Dean Jolliffe Economic Research Service, USDA

1. Rural Poverty and the Cost of Living:Implications of Current Discussions on Changing How We Measure Poverty Dean Jolliffe Economic Research Service, USDA Paper available for comments. The views expressed in this paper do not necessarily reflect the views of the Economic Research Service of the U.S. D. A. Email: Jolliffe@ers.usda.gov

2. Motivation Motivation • NAS Measuring Poverty Panel • Importance of poverty rates for distribution of benefits and funds Caveats • This analysis is restricted to the Fair Market Rent index • This is not intended to advocate use of FMR, rather to inform of the implications

3. Outline of Presentation • Review Foster-Greer-Thorbecke family of poverty indices. • Describe the data: March Supplement to the CPS from 1989 to 2001 and the Fair Market Rent Index • Results

4. Foster-Greer-Thorbecke Class of Poverty Indices • P = 1/n Σi I( y i < z)[(z-yi)/z] • =0  P0 Head count index, or proportion poor (Incidence or prevalence of Poverty) • =1  P1 Poverty-gap index • =2  P2 Squared, Poverty-gap index

5. A few examples of why the differences across P0, P1, and P2 matter • FSP Example: Transfer money to poor person, but not enough to convert into nonpoor. The person is better off, but this is not reflected in P0. P1 and P2 do reflect the change in welfare and are lowered. • Example: Transfer money from poor to someone who is extremely poor. (Same caveat as above). Inequality is less, mean income the same, any Shur-concave SWF increases; but P0 and P1 are unaffected. P2 is lowered. • Example: Is there a discrete difference in welfare at the poverty line? P0 suggests there is, P2 is differentiable at z. • Example: Measurement error and the recommendations of the National Academy of Science’s panel on poverty measurement. P1 and P2 are more robust to measurement error than P0. (next slide)

6. Brief Description of the CPS, March Supplement 1992-2003 • Administered by the Bureau of the Census for the Bureau of Labor Statistics • Basis for official poverty estimates. • Approximately 50,000 Households, Nationally Representative of civilian, non-institutionalized, housed population and members of the Armed Forces living off base or with their families on base • Problem: CPS is based on a stratified, multi-stage design, not SRS. The next slides illustrate that this matters.

7. Brief Description of Fair Market Rent (FMR) Data • FMR collected by HUD for Section 8 (housing voucher program) • Rent + utilities for ‘standard quality’ housing (FMR taken at 40th percentile) • Full coverage (354 metro, 2350 nonmetro), aggregated to 100 observations (met/nonmet by State) • ALTERNATIVES: CPI experiments, ACCRA

8. P Differences (No COLA adjustment)

9. Table 1: Scaled Fair Market Rent Index, Nonmetro-metro Comparison Notes: Fair Market Rent index weighted by individual weights to match weights used for poverty estimation.

11. P Differences (FMR adjustment)

12. Distribution of Income to Needs of the Poor, 2001

13. P Differences in the South (Official, no FMR adjustment)

15. P Differences in the South (FMR adjusted)

16. T-tests of P Differences in the South (FMR adjusted)

17. Table 3: Average Age of the Poor by Area Notes: Age in years of the poor by metro and nonmetro residence. FMR poor are those designated as poor after adjusting income with the Fair Market Rent index.

18. Age distribution of the Poor, 2001

19. Age distribution of the Nonmetro Poor, 2001

20. P Differences (1/3 FMR adjustment)

21. Conclusions • FMR as COLA fully reverses Metro – Nonmetro poverty rankings (national) • Of the Nonmetro poverty population, the elderly are disproportionately affected • Important to be aware of implications of FMR (could affect area distribution of social safety net) • 1/3 FMR moderates findings