In new published paper, Chen and Delmas compare aggregate corporate social performance across more than 2000 firms using new methods.
Stakeholders are becoming more and more concerned about the corporate social and environmental performance (CSP) of firms’ operations. For example, investors are increasingly using socially responsible investing (SRI) screens to select or avoid investing in firms according to their environmental and social preferences. Similarly, a growing number of consumers purchase eco-labeled products that signal a lower environmental and social impact of corporate operations. Some corporations are also developing socially responsible purchasing practices to promote more sustainable supply chains. However, measuring CSP has proven to be a daunting task because it covers a broad range of economic, social, and environmental impacts caused by business. To fully address its scope, firms and researchers must take into account a variety of metrics.
Stakeholders often need aggregate CSP measures to assess the overall corporate social performance of firms, or to compare one firm to another. Most approaches use simple linear aggregations, weighted or non-weighted, to derive a single CSP score from a set of metrics. These approaches, however, can be problematic because they require that we know the set preferences for various environmental and social criteria in advance. For example, are toxic emissions more or less important than worker safety—and by how much?
Chen and Delmas propose a new methodology to measure CSP in their forthcoming paper in the renowned peer reviewed journal Production and Operations Management, the journal of the Production and Operations Management Society (POMS).
Their methodology, based Data Envelopment Analysis (DEA), allows researchers to evaluate the relative efficiencies of firms. This method doesn’t require that we assign weights or prioritize particular metrics in advance DEA computes an efficient frontier that represents the best performers in a peer group. We can then compare overall CSP across firms against this benchmark.
The CSP scores yielded by DEA represent the distance from a particular firm to the best performers. DEA also identifies the opportunities for a given firm to reduce its current CSP concerns, given its CSP strengths relative to the best performers. In DEA, the weights of different criteria are generated through optimization, such that the firm will be assigned a set of “optimal weights” that maximizes the firm’s efficiency relative to the other firms in the sample.
Using their methodology, Chen and Delmas calculate the Using CSP data from 2,190 firms in three major industries (manufacturing, finance and service) from the Kinder, Lydenberg, and Domini Inc. database in 2007. The top 34 performers include Xerox and 3 M and Avon Products.