Abstract
The issue of management’s relations to the environment has received a significant amount of attention in the literature on corporate social responsibility. Yet the influence of religion on managers’ environmental decisions has until now remained unexamined despite its known importance. In this article, we examine the empirical association between religion—primarily Christianity—and the environmental practices a firm’s management undertakes by investigating their OLS, principal component, simultaneous, and endogenous effects. Employing a large and extensive U.S. sample, we find a negative association between the environmental practices initiated by a firm’s managers and the religiosity of the surrounding community, after controlling for various firm and demographic characteristics. In addition, after mitigating endogeneity with the dynamic system generalized method of moment, we still find an inverse association between religiosity and environmental-friendly decisions of management. We interpret these results as providing some support for the “dominion hypothesis” that claims Christian beliefs discourage environmental concern, but not for the “stewardship hypothesis” that implies that Christianity encourages people to “exercise a responsible stewardship over nature.” Nevertheless, additional analysis shows Christian groups differ significantly in how each influences managers’ environmental decisions.
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Notes
For some years the KLD data included as many as seven possible “strengths” in its environmental category. We exclude from our data set two of these strengths. We exclude the “Property, Plant and Equity” strength because KLD stopped using this strength in 1995. We also exclude the “Communication” strength because KLD did not begin using this strength until 1996 and in 2005 it removed this strength from its environmental category and made it part of its corporate governance category where it was combined with another corporate governance strength and was no longer assigned its own separate rating.
Based on a comprehensive data set from the CSR wire news service, Griffin and Sun (2013) find that firms disclose less in locations with strong religious beliefs (high adherence) but disclose more in locations with more non-evangelicals (high affiliation).
In our unreported tabulation of 503 counties, we found that Norfolk county, MA has the highest Catholic adherents of 55.2 %, and Winnebago county, IA has the highest mainline Protestant adherents of 67.7 % while Jones county, MS has the highest evangelical Protestant adherents of 53.6 %. These results are available from the authors upon request.
To check the existence of potential interpolation bias, we conduct our regressions using only the years for which we have direct survey data on religiosity (1990, 2000, and 2010) in our unreported results. Though the sample size is much smaller, the significant association between environment and religiosity measure suggests that our linear interpolation does not create systematic noise in our main results.
Since the religiosity index is provided on a county-level basis, we match the datasets using the counties where the firms’ headquarters are located. However, since the COMPUSTAT dataset for the most part does not provide the counties where firms’ headquarters are located, we utilize their ZIP codes instead. But while the ZIP codes of the firms are provided in the COMPUSTAT database, the ARDA only provide county codes, i.e., FIPS. We therefore match the FIPS codes with the ZIP codes, which enables us to obtain our final sample set.
Previous studies (e.g., Jo and Harjoto 2011, 2012; Ioannou and Serafeim 2012) do not employ firm fixed effects. To make our results comparable to the previous papers, we employ OLS regressions that omit firm fixed effects but control for year and industry effects. Because we use cross-sectional and time-series combined panel data, however, we also need to employ fixed effects regressions to account for fixed effects within each firm in the sample and to impose time independent effects for each variable that could be correlated with the regressors. Our unreported analysis with a fixed effects method based upon the assumption that the unobservable individual effects known to be correlated with regressors are non-random, basically provides results that are qualitatively similar to results with those of our OLS regressions, but with even stronger negative associations between ENV_IDX (andor ENV_NET) and REL with the t values of −6.479 to −7.045.
As we can see in Table 3 Panel B, the correlation between RACE and POVERTY is very high, i.e., 0.5855. As a result, when we included both RACE and POVERTY variables together with other demographic variables, the multicollinearity problems become serious (variance inflation factor is higher than 10), and our empirical results become unstable and insignificant. In econometrics, the variance inflation factor (VIF) quantifies the severity of multicollinearity in an OLS regression analysis. It provides an index that measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity. Because a VIF number larger than 10 indicates a serious multicollinearity problem (Greene 1993), we decide not to include the RACE and POVERTY variables in the subsequent analyses.
To conserve the space, we do not report the 2SLS and 3SLS results because those results confirm our earlier results from OLS regressions and principal component analysis. Those results, however, are available upon request.
The dynamic panel GMM model, in particular, enables us to estimate the diversity-religiosity relation by dealing with (i) past diversity scores due to autocorrelation problem of diversity ratings, (ii) fixed-effects to account for the dynamic aspects of the diversity-religiosity relation, and (iii) time-invariant unobservable heterogeneity, respectively.
Even when we add demographic variables in our dynamic system GMM regressions, our main results of the inverse association between ENV_IDX (or ENV_NET) and REL remains intact. In addition, when we employ the Arellano and Bond (1991) first-difference GMM estimator, our main results remain qualitatively the same.
Our data show that the religiosity of the local region (the county) influences the environmental choices a firm’s management makes. To show this we do not claim that the religiosity of the county is a proxy for the religiosity of the firm’s managers. Regardless of what the religiosity of the firm’s management might be (in fact, even if a firm’s management is atheistic), our data will still show that the firm’s management is influenced by the religiosity of the local region. Our study appeals to the “moral communities” research to motivate and support the premise that the religiosity of a region influences the choices of managers who work in that region.
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Acknowledgment
We appreciate the insightful guidance of section editor, Domenec Melé, and the many valuable comments from two anonymous referees. Jo appreciates Gerald and Bonita A. Wilkinson Professorship and Velasquez appreciates Charles J. Dirksen Professorship. Both Jo and Velasdquez thank the Bannan Institute Research Grant.
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Appendices
Appendix 1
See Table 9.
Appendix 2: The Implementation of Dynamic GMM Methodology in Stata (Version 11)
Following Wintoki et al. (2012), we use the xtabond2 command to implement dynamic system GMM estimation in Stata 11.
We applied the dynamic system GMM results described in Table 8, using the xtabond2 code in Stata as:
xi: xtabond2 env_net(env_idx) l. env_net(env_idx) l2. env_net(env_idx) rel logta(or logmve) mbve capexa advr rndr debtr saleg devret i.year i.ff_48industry, gmm(env_net(env_idx) rel logta(or logmve) mbve capexa advr rndr debtr saleg devret, lag(2 4) collapse) iv(i.year, i. ff_48industry) twostep robust small.
The ‘lag (2 4)’ indicates that we use lagged two-to-four periods as instruments, respectively. The Stata command incorporates our assumption that only FF48 industry dummies and the year dummies are exogenous. We also use the “collapse” to avoid instrument proliferation.
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Cui, J., Jo, H. & Velasquez, M.G. The Influence of Christian Religiosity on Managerial Decisions Concerning the Environment. J Bus Ethics 132, 203–231 (2015). https://doi.org/10.1007/s10551-014-2306-5
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DOI: https://doi.org/10.1007/s10551-014-2306-5