Purpose: This study analyzes how the technological efficiency, a key factor in economic growth measured by total factor productivity, varies according to the level of economic catch-up index and catch-up speed index. Research design, data, and methodology: Previous research has separately investigated the efficiency in catch-up theory and growth theory. In this study, we explicitly incorporate the economic catch-up variables into the efficiency estimation function, explaining efficiency from the perspective of catch-up theory. Data: Penn World Database (PWT10.0) Methodology: We establish a stochastic frontier model based on the transcendental logarithmic production function, with the distribution of inefficiency following an exponential function. To address issues related to small sample size and model specification, we employ Bayesian inference to derive posterior distributions. Results: The analysis results show that the model including heterogeneity such as the catch-up index is more appropriate for analyzing efficiency than existing efficiency analysis models. The catch-up index and catch-up speed index were found to significantly impact a country’s inefficiency. Additionally, the human capital index had a larger influence on inefficiency compared to percentile-based catch-up speed. Narrowing down the analysis period yielded more precise results. Implications: In the case of economic catch-up, the impact on efficiency varied across different economic stages that a country was in. The human capital index also emerged as an important variable in explaining efficiency.
1. Introduction
2. Catching-up Index and Bayesian Stochastic Frontier Model
3. Empirical Model and Analysis Results
4. Conclusion
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