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Does it Pay to Be Good...And Does it Matter? A Meta-Analysis of the Relationship between Corporate Social and Financial Performance

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Margolis, Joshua D., Elfenbein, Hillary Anger, Walsh, James P.·2009·SSRN Electronic Journal
DOI 10.2139/ssrn.1866371
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Abstract

In an era of rising concern about financial performance and social ills, companies’ economic achievements and negative externalities prompt a common question: Does it pay to be good? For thirty-five years, researchers have been investigating the empirical link between corporate social performance (CSP) and corporate financial performance (CFP). In the most comprehensive review of this research to date, we conduct a meta-analysis of 251 studies presented in 214 manuscripts. The overall effect is positive but small (mean r = .13, median r = .09, weighted r = .11), and results for the 106 studies from the past decade are even smaller. We also conduct sensitivity analyses to determine whether or not the relationship is stronger under certain conditions. Except for the effect of revealed misdeeds on financial performance, none of the many contingencies examined in the literature markedly affects the results. Therefore, we conclude by considering whether, aside from striving to do no harm, companies have grounds for doing good - and whether researchers have grounds for continuing to look for an empirical link between CSP and CFP.

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Metadata

Authors
Margolis, J. D., Elfenbein, H. A., & Walsh, J. P.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2009
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.1866371
OpenAlex
W1547562990
Cited by
1,392
References
18
Type
posted content
Language
en
Open access
green

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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.