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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?

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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?
the health wealth relationship
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)
deaton and subramanian 1996
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
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
main takeaways in deaton and subramanian
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
health and development11
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?
slide12

Under the Weather:

Health, Schooling, and Socioeconomic Consequences of Early-Life Rainfall

Sharon Maccini

University of Michigan

Dean Yang

University of Michigan

motivation
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
this paper
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
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
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
Critical periods in rat nutrition

Source: Figures 2.2 and 2.3, Barker (1998)

critical periods in rat nutrition19
Critical periods in rat nutrition

Source: Figures 2.2 and 2.3, Barker (1998)

identifying the impact of early life shocks
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
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
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
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
slide24
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)

potential selection concerns
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?
in closing
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 and development35
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?
health inputs and health
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
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
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
estimating 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