Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach

TL;DR

This paper develops a hierarchical panel data framework to analyze productivity convergence in manufacturing industries, addressing industry structure, technology heterogeneity, and cross-sectional dependence, revealing strong conditional convergence influenced by global and industry shocks.

Contribution

It introduces a novel hierarchical panel data estimation method that accounts for industry structure, heterogeneity, and dependence, with empirical application to manufacturing productivity.

Findings

Manufacturing industries show strong conditional convergence in productivity.

Both global and industry-specific shocks influence convergence.

Unconditional convergence is not observed in the data.

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.