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Corporate Social Responsibility and Firms’ Dynamic Productivity Change

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Advances in Efficiency and Productivity II

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 287))

Abstract

This chapter examines the relationship between corporate social responsibility (CSR) and firms’ productivity change. The application focuses on panel data of US firms from 2004 to 2015. The chapter uses a dynamic data envelopment analysis (DEA) model to measure productivity change and its technical, technical-inefficiency, and scale-inefficiency change components. A bootstrap regression model relates CSR and its dimensions of social, environmental, and governance CSR with dynamic performance measures. Results support a positive association between CSR and dynamic productivity change. The findings also provide evidence about the relevance of CSR dimensions, as well as the components of dynamic productivity change, adding interesting insights into the relationship between CSR and productivity change.

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Notes

  1. 1.

    Sun and Stuebs (2013) and Jacobs et al. (2016) did refer to firms’ productivity. However, in fact, they measured technical efficiency. Kapelko etal. (2020) analyzed the productivity change in the CSR context, but using input-specific productivity change measures.

  2. 2.

    In general, literature finds that the differences between productivity changes computed with unbalanced and balanced panel can be significantly different depending on the dataset used (Kerstens and Van de Woestyne 2014). However, it is also found that balancing an unbalanced panel results in a substantial loss of information (Kerstens and Van de Woestyne 2014); hence, we decide to use unbalanced panel in this chapter.

  3. 3.

    Quasi-fixed input (fixed assets) is not applied directly in the DEA model used to estimate dynamic productivity measures. Hence, it is not one of the variables directly used to estimate dynamic measures. Quasi-fixed input is used mainly to compute investments. Also, in the general dynamic DEA model, depreciation is given as a fraction of quasi-fixed input.

  4. 4.

    We did not apply bootstrap in the first stage when estimating the dynamic productivity change and its components, since no bootstrap approach has been developed in the context of both the static and dynamic Luenberger indicator. Although the bootstrap approach exists for the static directional distance function (see Simar et al. 2012), its adaptation to our context is not straightforward, since it requires previous analysis of the properties (such as consistency, rate of convergence, and asymptotic distributions) of the estimator of dynamic measures. Furthermore, the bootstrap approach exists in the literature for the first stage within the static Malmquist index (see Simar and Wilson 1999), but its adaptation in our context is not straightforward. More importantly, recent papers (Kneip et al. 2018; Simar and Wilson 2019) show that Simar and Wilson’s (1999) approach cannot be theoretically justified. Instead, these papers develop new, central limit theorems to allow for inference about Malmquist productivity change and its components. Again, these developments are not directly applicable in our context. Moreover, they allow to analyze if estimated productivity changes are significantly different from 1, which is out of the scope of this chapter.

  5. 5.

    In total, each statistic was run for each regression model. The under-identification test of the Kleibergen-Paap rk Lm statistic showed that the models were always identified (p-value = 0.0000), while the weak identification test using the Kleibergen-Paap rk Wald F statistic indicated that our instruments were relevant and strong (the F statistics oscillated between 17 and 36, depending on the model).

  6. 6.

    In the regression models on the relation between CSR and dynamic technical and scale-inefficiency changes, the dependent variable is dynamic inefficiency change (in its technical or scale variation), so, on the contrary to dynamic inefficiency itself, the larger the values of dynamic inefficiency change, the more positive change occurs. Therefore, the positive relation between dynamic technical-inefficiency change and CSR could imply that the larger the CSR, the larger the dynamic technical-inefficiency change that is implicitly a positive relation between CSR and dynamic technical-efficiency.

References

  • Alene, A. D. (2010). Productivity growth and the effects of R&D in African agriculture. Agricultural Economics, 41, 223–238.

    Google Scholar 

  • Aw, B. Y., Roberts, M. J., & Xu, D. Y. (2011). R&D investment, exporting, and productivity dynamics. American Economic Review, 101(4), 1312–1344.

    Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078–1092.

    Google Scholar 

  • Baum, C. F., Schaffer, M. E., & Stillman, S. (2003). Instrumental variables and GMM: Estimation and testing. The Stata Journal, 3(1), 1–31.

    Google Scholar 

  • Briec, W., & Kerstens, K. (2009a). The Luenberger productivity indicator: An economic specification leading to infeasibilities. Economic Modelling, 26, 597–600.

    Google Scholar 

  • Briec, W., & Kerstens, K. (2009b). Infeasibility and directional distance functions with application to the determinateness of the Luenberger productivity indicator. Journal of Optimization Theory and Application, 141, 55–73.

    Google Scholar 

  • Chambers, R. G., & Pope, R. D. (1996). Aggregate productivity measures. American Journal of Agricultural Economics, 78(5), 1360–1365.

    Google Scholar 

  • Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407–419.

    Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of the decision making units. European Journal of Operational Research, 2, 429–444.

    Google Scholar 

  • Cheng, B., Ioannou, I., & Serafeim, G. (2014). Corporate social responsibility and access to finance. Strategic Management Journal, 35, 1–23.

    Google Scholar 

  • Cummins, J. D., & Xie, X. (2013). Efficiency, productivity, and scale economies in the U.S. property-liability insurance industry. Journal of Productivity Analysis, 39(2), 141–164.

    Google Scholar 

  • Curi, C., Lozano-Vivas, A., & Zelenyuk, V. (2015). Foreign bank diversification and efficiency prior to and during the financial crisis: Does one business model fit all? Journal of Banking & Finance, 61, S22–S35.

    Google Scholar 

  • Deng, X., Kang, J.-K., & Low, B. S. (2013). Corporate social responsibility and stakeholder value maximization: Evidence from mergers. Journal of Financial Economics, 110, 87–109.

    Google Scholar 

  • Dilling-Hansen, M., Strøjer Madsen, E., & Smith, V. (2003). Efficiency, R&D and ownership – Some empirical evidence. International Journal of Production Economics, 83(1), 85–94.

    Google Scholar 

  • Eisner, R., & Strotz, R. H. (1963). Determinants of business investment. New York: Prentice-Hall.

    Google Scholar 

  • El Ghoul, S., Guedhami, O., Kwok, C., & Mishra, D. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance, 35(9), 2388–2406.

    Google Scholar 

  • Epstein, L. G. (1981). Duality theory and functional forms for dynamic factor demands. Review of Economic Studies, 48, 81–96.

    Google Scholar 

  • Färe, R., Grosskopf, S., & Margaritis, D. (2008). U.S. productivity in agriculture and R&D. Journal of Productivity Analysis, 30, 7–12.

    Google Scholar 

  • Flammer, C. (2015). Does corporate social responsibility lead to superior financial performance? A regression discontinuity approach. Management Science, 61(11), 2549–2568.

    Google Scholar 

  • Guillamon Saorin, E., Kapelko, M., & Stefanou, S. E. (2018). Corporate social responsibility and operational inefficiency: A dynamic approach. Sustainability, 10(7), 2277.

    Google Scholar 

  • Hannon, J., & Milkovich, G. (1996). The effect of human resource reputation signals on share prices: An event study. Human Resource Management, 35, 405–424.

    Google Scholar 

  • Hausman, J. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.

    Google Scholar 

  • Hou, C.-E., Lu, W.-M., & Hung, S.-W. (2017). Does CSR matter? Influence of corporate social responsibility on corporate performance in the creative industry. Annals of Operations Research, 278, 255–279.

    Google Scholar 

  • Huggett, M., & Ospina, S. (2001). Does productivity growth fall after the adoption of new technology? Journal of Monetary Economics, 48(1), 173–195.

    Google Scholar 

  • Jacobs, B. W., Kraude, R., & Narayanan, S. (2016). Operational productivity, corporate social performance, financial performance, and risk in manufacturing firms. Production and Operations Management, 25(12), 2065–2085.

    Google Scholar 

  • Kapelko, M., Oude Lansink, A., & Stefanou, S. E. (2014). Assessing dynamic inefficiency of the Spanish construction sector pre- and post-financial crisis. European Journal of Operational Research, 237(1), 349–357.

    Google Scholar 

  • Kapelko, M., Oude Lansink, A., & Stefanou, S. (2015a). Effect of food regulation on the Spanish food processing industry. PLoS One, 10(6), e0128217. https://doi.org/10.1371/journal.pone.0128217.

    Article  Google Scholar 

  • Kapelko, M., Oude Lansink, A., & Stefanou, S. (2015b). Analyzing the impact of investment spikes on dynamic productivity growth. Omega, 54, 116–124.

    Google Scholar 

  • Kapelko, M., Oude Lansink, A., & Stefanou, S. (2016). Investment age and dynamic productivity growth in the Spanish food processing industry. American Journal of Agricultural Economics, 98, 946–961.

    Google Scholar 

  • Kapelko, M., Oude Lansink, A., & Guillamon-Saorin, E. (2020). Corporate social responsibility and dynamic productivity change in the US food and beverage manufacturing industry. Agribusiness (forthcoming).

    Google Scholar 

  • Kerstens, K., & Van de Woestyne, I. (2014). Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data. European Journal of Operational Research, 233(3), 749–758.

    Google Scholar 

  • Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics, 133, 97–126.

    Google Scholar 

  • Kneip, A., Simar, L., & Wilson, P. W. (2018). Inference in dynamic, nonparametric models of production: Central limit theorems for Malmquist indices. In Discussion paper #2018/10, Institut de Statistique, Biostatistique et Sciences Actuarielles. Louvain-la-Neuve, Belgium: Université Catholique de Louvain.

    Google Scholar 

  • Lev, B., Petrovits, C., & Radhakrishnan, S. (2010). Is doing good for you? How corporate charitable contributions enhance revenue growth. Strategic Management Journal, 31, 182–200.

    Google Scholar 

  • Lu, W.-M., Wang, W.-K., & Lee, H.-L. (2013). The relationship between corporate social responsibility and corporate performance: Evidence from the US semiconductor industry. International Journal of Production Research, 51(19), 5683–5695.

    Google Scholar 

  • Lucas, R. E. (1967). Adjustment costs and the theory of supply. Journal of Political Economy, 75, 321–334.

    Google Scholar 

  • Lys, T., Naughton, J. P., & Wang, C. (2015). Signaling through corporate accountability reporting. Journal of Accounting and Economics, 60, 56–72.

    Google Scholar 

  • Margolis, J. D., Elfenbein, H. A., & Walsh, J. P. (2009). Does it pay to be good... And does it matter? A meta-analysis of the relationship between corporate social and financial performance. Available at SSRN: https://ssrn.com/abstract=1866371, http://dx.doi.org/10.2139/ssrn.1866371. Accessed 20 Jan 2019.

  • McWilliams, A., & Siegel, D. (2000). Corporate social responsibility and financial performance: Correlation or misspecification? Strategic Management Journal, 21(5), 603–609.

    Google Scholar 

  • McWilliams, A., & Siegel, D. (2001). Corporate social responsibility: A theory of the firm perspective. The Academy of Management Review, 26, 117–127.

    Google Scholar 

  • Morrison Paul, C. J., & Siegel, D. S. (2006). Corporate social responsibility and economic performance. Journal of Productivity Analysis, 26, 207–211.

    Google Scholar 

  • Orlitzky, M., Schmidt, F. L., & Rynes, S. L. (2003). Corporal social and financial performance: A meta-analysis. Organization Studies, 24, 403–441.

    Google Scholar 

  • Oude Lansink, A., Stefanou, S. E., & Serra, T. (2015). Primal and dual dynamic Luenberger productivity indicators. European Journal of Operational Research, 241, 555–563.

    Google Scholar 

  • Pergelova, A., Prior, D., & Rialp, J. (2010). Assessing advertising efficiency. Journal of Advertising, 39(3), 39–54.

    Google Scholar 

  • Puggioni, D., & Stefanou, S. E. (2019). The value of being socially responsible: A primal-dual approach. European Journal of Operational Research, 276(3), 1090–1103.

    Google Scholar 

  • Servaes, H., & Tamayo, A. (2013). The impact of corporate social responsibility on firm value: The role of customer awareness. Management Science, 59(5), 1045–1061.

    Google Scholar 

  • Silva, E., & Stefanou, S. E. (2003). Nonparametric dynamic production analysis and the theory of cost. Journal of Productivity Analysis, 19, 5–32.

    Google Scholar 

  • Silva, E., Oude Lansink, A., & Stefanou, S. E. (2015). The adjustment-cost model of the firm: Duality and productive efficiency. International Journal of Production Economics, 168, 246–256.

    Google Scholar 

  • Silva, E., & Stefanou, S. E. (2007). Dynamic efficiency measurement: Theory and application. American Journal of Agricultural Economics, 89(2), 398–419.

    Google Scholar 

  • Simar, L. (2003). Detecting outliers in frontier models: A simple approach. Journal of Productivity Analysis, 20(3), 391–424.

    Google Scholar 

  • Simar, L., & Wilson, P. W. (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operational Research, 115, 459–471.

    Google Scholar 

  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semiparametric models of production processes. Journal of Econometrics, 136, 31–64.

    Google Scholar 

  • Simar, L., & Wilson, P. W. (2019). Central limit theorems and inference for sources of productivity change measured by nonparametric Malmquist indices. European Journal of Operational Research, 277, 756–769.

    Google Scholar 

  • Simar, L., Vanhems, A., & Wilson, P. W. (2012). Statistical inference for DEA estimators of directional distances. European Journal of Operational Research, 220, 853–864.

    Google Scholar 

  • Skevas, T., & Oude Lansink, A. (2014). Reducing pesticide use and pesticide impact by productivity growth: The case of Dutch arable farming. Journal of Agricultural Economics, 65, 191–211.

    Google Scholar 

  • Sun, L., & Stuebs, M. (2013). Corporate social responsibility and firm productivity: Evidence from the chemical industry in the United States. Journal of Business Ethics, 118(2), 251–263.

    Google Scholar 

  • Surroca, J., Tribo, J. A., & Waddock, S. (2010). Corporate responsibility and financial performance: The role of intangible resources. Strategic Management Journal, 31(5), 463–490.

    Google Scholar 

  • Treadway, A. (1970). Adjustment costs and variable inputs in the theory of the competitive firm. Journal of Economic Theory, 2, 329–347.

    Google Scholar 

  • Vitaliano, D. F., & Stella, G. P. (2006). The cost of corporate social responsibility: The case of the community reinvestment act. Journal of Productivity Analysis, 6(3), 235–244.

    Google Scholar 

  • Waddock, S., & Graves, S. (1997). The corporate social performance - financial performance link. Strategic Management Journal, 18, 303–319.

    Google Scholar 

  • Wang, W.-K., Lu, W.-M., Kweh, Q. L., & Lai, H.-W. (2014). Does corporate social responsibility influence the corporate performance of the U.S. telecommunications industry? Telecommunications Policy, 38(7), 580–591.

    Google Scholar 

  • Wijesiri, M., & Meoli, M. (2015). Productivity change of micro finance institutions in Kenya: A bootstrap Malmquist approach. Journal of Retailing and Consumer Services, 25, 115–121.

    Google Scholar 

  • Worthington, A. (2000). Technical efficiency and technological change in Australian building societies. Abacus, 36(2), 180–197.

    Google Scholar 

  • Wright, P., & Ferris, S. (1997). Agency conflict and corporate strategy: The effect of divestment on corporate value. Strategic Management Journal, 18, 77–83.

    Google Scholar 

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Acknowledgments

The financial support for this article from the National Science Centre in Poland (grant number 2016/23/B/HS4/03398) is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Logistics, Wroclaw University of Economics and Business, Wrocław, Poland

    Magdalena Kapelko

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  1. Magdalena Kapelko
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Correspondence to Magdalena Kapelko .

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Editors and Affiliations

  1. Center of Operations Research, Miguel Hernandez University, Elche, Alicante, Spain

    Juan Aparicio

  2. School of Economics, University of Queensland, Brisbane, QLD, Australia

    C. A. Knox Lovell

  3. Center of Operations Research, Miguel Hernandez University, Elche, Alicante, Spain

    Jesus T. Pastor

  4. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA

    Joe Zhu

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Kapelko, M. (2020). Corporate Social Responsibility and Firms’ Dynamic Productivity Change. In: Aparicio, J., Lovell, C., Pastor, J., Zhu, J. (eds) Advances in Efficiency and Productivity II. International Series in Operations Research & Management Science, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-41618-8_9

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