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The impact of corporate sustainability performance on information asymmetry: the role of institutional differences

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Abstract

This paper empirically investigates whether corporate sustainability performance (CSP) affects information asymmetry (IA) for European firms listed in the STOXX Europe 600 from 2002 to 2013. We find a significantly negative effect of CSP on IA. By exploiting institutional differences between the European countries, we determine that the negative effect of CSP on IA is more pronounced in liberal market economies compared to coordinated market economies, thus pointing to a substitutive effect of CSP and economic coordination. Further, the impact is greater in countries with stricter disclosure requirements. In such countries, there is generally a greater appetite for company-specific information. However, disclosure requirements fulfil this need only partially because they concentrate on the corporate governance dimension of corporate sustainability. Hence, information on the social pillar especially matters to investors in a complementary manner and drives the overall effect. Our study contributes to the literature on the positive capital market effects of CSP by showing the proposed effect in European capital markets and the institutional determinants of its strength.

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Notes

  1. The difference between CSP, corporate social responsibility, and corporate social performance is discussed in Sect. 2. In this paper, CSP is the extent to which a company contributes to environmental, social, and economic development (Commission of the European Communities 2001).

  2. See Russo and Fouts (1997), Waddock and Graves (1997), Shen and Chang (2009), Barnett and Salomon (2012), Tang et al. (2012), and Wu and Shen (2013).

  3. See Brammer et al. (2006), Brammer and Millington (2008), Surroca et al. (2010), Aktas et al. (2011), Dhaliwal et al. (2011), El Ghoul et al. (2011), Goss and Roberts (2011), and Jo and Harjoto (2011).

  4. See Lee et al. (2009), Nelling and Webb (2009), Choi et al. (2010), Garcia-Castro et al. (2010), and Jo and Harjoto (2012).

  5. Russo and Fouts (1997) and Wu and Shen (2013) investigate a global sample without focusing on regions. Shen and Chang (2009) conduct a national study in Taiwan.

  6. A detailed description of the individual concepts and a concrete distinction is provided by van Beurden and Gössling (2008).

  7. Note that this understanding also captures the case in which a company improves its score by reporting sustainability activities that were previously pursued but unobservable. Simultaneously, the impact of favorable but unjustified reporting is limited because disclosed facts do not automatically impact the scores.

  8. Prominent examples are labor exploitation, child labor, and leakages on oil and gas exploration platforms or the planned sinking of the Brent Spar oil platform.

  9. Hall and Gingerich (2009) exemplify Germany as a typical example of a country that reflects more coordinated traits and the United States as a typical LME country. The United Kingdom and Ireland are further noted as typical LMEs.

  10. Leuz and Verrecchia (2000) exemplify portfolio rebalancing, liquidity shocks and changes in risk preferences.

  11. The baseline models for testing H1 are also calculated with trading volume and stock price volatility as dependent variables to exemplify the robustness of the results. However, given the number of statistical models needed to validly assess the hypotheses, we focus our main analyses on the bid-ask spread.

  12. We acknowledge that the KLD measure has the advantage of clearly defining strengths and weaknesses. This advantage allows Cho et al. (2013) to conclude that positive and negative CSP leads to effects of different magnitude. To replicate such an approach with ASSET4 data, we would need to rely on, for example, a median split based on industry and year. However, even if all firms in a given industry and year performed badly, the above-median performance (which would still be bad) would be rated as a strength. Hence, we refrain from such an approach.

  13. The data for the four variables (CSP_ECN, CSP_ENV, CSP_CGV, and CSP_SOC) are well suited for the principal component analysis. The variables correlate significantly at the 1 % level, and the Kaiser–Meyer–Olkin measure of sampling adequacy shows a satisfactory value (0.714). The factor in the one-factor solution has an eigenvalue of 2.372 (above the Kaiser criterion of one) and an explained variance of 59.31 %. The second factor would have an eigenvalue of only 0.720 and thus does not meet the Kaiser criterion. A visual scree test indicates that the one-factor solution is advisable. The component matrix reveals the following values: CSP_ECN, 0.700; CSP_ENV, 0.825; CSP_CGV, 0.647; and CSP_SOC, 0.885.

  14. We also repeat the principal component analysis for the index by incorporating only three pillars. The results remain qualitatively identical.

  15. Hüttenbrink et al. (2014) control for countries’ financial structure by including the standard measure of stock market development, the ratio of market capitalization of the listed firms to the gross domestic product. These authors also suggest additional non-institutional (firm-specific) governance measures in an executive compensation context. However, these measures do not appear to be relevant to our research question. Because the variables SH_PROT, RULE_LAW, and FIN_STRUC serve only as controls, we do not comprehensively discuss their peculiarities. The values for SH_PROT range from one to five, with higher values indicating stronger shareholder protection. RULE_LAW shows values of up to 10, with higher values reflecting stronger enforcement. Financial structures are marked with a dummy variable, in which 1 indicates a bank-based structure and 0 a market-based structure.

  16. We report the results, including financial and utility firms, as part of the robustness check.

  17. Most likely, the best-known example is the wage of married women in the labor force (Heckman 1974), which is only observable if the women are participating in the labor force. The likelihood of participation depends on certain factors, such as marriage, children, education, and age, and is modeled in the first stage, whereas the actual wage model is the second stage, which is linked to the first stage via the inverse Mills ratio, correcting the sample selection bias.

  18. The treatment effect model is occasionally known as the treatreg model (Guo and Fraser 2015). Given its close relationship to the Heckman selection model, it is also known as the Heckit model (Greene 2012).

  19. Indeed, initial attempts to employ the same two proxies led to overspecified models, which is another strong econometric argument for modifying the approach.

  20. El Ghoul et al. (2011) also employ the average CSP values of the industry. It is noteworthy that the use of the average values of an industry alone as an instrument is also discussed critically (e.g., Larcker and Rusticus 2010); however, the combination of two instruments relying on averages that are economically motivated should reveal a reliable estimation.

  21. We also conducted the analyses by including MTB. The results are inferentially identical, and the MTB variable is insignificant in all of the models.

  22. Tables 2 and 4 contain the market-to-book ratio (MTB). This variable is used in the first stages of the Heckman and treatment effect (treatreg) models, although they are not formally tabulated.

  23. Our panel is defined from an ex ante perspective as of January 1, 2002. Consequently, a decrease in the number of observations could be assumed over time. However, this is not the case, as can be concluded from Table 3, Panel C. The reason is—although the companies are theoretically in the sample from 2002 on—data restrictions lead to eliminations, especially through the CSP data in the early years of the sample period, which gives further impetus to the Heckman selection model to ensure that the results are not driven by data availability.

  24. The basic patterns of prior studies are reflected in the descriptive data, especially the divergence of means and medians for SPREAD, in addition to the development over time (Cho et al. 2013).

  25. All p-values throughout this paper are given as two-tailed values.

  26. Petersen (2009) describes this type of correlation as an unobserved company-specific effect. The procedure described leads to unbiased confidence intervals and coefficients because the 316 clusters in the sample are sufficient. Standard errors are not additionally clustered by year due to the short time horizon (2002–2013), which would yield few clusters and thus lead to biased standard errors (Petersen 2009).

  27. We reproduced the basic results by using the two additional proxies suggested by Leuz and Verrecchia (2000), relative trading volume and stock price volatility. The results remain inferentially identical.

  28. The sample size is reduced from 2999 to 2644 firm-year observations because lagged CSP values for 2002 (i.e., CSP values for 2001) were not consistently available, which is why this year was eliminated throughout.

  29. For the model with the coordination index, the coefficient showed a t-value of −1.52 (p = 0.129). For the model using the four institutional variables, the t-value was −1.35 (p = 0.179).

  30. This interdependency is also the reason why no maximum VIF values are reported for the treatment effect models (Lennox et al. 2012).

  31. Interaction models were not also considered due to the number of potential interactions and the difficulty of their unambiguous interpretation.

  32. The sign shift from the correlation analysis to the regression results could be the result of multicollinearity. This may especially hold true given the strong correlations between the ESG dimensions. To alleviate this concern, we included the three ESG dimensions separately into three distinct models, one dimension at a time. CSP_ENV was insignificant (p = 0.34), CSP_SOC was significantly negative (p = 0.05), and CSP_CGV was significantly positive (p = 0.01).

  33. The coefficient of determination for all of the models is relatively low compared to the corresponding models without financial and utility firms (Table 6), which points to the increasing heterogeneity of the companies in the sample.

  34. The OLS and IV estimates are computed according to the specifications presented as models 1–8 in Table 5. All of the models remain suitable specifications, and the CSP proxies and interaction effects remain significant at least at the 5 % level.

  35. The OLS and IV estimates are computed according to the specifications presented as models 1–8 in Table 5, with either sales, general costs, and administrative costs or the costs of goods sold as an additional control. This computation results in 16 additional models. All of the models are as similarly suitable as those in Table 5. The CSP proxies and interaction effects remain significant at least at the 5 % level. The newly introduced variables are insignificant according to conventional thresholds (p > 0.1, two-tailed).

  36. We emphasize that these results are not indicative of the relative impact of CSP information compared to other information.

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Acknowledgments

We thank Thorsten Knauer, Lisa Silge, and Arnt Wöhrmann, in addition to the participants at the University of Bayreuth Workshop, the Sixth International Conference on Sustainability and Responsibility (Berlin), the Annual Conference for Management Accounting Research 2015, and the European Accounting Association Annual Congress 2015 for their helpful comments and suggestions. We also thank the editor, Ralf Ewert, and the two anonymous reviewers for their helpful comments, which have greatly improved the paper.

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  1. University of Münster, Universitätsstr. 14-16, 48143, Münster, Germany

    Jan Diebecker & Friedrich Sommer

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  1. Jan Diebecker
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  2. Friedrich Sommer
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Correspondence to Friedrich Sommer.

Appendix

Appendix

See Table 9.

Table 9 Variables definitions
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Diebecker, J., Sommer, F. The impact of corporate sustainability performance on information asymmetry: the role of institutional differences. Rev Manag Sci 11, 471–517 (2017). https://doi.org/10.1007/s11846-016-0195-y

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