• Medientyp: E-Book
  • Titel: Replication report: a comment on Peter T. Leeson, R. August Hardy and Paola A. Suarez (2022)
  • Beteiligte: Melo, Lucas Braga de [Verfasser:in]; Cantone, Giulio G. [Verfasser:in]; Gabani, Jacopo [Verfasser:in]; Gao, Xiaoying [Verfasser:in]; Leeson, Peter T. [Verfasser:in des Bezugswerks]; Hardy, R. August [Verfasser:in des Bezugswerks]; Suarez, Paola A. [Verfasser:in des Bezugswerks]
  • Erschienen: Essen, Germany: Institute for Replication, August 2023
  • Erschienen in: I4R discussion paper series ; 55
  • Umfang: 1 Online-Ressource (circa 17 Seiten)
  • Sprache: Englisch
  • Identifikator:
  • Schlagwörter: Graue Literatur
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: Peter Leeson, August Hardy and Paola Suarez (2022) test maximizing behaviour of panhandlers at several Metrorail stations in Washington, D.C. Their main findings are that "stations with more panhandling opportunities attract more panhandlers" (the first statement) and that "cross-station differences in hourly panhandling receipts are statistically indistinguishable from zero" (the second statement). We test computational reproducibility and robustness replicability of their results. We can reproduce both statements, in Stata and R. Our robustness replications for the first statement confirm the authors' results in the vast majority of cases (replication was successful in 91% of the cases). Our robustness replications for the second statement might raise doubts on this finding. We run weighted ANOVA tests, we change the bounds in minutes used by authors by 5 minutes in their robustness checks, we run Bartlett's tests of equality of variances of means, and run pair-wise tests of equality of means. In three out of four cases we cannot replicate the results, and the differences (of either means, medians or variances of donations) across Metrorail stations are statistically different from zero. We hypothesize that panhandlers have a general idea about which stations have more passers-by, and will rationally go more often there. However, they are unlikely to have information about smaller variations in the number of passers-by (e.g., variations in passers-by at the same station over time due to non-public events), and therefore might find it difficult to perfectly maximize donations.
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