Total Factor Productivity and its determinants: an analysis of the relationship at firm level through unsupervised learning techniques

TL;DR

This study uses unsupervised learning to identify firm features that influence total factor productivity, revealing key determinants and their linear relationships across different periods.

Contribution

It introduces a bottom-up, unsupervised learning approach to analyze firm heterogeneity and determinants of productivity growth using large-scale data.

Findings

Profitability and credit measures are key determinants.

Cost and capital efficiency influence productivity.

A linear relationship exists between determinants and productivity growth.

Abstract

The paper is related to the identification of firm's features which serve as determinants for firm's total factor productivity through unsupervised learning techniques (principal component analysis, self organizing maps, clustering). This bottom-up approach can effectively manage the problem of the heterogeneity of the firms and provides new ways to look at firms' standard classifications. Using the large sample provided by the ORBIS database, the analyses covers the years before the outbreak of Covid-19 (2015-2019) and the immediate post-Covid period (year 2020). It has been shown that in both periods, the main determinants of productivity growth are related to profitability, credit/debts measures, cost and capital efficiency, and effort and outcome of the R&D activity conducted by the firms. Finally, a linear relationship between determinants and productivity growth has been found.