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Impacts on efficiency of merging the Swedish district courts

Author

Listed:
  • Per J. Agrell

    (Université catholique de Louvain)

  • Pontus Mattsson

    (Linnaeus University)

  • Jonas Månsson

    (Linnaeus University)

Abstract

Judicial courts form a stringent example of public services using partially sticky inputs and outputs with heterogeneous quality. Notwithstanding, governments internationally are striving to improve the efficiency of and diminish the budget spent on court systems. Frontier methods such as data envelopment analysis are sometimes used in investigations of structural changes in the form of mergers. This essay reviews the methods used to evaluate the ex post efficiency of horizontal mergers. Identification of impacts is difficult. Therefore, three analytical frameworks are applied: (1) a technical efficiency comparison over time, (2) a metafrontier approach among mergers and non-mergers, and (3) a conditional difference-in-differences approach where non-merged twins of the actual mergers are identified by matching. In addition, both time heterogeneity and sources of efficiency change are examined ex post. The method is applied to evaluate the impact on efficiency of merging the Swedish district courts from 95 to 48 between 2000 and 2009. Whereas the stated ambition for the mergers was to improve efficiency, no structured ex post analysis has been done. Swedish courts are shown to improve efficiency from merging. In addition to the particular application, this work may inform a more general discussion on public service efficiency measurement under structural changes, and their limits and potential.

Suggested Citation

  • Per J. Agrell & Pontus Mattsson & Jonas Månsson, 2020. "Impacts on efficiency of merging the Swedish district courts," Annals of Operations Research, Springer, vol. 288(2), pages 653-679, May.
    • Handle: RePEc:spr:annopr:v:288:y:2020:i:2:d:10.1007_s10479-019-03304-0
    DOI: 10.1007/s10479-019-03304-0
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    Cited by:

    1. Giacalone, Massimiliano & Nissi, Eugenia & Cusatelli, Carlo, 2020. "Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    2. Xiaoqing Chen & Kristiaan Kerstens & Qingyuan Zhu, 2021. "Exploring Horizontal Mergers in Swedish District Courts Using Convex and Nonconvex Technologies: Usefulness of a Conservative Approach," Working Papers 2021-EQM-05, IESEG School of Management.
    3. Aparicio, Juan & Ortiz, Lidia & Santín, Daniel, 2021. "Comparing group performance over time through the Luenberger productivity indicator: An application to school ownership in European countries," European Journal of Operational Research, Elsevier, vol. 294(2), pages 651-672.
    4. Jonas Månsson & Christian Andersson & Fredrik Bonander, 2022. "What lessons can be learned from cost efficiency? The case of Swedish district courts," European Journal of Law and Economics, Springer, vol. 54(3), pages 431-451, December.
    5. Chen, Xiaoqing & Kerstens, Kristiaan & Tsionas, Mike, 2024. "Does productivity change at all in Swedish district courts? Empirical analysis focusing on horizontal mergers," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    6. Kristiaan KERSTENS & Xiaoqing CHEN, 2022. "Evaluating Horizontal Mergers in Swedish District Courts Using Plant Capacity Concepts: With a Focus on Nonconvexity," Working Papers 2022-EQM-02, IESEG School of Management.
    7. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    8. Sila Mishra, 2023. "‘Cyclic syndrome’ of arrears and efficiency of Indian judiciary," SN Business & Economics, Springer, vol. 3(1), pages 1-27, January.
    9. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).
    10. Daniel Feliciano & Laura López-Torres & Daniel Santín, 2021. "One Laptop per Child? Using Production Frontiers for Evaluating the Escuela 2.0 Program in Spain," Mathematics, MDPI, vol. 9(20), pages 1-17, October.

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    More about this item

    Keywords

    Data envelopment analysis (DEA); Efficiency; Impact evaluation; Merging effects; Public sector;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • H50 - Public Economics - - National Government Expenditures and Related Policies - - - General
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • P41 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Planning, Coordination, and Reform
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance

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