Abstract
In contrast to the link between Corporate Social Responsibility (CSR) and financial performance, the connection between CSR and innovation has so far lacked research attention. This chapter investigates whether CSR and environmental innovations complement each other in such a way as to generate higher financial performance if both are present simultaneously compared to only one or none of them. We analyze if environmental innovators can increase their financial performance by signalling their environmental engagement through CSR. We use panel data on environmental R&D together with a CSR variable on the Global Reporting Initiative (GRI) and analyze their effect on financial performance. Although our results suggest that environmental R&D and the GRI are complementary, we cannot conclude that this is also true for other types of CSR.
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Notes
- 1.
Lyon and Maxwell (2008) additionally distinguish cost saving considerations and the avoidance of further threats of regulation as further reasons for such self-regulation.
- 2.
Akerlof (1970) originally addressed the problem of information asymmetry that arises either from adverse selection or from unobservable attributes (moral hazard).
- 3.
Please see Riley (2001) for an overview and discussion on the literature of signaling.
- 4.
The term Corporate Social Responsibility (CSR) is not commonly defined. In literature and practice, several definitions exist, but two common aspects of CSR can be found in these definitions: CSR activities relate to social and environmental issues and go beyond legal requirements. For our analyses, we apply this definition.
- 5.
In contrast, Harrington et al. (2008) find that firms' internal factors are the driving forces for implementing environmental management.
- 6.
- 7.
Loosely speaking, the adoption approach relies on the correlation of two firm strategies in order to account for complementarity. Note that this approach is only valid in case of continuous strategic measures (Miravete and Pernías 2010) and thus not applicable in this paper. The adoption approach can be traced back to the work of Arora and Gambardella (1990). They show that a positive covariance among a pair of activity variables indicates complementarity if the activity variables are conditioned on any other firm-specific characteristics. For an overview of empirical studies, please see Brynjolfsson and Milgrom (2013).
- 8.
The database can be accessed via the provider Thompson and Reuters.
- 9.
The A4 database offers other variables on CSR but the GRI fits our research question best and the database provides enough data points for the analysis.
- 10.
- 11.
This is the only indicator in the A4 database for environmental R&D investments which is usable for the analyses. The A4 data on environmental R&D investment costs provides not enough data for the analysis.
- 12.
- 13.
We conducted preliminary tests on fixed effects versus random effects models. First, the F-test on the null hypothesis of no fixed effects is rejected. The Hausman-Test with the null hypothesis of no correlation is rejected, too. Therefore, we use a fixed effects model with robust standard errors for the following dynamic panel estimations in a base model.
- 14.
In the appendix, we additionally provide Model 5 where the variables Greenit, CSRit, BOTHit are instrumented by the first and any further time lags and Model 6 with the moving average.
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Acknowledgements
This work is part of the project Impact Measurement and Performance Analysis of CSR (Corporate Social Responsibility), funded by the EU (7th Framework Program), Brussels, BE. We are indebted to the participants of the seminars and workshops held in Évora, Frankfurt (Oder), Mannheim, and Toulouse for their comments.
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Appendix
Appendix
Unbalanced panel data sample (6737 observations)
Overview panel data and innovation and CSR categories by continents
Overview panel data and innovation and CSR categories
8.1.1 GMM Estimators
Table 8.7 below provides the results for the basic GMM models, where the variables Greenit, CSRit, BOTHit are instrumented by the first and any further time lags (Model 5) or the moving average (Model 6).
Compared to Model 3, the GMM estimation in Model 5 shows smaller coefficients, except for the lagged P/S, and in some cases lower significance levels for the traditional variables influencing P/S. Nevertheless, the directions are comparable to the previous results. The coefficient estimates of the variables of interest, green innovation and CSR, again do not statistically differ from zero (however, their sign is now negative). Concerning engagement in both strategies (BOTHit), Model 5 confirms the results from the basic models, as there is a positive effect on P/S but it is only significant at the 10% level. The test developed by Arellano and Bond (1991) for auto-correlation in an auto-regressive process of the first order (AR1) shows significant serial correlation but no significant evidence of serial correlation in the first-differenced errors at order two (p = 0.713). This allows us to use lags of more than two-years as instruments. The Sargan-Hansen test for over-identification against the null—i.e. that the vector of instruments is orthogonal to the vector of the errors (or against the null that the instruments are exogenous)—shows that the instrumentation of the variable ln(P/S)it−1 is not strictly exogenous (p = 0.000). Unfortunately, the database does not provide better instruments and also estimations and tests with longer time lags reveal the same endogeneity problem. Therefore, we have to interpret the results with care. Although formal endogeneity is observable, it might not strongly affect the market value during the next year in reality as P/S is a year-end value. The tests for the subset of instruments with the lags of Greenit, CSRit, BOTHit confirm exogeneity to be slightly over the 1% level. This might indicate that the strict exogeneity assumption could be violated.
Therefore, we use the moving averages of the innovation and CSR variables as instruments in Model 6. For the moving average we calculate the average of the sum of the current year, one-year, and two-year time lag for each of the variables Greenit, CSRit, BOTHit. In this model the traditional variables are similar to Model 5, except for the coefficient estimate of the stock of patents (ln(Patents)it), which is not significant in all models. The variables of a pure CSR strategy and a joint CSR and R&D strategy are not significant. However, now green R&D affects P/S significantly negatively (5% level) but only with a small coefficient. Again the Arellano-Bond test at order one shows significant serial correlation, but no significant evidence of serial correlation in the first-differenced errors at order two (p = 0.636). We have to reject the null hypothesis of exogeneity of the subsets of instruments at the conventional 10% or 5% levels for both subsets (Tables 8.8 and 8.9).
8.1.2 Anderson-Hsiao Estimator
Table 8.10 below provides the results for the basic dynamic model setup, in which the endogeneity of the lagged dependent variables, but not the endogeneity of the key variables of interest, has been accounted for.
The most obvious insight from this table is that the coefficient estimate of the lagged dependent variable is far away from plausible values and also from the very basic OLS estimates provided in Table 8.5. The reason probably is a considerable instrumental variable bias due to a weak instrument problem. Recall that the results from Table 8.10 rely on a mode setup where all variables enter the model in differences. Although the correlation of the price-to-sales ratio in period t and t−1 is relatively high (0.883), the correlation of the first differences and lagged first differences is very small (−0.2354) making it a bad instrument. Also the first stage regressions support this view. The coefficient estimate of the excluded instrument in the structural equation is relatively low; let alone the fact that its level of significance is rather small, which supports the concern of a weak instrument problem. In particular, the F-statistic of the excluded instrument in the first stage regression (F = 5.81) is far away from areas considered to support non-weakness of instruments. Staiger and Stock (1997) propose a rule of thumb of a value of ten for the first stage F-statistic of a single excluded instrument to provide evidence for non-weakness. The central insight from this simple experiment is straightforward. Even in this basic setup, which only addresses the endogeneity of one variable, namely the lagged dependent variable, the Anderson-Hsiao estimator performs rather poorly given our data as lagged differences of the price-to-sales ratio, which is only loosely correlated with current values. Therefore, further lags as instruments might help mitigate this problem as in the Arellano-Bond GMM case. In this sense, the Arellano-Bond estimator seems to be a better choice allowing more consistent estimates, at least in part.
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Reif, C., Rexhäuser, S. (2018). Good Enough! Are Socially Responsible Companies the More Successful Environmental Innovators?. In: Horbach, J., Reif, C. (eds) New Developments in Eco-Innovation Research. Sustainability and Innovation. Springer, Cham. https://doi.org/10.1007/978-3-319-93019-0_8
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