Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach

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