unwanted pregnancy. A woman’s lack of control over her fertility has long-term impacts on members of her household and the accumulation of human capital of her children.

Inequalities In Health

There is a potential relationship between inequalities in health and various socioeconomic outcomes. It has been proposed that inequality in health contributes to a lack of social cohesion. There is evidence that relative deprivation contributes to stress, as well as other outcomes such as loss of dignity, shame, and stigmatization, which may have effects on labor market opportunities and incomes. High inequality also lowers the likelihood that social networks and mutual assistance relationships will mitigate the deleterious effects of health shocks that compromise health status directly. Inequalities in health (and other dimensions) may also contribute to differences in preference and thus reduce political support for investments in public goods. Health inequality, therefore, may partially explain why public institutions are both inefficient and fail to protect and promote the needs of those in greatest need. Furthermore, where inequality reduces trust and increases crime and violence, or where low social status makes people feel disrespected, it may generate violence or, at the very least, add to the political tensions contributing to disproportionate shares of budgets and state and private resources being allocated to political repression, internal security, and other nonproductive spending.

Yet another aspect by which health inequalities can slow economic growth is that there are decreasing returns in terms of productivity to health: populations with more health inequality will have lower productivity on average.

In regressing GDP, both in levels and growth rates, on health inequality and a range of other covariates, it has been found that the reduction in health inequality caused by a reduction in the number of children who die before the age of 5 of approximately 4.25 per 1000 children per year born to mothers with a low education level leads to an almost 8% increase in GDP per capita after a period of 10 years. Although this study is relatively unique and plagued by several serious methodological and data limitations, it does add to the limited empirical evidence on the relationship between health inequality and growth, and will hopefully motivate further research in this area.

Conclusions

There is considerable theoretical and empirical evidence that points to large productivity increases and economic gains from improved health and nutrition over the life course and across generations in developing countries. These are mediated by a wide range of pathways, including increases in strength and stamina, impacts on schooling (age of entry, duration, and attendance) and cognitive abilities, reduced fertility, and increased savings associated with reduced expenditures on health and greater incentives to invest in children who are expected to live longer and be more economically productive over their life course.

Information asymmetries, as well as market failures, such as for credit and insurance, will likely contribute to underinvestment in health-related human capital. This adds further justification for government policies to address these market failures. Additionally, there are likely to be large economic externalities associated with improving health that even further support government investments in the health sector. Thus, the large efficiency gains from investment in health find further justification in the likelihood that social rates exceed private rates of return.

References:

  1. Barker, D. J. P., Osmond, C., Forse´n, T. J., Kajantie, E. and Eriksson, J. G. (2005). Trajectories of growth among children who later develop coronary heart disease or its risk factors. New England Journal of Medicine 353(17), 1802–1809.
  2. Acemoglu, D. and Johnson, S. (2007). Disease and development: The effect of life expectancy on economic growth. Journal of Political Economy 115(6), 925–985.
  3. Ahuja, A., Wendell, B. and Werker, E. (2009). Male circumcision and AIDS: The macroeconomic impact of a health crisis. Harvard Business School Working Paper 07-025. Boston: Harvard Business School
  4. Alderman, H., Behrman, J. R., Lavy, V. and Menon, R. (2001). Child health and school enrollment: A longitudinal analysis. Journal of Human Resources 36(1), 185–205.
  5. Alderman, H., Hoddinott, J. and Kinsey, B. H. (2006). Long-term consequences of early childhood malnutrition. Oxford Economic Papers 58(3), 450–474.
  6. Almond, D. (2006). Is the 1918 influenza pandemic over? Long-term effects of in utero influenza exposure in the post-1940 U.S. Population. Journal of Political Economy 114(4), 672–712.
  7. Arndt, C. and Lewis, J. D. (2000). The macro-implications of HIV/AIDS in South Africa: A preliminary assessment. South African Journal of Economics 68(5), 380–384.
  8. Ashraf, Q. H., Ashley, L. and Weil, D. N. (2008). When does improving health raise GDP? In Acemoglu, D., Rogoff, K. and Woodford, M. (eds.) NBER macroeconomics annual 2008, vol. 23, pp. 157–204. Cambridge, MA: National Bureau of Economic Research, Inc.
  9. Banerjee, A., Iyer, L. and Somanathan, R. (2008). Public action for public goods. In Schultz, T. P. and Strauss, J. A. (eds.) Handbook of development economics, vol. 4, pp. 3117–3154. Amsterdam: Elsevier.
  10. Barker, D. J. P. (1994). Mothers, babies, and disease in later life. London: BMJ Publishing.
  11. Barnett, T., Tumushabe, J., Bantebya, G. B., et al. (1995). The social and economic impact of HIV/AIDS on farming systems and livelihoods in rural Africa: Some experience and lessons from Uganda, Tanzania, and Zambia. Journal of International Development 7(1), 163–176.
  12. Barro, R. (1997). Determinants of economic growth: A cross-country empirical study. Cambridge, MA: MIT Press.
  13. Basta, S., Soekirman, K. and Scrimshaw, N. (1979). Iron deficiency anemia and productivity of adult males in Indonesia. American Journal of Clinical Nutrition 32(4), 916–925.
  14. Beegle, K. (2003). Labor effects of adult mortality in Tanzanian households. World Bank Policy Research Working Paper 3062. Washington, DC: World Bank.
  15. Beegle, K., De Weerdt, J. and Dercon, S. (2005). Orphanhood and the long-run impact on children. Washington, DC, and Oxford: World Bank Economic Development Institute and Oxford University. Mimeo.
  16. Beegle, K., Goldstein, M. and Thirumurthy, H. (2010). Microeconomic perspectives on the impacts of HIV/AIDS. In Sahn, D. E. (ed.) The socioeconomic dimensions of HIV/AIDS in Africa: Challenges, opportunities and misconceptions, pp. 57–73. Ithaca: Cornell University Press.
  17. Behrman, J. and Deolalikar, A. (1988). Health and nutrition. In Chenery, H. and Srinivasan, T. N. (eds.) Handbook of development economics, vol. 1, pp. 631–712. Amsterdam: Elsevier.
  18. Behrman, J. R. and Rosenzweig, M. R. (2004). Returns to birthweight. Review of Economics and Statistics 86(2), 586–601.
  19. Bell, C., Devarajan, S. and Gersbach, H. (2006). The long-run economic costs of AIDS: Theory and an application to South Africa. World Bank Economic Review 20(1), 55–89.
  20. Bleakley, H. (2010). Malaria in the Americas: A retrospective analysis of childhood exposure. American Economic Journal: Applied Economics 2(2), 1–45.
  21. Bliss, C. and Stern, N. (1978a). Production, wages, nutrition: Part I: The theory. Journal of Development Economics 5(4), 331–362.
  22. Bloom, D. E., Canning, D., Mansfield, R. K. and Moore, M. (2007). Demographic change, social security systems, and savings. Journal of Monetary Economics 54(1), 92–114.
  23. Bloom, D. E., Canning, D. and Sevilla, J. (2004). The effect of health on economic growth: A production function approach. World Development 32(1), 1–13.
  24. Bongaarts, J. and Bulatao, R. A. (1999). Completing the demographic transition. Population and Development Review 25(3), 515–529.
  25. Bruner, E. and Marmot, M. (1999). Social organization, stress, and health. In Marmot, M. and Wilkinson, R. G. (eds.) Social determinants of health, pp. 17–43. Oxford: Oxford University Press.
  26. Case, A. and Ardington, C. (2005). The impact of paternal death on school enrollment and achievement: Longitudinal evidence from South Africa. Paper presented at the International Union of the Scientific Study of Population Seminar on Interactions between Poverty and HIV/AIDS, Cape Town, South Africa. Rondebosch: Centre for Social Science Research, University of Cape Town.
  27. Crimmins, E. M. and Finch, C. E. (2006). Infection, inflammation, height, and longevity. Proceedings of the National Academy of Sciences of the USA 103(2), 498–503.
  28. Deolalikar, A. B. (1988). Nutrition and labour productivity in agriculture: Estimates for rural South India. Review of Economics and Statistics 70(3), 406–413.
  29. Dow, W., Gertler, P., Schoeni, R., Strauss, J. and Thomas, D. (1997). Health care prices, health, and labor outcomes: Experimental evidence. Labor and Population Working Paper 97-01. Santa Monica, CA: RAND.
  30. Edgerton, V. R., Gardne, G., Ohira, Y., Gunawardena, K. A. and Senewiratne, B. (1979). Iron-deficiency anemia and its effect on worker productivity and activity patterns. British Medical Journal 2(6204), 1546–1549.
  31. Evans, D. K. and Miguel, E. (2007). Orphans and schooling in Africa: A longitudinal analysis. Demography 44(1), 35–57.
  32. Fogel, R. W. (1994). Economic growth, population theory, and physiology: The bearing of the long-term processes on making of economic policy. American Economic Review 84(3), 369–395.
  33. Fogel, R. W. (2004). Health, nutrition, and economic growth. Economic Development and Cultural Change 52(3), 643–658.
  34. Galor, O. and Weil, D. N. (1999). From Malthusian stagnation to modern growth. American Economic Review 89, 150–154.
  35. Glewwe, P. and Jacoby, H. G. (1995). An economic analysis of delayed primary school enrollment in a low-income country: The role of early childhood nutrition. Review of Economics and Statistics 77(1), 156–169.
  36. Glewwe, P., Jacoby, H. G. and King, E. (2001). Early childhood nutrition and academic achievement: A longitudinal analysis. Journal of Public Economics 81(3), 345–368.
  37. Glick, P. (2007). Reproductive health and behavior, HIV/AIDS, and poverty in Africa. Cornell Food and Nutrition Policy Program Working Paper No. 219. Ithaca, NY: Cornell University.
  38. Glick, P. and Sahn, D. E. (1997). Gender and education impacts on employment and earnings in West Africa: Evidence from Guinea. Economic Development and Cultural Change 45(4), 793–823.
  39. Glick, P., Sahn, D. E. and Walker, T. F. (2011). Household shocks and education investment in Madagascar. Cornell Food and Nutrition Policy Program Working Paper #240. Ithaca, NY: Cornell University.
  40. Godfrey, K. M. and Barker, D. J. P. (2000). Fetal nutrition and adult disease. American Journal of Clinical Nutrition 71(5), 1344S–1352S.
  41. Grimm, M. (2011). Does inequality in health impede economic growth? Oxford Economic Papers 63(3), 448–474.
  42. Haas, J. D. and Brownlie, IV, T. (2001). Iron deficiency and reduced work capacity: A critical review of the research to determine a causal relationship. Journal of Nutrition 131(supplement), 676S–690S.
  43. Haddad, L. J. and Bouis, H. E. (1991). The impact of nutritional status on agricultural productivity: Wage evidence from the Philippines. Oxford Bulletin of Economics and Statistics 53(1), 45–68.
  44. Hoddinott, J., Maluccio, J. A., Behrman, J. R., Flores, R. and Martorell, R. (2008). Effect of a nutrition intervention during early childhood on economic productivity in Guatemalan Adults. Lancet 371(9610), 411–416.
  45. Hosegood, V., McGrath, N., Herbst, K. and Timæus, I. M. (2004). The impact of adult mortality on household dissolution and migration in rural South Africa. AIDS 18(11), 1585–1590.
  46. Kirigia, J. M., Sambo, H. B., Sambo, L. G. and Barry, S. P. (2009). Economic burden of diabetes mellitus in the WHO African region. BMC International Health and Human Rights 9, 6.
  47. Lee, R. (2003). The demographic transition: Three centuries of fundamental change. Journal of Economic Perspectives 17(4), 167–190.
  48. Leigh, A., Jencks, C. and Smeeding, T. M. (2009). Health and economic inequality. In Salverda, W., Nolan, B. and Smeeding, T. M. (eds.) The oxford handbook of economic inequality, pp. 384–405. Oxford: Oxford University Press.
  49. Li, R., Chen, X., Yan, H., et al. (1994). Functional consequences of iron supplementation in iron-deficient female cotton workers in Beijing, China. American Journal of Clinical Nutrition 59(4), 908–913.
  50. Lucas, A. M. (2010). Malaria eradication and educational attainment: Evidence from Paraguay and Sri Lanka. American Economic Journal: Applied Economics 2, 46–71.
  51. Margo, R. A. and Steckel, R. H. (1982). The height of American slaves: New evidence on slave nutrition and health. Social Science History 6(4), 516–538.
  52. Martorell, R., Horta, B. L., Adair, L. S., et al. (2010). Weight gain in the first two years of life is an important predictor of schooling outcomes in pooled analyses from five birth cohorts from low-and middle-income countries. Journal of Nutrition 140(2), 348–354.
  53. McDonald, S. and Roberts, J. (2006). AIDS and economic growth: A human capital approach. Journal of Development Economics 80(1), 228–250.
  54. Meyerhoefer, C. and Sahn, D. E. (2010). The relationship between poverty and maternal morbidity and mortality in Sub-Saharan Africa. In Ajakaiye, O. and Mwabu, G. (eds.) Reproductive health, economic growth and poverty reduction in Africa: Frameworks of analysis. Nairobi, Kenya: University of Nairobi Press.
  55. Miguel, E. and Kremer, M. (2004). Worms: Identifying impacts on education and health in the presence of treatment externalities. Econometrica 72(1), 159–217.
  56. Murrugarra, E. and Valdivia, M. (2000). The returns to health for Peruvian urban adults by gender, age, and across the wage distribution. In Savedoff, W. D. and Schultz, T. P. (eds.) Wealth from health: Linking social investments to earnings in Latin America, pp. 151–188. Washington, DC: Inter-American Development Bank.
  57. Mushati, P., Gregson, S., Mlilo, M., Lewis, J. and Zvidzai, C. (2003). Adult mortality and the economic sustainability of households in towns, estates, and villages in AIDS-affected eastern Zimbabwe. Paper presented at the Scientific Meeting on Empirical Evidence for the Demographic and Socio-Economic Impact of AIDS, Durban, South Africa. Washington, DC: International Food Policy Research Institute.
  58. Over, M. (2010). Prevention failure: The ballooning entitlement burden of U.S. global AIDS treatment spending and what to do about it. In Sahn, D. E. (ed.) The Socioeconomic dimensions of HIV/AIDS in Africa: Challenges, opportunities and misconceptions, pp. 186–230. Ithaca: Cornell University Press.
  59. Pitt, M. and Rosenzweig, M. R. (1986). Agricultural prices, food consumption, and the health and productivity of Indonesian farmers. In Singh, I., Squire, L. and Strauss, J. (eds.) Agricultural household models: extensions, applications, and policy, pp. 335. Baltimore, MD: Johns Hopkins University Press.
  60. Pollitt, E. (2001). The developmental and probabilistic nature of the functional consequences of iron-deficiency anemia in children. Journal of Nutrition 131(2), 669S–675S.
  61. Sachs, J. D. (2003). Institutions don’t rule: Direct effects of geography on per capita income. NBER Working Paper 9490. Cambridge, MA: National Bureau of Economic Research.
  62. Sahn, D. E. (ed.) (2010). The socioeconomic dimensions of HIV/AIDS in Africa: Challenges, opportunities, and misconceptions. Ithaca, NY: Cornell University.
  63. Sahn, D. E. and Alderman, H. (1988). The effects of human capital on wages, and the determinants of labor supply in a developing country. Journal of Development Economics 29(2), 157–183.
  64. Schultz, T. P. (2008). Population policies, fertility, women’s human capital and child quality. In Schultz, T. P. and Strauss, J. (eds.) Handbook of development economics, vol. 4, pp. 3249–3304. Amsterdam: Elsevier.
  65. Schultz, T. P. and Tansel, A. (1997). Wage and labor supply effects of illness in Coˆte d’Ivoire and Ghana: Instrumental variable estimates for days disabled. Journal of Development Economics 53(2), 251–286.
  66. Sen, A. (1985). Commodities and capabilities. Amsterdam: North-Holland.
  67. Strauss, J. and Thomas, D. (1998). Health, nutrition, and economic development. Journal of Economic Literature 36(2), 766–817.
  68. Strauss, J. and Thomas, D. (2008). Health over the life course. In Schultz, T. P. and Strauss, J. (eds.) Handbook of development economics, vol. 4, pp. 3375–3474. Amsterdam: Elsevier.
  69. Thirumurthy, H., Graff Zivin, J. and Goldstein, M. (2008). The economic impact of AIDS treatment: Labor supply in western Kenya. Journal of Human Resources 43(3), 511–552.
  70. Thomas, D., Frankenberg, E., Friedman, J., et al. (2006). Causal effect of health on labor market outcomes: Experimental evidence. California Center for Population Research On-Line Working Paper Series CCPR-070-06. Los Angeles: University of California.
  71. Thomas, D. and Strauss, J. (1997). Health and wages: Evidence on men and women in urban Brazil. Journal of Econometrics 77(1), 159–185.
  72. Van den Berg, G. J., Lindeboom, M. and Portrait, F. (2006). Economic conditions early in life and individual mortality. American Economic Review 96(1), 290–302.
  73. Weil, D. N. (2007). Accounting for the effect of health on economic growth. Quarterly Journal of Economics 122(3), 1265–1306.
  74. Wilkinson, R. G. (1996). Unhealthy societies: The afflictions of inequality. London: Routledge.
  75. Wilkinson, R. G. (2000). The need for an interdisciplinary perspective on the social determinants of health. Health Economics 9(7), 581–583.
  76. Wolgemuth, J. C., Latham, M. C., Hall, A., Chesher, A. and Crompton, D. W. (1982). Worker productivity and the nutritional status of Kenyan road construction laborers. American Journal of Clinical Nutrition 36, 68–78.
Internal Geographical Imbalances
Pay-for-Performance Incentives in Health Programs