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The purpose of this project was to measure and estimate the distribution of personal income and related economic factors in both rural and urban areas of the People's Republic of China. The principal investigators based their definition of income on cash payments and on a broad range of additional components. Data were collected through a series of questionnaire-based interviews conducted in rural and urban areas at the end of 2002. There are ten separate datasets. The first four datasets were derived from the urban questionnaire. The first contains data about individuals living in urban areas. The second contains data about urban households. The third contains individual-level economic variables copied from the initial urban interview form. The fourth contains household-level economic variables copied from the initial urban interview form. The fifth dataset contains village-level data, which was obtained by interviewing village leaders. The sixth contains data about individuals living in rural areas. The seventh contains data about rural households, as well as most of the data from a social network questionnaire which was presented to rural households. The eighth contains the rest of the data from the social network questionnaire and is specifically about the activities of rural school-age children. The ninth dataset contains data about individuals who have migrated from rural to urban areas, and the tenth dataset contains data about rural-urban migrant households. Dataset 1 contains 151 variables and 20,632 cases (individual urban household members). Dataset 2 contains 88 variables and 6,835 cases (urban households). Dataset 3 contains 44 variables and 27,818 cases, at least 6,835 of which are empty cases used to separate households in the file. The remaining cases from dataset 3 match those in dataset 1. Dataset 4 contains 212 variables and 6,835 cases, which match those in dataset 2. Dataset 5 contains 259 variables and 961 cases (villages). Dataset 6 contains 84 variables and 37,969 cases (individual rural household members). Dataset 7 contains 449 variables and 9,200 cases (rural households). Dataset 8 contains 38 variables and 8,121 cases (individual school-age children). Dataset 9 contains 76 variables and 5,327 cases (individual rural-urban migrant household members). Dataset 10 contains 129 variables and 2,000 cases (rural-urban migrant households).

The Chinese Household Income Project collected data in 1988, 1995, 2002, and 2007. ICPSR holds data from the first three collections, and information about these can be found on the series description page. Data collected in 2007 are available through the China Institute for Income Distribution.

The data pertaining to this study was the result of an exhaustive investigation into the nature of the firms composing the Egyptian garment industry. The data capture various characteristics of the firms relating to each one's level and order of integration into the production of fabrics and garments and into retail. Part 1 of the study contains the data obtained from the initial screening interviews administered to each firm by phone to determine the prevalence and nature of integration present in its operations. This information was used to determine which one of the four study questionnaires would be administered to each firm during the final interview. Each questionnaire produced four datasets containing (in this order):

• general questions
• contracts
• lock in, switching costs, and temporal specificity
• product information.

Questionnaire 1 (Parts 2-5) was administered to the firms for which the following four scenarios was true: (1) garment production and retail occurred at the same time at the establishment, and both garment production and fabric production took place at the same time at the establishment, (2) garment production and retail occurred simultaneously at the establishment, but fabrics were not produced in-house, (3) garment production occurred before retail while garment and fabric production were simultaneous at the establishment, and (4) garment and fabric production that occurred simultaneously at the establishment but retail operations not performed in-house (i.e. did not own or rent its own retail stores). Questionnaire 2 (Parts 6-9) was completed by the firms for which the following two scenarios were true: (1) garment production was subsequent to fabric production, and garment production was started prior to retail, or (2) garment production was started prior to retail, and the firm did not produce any of its own fabrics. Questionnaire 3 (Parts 10-13) was given to the firms for which the following three scenarios were true: (1) garment production began simultaneously with fabric production but not at the onset, and for which retail started subsequent to both garment and fabric production, (2) both fabric production and retail had started subsequent to garment production, and (3) garment production started before fabric production, and the firm did not perform in-house retail operations. Questionnaire 4 (Parts 14-17) was administered to firms for which the following two scenarios were true: (1) garment production was subsequent to fabric production, but in-house retail operations were not performed, or (2) there was no fabric production or in-house retail operations. Each of the four questionnaires contained an identical screening section (in addition to the screening information found in Part 1) in order to ensure that the appropriate questionnaire was administered during the interview. Specific questions regarding each firm's management, sister companies, products, operations, and other firm-level characteristics varied depending on the questionnaire. However, sections eight and nine, dealing with fabrics and fabric suppliers, were identical across all questionnaires.

Durables represent a large share of households’ marginal propensity to spend (MPX). We develop a quantitative model of spending that takes durables into account and matches a rich set of empirical regularities simultaneously. Scaling the response of non-durables provides a poor approximation of the MPX on durables when it comes to its distribution in the population, its persistence over time, and its cyclicality. As an application, we study how the MPX varies with the size of stimulus checks and find that it declines more slowly compared to a model of purely non-durable spending.