Department of Labor Logo United States Department of Labor
Dot gov

The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Article
April 2023

Developing a consumption measure, with examples of use for poverty and inequality analysis: a new research product from BLS

This study provides an update on the work being done by the U.S. Bureau of Labor Statistics (BLS) to produce a research-based consumption measure and explores its potential use in poverty and inequality analysis. For this study, and for most U.S.-based studies in the literature, the Consumer Expenditure Surveys serve as the base to develop the measure. The consumption measure presented in this study accounts for the flow of services from homeownership and owned vehicles, in-kind transfers from government and private entities, and for the full value of health insurance; very few researchers have accounted for all of these. The main contribution is to provide the literature with an update on BLS activities, which include a plan to include home production in the consumption measure and information regarding an upcoming BLS research series. Using the measures produced in this study, we find that consumption without health insurance is 16 percent lower, on average, than total expenditures. Using a consumption measure, with or without health insurance, results in lower poverty rates than when using measures based on total expenditures or pretax income, and consumption for all the measures is more equal than the distributions of total expenditures or income. Using an absolute poverty measure, we find a noticeable decrease in the poverty rate from 2019 to 2021, when measured by consumption without health insurance.

Much of the economic well-being research focused on understanding the effects of behavior and policies on utility relies on the relationship between utility and household consumption or utility and household income. That research primarily uses income data from surveys. In the United States and other countries with more advanced economies, income data are more readily available from surveys and thus are more commonly used as a proxy for well-being than consumption.1 Additionally, researchers might prefer income as a measure of well-being because it reflects access to resources that could be used for consumption, whereas well-being measured by consumption could be artificially low because of preferences. However, income is more sensitive to short-run fluctuations, whereas consumption better reflects long-term resources and is more likely to capture disparities that result from differences across households in access to credit or the accumulation of assets.2 There is also evidence that components of consumption that are particularly important for poor people are well captured in household surveys, while many components of income important to poor people may not be as well captured in such surveys.3 Furthermore, some researchers have suggested that consumption is more strongly correlated with other indicators of economic well-being than is income.4 However, rather than promoting one measure over another, other research efforts support multidimensional measures of economic well-being, thereby noting that unidimensional measures are not sufficient.5

Although consumption may be a better measure of economic well-being than income, determining the actual consumption level for an individual or a group of individuals—a person or family that forms a consumer unit (CU), for example—is difficult because it depends on the individual’s or group’s particular circumstances, choices, use of purchases, and time usage.6 In addition, data limitations make the problem of valuing consumption even more difficult. For example, combining expenditures data with time-use data has been particularly challenging.7 As a result, many researchers have used expenditures as a proxy for consumption.8 The U.S. Bureau of Labor Statistics (BLS) Consumer Expenditure Surveys (CE) have been the primary source of expenditures data at the CU level for researchers and government agencies since the late 19th century. And, as such, the CE is the oldest BLS product that collects household or consumer expenditures as a measure of living conditions for the United States.9

BLS has long been interested in the creation of a measure of consumption with CE data as the base,10 as have other researchers.11 For example, Fisher, Johnson, and Smeeding (2015)12 produced a measure of consumption to study inequality, and Meyer and Sullivan (2012, 2013)13 produced a consumption measure to study poverty and inequality. However, recent experiences with the COVID-19 pandemic have highlighted the need for a more comprehensive consumption measure than has previously been produced. Throughout the pandemic, family and household members have played an increasingly important role in the well-being of other members of their households through the provision of services such as childcare, eldercare, more home-cooked meals, and education, all of which are nonmarket transactions and not captured with expenditures alone. For these transactions, a comprehensive consumption measure needs to account for the time household members spend in home production for their own consumption. These shortcomings of expenditures as an economic measure of well-being have motivated researchers at BLS to develop a more comprehensive consumption measure that accounts for a broader set of in-kind benefits than accounted for in previous measures and for the value of home production for own consumption.

Our first attempt at BLS to produce a consumption measure was published in the May 2022 edition of AEA Papers and Proceedings as “Building a consumption poverty measure: initial results following recommendations of a federal interagency working group.”14 In this article, we build upon our earlier work by expanding what is included in consumption and refining our methods to produce the measure. The primary difference between the earlier consumption measure and the one presented in this article is that we introduce a value of health insurance; this makes our most recent consumption measure more like the ones produced by Meyer and Sullivan (2012, 2013). This enables us to produce three consumption measures: one that excludes health insurance, one that includes health insurance capped as a percentage of consumption, and one that includes health insurance not capped. The measure based on capping health insurance expenditures for poverty measurement is based on a recommendation made by the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty (ITWG).15 As illustrations of how the measures can be used, we produce simple means for the consumption measures and components and inequality statistics for 2019, 2020, and 2021. The consumption-based poverty and inequality statistics are compared with statistics based on pretax income and CE-defined total expenditures.16

In our analysis, we find that the value of CU consumption without health insurance averaged about 16 percent less than CE total expenditures over the 3-year period, and that the values of CU consumption with health insurance (capped and uncapped) was about 14 percent more than CE total expenditures in 2019 and 2020, and about 12 percent more in 2021. For all consumption measures, compared with total expenditures and pretax income, poverty rates are lower when based on a relative concept. The absolute concept that we used for this study sets all poverty rates the same in 2019; such an approach allows us to see how poverty rates changed over the period while holding the base poverty rate the same. Our results show a noticeable decrease in the poverty rate from 2019 to 2021 when it is measured by consumption without health insurance. For all 3 years, consumption distributions are more equal than distributions of total expenditures and pretax income. Relative to 2019, we find that consumption poverty fell in 2020, with the onset of the pandemic, and that distributions of consumption became more equal. Compared with 2020, for all the consumption measures, poverty rose in 2021 when we used a relative measure, and inequality rose but not to the same levels as in 2019.

The consumption measures presented in this article represent work in progress. Next steps in the development of the BLS research consumption measure include the addition of the value of time for home production. During the 2021–23 period, BLS sponsored research to impute values of select home-production activities using the BLS American Time Use Survey and other data in combination with the CE. Once the value of home production is included, the goal of BLS to produce a more comprehensive measure of consumption can be realized. In addition, BLS plans to publish a consumption-measure research series based on internal CE data and imputation; this series will be available to the public through published tables of means. The series will not be an official BLS production series, but it can be referred to as an official research series. Depending upon available resources, BLS plans to release an auxiliary public-use data file that includes research-based consumption values.

Background and related literature

In standard economic models, individual utility is a function of consumption. In the life-cycle model, individuals choose the level of consumption in each period to maximize utility subject to lifetime income. The consumption level in a given period can be more or less than the income level in that period because individuals can use savings and borrowing to smooth consumption over their life cycle. The implication of the life-cycle model is that income, consumption, and savings will follow a predictable pattern over an individual’s life. Income is expected to exceed consumption during the prime working years and be less than consumption early in life and in retirement. Individuals can also use savings and borrowing to smooth consumption in response to unexpected fluctuations in income. This view of consumption was developed by Modigliani and Brumberg (1954) and extended by Friedman (1957).17 As noted by Jappelli and Pistaferri (2017), models of consumption developed in the 1970s through the 2000s further account for intertemporal choice under uncertainty, as well as the tradeoff between leisure and home production.18

Given its direct relationship with utility, consumption is often considered a better unidimensional measure of well-being compared with income or expenditures. In a framework focused on households, it is assumed that the members of the household collectively choose a level of consumption that maximizes utility for a given budget constraint defined by available resources. Consumption can be viewed as an outcome variable reflecting what has been achieved, with income acting as one of many inputs. This contrasts with economic well-being outcome measures that focus on what could be achieved, such as income, for example.

Many researchers have attempted to construct measures that reflect consumption. Early examples include Cutler and Katz (1991) and Slesnick (1991), who use a flow-of-services approach to value durables consumption.19 Later studies that examined household consumption as distinct from expenditures include Johnson, Smeeding, and Torrey (2005); Krueger and Perri (2006); Attanasio, Battistin, and Ichimura (2007); Heathcote, Perri, and Violante (2010); Aguiar and Bils (2011); Coibion, et al (2012), and Attanasio, Hurst, and Pistaferri (2012).20 The studies from the economics literature most related to our attempt to construct a consumption measure at the household level are those by Meyer and Sullivan (2013, 2023) and Fisher, Johnson, and Smeeding (2015).21 (The next section provides a more detailed discussion of the similarities and differences between the Meyer-Sullivan and Fisher-Johnson-Smeeding measures and those presented in this article.)

There is a large body of literature produced by international and national organizations that provides guidance on the development of a consumption measure. At the international level, guidance has been provided by the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD), the United Nations Economic Commission for Europe, and the World Bank.22 At the U.S. national level, the ITWG included in its 2021 report a comprehensive review of the literature on the development of consumption measures and their use in poverty measurement.23 Then, in 2022, BLS published a report by Curtin et al. called “A conceptual framework for the U.S. Consumer Expenditure Surveys,” which includes a measure of consumption.24

Data

In this section, we discuss the data and methods used to construct our consumption measures. We describe the methods used to produce the earlier consumption measure and those reflecting the addition of health insurance and improvements to our methodology. We also provide a discussion of the scope of our measures and then compare our measures with those produced by other researchers.

Consumer Expenditure Surveys and variables

The CE Interview Survey is the primary data source for the consumption measures presented in this article. The CE uses two survey instruments to collect expenditures at the CU level: the Diary Survey and the Interview Survey. Respondents are selected to participate in only one of the two surveys. The Diary Survey focuses on collecting expenditures for certain frequently purchased goods and services, such as food at home and food away from home, apparel, and other products and services purchased on a regular basis. In contrast, the Interview Survey collects expenditures more comprehensively, capturing approximately 95 percent of total expenditures, which is why we use it for our consumption measure.25 The Interview Survey is conducted throughout the year on a rolling basis and has a 3-month reference period. CUs complete the Interview Survey up to four times at 3-month intervals.

For our analysis, we compare consumption with CE-defined total expenditures and a measure of income. To define income, we start with the CE definition of pretax income available in the microdata files and subtract the value of Supplemental Nutrition Assistance Program (SNAP) benefits.26 This income measure matches the U.S. Census Bureau measure of money income used to produce the official U.S. poverty statistics.27 Note that unemployment benefits are included in this definition, so the impact of changes to benefit generosity are captured in the absolute magnitude of benefits collected in the CE from year to year. However, benefits related to the COVID-19 pandemic, such as the Economic Impact Payments, the expanded Child Tax Credit, and other tax credits are not included in the income measure because they are income-tax adjustments; however, the use of these credits are likely to be reflected in our consumption measures.

Consumption measure

In defining the scope of the consumption measure, we generally follow the ILO and OECD guidelines and the consensus recommendations of the ITWG.28 We deviate from the ILO and OECD guidelines, which include education and health insurance in final consumption, and instead follow the ITWG recommendations by excluding education but including values for health insurance. We also considered the research of others in the development of the consumption measures presented in this article.

In many countries, including the United States, household surveys are used to collect expenditure data that can be used, in part, to create a consumption measure. For most categories, consumption will equal expenditures in a given period. However, not all components of consumption satisfy this equality. For durable goods, the value of consumption is defined as a flow of services over the life of the product, rather than as the expenditures needed to acquire the good. International guidelines recommend that the flow of services from housing and vehicles be represented by, for example, rental equivalence or user cost. For our measures—the earlier measure and those presented in this article—we use rental equivalence for owner-occupied shelter as collected in the CE, and we impute elements of user costs for cars and trucks not collected in the CE (i.e., depreciation and the opportunity costs of capital).29

International guidelines and the ITWG also recommend that the value of in-kind benefits be counted in consumption; however, the value of these benefits is often not collected in national household surveys. In the CE, the value of most in-kind benefits—those from the government (e.g., subsidized school meals and energy assistance) and from employers (e.g., health insurance)—are not collected. One exception is that SNAP benefits are collected in the CE. Because SNAP benefits are administered in the form of electronic benefit transfer (EBT), we assume that they are used like money income and are implicitly included in reported food expenditures. For other government and employer in-kind benefits, imputed values are included in our measure of consumption. Government in-kind benefits for food, energy, and rental assistance were included in the earlier BLS consumption measure; two of the consumption measures presented in this study include government and employer in-kind benefits in the form of health insurance. Although we do not include home production in the current version of the consumption measure, we plan to include it in future versions.

Expenditures in categories such as education and health can be better thought of as investments rather than as providing for current consumption. Following the recommendation of the ITWG,30 in this and in our earlier consumption measure, we exclude education expenditures. We also exclude out-of-pocket spending on medical goods and services because the ITWG did not reach a consensus recommendation for the treatment of those expenditures. But health insurance provides current utility in the form of risk protection, unlike expenditures for medical goods and services, which provide utility indirectly by their effect on health. The inclusion of health insurance in a consumption measure for poverty analysis is controversial. Following the ITWG recommendation, and as presented in this article, we produce consumption measures with and without health insurance. But one concern with including health insurance in the measure for poverty analysis is that the value of government-provided health insurance can make up an outsized share of total consumption and almost singlehandedly push people out of poverty. To prevent overstating the effect of government-provided health insurance, we produce a consumption measure that restricts health insurance to be no more than half of total consumption with health insurance (uncapped) and use this measure in our poverty analysis. For consistency, we also use consumption with health insurance capped for our inequality analysis.

Several previous studies have created measures of consumption. Table 1 provides a comparison between the research consumption measures presented in this article and a selection of other measures. The measures most closely aligned with our measure are those created by Meyer and Sullivan (2012, 2013) and Fisher, Johnson, and Smeeding (2015).31 Although these measures are similar to ours, there are some important distinctions.32 With respect to the components of consumption, both Meyer and Sullivan and Fisher, Johnson, and Smeeding used rental equivalence for owner shelter but not for vacation homes.33 We include rental equivalence for owner shelter and vacation homes in our earlier consumption measure and in the current consumption measure. Our measures and those of Meyer and Sullivan replace reported rent with imputed market rents for renters who report receiving government subsidies and for those who reside in public housing. Meyer and Sullivan assigned the value of rent as the maximum of the reported rent versus the imputed rent.34 In contrast, we assign the imputed rent regardless of its size relative to the reported value. In addition, we impute rents for CUs living in rent-controlled units, those living rent free, and those living in college dormitories. Fisher, Johnson, and Smeeding imputed government rental and public-housing subsidies and added them to reported rents. For vehicles, Meyer and Sullivan estimated varying depreciation rates, but they did not account for opportunity cost.35 Fisher, Johnson, and Smeeding accounted for opportunity costs but used a fixed rate (5 percent); in addition, they used a fixed rate of depreciation (10 percent).36 Finally, Meyer and Sullivan did not include out-of-pocket health expenditures or education in their consumption measure. However, they included an imputed value of health insurance, which is capped at 30 percent of total consumption in their poverty analysis.37 We take a similar approach in this article, but we cap health insurance at 50 percent of consumption.38 In contrast, Fisher, Johnson, and Smeeding included out-of-pocket health and education expenditures in their consumption measure.39

Table 1. Comparison of various consumption measures with those of the present study
Spending categoryConsumption or expendituresNondurablesUnknown
Present study[1]Fisher, Johnson, and Smeeding (2015)Meyer and Sullivan (2013)Johnson, Smeeding, and Torrey (2005)Slesnick (2001)Cutler and Katz (1991)Attanasio, Hurst, and Pistaferri (2012)Coibion, Gorodnichenko, Kueng, and Silvia (2012)Aguiar and Bils (2011)Heathcote, Perri, and Violante (2010)Attanasio, Battistin, and Ichimura (2007)Krueger and Perri (2006)Hassett and Mathur (2012)

Food at home

YesYesYesYesYesYesYesYesYesYesYesYesYes

Food away from home

YesYesYesYesYesYesYesYesYesYesYesYesYes

Alcoholic beverages

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Housing

Rental equivalence for owned home

YesYesYesYesYesYesXXYesXXYesUnknown

Mortgage interest and principal

XXXXXXXXXXXXUnknown

Rent for renters

YesYesYesYesYesYesXXYesXXYesUnknown

Maintenance, repair, and insurance

PartialYesYesYesYesYesPartialXXXYesYesUnknown

Other lodging

PartialYesYesYesYesYesYesYesXXXYesUnknown

Rental equivalence for vacation home

YesXXXXXXXXXXXUnknown

Utilities (e.g., electricity, water, etc.)

YesYesYesYesYesYesYesYesYesXYesYesUnknown

Household operations (e.g., cleaning)

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Home furnishings and equipment

YesYesYesYesYesYesPartialYesYesXXYesUnknown

Apparel

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Transportation

Service flow from owned vehicles

YesYesYesXYesYesXXXXXYesUnknown

Net outlays for vehicles

XXXYesXXXXYesXXXUnknown

Gasoline and motor oil

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Maintenance and repair

YesYesYesYesYesYesUnknownXYesXYesYesUnknown

Insurance

YesYesYesYesYesYesYesXYesXXYesUnknown

Vehicle rental

YesYesYesYesYesYesYesYesYesXYesYesUnknown

Public transportation

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Out-of-pocket health expenditures

XYesXYesYesYesXXYesYesXYesUnknown

Imputed value of health insurance

YesXYesXXXXXXXXXUnknown

Entertainment

Fees and admissions

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Durable equipment

PartialYesYesYesYesYesXXYesYesXYesUnknown

Personal care items

YesYesYesYesYesYesPartialYesYesYesYesYesUnknown

Reading materials (e.g., books)

YesYesYesYesYesYesPartialXYesYesPartialYesUnknown

Education

XYesXYesYesYesXXYesYesXYesUnknown

Tobacco

YesYesYesYesYesYesYesYesYesYesYesYesUnknown

Miscellaneous

YesYesYesYesYesYesYesXYesXXYesUnknown

Life insurance

XXXXXXYesXXXXXUnknown

Sample information

Urban or rural

BothBothBothBothBothUnknownUrbanUnknownUrbanUrbanUrbanUrbanUnknown

Includes incomplete income reporters

YesYesYesXUnknownXXUnknownXXYesXUnknown

4-quarter consumer units

XYesXYesXXXXYesYesXYesUnknown

Age restriction

XXXXUnknownX25-65Unknown25-6425-6025-60NonelderlyX

Single females

YesYesYesYesXXYesYesYesYesXYesYes

Income data source

CECECPSCEXCPSPSIDCECECPSXCECPS

[1] The measure presented in this study is similar to the one presented in Armstrong, et al., with the exceptions that the previous measure included expenditures for individuals outside the consumer unit (e.g., gifts) and did not include a value of health insurance or a flow-of-services housing services for consumers living rent free or in college or university dormitories. See Grayson Armstrong, Caleb Cho, Thesia I. Garner, Brett Matsumoto, Juan Munoz, and Jake Schild, “Building a consumption poverty measure: initial results following recommendations of a federal interagency working group,” AEA Papers and Proceedings, vol. 112, May 2022, pp. 335–39, https://doi.org/10.1257/pandp.20221041.

Key: “Yes” means item is included, “X” means item is excluded, and “Partial” means that only part of the category is included in the measure. CE = Consumer Expenditure Surveys; CPS = Current Population Survey; PSID = Panel Study of Income Dynamics. 

Note: The information in this table, with the exception of the column for the present study, is taken from Jonathan Fisher, David S. Johnson, and Timothy M. Smeeding, “Inequality of income and consumption in the U.S.: measuring the trends in inequality from 1984 to 2011 for the same individuals,” Review of Income and Wealth, vol. 61, no. 4, December 2015, pp. 630–50, https://doi.org/10.1111/roiw.12129; see the appendix (supporting information available only in the online version), especially table A3, “Comparison of consumption definitions by terminology used to describe the measure”; see the references in Fisher, Johnson, and Smeeding for citation information for the various studies listed in this table. 

Imputations for consumption

In cases in which in-kind benefits are not collected as part of the CE, a value of in-kind benefits must be imputed. For some types of in-kind benefits, such as government rental assistance and health insurance (both employer provided and government), participation is captured in the CE; for these programs, we only need to assign a value to the benefits. For other in-kind benefits, such as the National School Lunch Program (NSLP), the Women Infants and Children (WIC) program, and the Low Income Home Energy Assistance Program (LIHEAP), the CE does not ask about participation. For these programs, participation and benefits must be imputed. Finally, in terms of the flow of services from housing and durable goods, as noted previously, the CE asks about rental equivalence for owner-occupied housing; however, rental equivalence is not asked for vehicles. Instead, we apply a user-cost approach to impute values of the service flows arising from vehicle (i.e., car and truck) ownership. For the consumption value of consumer units who are not homeowners but are identified as renters, select imputations are also produced. In this section, we provide a summary of the imputation methods. (See the appendix for a more detailed description of imputation methods.)

As previously noted, the CE does not collect program-participation or benefit values for NSLP, WIC, or LIHEAP. To impute benefit values to the CE, we start by modelling participation for these programs and LIHEAP benefits as reported in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Once participation in these programs is imputed to CUs in the CE, a value of the in-kind benefits is assigned. This value comes from administrative sources for NSLP and WIC. For LIHEAP, the reported benefit amounts from the CPS ASEC are used to impute a value of the benefits. Imputations for NSLP and LIHEAP for the measures presented in this article are the same as those included in the earlier measure; however, improvements in methodology to produce WIC imputations are introduced.

In addition to owner-occupied housing, in certain instances it is expected that reported rents do not reflect the full consumption value of rented shelter. Regarding renters living in units for which we consider their reported rents are less than market values, imputations for the value of shelter consumption are needed. We consider renters who are paying additional rental-related expenses—for example, those for maintenance and repairs—as not reporting rents that represent their consumption. In addition, the CE includes questions about whether the renter receives government assistance in paying rent, lives in public housing, lives in rent-controlled units, or lives rent free. However, not asked is the value of the difference in what renters pay for shelter and the market value of similar units. We use regression models for those who pay full market value to calculate the implied market value for those who pay less than the full value. The imputed market values represent the consumption value of rental shelter for these renters. Improvements in the rent imputations are introduced with the current measure, and imputed rents for renters living rent-free were added.

The CE asks about different types of health insurance coverage and collects data on out-of-pocket insurance premiums associated with each type of insurance. The CE also collects out-of-pocket expenditures on medical goods and services. As noted previously, for one of the measures presented in this article, the consumption value of health insurance is included. To derive this value, we first impute the full value of health insurance on the basis of insurance type. For private health insurance, the full value is based on the market price. This includes the out-of-pocket premiums that are captured by the CE and the employer contribution for employer-provided plans, or the value of any subsidy received for individual plans. For public insurance programs, the full value is based on the cost to the government (including administrative costs).

Finally, for vehicles, we begin with the CE definition for all transportation expenditures, which includes the expenditures for the purchase of all new and used vehicles, vehicle finance charges, gasoline and motor oil, maintenance and repairs, vehicle insurance, public transportation, and vehicle rental licenses and other charges. We remove expenditures for the purchase of all vehicles and vehicle finance charges that were made during the 3-month reference period. These expenditures are replaced with a flow-of-services value for cars and trucks that is imputed using a user-cost approach. The user cost is defined as the depreciation plus the opportunity cost of capital (current estimated value of the car multiplied by an interest rate) plus maintenance and repair costs. To derive a flow of services from the ownership of vehicles, which we limit to cars and trucks, we include imputed values of vehicle depreciation and opportunity costs of capital. Other components of user costs are already accounted for in the transportation expenditures that we keep. We estimate vehicle depreciation using vehicle purchase information in the CE by comparing similar vehicles purchased at different ages. A consumption flow-of-service value is imputed to CUs for the stock of cars and trucks owned for nonbusiness use. Although the flow of services from cars and trucks was included in the earlier BLS consumption measure, improvements in the methodology have been introduced and are reflected in the current measure. Note that our user costs will undervalue the flow of services from the stock of vehicles that are not cars or trucks; these other vehicles include, for example, planes, boats with motors, and motorized campers. 

Going from expenditures to consumption

To help readers understand the relationship between consumption and total expenditures as defined by BLS and presented in its published tables, we present a step-by-step description. Because we are interested in the CU’s consumption level, and not the consumption among people who do not live in that CU, we first remove expenditures for goods and services purchased to be given to someone outside the CU; these are identified as “gifts.” Next, we remove expenditures related to home ownership and the purchase of vehicles because these values will be captured instead by the flow-of-services value for shelter and vehicles, respectively. And, as noted previously, we also remove education and noninsurance medical expenditures because we view these as being more investment than consumption. For the measure of consumption with health insurance (uncapped and capped), we add the imputed value of health insurance. Finally, we do not include certain expenditures that are included in CE total expenditures that can be better thought of as financing future consumption (e.g., retirement contributions, life insurance purchases, etc.).

Methods for analysis

In this section, we present the methods used to examine the impact of moving to a consumption measure from CE total expenditures. Then, we describe the methods used to examine the impact of using consumption as opposed to total expenditures or income for poverty and inequality analysis.

Means

We analyze five measures of expenditures and consumption: total expenditures as defined by the CE, total expenditures as defined by the CE excluding gift expenditures, consumption without health insurance, consumption with the value of health insurance capped, and consumption with health insurance uncapped. To calculate the averages, we pool four quarters of data starting with the second quarter of the current calendar year through the first quarter of the following year. Quarters refer to the calendar period when the CE data were collected; for example, quarter one includes expenditures collected during interviews that took place from January to March; the reference period for January interviews is October through December of the previous year, while March interviews reference expenditures made in December of the previous year through February of the current year. Thus, when we refer to “quarterly” expenditures, these are actually 3-month values. For example, to calculate the quarterly averages for 2019, we use data from the second quarter of 2019 through the first quarter of 2020. We present CU-level quarterly averages that are weighted using one-fourth of the CU weight included in the CE data file; CE data are weighted quarterly and thus scaling by one-fourth accounts for the fact that four quarters of data are pooled together. Annual means can be produced by taking the quarterly mean and multiplying by four, but these values will differ from the means reported in the published CE tables.40

Poverty and inequality analysis

We present results for three consumption measures: one that does not include health insurance, a second that includes health insurance capped, and a third that includes health insurance uncapped. For comparison, we also present results for CE total expenditures and pretax income. The income measure that we use differs from the income measure that appears in BLS published tables with CE expenditures data.41 Unlike the pretax income measure in the published CE tables, the one in this study does not include SNAP benefits or food and rent as pay. For each measure, we create equivalized values using a three-parameter equivalence scale.42 Poverty and inequality statistics are based on equivalized values.

For studying poverty, we produce two sets of poverty rates. The first set is based on purely relative thresholds. These thresholds are defined to be 60 percent of the median equivalized value for each measure for each year. The second set of poverty rates is based on the implicit thresholds that result when all poverty rates are anchored to the same rate for a single measure; this is a type of absolute measure. We anchored the poverty rates for all the measures in 2019 to be the same as the 2019 relative poverty rate for consumption with health insurance capped. For 2020 and 2021, the implicit 2019 thresholds (based on equivalized-measure values) resulting from this anchoring are updated to account for inflation. To approximate inflation, we create an annual index as the average of the monthly Chained Consumer Price Index for All Urban Consumers for all items and all urban areas.43 When the respective measure for the CU is below the threshold, the CU is considered poor, and all members of that CU are considered poor. For example, if the CU’s equivalized consumption without health insurance is below the poverty threshold for consumption without health insurance, then the CU and everyone in it are considered poor. The poverty rates that we show refer to the percentage of people in the United States below the thresholds.

For studying inequality, we produce Gini indexes and Lorenz curves for each measure for each year. CUs are ranked on the basis of their equivalized value of each measure, and population weights are used to produce the distributions. For each measure, the Lorenz curve is a plot of the cumulative share of the population plotted against cumulative share of each measure. For example, 60 percent of the population accounts for 40 percent of overall consumption without health insurance.

Results

The results are calculated for 2019, 2020, and 2021 and are broken into three parts. The first section presents a discussion of the means. The second and third sections present the results of the poverty and inequality analysis, respectively. Results for 2021 are labeled as “preliminary” because some of the underlying non-BLS data used to produce the consumption measures are not finalized. Specifically, select 2021 health insurance values are subject to revision. In addition, WIC data are not finalized until up to a year after initial release by the U.S. Department of Agriculture (USDA). When updates to these non-BLS data are released, we will produce revised estimates for 2021.

Means

Quarterly CU averages are presented in tables 2 and 3. Table 2 includes results for 2019, 2020, and 2021 for the three consumption measures and two expenditures measures. Table 3 includes means for the consumption and expenditure subcomponents for 2020. (Detailed results for all 3 years are presented in table A-1 of the appendix for comparison at the subcomponent level.)

As shown in table 2, the trend from 2019 to 2021 is similar across all five measures, although the levels are slightly different. Means are lowest for 2020 relative to 2019 and 2021; this pattern is expected because of changes in consumption and expenditure patterns during the first year of the COVID-19 pandemic.44 Quarterly means are lowest for consumption without health insurance (from $12,158 to $13,562). The next highest means are for total expenditures that do not include those for gifts, followed by total expenditures that include those for gifts. Quarterly means for both expenditure measures are about $2,000 higher than those for consumption without health insurance. The quarterly means for consumption with health insurance capped and uncapped are more than $4,000 higher than the means for all years for consumption without health insurance.

Table 2. Nominal quarterly means for measures of expenditures and consumption, 2019 to 2021 
Measure201920202021[1]

CE-defined total expenditures

$14,717$14,555$16,196

CE-defined total expenditures (not including gifts)

14,50914,38615,955

Consumption without health insurance

12,39512,15813,562

Consumption with health insurance

16,79216,76718,062

Consumption with health insurance capped

16,71616,67518,004

[1] Data for 2021 are preliminary.

Note: CE = Consumer Expenditure Surveys.

Source: U.S. Bureau of Labor Statistics.

Focusing on 2020, table 3 presents a complete breakdown, by subcomponent, of the quarterly means for total expenditures with and without gifts, consumption without health insurance, and consumption with health insurance uncapped. The quarterly mean for total expenditures is $14,555. The quarterly mean drops, as expected, to $14,386, when we remove expenditures for goods and services purchased to be given to people who live outside the CU (i.e., identified as gifts by BLS). When we move to consumption without health insurance, the quarterly mean drops further, to $12,158. As revealed by the subcomponent means, the lower mean for total consumption without health insurance relative to the means for both measures of total expenditures is largely due to the removal of health insurance and the deduction of out-of-pocket expenditures, as well as switching from the net purchase price of vehicles to a flow of services. However, the decline was mitigated by increases in the consumption values of owned dwelling, rented dwelling, and other lodging; these reflect the movement from expenditures to rental equivalence for owned housing and imputed rents for renter shelter. Including health insurance increases the consumption measure to $16,767. Capping health insurance to 50-percent of consumption only slightly reduces the mean, to $16,675.

Table 3. Nominal quarterly means of expenditures and consumption, by subcomponent, 2020
CategoryTotal expendituresTotal expenditures, excluding giftsConsumption without health insuranceConsumption with health insurance uncappedConsumption with health insurance capped

Average quarterly expenditures or consumption

$14,555$14,386$12,158$16,767$16,675

Percent of consumption that is imputed

[1][1]11.31%31.87%29.86%

Food[2]

$2,139$2,136$2,142$2,142$2,142

Alcoholic beverages

112112112112112

Housing

5,0374,995[1][1][1]

Shelter

3,1333,114[1][1][1]

Owned dwellings[3]

1,8491,8493,5183,5183,518

Rented dwellings[4]

1,1001,0891,1881,1881,188

Other lodging[5]

184176308308308

Utilities, fuels, and public services[6]

1,0491,0441,0471,0471,047

Household operations

362357[1][1][1]

Child daycare expenses[7]

4848[1][1][1]

Out-of-pocket expenses, excluding child daycare

313309309309309

Household furnishings and equipment

493480[1][1][1]

Purchase of major kitchen appliances[8]

7272[1][1][1]

Out-of-pocket expenses, excluding household furnishings and equipment

422408408408408

Apparel and services

251237237237237

Transportation

2,4302,404[1][1][1]

Vehicle purchases (net outlay)[9]

1,2081,188[1][1][1]

Depreciation and opportunity costs of owning vehicles

[1][1]829829829

Gasoline, other fuels, and motor oil

386384384384384

Other vehicle expenses

782782782782782

Public and other transportation

5451515151

Health

1,2271,225[1][1][1]

Health insurance[10]

918917[1]46094517

Medical services

217215[1][1][1]

Prescription drugs

6565[1][1][1]

Medical supplies

2928[1][1][1]

Entertainment

626615[1][1][1]

Motorized recreational vehicles (net outlay)

5555[1][1][1]

Out-of-pocket expenses, excluding motorized recreational vehicles

571560560560560

Personal care products and services

6867676767

Reading

1615151515

Education[9]

284223[1][1][1]

Tobacco products and smoking supplies

7676767676

Miscellaneous

135124124124124

Personal insurance and pensions[9]

1,5921,592[1][1][1]

Life and other personal insurance[9]

121121[1][1][1]

Pensions and Social Security[9]

1,4711,471[1][1][1]

Cash contributions[9]

564564[1][1][1]

Income:

CE-defined quarterly pretax income[11]

$21,156$21,156$21,156$21,156$21,156

Census Bureau-defined quarterly pretax income[12]

$21,094$21,094$21,094$21,094$21,094

Number of consumer units (in thousands)

131,542131,542131,542131,542131,542

Number of sample interviews

20,15820,15820,15820,15820,158

Consumer unit characteristics:

Age of reference person

52.1452.1452.1452.1452.14

Average number in consumer unit:

People

2.472.472.472.472.47

Children under 18

0.580.580.580.580.58

Adults 65 and older

0.420.420.420.420.42

Earners

1.291.291.291.291.29

Vehicles:

Vehicles (owned)

1.81.81.81.81.8

Vehicles (leased)

0.080.080.080.080.08

Percent distribution:

Reference person:

Men

4747474747

Women

5353535353

Housing tenure:

Homeowner

6666666666

With mortgage

3939393939

Without mortgage

2727272727

Renter

3333333333

[1] Not applicable.

[2] For consumption, includes National School Lunch Program and Women, Infants, and Children program. Also includes an adjustment for board for students who report living in a dorm.

[3] For consumption, includes rental equivalence for primary residence.

[4] For consumption, includes market value of rental units. Consumer units residing in a college dorm were assigned the national average value for dorms using data from the U.S. Department of Education.

[5] For consumption, includes rental equivalence for vacation homes. Consumption also includes an adjustment for expenditures on dorms for students who report living in a dormitory.

[6] For consumption, includes energy assistance using Low-Income Home Energy Assistance Program..

[7] Not included in consumption because considered part of education.

[8] Not included in consumption because considered part of rental equivalence and rent.

[9] Item not included in consumption.

[10] For consumption, only the imputation for the full value of health insurance is included. For 2021, the value of health insurance is based on 2020 imputations adjusted for inflation.

[11] Definition excludes food and rent as pay. This definition differs from the definition of income used in the published Consumer Expenditure Surveys tables, which includes food and rent as pay.

[12] Does not include the value of Supplemental Nutrition Assistance Program or food and rent as pay.

Note: CE = Consumer Expenditure Surveys.

Source: U.S. Bureau of Labor Statistics.

Poverty

Chart 1 presents the poverty rates defined using relative thresholds (set at 60 percent of each equivalized measure value). For exposition purposes, we focus on consumption with health insurance capped, consumption without health insurance, total expenditures, and pretax income. Relative consumption poverty rates with or without health insurance (about 11 and 14 percent, respectively) or health insurance capped (about 11 percent) are lower than relative poverty rates based on total expenditures (averaging 20 percent) and those based on pretax income (averaging 28 percent).45 Relative poverty rates for all measures fell in 2020 and increased slightly in 2021. Focusing on 2019 relative to 2021, we see that there was little to no change in relative consumption poverty.46 For consumption without health insurance, total expenditures (that include gifts) and pretax income, there were declines in relative poverty.

Using the same relative thresholds as for the total population, chart 2 shows the poverty rates for the population who are less than 18 years of age. The rates for this younger population relative to the rates for the total population are higher for all measures. Unlike for the total population, for which poverty increased from 2020 to 2021, for people less than 18 years of age, there was a continued decline in consumption poverty and in total expenditures poverty in 2021.47 Income poverty increased in 2021, returning to its 2019 level. Note that the income measure we use does not include the expanded Child Tax Credit or the Economic Impact Payments issued during the pandemic. However, receipt of these subsidies could be reflected in the consumption estimates.48

Chart 3 presents the poverty rates based on an absolute concept of poverty, rather than a relative concept. Thresholds are absolute in that they are “fixed” in 2019 and updated only for inflation. The thresholds for each measure are derived in such a way that the 2019 poverty rates for all the measures equal the 2019 relative poverty rate based on consumption with health insurance capped. Thus, the starting poverty rates for all measures are set to equal 11.2 percent. Using this measure, we find that poverty fell from 2019 to 2021 for each of the consumption measures, with the largest drop occurring for consumption without health insurance. Poverty rates for total expenditures also dropped, while pretax income poverty rose from 2020 to 2021.

Chart 4 presents the poverty rates for children (under age 18) that are based on the same absolute thresholds as those used for the total population (with the poverty rate set at 11.2 percent). Child poverty rates are not anchored. As with the relative thresholds, poverty rates for children are higher than those for the total population. However, by 2021, consumption-based child poverty rates had fallen more (from 2.4 to 4.0 percentage points) than did the poverty rates for the total population (from 1.0 to 2.3 percentage points). In contrast, the pretax-income child poverty rate for 2020 to 2021 increased by more (2.0 percentage points) than did the total population income poverty rate (0.9 percentage point).

Inequality

Table 4 presents the Gini indexes for the different measures. Gini indexes are a summary measure of inequality. They range from 0 to 1, with higher values corresponding to less equal distributions. Consumption is distributed more equally than total expenditures, which are distributed more equally than pretax income. Adding health insurance to the consumption measure makes the distribution more equal. The greatest inequality across the 3 years occurred in 2019 for all measures except total expenditures. The distributions of all the measures became more equal in 2020; but inequality increased in 2021.

Table 4. Gini indexes for consumption with health insurance (capped and uncapped), consumption without health insurance, total expenditures, and pretax income, 2019 to 2021
Measure201920202021[1]

Consumption with health insurance capped

0.2500.2410.247

Consumption with health insurance uncapped

0.2470.2390.245

Consumption without health insurance

0.2950.2820.289

Total expenditures

0.3720.3640.376

Pretax income

0.4640.4490.455

[1] Data for 2021 are preliminary.

Source: U.S. Bureau of Labor Statistics.

Chart 5 presents the Lorenz curves for 2020. Lorenz curves are a visual representation of inequality and show the cumulative share of the population (ranked by the value of each measure from lowest to highest value) on the x-axis at or below the cumulative share of the measure as represented on the y-axis. Perfect equality is represented by the 45-degree line; the closer the Lorenz curve is to the line for perfect equality, the more equal the distribution. If a measure is equally distributed across the population, an equal share of the population would account for an equal share of the measure—for example, 50 percent of the population would account for 50 percent of consumption. An example of an unequal distribution is shown in chart 5 by the measure for consumption without health insurance: 50 percent of the population accounts for 30 percent of consumption. As with the Gini index results, pretax income is the least equally distributed of the measures, and consumption with health insurance capped is the most equally distributed. The Lorenz curves for consumption with health insurance and with health insurance capped are indistinguishable, and thus only one of the curves is presented. Lorenz curves for 2019 and 2021 (based on preliminary data) exhibit similar patterns.

Conclusion

In this article, we construct an initial version of a comprehensive consumption measure for the United States and use it to study poverty and inequality from 2019 to 2021. Our results show that the average value of consumption without including health insurance is lower than the average value of total expenditures. However, when we include health insurance, capped or uncapped, average consumption exceeds average total expenditures. We also find that consumption poverty rates defined using relative thresholds are lower than expenditure- or income-based poverty rates, regardless of whether health insurance is included. Consumption is also more equally distributed than expenditures or income. Poverty rates across all our measures fell in 2020 relative to 2019, and the distributions of the measures became more equal. This general finding is likely an effect of the COVID-19 pandemic, but verifying that claim is beyond the scope of this article.

BLS will continue to develop a comprehensive consumption measure for the United States with the CE providing the core data. The next main area of improvement is to incorporate the value of home production. Because home production is not a market transaction and relies instead on household members spending time performing tasks that add value to the household, we must impute time use to CUs participating in the CE. Home production includes the provision of childcare and eldercare. When purchased, both services can represent heavy financial burdens for families, some of whom will be priced out of the market. In such cases, children and elders in these families often receive care from other family members or friends through nonmarket transactions. We expect that the inclusion of home production in the CE will help us better understand consumption dynamics during the COVID-19 pandemic, when many childcare centers were closed.

The consumption measures presented in this article represent research in progress. However, BLS plans to release to the public a research series of consumption values in tables similar to the currently published data tables for CE total expenditures. These tables will include average consumption values. In addition, and dependent upon available resources, BLS is considering making available an auxiliary public-use microlevel data file that researchers could use to reproduce these consumption measures or create new ones of their own.49 Finally, BLS expects its ongoing development work to result in methodological improvements that will be reflected in the CE data and in the consumption measure particularly.

Appendix: Imputation methods

In this appendix, we present the details of the imputation methods for NSLP, WIC, and LIHEAP benefits, market rents for which reported rents are expected to undervalue consumption, health insurance, and vehicle flow of services that are used in the construction of our consumption measures.

NSLP, WIC, and LIHEAP

The basic approach for imputing these in-kind government benefits is based on that of Garner and Gudrais (2018) in their research on producing Supplemental Poverty Measure (SPM) thresholds that account for in-kind benefits.50 For the BLS consumption measure, we draw upon more recent methods developed by BLS (described below) and those developed by the Census Bureau to assign NSLP benefits to CPS-ASEC households for the SPM resource measure during the COVID-19 pandemic period.51

To produce the new consumption measure, the first step is to restrict, when necessary, the CPS-ASEC sample to households that are potentially eligible for in-kind programs. The full sample is asked the LIHEAP questions, and thus there are no sample restrictions for the imputation of LIHEAP benefits. However, only subsets of CPS-ASEC households are asked NSLP and WIC questions. The CPS ASEC only asks the NSLP-participation question of survey respondents in households with school-aged children (ages 5 to 18) who usually ate a hot lunch at school. During CPS-ASEC data collection, if a Census Bureau field representative was asked what is meant by “usually,” the response would be more than 50 percent of the time when the reference period was 2019; for the 2020 and 2021 reference periods, the response to what is meant by “usually” would have been when students were in school prior to the pandemic or when schools remained open during the pandemic.52 The WIC question is only asked of households with women ages 15 to 45 with no children (to include women who could potentially be pregnant) and women ages 15 and older with children under 5 years of age. These same sample restrictions for the NSLP and WIC benefit program are applied to the CE, with one exception. In the CPS ASEC, the NSLP questions are only asked of households with children who usually ate a hot meal at school; this information is not collected in the CE, which means we cannot restrict the CE sample along this dimension. The CE and CPS-ASEC data are then pooled. In the pooled data, NSLP and WIC participation and LIHEAP benefits are not missing in the CPS ASEC, but they are missing in the CE by design. A logistic regression model is used to impute participation when participation values are missing (in this case, the CE). For LIHEAP, a logistic regression model is used to first impute participation and then for those with imputed receipt, an ordinary least squares regression model is used to impute LIHEAP-benefit values from the CPS ASEC to the CE. For the statistical models, the explanatory variables are demographic variables that are defined the same for the CPS ASEC and the CE. For the imputed participation in these programs, the estimated model coefficients are used to generate participation probabilities for CE respondents, and then, using these predicted probabilities, participation (yes or no) is assigned randomly.53

From the CPS-ASEC data, there are three possible outcomes for NSLP: (1) receive free or reduced school lunch, (2) pay full price for school meals, and (3) does not consume school meals. All school lunches provided to children are subsidized. Thus, children in the second group are NSLP participants, but we refer to them as “paid” in that they paid fully for their school lunches. For “free” and “reduced,” the CPS-ASEC question for NSLP participation does not distinguish between these two levels of meal support; thus, we assign children to “free” and “reduced” by using a method similar to that used by the Census Bureau for assigning benefits to participating households when calculating SPM resources. NSLP-program benefit values for the three categories—free, reduced, and paid—are based on data from the USDA.54

Two different methods were used to assign free and reduced benefits, one for 2019 and a different one for 2020 and 2021. The method used for 2019 is based on CU pretax income (not including SNAP benefits) and a random assignment. If this pretax income measure is less than 150 percent of the official poverty threshold, children in the CU are all assumed to receive free meals. CUs with an NSLP-participation assignment of free or reduced and income equal to or above this threshold are randomly assigned to either being free or reduced NSLP participants.55 For 2019, the number of school days for which these benefits are assigned is based on the state average number of school days in an academic year.56 In contrast, for SPM resources, the Census Bureau assigns NSLP benefits using a national average of 179 school days.

The method to assign NSLP benefits for 2020 and 2021 for the BLS consumption measure is an adaption of the method developed by Census Bureau staff when accounting for NSLP benefits in SPM resources.57 The method accounts for both school closures and receipt of EBTs to assign school lunch values. It is based on a combination of school operating status, SNAP receipt, and an additional question that was added to the CPS ASEC for 2020 and 2021: “Did your children continue receiving free/reduced price meals through your school or school district if schools were closed during the pandemic?” For 2020 and 2021, free lunch NSLP benefits are assigned to consumers reporting SNAP and with imputed NSLP participation. For 2020 only, reduced lunch NSLP benefits were assigned to CUs with imputed NSLP participation but no SNAP benefits. There were no reduced benefits assigned for 2021. For 2020 and 2021, we only assign NSLP benefits to CUs with school-age children when they are expected to be in school, in person. Because many schools were closed during the pandemic in 2020 and 2021, the USDA used EBTs to administer NSLP benefits. For our measure of consumption, when EBTs were received, we assign a zero value to the NSLP benefit because we assume expenditures based on the use of the EBTs are already reflected in reported food expenditures. This is the same assumption that underlies the treatment of SNAP in the CE because SNAP benefits are administered via EBTs and thus are considered “like cash”; adding SNAP values to reported food expenditures would be double counting.58

For 2019 to 2021, if children in CUs were not assigned as receiving free or reduced lunch and were in school, in person, they were assumed to eat a hot lunch and to have received the NSLP-benefit values for paid meals. For 2019, the new BLS consumption measure used the average number of school days in an academic year, by state, to produce benefits for those who paid for their meals. For 2020 and 2021, we employed the method developed by the Census Bureau that accounted for school closings and changes in the NSLP when imputing NSLP benefits for those who paid for their meals.

For WIC, once participation receipt is imputed to the CE, WIC benefits are assigned. Average quarterly WIC-benefit values imputed to the CE are based on the benefits, infant-formula rebates, and infant participation. For those CE-respondent CUs who are imputed to have participants, the values for the in-kind benefits are assigned on the basis of state level per-beneficiary averages for WIC food benefits and infant-formula rebates, as well as timing of the adoption of WIC being electronically administered via EBT.59 For consumer units living in states that have fully implemented the distribution of WIC food benefits by EBT, only the infant-formula rebate is added to consumption. To produce quarterly WIC benefits, average monthly values are produced and then multiplied by 3. For example, to compute the average monthly value for 2019 (represented by the CE data for 2019 quarter two to 2020 quarter one), three-quarters of 2019 fiscal-year monthly averages are pooled with one quarter of the 2020 fiscal-year monthly average. In contrast, for our earlier measure, fiscal year 2019 monthly averages were used. Average WIC food benefits are assigned to women and children. In addition, average infant-formula rebates are added to WIC food benefits. The infant-formula rebate benefit is assigned to the fraction of all infants who are assumed to be only formula fed.60 WIC benefits and participation rates are based on administrative data published by the USDA.61

Consumption of rental shelter

For the consumption measure, there are several cases for which we expect the CE-reported out-of-pocket shelter expenditures not to reflect the full consumption value of this shelter. These include CUs receiving government rent subsidies, those living in public housing or those living in rent-controlled units, and those assumed to be paying less than market rents for other reasons. The CE asks about participation in the government programs, but not about the value of the difference in what renters pay for shelter and the market value of similar units, or about the market value of their units. Others who may pay less than the full consumption value of shelter include CUs consisting of people who are living rent free and those who are paying less rent because they are providing services to the landlord in lieu of paying the full rent. In addition, it is unlikely that the rents reported by college students living in dormitories reflect their consumption of shelter.

To impute rents for those receiving government rent subsidies, living in public housing, or living in rent-controlled units, we use the CE-participation variable for each program. In addition, we impute rents for renters who we assume are not paying full market rents: specifically, those reporting expenditures for maintenance and repairs in addition to reported rents. We also use the fact that some CUs consist of people living in dormitories to assign shelter consumption values.

We estimate a model of the full market rental value as a function of demographic, geographic, and housing-unit characteristics using a censored normal regression model, where the observed rental payment is censored from above if the CU received rental assistance (i.e., received government rental assistance, lived in public housing, or lived in a rent-controlled unit), or paid additional rent-related expenses. The estimation sample is restricted to traditional renters (i.e., respondents who report renting and do not report participating in a government rental assistance program, and who do not report out-of-pocket expenditures beyond rent alone). The estimation sample also excludes CUs consisting of people living in college or university dormitories. The rent used in the estimation model as the dependent variable is the logarithm of rent paid for the last month in the reference period, rather than the quarterly reported rent. This last month’s rent is used so that the period more closely matches that of the period covered in owners’ reports of rental equivalence: how much owners think their homes would rent for currently (at the time of the interview) without furnishings and without utilities.62 The imputed market rent multiplied by 3 is used in place of the CE-recorded rent for the last 3 months (the reference period) for all respondents who are identified as participating in a government rental assistance program, occupying their residence without payment of rent, and those with out-of-pocket maintenance and repair expenditures in addition to their reported rents. To derive shelter consumption for renters, out-of-pocket expenditures for tenant’s insurance are added to the rents reported by CUs assumed to be paying full market rents, and to the imputed rents for all other renters.

The consumption shelter value for CUs that consist of people living in college or university dormitories is imputed differently. Because dormitories are distinctly different from other rental units, we cannot apply our market-rent imputation model to these cases. Instead, we assign the national average dormitory cost, which we obtained from the National Center for Education Statistics.63 We use this value to calculate an average monthly cost, which is then adjusted to reflect the 3-month cost for the reference period. Because we do not expect dormitories to be rented year round, to get the 3-month reference-period cost, the monthly value is scaled by the number of months in the reference period that overlap with the August–May school year. For example, a CU interviewed in July has a reference period of April, May, and June. This reference period has 2 months that overlap with the August–May school year, so the consumption value for dormitories during this reference period is twice the average monthly cost for a dormitory. The same adjustment is made for the cost of board while living at college.

Health insurance

The CE Interview asks about different types of insurance coverage and measures the out-of-pocket insurance premium associated with each type of insurance. The CE also measures out-of-pocket expenditures on medical goods and services. For people with insurance, out-of-pocket expenditures on medical goods and services will reflect copayments and coinsurance. One limitation of the CE data on health insurance coverage is that for types of insurance other than Medicare, the only questions asked are whether anyone in the CU is covered and the number of people who are covered. So, there is no way to identify the specific individuals covered.

For the consumption measure with health insurance, we first impute the full value of health insurance on the basis of insurance type. For private health insurance, the full value is based on the market price. This includes the out-of-pocket premiums that are captured by the CE and the employer contribution for employer-provided plans, or the value of any subsidy received for individual plans. For public insurance programs, the full value is based on the cost to the government (including administrative costs).

The methods and data sources used for the imputation of the full value of health insurance are based on the work of Garner, et al. (2022).64 For that project, imputed values were added to CE health insurance expenditures to match the scope of the U.S. Bureau of Economic Analysis personal consumption expenditures (PCE) data, which, by definition, include employer contributions to employer-provided health insurance and government insurance programs. The data sources used to impute the values for the CE-PCE project are the same as those used to produce the consumption measures with health insurance (capped and uncapped) presented in this article. However, the values for health insurance used in the production of these consumption measures and those used for the CE-PCE project are not directly comparable because the two measures differ in scope.65

For employer-provided health insurance, we use data from the Medical Expenditure Panel Survey Insurance Component (MEPS-IC).66 MEPS-IC has data on the average employer contribution and average employer share. For employer-provided coverage, we impute different amounts on the basis of the type of plan: single coverage, plus one, and family. We assign individuals in the CE to different plan types on the basis of the number of people covered by the plan. So, individuals are assumed to have single coverage if the plan covers one CU member, plus one if the plan covers two CU members, and family coverage if the plan covers more than two CU members.

People who purchase individual health insurance plans can receive subsidies in the form of a tax credit. The CE asks individuals who purchase individual plans whether they receive a subsidy. We assign the average subsidy among those who receive a subsidy to CUs in the CE with individual plans who report receiving a subsidy. The data on the average subsidy amounts come from the Centers for Medicare & Medicaid Services (CMS).67

For public programs, the imputed amount is the per-enrollee cost to the government, including administrative costs. For Medicare, the average cost (including administrative costs) per beneficiary is calculated from the Trustee’s report for each of the Medicare programs: traditional Medicare (includes Parts A and B), Medicare Part C, and Medicare Part D. These programs also have out-of-pocket premiums. There are two ways to account for out-of-pocket premiums. One way is to add the average cost less premiums to the out-of-pocket premium amount reported in the CE. The other approach is to assign the average cost and ignore the out-of-pocket amounts. Which approach is preferable depends on whether the variation in out-of-pocket premiums for these programs is due to variation in the amount of coverage purchased or to variation in the amount the premium subsidized. If the coverage is the same, then adding the average cost less average premium amount will lead to a lower value for individuals who have received subsidies for their premiums. This is the case for traditional Medicare, so we ignore the CE-reported premiums and impute a value on the basis of the average cost. We treat Part D the same; although there is variation in Part D plans, the variation in out-of-pocket premiums due to subsidies is likely much larger. Part C can be thought of as a bundle of traditional Medicare and supplemental coverage. For the traditional Medicare component, we impute an average cost and ignore the CE-reported Part B premiums. Then, we add any additional premiums paid to reflect the supplemental coverage.

Other public programs are more straightforward because there is less variation in the types of coverage offered. So, we assign a single average-cost value to participants in other public programs and ignore any out-of-pocket premiums. For Medicaid and the Children’s Health Insurance Program, the per-enrollee average cost comes from the CMS National Health Expenditures tables.68 Per-enrollee costs for other public programs (e.g., TRICARE, Veterans Affairs benefits, and Indian Health Services) are calculated from the budgets for each of the programs.69

Depreciation and opportunity costs of owning vehicles

For vehicles, we employ a user-cost approach to assign a consumption value that is based on the flow of services. The user cost is defined as the depreciation plus the opportunity cost of capital (value of the car times the interest rate), plus maintenance and repair costs. We estimate the depreciation and opportunity costs; the other components of user costs are already included in consumption as nonvehicle-purchase-related expenditures. Depreciation and opportunity costs are imputed to CUs on the basis of vehicle ownership. For our research, we restrict vehicles to cars and trucks, with sport utility vehicles being categorized as trucks. For 2021, about 87 percent of vehicles reported in the CE as being owned were classified as cars or trucks. The remaining 13 percent are other vehicles such as planes, boats with motors, and motorized campers. Because the sample size for other vehicles is quite small and the CE does not collect data on characteristics for these vehicles, depreciation and opportunity costs for “other vehicles” are not calculated and thus are not included in the consumption measure.

We estimate the vehicle depreciation rate using data on vehicles owned as reported in the CE. For all vehicles owned, the CE collects data on the vehicle characteristics, including make, model, and year. The CE also asks when the vehicle was acquired and the purchase price of the vehicle. We estimate a depreciation rate by comparing the purchase price for vehicles purchased at different ages while controlling for vehicle characteristics. We estimate an age-specific depreciation rate for vehicles 10 years old or less. Vehicles over 10 years old are assumed to depreciate at a constant rate, as there are too few transactions involving older vehicles to estimate age-specific depreciation rates. The imputed depreciation value is calculated as the current market value of the vehicle times the age-specific depreciation rate. The current market value is calculated as the estimated new purchase price of the vehicle minus the estimated depreciation for prior years. The opportunity cost of capital is the product of an interest rate and the estimated current market value of each car and truck owned by the CU. The interest rates are the year-specific Treasury Long-Term Average (Over 10 Years), Inflation-Indexed annual rates, not seasonally adjusted, published by the Federal Reserve Bank of St. Louis.70

Table A-1. Nominal quarterly means of expenditures and consumption, by subcomponent, 2019 to 2021
CategoryTotal expendituresTotal expenditures, excluding giftsConsumption without health insuranceConsumption with health insurance uncapped
201920202021[1]201920202021[1]201920202021[1]201920202021[1]

Average quarterly expenditures or consumption

$14,717$14,555$16,196$14,509$14,386$15,955$12,395$12,158$13,562$16,792$16,767$18,062

Percent of consumption that is imputed

[2][2][2][2][2][2]12.16%11.31%10.95%30.11%31.87%29.99%

Food[3]

$2,184$2,139$2,472$2,175$2,136$2,462$2,220$2,142$2,492$2,220$2,142$2,492

Alcoholic beverages

134112142134112142134112142134112142

Housing

4,8625,0375,3674,8154,9955,312[2][2][2][2][2][2]

Shelter

3,0103,1333,3002,9873,1143,723[2][2][2][2][2][2]

Owned dwellings[4]

1,6621,8491,8611,6621,8491,8613,2463,5183,7623,2463,5183,762

Rented dwellings[5]

1,1101,1001,1771,1991,0891,1681,2141,1881,2901,2141,1881,290

Other lodging[6]

238184263224176244332308362332308362

Utilities, fuels, and public services[7]

1,0121,0491,0581,0081,0441,0521,0121,0471,0561,0121,0471,056

Household operations

390362404385357399[2][2][2][2][2][2]

Child daycare expenses[8]

754860754860[2][2][2][2][2][2]

Out-of-pocket expenses, excluding child daycare

315313344310309340310309340310309340

Household furnishings and equipment

451493604435480588[2][2][2][2][2][2]

Purchase of major kitchen appliances[9]

657297657297[2][2][2][2][2][2]

Out-of-pocket expenses, excluding household furnishings and equipment

386422508371408491371408491371408491

Apparel and services

310251363296237311296237311296237311

Transportation

2,6492,4302,7762,6162,4042,751[2][2][2][2][2][2]

Vehicle purchases (net outlay)[10]

1,1351,2081,2811,1161,1881,268[2][2][2][2][2][2]

Depreciation and opportunity costs of owning vehicles

[2][2][2][2][2][2]854829794854829794

Gasoline, other fuels, and motor oil

522386559518384554518384554518384554

Other vehicle expenses

792782807791782806791782806791782806

Public and other transportation

19954129191511231915112319151123

Health

1,2251,2271,3011,2171,2251,296[2][2][2][2][2][2]

Health insurance[11]

886918927886917927[2][2][2]4,3974,6094,500

Medical services

239217274231215270[2][2][2][2][2][2]

Prescription drugs

666568666568[2][2][2][2][2][2]

Medical supplies

342932342832[2][2][2][2][2][2]

Entertainment

643626801628615780[2][2][2][2][2][2]

Motorized recreational vehicles (net outlay)

265583265583[2][2][2][2][2][2]

Out-of-pocket expenses, excluding motorized recreational vehicles

617571718601560697601560697601560697

Personal care products and services

9668102966710296671029667102

Reading

141619141518141518141518

Education[10]

316284269245223209[2][2][2][2][2][2]

Tobacco products and smoking supplies

777684777684777684777684

Miscellaneous

126135149118124137118124137118124137

Personal insurance and pensions[10]

1,5721,5921,7411,5721,5921,741[2][2][2][2][2][2]

Life and other personal insurance[10]

129121119129121119[2][2][2][2][2][2]

Pensions and Social Security[10]

1,4431,4711,6231,4431,4711,623[2][2][2][2][2][2]

Cash contributions[10]

506564608506564608[2][2][2][2][2][2]

Income:

CE-defined quarterly pretax income[12]

$20,726$21,156$21,890$20,726$21,156$21,890$20,726$21,156$21,890$20,726$21,156$21,890

Census Bureau-defined quarterly pretax income[13]

$20,665$21,094$21,798$20,665$21,094$21,798$20,665$21,094$21,798$20,665$21,094$21,798

Number of consumer units (in thousands)

132,068131,542133,653132,068131,542133,653132,068131,542133,653132,068131,542133,653

Number of sample interviews

21,28020,15820,40621,28020,15820,40621,28020,15820,40621,28020,15820,406

Consumer unit characteristics:

Age of reference person

51.6252.1451.8751.6252.1451.8751.6252.1451.8751.6252.1451.87

Average number in consumer unit:

People

2.462.472.442.462.472.442.462.472.442.462.472.44

Children under 18

0.570.580.560.570.580.560.570.580.560.570.580.56

Adults 65 and older

0.40.420.420.40.420.420.40.420.420.40.420.42

Earners

1.31.291.281.31.291.281.31.291.281.31.291.28

Vehicles:

Vehicles (owned)

1.841.821.81.841.821.81.841.821.81.841.821.8

Vehicles (leased)

0.080.080.070.080.080.070.080.080.070.080.080.07

Percent distribution:

Reference person:

Men

484747484747484747484747

Women

525353525353525353525353

Housing tenure:

Homeowner

646665646665646665646665

With mortgage

373938373938373938373938

Without mortgage

272727272727272727272727

Renter

343333343333343333343333

[1] Data for 2021 are preliminary.

[2] Not applicable.

[3] For consumption, includes National School Lunch Program and Women, Infants, and Children program. Also includes an adjustment for board for students who report living in a dorm.

[4] For consumption, includes rental equivalence for primary residence.

[5] For consumption, includes market value of rental units. Consumer units residing in a college dorm were assigned the national average value for dorms based on data from the U.S. Department of Education.

[6] For consumption, includes rental equivalence for vacation homes. Consumption also includes an adjustment for expenditures on dorms for students who report living in a dormitory.

[7] For consumption, includes energy assistance using Low-Income Home Energy Assistance Program.

[8] Not included in consumption because considered part of education.

[9] Not included in consumption because considered part of rental equivalence and rent.

[10] Item not included in consumption.

[11 For consumption, only the imputation for the full value of health insurance is included. For 2021, the value of health insurance is based on 2020 imputations adjusted for inflation.

[12] Definition excludes food and rent as pay. This definition differs from the definition of income used in the published Consumer Expenditure Surveys tables, which includes food and rent as pay.

[13] Does not include the value of Supplemental Nutrition Assistance Program or food and rent as pay.

Source: U.S. Bureau of Labor Statistics.

ACKNOWLEDGMENT: We would like to thank former Commissioner of Labor Statistics William W. Beach, without whose support this research and the new consumption measure we introduce would not have been possible. We would also like to thank Associate Commissioner Jeffrey Hill of the Office of Prices and Living Conditions for his support throughout this project, as well as researchers Jonathan D. Fisher, David S. Johnson, Bruce D. Meyer, and James X. Sullivan, and our colleagues at the U.S. Census Bureau: John Creamer, Liana Fox, and Emily A. Shrider. Finally, we thank our research assistants for their excellent work in producing inputs for the consumption measure: Caleb Cho for vehicle inputs and Juan Munoz for in-kind benefit and rent inputs.

Suggested citation:

Thesia I. Garner, Brett Matsumoto, Jake Schild, Scott Curtin, and Adam Safir, "Developing a consumption measure, with examples of use for poverty and inequality analysis: a new research product from BLS," Monthly Labor Review, U.S. Bureau of Labor Statistics, April 2023, https://doi.org/10.21916/mlr.2023.8

Notes


1 The Luxembourg Income Study Database (LIS) and research series are excellent sources of information on income from household surveys; see the LIS Cross-National Data Center website at https://www.lisdatacenter.org/. For countries with developing economies, consumption and consumption expenditures are more meaningful concepts for household survey respondents than is income; thus, these measures are more often used for poverty and inequality analysis. See, for example, recent reports from the World Bank, including Piecing Together the Poverty Puzzle, Poverty and Shared Prosperity series (Washington, DC: World Bank, 2018), https://www.worldbank.org/en/publication/poverty-and-shared-prosperity-2018.

2 See Bruce D. Meyer and James X. Sullivan, “Consumption and income inequality in the United States since the 1960s,” Journal of Political Economy, vol. 131, no. 2, February 2023, https://doi.org/10.1086/721702; and Jonathan Fisher, David S. Johnson, and Timothy M. Smeeding, “Inequality of income and consumption in the U.S.: measuring the trends in inequality from 1984 to 2011 for the same individuals,” Review of Income and Wealth, vol. 61, no. 4, December 2015, pp. 630–50, https://doi.org/10.1111/roiw.12129. In addition, researchers have promoted the use of the joint distribution of income, consumption, and wealth as a better measure of well-being; see, for example, Jonathan D. Fisher, David S. Johnson, Timothy M. Smeeding, and Jeffrey P. Thompson, “Inequality in 3-D: income, consumption, and wealth,” Review of Income and Wealth, vol. 68, no, 1, March 2022, pp. 16–42, https://doi.org/10.1111/roiw.12509.

3 See Fernando Rios-Avila, “Quality of match for statistical matches using the American Time Use Survey 2013, the Survey of Consumer Finances 2013, and the Annual Social and Economic Supplement 2014,” Working Paper 914 (Annandale-on-Hudson, NY: Levy Economics Institute of Bard College, September 2018), https://www.levyinstitute.org/pubs/wp_914.pdf; Laura Wheaton, “Underreporting of means-tested transfer programs in the CPS and SIPP,” Research Report (Washington, DC: Urban Institute, February 2008), https://www.urban.org/research/publication/underreporting-means-tested-transfer-programs-cps-and-sipp; Graton Gathright and Tyler Crabb, “Reporting of SSA program participation in SIPP,” Working Paper (U.S. Census Bureau, 2014); Jonathan L. Rothbaum, “Comparing income aggregates: How do the CPS and ACS match the national income and product accounts, 2007–2012,” SEHSD Working Paper 2015-01 (U.S. Census Bureau, January 14, 2015); https://www.census.gov/library/working-papers/2015/demo/SEHSD-WP2015-01.html; Bruce D. Meyer, Nikolas Mittag, and Robert M. Goerge, “Errors in survey reporting and imputation and their effects on estimates of Food Stamp Program participation,” Working Paper 25143 (National Bureau of Economic Research, October 2018), https://doi.org/10.3386/w25143; and Bruce D. Meyer and Nikolas Mittag, “Using linked survey and administrative data to better measure income: implications for poverty, program effectiveness and holes in the safety net,” American Economic Journal: Applied Economics, vol. 11, no. 2, April 2019, https://www.aeaweb.org/articles?id=10.1257/app.20170478.

4 See Bruce D. Meyer and James X. Sullivan, “Measuring the well-being of the poor using income and consumption,” Journal of Human Resources, vol. 38, Special Issue on Income Volatility and Implications for Food Assistance Programs, 2003, pp. 1180-1220 https://www.jstor.org/stable/3558985; Bruce D. Meyer and James X. Sullivan, “Viewpoint: Further evidence on measuring the well-being of the poor using income and consumption,” Canadian Journal of Economics, vol. 44, no. 1, February 2011, pp. 52–87, https://www.jstor.org/stable/41336351; and Bruce D. Meyer and James X. Sullivan, “Identifying the disadvantaged: official poverty, consumption poverty, and the new Supplemental Poverty Measure,” Journal of Economic Perspectives, vol. 26, no. 3, Summer 2012, pp. 111–36, https://doi.org/10.1257/jep.26.3.111.

5 See, for example, the World Bank’s Multidimensional Poverty Measure, which draws from other prominent poverty measures, particularly the Multidimensional Poverty Index (MPI) developed by the United Nations Development Programme and Oxford University, https://www.worldbank.org/en/topic/poverty/brief/multidimensional-poverty-measure; and “Measuring well-being and progress: well-being research” (Organisation for Economic Co-operation and Development), https://www.oecd.org/wise/measuring-well-being-and-progress.htm.

6 In the U.S. Bureau of Labor Statistics (BLS) Consumer Expenditure Surveys (CE), a consumer unit is defined as follows: “A consumer unit comprises either: (1) all members of a particular household who are related by blood, marriage, adoption, or other legal arrangements; (2) 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 independent; or (3) two or more persons living together who use their income 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.” See “Consumer Expenditure Surveys: Glossary,” entry for “consumer unit” (U.S. Bureau of Labor Statistics, last modified February 13, 2015), https://www.bls.gov/cex/csxgloss.htm.

7 See Nancy Folbre, “Care data infrastructure: a U.S. case study,” Review of Income and Wealth, January 2023 (online version of record before inclusion in an issue), https://doi.org/10.1111/roiw.12633.

8 Although we cannot trace the origin of this assumption, we have found examples of expenditures being used as a proxy for consumption as far back as Hall (1978), Sargent (1978), and Hall and Mishkin (1982). These authors state that they use expenditures as a measure of consumption, but they do not provide any justification for doing so. The lack of justification suggests that using expenditures as a measure of consumption was an accepted idea at the time. See Robert E. Hall, “Stochastic implications of the life cycle-permanent income hypothesis: theory and evidence,” Journal of Political Economy, vol. 86, no. 6, December 1978, pp. 971–87, https://www.journals.uchicago.edu/doi/10.1086/260724; Thomas J. Sargent, “Estimation of dynamic labor demand schedules under rational expectations,” Journal of Political Economy, vol. 86, no. 6, December 1978, pp. 1009–44, https://www.journals.uchicago.edu/doi/10.1086/260726; and Robert E. Hall and Frederic S. Mishkin, “The sensitivity of consumption to transitory income: estimates from panel data on households,” Econometrica, vol. 50, no. 2, March 1982, pp. 461–81, https://www.jstor.org/stable/1912638?seq=8.

9 For a review of these early efforts, see David S. Johnson, John M. Rogers, and Lucilla Tan, “A century of family budgets in the United States,” Monthly Labor Review, May 2001, https://www.bls.gov/opub/mlr/2001/05/art3full.pdf.

10 For an updated version of the conceptual framework for the CE, see Scott Curtin, Adam Safir, Thesia I. Garner, Brett Matsumoto, and Jake Schild, “A conceptual framework for the U.S. Consumer Expenditure Surveys” (U.S. Bureau of Labor Statistics, last modified October 7, 2022), https://www.bls.gov/cex/research_papers/garner-et-al-conceptual-framework-for-CE.htm. An earlier version (September 2000) is available from the authors upon request.

11 For a select list of references, see those listed in “Consumer Expenditure Surveys: Consumption Research” (U.S. Bureau of Labor Statistics, last modified December 27, 2022), https://www.bls.gov/cex/consumption-research.htm.

12 Fisher, Johnson, and Smeeding, “Inequality of income and consumption in the U.S.: measuring the trends in inequality from 1984 to 2011 for the same individuals.”

13 See Bruce D. Meyer and James X. Sullivan, “Winning the war: poverty from the Great Society to the Great Recession,” Brookings Papers on Economic Activity, Fall 2012, pp. 133–83, https://www.brookings.edu/bpea-articles/winning-the-war-poverty-from-the-great-society-to-the-great-recession/; and Bruce D. Meyer and James X. Sullivan, “Consumption and income inequality and the Great Recession,” American Economic Review, vol. 103, no. 3, May 2013, pp. 178–83, https://www.aeaweb.org/articles?id=10.1257/aer.103.3.178.

14 See Grayson Armstrong, Caleb Cho, Thesia I. Garner, Brett Matsumoto, Juan Munoz, and Jake Schild, “Building a consumption poverty measure: initial results following recommendations of a federal interagency working group,” AEA Papers and Proceedings, vol. 112, May 2022, pp. 335–39, https://doi.org/10.1257/pandp.20221041.

15 See Final Report of the of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty, p. 29, https://www.bls.gov/evaluation/final-report-of-the-interagency-technical-working-group-on-evaluating-alternative-measures-of-poverty.pdf. Although most of the work of the group was completed in 2020, the report was posted to the BLS website in January 2021.

16 For the BLS definition of total expenditures in the CE, see “Consumer Expenditures and Income: Concepts,” Handbook of Methods (U.S. Bureau of Labor Statistics, last modified September 12, 2022), https://www.bls.gov/opub/hom/cex/.

17 See Franco Modigliani and Richard Brumberg, “Utility analysis and the consumption function: an interpretation of cross-section data,” in Kenneth K. Kurihara, ed., Post-Keynesian Economics (New Brunswick, NJ: Rutgers University Press, 1954), pp. 388–436; and Milton Friedman, A Theory of the Consumption Function (Princeton, NJ: Princeton University Press, 1957).

18 Tullio Jappelli and Luigi Pistaferri, The Economics of Consumption: Theory and Evidence (New York, NY: Oxford University Press, 2017).

19 David M. Cutler and Lawrence F. Katz, “Macroeconomic performance and the disadvantaged,” Brookings Papers on Economic Activity, vol. 1991, no. 2, 1991, pp. 1–74, https://doi.org/10.2307/2534589; and Daniel T. Slesnick, “The standard of living in the United States,” Review of Income and Wealth, vol. 37, no. 4, December 1991, pp. 363–86, https://doi.org/10.1111/j.1475-4991.1991.tb00379.x.

20 David S. Johnson, Timothy M. Smeeding, and Barbara Boyle Torrey, “Economic inequality through the prisms of income and consumption,” Monthly Labor Review, April 2005, https://www.bls.gov/opub/mlr/2005/04/art2full.pdf; Dirk Krueger and Fabrizio Perri, “Does income inequality lead to consumption inequality? Evidence and theory,” Review of Economic Studies, vol 73, no. 1, January 2006, pp. 163–93, https://doi.org/10.1111/j.1467-937X.2006.00373.x; Orazio Attanasio, Erich Battistin, and Hidehiko Ichimura, “What really happened to consumption inequality in the United States?,” in Ernst R. Berndt and Charles R. Hulten, eds., Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches, National Bureau of Economic Research Studies in Income and Wealth, vol. 67 (Chicago, IL: University of Chicago Press, 2007); Jonathan Heathcote, Fabrizio Perri, and Giovanni Violante, “Unequal we stand: an empirical analysis of economic inequality in the United States, 1967–2006,” Review of Economic Dynamics, vol. 13, no. 1, January 2010, pp. 15–51, https://doi.org/10.1016/j.red.2009.10.010; Mark A. Aguiar and Mark Bils, “Has consumption inequality mirrored income inequality,” Working Paper 16807 (Cambridge, MA: National Bureau of Economic Research, February 2011), https://doi.org/10.3386/w16807; Olivier Coibion, Yuriy Gorodnichenko, Lorenz Kueng, and John Silvia, “Innocent bystanders? Monetary policy in the U.S.,” Working Paper 18170 (Cambridge, MA: National Bureau of Economic Research, June 2012), https://doi.org/10.3386/w18170; and Orazio Attanasio, Erik Hurst, and Luigi Pistaferri, “The evolution of income, consumption, and leisure inequality in the U.S., 1980–2010,” Working Paper 17982 (Cambridge, MA: National Bureau of Economic Research, April 2012), https://doi.org/10.3386/w17982.

21 Meyer and Sullivan, “Consumption and income inequality and the Great Recession”; Meyer and Sullivan, “Consumption and income inequality in the U.S. since the 1960s”; and Fisher, Johnson, and Smeeding, “Inequality of income and consumption in the U.S.”

22 See the following sources for the international publications: Report II: Household Income and Expenditure Statistics, Seventeenth International Conference of Labour Statisticians, November 24–December 3, 2003 (International Labour Organization, 2003), https://www.ilo.org/public/libdoc/ilo/2003/103B09_182_engl.pdf; OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth (Paris: Organisation for Economic Co-operation and Development, 2013), https://dx.doi.org/10.1787/9789264194830-en; United Nations Economic Commission for Europe, Guide on Poverty Measurement (New York: United Nations, 2017), https://unece.org/DAM/stats/publications/2018/ECECESSTAT20174.pdf; and Giulia Mancini and Giovanni Vecchi, On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis (Washington, DC: The World Bank, March 2022), https://documents1.worldbank.org/curated/en/099225003092220001/pdf/P1694340e80f9a00a09b20042de5a9cd47e.pdf. For further background information, see the following two studies cited in the World Bank report: Angus Deaton and Salman Zaidi, “Guidelines for constructing consumption aggregates for welfare analysis,” Living Standards Measurement Study Working Paper 135 (Washington, DC: The World Bank, May 2002), https://documents1.worldbank.org/curated/en/206561468781153320/pdf/Guidelines-for-constructing-consumption-aggregates-for-welfare-analysis.pdf; and Jean Olson Lanjouw and Peter Lanjouw, “How to compare apples and oranges: poverty measurement based on different definitions of consumption,” Review of Income and Wealth, vol. 47, no. 1, March 2001, pp. 25–42, https://doi.org/10.1111/1475-4991.00002.

23 See Final Report of the of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty. See also Bruce Meyer and David Johnson, “Poverty measurement for the next generation: findings from the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty,” W75-2021, webinar from Institute for Research on Poverty, University of Wisconsin-Madison, April 21, 2021, https://www.irp.wisc.edu/resource/poverty-measurement-for-the-next-generation-findings-from-the-interagency-technical-working-group-on-evaluating-alternative-measures-of-poverty/

24 See Curtin et al., “A conceptual framework for the U.S. Consumer Expenditure Surveys.”

25 Not collected in the Interview but in the Diary are items such as postage and prescription drugs.

26 The definition of pretax income used in the microdata differs from the definition of pretax income used in the published CE tables. Specifically, the definition used in the published CE tables includes income from food and rent as pay, whereas the microdata definition of pretax income does not include these sources of income. Both definitions include Supplemental Nutrition Assistance Program (SNAP) benefits.

27 See “Income and Poverty” (U.S. Census Bureau, last modified July 6, 2022), https://www.census.gov/topics/income-poverty.html.

28 See Report II: Household Income and Expenditure Statistics (International Labour Organization); OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth; and Final Report of the of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty.

29 For owned primary residences and owned vacation homes, we use the CE variable that is created for use in the production of the CPI based on quarterly rental equivalence values adjusted for ownership. For owned timeshares, we use the same comparable variable but with the addition of an adjustment for duration of usage.

30 See Final Report of the of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty.

31 Meyer and Sullivan, “Winning the war: poverty from the Great Society to the Great Recession”; Meyer and Sullivan, “Consumption and income inequality and the Great Recession”; and Johnson and Smeeding, “Inequality of income and consumption in the U.S.: measuring the trends in inequality from 1984 to 2011 for the same individuals.”

32 In addition to the differences noted above, there are also differences regarding the samples used to create the consumption measures. For example, Meyer and Sullivan included consumer units that participated in the CE Interview during quarters that correspond to a calendar year (e.g., they use CE Interview data collected in the first calendar quarter of 2019 through the fourth calendar quarter of 2019 for their 2019 consumption measure). Fisher, Johnson, and Smeeding restrict their sample to only those respondents with four consecutive interviews and create annual consumption values by summing the quarterly values. Specifically, the estimation sample includes consumer units whose last interview was between April of the current year and March of the following year, with the restriction that there were to be four interviews (e.g., 2009 annual estimates of consumption are estimated as the sum of quarterly values, with their last interview as early as April 2009 or as late as March 2010). For the BLS measure, we base our sample on CE interviews conducted in the second quarter of the current calendar year through the first quarter of the following calendar year to create quarterly measures of consumption for the current year (e.g., we use CE Interview data collected from second quarter 2019 through first quarter 2020 for our 2019 consumption measure). Data collected in each calendar quarter reference expenditures made during the previous 3 months; for example, data collected in the second quarter of 2019 refer to expenditures made in the period from January through March 2019. Thus, our measure covers expenditures from January of the current year through February of the following year (i.e., the 2019 consumption measure covers January 2019 through February of 2020).

33 Meyer and Sullivan, “Winning the war: poverty from the Great Society to the Great Recession”; Meyer and Sullivan, “Consumption and income inequality and the Great Recession”; and Fisher, Johnson, and Smeeding, “Inequality of income and consumption in the U.S.”

34 Meyer and Sullivan, “Winning the war: poverty from the Great Society to the Great Recession”; Meyer and Sullivan, “Consumption and income inequality and the Great Recession.”

35 Ibid.

36 Fisher, Johnson, and Smeeding, “Inequality of income and consumption in the U.S.”

37 Meyer and Sullivan, “Winning the war: poverty from the Great Society to the Great Recession”; Meyer and Sullivan, “Consumption and income inequality and the Great Recession.”

38 The Interagency Technical Working Group on Evaluating Alternative Measures of Poverty (ITWG) recommends that health insurance be no more than half of total consumption. We chose to use 50 percent, rather than 30 percent or some other value, because it is the least binding cap that is still consistent with the ITWG recommendations.

39 Fisher, Johnson, and Smeeding, “Inequality of income and consumption in the U.S.”

40 This discrepancy occurs because the published tables use integrated data from both the Interview and Diary Surveys, whereas the results presented in this article are strictly based on the Interview Survey. Additionally, the published tables show calendar-year estimates—meaning, the expenditures used in the calculation are all within a specified calendar year. This study defines the measure on the basis of a collection year, which we defined as the second quarter of a specified year through the first quarter of the following year; therefore, the quarterly average means also include expenditures from outside a given calendar year. Finally, the adjustment to the quarterly weights, in order to produce the publication tables, is not exactly equivalent to the adjustment made in this study.

41 For example, see table 1800, “Region of residence: Annual expenditure means, shares, standard errors, and coefficients of variation, Consumer Expenditure Surveys, 2021,” https://www.bls.gov/cex/tables/calendar-year/mean-item-share-average-standard-error/cu-region-1-year-average-2021.pdf. Food and rent as pay are included in the “other income” category in the table.

42 This equivalence is the same one used by BLS to produce the Supplemental Poverty Measure (SPM) thresholds. See “Price and Index Number Research: 2021 Research Supplemental Poverty Measure Thresholds” (U.S. Bureau of Labor Statistics, last modified June 23, 2022), https://www.bls.gov/pir/spm/spm_thresholds_2021.htm.

43 See “Consumer Price Index: Chained Consumer Price Index For All Urban Consumers (C-CPI-U)” (U.S. Bureau of Labor Statistics, last modified December 3, 2021), https://www.bls.gov/cpi/additional-resources/chained-cpi.htm.

44 For further evidence of the impact of the pandemic, see “Changes to expenditures during the COVID-19 pandemic,” The Economics Daily, May 3, 2022, https://www.bls.gov/opub/ted/2022/changes-to-consumer-expenditures-during-the-covid-19-pandemic.htm: “After the COVID-19 pandemic began, consumer spending in the second quarter of 2020 was down 9.8 percent from the same period in 2019. One year later, in the second quarter of 2021, the pandemic was still affecting the economy, but businesses and consumers had begun to adapt. That resulted in consumer expenditures that were 15.7 percent higher in the second quarter of 2021 than a year earlier.”

45 These contrast with estimates of poverty rates based on CE pretax income data and official poverty thresholds; rates are estimated to be 12.2 percent for 2019, 11.4 percent for 2020, and 11.7 percent for 2021. These rates are not much higher than the rates based on the relative thresholds and consumption with health insurance, both capped and uncapped. The CE-based income poverty rates using official thresholds are similar to the official poverty rates published by the U.S. Census Bureau.

46 In this study, we do not examine whether the differences in poverty rates are statistically significant because no standard errors have been produced for these measures. BLS expects to produce standard errors for them in the future.

47 This result is in line with the change in child poverty as measured by the SPM. See Kalee Burns and Liana E. Fox, “The impact of the 2021 expanded Child Tax Credit on child poverty,” SEHSD Working Paper 2022-24 (U.S. Census Bureau, November 22, 2022), https://www.census.gov/content/dam/Census/library/working-papers/2022/demo/sehsd-wp2022-24.pdf.

48 Receipt of these payments resulted in increased expenditures, which will lead to an increase in consumption. See Jonathan Parker, Jake Schild, Laura Erhard, and David Johnson, “Economic Impact Payments and household spending during the pandemic,” Brookings Papers on Economic Activity: BPEA Conference Drafts, September 8–9, 2022 (Washington, DC: Brookings Institution, August 2022), https://www.brookings.edu/wp-content/uploads/2022/09/Parker-et-al-BPEA-Conference-Draft-BPEA-FA22.pdf; and Sophie M. Collyer, Thesia Garner, Neeraj Kaushai, Jiwan Lee, Jake Schild, Jane Waldfogel, and Christopher T. Wimer, “Effects of the expanded Child Tax Credit on household expenditures: preliminary intent-to-treat estimates from the Consumer Expenditure Survey,” BLS Working Paper 549 (U.S. Bureau of Labor Statistics, April 2022), https://www.bls.gov/osmr/research-papers/2022/pdf/ec220040.pdf.

49 This release would be similar to the release of state weights for the CE public-use data file. See “CE research products: State Weight files” (U.S. Bureau of Labor Statistics, last modified May 6, 2022), https://stats.bls.gov/cex/csxresearchtables.htm#stateweights.

50 The imputations of National School Lunch Program (NSLP) and Women Infants and Children (WIC) benefits that we use in this article are similar to those developed by Garner and Gudrais; however, for the Low-Income Home Energy Assistance Program (LIHEAP), the methods differ. Garner and Gudrais did not use the reported LIHEAP values from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC); rather, they assigned benefits based on heating and cooling degree days by geography. See Thesia I. Garner and Marisa Gudrais, “Alternative poverty measurement for the U.S.: Focus on Supplemental Poverty Thresholds.” Working Paper 510 (U.S. Bureau of Labor Statistics, September 25, 2018). https://www.bls.gov/osmr/research-papers/2018/pdf/ec180100.pdf. References to earlier work that focused on estimating and including in-kind benefits in SPM thresholds follow: Thesia I. Garner, Marisa Gudrais, and Kathleen S. Short, “Consistency in Supplemental Poverty Measurement: adding imputed inā€kind benefits to thresholds and impact on poverty rates for the United States,” Working Paper (U.S. Bureau of Labor Statistics, October 2015), https://www.bls.gov/osmr/research-papers/2015/st150120.htm; Thesia I. Garner and Charles Hokayem, “Supplemental Poverty Measure thresholds: imputing School Lunch and WIC benefits to the Consumer Expenditure Survey using the Current Population Survey,” Working Paper 457 (U.S. Bureau of Labor Statistics, July 2012), https://www.bls.gov/osmr/research-papers/2012/ec120060.htm; and Thesia I. Garner and Charles Hokayem. Supplemental Poverty Measure thresholds: imputing noncash benefits to the Consumer Expenditure Survey Using Current Population Survey—Parts I and II,” Working Paper, (U.S Bureau of Labor Statistics, 2011), https://www.bls.gov/osmr/research-papers/2011/st110100.htm.

51 The U.S. Census Bureau research focuses on the assignment of NSLP benefits to CPS ASEC households to produce the SPM resource measure. See the following research papers for the methods used to produce NSLP benefits during the COVID-19 pandemic: Em Shrider, “Alternative school lunch valuation in the CPS ASEC during COVID-19,” SEHSD Working Paper 2021-20 (U.S. Census Bureau, September 2021), https://www.census.gov/library/working-papers/2021/demo/SEHSD-WP2021-20.html; and Shrider, “School lunch and P-EBT valuation in the 2021 Supplemental Poverty Measure,” SEHSD Working Paper 2022-15 (U.S. Census Bureau, September 2022), https://www.census.gov/library/working-papers/2022/demo/SEHSD-wp2022-15.html. To see the impact on poverty rates of including imputed in-kind benefits in resources, see John Creamer, Emily A. Shrider, Kalee Burns, and Frances Chen, Poverty in the United States: 2021, Current Population Reports, P60-277 (U.S. Census Bureau, September 2022), https://www.census.gov/content/dam/Census/library/publications/2022/demo/p60-277.pdf.

52 According to Em Shrider of the U.S. Census Bureau, when the March 2020 CPS ASEC was administered (with reference period 2019), if the respondent asked, “‘Usually’?” or “What do you mean by ‘usually’?” the field representative (FR) would explain that it meant more than 50 percent of the time. For the CPS ASEC administered in March 2021 (reference period 2020) and March 2022 (reference period 2021), the FR notes say that the word “usually” refers to days when school was held in person, such as during the prepandemic period, or in areas where schools remained open during the pandemic. These directions are in the FR notes and are presented during training, but they are not made available to the public. Emily A. Shrider, email communication with Thesia I. Garner, March 29, 2023.

53 A monotone regression method is used to impute NSLP, WIC, and LIHEAP participation and ordinary least squares regression is used to impute LIHEAP benefits to the CE. We use SAS PROC MI for these imputations. For program participation, after the predicted probabilities are produced, a random number is then drawn for each respondent and imputation. If the random number is less than the participation probability, the respondent is identified as participating in the respective program. For example, suppose the estimated model predicts that consumer unit j has a 20-percent chance of participating in the NSLP. The SAS procedure will draw a random uniform [0,1] variable u. If u is less than 0.2, consumer unit j will be assigned a value of 1, and if u is greater than 0.2, consumer unit j will be assigned a value of 0. SAS repeats this for every observation with missing values for NSLP. See “The MI procedure,” chap. 2 in SAS/STAT 14.1 User’s Guide (Cary, NC: SAS Institute, 2015), https://support.sas.com/documentation/onlinedoc/stat/141/mi.pdf.

54 Although NSLP benefits are based on data from the U.S. Department of Agriculture (USDA), the benefit values that we use are those calculated by the Census Bureau and combine the NSLP values for meal reimbursement, bonus commodities, and entitlements. For USDA information about these programs, see “National School Lunch Programs: Rates of Reimbursement” (U.S. Department of Agriculture, Food and Nutrition Service, last modified July 26, 2022), https://www.fns.usda.gov/cn/rates-reimbursement; and “USDA Foods in Schools: Value of Donated Foods Notices” (U.S. Department of Agriculture, Food and Nutrition Service, last modified December 26, 2019), https://www.fns.usda.gov/usda-fis/value-donated-foods-notices. The USDA values are presented by academic year; the Census Bureau uses data from 2 academic years to produce benefit values that align more closely with a calendar year.

55 Shrider, “Alternative school lunch valuation in the CPS ASEC during COVID-19”; see note in figure 1, p. 7.

56 For the estimation of 2019 NSLP benefits in the consumption measure, we use the average number of school days in the 2018–19 academic year, by state. For the most recent data available relative to 2019, see table 234.20, “Minimum amount of instructional time per year and policies on textbooks, by state: selected years, 2000 through 2020,” in Digest of Education Statistics: 2019 (National Center for Education Statistics, February 2021), https://nces.ed.gov/programs/digest/d19/tables/dt19_234.20.asp .

57 Shrider, “Alternative school lunch valuation in the CPS ASEC during COVID-19”; and Shrider, “School lunch and P-EBT valuation in the 2021 Supplemental Poverty Measure.”

58 Ibid. In contrast to the treatment of NSLP benefits that are administered by electronic benefit transfer (EBT) being set to zero, when producing the SPM resource measure, the Census Bureau method assigns NSLP benefits that were administered by EBTs during months when schools were assumed to be closed.

59 See “WIC EBT Activities” (U.S. Department of Agriculture, Food and Nutrition Service, last updated December 2022), https://www.fns.usda.gov/wic/wic-ebt-activities.

60 This fraction is calculated as the number of WIC infants who are fully formula fed divided by the total number of infants participating in WIC. As evidence that most WIC infants are formula fed and receive the fully formula-fed package and not the partially formula-fed package, in their 2018 food package report, authors Nicole Kline, Kevin Meyers Mathieu, Jeff Marr write, “Fully formula-fed packages were prescribed for 30.9 to 66.0 percent of infants younger than 6 months old (800 ounces or more), and 54.5 percent of infants aged 6 months or older (at least 600 ounces). Quantities prescribed in the fully formula-fed [full nutrition benefit to maximum monthly allowance] ranges were most common across all infant age groups. Partially breastfed packages were prescribed for 8.5 percent of 0- to 0.9-month-old infants (less than 200 ounces), 13.7 percent of 1- to 3.9-month-old infants and 10.9 percent of 4- to 5.9-month-old infants (at least 200 but less than 600 ounces), and 4.8 percent of infants aged 6 months or older (at least 200 but less than 400 ounces).” See Kline, Mathieu, and Marr, WIC Participant and Program Characteristics 2018 Food Packages and Costs Report (Alexandria, VA: U.S. Department of Agriculture, Food and Nutrition Service, 2020), p. 21, https://fns-prod.azureedge.us/sites/default/files/resource-files/WICPC2018FoodPackage-1.pdf.

61 For 2019 through 2021, see “WIC Data Tables: Monthly Data—State Level Participation by Category and Program Costs” (U.S. Department of Agriculture, Food and Nutrition Service, last updated February 10, 2023), https://www.fns.usda.gov/pd/wic-program.

62 In the CE, owners are asked the following question: “If someone were to rent your home today, how much do you think it would rent for monthly, unfurnished and without utilities?” To derive the quarterly consumption of owner-occupied shelter using responses to this question, we multiply by 3.

63 The data we used are from table 330.10, “Average undergraduate tuition and fees and room and board rates charged for full-time students in degree-granting postsecondary institutions, by level and control of institution: 1963–64 through 2015–16,” Digest of Education Statistics: 2016 (National Center for Education Statistics, February 2018), https://nces.ed.gov/programs/digest/d16/tables/dt16_330.10.asp.

64 Thesia I. Garner, Robert S. Martin, Brett Matsumoto, and Scott Curtin, “Distribution of U.S. personal consumption expenditures for 2019: a prototype based on Consumer Expenditure Survey data,” Working Paper 557 (U.S. Bureau of Labor Statistics, August 8, 2022), https://www.bls.gov/osmr/research-papers/2022/ec220120.htm.

65 For example, care provided in government-operated facilities is out of scope in the personal consumption expenditures data, so the imputed values for Department of Veterans Affairs (VA) services, TRICARE (the health care program for uniformed service members, retirees, and their families), and Indian Health Services (IHS) only include the cost of care purchased from private providers. By contrast, the cost of the care provided in government facilities is in scope for the BLS consumption measure presented in this article. For more information on the personal consumption expenditures data, see “Consumer Spending” (U.S. Bureau of Economic Analysis, last updated February 24, 2023), https://www.bea.gov/data/consumer-spending/main.

66 See “Medical Expenditure Panel Survey Insurance Component” (Agency for Healthcare Research and Quality), https://datatools.ahrq.gov/meps-ic.

67 See U.S. Department of Health and Human Services, Centers for Medicare & Medicaid Services, https://www.cms.gov/.

68 Ibid.

69 For budget information on TRICARE, see “Under Secretary of Defense (Comptroller): Defense Budget Materials” (U.S. Department of Defense, last updated April 12, 2022), https://comptroller.defense.gov/Budget-Materials/; for VA benefits, see Department of Veterans Affairs FY2022 Appropriations, Report 46964 (Congressional Research Service, last updated June 28, 2022), https://crsreports.congress.gov/product/pdf/R/R46964; for IHS benefits, see U.S. Department of Health and Human Services, Indian Health Services, “Congressional Justifications,” https://www.ihs.gov/budgetformulation/congressionaljustifications/.

70 See “Economic Research,” Federal Reserve Bank of St. Louis, https://fred.stlouisfed.org.

article image
About the Author

Thesia I. Garner
garner.thesia@bls.gov

Thesia I. Garner is the chief research economist in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

Brett Matsumoto
matsumoto.brett@bls.gov

Brett Matsumoto is a research economist in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

Jake Schild
schild.jake@bls.gov

Jake Schild is a research economist in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

Scott Curtin
curtin.scott@bls.gov

Scott Curtin is a branch chief in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

Adam Safir
safir.adam@bls.gov

Adam Safir is a division chief in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

close or Esc Key