Real-time Monitoring of Economic Shocks using Company Websites
This provides a real-time, firm-level monitoring tool for economic shocks across industries and geographies, addressing data availability constraints for adaptive policy-making and economic resilience.
The paper tackles the problem of monitoring economic shocks on firms by introducing the Web-Based Affectedness Indicator (WAI), which uses LLM-assisted classification on over five million company websites to quantify firm responses, showing high correlation with pandemic measures and reliable prediction of firm performance in the COVID-19 context.
Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (WAI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, WAI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that WAI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, WAI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience.