Forecasting Economic Recession and Economic Growth Using Corporate Social Responsibility
Keywords:
Corporate Social Responsibility, Economic Recession, Economic Growth, , Logistic Regression, Panel Regression, Predictive IndicatorsAbstract
This study aims to examine whether corporate social responsibility (CSR) disclosures can serve as significant predictors of economic recession and real GDP growth. The research employed an ex post facto design using data from 105 firms listed on the Tehran Stock Exchange between 2012 and 2023. CSR disclosure was measured through a text-mining approach applied to board of directors’ reports, covering 24 CSR indicators grouped into six dimensions. Economic recession was identified using the Hodrick–Prescott filter on non-oil GDP, and economic growth was measured as the annual change in real GDP obtained from the Central Bank of Iran. Logistic regression was used to test the predictive power of CSR for recession likelihood, while panel regression was applied to examine CSR’s effect on GDP growth. Control variables included market return, stock return volatility, investor sentiment, exchange rate, consumer price index, earnings growth, firm size, and firm age. The logistic regression results showed that CSR was negatively and significantly associated with the probability of economic recession, confirming its predictive role in identifying downturns. The panel regression model demonstrated that CSR was positively and significantly related to subsequent GDP growth, even after controlling for financial and macroeconomic variables. These results indicate that CSR disclosures contain incremental predictive information about macroeconomic outcomes beyond traditional indicators. The findings provide strong evidence that CSR disclosures are not merely symbolic but embed valuable information regarding firms’ expectations and resilience, making them useful leading indicators of both recession and economic growth. Incorporating CSR into macroeconomic forecasting frameworks can enhance the accuracy and timeliness of predictions, particularly in emerging markets with evolving reporting standards.
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Copyright (c) 2025 Shirin Montashi, Mahnam Molaei, Maryam Farhadi, Javad Foruzandeh (Author)
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