Unemployment Insurance Tax Burdens and Benefits: Funding Family Leave and Reforming the Payroll Tax
We examine the distributional consequences of the UI payroll tax using representative individual microdata. We calculate taxes paid by individual wage and individual and household income deciles, incorporating the effects of multiple job holding and turnover. This tax distribution is compared with the distribution of UI benefits and benefits net of taxes, as well as to the burdens imposed by the federal income tax. We conclude that the UI payroll tax is indeed quite regressive. Within the context of the regular UI program, this regressivity is offset by the progressive nature of benefits, leaving the net benefit distribution progressive. We simulate a revenue-neutral increase to the OASDI level of the taxable wage base. The share of total UI taxes paid becomes fairly equal, and net benefits become positive across more deciles. Finally, we examine the effect of providing family leave within the UI system as recently proposed. We find that the share of such benefits going to relatively high-income groups is likely to be much larger than is the case for regular UI benefits.
|Date of creation:||Oct 2003|
|Date of revision:|
|Publication status:||published as Anderson, Patricia M. and Bruce D. Meyer. "Unemployment Insurance Tax Burdens And Benefits: Funding Family Leave And Reforming The Payroll Tax," National Tax Journal, 2006, v59(1,Mar), 77-95.|
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- Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
- Anderson, Patricia M & Meyer, Bruce D, 1997. "Unemployment Insurance Takeup Rates and the After-Tax Value of Benefits," The Quarterly Journal of Economics, MIT Press, vol. 112(3), pages 913-37, August.
- Robert Hutchens, 1981. "Distributional equity in the unemployment insurance system," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 34(3), pages 377-385, April.
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