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Estimating the economic costs of organized crime by synthetic control methods*

* This paper is a replication of an original study

Author

Listed:
  • Martin Becker
  • Stefan Klößner

Abstract

The economic costs of organized crime have been estimated for the case of southern Italy by Pinotti (Economic Journal 2015; 125, F203–F232, 2015): using synthetic control methods, he finds that, due to the advent of the Italian Mafia in the regions Apulia and Basilicata, GDP per capita dropped by 16%. Replicating this study in a narrow sense by estimating the same model with the same data, but using different software implementations, we observe minor differences stemming from the different implementations. By identifying the correct implementation, we find that the loss in GDP per capita due to the presence of the Mafia has been slightly overestimated.

Suggested Citation

  • Martin Becker & Stefan Klößner, 2017. "Estimating the economic costs of organized crime by synthetic control methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1367-1369, November.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:7:p:1367-1369
    DOI: 10.1002/jae.2572
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    Citations

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    Cited by:

    1. Monastiriotis, Vassilis & Zilic, Ivan, 2020. "The economic effects of political disintegration: Lessons from Serbia and Montenegro," European Journal of Political Economy, Elsevier, vol. 65(C).
    2. Echevarría, Cruz A. & Hasancebi, Serhat & García-Enríquez, Javier, 2022. "Economic Effects of Macao’s Integration with Mainland China: A Causal Inference Study," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 37(2), pages 179-215.
    3. Cruz A. Echevarría & Javier García-Enríquez, 2020. "The economic cost of the Arab Spring: the case of the Egyptian revolution," Empirical Economics, Springer, vol. 59(3), pages 1453-1477, September.
    4. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 120662, University Library of Munich, Germany, revised 27 Mar 2024.
    5. Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.
    6. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    7. Klößner, Stefan & Pfeifer, Gregor, 2015. "Synthesizing Cash for Clunkers: Stabilizing the Car Market, Hurting the Environment," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113207, Verein für Socialpolitik / German Economic Association.
    8. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    9. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 115373, University Library of Munich, Germany.
    10. 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.

    Replication

    This item is a replication of:
  • Paolo Pinotti, 2015. "The Economic Costs of Organised Crime: Evidence from Southern Italy," Economic Journal, Royal Economic Society, vol. 125(586), pages 203-232, August.

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    1. Estimating the economic costs of organized crime by synthetic control methods (Journal of Applied Econometrics 2017) in ReplicationWiki

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