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Using Non-Income Measures of Well-Being for Policy Evaluation

Using Non-Income Measures of Well-Being for Policy Evaluation. Stephen D. Younger Cornell University. Prepared for the Second Meeting of the Social Policy Monitoring Network on Health and Nutrition Rio de Janeiro November 6-7, 2003. Outline.

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Using Non-Income Measures of Well-Being for Policy Evaluation

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  1. Using Non-Income Measures of Well-Being for Policy Evaluation Stephen D. Younger Cornell University Prepared for the Second Meeting of the Social Policy Monitoring Network on Health and Nutrition Rio de Janeiro November 6-7, 2003 Food and Nutrition Policy Program

  2. Outline • Validity and utility of non-income measures of well-being • Using distributions of outcomes to compare more than just the mean impact of a policy or program • Modeling non-income outcomes in a regression framework Food and Nutrition Policy Program

  3. Validity and Utility of Non-Income Measures of Well-Being • Poverty is multidimensional (Sen) • Income is instrumentally important • Functionings are intrinsically important • There are things that money can’t buy • e.g. public goods • Low correlation between income and many health outcome measures Food and Nutrition Policy Program

  4. What Measures? • Children’s anthropometrics – z-scores • Adult’s anthropometrics – BMI or heights • Anemia • Self-reported morbidity • Mortality (probability) – life tables or predicted probabilities Food and Nutrition Policy Program

  5. Comparing Distributions • Standard methods compare means • Regression • Treatment/control differences (in differences) • Even distribution-sensitive statistics like stunting rates, povery measures, or gini indices, are scalars • Poverty (and inequality) analysis has to be concerned with entire distributions Food and Nutrition Policy Program

  6. Distribution of Children’s Heights in Assam and Utter Pradesh, India Food and Nutrition Policy Program

  7. Comparing Distributions • Scalar measures • Stochastic dominance • Nice foundation in welfare economics • Very general comparisons and results • But … subject to indeterminate results Food and Nutrition Policy Program

  8. Cumulative Distribution of Heights in Assam and Utter Pradesh, India Food and Nutrition Policy Program

  9. First-order dominance theorem • If one cumulative density function is everywhere below another, then poverty is lower for that group for any poverty line and for any poverty index (measure) in a large class of indices, viz. those that are : • Additively separable • non-decreasing (non-satiation) • anonymous • continuous at the poverty line Food and Nutrition Policy Program

  10. Dealing With CDF’s That Cross: Higher-Order Dominance Tests Food and Nutrition Policy Program

  11. Multivariate poverty comparisons • Say we want to measure well-being in two dimensions: income and height • Two important differences: • “union” vs. “intersection” poverty measures • Substitutability/complementarity of the two measures of well-being • The human development index Food and Nutrition Policy Program

  12. Bidimensional Poverty Surface Food and Nutrition Policy Program

  13. Union and Intersection Domains Food and Nutrition Policy Program

  14. Multivariate dominance results • For union poverty measures, multivariate dominance requires univariate dominance • It is possible to have univariate dominance in both dimensions but not bivariate dominance • It is possible to have bivariate intersection dominance but not univariate dominance Food and Nutrition Policy Program

  15. Example 1 – Central vs. Eastern Regions, Uganda, 1999 Food and Nutrition Policy Program

  16. Example 2 – Western vs. Northern Regions, Uganda, 1999 Food and Nutrition Policy Program

  17. Example 3 – Rural Central vs. Urban Eastern Regions, Uganda, 1999 Food and Nutrition Policy Program

  18. Example 4 –Eastern vs. Western Regions, Uganda, 1999 Food and Nutrition Policy Program

  19. Modeling Health and Nutrition Outcomes with Regression • Basic idea is straightforward: regress any health or nutrition outcome on individual, household, and community characteristics, including policy or program variables • Mostly reduced form or almost reduced form models • To date, far more attention to individual and household characteristics rather than policy variables Food and Nutrition Policy Program

  20. “Standard” List of Regressors for Anthropometric Models • Individual variables: age, gender, birth order, multiple birth, place of birth, health care history (e.g. vaccinations) • Household variables: size, composition, place of residence, head or parents’ education, head or parents’ height, income, assets, activities (e.g. type of work) • Community variables: health care and other infrastructure, prices Food and Nutrition Policy Program

  21. Standard List of Concerns • Endogenous regressors – mostly jointly determined with the outcome at the individual and household level • Selectivity bias for policy uptake • Program placement bias • Selectivity bias due to mortality Food and Nutrition Policy Program

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