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National Experimental Wellbeing Statistics - Version 1

Author

Listed:
  • Adam Bee
  • Joshua Mitchell
  • Nikolas Mittag
  • Jonathan Rothbaum
  • Carl Sanders
  • Lawrence Schmidt
  • Matthew Unrath

Abstract

This is the U.S. Census Bureau’s first release of the National Experimental Wellbeing Statistics (NEWS) project. The NEWS project aims to produce the best possible estimates of income and poverty given all available survey and administrative data. We link survey, decennial census, administrative, and third-party data to address measurement error in income and poverty statistics. We estimate improved (pre-tax money) income and poverty statistics for 2018 by addressing several possible sources of bias documented in prior research. We address biases from 1) unit nonresponse through improved weights, 2) missing income information in both survey and administrative data through improved imputation, and 3) misreporting by combining or replacing survey responses with administrative information. Reducing survey error substantially affects key measures of well-being: We estimate median household income is 6.3 percent higher than in survey estimates, and poverty is 1.1 percentage points lower. These changes are driven by subpopulations for which survey error is particularly relevant. For house holders aged 65 and over, median household income is 27.3 percent higher and poverty is 3.3 percentage points lower than in survey estimates. We do not find a significant impact on median household income for householders under 65 or on child poverty. Finally, we discuss plans for future releases: addressing other potential sources of bias, releasing additional years of statistics, extending the income concepts measured, and including smaller geographies such as state and county.

Suggested Citation

  • Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:23-04
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    File URL: https://www2.census.gov/library/working-papers/2023/adrm/ces/CES-WP-23-04.pdf
    File Function: First version, 2023
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    References listed on IDEAS

    as
    1. Bruce D. Meyer & Nikolas Mittag, 2019. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 176-204, April.
    2. Robert Moffitt & John Abowd & Christopher Bollinger & Michael Carr & Charles Hokayem & Kevin McKinney & Emily Wiemers & Sisi Zhang & James Ziliak, 2022. "Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 1-11, December.
    3. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    4. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    5. Kevin L. McKinney & John M. Abowd, 2022. "Male Earnings Volatility in LEHD Before, During, and After the Great Recession," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 33-39, December.
    6. Erik Hurst & Geng Li & Benjamin Pugsley, 2014. "Are Household Surveys Like Tax Forms? Evidence from Income Underreporting of the Self-Employed," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 19-33, March.
    7. Benjamin Cerf Harris, 2014. "Within and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records," CARRA Working Papers 2014-05, Center for Economic Studies, U.S. Census Bureau.
    8. Thomas Piketty & Emmanuel Saez & Gabriel Zucman, 2018. "Distributional National Accounts: Methods and Estimates for the United States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 553-609.
    9. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    10. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    11. Paul Bingley & Alessandro Martinello, 2017. "Measurement Error in Income and Schooling and the Bias of Linear Estimators," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 1117-1148.
    12. Andrew Garin & Emilie Jackson & Dmitri Koustas, 2022. "New gig work or changes in reporting?: Understanding self-employment trends in tax data," OECD Social, Employment and Migration Working Papers 278, OECD Publishing.
    13. Robert Moffitt & Sisi Zhang, 2022. "Estimating Trends in Male Earnings Volatility with the Panel Study of Income Dynamics," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 20-25, December.
    14. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    15. Brittany Bond & J. David Brown & Adela Luque & Amy O’Hara, 2014. "The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey," CARRA Working Papers 2014-08, Center for Economic Studies, U.S. Census Bureau.
    16. Bruce D. Meyer & Angela Wyse & Alexa Grunwaldt & Carla Medalia & Derek Wu, 2021. "Learning about Homelessness Using Linked Survey and Administrative Data," NBER Working Papers 28861, National Bureau of Economic Research, Inc.
    17. repec:hal:wpspec:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    18. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    19. 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.
    20. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    21. Kevin Corinth & Bruce D. Meyer & Derek Wu, 2022. "The Change in Poverty from 1995 to 2016 Among Single Parent Families," NBER Working Papers 29870, National Bureau of Economic Research, Inc.
    22. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    23. Marc Roemer, 2002. "Using Administrative Earnings Records to Assess Wage Data Quality in the March Current Population Survey and the Survey of Income and Program Participation," Longitudinal Employer-Household Dynamics Technical Papers 2002-22, Center for Economic Studies, U.S. Census Bureau.
    24. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    25. repec:hal:spmain:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    26. Michael D. Carr & Robert A. Moffitt & Emily E. Wiemers, 2022. "Reconciling Trends in Male Earnings Volatility: Evidence from the SIPP Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 26-32, December.
    27. Liana Fox & Jonathan Rothbaum & Kathryn Shantz, 2022. "Fixing Errors in a SNAP: Addressing SNAP Underreporting to Evaluate Poverty," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 330-334, May.
    28. Zhao Qingyuan & Percival Daniel, 2017. "Entropy Balancing is Doubly Robust," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-19, March.
    29. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    30. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    31. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    32. Jeff Larrimore & Jacob Mortenson & David Splinter, 2021. "Household Incomes in Tax Data: Using Addresses to Move from Tax-Unit to Household Income Distributions," Journal of Human Resources, University of Wisconsin Press, vol. 56(2), pages 600-631.
    33. Meyer, Bruce D. & Mittag, Nikolas, 2021. "An empirical total survey error decomposition using data combination," Journal of Econometrics, Elsevier, vol. 224(2), pages 286-305.
    34. Melissa C. Chow & Teresa C. Fort & Christopher Goetz & Nathan Goldschlag & James Lawrence & Elisabeth Ruth Perlman & Martha Stinson & T. Kirk White, 2021. "Redesigning the Longitudinal Business Database," NBER Working Papers 28839, National Bureau of Economic Research, Inc.
    35. Jeff Larrimore & Jacob Mortenson & David Splinter, 2023. "Unemployment Insurance In Survey And Administrative Data," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(2), pages 571-579, March.
    36. Charles Hokayem & Christopher Bollinger & James P. Ziliak, 2015. "The Role of CPS Nonresponse in the Measurement of Poverty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 935-945, September.
    37. Katharine G. Abraham & John C. Haltiwanger & Claire Hou & Kristin Sandusky & James R. Spletzer, 2021. "Reconciling Survey and Administrative Measures of Self-Employment," Journal of Labor Economics, University of Chicago Press, vol. 39(4), pages 825-860.
    38. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    39. John M. Abowd & Kevin L. McKinney & Nellie L. Zhao, 2018. "Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 183-300.
    40. 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.
    41. Bilal Habib, 2018. "How CBO Adjusts for Survey Underreporting of Transfer Income in Its Distributional Analyses: Working Paper 2018-07," Working Papers 54234, Congressional Budget Office.
    42. Nikolas Mittag, 2019. "Correcting for Misreporting of Government Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 11(2), pages 142-164, May.
    43. Christopher R. Bollinger & Barry T. Hirsch & Charles M. Hokayem & James P. Ziliak, 2019. "Trouble in the Tails? What We Know about Earnings Nonresponse 30 Years after Lillard, Smith, and Welch," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2143-2185.
    44. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    45. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Quantile and probability curves without crossing," CeMMAP working papers CWP10/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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