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Data Science for Justice: The Short-Term Effects of a Randomized Judicial Reform in Kenya

Author

Listed:
  • Matthieu Chemin

    (McGill University = Université McGill [Montréal, Canada])

  • Daniel L. Chen

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Vincenzo Di Maro

    (World Bank)

  • Paul Kieti Kimalu
  • Momanyi Mokaya

    (World Bank)

  • Manuel Ramos-Maqueda

    (World Bank)

Abstract

Can data science be used to improve the functioning of courts, and unlock the positive effects of institutions on economic development? In a nationwide randomized experiment in Kenya, we use algorithms to identify the greatest sources of court delay for each court and recommend actions. We randomly assign courts to receive no information, information, or an information and accountability intervention. Information and accountability reduces case duration by 22%. We find an effect on contracting behaviour, with more written labor contracts being signed by firms, and an effect on wage, since jobs with written labor contracts pay more. These results demonstrate a causal relationship between judicial institutions and development outcomes.

Suggested Citation

  • Matthieu Chemin & Daniel L. Chen & Vincenzo Di Maro & Paul Kieti Kimalu & Momanyi Mokaya & Manuel Ramos-Maqueda, 2023. "Data Science for Justice: The Short-Term Effects of a Randomized Judicial Reform in Kenya," Working Papers hal-03921938, HAL.
    • Handle: RePEc:hal:wpaper:hal-03921938
    Note: View the original document on HAL open archive server: https://hal.science/hal-03921938
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    References listed on IDEAS

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