Economic determinants of total factor productivity growth: The Bayesian modelling averaging approach


Abstract

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.

       

Keywords

total factor productivity; economic growth; growth accounting

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Published : 2021-11-19


Sobieraj, J., & Metelski, D. (2021). Economic determinants of total factor productivity growth: The Bayesian modelling averaging approach. Entrepreneurial Business and Economics Review, 9(4), 147-171. https://doi.org/10.15678/EBER.2021.090410

Janusz Sobieraj 
Warsaw University of Technology  Poland
http://orcid.org/0000-0002-0819-7384

Assistant professor in the Department of Building Engineering, Faculty of Civil Engineering, Warsaw University of Technology. He is a civil engineer with a degree from the Warsaw University of Technology, and holds a PhD in economic sciences in the field of management sciences from the Department of Computer Science and Management of the Wrocław University of Technology. His research interests concern issues related to construction management, applied economics, mobility factors, organisations & management, and financial markets.


Dominik Metelski  dmetelski@ugr.es
University of Granada  Spain
http://orcid.org/0000-0003-0195-0342

Research Assistant in the Department of Spanish and International Economics at University of Granada in Spain. His  research  interests  are  related  to  such  topics as  productivity, migration  as  well  as  issues in  the  area  of  entrepreneurship, innovation, real options and financial markets with particular emphasis on companies’ valuations, portfilio management and financial options.






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