Advances and Identification Challenges in Micro- Econometric Models of Firm Productivity

Abstract

Firm-level productivity analysis serves as a cornerstone for understanding the micro-foundations of economic growth, industrial competitiveness, and efficient resource allocation. This comprehensive review synthesizes and critically evaluates the primary statistical and econometric methodologies employed in the measurement and analysis of productivity at the firm level. We systematically delineate the evolution from traditional parametric techniques, such as production function estimation and Stochastic Frontier Analysis (SFA), to non-parametric approaches, including Data Envelopment Analysis (DEA) and the Malmquist Productivity Index. A significant focus is placed on addressing pervasive micro-level challenges, notably firm heterogeneity, measurement error, and endogeneity biases, which are endemic to firm-level data. The paper further explores recent methodological innovations, highlighting the integration of machine learning, quantile regression, and network analysis into the productivity research arsenal. By providing a structured guide for selecting and applying appropriate statistical tools, this review aims to equip researchers with the knowledge to conduct robust micro-level productivity analyses. Finally, we outline promising future research trajectories, emphasizing the potential of novel data sources and computational methods to deepen our understanding of productivity determinants.