Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach

Guohua Feng (), Jiti Gao () and Bin Peng ()

No 16/21, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Despite its paramount importance in the empirical growth literature, productivity convergence analysis has three problems that have yet to be resolved: (1) little attempt has been made to explore the hierarchical structure of industry-level datasets; (2) industry-level technology heterogeneity has largely been ignored; and (3) cross-sectional dependence has rarely been allowed for. This paper aims to address these three problems within a hierarchical panel data framework. We propose an estimation procedure and then derive the corresponding asymptotic theory. Finally, we apply the framework to a dataset of 23 manufacturing industries from a wide range of countries over the period 1963-2018. Our results show that both the manufacturing industry as a whole and individual manufacturing industries at the ISIC two-digit level exhibit strong conditional convergence in labour productivity, but not unconditional convergence. In addition, our results show that both global and industry-specific shocks are important in explaining the convergence behaviours of the manufacturing industries.

Keywords: growth regressions; convergence in manufacturing; cross-sectional dependence; hierarchical model; asymptotic theory (search for similar items in EconPapers)
JEL-codes: C23 L60 O10 (search for similar items in EconPapers)
Pages: 58
Date: 2021
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-tid
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Working Paper: Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach (2021)
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