Productivity Differences and Convergence Clubs in Latin America

Productivity Differences and Convergence Clubs in Latin America

Interactive slides: https://bcde2020.netlify.app
Paper: https://carlos-mendez.rbind.io/talk/20200728bcde

Abstract: There is a growing literature that highlights that the development potential of Latin America is highly constrained by its low productivity. In this context, this paper evaluates the productivity differences across 20 Latin American countries over the 1980-2014 period. Through the lens of a non-linear dynamic factor model, this paper finds that the productivity dynamics of Latin America appear to be characterized by a lack of overall convergence and the formation of multiple local convergence clubs. Two commonly used indicators support these findings. On the one hand, the dynamics labor productivity suggest the formation of four clubs of countries. On the other, the dynamics of total factor productivity suggest the formation of three clubs. Interestingly, in both indicators, it is the lowest-productivity club that is diverging from the rest at the highest speed. Overall, these results indicate that masked behind the average low productivity of Latin America, there are pockets of heterogeneity that need to be addressed to improve both economic cohesion and competitiveness in region.

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QuarRCS-lab

July 18, 2020
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  1. Productivity Differences and Convergence Clubs in Latin America Carlos Mendez

    https://carlos-mendez.rbind.io Associate Professor Graduate School of International Development Nagoya University JAPAN Prepared for the 2020 Bolivian Conference of Development Economics [ Slides and paper available at: http://bit.ly/bcde2020 ]
  2. This paper is a byproduct of the forthcoming book: 2

    / 22
  3. A summary of the paper in 2 slides... 3 /

    22
  4. Motivation: Development potential of Latin America constrained by low productivity

    (Daude and Fernndez-Arias, 2010; Pages 2010; Restuccia, 2013) Research Objective: (Re)evaluate the convergence hypothesis across economies in Latin America with particular emphasis on productivity differences and the formation of multiple convergence clubs. Methods: Clustering algorithm for panel data (Phillips and Sul, 2007, 2009) Data: 20 Latin American countries over the 1980-2014 period Inconclusive literature about Latin America: Convergence vs Divergence vs Convergence Clubs (Galvao and Reis-Gomes, 2007; Barrios et. al, 2018; Martin and Vazquez, 2015) Nonlinear dynamic factor model (Phillips and Sul, 2007, 2009) Labor productivity and total factor productivity (Fernandez-Arias, 2017) 4 / 22
  5. Main Results: 3. Convergence clubs characteristics: Labor productivity: Four clubs

    of countries Total factor productivity: Three clubs of countries Clubs show non-parallel trends: crossings, limited stability, and separating trends The lowest-productivity club (Honduras and Nicaragua) is diverging from the rest at the highest speed. 1. Lack of overall(global) convergence in both labor productivity and total factor productivity 2. Multiple local convergence clubs: above and below the average 5 / 22
  6. Outline of this presentation 1. Some stylized facts Large and

    heterogeneous productivity differences across Latin America 2. Convergence framework Global convergence test (intuition) Local convergence clubs (intuition) 3. Main results of the paper Lack of overall convergence Multiple convergence clubs above and bellow the average Convergence clubs characteristics [ Slides and paper available at: http://bit.ly/bcde2020 ] 6 / 22
  7. (1) Some stylized facts Large and heterogeneous productivity differences across

    Latin America 7 / 22
  8. Large and heterogeneous productivity differences across Latin America 8 /

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  9. Large and heterogeneous productivity differences across Latin America 9 /

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  10. Are there any signs of overall convergence/divergence or convergence clubs?

    10 / 22
  11. (2) Convergence framework Global convergence test (intuition) Local convergence clubs

    (intuition) 11 / 22
  12. Convergence framework (brief overview) First, define a relative transition parameter,

    , as Second, the convergence hypothesis is defined as In other words, when the relative transition parameter converges to unity, , the cross-sectional variance converges to zero, . Thrid, Phillips and Sul (2007) test this hypothesis by using the following log t regression model 12 / 22
  13. Convergence test (intuition) 13 / 22

  14. Convergence clubs (intuition) 14 / 22

  15. (3) Main results Lack of overall convergence Multiple convergence clubs

    above and below the average Convergence clubs characteristics 15 / 22
  16. Lack of overall convergence 16 / 22

  17. Multiple convergence clubs 17 / 22

  18. Multiple convergence clubs: Above and below the average 18 /

    22
  19. Convergence clubs characteristics: Labor productivity 19 / 22

  20. Convergence clubs characteristics: Total factor productivity 20 / 22

  21. Concluding Remarks The clubs show different convergence speeds and separating

    tendencies. The poor economic performance of Honduras and Nicaragua is driving the separation of clubs over time. Implications and further research Masked behind the low productivity of Latin America, there is still a high degree of heterogeneity that is worth exploring Next extension: (Re)evaluate the composition of convergence clubs using subnational data, which is to be constructed using satellite nightlight data. Reject the (overall) convergence hypothesis both in terms of labor productivity and total factor productivity Multiple convergence clubs below and above the mean Convergence clubs may help us identify economies facing similar challenges Call for better coordination and cooperation policies both within and between clubs International technology transfer initiatives. 21 / 22
  22. Thank you very much for your attention https://carlos-mendez.rbind.io Slides and

    working paper available at: http://bit.ly/bcde2020 Quantitative Regional and Computational Science lab https://quarcs-lab.rbind.io This research project was supported by JSPS KAKENHI Grant Number 19K13669 22 / 22