Arbeitspapier

Replicating Backfire Effects in Anti-Corruption Messaging: A Comment on Cheeseman and Peiffer (2022)

Cheeseman and Peiffer (2022) field a survey experiment in Nigeria to test the effect of five different anti-corruption messages on participants' willingness to bribe public officials. They find that these messages generally fail to reduce bribes and could, in fact, increase bribes. They further show that these counterproductive effects of anti-corruption messages are especially pernicious for participants who believe corruption is widespread, whom they call "Pessimistic Perceivers." We find that Cheeseman and Peiffer's findings are computationally reproducible: using the same data and estimation procedures, we arrive at the same output reported in the original article. Furthermore, we find that following Cheeseman and Peiffer's strategy to dichotomize a three-item scale used as a moderating variable, their results are robust to different estimation strategies. However, we draw attention to several shortcomings of the original analysis. First, the distribution of the moderating variable is highly skewed: on a 0-1 scale, the mean value is 0.81. Cheeseman and Peiffer's dichotomization procedure is also sensitive to the cutoff threshold and produces unstable results. Similarly, when we employ more flexible estimation strategies for heterogeneous treatment effects when the moderator is measured on a continuous scale, the results appear less robust.

Sprache
Englisch

Erschienen in
Series: I4R Discussion Paper Series ; No. 94

Klassifikation
Wirtschaft
Thema
Replication study
Corruption
Nigeria

Ereignis
Geistige Schöpfung
(wer)
Bergeron-Boutin, Olivier
Ciobanu, Costin
Cohen, Guila
Erlich, Aaron
Ereignis
Veröffentlichung
(wer)
Institute for Replication (I4R)
(wo)
s.l.
(wann)
2023

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Bergeron-Boutin, Olivier
  • Ciobanu, Costin
  • Cohen, Guila
  • Erlich, Aaron
  • Institute for Replication (I4R)

Entstanden

  • 2023

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