Economic Determinants of Total Factor Productivity Growth : the Bayesian Modelling Sveraging Spproach

• Autorzy: Janusz Sobieraj , Dominik Metelski
• Języki publikacji: EN

Abstrakty

EN

Objective: : The objective of this article is to use the most recent national-level data (reflecting heterogeneity) to explore determinants of total factor productivity (TFP) growth.
Research Design & Methods: The article examines the performance of a number of potential TFP growth determinants, relying on the Bayesian modelling analysis (BMA) methodology, which allows for isolating key regressors and assessing their actual contribution in relation to the phenomenon under study. As a scientific methodology, BMA is deeply rooted in statistical theory and directly results in posterior and predictive inferences. Moreover, BMA makes it easier to determine the relative impact of examined processes, while taking into account the uncertainty that accompanies the entire regressors' selection procedure (Raftery, Madigan, & Hoeting, 1997; Hoeting, Madigan, Raftery, & Volinsky, 1999; Sala-i-Martin, Doppelhofer, & Miller, 2004).
Findings: We indicate a number of determinants driving TFP growth, e.g. inequality measured by the Gini coefficient, the growth of information and communications technology (ICT) assets, logistics performance, the quality of logistics services, and migration.
Implications & Recommendations: We contribute to a more systematised knowledge of the determinants of TFP growth; the data shows that developed economies exhibit variable returns to scale (VRS). More importantly, there is an increasing contribution of ICT assets to economic growth and economies of scale, which is why whole economic systems exhibit increasing returns to scale (IRS). Some of the economic activity remains under-reported, meaning that economies of scale are even greater than the data reveals. In the era of globalisation, it becomes important to support digital technologies, address inequalities, create appropriate logistics infrastructure, and pay attention to mobility factors, e.g. labour migration.
Contribution & Value Added: We conduct an overview of the literature so as to better understand the importance of TFP growth. Based on the literature, we identify a number of potential TFP growth determinants and examine their relevance and robustness using the BMA approach, which has become increasingly popular in recent years. (original abstract)

Słowa kluczowe

EN

Bayesian models; Productivity; Accounting

PL

Modele bayesowskie; Produktywność; Rachunkowość

• Czasopismo: Entrepreneurial Business and Economics Review
• Rocznik: 2021
• Tom: 9
• Numer: nr 4
• Strony: 147--171

Twórcy

autor

Janusz Sobieraj

• Instytut Organizacji i Zarządzania w Przemyśle "ORGMASZ", Warszawa, doktorant

autor

Dominik Metelski

• University of Granada, Spain

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Identyfikatory

DOI

10.15678/EBER.2021.090410