IDEAS home Printed from https://ideas.repec.org/a/eee/jcjust/v73y2021ics0047235221000052.html
   My bibliography  Save this article

Public cooperation and the police: Do calls-for-service increase after homicides?

Author

Listed:
  • Brantingham, P. Jeffrey
  • Uchida, Craig D.

Abstract

Calls-for-service represent the most basic form of public cooperation with the police. How cooperation varies as a function of instances of police activity remains an open question. The great situational diversity of police activity in the field, matching the situational diversity of crime and disorder, makes it challenging to estimate causal effects. Here we use homicides as an indicator for the occurrence of a standardized set of highly visible, socially-intensive, acute police investigative activities and examine whether police calls-for-service change in response. We adopt a place-based difference-in-differences approach that controls for local fixed affects and common temporal trends. Estimates of the model using data from Los Angeles in 2019 shows that calls-for-service increase significantly in the week following a homicide. The effect pertains to both violent crime and quality of life calls for service. Partitioning the data by race-ethnicity shows that calls-for-service increase most when the homicide victim is Black. Partitioning the data by race-ethnicity and type of homicide shows that some types of calls are suppressed when the homicide is gang-related. The results point to opportunities for police to build trust in the immediate aftermath of homicides, when the public is reaching out for greater assistance.

Suggested Citation

  • Brantingham, P. Jeffrey & Uchida, Craig D., 2021. "Public cooperation and the police: Do calls-for-service increase after homicides?," Journal of Criminal Justice, Elsevier, vol. 73(C).
  • Handle: RePEc:eee:jcjust:v:73:y:2021:i:c:s0047235221000052
    DOI: 10.1016/j.jcrimjus.2021.101785
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047235221000052
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jcrimjus.2021.101785?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    2. Vaughn, Paige E., 2020. "The effects of devaluation and solvability on crime clearance," Journal of Criminal Justice, Elsevier, vol. 68(C).
    3. Pizarro, Jesenia M. & McGloin, Jean Marie, 2006. "Explaining gang homicides in Newark, New Jersey: Collective behavior or social disorganization?," Journal of Criminal Justice, Elsevier, vol. 34(2), pages 195-207.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rylan Simpson & Carlena Orosco, 2021. "Re-assessing measurement error in police calls for service: Classifications of events by dispatchers and officers," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-19, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    2. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    3. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    4. Susan Athey & Mohsen Bayati & Guido Imbens & Zhaonan Qu, 2019. "Ensemble Methods for Causal Effects in Panel Data Settings," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 65-70, May.
    5. Francesca Caselli & Matilde Faralli & Paolo Manasse & Ugo Panizza, 2021. "On the Benefits of Repaying," IMF Working Papers 2021/233, International Monetary Fund.
    6. Hao, Shiming, 2021. "True structure change, spurious treatment effect? A novel approach to disentangle treatment effects from structure changes," MPRA Paper 108679, University Library of Munich, Germany.
    7. Sviták, Jan & Tichem, Jan & Haasbeek, Stefan, 2021. "Price effects of search advertising restrictions," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    8. Goryunov, Alexander & Ageshina, Elena & Lavrentev, Igor & Peretyatko, Polina, 2023. "Estimating the effect of Russia’s development policy in the Far Eastern region: The synthetic control approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 72, pages 58-72.
    9. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2021. "Revisiting Event Study Designs: Robust and Efficient Estimation," Papers 2108.12419, arXiv.org, revised Jan 2024.
    10. Damon Jones & Ioana Marinescu, 2022. "The Labor Market Impacts of Universal and Permanent Cash Transfers: Evidence from the Alaska Permanent Fund," American Economic Journal: Economic Policy, American Economic Association, vol. 14(2), pages 315-340, May.
    11. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    12. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
    13. Alberto Abadie & Anish Agarwal & Raaz Dwivedi & Abhin Shah, 2024. "Doubly Robust Inference in Causal Latent Factor Models," Papers 2402.11652, arXiv.org, revised Apr 2024.
    14. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    15. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    16. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.
    17. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    18. Dmitry Arkhangelsky & Guido W. Imbens, 2019. "Doubly Robust Identification for Causal Panel Data Models," Papers 1909.09412, arXiv.org, revised Feb 2022.
    19. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy [Using synthetic controls: Feasibility, data requirements, and methodological aspects]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    20. Peter Ganong & Simon Jäger, 2018. "A Permutation Test for the Regression Kink Design," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 494-504, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jcjust:v:73:y:2021:i:c:s0047235221000052. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jcrimjus .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.