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      Cosponsoring and Cashing In: US House Members’ Support for Punitive Immigration Policy and Financial Payoffs from the Private Prison Industry

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      Business and Politics
      Cambridge University Press (CUP)

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          Abstract

          The private prison industry is a multi-million-dollar industry that has increasingly profited from the detention of undocumented immigrants. As a government contractor, therefore, the industry has a natural interest in government decision making, including legislation that can affect its expansion into immigrant detention. In this article, we examine the relationship between campaign donations made on behalf of the private prison industry and an untested form of position taking—bill cosponsorship—in the US House of Representatives. We hypothesize the private prison industry will reward House members for taking positions that benefit the industry. We also hypothesize the private prison industry will also reward House members who incur greater political risk by taking positions out of sync with the party. To test our hypotheses, we focus on punitive immigration legislation that has the potential to increase the supply of immigrant detainees over the course of eight years. We find support for our second hypothesis, that private prison companies are more likely to reward House Democrats who cosponsor punitive immigration policies even after accounting for possible endogeneity. The findings have important implications regarding the relationship between House members and private interests.

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          MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

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            Matching methods for causal inference: A review and a look forward.

            When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
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              Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Business and Politics
                Bus. Polit.
                Cambridge University Press (CUP)
                1469-3569
                July 05 2021
                : 1-18
                Article
                10.1017/bap.2021.6
                25941026-3fda-45bd-bdf7-68fd587cfcd8
                © 2021

                https://www.cambridge.org/core/terms

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