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Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach

Author

Listed:
  • Jorge Antunes

    (COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal, Lemme, 355, 21949-900 Rio de Janeiro, Brazil)

  • Goodness C. Aye

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Peter Wanke

    (COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal, Lemme, 355, 21949-900 Rio de Janeiro, Brazil)

  • Yong Tan

    (School of Management, University of Bradford, Bradford, West Yorkshire, UK, BD7 1DP, UK)

Abstract

Long-term productivity performance at the country level has been a research object under different theoretical lenses, scrutinized by different modelling approaches. This paper revisits the dataset used in Bergeaud et al. (2016) and investigates the endogenous sources of distinct productivity performance in different advanced countries. Country-level data series with more than one hundred year time-span were collected for each one of the following attributes: Labor productivity (LP), Total Factor Productivity (TFP), Capital Intensity (KI), Gross Domestic Product per capita (GDP pc), Average age of equipment capital stock (Age K), and Human Capital intensity (Human K).Differently from previous studies, a Two-Dimensional Fuzzy-Monte Carlo Analysis (2DFMC) approach is proposed here to decompose the sources of long-term productivity performance. In the first dimension, a novel multi-attribute decision-making (MADM) model based on Type-2 Fuzzy Sets (T2FS) is developed to compute and rank long-term productivity performance of each county using Unbiased-Power functions for Ideal Solutions (UP-IS). Next, in the second dimension, a Stochastic Structural Relationship Programming (SSRP) Model based on neural networks is proposed to evaluate the endogenous feedbacks among the aforementioned productivity attributes and overall productivity performance. Results suggest that the UP-IS presented higher cross-performance scores relative to the TOPSIS base-case. Norway is the best performing country with a positive performance score of 0.854 while Portugal is the worst with a score of 0.347. In terms of ordinal ranking of long-term productivity performance, UP-IS ranked first, followed by TPF and next LP and GDPpc. Further, Human K and Age K have positive and negative impact respectively on long-term productivity performance in advanced countries. On the other hand, productivity performance has positive impact on KI and TFP but negative impact on LP and GDPpc.

Suggested Citation

  • Jorge Antunes & Goodness C. Aye & Rangan Gupta & Peter Wanke & Yong Tan, 2020. "Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach," Working Papers 2020111, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:2020111
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    Keywords

    endogeneity; type-2 fuzzy sets; 2DFMC; stochastic performance; long-term productivity; advanced countries;
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