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

Food and Nutrition Policy Program Outline 1.Validity and utility of non-income measures of well-being 2.Using distributions of outcomes to compare more than just the mean impact of a policy or program 3.Modeling non-income outcomes in a regression framework

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

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

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

Food and Nutrition Policy Program 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 Bidimensional Poverty Surface

Food and Nutrition Policy Program Union and Intersection Domains

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

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

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

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

Food and Nutrition Policy Program 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 “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 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