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Title: The Perils of Relying Solely on the March CPS: The Case of Estimating the Effect on Employment of theTennCare Public Insurance Contraction
Citation Type: Miscellaneous
Publication Year: 2017
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Abstract: In a recent paper, Garthwaite, Gross and Notowidigdo (2014) report large positive labor supply effects of a major contraction in public insurance coverage in Tennessee, announced at the end of 2004 and implemented in mid-2005, using data from the March CPS. These results are important given the expansions of Medicaid coverage under the Affordable Care Act and the potential for substantial Medicaid contractions under President Trump and the Republican Congress. Their results are surprising given the previous work on the employment effects of health insurance expansions, but the authors argue that these differences in estimates are due to the fact that the Tennessee program went much higher into the income distribution than the programs studied by other researchers. In this paper we show, under reasonable parameter restrictions, that the framework used by Garthwaite, Gross and Notowidigdo (2014) only allows for estimating the lower bound on the labor supply response to the contraction, which makes their results all the more striking. However, we show next that their large estimates are the result of focusing on the March CPS in estimation. When we use their estimation strategy on a dataset based on all the months of the CPS, or a dataset based on the American Community Survey, we find much smaller, and sometimes negative, estimates of the lower bound on the labor supply response. This result holds when we use the whole data set or allow for parameter heterogeneity by hours worked, age and education. Note that compared to the March CPS, these alternative datasets offer much larger sample sizes and are not affected by seasonal factors. We then consider a number of possible explanations for the differences in the estimates, but our results continue when we consider these modifications. We attempt to distinguish between the estimates across databases using placebo tests. While these tests reject many estimates, there is still a very wide range in the surviving estimates. Hence, we conclude that, at best, we do not have good estimates of the treatment effect of interest.
Url: http://www.cirje.e.u-tokyo.ac.jp/research/workshops/emf/paper2017/emf0720.pdf
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Authors: Ham, John, C; Ueda, Ken
Publisher: National University of Singapore
Data Collections: IPUMS CPS
Topics: Labor Force and Occupational Structure, Methodology and Data Collection
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