Estimating Sampling Variance From The Current Population Survey: A Synthetic Design Approach To Correcting Standard Errors
AbstractEssentially all empirical questions that are addressed with sample data require estimates of sampling variance. The econometrics and statistics literatures show that these estimates depend critically on the design of the sample. The sample for the U.S. Current Population Survey (CPS), which serves as the basis for official poverty, unemployment, and earnings estimates, results from a stratified and clustered design. Unfortunately, analysts are frequently unable to estimate sampling variance for many CPS statistics because the variables marking the strata and clusters are censored from the public-use data files. To compensate for this, the Bureau of Census provides a method to approximate the sampling variance for several, specific point estimates, but no general method exists for estimates that differ from these cases. Similarly there are no corrections at all for regression estimates. This paper proposes a general approximation method that creates synthetic design variables for the estimation of sampling variance. The results from this method compare well with officially reported standard errors. This methodology allows the analyst to estimate sampling variance for a significantly wider class of estimates than previously possible, and therefore increases the usefulness of research resulting from the CPS data files.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2002 Annual meeting, July 28-31, Long Beach, CA with number 19628.
Date of creation: 2002
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Other versions of this item:
- Dean Jolliffe, 2001. "Estimating Sampling Variance from the Current Population Survey: A Synthetic Design Approach to Correcting Standard Errors," Econometrics 0110006, EconWPA, revised 20 Oct 2001.
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data
- I32 - Health, Education, and Welfare - - Welfare and Poverty - - - Measurement and Analysis of Poverty
- O18 - Economic Development, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
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- Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
- Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-38, May.
- Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-66, May.
- Tiehen, Laura & Jolliffe, Dean & Gundersen, Craig, 2012. "How State Policies Influence the Efficacy of the Supplemental Nutrition Assistance Program in Reducing Poverty," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124937, Agricultural and Applied Economics Association.
- Jolliffe, Dean, 2006. "The Cost of Living and the Geographic Distribution of Poverty," Economic Research Report 7254, United States Department of Agriculture, Economic Research Service.
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