NSF Award
2401824
Corporate social responsibility (CSR) can be broadly defined as activities undertaken by a for- profit business intended to increase social welfare rather than merely maximize profits. Examples of these activities include planting greenery, employee volunteering, product donations, and more. For instance, a firm may donate legal advice to an environmental conservation non-profit. Several rating agencies have attempted to quantify CSR by calculating Environmental, Social, and Governance (ESG) scores that capture the presence or absence of some of these activities. Nevertheless, existing CSR data suffer from fragmentation and inconsistencies because they are scattered across different sources (e.g., regulatory filings, news articles, social media, firm announcements) and lack uniformity in reporting. To date, there has been no single reliable source of information or database about the financial and non-financial impacts of CSR on society. Additionally, the current calculation of Gross Domestic Product (GDP) captures monetary transactions within the economy but ignores the intangible and qualitative activities involved in CSR that contribute to the well-being of society. This project aims to create a comprehensive database of consistent CSR metrics. It estimates the foregone earnings for US firms associated with CSR implementation and examines how measured GDP and productivity would change if these foregone earnings were treated as charitable donations. Both measured GDP and productivity could change when national accountants treat the foregone profits associated with CSR as charitable donations.<br/><br/>The project maps current CSR activities nationwide to provide an in-depth understanding of the determinants and mechanisms by which firms engage in CSR activities. It identifies the characteristics of firms that meet ambitious sustainability goals and assesses the real economic consequences of such investments for society. By measuring the economic outputs of CSR and their contribution to U.S. and global GDP, the project aims to highlight the significance of CSR in economic terms. To achieve these goals and create a comprehensive CSR database, the project utilizes Artificial Intelligence and Machine Learning techniques that can automate the extraction of CSR data from various sources, a task that would otherwise require labor-intensive analysis by skilled workers. The project enhances the consistency and comparability of CSR data by creating a uniform reporting method. This is achieved by identifying patterns of similarities across different key data points and converting unstructured reporting into a structured and standardized format. Furthermore, bias in CSR data is assessed by analyzing sentiment, tone, and context within non- financial data. The project benefits multiple stakeholders. Investors will be able to allocate their resources more effectively, scholars will gain advanced tools for research on CSR, and decision makers will be equipped with evidence-based insights for informed decision-making. The resulting database of CSR metrics will be made publicly accessible, allowing diverse stakeholders to use it for various purposes. The project not only offers research database and tools but also sets the stage for future research, advancing our knowledge and contributing to the existing body of knowledge on CSR and economic growth.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
