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The Promise of Health: Evidence of the Impact of Health on Income and Well-Being

The Promise of Health: Evidence of the Impact of Health on Income and Well-Being

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this chapter.

The next section outlines a number of mechanisms by which health innovations can affect incomes.

Section 5.3 presents empirical evidence that speaks to this issue, much of it produced over the last

decade. Section 5.4 concludes.



5.2 M ECHANISMS

Better health comes in many forms and at different stages of life. The life-cycle patterns of human

capital and other investments, of labor market participation and income generation, and of fertility

decisions, mean that the effects of health improvements are likely to vary depending on when, and by

whom, they are experienced. This section outlines the various routes by which improved health can

plausibly lead to increases in measured incomes, taking account of the expected wide range of

heterogeneity in effects.

We classify health improvements loosely into three categories, corresponding to improved

nutrition, reduced morbidity, and reduced mortality. These categories clearly are not mutually

exclusive, as better nourished individuals fall sick less often, and death is often, but not always,

portended by illness. Nonetheless, with this classification in mind, we set out the routes by which

improved health can affect income—namely direct productivity effects, effects on incentives to

accumulate human and physical capital, and aggregation effects, which are important to assess in

extrapolating the effects of improvements at the individual level to improvements in population

health.

5.2.1 Direct Effects

The obvious route from health to income is a direct productivity link. Higher levels of nutrition and

energy intake can likely increase the productivity of both manual work and tasks that require

concentration. Fogel (2004) traces increases in economic growth to surges in agricultural

productivity, and the impact this had on hunger and nutrition. Using historical records, he proposes

that there simply were not enough calories consumed to exhaust the productive capacity of workers in

nineteenth-century Europe, and that increases in food availability led directly to increases in the size

and stature, strength, and effort of the workforce. As reviewed by Strauss and Thomas (1998), the

historical evolution of height, attributed in large part to nutritional improvements, provides a useful

proxy for health status, and is closely linked to economic development.

Nutrition does not only increase physical strength, but it provides protection against both chronic

and infectious disease. Fogel develops the concept of physiological capital to denote the biological

capacity of an individual to learn, acquire skills, and work, and focuses on the impact of in utero,

infant, and early childhood nutrition and parental behaviors (such as smoking and drinking) that can

lead to impairment of the nervous system and neurological damage. Longer term health effects of low

birth-weight and malnutrition early in life include increased risk of heart disease, stroke,

hypertension, and diabetes.

Lower morbidity can similarly affect both the productivity of a worker’s time spent on the job, but

also the length of time devoted to work, and absenteeism. In countries in which children contribute to

household income, often by undertaking domestic and farm chores such as cleaning, fetching water,

and guarding livestock, reductions in morbidity and increases in nutrition can have direct impacts on

total, if not always market, income. And reductions in mortality have to be (weakly) good for a given

individual’s output. Total, if not per capita, output is also likely to rise in the presence of mortality



reductions, unless congestion costs are especially large.

Alternatively, health improvements of one individual can directly increase the incomes of others.

When parents and grandparents who tend to sick children are relieved of this task, their measured

incomes may rise as they (re-)enter the labor market. The same is true for children who would

otherwise be working if not for the fact that they have to tend to a sick parent. In both these cases, one

person’s health improvement increases the labor supply, and hence the income, of another. To the

extent that both individuals enter the labor market, measured incomes will increase further. However,

even if health improvements for one individual do not free up time of another, they could nonetheless

increase the productivity of both, for example due to the existence of either task complementarity or

increasing returns in household production.

Finally, improvements in one person’s health can lead to improvements in the health of others,

with direct or indirect effects on their incomes. This mechanism appears most likely to be operational

in the context of reducing the transmission of communicable and infectious diseases, in which either

behavioral change is important (such as the use of insecticide-treated bed nets to prevent malaria, or

condoms to prevent transmission of HIV and other STIs), or when public goods such as water and

sanitation facilities are used. There could well be increasing returns to improved health associated

with network externalities in such cases, which require that a certain critical mass of individuals

adopt preventive behaviors or gain access to public goods before the health (and incomes) of the

broader community can improve.

5.2.2 Effects on Capital Accumulation

Grossman (1972) was among the first to model health improvements as investments in human capital,

and indeed, the direct economic impacts of better health at early ages tend to show up later in life, if

only for life-cycle reasons. But improvements in health, as well as adding directly, by definition, to

the stock of human capital, can also strengthen incentives of individuals to increase the rate of human

capital investment, particularly in the form of education, either because the effective price falls, or

because the returns to education increase.

Healthier children may well attend school more regularly, learn more efficiently while there, and

perform better on tests, all of which increase their acquisition and maintenance of human capital for a

given level of effort and cost. The incentive to invest in education therefore increases when the

effective price falls, increasing the number of years, the quality of the education sought, and the

willingness (most often of parents) to pay for that education. Although a similar mechanism could

affect the incentive of those who are past school age to invest in training, we expect that better health

among adults is unlikely to have as large an effect on human capital investment and future income.

However, the prospect of better health in adulthood may well induce greater levels of human

capital investment, among those of school age or older. Even if the effective price of education or

training does not fall, better health in adulthood can increase both the length of time over which the

returns to education are earned because of a longer working life, and the length of time over which

these returns are consumed, as life expectancy increases. Again, the effects of morbidity declines

among adults, which are likely to affect adult labor supply, and the impact of reduced adult mortality

rates, which might affect life expectancy more, could have similar impacts on human capital

investment, but for different reasons.

We note one important difference between health improvements that reduce the price of human

capital investment or the direct return to it, and those that simply affect life expectancy. In principle,



the first kind of health improvement increases the incentive to invest in human capital, while the

second, which simply affects the return to saving, induces an increase in investment in all forms of

capital. The difference may be difficult to observe in practice however, given that a majority of

individuals hold a large share of their total asset base in the form of human capital, while credit

market imperfections, which are particularly acute in developing countries, could limit the ability of

individuals to invest in any capital other than through on-the-job training. On the other hand, Bloom et

al. (2003) develop a model in which increases in life expectancy lead to higher savings rates at every

age, and present some cross-country evidence in support of this result.

5.2.3 Aggregate Effects

Most of the mechanisms by which health improvements increase income described above involve

changes in inputs to the production process—for example, through increases in the quantity or quality

of labor supply in the short term, and changes in the stock of human and possibly other capital over

the longer term. If income generation depends on other inputs that are in fixed supply, such as land,

then the per capita effects of population-wide health improvements might be much smaller than those

experienced by a single individual who enjoys a similar health improvement. These general

equilibrium effects limit the extent to which studies at the micro-level can be extrapolated, and can

also account for at least some of the relatively weak cross-country empirical evidence on the impact

of health on income.

Similarly, undisputedly positive health improvements can nonetheless reduce per capita income if

they adversely alter the dependency ratio. This seems particularly likely for a number of interventions

that directly target mortality: in rich countries mortality rates have recently fallen most among the

elderly, while in the developing world infant (under one year) and child (under 5 years) mortality

rates have been reduced through expansions of immunization and nutrition programs, and improved

sanitation. The short run impacts of such health improvements on per capita income are almost

certainly negative, as neither group contributes significantly to measured GDP.

Some authors have even suggested that the impact on total income of reductions in mortality rates

among adults of working age may not be large enough to offset the population effect, thereby reducing

per capita income. Conversely, high-mortality epidemics can have ambiguous effects on per capita

income. The Black Death in fourteenth-century Europe, and the HIV/AIDS epidemic in southern

Africa, both led in the short run to reductions in the labor force without offsetting reductions in the

capital stock, leading to higher labor productivity and greater incomes for those lucky enough to be

spared. The expansion of antiretroviral therapy (ART) across Africa in the last five years, which has

increased survival rates among HIV-infected individuals, many in the prime of working life, could

thus have lowered per capita income.

How should we interpret these arithmetic consequences of changes in population structure and

size? The natural implication is that a focus on per capita incomes is not necessarily well-placed, and

that it is important to adopt meaningful welfare measures that reflect the obvious social costs of

mortality. One straightforward adjustment is to divide total income produced by the total number of

people, living and dead, who were once alive and who would be now if not for a given mortality

risk. Let’s call this per nata income. Higher mortality thus decreases total income (the numerator) but

not the denominator.

This measure is not entirely satisfactory, of course, as it implicitly assumes fixed fertility rates. It

can thus be argued that in calculating the income effects of reductions in child mortality rates that lead



women to have fewer children, the denominator should in any average measure indeed fall. In fact,

even health improvements that reduce morbidity among children can induce parents to move along the

quality–quantity frontier (Becker 1981), reducing the desired number of births, and the denominator in

a meaningful measure of mean welfare.



5.3 EMPIRICAL EVIDENCE

Perhaps the starkest differences in health and income are apparent in cross-country comparisons.

While the identification challenges that must be overcome in estimating the impact of improved health

on income and economic activity in such settings are manifold, they have nonetheless not deterred

researchers from addressing the issue at this level of aggregation. A sizeable empirical literature has

thus focused on using national-level data to investigate the relationships between health and income.

Alternatively, researchers have used natural or quasi-experiments, in which arguably exogenous

shocks to health status, associated with either sudden disease outbreaks or discrete policy-driven

improvements in the health environment, to identify the impact of health on income. At a more microlevel, randomized control trials have been employed to improve nutrition or health, and the impacts

on schooling, labor supply, and income measured. Of course, as identification has become more

reliable with these methods, the parameters of interest have focused on progressively narrower

relationships.

5.3.1 Cross-country Studies

Cross-country correlations between population health (as measured by life expectancy) and income

per capita, as exemplified by the Preston Curve (Preston 1975), are strong and have remained so over

the last hundred years (Pritchett and Viarengo 2010). Figure 5.1 uses 2007 data from the United

Nations Development Programme’s Human Development Report database1 to illustrate this

relationship. While advances in technology and its diffusion have shifted the relationship between

income and health, the cross-country correlation between the variables has proven robust over time.

One strand of the literature thus employs cross-country regression techniques in an attempt to identify

a causal link from health to income at the national level. These studies have had to make strong

identifying assumptions that are sometimes difficult to sustain.

A number of authors have used cross-country regression analysis to isolate the impact of

improvements in population health on macroeconomic performance, in particular per capita GDP, that

could underlie the correlations observed in the Preston curve. Pritchett and Summers (1996) were

among the first to address this issue, using infant and child mortality rates as measures of population

health. As instruments for economic growth, they employed terms of trade shocks, the investment to

GDP ratio, the black market premium for foreign exchange, and the deviation of the official exchange

rate from its purchasing power parity level, all of which were argued to be correlated with income,

but would not directly affect health. While their estimates provide evidence that income growth does

drive health improvement, they do not rule out the reverse effect.2



FIGURE 5.1 Life expectancy per capital GDP (US dollars)



Gallup and Sachs (2001) showed that better health was correlated with larger subsequent changes

in income, suggesting that health could affect not only the level, but the growth rate of income.

Obvious endogeneity problems plague this strand of the literature, as omitted variables that determine

health could explain future income growth. They noted, however, that the geographic concentration of

infectious diseases in tropical regions of the world suggested a plausible instrument for health status,

namely distance from the equator, and found a strong impact of health on income growth using this

approach.

Geography as a proxy for health has however been challenged by a number of authors (e.g.

Acemoglu, Johnson, and Robinson 2001; Easterly and Levine 2003; Rodrik, Subramanian, and Trebbi

2004), who point out that geographic location may well influence economic growth either directly or

through historical patterns of institutional development and adoption. Once the impact of geography

on the choice of institutions is accounted for, there remains no independent link from geography (and

thus health) to income. The most acute problem these studies face is simply the availability of reliable

historical data.3 However, even with reasonable data, a remaining methodological problem is that

both health and institutional choice are potentially endogenous, and a single instrument cannot identify

the contribution of each to growth. In response, McArthur and Sachs (2001) note the relatively small

sample size employed in that study, and its limited geographic diversity, and present evidence on a

wider sample of countries and claim that both institutions and the disease environment—in particular

the prevalence of malaria—influence future economic growth. Sachs (2003) attempts to further

identify the specific impact of malaria by constructing an index of “malaria ecology” and a separate

instrument for institutional choice based on mortality rates of colonial settlers. He finds both affect

future income, suggesting again that both health and institutions matter for growth. But since malaria

ecology could easily be associated with the conditions encountered by early settlers, and hence the

choice of institutions, the validity of the estimates remains in question.

Bloom, Canning, and Sevilla (2004) present results from thirteen cross-country analyses that

complement or refine the Gallup and Sachs methodology, all of which report large effects of health on



income growth. The authors themselves try to resolve the problem of omitted variables by regressing

income growth on changes in health, and other lagged variables. This general approach does not find

significant support in the wider macroeconomic literature, however (Mankiw 1995; Weil 2007).

Acemoglu and Johnson’s (2007) study is probably the most innovative recent cross-country

analysis of the impact of health on income. It uses the international epidemiological transition of the

1940s, associated with the discovery and diffusion of penicillin and sulfa drugs, vaccines,

insecticides, and the creation of the World Health Organization, as an exogenous source of health

improvements. Specifically, the authors use variation in the potential health improvement associated

with full adoption of these innovations, which they argue are uncorrelated with future income growth,

as an instrument for actual health improvements. They find that population growth rates increased in

response to the innovations, as did aggregate income, but per capita income fell.

Acemoglu and Johnson’s methodology—using potential health improvements as an instrument for

actual changes in health status—has been used in a number of other studies on the impact of malaria

on income (see below). However, some commentators have questioned the mechanisms underlying

the empirical results. In particular, while capital constraints may well bind in the short run, reducing

the potential for increases in per capita income, over the forty-year period of their analysis one would

expect the capital stock, including the productivity of land, to increase accordingly, and for fertility

rates to adjust. Focusing instead on the effect of higher mortality on economic outcomes, Young

(2005) uses micro-data to calibrate a model of the AIDS epidemic in South Africa. He incorporates

fertility effects and the impact of adult mortality on the intergenerational transmission of human

capital, but still finds that the increase in capital–labor ratios associated with the disease leads to

higher per capita income, consistent with Acemoglu and Johnson’s macro-analysis.

Weil (2007) uses microeconomic estimates of the effect of health on individual incomes (some of

which are reviewed below) to calibrate a model of production which is then used to estimate the

share of cross-country variation in per capita GDP that is attributable to variations in health

indicators. That is, per capita output differs across countries because of differences in physical

capital assets, worker skills (or educational capital), the capacity of workers to work hard and long,

and to think clearly (health capital), and other factors. Using this methodology, he finds that between

about 10 and 30 percent of the variation of log GPD per capita across countries can be attributed to

differences in health human capital. His preferred estimate (23%) is “roughly the same as the share

accounted for by human capital from education, and larger than the share accounted for by physical

capital.”

The strength of Weil’s approach is that it relies on well-identified microeconomic estimates of the

causal impact of health on income. One limitation, recognized by the author, is that it is difficult to use

the framework to estimate the “full” contribution of differences in health to cross-country variation in

incomes. Allocating national output as the return to various factors becomes problematic when one

factor (say health) determines investment in others (say education). While many microeconomic

studies of the effect of health on income examine this and other similar routes, Weil’s analysis

focuses on the proximate effect of health on income—that which arises due to the physical and mental

capacity of workers. That is, his analysis can be thought of as providing an estimate of the impact of

removing health differences across countries, while holding all other factors, including physical and

educational capital, fixed. Allowing those factors to endogenously respond to improvements in health

would likely lead to somewhat larger income responses.

5.3.2 Micro-level Evidence



The labyrinth of channels from health to income and back, the time lags between innovations in one

and effects on the other, and the dependence of these effects on a range of other complementary

institutional and environmental factors, all suggest the debates surrounding macro-level empirical

analyses using aggregate data will continue for some time. An alternative approach has been to

investigate the individual mechanisms by which health affects income using quasi-experimental data,

and randomized control trials.

Nutrition is perhaps most important for human growth and development, and so has been heavily

studied among children. However, nutritional intake in adulthood can directly affect economic output,

as reviewed by Thomas and Frankenberg (2002). They summarize their findings as indicating that

“[w]hile the establishment of this link is not straightforward, the weight of evidence points to

nutrition, and possible other dimensions of health, as significant determinants of economic

productivity.”

For example, Thomas et al. (2004) report the results of a large-scale iron supplementation

intervention in Indonesia that covered over 17,000 adults. They find that iron supplements had

significant effects on men who were otherwise anemic, and who experienced increased energy levels,

better work attendance, and up to 20 percent higher productivity.4 There was however no discernible

effect for women.

On the other hand, the physiological benefits of better nutrition do not always show up in greater

measured output. For example, Thomas and Frankenberg report the results of randomized experiments

in which iron supplementation improved energy use on the job, but did not lead to higher measured

incomes. For example, in a study of female Chinese cotton workers, Li et al. (1994) found iron

supplements were effective in increasing energy levels and that they reduced energy expenditure per

work task completed. However, the subjects did not increase output in their primary jobs, but spent

more time on non-work activities.5 This could have been due to constraints in the work-place that

made it difficult to increase output (reliance on other workers, technology constraints), or simply to

the fact that optimizing individuals chose to spend the additional energy on non-work activities.

In contrast to iron supplementation, the impact of increases in caloric intake is less clear, with

some randomized experiments showing no effect, and others a small impact. Observational studies

(i.e. those with non-random variation in caloric intake) have shown positive correlations between

calories and productivity, but suffer from problems of unobserved heterogeneity.6 For example,

Foster and Rosenzweig (1994) report that caloric intake is correlated with hourly productivity, but

the causality is likely in the opposite direction, as the variation they exploit rests in the strength of

effort incentives—higher powered incentives lead to more effort, higher income, and higher caloric

consumption. Croppenstedt and Muller (2000), however, estimate a positive impact of health and

nutritional status on the productivity of peasant farmers in Ethiopia.

Childhood nutrition can affect future incomes either through a direct effect on future productivity or

by inducing improved attendance and performance at school. Some studies can estimate only the net

impact of these two channels, while others have been able to isolate the effect of the link through

education from the more direct effect.

Hoddinott et al. (2008) report results from a long-term study of the impact of child nutrition in

Guatemala, in which children in two villages were offered a highly nutritious dietary supplement,

while those in two comparison villages were offered a supplement of minimal nutritional value. Sixty

percent of village residents, by then aged between 25 and 42, were tracked more than twenty years

later. Wages rates of those from the treatment villages were found to be 46 percent higher than those



of control villages, but they nonetheless did not have significantly higher earned incomes.

Case and Paxson (2008) find that the labor market pays a wage premium to individuals of greater

height, itself a robust indicator of childhood nutrition. They go further and argue that the premium is

not a reward for height itself, either due to discrimination or the productivity that height might

portend, but that height serves as a marker for cognitive ability, which is rewarded by the market.

Similarly, Behrman and Rosenzweig (2004) use data on twins and find that fetal growth is associated

with future height and years of completed schooling in adulthood. These studies suggest that in utero

and early childhood health can have important effects on the efficiency with which children learn

while at school, and hence their future economic performance.

In a similar vein, Case, Fertig, and Paxson (2004) control for parental income, education, and

social class and find that children who experience poor health attain lower education, worse health,

and lower social status in adulthood. Poor health in childhood thus acts as a mechanism of intergenerational poverty transmission.

In the developing world, childhood malnutrition and morbidity not only can reduce returns on the

intensive margin by compromising learning efficiency at school, but they can have large effects on the

extensive margin by reducing school enrollment and attendance. In a field experiment in western

Kenya, Miguel and Kremer (2004) assigned simple and cheap de-worming treatment to randomly

selected students across randomly selected schools. They found that absenteeism in treated schools

was reduced by one-quarter, and that by reducing transmission of the infection the intervention also

improved the health and school participation of untreated students in both treated and nearby

untreated schools. However, despite the impact on school participation, the intervention did not have

a significant effect on test scores, so the contribution to human capital and future economic activity

remains unknown.

In contrast, Field et al. (2009) use temporal and geographic variation in the implementation of

intensive iodine supplementation in Tanzania to study the impact on schooling of reductions in iodine

deficiency syndrome. They find that children treated in utero attain between one-third and one-half a

year more education relative to their (untreated) siblings and peers, and that this effect is particularly

great for girls. They also infer from the data that this increase in years of schooling attained is driven

in part by better performance while in school, and the associated higher likelihood of passing tests

which allow grade progression, and that the program did not affect the age at which children begin

formal education. Thus iodine supplementation can be interpreted as reducing the price of completed

school grades, and hence the demand for education.

Like iodine deficiency syndrome, anemia can impact childhood educational attendance and

attainment. Bobonis et al. (2006) report the results of a field experiment in which iron supplements

were randomized across 2–6-year-old children in Delhi slums, where the pre-intervention rate of

anemia was 69 percent. This study focused on very young children, for whom test score data were of

little relevance. They find that child weight increased, and that pre-school participation rates rose, at

least in the short run. On the other hand, the longer run impacts of the intervention were harder to

interpret, due to sample attrition and non-random sorting of new cohorts.

Following a similar strategy to that employed by Field et al., Bleakley (2007a and 2007b) uses

two specific historical health interventions—hookworm eradication in the southern United States and

malaria eradication campaigns in selected countries in the Americas (US, Brazil, Colombia, and

Mexico)—to estimate the effect of health on income. In his study of the impact of hookworm

eradication efforts under the Rockefeller Sanitary Commission in the American South in the early



twentieth century, Bleakley measures pre-existing infection rates in 1913 by location (on average, 40

percent of school-aged children were infected prior to the intervention). In a similar fashion to the

approach adopted by Acemoglu and Johnson (2007), the author uses variation in infection rates by

location, which reflect the potential benefits from eradication, to identify the impact of changes in the

health environment on economic outcomes. He finds that areas with higher pre-existing infection rates

saw greater increases in school enrollment, attendance, and literacy after the intervention. For

example, he finds that before 1910 the impact of infection rates in 1913 on school attendance is

negative, but that by 1920 there is no impact of 1913 infection rates on attendance. That is, those

areas that had the most to gain from the eradication saw enrollment rates increase more. Similar

results are found for literacy.

There may have been other changes in the economic environment that could have led to similar

trends over this period, but it is argued that these should have affected adults in different areas in a

similar way. However, no similar pattern is found among adults across the affected areas, who, by the

nature of the disease, had virtually no pre-existing infection.

A similar exercise is performed using the malaria eradication campaigns in the United States

c.1920 and in Brazil, Colombia, and Mexico c.1955 (Bleakley 2006). Pre-existing prevalence rates

provide the exogenous variation permitting him to identify the impact of childhood exposure to

malaria on future adult literacy and incomes. He finds that for individuals born well before the

relevant eradication campaign, those born in more malarious regions had lower wages and literacy

later in life, but for those born well after the campaigns, pre-eradication malaria prevalence had little

effect on future wages and literacy. He concludes that “persistent childhood malaria infection reduces

adult income by 40 to 60 percent.”

Interestingly, Bleakley is able to differentiate between the impact of morbidity and mortality on

future income. He finds that eradication of vivax malaria (which causes high morbidity, but relatively

few deaths) leads to significant increases in human capital formation and future income, but that

eradication of falciparum malaria (which is relatively fatal) produces no such gains. His preferred

rationalization of this result is that reductions in mortality rates increase the marginal benefit of human

capital acquisition (as there are more years in which to earn a return on human capital investments),

but this might have little impact on the level of investment if marginal costs are increasing steeply.7

On the other hand, a reduction in morbidity makes it easier to attend school and to learn while there,

thereby flattening the marginal cost curve, and leading to significant increases in human capital

acquisition.

Bleakley uses his estimates to extrapolate across countries, and estimates that malaria can account

for about 10–16 percent of the income gap between the US and Latin America. For the Americas at

least then, this evidence suggests that eradicating malaria would modestly narrow the cross-country

income gap by inducing higher growth in Latin America. He concludes that “…while reducing

malaria could bring substantial income gains to some countries, the estimated effect is approximately

an order of magnitude too small to be useful in explaining the global income distribution” (2006: 26).

According to this research, improving health could be important for growth, but is unlikely to be a

panacea.

Using a similar approach, Cutler et al. (2007) examine the impact of a malaria eradication

program across Indian states during the 1950s and find that the program increased literacy and

primary school completion by ten percentage points, accounting for about half the observed gains in

these measures over the period spanning the intervention in malarious regions. Hong (2007) and



Lucas (2009) both find significant affects of either exposure to malaria or its eradication on a variety

of economic outcomes such as schooling, literacy, labor force participation, and/or wealth.

Almond (2006) uses a negative historical health innovation as opposed to a policy-driven

improvement to again estimate the link from in utero health to economic performance later in life. In

particular, he studies the effects of the 1918 influenza epidemic, which was harsh and short, and

whose incidence varied geographically. In comparing cohorts exposed in utero to the flu with those

who were born just before or conceived just after, he found the former had “reduced educational

attainment, increased rates of physical disability, lower income, lower socioeconomic status, as well

as accelerated adult mortality compared with other cohorts.” Similarly, those born in states where the

pandemic was less severe fared better than those in other states.

The role of health on the incentive to invest in education has attracted both theoretical and

empirical attention of researchers. For example, Kalemli-Ozcan et al. (2000) and Soares (2005)

incorporate both education and fertility choices in models of demographic transition and the impact of

health on growth. The essential feature of these models is that longer life expectancy increases the

returns to investment in general, including education. Those cross-country analyses above that assess

the impact of life expectancy on income are unable to identify a large effect partly because much of

the improvement in life expectancy over the twentieth century was associated with reductions in

infant and child mortality, which occurs before most educational decisions are made.

Jayachandran and Lleras-Muney (2009) present evidence that reductions in adult mortality, which

increase the life expectancy of school-aged children, can have important effects on educational

attainment. They exploit a sharp fall in maternal mortality, from 1.8 to 0.5 maternal deaths per

hundred live births over a short seven-year period in Sri Lanka that translated into a 4.1 percent

increase in life expectancy of 15-year-olds, and find that female literacy increased 2.5 percent and

years of schooling rose by 4.0 percent. Their empirical strategy is strengthened by a number of unique

features of the environment: first, as well as occurring after education decisions have been made,

maternal mortality affects relatively young adults, and reducing it saves potentially many future years

(and hence induces a stronger human capital acquisition response); second, the authors are able to

compare outcomes for women across districts (in which the reductions in maternal mortality rates

varied); and third, the outcomes for both women and men (whose mortality rates and life expectancy

changed less over the same period) can be usefully compared.

At the cross-country level, Fortson (2007) uses data from Demographic and Health Surveys in

southern Africa to assess the effect of adult mortality on the incentive to invest in human capital. She

finds that living in an area with higher HIV prevalence, and hence a higher perceived risk of

premature death, is associated with lower educational attainment and slower grade progression. This

effect is not observed just for orphans, but for non-orphans as well. Reduced life expectancy appears

to dampen the incentive to invest in human capital.

Most often it is adults who make education decisions on behalf of their children, so reductions in

adult mortality can increase the incentive to invest in human capital simply because those who would

finance it are alive to do so. For example, Case, Paxson, and Ableidinger (2004) find that

orphanhood reduces the school enrollment of children compared with other children with whom they

live, suggesting that biological ties seem to matter in the allocation of investment.



5.4 CONCLUSIONS

This chapter has outlined a number of mechanisms by which improvements in health could lead to



increases in income, and has documented a growing body of empirical evidence that assesses the

strength of these links. From a policy perspective, it is not necessarily useful to dwell on the question

of whether health determines income or vice versa as both directions of causality are likely to be

operative. The more useful question is, “Are the income returns to some health improvements likely

to be enough to tip the balance in favor of interventions that would otherwise not pass a cost–benefit

test?” It remains very difficult to address this question in general, the answer to which depends on the

nature of the health improvement, the intervention itself, and the economic and institutional

environment.

Basic public economics suggest that policymakers first investigate the prevalence of market

failures that inhibit the adoption of the kinds of interventions that might otherwise have large health,

and income, effects. Obvious candidates are public goods (e.g. vector control), and perhaps goods

that exhibit increasing returns to scale in production, such as in the provision of some private goods

including e.g. iodized salt, which might be best provided, financed, or simply mandated by

government.

Alternatively, the correlation between health and income suggests that health policy should be an

integral part of social policy in general and of redistributional policies in particular. First,

independent of any causation between the two, either (poor) health status can be used as a marker of

(low) income, so income transfers in the form of tax credits and subsidies might be directed to the

sick and infirm; or publicly provided health-improving goods and services, of little value to those

who are not sick, could serve as an efficient self-targeted means of redistributing to the poor.

However, the scope for errors of both inclusion and exclusion is wide, and some health conditions

concentrated particularly among the poor, and some services valued particularly by them, might have

better targeting properties than others.

Second, absent issues of targeting efficiency, the causal link from health to income, and the fact

that income does not fully explain health outcomes, suggests that the provision of health services to

the poor could be an efficient means of improving their well-being. There is a taste of a merit–good

argument to this reasoning: the fact that education, itself correlated with income, is an important

independent determinant of health, suggests that demand for certain health services among the poor

might not be as high as it could be. Whether this means the poor should be provided with health

services or education is not clear—perhaps both?

In the poorest regions of the world, the potential gains to health improvement can only be realized

if complementary institutions such as schools with effective teachers and well-functioning labor and

credit markets exist. The promise of health, however, is matched by the challenges of delivering

health services, an issue that we have not addressed here but which must be incorporated into any

policy choice involving public provision or financing of health interventions. In these environments

especially, the mobilization of resources to finance improved health, and the income gains it

promises, must go hand in hand with the design of incentives for effective service delivery, embedded

in responsive and well-governed institutions.

REFERENCES

ACEMOGLU, DARON and JOHNSON, SIMON (2007), “Disease and Development: The Effect of Life

Expectancy on Economic Growth,” Journal of Political Economy, 115(6): 925–85.

—— and ROBINSON, JAMES A. (2001), “The Colonial Origins of Comparative Development: An

Empirical Investigation,” American Economic Review, 91(5): 1369–401.



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