NIFTY50 Employee Productivity FY2024

This dataset provides a comprehensive analysis of employee productivity across all companies included in the NIFTY-50 index for the financial year 2023–24. The objective of the dataset is to evaluate how efficiently India’s largest listed companies utilize their human capital to generate revenue and profits. As Indian companies do not disclose a direct measure of employee productivity, the dataset relies on widely accepted financial proxies derived from publicly available and audited disclosures, making it suitable for academic and analytical use. The dataset covers 50 companies across diverse sectors, including banking and financial services, information technology, FMCG, pharmaceuticals, automobiles, oil and gas, power utilities, metals, cement, telecom, insurance, retail, infrastructure, and consumer technology. This broad coverage allows for both firm-level and sector-level analysis of productivity patterns within the Indian corporate landscape. All financial data has been taken from FY2023–24 annual reports, investor presentations, and NSE filings, ensuring reliability and consistency. No third-party estimates or assumptions have been incorporated. Key variables included in the dataset are company name, sector classification, total revenue, profit after tax (PAT), total number of employees, revenue per employee, and PAT per employee. Revenue and PAT are expressed in ₹ crore, while productivity indicators are calculated in ₹ lakh per employee. Revenue per employee serves as a measure of operational output generated by each employee, while PAT per employee reflects the profitability contribution of the workforce after accounting for costs and taxes. These metrics are commonly used in equity research, strategic analysis, and academic studies to assess human-capital efficiency. The dataset highlights significant variation in productivity across sectors. Asset-heavy industries such as oil, power, and utilities tend to exhibit very high revenue per employee due to large-scale operations and capital intensity, whereas IT services and consumer-oriented firms display comparatively lower but more human-capital-driven productivity levels. As a result, intra-sector comparisons are more meaningful than direct cross-sector comparisons, and the dataset should be interpreted with this context in mind. Loss-making firms are also included, allowing productivity challenges to be observed through negative PAT per employee values. While the dataset offers a robust view of employee productivity using standardized proxies, it has certain limitations. It does not account for differences in outsourcing, automation, or contractual labor, and employee headcounts are based on reported figures as of the financial year end.