Test direct, spillover, and aggregate effects of hot spots policing on crime in a high-crime environment.
We identified 967 hot spot street segments and randomly assigned 384 to a six-month increase in police patrols. To account for the complications resulting from a large experimental sample in a dense network of streets, we use randomization inference for hypothesis testing. We also use non-experimental streets to test for spillovers onto non-hot spots and examine aggregate effects citywide.
Our results show an improvement in short-term security perceptions and a reduction in car thefts, but no direct effects on other crimes or satisfaction with policing services. We see larger effects in the least secure places, especially for short-term security perceptions, car thefts, and assaults. We find no evidence of crime displacement but rather a decrease in car thefts in nearby hot spots and a decrease in assaults in nearby non-hot spots. We estimate that car thefts decreased citywide by about 11%.
Our study highlights the importance of context when implementing hot spots policing. What seems to work in the USA or even in Bogotá is not as responsive in Medellín (and vice versa). Further research—especially outside the USA—is needed to understand the role of local crime patterns and police capacity on the effectiveness of hot spots policing.
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Some of the most prominent experimental studies in criminology are Sherman et al. (1989) in Minneapolis, MN; Sherman and Weisburd (1995) in Minneapolis, MN; Weisburd and Green (1995) in Jersey City, NJ; Sherman et al. (1995) in Kansas City, KS; Braga et al. (1999) in Jersey City, NJ; Mazerolle et al. (2000) in Oakland, CA; Braga and Bond (2008) in Lowell, MA; Taylor et al. (2011) in Jacksonville, FL; Ratcliffe et al. (2011) in Philadelphia, PA; Groff et al. (2015) in Philadelphia, PA; and Santos and Santos (2016) in Port St. Lucie, FL. There is also non-experimental evidence on hot spots policing in the criminology literature as Sviridoff et al. (1992) in New York, NY; Cohen et al. (2003) in Pittsburgh, PA; and Lawton et al. (2005) in Philadelphia, PA. Studies on police and crime in the economics literature include Di Tella and Schargrodsky (2004) in Buenos Aires, Argentina; and Draca et al. (2011) in London, U.K.
See Police Executive Research Forum (2008) for US data. See the report on hot spots policing by one of the major national newspapers in Argentina (La Nación). In Colombia, the National Police Department requires that police patrols intensify their activities in crime hot spots. This is outlined in the Quadrants Policing Guidelines. For the case of Uruguay, see the report from the Ministry of the Interior. For Trinidad and Tobago, see Sherman et al. (2014). In Venezuela, there was an unsuccessful initiative to implement hot spots policing strategies in Sucre. One of the coauthors of this study was involved in the early evaluation efforts.
See Consejo Ciudadano para la Seguridad Pública y Justicia Penal, Global Study on Homicide 2013 ,and Transnational Organized Crime in Central America and the Caribbean.
As Chong et al. (2014) show, there is wide variation in implementation capabilities even for simple policies, as returning mail when the address is non-existent.
Blattman et al. (2018) classify control streets using 250 and 500 meters radii. We use 125 and 250 meters for two reasons. First, in our case, there are only 20 control streets beyond 500 meters and that small sample size would drive the results. Second, Blattman et al. (2018) don’t find crime displacement beyond 250 meters.
See Petrosino et al. (2003). In Colombia, the program is called Delinquir no paga, and it is run by the prison authority countrywide.
Data on population and gross domestic product is from the National Department of Statistics.
See the report The Worlds’ Most Crowded Cities by the World Economic Forum.
The war for power after Don Berna’s extradition was documented by local media. See for instance La Guerra que Desangró a Medellín in the major regional newspaper El Colombiano.
See for instance McDermott (2014) and the report Así Funciona la Oficina, published in the newspaper El Colombiano.
The influence of organized crime in the regulation of violence becomes apparent with the case of El Popular, a low-income comuna where the presence of some criminal organizations is prevalent. See for instance Duncan et al. (2015).
Estimates are from the Secretariat of Security of Medellín.
Data is from the National Police of Colombia.
Specifically, it is known as the Modelo Nacional de Vigilancia Comunitaria por Cuadrantes. For more details on the model see the Quadrants Policing Guidelines. Throughout the paper, we use quadrants as a translation for cuadrante.
These are usually police stations or special locations managed by the National Prosecutor’s Office.
The activities are recorded in the Tabla de Acciones Mínimas Requeridas, which specify the activity, the time of the day, general places to focus on, and other relevant details to provide surveillance to the quadrant.
All figures are for active police and exclude those performing solely administrative tasks. Data from US cities is from the FBI Uniform Crime Report .
This is specified in the Quadrants Policing Guidelines.
One major motivation for this hot spots policing intervention was the concern by senior officers at the National Police on how to identify and systematically target crime hot spots. Indeed, this concern allowed us to collaborate with the police in designing this and other similar interventions.
According to aggregate statistics reported by the Metropolitan Police, during the year 2015, roughly 91% of motorbike thefts, 88% of car thefts, 75% of personal robberies, 71% of assaults, and 74% of homicides occurred in the public view. These data, however, are not available for each individual crime report. Broadly, this implies that most crimes are subject to be deterred by increased police presence in the streets. This particularly low concentration of crime in private spaces is explained by the fact that private property in Colombia is often secured both by physical barriers and by armed private security guards.
In some crime reporting locations, the individual filing the report is allowed to point to the specific location in an interactive map. In these cases, the exact coordinates are automatically recorded. In other reporting locations, the individual filing the report specifies the address. In these cases, the Metropolitan Police records the exact coordinates after the crime is reported. The main reporting locations are police stations.
Reporting rates are from the National Survey on Citizen Security conducted by the National Department of Statistics.
These data were subject to some amount of uncertainty and were erratic during some periods. In the best cases, each device sent the signals frequently, and we were able to identify clearly the time of entry and exit to the hot spots. When this was not the case—for instance, when time stamps were separated by large periods of several hours with changing locations—we had to assign patrolling time making ad-hoc decisions. In general, when we observed only one signal from a street with the next one being in a different location, we assigned three minutes of police patrolling time. When we observed one signal from a street with the next one being assigned to the same, but separated by many hours, we top-coded the entry at the duration of the shift. These decisions resulted from discussions with police patrols and officials.
Authors’ estimations based on data from the Metropolitan Police and the Secretariat of Security. Patrolling times were estimated during pilots between January and April 2015. We estimate the distribution of total reported crimes using a crime index that weights crimes according to the average prison sentence. See “Selection of crime hot spots” for more details.
This was a result of budgetary constraints from the Secretariat of Security, which covered for the expenses for the endline survey.
The original survey measures were the following: (i) indicator for direct and indirect victimization of the respondent; (ii) score from 1 to 3 on perceptions of security for last 6 months; (iii) score from 1 to 4 on perceptions of security for last 12 months; (iv) score from 1 to 4 on general perceptions of security; (v) score from 1 to 4 on quality of police work; (vi) score from 1 to 4 on satisfaction with police service; (vii) indicator for an increase in police presence.
Institutional facilities are mainly from the local government to provide different public services.
We used the following weights for each crime: 0.550 for homicides, 0.112 for assaults, 0.221 for car and motorbike theft, and 0.116 for personal robbery.
There were different reasons to include or exclude streets. For instance, streets nearby metro stations had a disproportionate number of personal robberies reported, but the location was usually the transport system rather than the station. Other streets were not pre-selected because there were few reports.
See Klofas et al. (2010) for a discussion on the precision of mandates for police interventions and strategies.
In effect, these instructions were included in the weekly meeting to define the patrolling strategy and recorded in the Tabla de Acciones Mínimas Requeridas.
We account for the clustered assignment using clustered standard errors, as we explain in “Estimating equations.”
For a similar analysis with pre-specified spillover ranges, see Blattman et al. (2018).
Recall from “Randomization and schedule of potential outcomes” that a bug in the randomization code assigned 7 streets to treatment with probability 1—hence they had a probability of 0 to be in the control condition. We drop these streets from the analysis.
See also Blattman et al. (2018) for a similar analysis in the city of Bogotá, where all these considerations were pre-specified.
We could assume even farther spillover effects (i.e., violations of the SUTVA) but the number of pure control units largely decreases. See Blattman et al. (2018) for a pre-specified design that allows testing for spillover effects within 500 meters.
See also Gerber and Green (2012).
One obvious concern with our approach is that, while we are evaluating program effects using multiple outcomes, we are not correcting p values for multiple comparisons. We acknowledge this limitation and note that common corrections as the Bonferroni adjustment will render mostly non-significant estimates, as sometimes they are extreme (Dunn 1961). We leave the interpretation to the reader, but note that the main effects are generally stable across specifications. We also note that the outcome measures using police crime data correspond to the original set of outcomes we used to identify the experimental hot spots in the first place.
Note the pure control group for the case of short-range spillovers includes control streets located at more than 125 meters from targeted streets: 189 that are between 125 and 250 meters plus 123 that are at more than 250 meters. On the other hand, the pure control group for the case when we assume both short- and long-range spillovers includes only the 123 streets located at more than 250 meters from treated streets. See Table 2.
We report these heterogeneous effects as do Blattman et al. (2018).
See the notes in each table for details.
Note that we observe some evidence of long-range positive spillover effects when we assume both levels of spillovers. As a result, the decrease in reported assault cases in streets located 125 and 250 meters lead us to underestimate the short-range spillover effects when we only assume short-range spillovers. Recall that when we assume only short-range spillover effects, streets located between 125 and 250 meters are in the control group.
They find smaller effects for property crime in general, so the comparison is not direct for the exact same type of crime.
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This project was possible because of the collaboration of the Ministry of Defense of Colombia, the National Police of Colombia, and the City of Medellí n. In particular, we are grateful to the former Minister of Defense Juan C. Pinzón, Director of the National Police General Jorge H. Nieto, and Mayor of Medellín Anibal Gaviria. CESED at Universidad de los Andes and the Latin American Development Bank (CAF) coordinated research activities. Cifras y Conceptos collected the data. For research assistance we are grateful to Juan Carlos Angulo and Eduardo Fagre. For comments we thank Chris Blattman, Marcela Eslava, Don Green, Patryk Perkowski, Juan F. Vargas, Hernando Zuleta and numerous conference participants.
For financial support, we thank the Ministry of Defense of Colombia, the Latin American Development Bank (CAF), the City of Medellín, Organización Ardila Lülle, and Open Society Foundations.
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Collazos, D., García, E., Mejía, D. et al. Hot spots policing in a high-crime environment: an experimental evaluation in Medellín. J Exp Criminol (2020). https://doi.org/10.1007/s11292-019-09390-1
- Spillover effects
- Hot spots
- Field experiment