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Economic well-being has been an issue relevant to American public discourse for some time and has become a topic of particular interest among journalists, policymakers, and ordinary American families. The Consumer Expenditure Survey (CE) is often used as a tool to analyze the effects of economic well-being. Though the Census Bureau independently collects and processes income data—the CE is the only Federal survey that collects information on income, expenditures, and associated demographic characteristics from U.S. consumers. As a result, CE data are useful for answering many salient questions related to economic well-being. However, CE data are also complex and should be used with an understanding of the limitations of these data.

This Beyond the Numbers article examines assumptions users often make regarding how the CE measures household wealth, by providing examples of the nuances in the data and composition of five household groupings. The examples provide a clearer snapshot of economic well-being found in the income quintiles. The article uses tabulations of households by quintiles of income before taxes that many researchers use to identify the “poor” and the “rich” in the CE data. These terms are subjective and potentially pejorative. Another common set of terms for these types of analyses are “low-income” and “high-income.” These are also subjective terms, and they are imprecise for communicating the concept of economic well-being, which involves other measures of wealth (e.g. stocks, bonds, cash assets, etc.). Misunderstandings arise potentially when researchers use terms like “rich” and “poor” to describe households rather than focus solely on income measures. So, what are the common assumptions made about the economic well-being of the “poor” and “rich” when using CE data to compare income groups?


Suggested Citation
Crain, V., & Wilson, T. J. (2017). Use with caution: Interpreting Consumer Expenditure income group data. Beyond the Numbers, 6(5). Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics.