IDEAS home Printed from https://ideas.repec.org/p/ays/ispwps/paper1821.html
   My bibliography  Save this paper

Fiscal Fragmentation and the Spatial Distribution of Crime in the United States

Author

Listed:
  • Jinghua Lei

    (School of Finance,Renmin University of China)

  • Jenny Ligthart (deceased)
  • Mark Rider

    (Department of Economics, Andrew Young School of Policy Studies)

  • Ruixin Wang

    (School of Economics and Management, Harbin Institute of Technology, Shenzhen)

Abstract

We investigate the effect of fiscal fragmentation on crime and its spatial distribution among jurisdictions in metropolitan areas in the United States. We begin by developing a positive model of local government provision of public safety, in which fiscal fragmentation influences the provision of public safety through three channels: X-efficiency, interjurisdictional-spillover effects, and Tiebout-sorting effects. Our model predicts that fiscal fragmentation creates an efficiency-equity trade-off in the provision of public safety. To investigate this tradeoff, we estimate several models using U.S. county-level, panel data drawn from a sample of metropolitan areas for census years 1990, 2000, and 2010. Our findings suggest that fiscal fragmentation has a negative effect on the aggregated crime rate in a metropolitan area which we interpret as evidence of an increase in efficiency. We also find that fiscal fragmentation increases the disparities in crime rates among jurisdictions in a metropolitan area. To further explore the underlying mechanisms, we examine interjurisdictional-spillover and Tiebout-sorting effects of fiscal fragmentation in a Spatial-Autoregressive Durbin model with multiplicative spatial interaction terms. Since conventional estimation methods are not suitable for the task at hand, we derive an innovative Maximum Likelihood Estimator for our empirical model. As predicted by the theory, we find strong evidence that both interjurisdictional spillover and Tiebout-sorting effects have a positive effect on the spatial correlation in crime rates among jurisdictions in a metropolitan area.

Suggested Citation

  • Jinghua Lei & Jenny Ligthart (deceased) & Mark Rider & Ruixin Wang, 2018. "Fiscal Fragmentation and the Spatial Distribution of Crime in the United States," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper1821, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    • Handle: RePEc:ays:ispwps:paper1821
    as

    Download full text from publisher

    File URL: https://icepp.gsu.edu/files/2018/10/paper1821.3.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    3. Lee, Lung-fei & Liu, Xiaodong, 2010. "Efficient Gmm Estimation Of High Order Spatial Autoregressive Models With Autoregressive Disturbances," Econometric Theory, Cambridge University Press, vol. 26(1), pages 187-230, February.
    Full references (including those not matched with items on IDEAS)

    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. Tizheng Li & Xiaojuan Kang, 2022. "Variable selection of higher-order partially linear spatial autoregressive model with a diverging number of parameters," Statistical Papers, Springer, vol. 63(1), pages 243-285, February.
    2. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian estimation of multivariate panel probits with higher‐order network interdependence and an application to firms' global market participation in Guangdong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1356-1378, November.
    3. Harald Badinger & Peter Egger, 2013. "Estimation and testing of higher-order spatial autoregressive panel data error component models," Journal of Geographical Systems, Springer, vol. 15(4), pages 453-489, October.
    4. Liu, Shew Fan & Yang, Zhenlin, 2015. "Modified QML estimation of spatial autoregressive models with unknown heteroskedasticity and nonnormality," Regional Science and Urban Economics, Elsevier, vol. 52(C), pages 50-70.
    5. Harald Badinger & Peter Egger, 2015. "Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(1), pages 11-35, March.
    6. Moscone, Francesco & Tosetti, Elisa & Canepa, Alessandra, 2014. "Real estate market and financial stability in US metropolitan areas: A dynamic model with spatial effects," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 129-146.
    7. Wang, Wei & Lee, Lung-Fei & Bao, Yan, 2018. "GMM estimation of the spatial autoregressive model in a system of interrelated networks," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 167-198.
    8. Shew Fan Liu & Zhenlin Yang, 2015. "Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model," Econometrics, MDPI, vol. 3(2), pages 1-36, May.
    9. Jinghua Lei & Jenny Ligthart & Mark Rider & Ruixin Wang, 2022. "Fiscal fragmentation and crime control: Is there an efficiency-equity tradeoff?," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(3), pages 751-787, June.
    10. Matthias Arnold & Dominik Wied, 2014. "Improved GMM estimation of random effects panel data models with spatially correlated error components," Papers in Regional Science, Wiley Blackwell, vol. 93(1), pages 77-99, March.
    11. Luis Eduardo Sandoval, 2018. "Socio-economics characteristics and spatial persistence of homicides in Colombia, 2000-2010," Estudios de Economia, University of Chile, Department of Economics, vol. 45(1 Year 20), pages 51-77, June.
    12. Qu, Xi & Lee, Lung-fei, 2015. "Estimating a spatial autoregressive model with an endogenous spatial weight matrix," Journal of Econometrics, Elsevier, vol. 184(2), pages 209-232.
    13. repec:rri:wpaper:201303 is not listed on IDEAS
    14. Gupta, Abhimanyu & Robinson, Peter M., 2015. "Inference on higher-order spatial autoregressive models with increasingly many parameters," Journal of Econometrics, Elsevier, vol. 186(1), pages 19-31.
    15. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," CESifo Working Paper Series 5428, CESifo.
    16. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
    17. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    18. Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 115-168, Elsevier.
    19. Gianfranco Piras, 2013. "Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 370-388, September.
    20. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," Working Papers 749, Queen Mary University of London, School of Economics and Finance.
    21. Li, Kunpeng, 2017. "Fixed-effects dynamic spatial panel data models and impulse response analysis," Journal of Econometrics, Elsevier, vol. 198(1), pages 102-121.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ays:ispwps:paper1821. 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: Paul Benson (email available below). General contact details of provider: https://edirc.repec.org/data/ispgsus.html .

    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.