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Survey databases like the ACS and CPS frequently have safety net program utilization counts that differ
<br />from program administrative data. We adjusted the CPS so that its program utilization estimates match
<br />the program administrative data. The CPS does not provide a large enough sample size to accurately
<br />estimate program utilization for construction workers at the state or county levels. The ACS does have
<br />sufficient sample size for this analysis but lacks specific questions about program utilization, and its
<br />occupational employment counts differ from more accurate data like the OES. On the other hand,
<br />while the OES has accurate employment counts for wage workers, it does not include independent
<br />contractors. To overcome these issues, we built a model using CPS data to predict program utilization
<br />based on income, demographics, and family structure. We then used that model to impute program
<br />utilization onto the ACS data. We calculated the ratio of wage workers to non -incorporated self-
<br />employed workers based on the ACS and used it to adjust the OES data for non -incorporated self-
<br />employed workers, and then adjusted the employment counts in the ACS to match the adjusted OES
<br />data. Finally, we used that imputed and adjusted ACS data to analyze safety net program utilization in
<br />families of construction workers.
<br />For a detailed explanation of methodology, please see Appendix A: Methodology from Fast Food,
<br />Poverty Wages: The Public Cost of Low -Wage Jobs in the Fast -Food Industry.37
<br />Endnotes
<br />1 US Census Bureau, ACS 2019 1-year estimates, table C24070, Industry By Class Of Worker For
<br />The Civilian Employed Population 16 Years And Over. "People employed in the construction industry"
<br />excludes self-employed in own incorporated business workers. Accessed 12/2/2021.
<br />2 Bureau of Economic Analysis, Value Added bv Industry, accessed 12/2/2021.
<br />3 U.S. Bureau of Economic Analysis, SAINC5N Personal Income bv Maior Component and
<br />Earninas bv NAICS Industry 1/, accessed 12/2/2021.
<br />4 Russell Ormiston, Dale Belman, and Mark Erlich, "An Empirical Methodology to Estimate
<br />the Incidence and Costs of Payroll Fraud in the Construction Industry," January 2020, 2, https://
<br />stootaxfraud.net/wo-content/unloads/2020/03/National-Carpenters-Studv-Methodoloav -for-Waae-
<br />and-Tax-Fraud-Report-FINAL.adf.
<br />5 There are several complementary explanations for the development of the bifurcated
<br />construction industry and the decline of unionization. See Erlich (2020) , Theodore (2015), Weil (2005),
<br />and Ormiston et al. (2020). Mark Erlich, "Misclassification in Construction: The Original Gig Economy,"
<br />ILR Review, November 26, 2020, 1-29, https://doi.ora/10.1177/0019793920972321: Nik Theodore,
<br />"Rebuilding the House of Labor: Unions and Worker Centers in the Residential Construction Industry,"
<br />WorkingUSA 18 (March 1, 2015): 59-76, httos://doi.orQ/10.1111/wusa.12153: David Weil, "The
<br />Contemporary Industrial Relations System in Construction: Analysis, Observations and Speculations,"
<br />Labor History 46, no. 4 (November 1, 2005): 447-71, httos://doi.ora/10.1080/00236560500266258:
<br />Russell Ormiston et al., "Rebuilding Residential Construction," in Creating Good Jobs: An Industry -Based
<br />Strategy, ed. Paul Osterman (Cambridge, MA: MIT Press, 2020), 75-113.
<br />6 "Union Membership and Coverage Database from the CPS," htto://www.unionstats.com: 1971
<br />figure from Andrew Elrod, "Built Trades," Phenomenal World (blog), August 11, 2021, httos://www.
<br />ohenomenalworld.ora/analvsis/built-trades/ When considering only blue-collar construction workers,
<br />the numbers are significantly higher, though the trend of deunionization remains: the Bureau of Labor
<br />The Public Cost of Low -Wage Jobs in the US Construction Industry
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