BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRICES AND LIVING

CONDITIONS 

 

Developing a New Poverty Line for the USA: Are There Lessons for India?

Thesia I. Garner, U.S. Bureau of Labor Statistics 

Kathleen Short, U.S. Census Bureau 

 Working Paper 378

 March 2005 

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. 

Bureau of Labor Statistics.
 1

Developing a New Poverty Line for the USA: 

Are There Lessons for India?

 Thesia I. Garner • Kathleen Short 

Published in: National Income Accounts and Data Systems, edited by B.S. Minhas, Oxford 

University Press, New Delhi, 2002. 

This paper is drawn from a paper presented by Kathleen Short, Thesia I. Garner and David S. 

Johnson in August 1998, ‘Redefining Poverty Measurement in the US: Examining the Impact on 

Inequality and Poverty’, at a conference organized by the International Association for Research 

of Income and Wealth in Cambridge, UK. 
 2

Abstract 

This paper reviews a procedure that is being followed in the United States of America (USA) to 

experimentally test and evaluate recommendations made for redefining poverty measurement in 

that country. The recommendations were made in 1995 by the US National Academy of 

Sciences (NAS) Panel on poverty measurement. In this paper these recommendations are 

reviewed and the impact of implementing the recommendations on measures of inequality and 

poverty are examined. In conclusion, a discussion concerning possible lessons for India is 

provided. 

The recommended poverty measure (based on new measures of thresholds and resources) is 

examined in terms of its impact on inequality statistics, as well as poverty statistics, and results 

are compared to similar statistics based on the official measure. The standard Gini index, and 

three generalized entropy inequality measures are used to examine inequality. For the poverty 

analysis simple head count ratios, poverty gaps, and Foster-Greer-Thorbecke poverty measures 

are computed. Data from the 1991 U.S. Consumer Expenditure Survey (CE) Interview are used 

to produce the thresholds, and data from the 1992 through 1997 Current Population Survey 

(CPS), and in some analyzes, the 1991 panel of the Survey of Income and Program Participation 

(SIPP), are used to define resources. 

The proposed measure produces a distribution of resources that is, in general, more equal than is 

the distribution of official income. The poverty analysis reveals that changes in the poverty rates 

based on the official and the experimental measures are similar over time. However, poverty as 

measured by the NAS measure is greater than official poverty. The experimental poverty 

measure yields a poverty population that looks slightly more like the total U.S. population in 

terms of various demographic and socioeconomic characteristics than does the current official 

measure. Geographically adjusting the thresholds results in greater equality and lower poverty 

rates than when non-adjusted thresholds are used. 

With regard to India, poverty measurement is likely not to be based on income and expenditures 

primarily. Alternative measures based on other needs and resources are reviewed. However, 

regardless of the measure used, systematic evaluations of the measure are necessary and the USA 

model may be one to consider in this evaluation process. 
 3

In the 1996-7 budget for India, the Common Minimum Programme (CMP) announced by 

the government in June 1996 showed a strong commitment to the development of social sectors 

for achieving distributive justice. The Conference of Chief Ministers on Basic Minimum 

Services (held in New Delhi during the 4-5 July 1996) recommended the adoption of several 

objectives to provide these services. Among these are programs to assist the poor including the 

provision of public housing to all shelter-less poor families, streamlining the public distribution 

system targeted at families below the poverty line, increased funding for nutritional support for 

primary school students, and increased funding allocations for health. 

In order to achieve these objectives it is necessary to identify those who are below the 

poverty line and in most need. However, this process is sensitive to the estimation procedure 

adopted. The official poverty measure used in India is based on food energy requirements and 

was originally developed by the 1979 Planning Commission and endorsed by the 1993 Planning 

Commission.1

 It is based on the nutritional norm of 2400 calories per person per day in rural 

areas and 2100 calories per persons per day in urban areas, and is defined as the level of average 

per capita total expenditures at which this norm is typically attained. 

In 1993 an expert group was formed by the Planning Commission to consider 

methodological and computational aspects of estimation of the proportion and number of poor in 

India.2

 This Group conducted an extensive review and analysis of the methods that can and have 

been used to measure poverty in India. These include ones based on a hunger criterion, food 

share criterion, and consumption of calories criterion. For official poverty, consumption 

expenditure data from the National Sample Survey (NSS) are used. Issues addressed by the 

expert group included differences between urban and rural areas, inter-area price differences, and 

poverty statistics, among many other issues. 

In 1998, the Department of Statistics commissioned a study: ‘Counting the Poor: Where 

are the Poor in India?’3

 The purpose of this report was to produce recent poverty statistics for 

India using micro-level data. They produced estimates using NSS data from 1987-8 and 1993-4. 

Six poverty lines were used in the analysis. Head count ratios as well as severity indices were 

produced for urban and rural areas, as well as the states. 

According to information from the Ministry of Finance, the methodology for the 

estimation of poverty is again under review by the Planning Commission.4

 The goal of this 

paper is not to suggest to the Government of India the poverty measure that is most appropriate 

for that country, but to review an approach that is being followed in the United States of America 

(USA) to evaluate recommendations for revising poverty measurement there. Based on this 

example, it is hoped that lessons for India can be drawn. Of course, we in the USA can also 

learn from India’s experience and review of poverty measurement. 

Poverty measurement enables us to identify who is ‘poor’ and how ‘poor is poor.’ Once 

appropriate measures for a country have been adopted, resulting data can be used to assist in 

 

1

 See Datt and Ravallion (1998). Gulab Singh of the Department of Statistics, Government of India told me that two 

alternative measures have been proposed. One is the share of total consumption expenditures allocated to food for a 

person, family, or household. The other is based on a National Sample Survey-Consumption Expenditure Survey 

section question in which respondents are asked ‘Do you manage to get two square meals a day which you think are 

sufficient?’ If the person says ‘no’ then the person is considered to be absolutately poor. 

2

 See Planning Commission, July 1993. 

3

 Dubey and Gangopadhyay, February 1998. 

4

 Based on information provided by the Ministry of Finance, Government of India on the following web site: 

hhtp://www.nic.in.indiabudget/es97/CHAP1.HTM and /CHAP10.HTM. 
 4

policy development, planning, and the evaluation of poverty alleviation programs. Poverty 

statistics are important in that they provide us with indicators that reflect who we are as a 

country, a culture, and a society. 

THE US EXPERIENCE 

In the spring of 1995 the National Academy of Sciences (NAS) Panel on Poverty and 

Family Assistance (see Citro and Michael 1995) released a report in which they evaluated the 

current method of poverty measurement in the US and made recommendations for a change. In 

their report, the panel recommended changing the definitions of both the thresholds and the 

resources that are used to measure poverty. In this paper, we implement many of the panel’s 

basic procedures, with slight modifications, to obtain experimental poverty thresholds and a ‘new 

income’ or resource measure. Although the panel did not address the issue of whether the new 

resource measure would be used as the basis for a new official income measure, we decided to 

examine the distribution of this measure since many of the calculations used to produce the 

resource measure are redistributional in nature (the panel stated that the new resource measure 

was to be used in conjunction with the new threshold and that the income to needs ratio based on 

the new measure would be relevant). One way to do this is to examine inequality in the 

experimental resource measure as compared to the official measure. The treatment of zero and 

negative incomes and resources is important for this part of the analysis. Poverty, based on the 

experimental and official definitions, also is examined in detail using a variety of measures. The 

thresholds used for this analysis are based on the interview component of the US Consumer 

Expenditure Survey (CEX) with data from 1989-91. Thresholds for 1992 through 1996 are 

produced using the all items Consumer Price Index for Urban Consumers (CPI-U). The 

inequality estimates are based on Current Population Survey (CPS) data for 1996. Poverty rates 

and distributions for 1991 are produced using family resource data that come from two different 

sources: the 1991 panel of the Survey of Income and Program Participation (SIPP) and the 

March 1992 Current Population Survey (CPS).5

 The panel recommended that the SIPP, rather 

than the CPS, become the official source of poverty statistics. Both surveys are used to produce 

estimates of poverty for the same year, 1991. Additional estimates for 1992 to 1996 are based on 

CPS data and are presented in order to examine the behavior of the experimental poverty rates 

over time. Foster-Greer-Thorbecke (1984) measures of poverty are produced for 1996 using 

CPS data alone. 

With one exception, our results from the inequality analysis generally reveal there to be 

slightly more equality in the distribution of income when the experimental resource definition is 

used as compared to the current official measure of income regardless of the treatment of zero 

and negative values. This was expected due to the redistributional nature of the additions and 

subtractions to current gross money income. The opposite relationship between the experimental 

measure and official income occurs when both of the following conditions hold in combination: 

the inequality index is more sensitive to transfers at the lower end of the distribution, and the 

zero and negative values are truncated. We also find that the use of geographically adjusted 

thresholds results in resource distributions that are statistically significantly more equal than 

distributions based on thresholds with no geographic price adjustment. 

Concerning poverty, we find that changes in the poverty rates based on the official and 

the experimental measures are similar over time. For 1991, use of the SIPP data results in lower 

 

5

 See Appendix for a description of the three surveys. For these surveys, the homeless, persons in jails or prisons, 

and some military personnel are not sampled. 
 5

poverty rates than when the CPS data are used for both the current official measure of income 

and for the experimental measure. We show that using the experimental poverty measure yields 

a poverty population that looks slightly more like the total population in terms of various 

demographic and socioeconomic characteristics than does the current official measure. The 

poverty gaps resulting from the use of the experimental measure are generally less than the gaps 

based on the official measure. Only for the elderly and households with male householders are 

the gaps wider. However, using the Foster-Greer-Thorbecke (FGT) statistics reveals statistically 

significantly greater normalized gaps for the proposed measure. Greater severity in poverty also 

results with the proposed measure when the entire (negatives, zeroes, and positive values) 

distributions of income and resources are analyzed. Restricting the analysis to observations with 

positive incomes or resources results in poverty severity indices that are not statistically different 

for either measure of income. 

The paper is divided into three remaining sections. First we present the panel’s 

recommendations for producing the new poverty measure focusing on the thresholds and 

resources. The experimental thresholds used in this analysis are compared to the official 

thresholds for the reference unit. Also in this section we describe the new resource measure, 

followed by some basic statistics using data from the March 1997 CPS as a precursor to the 

inequality analysis. The second section includes a description of the inequality measures and our 

results for the proposed resource versus current income measures; also included is our poverty 

analysis using the new measure in contrast to the old measure. In the last section, we provide our 

conclusions. 

Revising the Poverty Measure 

Thresholds

The procedure recommended by the NAS Panel to calculate the thresholds for a 

particular year includes the use of CEX data. The data are to be used to determine the median 

expenditures (adjusted to current dollars) for food, clothing, shelter, and utilities for some 

reference family, for the three year period previous to the current year. However, the panel stated 

that if data from the most recent three years are available, then these should be used. In the 

panel’s study and in this one, we define food to include food at home and food away from home, 

but not alcoholic beverages. Clothing includes apparel, upkeep, sewing materials, and related 

items. Shelter includes rent, maintenance and repairs, and insurance for renters; for owners, 

shelter is defined as including payments on mortgage interest (but not principal), property taxes, 

electricity, telephone, and such public services as water and sewer. The resulting thresholds are 

based on a percentage of the median level of expenditures for this basic bundle composed of 

food, clothing, shelter, utilities, and ‘a little bit more.’ The ‘little bit more’ would be accounted 

for by applying a small multiplier to the median expenditure value for the basic bundle. This 

additional amount would allow for other needs (e.g., household supplies, personal care). This is 

in contrast to the current official threshold that is based on food expenditures and a larger 

multiplier to account for other goods and services. See Citro and Michael (1995) for a 

description of the method used to derive the original official thresholds. 

The panel recommended that the reference unit for the basic threshold should be one in 

which two adults and two children are present. This recommendation was operationalized by 

producing the thresholds for a consumer unit6

 composed of two adults and two children. This 

 

6

 A ‘consumer unit’ comprises either: (a) all members of a particular household who are related by blood, marriage, 

adoption, or other legal arrangements; (b) a person living alone or sharing a household with others or living as a 

roomer in a private home or lodging house or in permanent living quarters in a hotel or motel, but who is financially 
 6

type of reference unit accounts for about 10 percent of all consumer units in the CEX Interview 

data file. Following the panel (Citro and Michael 1995) and Garner et al. (1998), we use the 

average of upper and lower values for the percentages and multipliers to obtain a poverty 

threshold for the reference unit. The formula for deriving the proposed reference unit poverty 

threshold is: 

⎥

⎦

⎤ ⎢

⎣

⎡ + = 2

( * * ) ( * * ) M1 P1 median M2 P2 median T *median

2

(1.15* 0.78) (1.25* 0.83)

⎥

⎦

⎤ ⎢

⎣

⎡ + = , (1) 

 = 0.96725*median

where 

T = the reference unit poverty threshold, 

M1,M2 = multipliers for smaller and larger additional amounts, 

P1,P2 = lower and higher percentages, 

median = median expenditures for the basic bundle of food, clothing, shelter, and 

 utilities. 

The panel set the percentages of the median (P1, P2) at 78 percent and 83 percent. These 

percentages roughly correspond to the 30th and 35th percentile distribution of expenditures for the 

basic bundle for the reference unit. The multipliers (M1, M2) were set at 1.15 and 1.25. 

Mathematically, these factors collapse to median expenditures for the basic bundle multiplied by 

0.96725 when we average the lower and upper ranges. Thus, the resulting threshold is very close 

to median expenditures for the basic bundle. The underlying assumption concerning these 

thresholds is that a family’s basic needs can be met if its resources are above the threshold value. 

For our analysis, we use 1991 thresholds based on the median of the three most recent 

years of data, 1989-91, with all expenditures updated to 1991 dollars. Specifically, the 1991 

threshold is calculated by using the interview quarterly expenditure data (annualized) for 1989-

91, updating the annualized expenditures to 1991 dollars (using the CPI-U for all items), 

estimating the median, and then multiplying by the factor, 0.96725. 

To produce the thresholds for 1992 to 1996, we update the threshold from 1991 using the 

all items CPI-U. While the panel recommended updating by the change in median expenditures 

each year, Johnson et al. (1997) showed that the change in median expenditures were similar to 

the inflation rate over this entire period, but the annual changes were more volatile than the 

inflation rate. 

The experimental and official thresholds for a consumer unit with two adults and two 

children are presented in Table 1. Experimental thresholds updated by the CPI-U and by the 

change in median expenditures are presented for comparison. As noted above, for this analysis, 

we use the ones updated by the CPI-U only. The experimental thresholds adjusted by the CPI-U 

are only slightly higher than the official thresholds; they are US$79 higher in 1991 and US$91 

higher in 1996. If the change in median expenditures were used, the thresholds for 1992 through 

1995 would be somewhat higher. As shown in an earlier paper (Johnson et al. 1997), there is a 

large standard deviation for the median expenditures for the reference family (about US$300). 

This is why for some years the change in the thresholds is larger than the inflation rate (for 

 

independent; or (c) two or more persons living together who use their incomes to make joint expenditure decisions. 

Financial independence is determined by the three major expense categories: housing, food, and other living 

expenses. To be considered financially independent, at least two of the three major expense categories have to be 

provided entirely or in part by the respondent (USDL 1995). 
 7

example, 1993), while it is smaller in other years (for example, 1995 and 1996). 

The panel recommended adjusting the reference threshold to reflect geographic 

differences in costs. They stated that poverty thresholds should be higher in areas with higher 

prices. In addition, because many spells of poverty are short, geographic adjustment is called for 

since families cannot be expected to quickly change location when they experience a decline in 

income. Kakwani (1993) also recommended that a household welfare measure should be 

adjusted to take into account regional price variations, since prices may vary substantially across 

regions. We follow the panel’s recommendations and make a price adjustment using their interarea housing price indices based on data from the 1990 census on gross rent for apartments. This 

is the same approach as followed by the panel. (Currently inter-area price indices for all items 

are not available for the entire US7) In our analysis, we examine whether this adjustment affects 

our inequality and poverty results. 

The panel recommended the use of a two-parameter equivalence scale to produce 

thresholds for other types of units (for example, families). This scale explicitly accounts for the 

differing needs of adults and children and the economies of scale of living in larger families or 

households. This scale is 

F (A + PC) , (2) 

where 

A = number of adults in the family, 

C = number of children in the family, 

P= adult-equivalent of one child, and 

F= the economies of scale factor. 

For our analysis we use P=0.7 and a scale economy factor F=0.65. These scales were 

chosen since they minimize the effect on overall poverty and are most similar to the current 

scales. However, we note that different equivalence scales can change one’s results regarding 

inequality (see Coulter et al. 1992; Lancaster, Ray, and Valenzuela 1998a) and the composition 

of poverty (see Citro and Michael 1995; Lancaster, Ray, and Valenzuela 1998b; and Johnson et 

al. 1997). 

Resources 

Resources Definition: Following the panel’s recommendation, we use an experimental resource 

measure that is based on annual gross money income (the income used for current official 

poverty measurement in the US) plus the value of various in-kind transfers, but which excludes 

selected expenses. In this paper we include the following in-kind transfers in both the CPS and 

the SIPP measures: food stamps, school lunch, and housing subsidies (see Shea et al. 1997). 

Benefits from the Women, Infants and Children (WIC) program, school breakfast, and energy 

assistance programs are added to the SIPP resource measure but not the CPS measure.8

 From the 

cash and in-kind transfers total we subtract the following expenses: work-related transportation 

 

7

 Kokoski, Cardiff, and Moulton (1994) have produced experimental inter-area price indices for urban areas in the 

US 

8

 To examine the impact on poverty when these three benefits are not included in the resource measure, we produced 

standardized poverty rates in an earlier study (Short et al. 1998a). Standardized rates were produced by adjusting 

the experiment thresholds by a percentage of the threshold to obtain an overall poverty rate equal to the official rate. 

Not including these three benefits increased the standardized experimental poverty rate by 0.2 percentage points in 

the SIPP measure in 1991. 
 8

and miscellaneous expenses,9 child care expenses (see Short et al. 1996), medical out-of-pocket 

expenditures,10 income and social security taxes, and child support payment. Our treatment of the 

latter two elements differs between the two surveys. In the CPS, taxes paid are modeled in every 

year, including the value of the Earned Income Credit (EIC) received. The SIPP collects 

information on taxes paid in an annual tax module; we are currently evaluating these data to 

develop a tax estimation procedure for the SIPP. For the purpose of this paper, we do not 

subtract taxes from income for the SIPP analysis.11 Further, information on child support 

payments are not available in the CPS and, therefore, are not included in the CPS estimates 

reported here, but are subtracted from the SIPP resource measure.12 

Descriptive Statistics: CPS 1996 

Table 2 shows some basic descriptive statistics for the official money income measure 

and the alternative National Academy of Sciences (NAS) resource measure in 1996 using the 

CPS. These results are based on the incomes and resources of families and unrelated 

individuals;13 data are weighted by persons. Looking at average income and resources, it is 

clear that the NAS measure results in a net subtraction (costs outweigh benefits) in the aggregate. 

The result of our implementation of the NAS measure for 1996 is that average family income is 

$37,573, down from $50,569 under the official definition. Median income is also much lower, 

$27,868 under the NAS definition, $37,992 under the official measure. The ranges of the two 

measures are also quite different, the NAS distribution being narrower, due again to the net 

deductions in that measure.

Inequality and Poverty 

Unit of Analysis 

For our inequality analysis, we show results for persons (these include persons living in 

families and those considered to be unrelated individuals), families and unrelated individuals, 

and families alone. Our poverty analysis includes results for these same groups, but not for all 

poverty measures. As noted earlier, the thresholds are based on consumer units; then the 

thresholds for the different families and unrelated individuals are produced using the equivalence 

 

9

 A fixed amount per week per working adult, not to exceed earnings, was subtracted. The panel estimate of $14.42 

for 1992 was price-adjusted for other years. 

10 These expenditures are imputed (see Betson 1997a, 1997b). 

11 Our previous calculations have shown that accounting for taxes in our standardized experimental CPS measure 

increased the poverty rate by about 1.0 percentage point in 1991 (Short et al., 1998a). 

12 Calculations have shown that accounting for child support paid in the SIPP experimental measure increased the 

poverty rate by less than 0.1 percentage points in 1991 (Short et al., 1998a). 

13 The Census Bureau definition of a ‘family’ refers to a group of two or more persons related by birth, marriage, or 

adoption who reside together; all such persons are considered as members of one family. For example, if the son of 

the person who maintains the household and the son’s wife are members of the household, they are treated as 

members of the parent’s family. Every family must include a reference person. Two or more people living in the 

same household who are related to one another, but are not related to the householder, form an ‘unrelated 

subfamily.’ These unrelated subfamilies are excluded from the count of families and unrelated subfamily members 

are excluded from the count of family members. Beginning with the 1980 CPS, this procedure has been followed. 

The term ‘unrelated individuals’ refers to persons 15 years of age and over (other than inmates of institutions) who 

are not living with any relatives. An unrelated individual may (a) constitute a one-person household, (b) be part of 

a household including one or more other families or unrelated individuals, or (c) reside in group quarters such as a 

rooming house. Thus, a widow living by herself, or with one or more other persons not related to her, a lodger not 

related to the householder or to anyone else in the household, and a maid living as a member of his or her 

employer’s household with no relatives in the household, are all examples of unrelated individuals (Census 1995 and 

Census Web page). 
 9

scale adjustment. Here we assume that a consumer unit composed of two adults and two children 

is like a family with the same composition. This assumption is supported by research conducted 

by Johnson et al. (1997) who found there to be no statistically significant difference in the 

median expenditures of two adult-two children consumer units and those of families composed 

of a married couple with two children. 

For the person-level inequality analysis, we produce results with and without adjustments 

for differences in family composition. Specifically, first we conduct our inequality analysis of 

the income and resources of families and unrelated individuals (hereafter we refer to this group 

as ‘families’ unless otherwise noted); then we produce inequality indices using scale 

adjustments. To account for differences in family composition, we first simply use a per capita 

adjustment that accounts for differences in family size only. Then we use an adjustment that 

explicitly accounts for differences in needs between adults and children and for differences in 

economies of scale within the family. For this adjustment we use scale adjustment factors 

proposed by the NAS Panel (1995) and presented earlier in this paper. Implicit in these 

thresholds is an adjustment for differences in prices across geographic areas. To capture the 

implicit scales in the official and experimental thresholds, the value that we actually use for the 

inequality analysis of income or resources per equivalent adult is the income to needs ratio. This 

ratio is defined as the family income divided by the family threshold. 

 When the focus of one’s research is the economic well-being of individuals, it is most 

appropriate to allocate the income values to each person in the family for the person-level 

analysis. This weighting results in the individual distribution, rather than the family distribution 

of incomes and resources. We follow this procedure in this study. Then we apply person weights 

from the income and resource data file to produce population estimates. For the family-based 

analysis, we use family population weights. 

For the poverty analysis, thresholds, income and resources for different family types are 

used to determine poverty status. To obtain the person level results, the data are personpopulation weighted. For the families and unrelated individuals analysis and the families alone 

analysis, population weights for these groups are applied. 

Inequality Analysis 

Given that the operations we perform to compute the NAS measure are essentially 

redistributional in nature, we expect that the distribution of the experimental income measure 

will differ from the distribution of the current income measure. The subtraction of taxes and the 

addition of in-kind benefits are expected to be equalizing. However, the subtraction of medical 

expenditures and work-related expenditures could be more or less equalizing. Therefore we have 

no hypothesis concerning what the net effect of the changes taken together will be on the 

distribution of the NAS income measure. In order to examine the aggregate impact of these 

changes, we use the CPS data for 1996 to produce inequality indices using both the NAS 

measure and the official money poverty measure. 

Inequality Indices 

The inequality indices that we use to examine the distribution of income and resources 

across the population include the standard Gini coefficient (G), and three generalized entropy 

(GE) measures with the index designation α I . When α =0 the GE index corresponds to the 

mean logarithmic deviation (D), when α =1 to the Theil coefficient (T), and when α =2 to half of 
 10

the coefficient of variation squared ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

2

2 C

; α is called the ‘income share-distance’ parameter. 

The formulae for the different indices are given in equations (3a) through (4d) below. 

Here we refer to persons; however, the analyses could be done for families or other groups of 

persons as well. In each of the equations, yi is the income of the i-th person, y is the mean 

income, and n = the number of observations or population size. The Gini index can be defined as 

in (3a) or (3b) below. For equation (3b) an ordered vector is needed: 

| | 2

1

1 1

2 i j

n

j

n

i

y y

n y

G ⎥ −

⎦

⎤ ⎢

⎣

⎡ = ∑ ∑ = =

 (3a) 

∑( ) =

⎥ − +

⎦

⎤ ⎢

⎣

⎡ ⎟ −

⎠

⎞ ⎜

⎝

⎛ = +

n

i

i n i y

n n y 1

2 1 1 2 1 . (3b) 

The GE inequality measures are given by the following formulas: 

Iα ( y, n) = ∑= ⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

−

n

i

i

y

y

n 1

1

( 1)

1 α

α α α ≠ 0,1 (4a) 

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛ = =

n

i i y

y

n

I D

1

0 log 1 (4b) 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛ = = ∑= y

y

y

y

n

I T i

n

i

i log 1

1

1 . (4c) 

Thus ∑= ⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = =

n

i

i

y

y

n

C I

1

2 2

2 1

2

1

2 . (4d) 

The aversion parameter reflects different ‘perceptions of inequality,’ with lower values 

indicating a higher degree of inequality aversion. Changing this parameter alters the way income 

differences in different parts of the income distribution are aggregated. The more negative 

(positive) is the parameter, the more sensitive the index is to income differences at the bottom 

(top) of the income distribution rather than at the top (bottom). 

Standard errors are produced for each measure using the methods specified by Kakwani 

(1990).14 For all measures, we use population weights and account for the complex sample 

designs in the CPS and SIPP by using a sample design factor to adjust the standard errors.15 

We produce this set of indices in order to examine how inequality is affected when the 

same information is being weighted differently in the aggregation process. The Gini coefficient 

is responsive to transfers based on the ranking of persons by their incomes, and for equi-distant 

transfers, is most sensitive to transfers at the mode of the distribution. The GE measures are 

developed by considering the relationships using an axiomatic approach in that a set of desirable 

 

14 The Kakwani approach assumes that the inequality indices are computed on the basis of independently drawn 

random samples of households. Since we compare inequality indices for the same families, for example, and 

examine whether the indices are statistically significantly different, we need to modify the t-test to account for this 

fact. However, using the formula for uncorrelated data for the correlated data that we have here results in our 

applying a more stringent test to the data than is actually necessary. Thus our statistical conclusions hold even more 

strongly. 

15 To estimate each standard error, we multiplied the simple random sampling standard error by 1.4. 
 11

properties for the measure itself is specified at the outset. These properties are then used to 

characterize the index. The GE measures can be interpreted as making assumptions about how 

distances between individuals’ income shares are measured. The α parameter summarizes the 

sensitivity of α I to income differences in different parts of the distribution. As the parameter α

increases, the index becomes more sensitive to transfers at the top of the distribution. The Theil 

coefficient and one-half of the coefficient of variation squared are more sensitive to transfers at 

the top of the distribution, while the mean logarithmic deviation is relatively more responsive to 

transfers at the lower end (see Cowell and Kuga 1981, Jenkins 1991, and Coulter et al. 1992 for 

discussions of these measures and the principles which they satisfy). This is relative to other 

measures of the GT class with lower parameters. 

Treatment of Zeroes and Negative Values

In conducting our analysis we found that, for 1996, about 0.9 percent of all persons were 

in families with zero or negative incomes. In contrast, 1.7 percent of all persons had zero or 

negative family resources. Negative values in the CPS data file can result for business, farm, and 

rental income. For the SIPP, negatives are possible for rental income. For resources, the cause 

of the negatives is most likely due to the imputations, particularly those for medical out-ofpocket expenditures, that we use to produce the measure. Because of the imputations, we wanted 

to give particular attention to the impact of the imputations for analyses that would involve the 

entire income distribution. In our study, the lowest value for income is -$2,513 while the lowest 

value for resources is -$25,860. Given that for part of our inequality analysis we use indices 

based on log incomes and resources, we had to make a decision concerning how to treat 

observations with zero and negative values. We produce results based on two different 

treatments of these values. First we set all negative and zero values to 0.01 and produce the 

inequality indices; then we conduct our analysis using only those observations with incomes or 

resources greater than 0. The first treatment is similar to that followed by the US Census Bureau 

when producing Gini indices for income based on detailed shares.16 Jenkins (1995) and Cowell 

and Victoria-Feser (1996) have used the second method.17 Truncating the zero and negative 

values to 0.01 is expected to increase the mean log deviation estimates because this measure is 

bottom-sensitive. To examine how the indices would be affected when the zeroes and negatives 

are included, we also produce Gini coefficients and estimates of half the coefficient of variation 

squared. Both of these allow for the inclusion of such values. This analysis is only conducted 

for income and resources per equivalent adult; equivalency is based on the scales implicit in the 

experimental thresholds. 

Results of Inequality Analysis 

Table 3 shows the results of the inequality analysis for income and NAS-defined 

resources using CPS data for calendar year 1996 and both assumptions concerning the treatment 

of zero and negative income and resource values. Indices are presented for persons, families and 

 

16 See Census (1995) for the Gini index calculation description. In creating a data set on transitional economies for 

use at the World Bank, researchers (Ackland et al. 1996) took steps to ensure that total disposable income was 

always positive, stating that, ‘...a practical reason for ensuring this is the fact that the existence of zero or negative 

TOTHHY [income] would complicate data manipulation and analysis (e.g., log transformations and the calculation 

of Gini coefficients).’ In the World Bank data file, zero and negative values were made to be greater than 0 but less 

than the smallest positive amount in the full income distribution in order to preserve rankings. 

17 Both Jenkins (1995) and Cowell and Victoria-Feser (1996) have suggested that one may also want to drop 

unreasonably high values as well. 
 12

unrelated individuals, and families. Statistical significance18 is noted in the table by an asterisk 

when the indices based on the official and experimental measures are compared. When zero and 

negative income and resources are set equal to 0.01, we find that the NAS measure is only 

slightly more equally distributed than the official income measure when the Gini coefficient, 

Theil coefficient, and half the coefficient of variation squared are used. This is true for persons, 

for families, and for families including unrelated individuals living alone. The mean log 

deviation, when produced for persons and the family groups, results in higher inequality for the 

NAS income measures. While the differences are statistically significant, they are very small. 

In contrast, when we trim the data to include only observations with positive income or 

resource values, the NAS measure always results in greater equality in the distributions. For the 

person level analysis, the official based indices are statistically significantly greater for each of 

the incomes and resources upon which the Gini coefficient and mean log deviation are based, 

and for the family size and composition adjusted incomes and resources using the Theil 

coefficient. The family analyses reveal the same pattern with statistical significance between the 

official and NAS measures for the Gini and mean log deviation. 

 Using the full distributions (negatives, zeroes, and positive values) for income and 

resources per equivalent adult, we find that the Gini based on the official measure is statistically 

significantly greater than the NAS measure (results not presented in table).19 This is consistent 

with our findings based on the truncated (when income or resources ≤ 0, the value is set = 

$0.01) and trimmed (data are restricted to values > 0) distributions. In addition, we find that the 

Gini indices based on the full sample are statistically greater than the indices when trimmed data 

are used. This result holds for both income and NAS resources. Truncating the data has no 

affect. However we conclude from this analysis that the treatment of the zeroes and negative 

values matters in analyses of inequality when a measure such as the Gini coefficient is used. 

In contrast, there is no statistical difference between the indices based on official income 

and NAS resources for the coefficient of variation index for either the truncated or trimmed 

distributions. When we compare the inequality indices for the full distributions to those based on 

the truncated distributions, we also find no statistically significant differences in the indices 

within income and resource measures and across measures.20 Thus we conclude that using the 

entire distributions, truncating, or trimming the data makes little difference in inequality analyses 

based on the coefficient of variation index. 

As might be expected, geographic price variations are likely to matter in assessing 

equality in distributions of economic well-being. Our Gini index analysis of NAS resources per 

equivalent adult, using the trimmed distributions, reveals that geographic adjustment in the 

thresholds result in statistically greater equality than when non-geographically adjusted 

thresholds are used.21 There are no statistically significant differences between the GE indices 

when geographic adjustment is used and when it is not. 

Income per equivalent adult results in the lowest index values, followed by those using 

unadjusted family income or resources and then the per capita adjusted income and resources. 

This result holds over both the official measure and the experimental measure for both treatments 

 

18 One-way t-tests are conducted. The 95 percent confidence interval is used; the critical value is 1.65. 

19 The Gini for the full distribution using official income is 0.441 and for NAS resources the Gini is 0.424. Standard 

errors are available from the authors upon request. 

20 Half the coefficient of variation squared is 0.686 for the official measure and 0.658 for the experimental measure 

when the full distribution is used. 

21 The Gini based on income per equivalent adult, using the trimmed NAS resource distribution, is 0.416 when no 

geographic adjustment is used as compared to 0.411 when geographic adjustment is used. The first is statistically 

significantly greater than the second using a one-tail t-test when α = 0.05 and the critical value = 1.65. 
 13

of zeroes and negative values. 

Poverty Analysis 

 In this section we use the above described NAS resource measure and the experimental 

threshold to examine resulting poverty statistics using CPS data from 1991 to 1996 and SIPP 

data from 1991. We expect there to be differences in poverty rates and in the composition of the 

poor when the two different measures are compared, given the additions and subtractions from 

income, as well as when the two different surveys are used. Throughout this analysis, we also 

produce results using standardized experimental poverty rates. For this, experimental rates are 

produced using thresholds adjusted to produce an overall poverty rate roughly equal to the 

official rate. The poverty rate of persons is used as our standard. For the 1991 analysis, the 

standardization was achieved by applying a factor of 0.845 to the experimental thresholds when 

using the CPS based measures and 1.025 when using the SIPP measure (Short et al. 1998b). For 

the 1996 analysis, the standardization was achieved by applying a factor of 0.856 to the 

experimental thresholds when using the CPS based measure. The standardized rates are an 

informative way to examine which groups have a differential probability to be classified as poor 

under the experimental measures. Before presenting our results, we describe the poverty 

statistics employed. 

Poverty Statistics 

Several poverty statistics are used to conduct our analysis. Statistics other than those 

used in this study could have been used (see Hagenaars 1986 and Ruggles 1990). Each measure 

involves comparing an income or resource measure to a poverty threshold. The simplest statistic 

is the head count ratio, which is simply the proportion of the population with incomes below the 

poverty threshold (in other words, 

n

q where q = the number of poor families or persons and n =

the total number of families or persons). However, this measure does not account for the depth 

of poverty in the population. Producing the average poverty gap (computed as ∑( ) =

−

q

i

i i z y

q 1

1 , 

where i y is family income and i z is the appropriate poverty threshold) can solve this problem. 

This measures the average shortfall of income (resources) below the poverty threshold. Here, 

deprivation depends on the distance between a poor family’s income and the poverty threshold. 

A difficulty with this measure is that it is invariant to the distribution of incomes within the poor 

population. Foster et al. (1984) proposed a class of poverty measures, the Foster-GreerThorbecke (FGT) measures, which do not suffer from this problem. These measures take the 

form of: α

α α ∑

= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − =

q

i i

i i

z

z y

n

P y z

1

1 ( (5) , , )

where 

P is the poverty measure, 

α is a measure of poverty aversion (a larger α gives greater emphasis to the poorest poor) 

and α≥ 0 , 

( ) i n y y , y ..., y = 1 2, is a vector of incomes or resources in increasing order, 

i z = is the poverty threshold for family i and i z > 0, 
 14

, z y i − is the income or resource shortfall of the i-th family, 

q = q( ) y;z is the number of poor families or persons (having incomes or resources no 

greater than z), and 

n = n( ) y is the total number of families or persons. 

This class of measures has several attractive features. First it collapses to the head count 

ratio if α = 0 and to the normalized poverty gap if α =1; when α = 2 , the index is sensitive to 

the distribution of incomes among the poor. As α increases, more weight is placed on those 

families or persons with the lowest incomes, until in the limit it measures only the condition of 

the family or person with the lowest income in the economy. Thus, the weights are based on a 

notion of relative deprivation experienced by the poor families or persons. In addition, the 

properties of the FGT family of measures satisfy three basic axioms. First, when α > 0 , the 

measures satisfy Sen’s monotonicity axiom (see Sen 1976). This axiom can be stated as, ‘given 

other things, a reduction in the income of a poor family must increase the poverty measure.’ 

Second, when α >1 the measures satisfy Sen’s transfer axiom (Sen 1979). The transfer axiom 

can be stated as, given other things, a pure transfer of income from a poor household to any other 

household that is richer must increase the poverty measure. Third, Kakwani’s (1980) transfer 

sensitivity axiom is only met with the FGT class of measures when α > 2 (Foster et al. 1984). 

For this axiom, if a transfer t > 0 of income takes place from a poor household with income i y to 

a poor household with income ) yi + d(d > 0 , then the magnitude of the increase in poverty must 

be smaller for larger i y . 

Standard errors are produced for the FGT measures using the specifications presented by 

Kakwani (1993).22 Again, a sample design factor is used to adjust the sample standard errors to 

account for the complex sample design of the surveys. 

Results of Poverty Analysis 

 In this section we present our poverty results. Included are head count ratios for selected 

demographic groups (in Tables 4 and 6), a comparison of the total population and the poor 

population according to demographic groups based on both the official and experimental 

measures (Table 5), average poverty gaps of families (Table 7), overall poverty rates and gaps 

for families and unrelated individuals and for families alone (Table 8), and overall poverty 

statistics for persons using the income to needs ratio and the FGT measures (Tables 9). 

First, we begin by analyzing the poverty rates of persons to examine trends and the 

composition of the poverty population. In Table 4, we present the results for 1991 based on both 

the CPS and SIPP. Poverty rates using the official thresholds and income measure for different 

demographic groups are compared to the poverty rates based on our implementation of the 

panel’s proposed method (labeled as NAS experimental and NAS standardized). As shown, 

poverty rates using the official definition with SIPP data are smaller than official CPS-based 

poverty rates. As noted earlier, in order to examine the effects on the composition of the 

poverty population, we adjust the experimental thresholds by a factor in order to obtain an 

 

22 The Kakwani approach assumes that the poverty indices are computed on the basis of two independently drawn 

random samples of households. However, as for the inequality indices, we compare indices for the same families, 

for example, and examine whether they are statistically significantly different. Thus, we need to modify the t-test to 

account for this fact. Again, use of the formula for uncorrelated data for the correlated data that we have here results 

in our applying a more stringent test to the data than is actually necessary; given this, our statistical conclusions hold 

even more strongly. 
 15

overall poverty rate equal to the official rate. The standardized rates in Table 4 show that 

children, Blacks, and people in female householder families are less likely to be classified as 

poor under the new measure while all other groups shown are more likely to be classified as 

poor. 

Since the experimental standardized poverty rate is lower than the official rate for 

children, Blacks, and persons in female householder families, we would expect that their 

representation in the poverty population would be lower, and vice versa for those with higher 

rates. As seen in previous research (see Citro and Michael 1995; Garner et al. 1998; Short et al. 

1998a, 1998b), using the new measure results in a poverty population that more closely 

resembles the total population. This is illustrated in Table 5, which shows the composition of the 

total population versus that of the poverty population under the different measures for 1991. 

 Table 6 shows that over the 1991-6 period, rates under the official and experimental 

methodologies behave similarly, increasing over the 1991-3 period and decreasing over the 

1993-6 period. The table shows standardized experimental poverty rates controlled to the 1996 

official rate. The official rate rises from 14.2 to 15.1 percent from 1991 to 1993 and falls to 13.7 

percent by 1996. The standardized experimental rate rises from 14.5 to 15.7 percent from 1991 

to 1993 and falls to 13.7 percent by 1996. However, over the 1993-96 period, poverty rates drop 

more under the experimental measure for some groups, such as children and Blacks. This drop 

appears to be due to the addition of the Earned Income Tax Credit23 in the resource measure. 

This result highlights the ability of the new measure to capture the effects of many tax and 

transfer policies.

Poverty gaps give us some additional information about the difference between the 

official and the NAS measures. Average poverty gaps, computed on a family basis, are 

presented in Table 7. In this and the following tables, the NAS standardized measure is based on 

the 1996 official poverty rate for persons and is referred to in the tables as ‘Std96.’ For the 

poverty gap analysis, the data are restricted such that the gap cannot be greater than the 

threshold. This means that the gap for families with negative incomes or resources is set equal to 

the threshold. The same approach is used by the Census Bureau to produce poverty gaps for 

official publication. Based on this analysis, we find that the NAS measure (both standardized and 

non-standardized) results in lower poverty gaps on average than the official measure. The 

standardized NAS measure results in the lower of the two NAS gaps. This is not surprising since 

the thresholds are lower by definition. Overall, the results presented in Table 7 suggest that the 

intensity of poverty is softened considerably by the addition of in-kind transfers to the needy and 

the subtractions from current gross money income; however, it is important to remember that all 

negative values have been truncated for producing the gaps. The conclusion does not hold for all 

the demographic groups considered. The elderly experience greater average poverty gaps when 

both of the NAS experimental measures are used versus the official measure. This may not be 

surprising since the elderly are most likely to have larger expenses deducted from resources for 

medical care than are the non-elderly. The non-standardized NAS measure also results in higher 

poverty gaps for male headed households. 

Table 8 includes poverty rates and gaps for families and unrelated individuals, and for 

families alone. As for Table 7, gaps are restricted such that they cannot be greater than the 

threshold. The rates are based on incomes and resources from the entire distribution. These 

results show that the rates based on the NAS experimental measure (not standardized) are 

statistically significantly greater than the official rates. The poverty gaps are statistically 

significantly smaller for the experimental measure (for both NAS96 and Std96) than for the 

 

23 This is a refundable tax credit for low-income working families. 
 16

official measure.

Table 9 lists additional poverty statistics for persons, including those based on the FGT 

class of measures; income and resources are based on CPS data for 1996. Here we see a similar 

pattern for persons emerge as for families and for families and unrelated individuals. First, the 

table shows the number of poor people under the three poverty measures, with the largest 

number of poor being counted under the NAS experimental measure. In contrast, the income to 

poverty ratio is lowest for the non-standardized experimental measure (2.71), and is highest for 

the official measure (3.78). The NAS standardized and non-standardized ratios are statistically 

significantly less than the ratio based on the official measure. 

The FGT poverty measures that we show, computed only for persons, provide us with 

additional information about poverty. For this examination, we present results for (a) the entire 

distribution, including negative values, (b) distributions when negative values are truncated at 

greater than or equal to zero income or resources, and (c) distributions when the data are 

trimmed to be greater than zero. As noted previously, when α = 0 the FGT equals the poverty 

rate. Before standardizing the experimental rate, we see that the overall poverty rate increases 

from 13.7 to 18.0 percent. The normalized poverty gap, FGT1, shows a different pattern than the 

non-normalized gap results. Based on the FGT1 and before standardization (and after 

standardization too when results are based on the entire distribution), the normalized gap 

increases with the new measure as compared to the official measure of poverty. The measure of 

intensity, FGT2, suggests a higher concentration of poor at the very bottom of the distribution 

when the experimental measure is used. However, when we standardize the experimental 

measure, the FGT statistics are more similar to those of the official poverty measure, yet they are 

statistically significantly different. 

When data including negative values (all income or resource values) are used, the FGT1 

and FGT2 measures reveal that poverty based on the experimental measure is more severe than 

when the official measure is used. This result holds for the standardized and non-standardized 

measures; here the differences are statistically significant. 

As seen in Table 9, the negative values in the data affect the FGT results. When income 

and resources are restricted to values greater than or equal to zero (the truncation case), we find 

that the FGT1 and FGT2 indices are statistically significantly greater for the NAS96 measure but 

less for the Std96 measure when compared to the indices based on the official measure. When 

the data are restricted to income and resource values greater than zero and the non-standardized 

measure is used, only the poverty rates and normalized poverty gaps are greater with the NAS 

measure.24 The FGT indices based on this trimming of the distributions and the standardization 

are all statistically significantly less than the indices based on the official measure. 

Within the measures, the FGT results are compared across the treatments of the distributions 

(significance not shown in table). For this we find there to be no difference between the official 

based indices for the entire distribution and when the data are truncated to be greater than or 

equal to zero. However, when the data are restricted to values greater than zero, there are 

statistically significant differences for each of the three FGT indices. For the NAS measure (for 

both the NAS96 and Std96), the FGT1 and FGT2 indices are statistically significantly different 

when the indices with the negative values are compared to the indices based on distributions 

without negative values. In addition, the FGT0 indices are also statistically significantly 

different when the data are trimmed such that income or resources are greater than zero (using 

 

24 When the data are restricted to positive income or resource values only, the same thresholds as are used for the 

total distribution are applied. This is the reason why the standardized rate is not equal to the official rate for the 

analysis based on the trimmed data. 
 17

both the NAS96 and STD96 measures) as compared to the indices when all values are used for 

the analysis. 

The effect of geographically adjusting the thresholds for price differences across areas is 

examined when incomes were trimmed to be greater than zero (results not shown). Based on this 

analysis we find that geographic adjustment results in statistically lower person poverty rates 

than when no geographic adjustments are applied.25 There are no statistical differences between 

the FGT1 and FGT2 measures with and without price adjustments in the thresholds for either the 

standardized or non-standardized measures. 

Concluding Remarks 

 

The results presented here have shown, generally, that the alternative experimental poverty 

measures are not much different from the official measure in terms of general distributional 

properties, that is, inequality. By examining several inequality statistics, we have seen that the 

distribution of experimental resources is only slightly more equal than that of official money 

income, although some of the differences are statistically significant. Poverty rates are higher 

with the NAS measure than when the official measure is used. Our examination of family 

poverty gaps suggests that the experimental measure implies less severe poverty than the current 

measure. In contrast, when normalized poverty gaps are produced using the entire or the trimmed 

distributions for persons, and when more weight is attached to the lower end of the distribution 

(as for the severity index represented by the FGT2 index), poverty is a greater problem with the 

NAS experimental measure. Official poverty is significantly more of a problem when examined 

in terms of the FGT class of poverty measures and standardization is used. Whether the official 

or NAS experimental measure results in more severe poverty is likely to be related to the 

treatment of negative values and to whether one is talking about persons or families. 

Based on our inequality analyses we conclude that scale adjustment matters, the 

treatment of zero and negative income and resource values matters, and the choice of inequality 

index matters in analyses of official income and the NAS experimental resource measure. The 

treatment of zeroes and negatives also matters for poverty measurement as does the use of 

standardization. Geographic adjustment may or may not matter, depending upon the inequality 

and poverty measures considered. 

If the NAS resource measure were used as another measure of economic well-being, in 

addition to its use for poverty measurement, one might want to examine whether this measure 

produces higher economic well-being than does the official measure. For this, further analyses, 

including tests of stochastic dominance, are required. Preliminary analysis suggests that the 

official income distribution dominates that of the NAS measure within an interior range of 

incomes and resources based on a test of first degree stochastic dominance.26 However, neither 

dominates the other throughout the distributions. Through analyses of higher order stochastic 

dominance, we would be able to make a statement about whether overall economic well-being is 

higher using the official measure or when the NAS measure is used. 

Regardless of how the NAS measure is used, for poverty, inequality, or other analyses of 

economic well-being, the researcher and policy analyst need to understand how the distributions 

 

25 The poverty rate for persons is 0.169 when non-geographically adjusted and non-standardized thresholds are used, 

as compared to 0.165 with geographically adjusted, non-standardized thresholds. These rates refer to the trimmed 

data. 

26 We thank Stephen Howes of the World Bank, for sharing his ‘SAS Dominance Module’ (Howes 1995) for this 

part of our analysis. 
 18

are defined and what the indices of well-being are suggesting. Based on this study, we caution 

that if the NAS resource measure is used for such analyses, greater attention needs to be given to 

how zero and negative values are treated and to understanding what these values represent. 

Again, as noted before, the treatment of these values for both inequality and FGT poverty 

measure analyses matters. If we assume that current gross money incomes are correctly reported 

(with zeroes and negative values representing income losses for example), then are we adding 

appropriate amounts for in-kind transfers and are we subtracting appropriate amounts for the 

families? Is it reasonable to assume that the expenditures for child care expenses, work-related 

expenses, out-of-pocket medical expenses, and taxes and social security payments are being paid 

for out of current gross money income? Is it perhaps more reasonable to assume that some of 

these costs, particularly medical out-of-pocket costs, are being financed out of savings, 

borrowing, or a reduction in assets? As we have shown, the resource measure, as calculated 

here, results in a large number of persons in families with non-positive values. The presence of 

these values has consequences for our description and understanding of economic well-being in 

the United States. As our results of inequality and poverty here display, this does affect our 

ability to provide a complete picture of the relative conditions of persons below the poverty line. 

It is clear from this study that continued research is needed and public discussion desirable. 

LESSONS FOR INDIA 

In this paper we have described a systematic approach that we are using in the US to 

evaluate recommendations that have been made for revising poverty measurement. We are 

considering the impact on poverty measurement of using different equivalence scales, the 

treatment of owner-occupied housing in our measure of the basic bundle of threshold needs, 

geographic and updating adjustments for the thresholds, and different imputations for resource 

components. As we engage in this process, we regularly meet with other analysts and 

administrative persons within the federal government. For example, about once every six weeks 

we meet with the Office of Management and Budget Interagency Technical Group on Poverty 

and Income Measurement. This group has provided us with additional guidelines and 

suggestions for evaluation. In June 1999, we published a report of our latest findings under the 

auspices of the Bureau of the Census. We expected there to be substantial public discussion and 

further analyses. In preparation for these discussions, we made available to the public the data 

files and programs that we used to test our assumptions and produce the Census report. Forums 

for discussions were organized in which our work was presented and scrutinized. For example, 

in April 1999, the University of Wisconsin-Madison, Institute for Research on Poverty convened 

such a forum inviting academics, policy analysts, and other researchers to evaluate and comment 

on what we had done. 

Whether an approach such as the one we are following in the US is applicable to India is 

a question only those in India can answer. However, as poverty measurement is most important 

to both our societies, we strongly believe that such systematic analyses are desirable and 

necessary. This belief is also reflected in the reports published by the Planning Commission and 

the Department of Statistics. Not only should the overall measure be considered in the analyses, 

but also assumptions concerning equivalence scale adjustment (as noted also by Lancaster, Ray, 
 1

and Valenzuela 1998a, b), inter-area price and updating adjustments, the treatment of housing 

needs, and other issues. 

Should India use an income based measure of poverty? The answer to this question is 

‘probably not.’ Other alternative measures are likely to provide more appropriate information 

concerning poverty in India. The food energy requirements approach may not be the only 

measure one would want to use, as was recognized by researchers in India. This one has 

problems in that requirements vary across individuals and over time for a given individual. An 

assumption must also be made about activity levels which determine energy requirements 

beyond those needed to maintain the human body’s metabolic rate at rest (see Datt and Ravallion 

1998). As alternatives, one might want to consider other aspects of food adequacy and hunger. 

Other measures might also include those reflecting water quality, housing and energy adequacy, 

medical care access, health status, and resources available from others. Subjective assessments 

of economic well-being, based on the responses of the people in the society, may be a desirable 

complement to an absolute measure. By using a multi-dimensional approach to measure 

poverty, a clearer picture of who is poor in India should result. This can be then used to provide 

guidance in developing programs for poverty alleviation.
 2

Table 1. Poverty Thresholds for Two Adults and Two Children: 1991 to 1996 

 Experimental Experimental

Year Official CPI-U updated CEX updated

1991 $13,812 $13,891 $13,891

1992 14,228 14,309 14,349

1993 14,654 14,738 14,936

1994 15,029 15,115 15,211

1995 15,455 15,543 15,561

1996 15,911 16,002 15,743

Table 2. Population Size and Summary Statistics of CPS-Based Family 

Income and Resource Measures: 1996 

 Official NAS96

Number of 

Families (000) 70,241 70,241

Families and unrelated individuals (000) 111,582 111,582

Persons (000) 266,218 266,218

Income and Resource Statistics 

 For families and unrelated individuals 

 Averaged across persons 

Mean $50,569 $37,573

Median 37,992 27,868

Minimum -2,513 -25,860

Maximum 1,404,998 1,008,454
 3

Table 3. Inequality Statistics of CPS-Based Family Income and Resource Measures: 1996 

If income or resource Restrict to 

≤ 0, set value = 0.01 income or resource > 0

 Official NAS96 Official NAS96

Persons 

Number of persons (000) 266,218 266,218 263,822 261,692

Gini coefficient 

Income 0.448 0.440 * 0.442 0.430 * 

Income per equivalent adult 0.441 0.421 * 0.435 0.411 * 

Per capita 0.461 0.458 0.456 0.448

Mean log deviation ( α =0 I ) 

Income 0.524 0.597 * 0.393 0.356 * 

Income per equivalent adult 0.414 0.552 * 0.374 0.316 * 

Per capita 0.534 0.607 * 0.407 0.374 * 

Theil coefficient ( α =1 I ) 

Income 0.379 0.371 0.370 0.353

Income per equivalent adult 0.370 0.347 * 0.361 0.329 * 

Per capita 0.410 0.408 0.400 0.391 * 

Half coefficient of variation2 ( α =2 I )

Income 0.680 0.650 0.669 0.630

Income per equivalent adult 0.685 0.653 0.674 0.633

Per capita 0.837 0.834 0.825 0.811

Families and Unrelated Individuals (no scale adjustment)

Number of families and 

 unrelated individuals (000) 111,582 111,582 109,865 108,602

Gini coefficient 0.477 0.472 0.469 0.457 * 

Theil coefficient 0.429 0.425 0.413 0.398

Mean log deviation 0.652 0.768 * 0.439 0.405 * 

Half coefficient of variation2 0.794 0.774 0.775 0.740

Families (no scale adjustment)

Number of families (000) 70,241 70,241 68,840 69,278

Gini coefficient 0.425 0.420 0.422 0.412 * 

Theil coefficient 0.345 0.340 0.339 0.327

Mean log deviation 0.432 0.510 * 0.351 0.320

Half coefficient of variation2 0.620 0.600 0.613 0.585

*statistically significant difference between official and experimental value based on one-way t-test, α =0.05 level, 

critical value=1.65 
 4

Table 4. Poverty Rates of Persons (percentages): 1991 

 Official Definition NAS Experimental Measure 

 Standardized 

CPS SIPP CPS SIPP CPS SIPP

All Persons 14.2 12.1 18.9 13.6 14.2 14.2

Children 21.8 19.6 26.4 18.9 19.9 20.0

Elderly 12.4 9.0 20.3 14.5 14.9 15.3

White

11.3 9.3 16.1 11.5 12.1 12.0

Black 32.7 29.0 36.7 26.8 27.4 28.4

Hispanic 28.7 27.6 40.0 29.5 30.6 30.8

One or more workers 9.3 6.6 14.3 9.0 10.4 9.6

Persons in family of type 

Married couple 7.2 6.3 11.9 8.8 8.3 9.3

Female householder 39.7 35.5 45.0 33.6 35.7 35.2

 

Table 5. Distribution of the Total Population and Poor Population (percentages): 1991 

 Total Population Poverty Population 

 NAS Experimental 

Official Measure Standardized 

CPS SIPP CPS SIPP CPS SIPP

All persons 100.0 100.0 100.0 100.0 100.0 100.0

Children 26.0 27.0 40.0 44.0 37.0 38.0

Elderly 12.0 12.0 11.0 9.0 13.0 12.0

White 84.0 83.0 67.0 64.0 71.0 70.0

Black 13.0 13.0 29.0 30.0 24.0 25.0

Hispanic origin 9.0 9.0 18.0 21.0 19.0 20.0

One or more workers 85.0 82.0 55.0 45.0 62.0 55.0

Persons in family of type

 Married couple 80.0 80.0 45.0 45.0 51.0 54.0

 Female householder 16.0 17.0 51.0 52.0 44.0 42.0
 5

Table 6. Poverty Rates (percentages) of Persons Based on CPS Family Income and Resource 

Measures: 1991 to 1996 

Year 

1991 1992 1993 1994 1995 1996

Official Measure 

All persons 14.2 14.8 15.1 14.6 13.8 13.7

Children 21.8 22.4 22.7 21.8 20.8 20.5

Nonelderly adults 11.0 12.0 12.4 12.0 11.0 11.0

Elderly 12.4 12.9 12.2 11.7 10.5 10.8

White 11.3 11.9 12.2 11.7 11.2 11.2

Black 32.7 33.4 33.1 30.6 29.3 28.4

Hispanic origin 28.7 29.6 30.6 30.7 30.3 29.4

One or more workers 9.3 9.7 9.9 9.6 9.5 9.5

Persons in family of type 

Married couple 7.2 7.7 8.0 7.4 6.8 6.9

Female householder 39.7 39.0 38.7 38.6 36.5 35.8

NAS Experimental - controlled to 1996 rate 

All persons 14.5 15.3 15.7 14.7 13.8 13.7

Children 20.0 21.0 21.0 20.0 18.0 18.0

Nonelderly adults 12.0 13.0 13.0 12.0 12.0 12.0

Elderly 15.0 17.0 17.0 16.0 15.0 16.0

White 12.0 13.0 13.0 13.0 12.0 12.0

Blacks 28.0 30.0 31.0 26.0 25.0 25.0

Hispanic origin 31.0 32.0 32.0 31.0 29.0 29.0

One or more workers 11.0 11.0 11.0 11.0 10.0 10.0

Persons in family of type 

Married couple 9.0 9.0 9.0 9.0 8.0 8.0

Female householder 36.0 36.0 36.0 35.0 32.0 32.0

 
 6

Table 7. Average Poverty Gaps of Families, Based on CPS Family Income and Resource 

Measures, by Characteristics of Reference Person and Family: 1996 

(restriction: gap cannot be > threshold) 

Official NAS96 Std96

All Families $6,252 $5,447 $4,815

Reference Person Characteristic 

Age 

< 18 years 5,769 4,652 3,720

18-64 years 6,468 5,500 4,812

65 years and older 4,000 5,223 4,859

Race 

White 5,907 5,406 4,866

Black 6,967 5,420 4,513

Other 6,628 6,127 5,535

Hispanic origin 6,366 5,700 4,835

Family Characteristic 

Workers present 

None 7,334 6,032 5,376

One or more 5,566 5,132 4,483

Family type 

Married couple 5,850 5,432 4,991

Male householder 5,347 5,473 4,696

Female Householder 6,657 5,461 4,656

Geographic region 

Northeast 6,473 5,738 5,068

Midwest 5,931 5,056 4,554

South 6,383 5,193 4,560

West 6,098 5,886 5,207

Metropolitan area 

Central city 6,676 5,783 5,001

Not central city 5,993 5,472 4,938

Non-metropolitan area 5,865 4,741 4,216
 7

Table 8. Poverty Statistics for Families and Unrelated Individuals Based on CPS 

Family Income and Resource Measures: 1996 

(restriction: gap cannot be > threshold)

 Official NAS96 Std96

Families and Unrelated Individuals 

 Number of (000) 111,582 109,865 108,602

 Poverty rate 14.7 19.0 * 14.8

 Poverty gap $4,962 $4,668 * $4,190 * 

Families 

 Number of (000) 70,241 68,840 69,278

 Poverty rate 11.0 15.8 * 12.0 * 

 Poverty gap $6,252 $5,448 * $4,815 * 

*statistically significant difference between official and experimental value based on one-way t-test, 

α =0.05 level, critical value=1.65 

Table 9. Poverty Statistics for Persons Based on CPS Family Income and Resource 

Measures: 1996 

 Official NAS96 Std96

Persons (000) 266,218 266,218 266,218

Poverty rate 13.7 18.0 * 13.7

Number poor (000) 36,529 47,812 * 36,529

Income/poverty ratio 3.78 2.71 * 3.18 * 

FGT Poverty Measures 

Based on entire distribution 

Number of persons (000) 266,218 266,218 266,218

FGT0 0.137 0.180 * 0.137

FGT1 0.060 0.079 * 0.065 * 

FGT2 0.039 0.063 * 0.060 * 

Distribution with income or resources ≥ 0 

(with negative values set =0) 

Number of persons (000) 266,218 266,218 266,218

FGT0 0.137 0.180 0.137

FGT1 0.060 0.073 * 0.059 * 

FGT2 0.039 0.047 * 0.040 * 

Distribution with income or resources > 0 

Number of persons (000) 263,822 261,692 261,692

FGT0 0.129 0.165 * 0.122 * 

FGT1 0.052 0.057 * 0.042 * 

FGT2 0.030 0.030 0.023 * 

*statistically significant difference between official and experimental value based on one-way t-test, 

α =0.05 level, critical value=1.65 
 8

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http://www.census.gov/www/hhes/povmeas.htm 
 12

Appendix: Data Sources 

Consumer Expenditure Survey 

The Consumer Expenditure (CEX) Interview Survey is the source of data used to 

compute poverty thresholds in this paper. Also as part of the CEX is a Diary survey; data from 

this survey are not used for producing the poverty thresholds (see USDL 1995 for a description 

of the Diary survey). The CEX is a Bureau of Labor Statistics survey with data collected by the 

Census Bureau. The CEX Interview has been a continuing quarterly survey since 1980. The 

population covered by the survey is the total civilian non-institutional population of the United 

States as well as a portion of the institutional population in various group quarters. Excluded are 

military personnel living on military bases and nursing home residents. The unit of data 

collection is the consumer unit. A consumer unit comprises members of a household who are 

related or share at least two out of three major expenditures--housing, food, and other living 

expenses. A person living alone is a single consumer unit. About 5,000 consumer units 

participated in the Interview portion of the CEX each quarter during the period of this study. 

Data are collected from consumer units five times over a 13-month period. The first interview, 

based on a one-month recall, is used to ‘bound’ the interview or to reduce the likelihood that 

expenditures will be reported in the next interview. Data reported in the first interview are not 

released nor are they used in for any estimation. Expenditure data are collected in the second 

through fifth interviews for the previous three months. For example, a consumer unit that is 

visited in March reports expenditures for February, January, and December. The sample is a 

rotating panel in which 20 percent of the sample are interviewed for the first time each quarter 

while 20 percent are interviewed for the last time. The Interview survey covers about 95 percent 

of total expenditures (USDL 1995). 

Current Population Survey 

 The Bureau of the Census conducts the Current Population Survey (CPS) for the Bureau 

of Labor Statistics. Each year the March Supplement or Annual Demographic Supplement is 

used to collect income data. At various other times during the survey cycle, supplementary 

questions are asked concerning various topics. The population covered includes the civilian noninstitutional population of the United States and members of the Armed Forces in the United 

States living off post or with their families on post, but excludes all other members of the Armed 

Forces. The sample is about 60,000 households, including families and unrelated individuals; 

data are reported for more than 150,000 persons. Coverage does not include residents of US 

territories or other areas outside the 50 States and the District of Columbia. During the 1993-5 

period, three changes were introduced in the CPS: (1) for the 1993 survey, 1990 Census 

population controls were introduced; (2) for the 1994 survey, interviewing was converted from 

paper and pencil to Computer Assisted Personal Interviewing (CAPI); and (3) for the 1995 

survey, a new sample based on the 1990 Census design was introduced. 

Survey of Income and Program Participation 

The Survey of Income and Program Participation (SIPP) is a continuing panel survey, 

begun in 1983, which is sponsored and conducted by the Bureau of the Census. The current 

design introduces a new sample panel each February. Each sample of households is interviewed 

every 4 months for 32 months. (Most SIPP panels have eight interviews.) There are monthly 

rotation groups. The sample covers the US civilian noninstitutionalized population and members 

of the Armed Forces living off post or with their families on post. Sample size has varied from 

12,500 to 23,500 households per panel; the 1996 panel is composed of 36,700 households. The 
 13

reporting unit is the household with unrelated individuals and families also identified. 

Special thanks go to Gulab Singh of the Department of Statistics, Government of India 

and Martin Ravallion of the World Bank for sharing materials and providing insights concerning 

poverty measurement in India. Special thanks are also extended to participants at the 

International Conference on Income and Wealth held in New Delhi, India in November 1998 to 

commemorate the Golden Jubilee of India’s Independence, especially Michael Ward and B.S. 

Minhas for their comments. 

Appreciation for comments and suggestions is extended to Connie Citro of the National 

Academy of Sciences, Travis Nesmith of the BLS, John Iceland and Dan Weinberg of the 

Census Bureau, and Stephen Jenkins and others participating in the IARIW conference session in 

which the basic text of this paper was presented. Thanks are also extended to James Foster for 

discussions concerning FGT poverty measures and Stephen Jenkins concerning inequality 

measurement, Mike Fratantoni, Stephen Howes, and Klaas do Vos for sharing computer code, 

and Patricia Rozaklis for research assistance. 

The core of this paper reports the results of research and analysis undertaken by BLS and 

Census Bureau staff. It has undergone a more limited review than official publications. This 

paper is released to inform interested parties of research and to encourage discussion. 

 As applied to Garner, the views expressed are those of the author and do not 

reflect the policies of the Bureau of Labor Statistics (BLS) or the views of other BLS staff 

members.