Health and Development

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?

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)

Income and expenditure elasticities of calorie demand

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 estimates y = g(x) + e • How can we estimate g(x)? • Kernel regression • Fan (1992) locally weighted regression

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

Calories vs. expenditure (nonparametrics)

Price per calorie vs. expenditure (nonparam.)

Parametric analysis

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

Under the Weather: Health, Schooling, and Socioeconomic Consequences of Early-Life Rainfall Sharon Maccini University of Michigan Dean Yang University of Michigan

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

Pigs

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

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

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

Critical periods in rat nutrition Source: Figures 2.2 and 2.3, Barker (1998)

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

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

Regression equation • For outcome Yijst for individual i born in district j in season s of year t: Yijst = qRjt + mjs + pst + gjsTREND + eijst • Rainfall shock Rjt is at district-year level • Birthdistrict-season fixed effects (mjs) account for time-invariant differences across people born in the same district in the same season • Birthyear-season (cohort) fixed effects (pst) 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

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)

Early-life rainfall and adult health

Early-life rainfall and other outcomes

Nonparametric estimates

Effect of rainfall before and after birthyear

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?

Rainfall and inclusion in sample

Rainfall and parental characteristics

Pathways to adult SES

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

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

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

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

Table 1

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

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

Regression equation

Table VII: Externality estimates

Table IX

Table X: No effect on test scores

Health and Development