Promise and Pitfalls in the Use of 'Secondary' Data-Sets: Income Inequality in OECD Countries
AbstractSecondary data-sets have come to play an increasing role in empirical economic research. This paper examines the major new secondary data-set assembled by Klaus Deininger and Lyn Squire (DS) at the World Bank. We concentrate on its coverage of the OECD countries. We have particularly in mind the user of income inequality statistics who does not wish to go back to the original data. In order to motivate the analysis, we first present two examples of the problems which may arise, showing how both cross-country comparisons and time-series analysis may depend sensitively on the choice of data. Section 3 of the paper sets the DS data-set in the historical context of earlier exercises in assembling comparative information on income inequality. In Section 4, we consider the methodological issues which arise in the use of income distribution data and their relation to the different sources of evidence. In Section 5, we discuss their implications for the comparison of income inequality across OECD countries, and the use of dummy variables to allow for definitional and data differences. Section 6 is concerned with changes in income inequality over time, and the establishment of consistent series for individual countries. The lessons to be drawn for use of secondary data-sets in the field of income distribution are summarised at the end of the paper.
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Bibliographic InfoPaper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 379.
Date of creation: Oct 2000
Date of revision:
personal income distribution; secondary data-sets;
Other versions of this item:
- Atkinson, A.B. & Brandolini, A., 2000. "Promise and Pitfalls in the Use of 'Secondary' Data -Sets: Income Inequality in OECD Countries," Papers 379, Banca Italia - Servizio di Studi.
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
- C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
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