Abstract
We analyse the incidence of the productive capacity of twenty Latin America and Caribbean countries on their international competitiveness (IC) for the period 2000–2018. We propose two indicators of IC and measure productive capacity through seven of the eight indicators embodied in the productive capacity index of the UNCTAD. The evidence gathered reveals three areas that have played an important role in the competitiveness of the region; these are natural resources, energy, business regulations, and information and communications technology. It follows structural change and the quality of institutions. The availability of infrastructure has had a positive impact on regional competitiveness, especially in relation to energy. However, information and communication technologies as well as transport capabilities have had a minor effect. The outstanding performance verified by the variables identifying the quality of institutions and business regulations highlights the role that politicians and policymakers can play considering they hold the tools of a major transformation in their hands. Finally, our findings indicate that regional competitiveness is highly dependent on natural and energy resources and quite influenced by institutions and regulatory frameworks.
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Notes
We recommend UNCTAD (2006, chapter 1) for a discussion of the different meanings of “productive capacity” and the theoretical foundation of its own definition.
The countries included are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela.
The concept of welfare presented here has been defined by Aiginger & Vogel (2015, p. 506) as the “ability to provide Beyond GDP goals”.
These measures of productivity, the appropriate technology and the inefficiency views, were analysed by Jerzmanowski (2007). He concludes that the inefficiency approach is more important than the technology approach for understanding the disparities across countries, i.e., that the catching up effect has a more relevant impact than the technology shift in improving economic performance.
Jerzmanowski (2007) has also discussed the advantages of DEA.
To estimate the production function, we consider the output approach and we have conducted our estimations allowing for variable returns to scale and technical regress.
These authors also include education. However, we are not going to take it into account because it has been already included in the productivity and efficiency calculation.
In Calderon & Servén (2014) there is a more detailed discussion on the linkage between infrastructure and productivity.
We have also verified these results with other first-generation tests resulting in the same conclusions.
The Quadratic-Spehere kernel was chosen and the bandwidth was set in 2, which is an usual value for this time dimension.
All the limitations due to the short length of the time dimension and the number of variables included, affect also to other related techniques such as PMG-ARDL or CS-DL.
The process to demean each variable is simply. First estimate the cross-sectional variable’s mean as \({\overline{Z} }_{t}={N}^{-1}*{\sum }_{i=1}^{N}{Z}_{it}\). Then, the demeaned variable is \({\widetilde{Z}}_{it}={{Z}_{it}-\overline{Z} }_{t}\).
We recommend the interesting discussion about these rules of thumb presented by O’brien (2007).
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Le Clech, N.A. Productive capacity and international competitiveness: evidence from Latin America and Caribbean countries. Empirica 50, 695–724 (2023). https://doi.org/10.1007/s10663-023-09581-0
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DOI: https://doi.org/10.1007/s10663-023-09581-0