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

Economics > General Economics [Submitted on 24 Nov 2025 (v1), last revised 31 May 2026 (this version, v2)] Title:Total Factor Productivity and its determinants: an analysis of the relationship at firm level through unsupervised learning techniques View PDFAbstract: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/efficiency of the R&D activity conducted by the firms. Finally, a linear relationship between determinants and productivity growth has been found. Submission history From: Paolo Pedotti [view email][v1] Mon, 24 Nov 2025 19:02:37 UTC (1,812 KB) [v2] Sun, 31 May 2026 17:39:10 UTC (1,094 KB) Current browse context: econ.GN References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.