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Special Issue Editors’ Essay

Author

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  • Burt S. Barnow
  • David H. Greenberg

Abstract

Background: Impact evaluations draw their data from two sources, namely, surveys conducted for the evaluation or administrative data collected for other purposes. Both types of data have been used to estimate program impacts. This is an introductory essay to a Special Issue entitled “Do the Estimated Effects of Social Programs Depend on the Source of Data Used to Measure Them? Survey Data Versus Administrative Data.†In addition to this essay, the Special Issue contains six articles, which appear in Volume 42, Issue 5–6 (October–December 2018) and in this issue (Volume 43, Issue 5 (October 2019)) of Evaluation Review. Objective: To describe and summarize each of the six papers and draw lessons from them. The papers investigate the relative strengths and weaknesses of survey and administrative data for estimating the impacts of policy interventions. Results: This essay first describes a simple model of the mechanisms that can cause impacts estimated with survey data to differ from those estimated with administrative data. It then describes and summarizes each of the papers appearing in this Special Issue and uses the model described to interpret the findings when it is applicable. The final section draws general lessons from the papers. Conclusions: The decision on whether to use survey or administrative data to estimate program impacts can be highly consequential because the estimates can differ considerably. All the papers in this Special Issue point to the importance of using both survey data and administrative data whenever possible.

Suggested Citation

  • Burt S. Barnow & David H. Greenberg, 2019. "Special Issue Editors’ Essay," Evaluation Review, , vol. 43(5), pages 231-265, October.
    • Handle: RePEc:sae:evarev:v:43:y:2019:i:5:p:231-265
    DOI: 10.1177/0193841X19865076
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    References listed on IDEAS

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    1. Bruce D. Meyer & Derek Wu, 2018. "The Poverty Reduction of Social Security and Means-Tested Transfers," NBER Working Papers 24567, National Bureau of Economic Research, Inc.
    2. Annalisa Mastri & Dana Rotz & Elias S. Hanno, "undated". "Comparing Job Training Impact Estimates Using Survey and Administrative Data," Mathematica Policy Research Reports 157778d936f848ddb0b4e8e32, Mathematica Policy Research.
    3. Bruce D. Meyer & Derek Wu, 2018. "The Poverty Reduction of Social Security and Means-Tested Transfers," ILR Review, Cornell University, ILR School, vol. 71(5), pages 1106-1153, October.
    4. Burt S. Barnow & David Greenberg, 2015. "Do Estimated Impacts on Earnings Depend on the Source of the Data Used to Measure Them? Evidence From Previous Social Experiments," Evaluation Review, , vol. 39(2), pages 179-228, April.
    5. V. J. Hotz & J. K. Scholz, "undated". "Measuring Employment and Income for Low-Income Populations with Administrative and Survey Data," Institute for Research on Poverty Discussion Papers 1224-01, University of Wisconsin Institute for Research on Poverty.
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    7. Katharine G. Abraham & John Haltiwanger & Kristin Sandusky & James R. Spletzer, 2013. "Exploring Differences in Employment between Household and Establishment Data," Journal of Labor Economics, University of Chicago Press, vol. 31(S1), pages 129-172.
    8. Geoffrey L. Wallace & Robert Haveman, 2007. "The implications of differences between employer and worker employment|earnings reports for policy evaluation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 737-754.
    9. Reuben Ford & Douwêrê Grékou & Isaac Kwakye & Taylor Shek-wai Hui, 2018. "The Sensitivity of Impact Estimates to Data Sources Used: Analysis From an Access to Postsecondary Education Experiment," Evaluation Review, , vol. 42(5-6), pages 575-615, October.
    10. W. Norton Grubb, 1993. "The Varied Economic Returns to Postsecondary Education: New Evidence from the Class of 1972," Journal of Human Resources, University of Wisconsin Press, vol. 28(2), pages 365-382.
    11. Judith Scott-Clayton & Qiao Wen, 2019. "Estimating Returns to College Attainment: Comparing Survey and State Administrative Data–Based Estimates," Evaluation Review, , vol. 43(5), pages 266-306, October.
    12. Richard Hendra & Aaron Hill, 2019. "Rethinking Response Rates: New Evidence of Little Relationship Between Survey Response Rates and Nonresponse Bias," Evaluation Review, , vol. 43(5), pages 307-330, October.
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