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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. - PowerPoint PPT Presentation
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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. YoungerCornell 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
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
60 65 70 75 80 85 90 95 100
Standardized height
dens
ity U. Pradesh
Assam
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
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
65 70 75 80 85 90 95 100
Standardized height
cum
ulat
ive
dens
ity
U. Pradesh
Assam
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
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
65 70 75 80 85 90 95 100
Standardized height
U. Pradesh
Assam
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
-3.8
2
-2.8
2
-1.8
2
-0.8
2
0.18
1.18
9.50
10.50
11.50
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0C
umul
ativ
e di
stri
buti
on
Height-for-age z-score
log HH expenditure p.c.
0.9-1
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0.3-0.4
0.2-0.3
0.1-0.2
0-0.1
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
ln(y)
HAZ 7.824 8.068 8.249 8.395 8.527 8.664 8.803 8.996 9.276 11.660-3.100 -7.453 -10.520 -10.700 -10.870 -11.380 -11.590 -10.060 -8.190 -7.680 -6.807-2.450 -7.967 -11.840 -13.040 -12.620 -11.160 -11.110 -9.639 -7.495 -6.744 -4.829-1.970 -8.205 -13.040 -14.190 -14.440 -12.590 -12.730 -11.360 -8.336 -6.632 -3.375-1.580 -9.345 -13.080 -14.580 -15.640 -14.040 -14.280 -13.280 -10.580 -7.773 -3.908-1.220 -10.160 -15.170 -17.050 -18.260 -17.010 -16.560 -14.890 -11.720 -8.991 -4.464-0.880 -10.710 -16.050 -18.080 -19.430 -18.440 -18.440 -15.710 -12.570 -9.416 -3.587-0.500 -10.680 -16.520 -19.220 -21.040 -19.580 -19.660 -16.830 -14.150 -10.070 -3.785-0.010 -12.560 -18.100 -20.520 -22.160 -21.660 -21.270 -17.620 -14.690 -10.790 -4.2290.690 -13.130 -18.510 -20.470 -21.830 -20.960 -20.830 -17.250 -13.420 -9.248 -1.9595.820 -13.690 -19.410 -21.110 -22.360 -21.340 -20.240 -16.580 -12.570 -8.105 0.024
Food and Nutrition Policy Program
Example 2 – Western vs. Northern Regions, Uganda, 1999
ln(y)
HAZ 7.824 8.068 8.249 8.395 8.527 8.664 8.803 8.996 9.276 11.660-3.100 -10.690 -8.402 -8.641 -7.858 -6.373 -4.052 -3.795 -1.640 -1.093 0.499-2.450 -15.440 -10.740 -10.670 -9.503 -7.967 -6.025 -4.949 -1.681 0.402 2.910-1.970 -18.760 -15.300 -12.620 -10.490 -8.819 -5.842 -3.855 -0.371 1.978 5.131-1.580 -23.000 -20.210 -17.420 -13.860 -11.920 -8.762 -5.268 -0.872 2.065 5.284-1.220 -26.220 -22.880 -19.900 -14.640 -12.250 -9.037 -5.312 -1.129 2.664 6.231-0.880 -29.170 -25.040 -22.110 -17.680 -15.390 -11.800 -8.179 -3.188 1.031 5.073-0.500 -31.870 -27.860 -24.920 -20.320 -17.660 -13.900 -9.762 -4.382 -0.246 4.297-0.010 -34.810 -31.240 -27.850 -23.810 -20.790 -17.450 -12.670 -7.210 -3.017 1.6490.690 -35.890 -32.390 -29.230 -24.150 -20.990 -17.970 -12.910 -6.916 -2.202 2.3355.820 -39.800 -36.450 -33.200 -27.830 -24.320 -20.890 -16.460 -10.130 -5.286 -0.008
Food and Nutrition Policy Program
Example 3 – Rural Central vs. Urban Eastern Regions, Uganda, 1999
ln(y)HAZ 7.824 8.068 8.249 8.395 8.527 8.664 8.803 8.996 9.276 11.660
-3.100 4.698 -4.756 -1.582 -2.842 0.062 0.610 2.559 5.519 3.458 2.637-2.450 8.001 1.731 5.582 5.754 8.763 8.643 11.910 14.940 13.930 12.510-1.970 8.184 -4.960 -0.307 -0.025 5.016 4.224 7.156 10.590 9.712 8.720-1.580 9.695 -1.046 2.743 2.692 8.366 7.651 10.320 13.920 12.030 7.938-1.220 7.846 0.140 2.801 4.249 9.201 9.988 13.760 17.110 15.540 9.993-0.880 10.090 2.003 5.305 6.958 12.340 9.820 14.730 17.110 15.410 7.941-0.500 12.120 4.765 8.590 10.650 17.300 15.270 21.160 22.360 20.020 11.170-0.010 12.850 6.872 11.310 12.260 15.860 15.010 18.730 18.330 13.550 4.4840.690 13.900 9.221 13.020 13.580 17.390 16.430 19.030 18.410 14.130 1.1095.820 12.290 8.636 13.520 15.240 19.570 19.950 21.460 20.250 16.400 0.000
Food and Nutrition Policy Program
Example 4 –Eastern vs. Western Regions, Uganda, 1999
lny
HAZ 7.824 8.068 8.249 8.395 8.527 8.664 8.803 8.996 9.276 11.660-3.100 3.962 3.274 1.154 -0.249 -0.253 -0.563 -1.147 -2.404 -2.460 -3.448-2.450 3.499 2.267 0.374 -0.213 -0.613 -0.742 -1.754 -3.662 -4.439 -6.118-1.970 3.130 2.778 0.075 -0.367 -1.018 -1.244 -2.070 -4.503 -5.687 -7.822-1.580 3.577 3.577 2.020 1.654 0.690 0.649 -0.912 -3.639 -5.142 -7.320-1.220 5.665 5.546 3.654 2.590 1.802 1.770 -0.001 -2.377 -4.273 -6.709-0.880 6.966 6.006 4.577 4.198 3.674 4.030 2.191 -0.354 -2.321 -4.865-0.500 8.065 7.614 6.957 6.882 6.283 6.328 4.079 1.842 0.049 -2.758-0.010 10.230 9.380 8.483 8.977 8.377 8.720 6.055 3.836 2.134 -0.8860.690 11.510 11.000 9.631 9.446 8.781 9.506 6.837 3.905 2.082 -0.4525.820 11.950 10.920 10.430 10.290 9.509 10.450 7.906 4.711 2.959 -0.022
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