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Estimating Sampling Variance from the Current Population Survey: A Synthetic Design Approach to Correcting Standard Errors

Author

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  • Dean Jolliffe

    (Economic Research Service)

Abstract
Essentially 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.

Suggested Citation

  • Dean Jolliffe, 2001. "Estimating Sampling Variance from the Current Population Survey: A Synthetic Design Approach to Correcting Standard Errors," Econometrics 0110006, University Library of Munich, Germany, revised 20 Oct 2001.
    • Handle: RePEc:wpa:wuwpem:0110006
    Note: Type of Document - PDF; prepared on IBM PC; pages: 32 . The views and opinions expressed in this paper do not reflect the views of the Economic Research Service of the U.S. Department of Agriculture.
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    References listed on IDEAS

    as
    1. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    2. Stephen Howes & Jean Olson Lanjouw, 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.
    3. 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 334-338, May.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. 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.
    2. Dean Jolliffe & Juan Margitic & Martin Ravallion & Laura Tiehen, 2024. "Food stamps and America's poorest," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(4), pages 1380-1409, August.
    3. Tiehen, Laura & Jolliffe, Dean & Gundersen, Craig, 2012. "Alleviating Poverty in the United States: The Critical Role of SNAP Benefits," Economic Research Report 262233, United States Department of Agriculture, Economic Research Service.
    4. 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.
    5. Dean Jolliffe, 2003. "On the Relative Well‐Being of the Nonmetropolitan Poor: An Examination of Alternate Definitions of Poverty during the 1990s," Southern Economic Journal, John Wiley & Sons, vol. 70(2), pages 295-311, October.

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    More about this item

    Keywords

    Current Population Survey; Sampling Variance; Sample Design; Regional Analysis; Rural; Poverty;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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