receipts, although the refund amounts are known to households before the checks arrive. Stephens (2008) found that household consumption increases once vehicle loans are paid off even though the date and amount of the final payment are known in advance by households. Finally, using the CE Diary data, Stephens (2003) found that households increase their daily nondurable consumption when their Social Security checks arrive, in contrast to the predictions of the LCPIH.
Since the LCPIH models the decisions of individual households, households are able to insure themselves against bad outcomes, such as a job loss or disability, only through their own savings. An alternative model that is an important economic benchmark for understanding the amount of risk that households face is the model of full insurance. In this model, individual households are fully insured against their own household-level changes in income, although aggregate-level income changes will influence household consumption (e.g., a village that pools all of its resources in each year and then redistributes them across all households in the village).
Mace (1991) tested the full-insurance hypothesis by exploiting the panel feature of the CE to regress changes in household consumption on changes in both aggregate consumption and household level “shocks” (e.g., changes in income and employment status). She found mixed evidence in support of this benchmark depending on the choice of empirical specification, although the preponderance of the evidence favors the model. However, Nelson (1994) found that alternative methods of measuring key variables, including using more expansive measures of consumption and employment changes, consistently reject the full insurance model. Attanasio and Davis (1996) focused on the large, observable wage changes that occurred between groups, as defined by education level and year of birth, during the 1980s to test the full insurance model. They concluded that the full insurance model overwhelming fails to explain the large between-group changes in consumption found in the CE over the same period.
CE Data Help Measure Well-Being Across Households
Income Inequality Across Households
Heathcote, Perri, and Violante (2010) combined data from the CE, the Panel Study of Income Dynamics, the Current Population Survey, and the Survey of Consumer Finances to conduct a systematic study of cross-sectional inequality in the United States. They found both a continuous and sizable increase in wage inequality over the study period.
Krueger and Perri (2006) investigated welfare consequences of this growing inequality. The CE helped reveal that poor households do not measurably change their consumption in response to lower wages, but instead