Developing Data-Driven Compensation Strategies Based on Productivity Elasticity: An Empirical Multi-Sector Analysis
Savanam Chandra Sekhar *
KL Business School, Koneru Lakshmaiah Education Foundation, KL University, India.
*Author to whom correspondence should be addressed.
Abstract
This study develops and empirically evaluates data-driven compensation strategies based on productivity elasticity using employee-level panel data across manufacturing, information technology, and public sectors. It examines the nonlinear and heterogeneous relationship between salary and employee productivity and evaluates whether elasticity-based compensation improves productivity, efficiency, and wage–productivity alignment compared with traditional pay systems. The study adopts a quantitative research design integrating panel econometric modelling, structural equation modelling (SEM), Difference-in-Differences (DiD), and stochastic frontier analysis. Results indicate a positive and statistically significant effect of salary on productivity (β₁ = 0.287, p < 0.001), with diminishing marginal returns (β₂ = −0.064, p < 0.001). Significant heterogeneity in productivity elasticity is observed across sectors, with higher responsiveness in knowledge-intensive roles. Elasticity-based compensation increases productivity by approximately 8.3%, improves efficiency (0.82 vs 0.71), and reduces wage–productivity misalignment by about 30%. Dynamic panel estimates confirm sustained productivity gains from time-varying compensation. The findings demonstrate that elasticity-based, data-driven compensation systems provide a more efficient, adaptive, and equitable alternative to traditional salary structures.
Keywords: Productivity elasticity, compensation optimization, panel data, nonlinear modelling, wage–productivity alignment, data-driven HR analytics