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
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
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
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
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)
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.
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
P Differences (No COLA adjustment)
%(Nonmetro-Metro)/Metro: P0
%(Nonmetro-Metro)/Metro: P2
%(Nonmetro-Metro)/Metro: P1
30
Percent Difference
15
0
-15
-30
1991
1993
1995
1997
1999
2001
Table 1: Scaled Fair Market Rent Index, Nonmetro-metro Comparison
Fair Market Rent
Index
Average
Median
(Min, Max)
National
1.00
1.00
(0.74, 1.21)
Nonmetropolitan
0.82
0.81
(0.74, 1.21)
Metropolitan
1.04
1.01
(0.85, 1.19)
Nonmetro South
0.79
0.78
(0.74, 0.93)
Metro South
0.98
1.00
(0.85, 1.19)
Notes: Fair Market Rent index weighted by individual weights to match weights
used for poverty estimation.
Table 2: 2001 & 2002 Poverty Measures, Nonmetro-metro Comparison
Headcount,
P0 Measure
Actual
FMR
adjusted
Poverty-gap,
P1 Measure
Actual
FMR
adjusted
Squared Poverty-gap,
P2 Measure
Actual
FMR
adjusted
0.142
(0.004)
0.111
(0.002)
0.105
(0.003)
0.120
(0.002)
0.063
(0.002)
0.052
(0.001)
0.050
(0.002)
0.055
(0.001)
0.043
(0.002)
0.036
(0.001)
0.036
(0.002)
0.038
(0.001)
28%
(3.80)
-12%
(3.01)
21%
(4.71)
-9%
(3.97)
18%
(5.64)
-5%
(4.97)
2001
Nonmetro
Metro
Nonmetro-Metro
Difference
P Differences (FMR adjustment)
%(Nonmetro-Metro)/Metro: P0
%(Nonmetro-Metro)/Metro: P2
%(Nonmetro-Metro)/Metro: P1
30
Percent Difference
15
0
-15
-30
1991
1993
1995
1997
1999
2001
Distribution of Income to Needs of the Poor,
2001
Nonmetro
Metro
Density of Welfare Ratio, 2001
1.5
1.2
.9
.6
.3
0
.25
.5
Welfare Ratio, 2001
.75
1
P Differences in the South
(Official, no FMR adjustment)
%(Nonmetro-Metro)/Metro: P0
%(Nonmetro-Metro)/Metro: P2
%(Nonmetro-Metro)/Metro: P1
45
Percent Difference
30
15
0
-15
-30
1991
1993
1995
1997
1999
2001
Table 2S: 2001 & 2002 Poverty Measures, Nonmetro-metro Comparison
2001
Nonmetro South
Metro South
Nonmetro-Metro
Difference
Headcount,
P0 Measure
Actual
FMR
adjusted
Poverty-gap,
P1 Measure
Actual
FMR
adjusted
Squared Poverty-gap,
P2 Measure
Actual
FMR
adjusted
0.176
(0.006)
0.122
(0.003)
0.124
(0.006)
0.119
(0.003)
0.079
(0.004)
0.057
(0.002)
0.060
(0.004)
0.056
(0.002)
0.054
(0.003)
0.040
(0.002)
0.044
(0.003)
0.039
(0.002)
44%
4%
39%
7%
35%
11%
P Differences in the South
(FMR adjusted)
%(Nonmetro-Metro)/Metro: P0
%(Nonmetro-Metro)/Metro: P2
%(Nonmetro-Metro)/Metro: P1
20
Percent Difference
10
0
-10
-20
1991
1993
1995
1997
1999
2001
T-tests of P Differences in the South
(FMR adjusted)
T-stat: P0 Relative Diff
T-stat: P2 Relative Diff
T-stat: P1 Relative Diff
4
1.96
0
-1.96
-4
1991
1993
1995
1997
1999
2001
Table 3: Average Age of the Poor by Area
2001
Poor
FMR
Poor
2002
Metro
Nonmetro
Metro
Nonmetro
29.75
32.27
30.20
32.00
(0.257)
(0.506)
(0.252)
(0.484)
30.08
30.59
30.40
30.25
(0.252)
(0.561)
(0.245)
(0.516)
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.
Age distribution of the Poor, 2001
Nonmetro
Metro
Density of Age, 2001
.025
.01
0
0
10
20
30
40
Age, 2001
50
60
70
80
Age distribution of the Nonmetro Poor, 2001
Age, Poor
Age, FMR Poor
Density
.03
.015
0
0
10
20
30
40
Age
50
60
70
80
P Differences (1/3 FMR adjustment)
%(Nonmetro-Metro)/Metro: P0
%(Nonmetro-Metro)/Metro: P2
%(Nonmetro-Metro)/Metro: P1
30
Percent Difference
15
0
-15
-30
1991
1993
1995
1997
1999
2001
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