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November 2006, Vol. 129, No. 11
Income imputation and the analysis of consumer expenditure data
Jonathan D. Fisher
The Bureau of Labor Statistics (BLS) Consumer Expenditure Survey (CE) began imputing income in its 2004 data. Imputation predicts income for households that reported receiving income but failed to report a specific value. Many national household surveys such as the Current Population Survey and the Survey of Consumer Finances impute missing income values. While imputation is common practice, researchers should take some precautions when using imputed data.
This article examines how income imputation affects analysis of the CE expenditure data. Most importantly, researchers who use both income and expenditures data from 2004 forward no longer have to restrict their sample to households that reported income. This study presents results for the restricted sample employed before imputation was introduced and compares them with results using the sample that should be employed after imputation. The study also compares the distribution of expenditures and measures of well-being—such as the Gini coefficient and the poverty rate—in the two samples.
The other large effect of adopting income imputation is that there may be a break in time series data that use multiple years of CE data. Because BLS will only provide imputed income data from 2004 forward, researchers who want to create a time series using income and expenditures will not have imputed income data for the period before 2004. This study uses data from 2002 to 2004 to show how the introduction of income imputation creates a break in the time series for some statistics (such as the poverty rate).
This excerpt is from an article published in the November 2006 issue of the Monthly Labor Review. The full text of the article is available in Adobe Acrobat's Portable Document Format (PDF). See How to view a PDF file for more information.
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