BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Prices and Living Conditions 

Income Imputation and the Analysis of Expenditure Data 

in the Consumer Expenditure Survey 

Jonathan Fisher, U.S. Bureau of Labor Statistics 

Working Paper 394

May 2006

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
1

INCOME IMPUTATION AND THE ANALYSIS OF EXPENDITURE

DATA IN THE CONSUMER EXPENDITURE SURVEY

Jonathan D. Fisher* 

Research Economist 

Bureau of Labor Statistics, Division of Price and Index Number Research 

2 Massachusetts Ave., NE, Room 3105 

Washington, DC 20212-3105 

Fisher.Jonathan@bls.gov 

T: 202-691-6596 

F: 202-691-6583 

Abstract 

The Consumer Expenditure (CE) Survey began imputing income in its 2004 data. Imputation predicts 

income for households that reported receiving income but failed to report a specific value. In this study, I 

examine how income imputation affects analysis of the CE expenditure data. Most importantly, research 

that uses both income and expenditures from 2004 on will not have to restrict the sample to households 

that reported income. The expenditure results most sensitive to the introduction of income imputation are 

statistics that focus on households with low levels of expenditures. 

JEL Codes: C81; D12; I32 

Key words: Income imputation; expenditures; poverty 

* I want to thank Thesia Garner and Carla Engelbrecht for their helpful comments and suggestions. All 

views expressed in this paper are those of the authors and do not reflect the views or policies of the 

Bureau of Labor Statistics (BLS) or the views of other BLS staff members. 
2

The Bureau of Labor Statistics’ (BLS) Consumer Expenditure (CE) Survey began imputing 

income in its 2004 data. Imputation predicts income for households that reported receiving income but 

failed to report a specific value. Many national household surveys such as the Current Population Survey 

and the Survey of Consumer Finances impute missing income values. While imputation is common 

practice, it remains beneficial to highlight precautions researchers should take when using imputed data. 

In this study, I examine how income imputation affects analysis of the CE expenditure data. 

Most importantly, research that uses both income and expenditures from 2004 on will not have to restrict 

the sample to households that reported income. I present results with the sample employed before 

imputation was introduced and compare it to results using the sample that should be employed after 

imputation. I compare the distribution of expenditures and measures of well-being such as the Gini 

coefficient and the poverty rate in the two samples. 

Researchers that want to create a time series using income and expenditures will not have 

imputed income data before 2004 because the BLS will not go back to impute income before 2004. Not 

imputing before 2004 could create a break in a time series of statistics in 2004, which this study 

examines. Using data from 2002-2004 allows me to show how the introduction of income imputation 

creates a break in the time series for some statistics such as the poverty rate. The Data section describes 

the relevant factors of the CE, and the imputation section provides an overview of the imputation 

procedure and how it interacts with expenditures. The Methodology section describes the sample, defines 

the measure of expenditures, and defines other key variables. Finally I compare the distributions of 

expenditures before and after imputation as well as look how measures of well-being are affected by the 

introduction of income imputation. 

DATA 

The CE Survey is a continuing quarterly survey of consumer units. A consumer unit consists of 

members of a household who are related or share at least two out of three major expenditures: housing, 
3

food, and other living expenses. In each consumer unit, one individual is designated as the reference 

person, who is the person who rents or owns the residence as designated by the respondent. Data are 

collected from consumer units and the individuals within these consumer units five times over a 13-month 

period. The first interview is used for bounding purposes and is not released publicly. The remaining 

four quarters of data are used in this analysis. I restrict the sample to consumer units that appear in all 

four quarterly interviews, which allows me to create a measure of actual yearly expenditures for each 

consumer unit. 

The CE asks eighteen income variables. Six are collected for each individual in the consumer 

unit including: wage and salary, self-employment (non-farm) income, farm income, Social Security 

benefits, Railroad retirement benefits, and, Supplement Security Income benefits. The remaining twelve 

variables are collected for the consumer unit and are pension income, interest income, dividend and 

royalty income, unemployment benefits, worker’s compensation benefits, child support, alimony, income 

from roomers or boarders, income from other rental units, food stamp benefits, and, other income. 

The Bureau of Labor Statistics (BLS) creates a complete income reporter designation to 

determine whether consumer units provided sufficient income data for use in official publications (Garner 

and Blanciforti, 1994).1

 A consumer unit is designated a complete income reporter if: 

1) The reference person reports a non-zero amount for a major income source (i.e., wage and 

salary, self-employment, farm income, Social Security benefits, Railroad retirement benefits, 

or Supplemental Security Income benefits); or, 

2) At least one other consumer unit member reports a non-zero amount for a major income 

source and reports valid zeros for all major income sources for the reference person; or, 

3) The consumer unit reports a non-zero amount for at least one other income source and valid 

zeros for all major sources for all members. 

 

1

 T. I. Garner and L. A. Blanciforti “Household Income Reporting: An Analysis of U.S. Consumer Expenditure 

Survey Data,” Journal of Official Statistics, 1994, vol. 10 no. 1, pp. 69-91. 
4

A consumer unit could be classified as a complete income reporter and still not provide a full 

accounting of its income. For example, the reference person could report wage and salary income but fail 

to report a valid amount for its alimony income, and this consumer unit would be classified as a complete 

income reporter under condition (1). In 2004, 13 percent of consumer units are complete income 

reporters and only 64 percent of complete income reporters reported valid amounts for every income 

source. Overall 44 percent of consumer units in 2004 reported an invalid amount for at least one of the 

eighteen income components.2

INCOME IMPUTATION IN THE CE 

For the 44 percent of consumer units that reported at least one invalid income amount, the BLS 

imputes income using the multiple imputation technique described in Rubin (1987).3

 Multiple imputation 

is an iterative regression-based approach, where the data for the regression comes from the valid non-zero 

reporters for each income component. The dependent variable equals the income component being 

imputed, and each of the eighteen components is imputed in a separate regression. The independent 

variables include demographic characteristics of the consumer unit and a variable that equals the quarterly 

expenditure-outlays for the consumer unit.4

An initial regression is run with all of the independent variables. Any independent variable that is 

not statistically significant at the 15 percent level using a two-sided test is removed from the regression 

model. A second regression is then run with the variables that were statistically significant in the initial 

regression. This iterative process continues until all remaining variables are statistically significant at the 

15 percent level. The coefficients from this last regression are used to predict income for invalid 

 

2

 All results are population weighted. The weighting methodology is described in the data appendix. 

3

 Rubin, D.B. Multiple Imputation for Nonresponse in Surveys (New, York, NY J. Wiley & Sons, 1987). 4

 Specifically, the quarterly expenditure-outlays variable is named ERANKMTH, and it equals the dollar amount of 

expenditure outlays made during the last three months. 
5

reporters.5 Through this iterative removal of independent variables, the quarterly expenditure-outlays 

variable may or may not be in the final regression for a given income variable. If the expenditure-outlays 

variable remains in the regression model, the level of quarterly expenditure-outlays affects imputed 

income. This creates dependence between expenditure-outlays and income that may affect conclusions 

about the relationship between the expenditure and income variables. 

Imputed income also directly affects the BLS’ official measure of expenditures. The publication 

category “Personal insurance and pensions” includes employee contributions to Social Security that is 

derived by the BLS from the wage and salary variable. In the 2004 sample used in this study, 25 percent 

of consumer units had wage and salary imputed. After the BLS imputes missing wage and salary for 

these 25 percent of individuals, the BLS then assumes that each individual paid Social Security taxes at 

the required 7.65 percent rate.6

 This 7.65 percent is added to the official “Personal insurance and 

pensions” category and in the official BLS measure of total expenditures. Because of this imputation of 

Social Security taxes, the BLS warns in its publications that “average annual expenditures and 

expenditures for personal insurance and pensions are not strictly comparable to previous [year’s] data.”7

METHODOLOGY 

The expenditure variable used in this study differs from the official BLS measure of total 

expenditures. I exclude the “Personal insurance and pensions” category from my measure because it is 

more accurately classified as savings or taxes. My measure of consumption-expenditures equals the sum 

of outlays for housing, food, transportation, apparel, medical care, entertainment, gifts, and miscellaneous 

items. The data appendix provides additional details about consumption-expenditures. The definition 

 

5

 Paulin, Tsai, and Grance (2005) provide a more detailed description of the BLS’ imputation procedure. G. Paulin, 

S. Tsai, and M. Grance “Model-Based Multiple Imputation,” Proceedings of the Twenty-Ninth Annaul SAS Users 

Group International Paper 210-29, 2004. 6

 Officially, 6.2 percent is paid for Old-Age Survivors and Disability Insurance (OASDI), and 1.45 percent is for 

Medicare deductions. 

7

 U.S. Bureau of Labor Statistics, “Consumer Expenditures in 2004.” Accessed at 

https://www.bls.gov/news.release/cesan.nr0.htm, on December 5, 2005. 
6

follows the literature that defines expenditures as outlays (Rogers and Gray, 1994; Fisher et al, 2005),8

but there is no consensus about the proper definition of outlays or expenditures. Rogers and Gray (1994) 

define three measures of expenditures, and all of the studies referenced throughout this paper deviate from 

these three definitions. Even the definition I employ in this paper differs slightly from Rogers and Gray 

(1994) because they include insurance and pension contributions in their expenditure outlays measure 

while I exclude it. 

I employ two samples of consumer units to show how income imputation affects the analysis 

consumption-expenditures. The first is complete income reporters, as defined above. Complete reporters 

is the sample often used by past research that studies income and expenditures together. Income 

imputation will allow researchers to utilize all consumer units, which includes complete and incomplete 

income reporters.9

One might be concerned that adding incomplete reporters may alter the sample in observable 

ways. Research that excludes incomplete income reporters implicitly assumes that incomplete reporters 

are a random sample of the population. The assumption that incomplete reporters are a random sample 

denotes that the inclusion of them would not affect the sample. The purpose of this study is to show 

whether the inclusion of incomplete reporters affects conclusions about the distribution and the analysis 

of consumption-expenditures after income imputation. The study also shows how a time series of 

economic statistics might be affected by the introduction of income imputation. 

 I start by comparing the distribution of consumption-expenditures for the two samples by 

presenting the percentiles, the Gini coefficient, and the poverty rate. For the poverty rate, I compare the 

level of consumption-expenditures to the official poverty thresholds. Then I use consumptionexpenditures in an Engel curve regression as a further test of the sensitivity of results to the two samples. 

 

8

 J. M. Rogers and M. Gray, “CE Data: Quintiles of Income versus Quintiles of Outlays,” Monthly Labor Review, 

1994, vol. 117, no. 12, pp. 32-37. J. D. Fisher, D. S. Johnson, J. Marchand, T. Smeeding, and B. B. Torrey, “The 

Retirement Consumption Conundrum: Evidence from a Consumption Survey,” Center for Retirement Research 

Working Paper 2005-14, 2005. 9

 Researchers will still have the option to restrict the sample to complete income reporters. The variable identifying 

which households are complete income reporters is included in the 2004 dataset and beyond. 
7

An Engel curve estimates how the share of food expenditures in total consumption-expenditures depends 

on permanent income and other factors. Engel curves have been estimated for all households (Phipps and 

Garner 1994), for Hispanic households (Paulin 2003), and for the poor (Attanasio, Battistin, and Leicester 

2004).10 The dependent variable equals the share of total consumption-expenditures devoted to food. The 

key independent variable is the log of permanent income. Most research including Paulin (2003) and 

Attanasio et al. (2004) use consumption-expenditures as a proxy. I follow this methodology and include 

the log of consumption-expenditures and its square. The regression also contains a number of control 

variables: the number of adults in the household, the number of children in the household, and the square 

for each as well as dummy variables for region, education, race, and year. 

COMPARING DISTRIBUTIONS 

Most analysis in this paper compares two samples, all consumer units and complete income 

reporters. Focusing on these two groups obscures some of the differences in the data because incomplete 

income reporters are a small proportion of all consumer units. Incomplete reporters are only 13.6 percent 

of all consumer units. In the first two tables and in the Figure 1, I include separate results for incomplete 

income reporters. 

Table 1 compares selected demographic characteristics for complete income reporters, 

incomplete income reporters, and all consumer units. The mean age of the reference person for each 

group is almost identical at 51.5 years old. The mean masks some heterogeneity in the distribution of 

age, as shown by the age brackets in Table 1. Incomplete income reporters are less likely than complete 

reporters to be under age 35 and over age 65. Garner and Blanciforti (1994) find a similar pattern for the 

ages of complete and incomplete reporters. Family size is identical across samples, with each sample 

 

10 G. Paulin, “A Changing Market: Expenditures by Hispanic Consumers, Revisited,” Monthly Labor Review, 2003, 

vol. 126, no. 8, pp. 12-35. S. Phipps and T. I. Garner, “Are Equivalence Scales the same for the United States and 

Canada?” Review of Income and Wealth, 1994, vol. 40, no. 1, pp. 1-17. O. Attanasio, E. Battistin, and A. Leicester, 

“Inequality, Poverty, and Their Evolution in the US: Consumption and Income Information in the Consumer 

Expenditure Survey,” Notes prepared for the National Poverty Center’s ASPE-Initiated Workshop on Consumption 

among Low-income families, 2004. Available at http://www.npc.umich.edu/research/npc_research/consumption. 
8

having 1.8 adults and 0.8 children on average. There are also only small differences across samples for 

family type, education, region of residence, and urban status. There are noticeable differences in race, 

with incomplete reporters more likely to be black and less likely to be white. 

 Table 2 moves to the consumption-expenditures data and presents percentiles of the consumptionexpenditures distribution. I present the mean and median as measures of central tendency. The mean 

exceeds the median by approximately $6,000 for complete and incomplete reporters, indicating that the 

distributions are right-skewed. Figure 1 shows the skewness in more detail by presenting the frequency 

distribution of consumption-expenditures for complete income reporters, all consumer units, and 

incomplete income reporters. All three distributions exhibit a long right-tail. 

Table 2 and Figure 1 also reveal that consumption-expenditures are higher for complete reporters 

across all percentiles of the consumption-expenditure distribution. Incomplete reporters are more likely 

to be in the lower half of the consumption-expenditures distribution of complete income reporters. The 

comparison of interest is between complete reporters and all consumer units because these are the two 

samples researchers will use. And because incomplete reporters are a small fraction of the all consumer 

units sample, the distributions for complete reporters and all consumer units virtually overlap at 

consumption-expenditures greater than $23,000. The large grouping of incomplete reporters with 

consumption-expenditures less than $23,000 causes the distributions of complete reporters and all 

consumer units to visibly differ in this range. 

An additional way to describe a distribution is to look at inequality, and I present the Gini 

coefficient in the bottom of Table 2.11 The Gini coefficient for complete income reporters equals the Gini 

for all consumer units. The two Gini coefficients differ only in the third digit after the decimal place. 

Another common statistic for economists to focus on is the poverty rate (e.g., Cutler and Katz 

1992; Slesnick 1993; and Federman et al. 1996).12 Table 3 presents the percent of consumer units with 

 

11 For an example of research that examines consumption inequality, see D. S. Johnson, T. M. Smeeding, and B. B. 

Torrey, “Economic inequality through the prisms of income and consumption,” Monthly Labor Review, 2005, vol. 

128, no. 4, pp. 11-24. 
9

consumption-expenditures below the official U.S. Census Bureau poverty threshold. I find that 10.8 

percent of complete reporters had consumption-expenditures below the poverty threshold, while 11.5 

percent of all consumer units were consumption-expenditure poor. With over 102 million consumer units 

represented by the 2004 CE, this 0.7 percentage point difference in the poverty rate means that an 

additional 748,000 consumer units are considered consumption-expenditure poor in the all consumer units 

sample. This difference persists when I split the sample by family type and race in Table 3. 

TIME SERIES ANALYSIS 

Because of this difference in the poverty rate between the two samples, the introduction of 

income imputation could affect conclusions about the change in the poverty rate between 2003 and 2004. 

A researcher that recreates the time series analysis of poverty rates as in Slesnick (1993) might use 

complete income reporters before 2004 and use all consumer units in 2004. Table 4 shows that the 

poverty rate for complete reporters equals 10.6 percent in 2003 and 10.9 percent in 2004. If I instead use 

all consumer units in 2004 then the poverty rate equals 11.9 percent. Either the poverty rate increased 0.3 

percentage points or 1.3 percentage points between 2003 and 2004, which is a large difference that cannot 

be ignored. 

 A similar issue might arise for the Gini coefficient and other statistics of interest. Using complete 

reporters, the results suggest that the Gini for consumption-expenditures increased by 3.8 percent between 

2003 and 2004 (Table 4). If I use complete reporters in 2003 and all consumers in 2004 instead, 

inequality increased by 4.1 percent. 

Table 4 also presents the change in consumption-expenditures between 2003 and 2004 at 

different points of the consumption-expenditures distribution. The growth in mean consumptionexpenditures between 2003 and 2004 equals 0.6 percent when using complete income reporters in both 

 12 D. M. Cutler and L. Katz, “Rising Inequality? Changes in the Distribution of Income and Consumption in the 

1980’s,” American Economic Review, 1992, vol. 82, no. 2, pp. 546-551. D. T. Slesnick, “Gaining Ground: Poverty 

in the Postwar United States,” Journal of Political Economy, 1993, vol. 101 no. 1, pp. 1-38. M. Federman, T. I. 
10

years, while it equals –1.7 percent using complete reporters in 2003 and all consumer units in 2004. The 

change in expenditures is always lower when using all consumer units in 2004 than using complete 

income reporters in 2004. Taken as a whole, Table 4 suggests that researchers using multiple years of CE 

data must be aware of the fundamental change in the sample between 2003 and 2004. Statistics that focus 

on the bottom of the consumption-expenditures distribution, such as the poverty rate, will be most 

sensitive to the change in the sample. Other statistics that consider the whole distribution, like the Gini 

coefficient, may not be as sensitive to the change.

REGRESSION ANALYSIS 

As a final test of the sensitivity of the consumption-expenditure data to the introduction of 

income imputation, I use the data in a regression framework. As described in the methodology section, I 

estimate Engel curves, which means I regress the share of food on consumption-expenditures and other 

control variables. Table 6 displays ordinary least squares estimates of the coefficients on consumptionexpenditures and its square. The coefficients match expectations for complete income reporters and all 

consumer units; the linear term on consumption-expenditures is negative and statistically significant, 

while the quadratic term is positive, statistically significant, and small relative to the linear term. These 

results are very similar to the ones presented in Attanasio et al. (2004). 

I next restrict the sample to those households that have consumption-expenditures below the 

official poverty threshold. Restricting it to low consumption-expenditure consumer units allows me to 

test the sensitivity of the regression results in the sub-sample most likely to be affected by income 

imputation. Instead of a negative coefficient on consumption-expenditures, the coefficient is positive and 

statistically significant, which again matches the findings in Attanasio et al. (2004). While the coefficient 

on consumption-expenditures is positive and significant for both complete reporters and all consumer 

units, the point estimate for the linear term for all consumer units is 41 percent higher. The regression 

 

Garner, K. Short, W. B. Cutter IV, J. Kiely, D. Levine, D. McGough, and M. McMillen, “What does it Mean to be 

Poor in America?” Monthly Labor Review, 1996, vol. 119, no. 5, pp. 3-17. 
11

results are sensitive to the sample chosen, especially when the sample comes from the bottom part of the 

consumption-expenditures distribution. 

CONCLUSIONS 

 Income imputation has been a needed improvement for the CE Survey data, but researchers need 

to be aware of the consequences of income imputation. I discussed four consequences. 

• A BLS created measure of expenditure-outlays is used to impute income, which means that there 

may be an artificial dependence between income and expenditures. 

• Imputed income also directly affects the BLS’ official published measure of expenditures. After 

imputing wage and salary income, the BLS assumes each consumer unit pays Social Security 

taxes from its wage or salary income. The amount of Social Security taxes is added to the official 

“Personal insurance and pensions” category and in the official BLS measure of total expenditures. 

• From 2004 on, researchers will not have to restrict the CE sample to complete income reporters if 

they want to examine income and expenditures together. Because incomplete income reporters 

are more likely to have lower consumption-expenditures than complete income reporters, 

research that focuses on low consumption-expenditure households is more likely to be affected by 

the use of all consumer units. In this study, the results most affected by the inclusion of 

incomplete income reporters are the poverty rate and the Engel curve for consumptionexpenditure poor households. Alternatively, the Gini coefficient and the Engel curve for all 

households were not affected by the inclusion of incomplete reporters. 

• Finally, there may be a break in any time series of statistics that uses 2004 data with earlier data. 

I show that the difference in the poverty rate in 2003 and 2004 depends greatly on what sample is 

used in each year. The poverty rate is much higher when using all consumer units than when 

using complete income reporters. 
12

Overall, there are legitimate concerns about using the consumer units with imputed income data. 

There may be a temptation to continue using the complete income reporter sample rather than using all 

consumer units. As long as researchers are aware of the potential problems, researchers should use all 

consumer units starting in 2004. The biggest advantage of using all consumer units is in the precision of 

estimates. In 2004, 13 percent of all consumer units were classified as incomplete income reporters. By 

having a larger sample size after imputation, the precision of analysis using the CE Survey will increase. 

It has taken the CE longer than other surveys to introduce income imputation, but researchers should be 

satisfied that a long term issue with the CE data is being addressed while also being aware what affect the 

solution may have on analysis of the data. 
13

DATA APPENDIX 

The consumption-expenditures measure includes the amount that the consumer unit actually 

spends for current consumption. This includes expenditures for food, housing, transportation, apparel, 

medical care, entertainment, gifts (of cash, goods and services) to organizations or persons outside the 

consumer unit, and miscellaneous items for the consumer unit. Excluded are expenditures for pensions 

and social security, savings, and life insurance. 

Housing includes expenses associated with owning or renting a home or apartment, including 

rental payments, mortgage principal and interest, property taxes, maintenance, repairs, insurance, and 

utilities. 

Transportation includes expenditures for the net purchase price of vehicles, finance charges, 

maintenance and repairs, insurance, rental, leases, licenses, gasoline and motor oil, and public 

transportation. Public transportation includes fares for mass transit, buses, airlines, taxis, school buses, 

and boats. 

Medical care expenditures are for out-of-pocket expenses including payments for medical care 

insurance, medical services, and prescription drugs. 

Entertainment expenditures are for fees and admissions, televisions, radios, sound equipment, 

pets, toys, playground equipment, and other entertainment supplies, equipment, and services. 

Miscellaneous expenditures are for personal care services, reading, education, tobacco products 

and smoking supplies, alcoholic beverages, other lodging, and house furnishings and equipment. 

I adjust all expenditure data using the Consumer Price Index research series (CPI-U-RS). The CE 

weight named FINLWT21 is used. Because young renters are under-represented in the sample of 

consumer units who remain in the survey for all five interviews, I use a procedure presented in Sabelhaus 

(1993) to adjust the weight variable by age and housing tenure (homeowner/ renter) to obtain a better 

representation of the population as a whole.13

 

13 J. Sabelhaus, “What is the Distributional Burden of Taxing Consumption?” National Tax Journal, 1993, vol. 46, 

no. 3, pp. 331-344. 
14

For measures of inequality such as the Gini coefficient, it is desirable to have a comparable unit 

of measurement. It is difficult to compare the consumption-expenditures of a single-person consumer 

unit to a four-person consumer unit. I expect the four-person consumer unit to have higher consumptionexpenditures everything else equal. To overcome this difficulty, I adjust the consumption-expenditures of 

a consumer unit by an equivalence scale, which allows me to make comparisons across consumer units of 

different sizes. The scale is given by the square root of the family size and indicates that the resources for 

a four-person consumer unit must be twice that of a single-person consumer in order for the two to have 

an equivalent standard of living. The equivalence scale is only used for calculation of the Gini 

coefficient. 
15

TABLE 1

DEMOGRAPHIC CHARACTERISTICS BY INCOME REPORTER 

STATUS (2002-2004) 

 

 

Complete income 

reporters

Incomplete income 

reporters

All consumer 

units 

Number of observations 11,271 1,780 13,051 

Age of reference person (%) 

 Age 25 or less 4.1 3.5 4.0 

 Age 26-35 15.1 12.9 14.8 

 Age 36-45 21.6 23.1 21.9 

 Age 46-55 21.4 23.5 21.7 

 Age 56-65 15.3 17.3 15.5 

 Age 66 plus 22.5 19.7 22.1 

Family size 

 Adults 1.8 1.8 1.8 

 Children 0.8 0.8 0.8 

Family type (%) 

 Single -- no children 26.6 26.1 26.5 

 Single parent 5.5 4.6 5.4 

 Married -- no children 23.7 24.3 23.8 

 Married with children 26.9 27.0 26.9 

 Married -- other 4.3 4.7 4.3 

 Other family type 13.0 13.3 13.1 

Education of reference person (%) 

 High school dropout 15.6 13.8 15.4 

 High school graduate 27.8 29.6 28.0 

 Some college 28.8 30.2 29.0 

 College graduate 27.8 26.4 27.6 

Race of reference person (%) 

 White 84.5 81.0 84.0 

 Black 10.5 15.2 11.1 

 Other race 5.0 3.8 4.9 

Region of residence (%) 

 Northeast 19.1 20.1 19.2 

 West 20.9 18.9 20.6 

 South 36.0 34.1 35.8 

 Midwest 24.0 26.9 24.4 

Live in urban area (%) 72.4 71.3 72.3 

Source: Consumer Expenditure Survey (2002-2004). All data are population weighted. 
16

TABLE 2

DISTRIBUTION OF CONSUMPTION-EXPENDITURES BY INCOME 

REPORTER STATUS (2002-2004) 

 

Complete

income reporters

Incomplete income 

reporters

All 

consumer units 

Mean 35,441 31,099 34,845 

 

10th percentile 11,899 9,973 11,568 

25th percentile 18,870 15,614 18,386 

50th percentile 29,542 25,905 28,953 

75th percentile 45,060 40,093 44,436 

90th percentile 64,577 58,543 63,703 

 

Inequality measure 

Gini 0.32 0.32 0.32 

Source: Consumer Expenditure Survey (2002-2004). All data are population weighted. Expenditure data 

is in real 2004 dollars using the CPI-U-RS. 
17

TABLE 3

PERCENT OF CONSUMER UNITS BELOW OFFICIAL 

POVERTY THRESHOLD USING 

CONSUMPTION-EXPENDITURES (2002-2004)

Complete

income reporters

All

consumer units

 

All 10.8 11.5

By family type 

With children in consumer unit 10.7 11.3

Elderly head of household 16.3 17.4

By race of the reference person 

White 8.9 9.4

Black 23.3 26.9

Other race 12.5 12.7

Source: Consumer Expenditure Survey (2002-2004). All data are population weighted. Expenditure data 

is in real 2004 dollars using the CPI-U-RS. The poverty thresholds are the official thresholds published 

by the U.S. Census Bureau. 
18

TABLE 4

EXAMINING THE CHANGE IN CONSUMPTIONEXPENDITURES BETWEEN 2003 & 2004 

 

Complete income 

reporters 

All consumer 

units % change % change

 2003 2004 2004 CIR only CIR to ACU

 

 (A) (B) (C) [(B)-(A)] / (A) [(C)-(A)] / (A)

 

Poverty rate 10.64 10.94 11.85 2.8% 11.3%

Gini 0.32 0.33 0.33 3.8% 4.1%

 

Mean 23,143 23,291 22,739 0.6% -1.7%

25th percentile 13,470 13,124 12,666 -2.6% -6.0%

50th percentile 19,899 19,534 19,022 -1.8% -4.4%

75th percentile 28,799 28,565 28,055 -0.8% -2.6%

Source: Consumer Expenditure Survey (2002-2004). All data are population weighted. Expenditure data 

is in real 2004 dollars using the CPI-U-RS. The poverty thresholds are the official thresholds published 

by the U.S. Census Bureau. 
19

TABLE 5

ORDINARY LEAST SQUARES ESETIMATES OF ENGEL 

CURVES FOR FOOD EXPENDITURES (2002-2004)

 

Complete income 

reporters

All consumer 

units

All 

-0.329 -0.340 Consumption-expenditures 

(0.020) (0.020)

Consumption-expenditures 0.012 0.012

squared (0.001) (0.001)

Consumption-expenditure poor

0.630 1.075 Consumption-expenditures 

(0.246) (0.227)

Consumption-expenditures -0.043 -0.067

squared (0.014) (0.013)

Source: Consumer Expenditure Survey (2002-2004). All data are population weighted. Expenditure and 

income data are in real 2004 dollars using the CPI-U-RS. Consumer units that have consumptionexpenditures below the official Census poverty threshold are classified as consumption-expenditure poor. 

The poverty thresholds are the official thresholds published by the U.S. Census Bureau. 

Notes: The dependent variable equals the share of food expenditure in total consumption-expenditures. 

The other independent variables in each regression are age, the number of adults, the number of children, 

and the square of each. There are also dummy variables for region, education, race, and year. 
20

Figure 1: Frequency distribution for consumption expenditures

0

0.010.020.030.040.050.060.07

1,000 11,000 21,000 31,000 41,000 51,000 61,000 71,000 81,000 

Consumption expenditures ($2004)

%

All consumer units Complete income reporters Incomplete income r