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Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics

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  • Czajka, John L, et al

Abstract

This article proposes and evaluates two new methods of reweighting preliminary data to obtain estimates more closely approximating those derived from the final data set. In the authors' motivating example, the preliminary data are an early sample after all tax returns have been processed. The new methods estimate a predicted propensity for late filing for each return in the advance sample and then poststratify based on these propensity scores. Using advance and complete sample data for 1982, the authors demonstrate that the new methods produce advance estimates generally much close to the final estimates than those derived from the current advance estimation techniques. The results demonstrate the value of propensity modeling, a general-purpose methodology that can be applied to a wide range of problems, including adjustment for unit nonresponse and frame undercoverage as well as statistical matching. Coauthors are Sharon M. Hirabayashi, Roderick J. A. Little, and Donald B. Rubin.

Suggested Citation

  • Czajka, John L, et al, 1992. "Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 117-131, April.
    • Handle: RePEc:bes:jnlbes:v:10:y:1992:i:2:p:117-31
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    Citations

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    Cited by:

    1. Amang Sukasih & Donsig Jang & Sonya Vartivarian & Stephen Cohen & Fan Zhang, "undated". "A Simulation Study to Compare Weighting Methods for Survey Nonresponses in the National Survey of Recent College Graduates," Mathematica Policy Research Reports 613f000cac94492f91b53813f, Mathematica Policy Research.
    2. Amarendra Sharma, 2019. "Indira Awas Yojana and Housing Adequacy: An Evaluation using Propensity Score Matching," ASARC Working Papers 2019-05, The Australian National University, Australia South Asia Research Centre.
    3. repec:mpr:mprres:6579 is not listed on IDEAS
    4. Hao Chen & Dylan S. Small, 2022. "New multivariate tests for assessing covariate balance in matched observational studies," Biometrics, The International Biometric Society, vol. 78(1), pages 202-213, March.
    5. Matthias Schonlau & Arthur van Soest & Arie Kapteyn & Mick Couper, 2009. "Selection Bias in Web Surveys and the Use of Propensity Scores," Sociological Methods & Research, , vol. 37(3), pages 291-318, February.
    6. Richard Valliant & Jill A. Dever, 2011. "Estimating Propensity Adjustments for Volunteer Web Surveys," Sociological Methods & Research, , vol. 40(1), pages 105-137, February.
    7. Jason K. Luellen & William R. Shadish & M. H. Clark, 2005. "Propensity Scores," Evaluation Review, , vol. 29(6), pages 530-558, December.
    8. Lingxiao Wang & Barry I. Graubard & Hormuzd A. Katki & and Yan Li, 2020. "Improving external validity of epidemiologic cohort analyses: a kernel weighting approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1293-1311, June.
    9. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    10. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
    11. Petreski, Marjan & Jovanovic, Branimir, 2013. "Do Remittances Reduce Poverty and Inequality in the Western Balkans? Evidence from Macedonia," MPRA Paper 51413, University Library of Munich, Germany.
    12. Sunghee Lee & Richard Valliant, 2009. "Estimation for Volunteer Panel Web Surveys Using Propensity Score Adjustment and Calibration Adjustment," Sociological Methods & Research, , vol. 37(3), pages 319-343, February.

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