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Corporate Social Responsibility and Firm Financial Performance: The Mediating Role of Productivity

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Abstract

This study treats firm productivity as an accumulation of productive intangibles and posits that stakeholder engagement associated with better corporate social performance helps develop such intangibles. We hypothesize that because shareholders factor improved productive efficiency into stock price, productivity mediates the relationship between corporate social and financial performance. Furthermore, we argue that key stakeholders’ social considerations are more valuable for firms with higher levels of discretionary cash and income stream uncertainty. Therefore, we hypothesize that those two contingencies moderate the mediated process of corporate social performance with financial performance. Our analysis, based on a comprehensive longitudinal dataset of the U.S. manufacturing firms from 1992 to 2009, lends strong support for these hypotheses. In short, this paper uncovers a productivity-based, context-dependent mechanism underlying the relationship between corporate social performance and financial performance.

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Notes

  1. Please refer to the NBER website for more information (http://www.nber.org/nberces/).

  2. We include the qualitative category of “human rights” to measure CSR because many U.S. firms have significant non-U.S. operations involving indigenous employees as an important group of stakeholders. To be consistent with existing literature (Berman et al. 1999; Hillman and Keim 2001), we also construct our CSP measures without the “human rights” category. However, such modification does not change our results in a material way.

  3. Note that our TFP measure is logged because it is estimated from a log-linear production function.

  4. According to existing literature (Muller and Judd 2005), we recognize that the combination of \(\beta_{11} \ne 0\) \(\beta_{13} \ne 0\), \(\beta_{21} \ne 0\), and \(\beta_{35} \ne 0\), implies another possible moderated mediation process which we do not hypothesize. Specifically, the indirect effect of the mediator (TFP) on outcome variable (Tobin’s Q) may depend on the moderator. However, we do not find evidence supporting this moderated mediation process for moderators in our empirical analysis.

  5. To save space, we do not report the first-stage probit model result, which is available upon request.

  6. We report the fraction correctly predicted is 72 %. As Hoetker (2007) indicates, this percentage can be misleading because it does not account for the fact that around 66 % of sample firms have positive CSP scores. Therefore, the percentage needs to be adjusted. Following Veall (1996), we calculate λ′=(0.72 − 0.66)/(1−0.66) = 0.18 and compare the performance of our model (0.72) with a blind guess (0.66), which reveals a very significant 18 % improvement.

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Acknowledgments

This research project is generously supported by a Summer Research Grant awarded by the Stuart School of Business, Illinois Institute of Technology. This project is also supported by the Research Bureau at People's Bank of China, and the Major Program of the National Natural Science Foundation of China (Grant 13&ZD016), and the Key Program of the National Natural Science Foundation of China (Grant 12AZD095).

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Correspondence to Haizhi Wang.

Appendix: Measuring Total Factor Productivity

Appendix: Measuring Total Factor Productivity

Total factor productivity, or TFP, is the conventional measure of firm-level productivity (Schoar 2002). Productivity is often estimated as the deviation between observed output and output predicted by a Cobb–Douglas production function estimated by ordinary least squares (OLS). Such estimates, however, may suffer from simultaneity and selection biases (Olley and Pakes 1996) (hereafter OP). Simultaneity arises because profit-maximizing firms can choose their input levels to accommodate productivity shocks (Marschak and Andrews 1944). Firms can increase their use of inputs where there are positive productivity shocks, which makes the input factors endogenous. Consequently, OLS yields biased estimations because of the simultaneity issue.

Another endogeneity concern, selection bias, needs to be addressed in order to estimate production function parameters. Selection bias results from the fact that firms’ decisions to exit markets are correlated with productivity shocks. For example, if there is a positive relation between a firm’s profitability and its capital stock, a firm with lots of capital stock may continue to operate despite a low productivity shock. In other words, a firm with a low productivity shock but a high capital stock can still survive because it can produce greater future profits based on its large capital stock. Therefore, the estimated coefficient on the capital variable can be biased downward because of the negative relation between capital stock and likelihood of exit for a given productivity shock.

To estimate the production function parameters and firm-level productivity, OP propose a novel approach to control for the simultaneity and selection issues. To be specific, OP use investment to proxy for the unobserved time-varying productivity, with the assumption that firms invest more in observing a positive productivity shock. In addition, the OP approach generates an exit rule to account for the selection problem. Notably, the OP approach allows firm-specific productivity to vary over time, and it endogenizes exit decisions induced by inefficient operation. These features address two major concerns in estimating productivity.

Following OP, we estimate the output as a log-linear Cobb–Douglas production function of several input factors for all U.S. manufacturing firms in the Compustat database from 1992 to 2009 (Schoar 2002). Specifically, using a Stata code (opreg) developed by Yasar et al. (2008), we estimate the following log-linear function:

$$\log Sale{s_{ijt}}\, = \, \propto \, + \,{\beta_1}\log {\text{Capita}}{{\text{l}}_{ijt}}\, + \,{\beta_2}\log {\text{Labo}}{{\text{r}}_{ijt}}\, + \,{\beta_3}\log {\text{Materia}}{{\text{l}}_{ijt}}\, + \,{a_{ijt}}\, + \,{\varepsilon_{ijt}},$$

where i indexes firms, j indexes industries, and t indexes years. We measure output by company sales (Compustat data #12). Capital is the value of property, plant, and equipment net of depreciation (Compustat data #8). Labor is the number of employees using Compustat data #29. Material represents total expenses (Compustat data #12–Compustat data #13) minus labor expenses (Compustat data #29 multiplied by average industry wage). TFP is the residual difference between predicted and actual outputs (\({\alpha_{ijt}}\)). Thus, TFP reflects the output that input factors cannot explain.

To proxy for the unobserved time-varying productivity and to facilitate the estimation, we measure investment as total capital expenditures (Compustat #128). We further obtain deflating factors for each four-digit industry-year from the NBER-CES database (http://www.nber.org/nberces/) to deflate the sales, capital, materials, and investment measures (Bartelsman and Gray 2001). Because coefficients on capital, labor, and material inputs vary by industry and year, this model specification allows for different industry-level factor intensities. Note that our TFP measure is a logged measure of raw productivity in the log-linear Cobb–Douglas production function. In the regression analysis, we use the logged measure instead of raw measure as our dependent variable, and therefore we interpret the estimated coefficients as percentages.

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Hasan, I., Kobeissi, N., Liu, L. et al. Corporate Social Responsibility and Firm Financial Performance: The Mediating Role of Productivity. J Bus Ethics 149, 671–688 (2018). https://doi.org/10.1007/s10551-016-3066-1

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