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Who Watches the Watchmen? Local News and Police Behavior in the United States. (2020). mastrorocco, nicola ; Ornaghi, Arianna.
In: Trinity Economics Papers.
RePEc:tcd:tcduee:tep0720.

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  1. Political Campaigning, and Racial Discrimination in Arrests for Drugs.. (2023). Zambiasi, Diego ; Barilari, Francesco.
    In: Trinity Economics Papers.
    RePEc:tcd:tcduee:tep0223.

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  2. Do civilian complaints against police get punished?. (2023). Romaniuc, Rustam ; Gomies, Matthew ; Deangelo, Gregory.
    In: Public Choice.
    RePEc:kap:pubcho:v:196:y:2023:i:3:d:10.1007_s11127-023-01052-1.

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  3. Strategic Bureaucratic Opacity: Evidence from Death Investigation Laws and Police Killings. (2023). Kastoryano, Stephen ; Celislami, Elda ; Mastrobuoni, Giovanni.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp16609.

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  6. Hosting Media Bias: Evidence from the Universe of French Broadcasts, 2002-2020. (2022). Urvoy, Camille ; Herve, Nicolas ; Hengel, Moritz ; Cage, Julia.
    In: Working Papers.
    RePEc:hal:wpaper:hal-03878119.

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  7. Media Competition and News Diets. (2022). Sinkinson, Michael ; Cage, Julia ; Angelucci, Charles.
    In: Post-Print.
    RePEc:hal:journl:hal-03880088.

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  8. The economics of policing and crimeThe economics of policing and crime. (2022). Masera, Federico.
    In: Chapters.
    RePEc:elg:eechap:19378_2.

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  9. Media Slant is Contagious. (2022). Widmer, Philine ; Ash, Elliott ; Galletta, Sergio.
    In: Papers.
    RePEc:arx:papers:2202.07269.

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  1. 40Mello (2019) supports this choice by noting that the percent differences tend to be more dispersed for smaller than for larger cities, perhaps because the number of crimes and arrests is increasing with city size. We follow the same size categories: 10,000-15,000, 15,000-25,000, 25,000-50,000, 50,000-100,000, 100,000-250,000, and >250,000. 78 Google Trends Data The Google Trends API normalizes the search interest between 0 and 100 for the time and location of each query. In particular, each data point is divided by the total searches of the geography and time range it represents to compare relative popularity. [...] The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics (Stephens-Davidowitz, 2014). We modify the script provided byGoldsmith-Pinkham and Sojourner (2020) to query the Google trends API. Importantly, the Google trends API limits the number of geographic locations per query to five.
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  4. Appendix B – Data Cleaning Newscast Transcripts Separating Newscasts into News Stories. We segment each newscast into separate stories using an automated procedure based on content similarity across sentences. We begin by selecting the number of stories each newscast is composed of using texttiling (Hearst, 1997), an algorithm that divides texts into passages by identifying shifts in content based on word co-occurrence. We then divide sentences into passages using the Content Vector Segmentation methodology proposed by Alemi and Ginsparg (2015), which identifies content shifts by leveraging the representation of sentences into a vector space using word embeddings. In addition, we show that our results are robust to a simple segmentation procedure that separates the newscast into stories of 130 words, based on the fact that the average person speaks at around 130 words per minute.
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  5. Appendix Figure XIII: Local News Viewership and Political Participation, by Age Below 55 Above 55 (a) Watched Local TV News Below 55 Above 55 (b) Attended a Local Political Meeting Notes: This figure reports the share of people who reported watching local TV news in the last day (Panel (a)) or attended a local political meeting in the last year (Panel (b)), separately for individuals below and above 55. Data are from the 2010 Cooperative Congressional Election Study.
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  14. Covered Non-Covered 0.686 0.550 0.170 0.110 Watched Local TV News in the Last Day 0.10 0.20 0.30 0.40 0.50 0.60 0.00 0.70 Coefficient Estimate, 95% CI -.15 -.1 -.05 0 .05 -.2 .1 .15 Attended Local Political Meeting in the Last Year 0.03 0.05 0.07 0.10 0.13 0.15 0.17 0.00 57 Appendix Tables Appendix Table I: Sample Summary Overall Included in the Content Analysis (1) (2) # of Stations 835 323 # of Stations Ever Controlled by Sinclair 121 38 # of Stations Ever Owned and Operated by Sinclair 110 37 # of Stations Ever Owned and Operated by Cunningham 10 1 # of Stations Ever Controlled by Sinclair through a Local Marketing Agreement 10 4 Notes: This table presents summary counts for full-powered commercial TV stations affiliated with a big four network 2010-2017, separately for all stations (column (1)) and for the sample of stations included in the content analysis (column (2)).
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  22. Figure II: Number of Stations Controlled by Sinclair 2010-2017 2010 2011 2012 2013 2014 2015 2016 2017 Notes: This figure shows the number of big-four affiliate stations controlled by Sinclair in each month from January 2010 to December 2017. A station is considered controlled by Sinclair if it is owned and operated by the Sinclair Broadcast Group, if it is owned and operated by Cunningham Broadcasting, or if Sinclair controls programming through a local marketing agreement. Share of Stories of a
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  23. For each city, we fit the time series of crimes and clearances 2009-2017 using a local linear regression with bandwidth two. We compute the absolute value of the percent difference between actual and predicted values (adding 0.01 to the denominators to avoid dealing with zeros) and identify an observation to be a record error if the percent difference exceeds a given threshold. The threshold is computed as the 99th percentile of the distribution of percent differences for cities within a population group.40 We substitute observations that are identified as record errors using the predicted value from the time-series regression. We follow this procedure to clean the crime and clearance series of each type of crime (property, violent, murder, assault, robbery, rape, burglary, theft, and motor vehicle theft). Overall, around 1% of observations are substituted using this procedure.
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  26. Given Type .05 .1 .15 .2 0 .25 .3 # of Stations Controlled by Sinclair 20 40 60 80 100 120 0 140 Mean Topic Share (Local News) 0.05 0.10 0.15 0.20 0.00 0.25 0.30 39 Figure III: Map of Media Markets Experiencing Sinclair Entry 2010-2017 Notes: This map shows year of Sinclair entry across media markets in the United States. Lighter colors correspond to later entry. Never treated are media markets that never experience Sinclair entry; always treated are media markets that have at least one station controlled by Sinclair at the beginning of the period of interest (January 2010). There were no additional stations that were acquired in 2010.
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  61. P-value (-3 = -2 = 0): 0.669 Coefficient Estimates, 95% CI -.06 -.04 -.02 0 -.08 .02 40 Figure V: Local Crime News of Violent and Property Crimes 0.750 Violent Property (a) Share of Crime Stories Violent Property (b) Crime Stories per Offense Notes: This figure shows what crimes are covered in local TV news. Panel (a) shows the average share of a municipality’s crime stories that are about violent crimes (i.e. murder, assault, rape, and robbery) and property crimes (i.e. burglary, theft, and motor vehicle theft). Panel (b) shows the average number of crime stories per reported offense across municipalities. Note that this does not exactly correspond to the probability that a crime of a given type appears in the news because we have information on news coverage only for one randomly selected day per week. In both graphs, the sample is restricted to 2010 and to media market that never experience Sinclair entry.
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  63. People People People (1) (2) (3) (4) (5) Sinclair * Covered-0.001-0.002 0.085-0.028-0.012 (0.005) (0.002) (0.173) (0.029) (0.022) Observations 8449 8449 9472 14015 14015 Clusters 109 109 111 111 111 Municipalies 1371 1371 1501 1752 1752 Outcome Mean in 2010 0.240 0.019 2.967 2.370 1.846 Media Market by Year FE X X X X X Covered by Year FE X X X X X Municipality FE X X X X X Sinclair * Controls X X X X X Appendix Table XVII: Effect of Sinclair Entry on the Violent Crime Clearance Rate, Robustness Dependent Variable Violent Crime Clearance Rate Baseline Data Cleaning Treatment Definition Robustness to… No Winsorizing No Imputation Drops DMAs with Divested Stations Owned and Operated by Partially Treated Years as Group Acquis.
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  64. Population smoothing. To define crime rates we use a smoothed version of the population count included in the UCRs, again following the crime literature. In particular, we fit the population time series of city using a local linear regression with a bandwidth of 2 and replace the reported population with the predicted values. This is necessary because population figures are reported yearly, but tend to jump discontinuously in census years (Chalfin and McCrary (2018)).
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  65. Prat, Andrea. 2018. ‘Media Power’, Journal of Political Economy 126(4), 1747–1783.
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  66. Premkumar, Deepak. 2020, Intensified Scrutiny and Bureaucratic Effort: Evidence from Policing After High-Profile, Officer-Involved Fatalities. Working Paper. Ramırez-Alvarez, Aurora. Forthcoming. ‘Media and Crime Perceptions: Evidence from Mexico ’, Journal of Law, Economics, and Organization .

  67. Rate (1) (2) (3) (4) Sinclair * Covered-0.004-0.013-0.004-0.006 (0.009) (0.009) (0.011) (0.015) Observations 14016 14013 14009 13953 Clusters 111 111 111 111 Municipalities 1752 1752 1752 1752 Outcome Mean in 2010 0.191 0.131 0.211 0.172 Media Market by Year FE X X X X Covered by Year FE X X X X Municipality FE X X X X Sinclair * Controls X X X X 44 Table IV: Effect of Sinclair Entry on Salience of Crime and Police Dependent Variable Monthly SearchVolume Keyword Crime Police Weather Youtube (1) (2) (3) (4) Sinclair-0.040*** -0.040*** -0.009-0.011 (0.014) (0.014) (0.016) (0.009) Observations 14880 14880 14880 14880 Clusters 155 155 155 155 Outcome Mean in 2010 3.624 3.920 3.872 4.284 Media Market FE X X X X Month FE X X X X Media Market Controls X X X X Notes: This table shows the effect of Sinclair entry on the salience of crime and police using Google trend data in differences-in-differences design.
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  68. Rolnik, Guy, Cagé, Julia, Gans, Joshua, Goodman, Ellen, Knight, Brian, Prat, Andrea, Schiffrin, Anya and Raj, Prateek. 2019, Protecting Journalism in the Age of Digital Platforms. Committee for the Study of Digital Platforms Media Subcommittee. Stigler Center for the Study of the Economy and the State. University of Chicago Booth School of Business.
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  69. Sheriffs are the only law enforcement heads that can be elected as well as appointed, again depending on the state. Finally, the FBI has jurisdiction over federal crimes (i.e. crimes that violate U.S. federal legal codes or where the individual carries the criminal activity over multiple states). However, most crimes are prosecuted under state criminal statutes. Owens (2020) explains in detail the functioning of law enforcement agencies in the United States.
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  72. Snyder Jr, James M. and Strömberg, David. 2010. ‘Press Coverage and Political Accountability ’, Journal of Political Economy 118(2), 355–408.

  73. Standard errors are clustered at the media market level. The dataset is a municipality by year panel. Treatment is defined at the year level. A media market is considered treated in a given year if Sinclair was present in the market in the January of that year. Panel A includes the full sample; Panel B restricts the sample to municipalities that experience at least one assault, one robbery, and one rape in every year. Covered municipalities are municipalities that are mentioned in the news more than the median municipality in 2010. Clearance rates are defined as total number of crimes cleared by arrest or exceptional means over total number of crimes, winsorized at the 99% level.
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  74. Standard errors are clustered at the media market level. The dataset is a municipality by year panel. Treatment is defined at the yearly level. A media market is considered treated in a given year if Sinclair was present in the market in the January of that year. Covered municipalities are municipalities that are mentioned in the news more than the median municipality in 2010. Crime rates are defined crimes per 1,000 people under an inverse hyperbolic sine transformation, and are winsorized at the 99% level.
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  75. Standard errors are clustered at the media market level. The dataset is a municipality by year panel. Treatment is defined at the yearly level. A media market is considered treated in a given year if Sinclair was present in the market in the January of that year. In Panel A, reports outcomes are defined as crime rates; in Panel B, outcomes are defined as indicator variables for experiencing at least one crime. Covered municipalities are municipalities that are mentioned in the news more than the median municipality in 2010. Crime rates are defined as crimes per 1,000 people under an inverse hyperbolic sine transformation, and are winsorized at the 99% level.
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  76. Stashko, Allison. 2020, Do Police Maximize Arrests or Minimize Crime? Evidence from Racial Profiling in U.S. Cities. SSRN # 3132046.
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  77. Stephens-Davidowitz, Seth. 2014. ‘The Cost of Racial Animus on a Black Candidate: Evidence using Google Search Data’, Journal of Public Economics 118, 26–40.

  78. Stories are defined to be local if they mention at least one of the municipalities with more than 10,000 people in the media market. 49 Appendix Figure IV: Map of Media Markets Included in the Content Sample Notes: This map shows the share of stations for which we have content data continuously from 2010-2017 across media markets in the United States. Darker colors correspond to higher shares of media market stations included in the content data. 61% of media market have at least one station included in our sample, and for 88% of them the sample includes more than half of the stations present in the market.
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  79. The characteristics included are log population, share male, share male between 15 and 30, share over 55, share white, share Hispanic, share with 2 years of college, log median income, share of population below the poverty rate, share unemployed, log municipality area, and Republican vote share in the 2008 presidential election. Standard errors are clustered at the media market level. The dataset is a municipality by year panel.
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  80. To create a balanced sample, we exclude municipalities that do not continuously report crime data to the FBI 2010-2017 (236 municipalities) and do not have at least one violent and one property crime in every year (29 municipalities). This leaves us with 2358 municipalities. The empirical strategy requires restricting the sample to municipalities located in media markets included in the content data (which further drops 601 municipalities) and the regressions drops 5 singleton municipalities (Correia (2015)). The final sample includes 1752 municipalities.
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  81. Treatment is defined at the year level. A media market is considered treated in a given year if Sinclair was present in the market in the January of that year. Covered municipalities are municipalities that are mentioned in the news more than the median municipality in 2010. Clearance rates are defined as total number of crimes cleared by arrest or exceptional means over total number of crimes. Clearance rates are winsorized at the 99% level.
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  82. United States Department of Justice. Federal Bureau of Investigation. 2017, ‘Uniform Crime Reporting Program Data: Offenses Known and Clearances by Arrest, 2010-2017’. Ann Arbor, MI: Inter-university Consortium for Political and Social Research. 37 Velásquez, Daniel, Medina, Santiago, Yamada, Gustavo, Lavado, Pablo, Nunez-del Prado, Miguel, Alatrista-Salas, Hugo and Morzán, Juandiego. 2020. ‘I Read the News Today, Oh Boy: The Effect of Crime News Coverage on Crime Perception’, World Development 136.
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  83. Weisburst, Emily K. 2019. ‘Safety in Police Numbers: Evidence of Police Effectiveness from Federal COPS Grant Applications’, American Law and Economics Review 21(1), 81–109.

  84. Wenger, Debora and Papper, Bob. 2018a, Local TV News and the New Media Landscape: Part 1, The State of the Industry. Knight Foundation.
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  3. The demand for protection and the persistently high rates of gun violence among young black males. (2024). Kotowski, Maciej ; Evans, William N.
    In: Journal of Public Economics.
    RePEc:eee:pubeco:v:234:y:2024:i:c:s0047272724000501.

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  4. Monitoring Police with Body-Worn Cameras: Evidence from Chicago. (2024). Ferrazares, Toshio.
    In: Journal of Urban Economics.
    RePEc:eee:juecon:v:141:y:2024:i:c:s0094119023000086.

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  5. Reducing racial disparities in crime victimization: Evidence from employment discrimination litigation. (2024). Mattia, Taylor ; Harvey, Anna.
    In: Journal of Urban Economics.
    RePEc:eee:juecon:v:141:y:2024:i:c:s0094119022000365.

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  6. When do more police induce more crime?. (2023). Weinschelbaum, Federico ; de la Vega, Casilda Lasso ; Volij, Oscar.
    In: Economic Theory.
    RePEc:spr:joecth:v:76:y:2023:i:3:d:10.1007_s00199-022-01477-7.

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  7. Crime, inequality and subsidized housing: Evidence from South Africa. (2023). Piraino, Patrizio ; Manea, Roxana Elena ; Viarengo, Martina.
    In: World Development.
    RePEc:eee:wdevel:v:168:y:2023:i:c:s0305750x2300061x.

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  8. The unintended effects of minimum wage increases on crime. (2023). Fone, Zachary ; Cesur, Resul ; Sabia, Joseph J.
    In: Journal of Public Economics.
    RePEc:eee:pubeco:v:219:y:2023:i:c:s0047272722001827.

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  9. Main street business initiatives and crime in small towns. (2023). Smith, Rhet A ; Johnson, Josiah.
    In: Journal of Economic Behavior & Organization.
    RePEc:eee:jeborg:v:209:y:2023:i:c:p:91-112.

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  10. Federal-Local Partnerships on Immigration Law Enforcement: Are the Policies Effective in Reducing Violent Victimization?. (2023). Xie, Min ; Baumer, Eric P.
    In: Working Papers.
    RePEc:cen:wpaper:23-18.

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  11. Office?based mental healthcare and juvenile arrests. (2022). Lu, Thanh ; Deza, Monica ; MacLean, Johanna Catherine.
    In: Health Economics.
    RePEc:wly:hlthec:v:31:y:2022:i:s2:p:69-91.

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  12. The economics of policing and crimeThe economics of policing and crime. (2022). Masera, Federico.
    In: Chapters.
    RePEc:elg:eechap:19378_2.

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  13. Speed limit enforcement and road safety. (2022). Bauernschuster, Stefan ; Rekers, Ramona.
    In: Journal of Public Economics.
    RePEc:eee:pubeco:v:210:y:2022:i:c:s0047272722000652.

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  14. Local access to mental healthcare and crime. (2022). Solomon, Keisha ; MacLean, Johanna Catherine ; Deza, Monica.
    In: Journal of Urban Economics.
    RePEc:eee:juecon:v:129:y:2022:i:c:s0094119021000929.

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  15. Rare homicides, criminal behavior, and the returns to police labor. (2022). Xue, Yuhan ; Lovett, Nicholas.
    In: Journal of Economic Behavior & Organization.
    RePEc:eee:jeborg:v:194:y:2022:i:c:p:172-195.

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  16. Crime and the Mariel Boatlift. (2022). Packard, Michael ; Billy, Alexander.
    In: International Review of Law and Economics.
    RePEc:eee:irlaec:v:72:y:2022:i:c:s0144818822000503.

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  17. Prescription drug monitoring programs, opioid abuse, and crime. (2021). Horn, Brady ; Deza, Monica ; Dave, Dhaval.
    In: Southern Economic Journal.
    RePEc:wly:soecon:v:87:y:2021:i:3:p:808-848.

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  18. The Effects of Police Violence on Inner-City Students*. (2021). Ang, Desmond.
    In: The Quarterly Journal of Economics.
    RePEc:oup:qjecon:v:136:y:2021:i:1:p:115-168..

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  19. COVID-19 Has Strengthened the Relationship Between Alcohol Consumption and Domestic Violence. (2021). Danagoulian, Shooshan ; Deza, Monica ; Chalfin, Aaron.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:28523.

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  20. Do Police Make Too Many Arrests? The Effect of Enforcement Pullbacks on Crime. (2021). Weisburst, Emily ; Gonalves, Felipe ; Cho, Sungwoo.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp14907.

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  21. Collective Bargaining Rights, Policing, and Civilian Deaths. (2021). Feir, Donna ; Gillezeau, Rob ; Cunningham, Jamein.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp14208.

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  22. Crime, Inequality and Subsidized Housing:Evidence from South Africa. (2021). Patrizio, Roxana Manea.
    In: CIES Research Paper series.
    RePEc:gii:ciesrp:cies_rp_66.

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  23. Law enforcement with motivated agents. (2021). Yahagi, Ken.
    In: International Review of Law and Economics.
    RePEc:eee:irlaec:v:66:y:2021:i:c:s0144818821000065.

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  24. The ineffectiveness of ‘observe and report’ patrols on crime. (2021). Klick, Jonathan ; Fabbri, Marco.
    In: International Review of Law and Economics.
    RePEc:eee:irlaec:v:65:y:2021:i:c:s014481882030185x.

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  25. Crime, Inequality and Subsidized Housing: Evidence from South Africa. (2021). Viarengo, Martina ; Piraino, Patrizio ; Manea, Roxana.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8914.

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  26. .

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  27. Who Watches the Watchmen? Local News and Police Behavior in the United States. (2020). mastrorocco, nicola ; Ornaghi, Arianna.
    In: Trinity Economics Papers.
    RePEc:tcd:tcduee:tep0720.

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  28. Local Access to Mental Healthcare and Crime. (2020). Maclean, Johanna ; Solomon, Keisha T ; Deza, Monica.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:27619.

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  29. Do immigrants increase crime? Spatial analysis in a middle-income country. (2020). Vásquez Lavín, Felipe ; Ponce, Roberto D ; Vasquez-Lavin, Felipe ; Leiva, Mauricio.
    In: World Development.
    RePEc:eee:wdevel:v:126:y:2020:i:c:s0305750x19303778.

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  30. Unlocking amenities: Estimating public good complementarity. (2020). Sarmiento-Barbieri, Ignacio ; Christensen, Peter ; Albouy, David.
    In: Journal of Public Economics.
    RePEc:eee:pubeco:v:182:y:2020:i:c:s0047272719301720.

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  31. Are criminals strategic? Offender responses to drug sentencing cutoffs. (2020). Lepage, Louis-Pierre.
    In: Labour Economics.
    RePEc:eee:labeco:v:66:y:2020:i:c:s092753712030110x.

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  32. Urban revival in America. (2020). Handbury, Jessie ; Couture, Victor.
    In: Journal of Urban Economics.
    RePEc:eee:juecon:v:119:y:2020:i:c:s0094119020300383.

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  33. “Crime” on the Field. (2019). Makofske, Matthew ; Kitchens, Carl ; Wang, LE.
    In: Southern Economic Journal.
    RePEc:wly:soecon:v:85:y:2019:i:3:p:821-864.

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  34. The Minimum Legal Drinking Age and Crime Victimization. (2019). Hansen, Benjamin ; Ryley, Rachel ; Chalfin, Aaron.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:26051.

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  35. Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City. (2019). Hansen, Benjamin ; Parker, Lucie ; Lerner, Jason ; Chalfin, Aaron.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:25798.

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  36. Shaking Criminal Incentives. (2019). Koutmeridis, Theodore ; Aoki, YU.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp12781.

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  37. The Effect of Social Connectedness on Crime: Evidence from the Great Migration. (2019). Stuart, Bryan ; Taylor, Evan J.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp12228.

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  38. Shaking Criminal Incentives. (2019). Koutmeridis, Theodore ; Aoki, YU.
    In: Working Papers.
    RePEc:gla:glaewp:2019_13.

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  39. Shaking Criminal Incentives. (2019). Koutmeridis, Theodore ; Aoki, YU.
    In: Working Papers.
    RePEc:gla:glaewp:2019-13.

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  40. Schools as places of crime? Evidence from closing chronically underperforming schools. (2019). MacDonald, John M ; Ukert, Benjamin ; Steinberg, Matthew P.
    In: Regional Science and Urban Economics.
    RePEc:eee:regeco:v:77:y:2019:i:c:p:125-140.

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  41. Police disruption and performance: Evidence from recurrent redeployments within a city. (2019). Mastrobuoni, Giovanni.
    In: Journal of Public Economics.
    RePEc:eee:pubeco:v:176:y:2019:i:c:p:18-31.

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  42. More sneezing, less crime? Health shocks and the market for offenses. (2019). Danagoulian, Shooshan ; Chalfin, Aaron ; Deza, Monica.
    In: Journal of Health Economics.
    RePEc:eee:jhecon:v:68:y:2019:i:c:s0167629618308221.

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