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The Influence of Primary Study Characteristics on the Performance Differential Between Socially Responsible and Conventional Investment Funds: A Meta-Analysis

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Abstract

Empirical studies, which analyze the performance of socially responsible investment (SRI) funds relative to conventional funds, find contradictory results. The aim of this paper is to investigate, with the help of a meta-analysis, how selected primary study characteristics influence the probability of a significant under- or outperformance of SRI funds compared with conventional funds. 25 studies with more than 500 observations are included in the meta-analysis. The results of this paper suggest that the consideration of the survivorship bias in a study increases (decreases) the probability of a significant outperformance (underperformance) of SRI funds relative to conventional funds. The focus on United States (US) SRI funds increases (decreases) the probability of a significant outperformance (underperformance) too. The time period influences the probability of a significant under- and outperformance of SRI funds as well, but based on the results of this paper, it is not possible to draw general conclusions on this variable.

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Notes

  1. For more information on the definition of “broad” and “core” SRI, see Eurosif (2010), p. 9.

  2. Thus, the most pressing question is if there are any reasons why a “good” company may be a successful company as well? Heal (2008) provides an overview of theoretic reasons. This topic is investigated empirically by a vast amount of studies. For example, the often-cited meta-analysis of Orlitzky et al. (2003) finds a positive relationship between Corporate Social Performance and Corporate Financial Performance. Furthermore, a recent literature review was conducted by Van Beurden and Goessling (2008).

  3. An in-depth discussion of the potential influence of selected primary study characteristics on the observed results is provided in Sect. Literature overview and hypotheses.

  4. Renneboog et al. (2008b) do not find significant performance differences for the following countries: Belgium, Germany, Italy, Luxembourg, Netherlands, Norway, Switzerland, UK, US, Canada, Australia, Malaysia, and Singapore.

  5. The base group of these two dummy variables in the regression framework is “no matching procedure.”

  6. A similar approach was used by García-Quevedo (2004).

  7. A better approach would be to include a dummy variable for every single study (“study” fixed effects). Unfortunately, this is not possible in my specification as lots of studies do not report an out- and an underperformance of SRI funds. Hence, it would not be possible to estimate the underperformance and the outperformance logit model (study dummy variables would predict the dependent variables perfectly).

  8. I suppose the influence of the publication bias on this body of literature to be smaller than on other subjects because lots of studies with insignificant results were published. Table 2 reports that almost 75 % of the primary studies’ results are insignificant. Nevertheless, a publication bias may be present. Unfortunately, it is not possible to control for publication bias with a dummy variable in my specification as there is no unpublished effect which reports an outperformance of SRI funds. As a conclusion, a dummy variable Published paper would predict the dependent variable (Outperformance) perfectly.

  9. All included studies are marked in the reference list with an asterisk.

  10. Unfortunately, not every study provides information on the sample period of all effects.

  11. A similar procedure to divide the sample period is used, for example, by *Bauer (2005) and Bauer et al. (2006) who divide their sample periods into three equal and non-overlapping subperiods.

  12. Some studies do not provide information on the consideration of survivorship bias.

  13. Average marginal effects are calculated by computing individual marginal effects at every observation and by averaging these individual marginal effects across the sample.

  14. For instance, some studies use several models to evaluate the performance of their fund samples. The results of the models of one study may be correlated to a certain degree because all models use the identical dataset.

  15. Louche and Lydenberg (2006) investigate this issue from a historic perspective.

  16. For the empirical estimation, the dummy variables Time period 1981–1990 and time period 1991–2000 are taken together because there are only eight observations in the first subperiod with information on all variables of the logit models. All of these observations have the identical outcome in the dependent variable and hence, Time period 1981–1990 would predict the dependent variable perfectly.

  17. It is not possible to include a dummy variable for the study of Renneboog et al. (2008b) because they do not find an outperformance of SRI funds, and hence it would not be possible to estimate the logit model (dependent variable Outperformance). This is the reason why I exclude the effects of the two studies.

  18. Detailed results on this robustness check are not reported in the paper, but can be obtained from the author upon request.

  19. I want to acknowledge that the weighting by 1/n is a little less precise than weighing the observations by their true independence in light of the study sample (e.g., if a study has two subsample periods of equal size and one main sample regression period, the first two observations should carry a weight of 0.25 and the latter a weight of 0.5). Based on the fact that studies use more characteristics than the sample period to create subsamples the weighting scheme would get really complex and such a weighting would provide only a marginal gain. Therefore, I decided to follow Horvathova (2010) and weigh the observations by 1/n. I want to thank the unknown ‘PRI Academic Fellow’ for raising the aspects mentioned in this endnote and the suggestion of the weighted regression approach in general.

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Acknowledgments

I want to thank Hannes Winner for his valuable suggestions throughout the process of conducting this paper and Claudia B. Woehle for her general support and feedback. Furthermore, I appreciate the very helpful comments of the two anonymous reviewers. Several (former) colleagues at the University of Salzburg helped to improve the paper significantly – namely, Harald Oberhofer, Benjamin Furlan, Andreas Pacher, Matthias Stöckl, Philipp Weigl, Klaus Nowotny and Alex Avedikjan. I am deeply grateful to Magdalena Braendle for our fruitful discussions and her suggestions for improvement.

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  1. Department of Economics and Social Sciences, University of Salzburg, Residenzplatz 9, 5010, Salzburg, Austria

    Sebastian Rathner

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  1. Sebastian Rathner
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Correspondence to Sebastian Rathner.

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Sections for reviewing purposes: Corporate Responsibility (quantitative issues); Finance.

Appendix

Appendix

See Tables 9 and 10

Table 9 Detailed information on the included studies
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Table 10 Detailed information on the included studies
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Rathner, S. The Influence of Primary Study Characteristics on the Performance Differential Between Socially Responsible and Conventional Investment Funds: A Meta-Analysis. J Bus Ethics 118, 349–363 (2013). https://doi.org/10.1007/s10551-012-1584-z

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