1 Health and Development. Health and development An observation: health and wealth are correlated both across countries and across people within societies

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Health and Development

Health and development

• An observation: health and wealth are correlated both across countries and across people within societies. Why?

• Question #1: What is the impact of income on health and nutrition?

• Question #2: What is the impact of health/nutrition on economic outcomes?

• Question #3: Which policies / institutions improve the delivery of public health services in poor countries?

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The health-wealth relationship

• Disentangling the relationships between health and wealth and uncovering causal relationships in either direction is very tricky– Fundamental endogeneity problem– Measurement issues– Health: inputs (nutrition, expenditure) or output (health

status)– Proper measurement of inputs: adjustment for quality,

wastage– Wealth: short or long run? Measurement error in income– Functional form: non-linearities are key to the story, but it

may not be possible to observe them

• Table 1 (Strauss and Thomas): Wide variety in the estimates of the elasticities of calorie demand with respect to household resources (0.01 to 0.82)

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Income and expenditure elasticities of calorie demand

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Deaton and Subramanian (1996)

• Nonparametric approach to examining the impact of wealth on health

• Data set: 5,630 households in 563 villages• Recall data on 149 food items, meals taken out and given

away, etc.– From those 149 food items, they calculate caloric intake

using a conversion table. Also correct for meals taken out and meals given to people.

• Interesting aspect of this work: non-parametric estimatesy = g(x) + e

• How can we estimate g(x)?– Kernel regression– Fan (1992) locally weighted regression

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Results

• Positive relationship between income and nutrition, precisely estimated even non-parametrically

• The elasticity declines with outlay, but not dramatically. Sample of poor people.

• Price per calories paid increase with outlay. Richer households pay more per calorie– Rich: 1.50 rupees per 1000 calories– Average: 1.14 rupees per 1000 calories– Poor: 0.88 rupees

• Price elasticity of calories seems constant

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Calories vs. expenditure (nonparametrics)

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Price per calorie vs. expenditure (nonparam.)

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Parametric analysis

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Main takeaways in Deaton and Subramanian

• Nutrition does increase with per capita PCE– Elasticity of calories declines with PCE, from 0.65 to 0.4– But they do substitute towards more expensive calories

• Income elasticity of food expenditure about 0.75, roughly evenly split between increased calories and increased price per calorie

• Implausible that malnutrition is the cause of poverty, rather than vice versa: adequate nutrition can be purchased for 4% of daily wage

• Nice exploration of data, but endogeneity problem is not solved here

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Sharon MacciniUniversity of Michigan

Dean YangUniversity of Michigan

Under the Weather:Health, Schooling, and Socioeconomic

Consequences of Early-Life Rainfall

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Motivation

• Life in rural areas of developing countries is prone to many kinds of risk

• In addition to short-run effects, consequences of certain shocks may be felt many years or even decades later– Important for targeting of public resources that help

cushion impact of shocks

• Health shocks at the earliest stages of life, by affecting long-run health human capital, may have effects that extend into adulthood

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Pigs

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This paper …

• Examines the long-run impact of exogenous environmental shocks in early life– Rainfall shocks in locality and year of birth, for

Indonesian adults– Health as well as socioeconomic outcomes

• Compares long-run impact of shocks experienced at different points in early life– Tests for the existence of “critical periods” in child

development

• Provides suggestive evidence on the pathways through which early-life rainfall affects adult outcomes

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Summary of results

• For women, 20% higher birthyear rainfall leads to:

– Better health: 0.57 centimeters greater height, 3.8 percentage points less likely to report poor/very poor health

– More education: 0.22 more completed grades of schooling

– Improved socioeconomic status: 0.12 standard deviation higher asset index in household

• No corresponding effects for men, possibly due to gender bias in household resource allocation in hard times

• Rainfall in the first year of life has greatest effect on adult outcomes

• Evidence consistent with the following chain of causation: Early-life rainfall infant health schooling adult SES

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Critical-period programming

• Exposure to certain stimuli during a sensitive time span may have irreversible effects on living organisms

• “Fetal origins hypothesis”: The fetal stage and infancy are critical periods in human physical development– Early-life shocks can have long-lasting effects on health

(Barker 1998)

• Faced with poor nutrition/health conditions, limited resources prioritized for brain, compromising physical growth and development of other organ systems– Individuals are “programmed” for smaller body size, worse

health later in life

• Evidence:– Animal studies (see figures)– Epidemiological research in human populations

• But causality often questionable

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Critical periods in rat nutrition

Source: Figures 2.2 and 2.3, Barker (1998)

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Critical periods in rat nutrition

Source: Figures 2.2 and 2.3, Barker (1998)

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Identifying the impact of early-life shocks

• Relate later life outcomes to early-life health conditions– e.g., cross-sectional differences in birthplace infant

mortality, individual self-reported health status Open to omitted variable concerns

• Examine impact of shocks to health conditions at birth– e.g., within-twin birthweight differences, epidemics Difficult to generalize results from unusual events Data often a serious limitation

• We examine impact of an important source of environmental variation in developing countries: rainfall– Using high-quality survey data in IFLS

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Contemporaneous impact of rainfall

• Higher rainfall raises agricultural productivity in Indonesia– Secondary sources verify that droughts are

associated with food insecurity historically– Levine and Yang (2006): positive rainfall shocks

associated with increases in rice output across Indonesian districts in 1990s

• Higher agricultural output should lead to higher household income– Better ability to purchase nutrition, health inputs

and otherwise nurturing environments for infants

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Regression equation

• For outcome Yijst for individual i born in district j in season s of year t:

Yijst = Rjt + js + st + jsTREND + ijst

• Rainfall shock Rjt is at district-year level

• Birthdistrict-season fixed effects (js) account for time-invariant differences across people born in the same district in the same season

• Birthyear-season (cohort) fixed effects (st) account for Indonesia-wide shocks

• District-season-specific linear time trends absorb long-running linear trends in outcomes that vary across districts– Mainly helps absorb residual variation

Measurement error

• Rainfall is measured at the closest weather station to the birth district in the birth year– But is only imperfectly correlated with actual rainfall

in the individual’s birth locality

• Leads to attenuated coefficient estimates

• Solution: instrument early-life rainfall with similar variables whose errors are likely to be orthogonal– Instruments: early-life rainfall in 2nd- through 5th-

closest weather stations to birth district in birth year

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Data

• Indonesia Family Life Survey (IFLS)– Adults observed in wave 3 (2000): ~4,600 women, ~4,300

men born outside of major cities between 1953-1974– Anthropometrics, other health outcomes, socioeconomic

outcomes

• Global Historical Climatology Network (GHCN) Precipitation and Temperature Data (Version 3)– National Climatic Data Center, NOAA– Contains monthly rainfall records at 200+ rainfall stations

in Indonesia– Each IFLS birth district matched with closest rainfall

station

– Rainfall variable: log rainfall - log mean district rainfall

(from 1953-1999)

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Early-life rainfall and adult health

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Early-life rainfall and other outcomes

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Nonparametric estimates

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Nonparametric estimates

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Effect of rainfall before and after birthyear

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Potential selection concerns

• Potential negative bias – High rainfall differential survival of weakest infants

• Potential positive bias:– High-SES parents may time births to occur during good

rainfall years • Unlikely that parents can forecast rainfall so far in

advance• Also: controlling for past rainfall doesn’t change results

• Tests for selection:– Is early-life rainfall associated with size of cohorts

observed in our data?– Is early-life rainfall associated with parental education?

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Rainfall and inclusion in sample

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Rainfall and parental characteristics

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Pathways to adult SES

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In closing

• For women, higher early-life rainfall leads to better health, higher educational attainment, and improved socioeconomic status– No corresponding effects for men– Likely pathway to adult SES is via schooling

• Link to consumption smoothing literature– Does not mean that consumption smoothing mechanisms

were not operative– But does suggest that they were only partially effective, and

this partial failure had long-run effects

• Implications for policy– Identifies a group—female infants—whose later-life fortunes

are strongly tied to early-life conditions– Justification for interventions that shield infants from the

health consequences of temporary environmental and economic shocks

• E.g., weather insurance, social insurance schemes, public health investments, food security policies






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Health inputs and health

• Question: why might there be scope for public intervention in the health sector? In other words, why don’t households provide the necessary health investments themselves privately?

• Within-household agency problems or imperfect parental altruism towards children

• Positive treatment externalities

• Poor (or incorrect) knowledge of new health technologies among individuals

• Credit constraints prevent good health investments

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Kremer and Miguel (2004)

• Worm infections (e.g., hookworm, whipworm, roundworm, schistosomiasis) are among the world’s most common infections

• Paper studies school-based deworming treatment– In sample of rural Kenyan school children, over 90%

were infected at baseline. Between one third and one half had “serious” infections

• Worms pass larvae out through human fecal matter, infecting others– Treatment generates a positive externality by

reducing this transmission to others

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Study set-up

• 75 primary schools, over 30,000 children (aged 6-18)

• Deworming treatment (drugs, health education) phased in randomly across three treatment groups– Groups are similar along observables– Listed school alphabetically (by zone), and counted off 1-

2-3, 1-2-3, etc. – Thus the placement of schools into groups was not done

by a random number generator, but is completely arbitrary and orthogonal to omitted variables

• Group 1: treatment 1998 and 1999• Group 2: no treatment 1998, treatment 1999• Group 3: no treatment in 1998 or 1999

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Table 1

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Table V: Simple T vs. C comparison(no account for externalities)

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Estimating externalities

• One of the goals of the paper is to compare the naive treatment effect estimator, “Treatment minus comparison”, E( Yij | T1i =1) – E( Yij | T1i =0), to estimators that take into account “contamination” of the experiment from externalities

• This contamination may produce gains in the comparison group

• Externalities would lead to doubly under-estimating treatment effects– Miss impacts in the comparison group– Understate impacts in the treatment group

• A real concern in existing studies that randomize within schools and often found no significant impact

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Regression equation

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Table VII: Externality estimates

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Table IX

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Table X: No effect on test scores

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Documents

1 Health and Development. Health and development An observation: health and wealth are correlated both across countries and across people within societies