Analyzing the Impact of Economic Indicators on Tech Employees’ Depression - A Mixed-Methods Approach Using LLM-Based Labeling and Quantitative-Qualitative Analysis

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The interaction of economic instability and mental health in the tech company has become a primary concern. Our research examines how economic indicators–such as GDP growth, unemployment rates, inflation, and stock market performance–correlate with depression levels among tech employees. Using a mixed-methods approach, we analyze large-scale data sourced from Blind, an anonymous professional network, where tech employees openly discuss workplace questions. Depression levels in employee comments are assessed using large language models (LLMs)-based sentiment analysis and categorized by severity. Quantitative findings are then compared with economic trends to identify patterns, while qualitative insights from employee narratives offer deeper perspectives on the emotional impact of economic fluctuations. Our research aims to provide a holistic understanding of how broader economic forces influence employee mental health in the tech sector and inform the development of targeted mental health interventions. Our study contributes to ongoing discussions on workplace wellbeing and mental health support in tech companies.