The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index.

The CE program is comprised of two separate components (each with its own survey questionnaire and independent sample), the Diary Survey and the quarterly Interview Survey (ICPSR 36237). This data collection contains the Diary Survey component, which was designed to obtain data on frequently purchased smaller items, including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. Each consumer unit (CU) recorded its expenditures in a diary for two consecutive 1-week periods. Although the diary was designed to collect information on expenditures that could not be easily recalled over time, respondents were asked to report all expenses (except overnight travel) that the CU incurred during the survey week.

The 2013 Diary Survey release contains five sets of data files (FMLD, MEMD, EXPD, DTBD, DTID), and one processing file (DSTUB). The FMLD, MEMD, EXPD, DTBD, and DTID files are organized by the quarter of the calendar year in which the data were collected. There are four quarterly datasets for each of these files.

The FMLD files contain CU characteristics, income, and summary level expenditures; the MEMD files contain member characteristics and income data; the EXPD files contain detailed weekly expenditures at the Universal Classification Code (UCC) level; the DTBD files contain the CU's reported annual income values or the mean of the five imputed income values in the multiple imputation method; and the DTID files contain the five imputed income values. Please note that the summary level expenditure and income information on the FMLD files permit the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

The DSTUB file provides the aggregation scheme used in the published consumer expenditure tables. The DSTUB file is further explained in Section III.F.6. 'Processing Files' of the Diary Survey Users' Guide. A second documentation guide, the 'Users' Guide to Income Imputation,' includes information on how to appropriately use the imputed income data.

Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over.

The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on 'Other' in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending estimate. The BLS unpublished integrated CE data tables are provided as an easy-to-use tool for obtaining spending estimates. However, users are cautioned to read the BLS explanatory letter accompanying the tables. The letter explains that estimates of average expenditures on detailed spending items (such as leisure and art-related categories) may be unreliable due to so few reports of expenditures for those items.

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.

In May 2025, the index for consumer confidence in China ranged at ** points, up from **** points in the previous month. The index dropped considerably in the first half of 2022 and performed a sideways movement during 2023 and 2024. Consumer confidence Index The consumer confidence index (CCI), also called Index of Consumer Sentiment (ICS) is a commonly used indicator to measure the degree of economic optimism among consumers. Based on information about saving and spending activities of consumers, changes in business climate and future spending behavior are being projected. The CCI plays an important role for investors, retailers, and manufacturers in their decision-making processes. However, measurement of consumer confidence varies strongly from country to country. As consumers need time to react to economic changes, the CCI tends to lag behind other indicators like the consumer price index (CPI) and the producer price index (PPI). Development in China As shown by the graph at hand, confidence among Chinese consumers picked up since mid of 2016. In October 2017, the CCI hit a record value of 127.6 index points and entered into a sideward movement. Owing to a relative stability in GDP growth, a low unemployment rate, and a steady development of disposable household income, Chinese consumers gained more confidence in the state of the national economy. Those factors also contribute to the consumers’ spending power, which was reflected by a larger share of consumption in China’s GDP. After the outbreak of the coronavirus pandemic, consumer confidence dropped quickly in the beginning of 2020, but started to recover in the second half of the year, leading to a v-shaped movement of the index in 2020.