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Regional Disparities and Convergence Clubs in Indonesia: New District-Level Evidence

QuarRCS-lab
November 21, 2020
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Regional Disparities and Convergence Clubs in Indonesia: New District-Level Evidence

This paper aims to re-examine the regional convergence hypothesis on income in Indonesia over the 2000-2017 period. By applying a non-linear dynamic factor model, this paper tests the club convergence hypothesis using a novel dataset of income at the district level. The results show significant five convergence clubs in Indonesian districts' income dynamics, implying the persistence of income disparity problems across districts even after implementing the decentralization policy. The subsequent analysis reveals two appealing features regarding the convergence clubs. First, districts belonging to the same province tend to be in the same club, and second, districts with specific characteristics (i.e., big cities or natural resources-rich regions) dominate the highest income club. Overall, our findings suggest some insightful policy implications, including the importance of differentiated development policies across convergence clubs and inter-provincial development strategies.

QuarRCS-lab

November 21, 2020
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  1. Regional Disparities and Convergence Clubs in Indonesia: New District-Level Evidence

    2000-2017 Carlos Mendez https://carlos-mendez.rbind.io Associate Professor Graduate School of International Development Nagoya University JAPAN Prepared for the 2020 Fall Meeting of the Japan Association for Applied Economics (JAAE) [ Slides and paper available at: https://quarcs-lab.org/research ]
  2. Joint work with Anang Gunawan (Ministry of Development Planning of

    Indonesia) Harry Aginta (Central Bank of Indonesia and Nagoya University) 2 / 26
  3. A summary of the paper in 2 slides... 3 /

    26
  4. Motivation: Large regional disparities across Indonesia (Akita, 2002, Esmara 1975;

    Mishra 2009) Provinces of Indonesia appear to convergence to TWO separate clubs (Kurniawan et al., 2019; Mendez 2020; Mendez and Kataoka, 2020) Lack of studies using district-level data Research objective: (Re)evaluate the evolution of regional income disparities with an emphasis on the formation of multiple convergence clubs at the district level Would all districts eventually converge to a common long-run equilibrium? If not, how many long-run equilibria can be identified? Methods: Classical sigma and beta convergence (Barro and Sala-i-Martin, 1992) Relative convergence (Phillips and Sul, 2007, 2009) Data: New data covering 514 districts over the 2000-2017 period (Gunawan et al., 2019) 4 / 26
  5. Main Results: 4. The spatial distribution of the clubs suggest

    a potential role for spatial externalities 1. On average, income disparities have reduced Initially poor regions are catching up with initially rich regions 2. The hypothesis of a common long-run equilibrium is rejected Districts are converging to FIVE clubs with parallel trends 3. Regional convergence is stronger within clubs 5 / 26
  6. Outline of this presentation 1. Overview of the data Based

    on the classical convergence approach (Barro and Sala-i-Martin, 1992) 2. A modern convergence framework (Phillips and Sul, 2007, 2009) Convergence test (intuition) Convergence clubs (intuition) 3. Main results of the paper Convergence and the role of spatial dependence Convergence and the role of spatial heterogeneity [ Slides and paper available at: https://quarcs-lab.org/research ] 6 / 26
  7. (1) Overview of the data Evolution of regional disparities on

    average 7 / 26
  8. Beta convergence approach On average, initially poor regions are growing

    faster than initially rich regions 8 / 26
  9. Sigma convergence approach On average, regional income disparities have been

    decreasing over time 9 / 26
  10. (2) A modern convergence framework Convergence test (intuition) Convergence clubs

    (intuition) 10 / 26
  11. Relative convergence framework (sketch) First, define a relative transition parameter,

    , as Second, define the convergence hypothesis 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 hit hit = yit ∑ N i=1 y it 1 N Ht = N ∑ i=1 (hit − 1) 2 → 0 1 N h it → 1 H t → 0 log ( ) − 2log {log (t)} = a + b log (t) + ϵt H1 Ht 11 / 26
  12. Convergence test (intuition) 12 / 26

  13. Convergence clubs (intuition) 13 / 26

  14. The task ahead ... ... reevaluate convergence beyond the average

    14 / 26
  15. (3) Main results Reject the hypothesis of a common long-run

    equilibrium Five local convergence clubs Stronger patterns of beta and sigma convergence within clubs Lack of convergence across clubs The clubs show strong signs of spatial dependence 15 / 26
  16. Reject the hypothesis of a common long- run equilibrium 16

    / 26
  17. There are five convergence clubs 17 / 26

  18. Convergence within clubs 18 / 26

  19. Beta convergence within clubs 19 / 26

  20. Sigma convergence within clubs 20 / 26

  21. Lack of convergence across clubs 21 / 26

  22. Spatial distribution of the clubs 22 / 26

  23. Concluding Remarks Main Results: 1. On average, income disparities have

    reduced 2. The hypothesis of a common long-run equilibrium is rejected. Convergence towards FIVE clubs with parallel trends 3. Regional convergence is stronger within clubs 4. The spatial distribution of the clubs suggest a potential role for spatial externalities Implications, discussion, and further research The need for an analytical framework that focuses on regional heterogeneity and goes beyond single summary measures Convergence clubs help us identify regions facing similar challenges Call for better coordination and cooperation policies both within and between clubs Studying the determinants of the convergence clubs is the next step. 23 / 26
  24. Thank you very much for your attention https://carlos-mendez.rbind.io Slides and

    working paper available at: https://quarcs-lab.org/research Quantitative Regional and Computational Science lab https://quarcs-lab.org This research project was supported by JSPS KAKENHI Grant Number 19K13669 24 / 26
  25. References (I) Akita, T. (2002). Regional income inequality in Indonesia

    and the initial impact of the economic crisis. Bulletin of Indonesian Economic Studies, 38(2), 201-222. Barro, R., & Sala‐i‐Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223– 251. Esmara, H. (1975). Regional income disparities. Bulletin of Indonesian Economic Studies., 11(1), 41– 57. Gunawan, A., Mendez, C. & Santos‐Marquez, F. (2019). Regional income disparities, distributional convergence, and spatial effects: Evidence from Indonesia (MPRA Working Paper No. 95972). Kurniawan, H., de Groot, H. L., & Mulder, P. (2019). Are poor provinces catching‐up the rich provinces in Indonesia? Regional Science Policy & Practice, 11(1), 89– 108. Mendez, C. (2020). Convergence Clubs in Labor Productivity and its Proximate Sources: Evidence from Developed and Developing Countries. Springer. https://doi.org/10.1007/978-981-15-8629-3 Mendez, C. (2020). Regional efficiency convergence and efficiency clusters: Evidence from the provinces of Indonesia 1990-2010. Asia-Pacific Journal of Regional Science, 4(2), 391–411. https://doi.org/10.1007/s41685-020-00144-w Mendez, C., & Kataoka, M. (2020). Disparities in Regional Productivity, Capital Accumulation, and Efficiency across Indonesia: A Club Convergence Approach. Review of Development Economics, Online First. https://doi.org/10.1111/rode.12726 25 / 26
  26. References (II) Mishra, S. C. (2009). Economic inequality in Indonesia:

    Trends, causes and policy response. Colombo, Sri Lanka: UNDP Regional Office. Phillips, P., & Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica, 75(6), 1771– 1855. Phillips, P., & Sul, D. (2009). Economic transition and growth. Journal of Applied Econometrics, 24(7), 1153– 1185. 26 / 26