using non-income measures of well-being for policy evaluation

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


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


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


What Measures?

• Children’s anthropometrics – z-scores

• Adult’s anthropometrics – BMI or heights

• Anemia

• Self-reported morbidity

• Mortality (probability) – life tables or predicted probabilities


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


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


Comparing Distributions

• Scalar measures

• Stochastic dominance– Nice foundation in welfare economics– Very general comparisons and results– But … subject to indeterminate results


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


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


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


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


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

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0.5

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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


Union and Intersection Domains


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


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


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


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


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


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


“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


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

using non-income measures of well-being for policy evaluation

Documents

povery measures

policy evaluationprepared

regression frameworkvalidity

stunting rates

utter pradesh

gini indices

multidimensional senincome

public goodslow correlation