Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach
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.
Cite
@article{arxiv.2111.00449,
title = {Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach},
author = {Guohua Feng and Jiti Gao and Bin Peng},
journal= {arXiv preprint arXiv:2111.00449},
year = {2021}
}