NIFTY50 Employee Productivity FY2024

Description

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.

Steps to reproduce

The above dataset is collected entirely from publicly available, official company disclosures, not from estimates or private databases. Specifically, the data has been compiled from the following sources: The financial data (Revenue and Profit After Tax) for each NIFTY-50 company has been taken from their FY2023–24 Annual Reports, which are released every year and audited as per Indian regulatory requirements. These annual reports are published on the companies’ official websites and filed with the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). The employee headcount data has been sourced from the Human Capital / Employee Information sections of the same annual reports or from official investor presentations released for FY2023–24. Where companies disclose employee numbers quarterly, the figure closest to 31 March 2024 has been used to maintain consistency. Using these primary inputs, the productivity metrics—Revenue per Employee and PAT per Employee—were calculated by the analyst using standard financial formulas. No third-party productivity indices, forecasts, or assumptions were used in this process. In summary, the dataset is based on: Company Annual Reports (FY2023–24) NSE & BSE corporate filings Official investor presentations Company websites (Investor Relations sections) This ensures that the dataset is verifiable, transparent, and suitable for academic and professional analysis.

Institutions

• Alliance University

  Karnataka, Bangalore

Categories

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