IDEAS home Printed from https://ideas.repec.org/p/usg/econwp/202208.html
   My bibliography  Save this paper

An empirical evaluation of environmental Alternative Dispute Resolution methods

Author

Listed:
  • Bonev, Petyo
  • Matsumoto, Shigeru

Abstract

This paper empirically evaluates different Alternative Dispute Resolution methods. Using a novel dataset on environmental disputes from Japan, we show that consensus-based approaches such as mediation lead on average to shorter duration and higher satisfaction than top-down approaches such as arbitration. Moreover, our findings suggest that the benefits depend on the transaction cost of resolving a dispute: while disputes with high transaction costs tend to benefit more from top-down approaches, disputes with lower costs benefit more from consensual resolution methods.

Suggested Citation

  • Bonev, Petyo & Matsumoto, Shigeru, 2022. "An empirical evaluation of environmental Alternative Dispute Resolution methods," Economics Working Paper Series 2208, University of St. Gallen, School of Economics and Political Science.
    • Handle: RePEc:usg:econwp:2022:08
    as

    Download full text from publisher

    File URL: http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-2208.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alan Speight & Dennis Thomas, 1997. "Arbitrator Decision‐Making in the Transfer Market: an Empirical Analysis," Scottish Journal of Political Economy, Scottish Economic Society, vol. 44(2), pages 198-215, May.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Bruno S. Frey & Alois Stutzer, 2005. "Beyond outcomes: measuring procedural utility," Oxford Economic Papers, Oxford University Press, vol. 57(1), pages 90-111, January.
    4. Andrew Chesher, 2002. "Semiparametric identification in duration models," CeMMAP working papers CWP20/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Tatyana Deryugina & Frances C. Moore & Richard S.J. Tol, 2020. "Applications of the Coase Theorem," Working Paper Series 0820, Department of Economics, University of Sussex Business School.
    6. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    7. Ashenfelter, Orley, et al, 1992. "An Experimental Comparison of Dispute Rates in Alternative Arbitration Systems," Econometrica, Econometric Society, vol. 60(6), pages 1407-1433, November.
    8. Farber, Henry S & White, Michelle J, 1994. "A Comparison of Formal and Informal Dispute Resolution in Medical Malpractice," The Journal of Legal Studies, University of Chicago Press, vol. 23(2), pages 777-806, June.
    9. Wojciech Olszewski, 2011. "A Welfare Analysis of Arbitration," American Economic Journal: Microeconomics, American Economic Association, vol. 3(1), pages 174-213, February.
    10. Johannes Hörner & Massimo Morelli & Francesco Squintani, 2015. "Mediation and Peace," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1483-1501.
    11. R. H. Coase, 2013. "The Problem of Social Cost," Journal of Law and Economics, University of Chicago Press, vol. 56(4), pages 837-877.
    12. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460, Elsevier.
    13. Schotter, Andrew & Zheng, Wei & Snyder, Blaine, 2000. "Bargaining Through Agents: An Experimental Study of Delegation and Commitment," Games and Economic Behavior, Elsevier, vol. 30(2), pages 248-292, February.
    14. Steven G. Medema, 2020. "The Coase Theorem at Sixty," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1045-1128, December.
    15. Matthew Backus & Thomas Blake & Jett Pettus & Steven Tadelis, 2020. "Communication and Bargaining Breakdown: An Empirical Analysis," NBER Working Papers 27984, National Bureau of Economic Research, Inc.
    16. Adam Meirowitz & Massimo Morelli & Kristopher W. Ramsay & Francesco Squintani, 2019. "Dispute Resolution Institutions and Strategic Militarization," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 378-418.
    17. Goltsman, Maria & Hörner, Johannes & Pavlov, Gregory & Squintani, Francesco, 2009. "Mediation, arbitration and negotiation," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1397-1420, July.
    18. Matsumoto, Shigeru, 2011. "A duration analysis of environmental alternative dispute resolution in Japan," Ecological Economics, Elsevier, vol. 70(4), pages 659-666, February.
    19. Bonev, Petyo, 2020. "Nonparametric identification in nonseparable duration models with unobserved heterogeneity," Economics Working Paper Series 2005, University of St. Gallen, School of Economics and Political Science.
    20. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    21. Shavell, Steven, 1995. "Alternative Dispute Resolution: An Economic Analysis," The Journal of Legal Studies, University of Chicago Press, vol. 24(1), pages 1-28, January.
    22. Jack Fanning, 2021. "Mediation in Reputational Bargaining," American Economic Review, American Economic Association, vol. 111(8), pages 2444-2472, August.
    23. Heckman, James J, 1991. "Identifying the Hand of the Past: Distinguishing State Dependence from Heterogeneity," American Economic Review, American Economic Association, vol. 81(2), pages 75-79, May.
    24. Vira Semenova & Victor Chernozhukov, 2021. "Debiased machine learning of conditional average treatment effects and other causal functions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 264-289.
    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. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    2. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    3. Bonev, Petyo, 2020. "Nonparametric identification in nonseparable duration models with unobserved heterogeneity," Economics Working Paper Series 2005, University of St. Gallen, School of Economics and Political Science.
    4. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    5. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    6. Baudry, Marc & Faure, Anouk & Quemin, Simon, 2021. "Emissions trading with transaction costs," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
    7. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    8. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    9. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
    10. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    11. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    12. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    13. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    14. Björn Gehrmann, 2019. "Third-party diplomacy," HiCN Working Papers 312, Households in Conflict Network.
    15. Farmer, Amy & Pecorino, Paul, 2024. "Proposal convergence and settlement under final offer arbitration," International Review of Law and Economics, Elsevier, vol. 77(C).
    16. Bonev, Petyo & Emmenegger, Rony & Forero, Laura & Ganev, Kaloyan & Simeonova-Ganeva, Ralitsa & Söderberg, Magnus, 2024. "Nuclear waste in my backyard: Social acceptance and economic incentives," Energy Policy, Elsevier, vol. 185(C).
    17. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    18. Gottardi, Piero & Mezzetti, Claudio, 2024. "Shuttle diplomacy," Journal of Economic Theory, Elsevier, vol. 216(C).
    19. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    20. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.

    More about this item

    Keywords

    Environmental policy; environmental disputes; Alternative Dispute Resolution; Coase Theorem;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • Q34 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Natural Resources and Domestic and International Conflicts
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    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:usg:econwp:2022:08. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/vwasgch.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.