Chapter 16.
Consumer Expenditures and Income

Uses and Limitations
The importance of the Consumer Expenditure Survey is that it allows data users to relate the expenditures and income of consumers to the characteristics of those consumers. The survey data are of value to government and private agencies interested in studying the welfare of particular segments of the population, such as the elderly, low-income families, urban families, and those receiving food stamps. The survey data are used by economic policy makers interested in the effects of policy changes on levels of living among diverse socioeconomic groups. Econometricians find the data useful in constructing economic models. Market researchers find them valuable in analyzing the demand for groups of goods and services. The U.S. Department of Commerce uses the survey data as a source of information for revising its benchmark estimates of selected items in the expenditure and income components of the National Accounts.

As in the past, the revision of the Consumer Price Index remains a primary reason for undertaking such an extensive survey. The results of the Consumer Expenditure Survey have been used to select new market baskets of goods and services for the index, to determine the relative importance of components, and to derive new cost weights for the baskets. In August 2002, the Bureau of Labor Statistics began publishing a new index called the “Chained Consumer Price Index for All Consumers” (C-CPI-U), which supplements the existing consumer price indexes. The use of expenditure data from different time periods distinguishes the C-CPI-U from the existing CPI measures, which use only a single expenditure base period to compute the price change over time. The new index is designed to measure the change in the “cost of living,” as compared to the existing indexes that are designed to measure the change in the fixed market basket of goods and services in retail outlets. The C-CPI-U uses expenditure data from different time periods to reflect the effect of substitution that consumers make across item categories in response to changes in the relative prices of goods and services.

Sample surveys are subject to two types of errors—sampling and non-sampling. Sampling error is the uncertainty caused by the fact that observations are taken from a random sample of population members and not from the entire population. Non-sampling error is the rest of the error. Non-sampling errors can be attributed to many sources, such as differences in the interpretation of questions, inability or unwillingness of respondents to provide correct information, data processing errors, and so on. Non-sampling error arises regardless of whether data are collected from a sample or from the entire population.

Another way of analyzing error is to divide it into variance and bias. The variance is a measure of how close different estimates would be to each other if it were possible to repeat the survey over and over using different samples. While it is not feasible to repeat the survey over and over, statistical theory allows the variance to be estimated anyway. A small variance indicates that multiple independent samples would produce values that are consistently very close to each other. Bias is the difference between the “expected” value of an estimate and its “true” value. A statistic may have a small variance but a large bias, or it may have a large variance but a small bias. For an estimate to be considered accurate, both its variance and its bias need to be small.

The Bureau of Labor Statistics is constantly trying to reduce the error in the Consumer Expenditure Survey estimates. Variance and sampling error are reduced by using a sample of respondents that is as large as possible given resource constraints. Improving the accuracy of the estimates was the primary reason for the significant expansion in the sample for both the Interview and Diary surveys that occurred in 1999. The Bureau reduces non-sampling error through a series of computerized and professional data reviews, as well as through continuous survey process improvements and through theoretical research.

Next: In chapter 17