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The United States is embroiled in an important debate about police use of force tactics. I find that black civilians are disproportionately likely to be involved in a use of force incident during an arrest, examining data from Dallas, Texas. However, this race disparity stems from differences in the initial likelihood of arrest. Further, I fail to find evidence of taste-based racial bias in use of force conditional on arrest, leveraging variation across officer and civilian race. The results suggest that reforms that narrowly focus on force-related protocols may be unlikely to reduce racial disparities in use of force.
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These files contain citations from U.S. patents 1926-2018 to articles in the Microsoft Academic Graph from 1800-2018. Please cite Marx, M. and A. Fuegi, "Reliance on Science in Patenting." available in this distribution as reliance_on_science.pdf and also at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686. These data make use of and redistribute portions of the Microsoft Academic Graph. Please see https://www.microsoft.com/en-us/research/project/academic/ for details about the Microsoft Academic Graph (MAG), which is provided under an ODC-BY license (https://opendatacommons.org/licenses/by/1-0/index.html). Academic papers making use of the MAG data should cite the following paper: Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246.
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These files contain citations from U.S. patents 1926-2018 to articles in the Microsoft Academic Graph from 1800-2018. Please cite Marx, M. and A. Fuegi, "Reliance on Science in Patenting." available in this distribution as reliance_on_science.pdf and also at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686. These data make use of and redistribute portions of the Microsoft Academic Graph. Please see https://www.microsoft.com/en-us/research/project/academic/ for details about the Microsoft Academic Graph (MAG), which is provided under an ODC-BY license (https://opendatacommons.org/licenses/by/1-0/index.html). Academic papers making use of the MAG data should cite the following paper: Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246.
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The National Surveys on Energy and Environment (NSEE) is an on-going biannual national opinion survey on energy and climate policy. Launched in 2008, over time the NSEE has covered topics such as public policy approaches to address climate change including federal, state, and international action; energy policies such as cap-and-trade, carbon taxes, renewable energy requirements, vehicle emissions standards, and many more; and knowledge and attitudes about global warming, climate adaptation, fracking, and geoengineering. From 2008-2012 the survey was called the “National Survey of American Public Opinion on Climate Change” (NSAPOCC); starting in 2013 the survey was renamed to the “National Surveys on Energy and Environment” (NSEE). NSEE was co-founded by professor Barry Rabe at the University of Michigan and professor Christopher Borick at Muhlenberg College, and is fielded by the Muhlenberg College Institute of Public Opinion. For more information about the NSEE, contact closup-nsee@umich.edu. The NSEE is committed to transparency in all facets of our work, including timely release and posting of data from each survey wave. A grant from the Office of the Provost at the University of Michigan has allowed us to provide online access to earlier waves of the NSEE, including frequency tables, survey instruments, and datasets. Users can see a list of topics covered by the NSEE, and search for questions by text, variable name, or variable category on CLOSUP's website. Although the datasets are listed by survey wave, the NSEE is a valuable source of longitudinal public-opinion data on climate change and energy policy. Many questions have been asked over multiple waves, including questions about belief in global warming that have been asked in every wave of the NSEE. Consult the NSEE Crosswalk to see which questions have been asked in prior and subsequent waves of the NSEE. To facilitate longitudinal analysis, the NSEE datasets use a longitudinal variable naming scheme to facilitate longitudinal analysis. Variable names include two parts: a subject category for the question, and a description of the contents of the question. When a question has been asked with the same text and response options over multiple waves, the same variable name will be used in each dataset. For more information on the longitudinal naming scheme users should consult the codebooks for the datasets. ,Weight variables are provided for each dataset. Data are weighted by gender, age, race, income, and education, to reflect population characteristics of the United States as reported by the United States Census Bureau.,Presence of Common Scales: Several likert-type scales were used.,computer-assisted telephone interview (CATI)~~,Adult (age 18 or older) residents of the United StatesSmallest Geographic Unit: State,The NSEE is conducted as a telephone survey adult (age 18 or older) residents of the United States. In 2008, only landlines were included in the sampling frame, starting in 2009 both landline and cell phones have been included in the sampling frames. See individual waves for more detailed sampling information.,
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The data contain records of defendants in criminal cases filed in United States District Court during fiscal year 2015. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt Associates and the Bureau of Justice Statistics. Data and documentation were prepared by Abt Associates.,Datasets: DS1: 2015 Cases Filed Data,Federal Justice Statistics Program Data Series,Criminal cases filed in United States District Court during fiscal year 2015.,
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These files contain citations from U.S. patents 1926-2018 to articles in the Microsoft Academic Graph from 1800-2018. Please cite Marx, M. and A. Fuegi, "Reliance on Science in Patenting." available in this distribution as reliance_on_science.pdf and also at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686. These data make use of and redistribute portions of the Microsoft Academic Graph. Please see https://www.microsoft.com/en-us/research/project/academic/ for details about the Microsoft Academic Graph (MAG), which is provided under an ODC-BY license (https://opendatacommons.org/licenses/by/1-0/index.html). Academic papers making use of the MAG data should cite the following paper: Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246.
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We use state-of-the-art, satellite-based PM 2.5 data products to assess the extent to which the Environmental Protection Agency's existing, monitor-based measurements over- or underestimate true exposure to PM 2.5 pollution. Treating satellite-based estimates as truth implies a substantial number of "policy errors"—overregulating areas that are in compliance with the air quality standards and under-regulating other areas that appear to be in violation. We investigate the health implications of these apparent errors. We also highlight the importance of accounting for prediction error in satellite-based estimates. Once prediction errors are accounted for, conclusions with regards to "policy errors" become substantially more uncertain.
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We combine a randomized experiment with administrative data to study the effects of mandatory job search periods in the Dutch welfare system. Job search periods postpone the first welfare benefits payment and encourage applicants to start searching for jobs actively. Job search periods substantially reduce benefits take up. The decline in benefits receipt is permanent, but fully compensated by increased earnings because of higher reemployment rates. We do not find detectable effects on health and crime outcomes, nor do we observe income declines for more vulnerable applicants. Our results suggest that job search periods are an effective instrument for targeting benefits to welfare applicants.
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I estimate the effect of access to food stamps on criminal recidivism. In 1996, a federal welfare reform imposed a lifetime ban from food stamps on convicted drug felons. Florida modified this ban, restricting it to drug traffickers who commit their offense on or after August 23, 1996. I exploit this sharp cutoff in a regression discontinuity design and find that the ban increases recidivism among drug traffickers. The increase is driven by financially motivated crimes, suggesting that the cut in benefits causes ex-convicts to return to crime to make up for the lost transfer income.
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As states continue to implement the Common Core State Standards (CCSS), state educational agencies (SEAs) are providing professional development and curricular resources to help districts and teachers understand the standards. However, little is known about the resources SEAs endorse, the states and/or organizations sponsoring these resources, and how states and organizations are connected. This study investigates the secondary English/language arts resources provided by 51 SEAs (2,023 resources sponsored by 51 SEAs and 262 intermediary organizations). Social network analysis of states and sponsoring organizations revealed a core-periphery network in which certain states and organizations were frequently named as the sponsors of resources, while other organizations were named as resource sponsors by only one state. SEAs are providing a variety of types of resources, including professional development, curriculum guidelines, articles, and instructional aids. This study offers insight into the most influential actors providing CCSS resources at the state level, as well as how SEAs are supporting instructional capacity through the resources they provide for teachers.
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