Competition and Moral Behavior: A Meta-Analysis of Forty-Five Crowd-Sourced Experimental Designs
Anna Dreber, Felix Holzmeister, Sabrina Jeworrek, Magnus Johannesson, Joschka Waibel, Utz Weitzel, et al.
Proceedings of the National Academy of Sciences of the United States of America (PNAS),
No. 23,
2023
Abstract
Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity—variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity—estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs—indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.
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A Note on the Use of Syndicated Loan Data
Isabella Müller, Felix Noth, Lena Tonzer
IWH Discussion Papers,
No. 17,
2022
Abstract
Syndicated loan data provided by DealScan is an essential input in banking research. This data is rich enough to answer urging questions on bank lending, e.g., in the presence of financial shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice and we provide guidelines for applied research.
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Predicting Free-riding in a Public Goods Game – Analysis of Content and Dynamic Facial Expressions in Face-to-Face Communication
Dmitri Bershadskyy, Ehsan Othman, Frerk Saxen
IWH Discussion Papers,
No. 9,
2019
Abstract
This paper illustrates how audio-visual data from pre-play face-to-face communication can be used to identify groups which contain free-riders in a public goods experiment. It focuses on two channels over which face-to-face communication influences contributions to a public good. Firstly, the contents of the face-to-face communication are investigated by categorising specific strategic information and using simple meta-data. Secondly, a machine-learning approach to analyse facial expressions of the subjects during their communications is implemented. These approaches constitute the first of their kind, analysing content and facial expressions in face-to-face communication aiming to predict the behaviour of the subjects in a public goods game. The analysis shows that verbally mentioning to fully contribute to the public good until the very end and communicating through facial clues reduce the commonly observed end-game behaviour. The length of the face-to-face communication quantified in number of words is further a good measure to predict cooperation behaviour towards the end of the game. The obtained findings provide first insights how a priori available information can be utilised to predict free-riding behaviour in public goods games.
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The Impact of R&D Collaboration Networks on the Performance of Firms and Regions: A Meta-Analysis of the Evidence
Gunnar Pippel
International Journal of Networking and Virtual Organisations,
No. 4,
2013
Abstract
Wissensintensive Interaktionen sind mit einer Reihe von Vor- und Nachteilen für die beteiligten Akteure verbunden. Deshalb hat sich ein Literaturstrang entwickelt, welcher der Fragestellung nachgeht, wie sich FuE-Kooperationen auf die Leistungsfähigkeit von Firmen und Regionen auswirken. Diese Studien kommen zu unterschiedlichen Ergebnissen. Das Papier versucht die verschiedenen Ergebnisse der Literatur zusammenzufassen. Ein weiteres Ziel ist es, die in der Literatur verwendeten Methoden zur Bearbeitung der Fragestellung kritisch zu diskutieren. Zudem geht das Papier der Frage nach, inwiefern die in der Literatur verwendeten Methoden und Ansätze die Ergebnisse der Studien beeinflussen. Zur Bearbeitung dieser Fragestellungen wird eine Metaanalyse der relevanten Literatur durchgeführt. Dabei wird gezeigt, dass FuE-Kooperationen einen positiven Effekt auf die Leistungsfähigkeit von Firmen und Regionen haben. Zudem gibt es starke Evidenz dafür, dass die verwendeten Methoden und Ansätze die Ergebnisse der Studien beeinflussen.
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