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Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach

Author

Listed:
  • Guohua Feng
  • Jiti Gao
  • Bin Peng
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.

Suggested Citation

  • Guohua Feng & Jiti Gao & Bin Peng, 2021. "Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach," Monash Econometrics and Business Statistics Working Papers 16/21, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2021-16
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    References listed on IDEAS

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    More about this item

    Keywords

    growth regressions; convergence in manufacturing; cross-sectional dependence; hierarchical model; asymptotic theory;
    All these keywords.

    JEL classification:

    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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