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Regional Efficiency Dispersion, Convergence, and Efficiency Clusters: Evidence from the Provinces of Indonesia 1990-2010

QuarRCS-lab
September 14, 2019

Regional Efficiency Dispersion, Convergence, and Efficiency Clusters: Evidence from the Provinces of Indonesia 1990-2010

Improving production efficiency at the regional level is often considered a means to reduce regional inequality. This presentation is about regional efficiency convergence across provinces in Indonesia over the 1990–2010 period. Through the lens of both classical and distributional convergence frameworks, the dispersion dynamics of the following three indicators are contrasted: overall efficiency, pure efficiency, and scale efficiency. Results from the classical convergence approach suggest that—on average—there is regional convergence in all these three efficiency measures. However, results from the distributional convergence approach indicate the existence of two local convergence clusters within the overall and pure efficiency distributions. Moreover, since scale efficiency is characterized by only one convergence cluster, the two clusters of pure efficiency appear to be driving the overall regional efficiency dynamics in Indonesia. The presentation concludes highlighting the importance of monitoring and evaluating heterogeneous (beyond average) behavior, multiple convergence clusters, and geographic proximity when formulating regional policies that aim to reduce regional inequality.

QuarRCS-lab

September 14, 2019
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  1. Regional Efficiency Dispersion, Convergence, and
    Efficiency Clusters:
    Evidence from the Provinces of Indonesia 1990-2010
    Carlos Mendez
    https://carlos-mendez.rbind.io
    Associate Professor
    Graduate School of International Development
    Nagoya University, JAPAN
    Prepared for the 56th Annual Meeting of the Japan Section of the RSAI
    [ Slides available at: http://bit.ly/rsai2019japan ]

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  2. Motivation:
    Large per-capita income differences across provinces in Indonesia
    Persistent income differences despite several policy efforts
    Differences in efficiency explain a larger fraction of the differences in income (Caselli 2005; Hall
    and Jones 1999; Hsieh and Klenow 2010)
    Research Objective:
    Study efficiency convergence/divergence across provinces in Indonesia over the 1990-2010
    period.
    Methods:
    Classical convergence framework (Barro and Sala-i-Martin 1992)
    Distributional convergence framework (Quah 1996; Hyndman et. al 1996; Menardi and Azzalini
    2014)
    Data:
    Overall efficiency = Pure technical efficiency x Scale efficiency (DEA framework)
    26 provinces over the 1990-2010 period (Kataoka 2018)

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  3. Main Results:
    1. Convergence on average in the three measures of efficiency
    2. Regional heterogeneity matters: Local convergence clusters
    3. Clustering dynamics
    Overall efficiency: Two convergence clusters
    Pure technical efficiency: Two convergence clusters
    Scale efficiency: One convergence cluster
    Policy Implication: Policy should be focalized at the cluster level

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  4. Outline of this presentation
    1. Global convergence "on average": Using classical summary measures
    Beta convergence
    Sigma convergence
    2. Let's go beyond the average: Regional heterogeneity still matters
    Distribution dynamics framework
    Distributional convergence
    3. Local convergence clusters:
    Overall efficiency: Two convergence clusters
    Pure technical efficiency: Two convergence clusters
    Scale efficiency: One convergence cluster

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  5. (1) Global convergence "on average"
    Using classical summary measures
    Beta convergence
    Sigma convergence

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  6. Beta convergence

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  7. Sigma convergence

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  8. (2) Let's go beyond the average
    Regional heterogeneity still matters
    Distribution dynamics framework
    Distributional convergence

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  9. Regional heterogeneity matters
    Let's GO beyond the average!
    Study the dynamics of the entire regional distribution
    Let's move from conditional mean to conditional density estimation.
    Recent advances in nonparametric econometrics: Distribution dynamics

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  10. The distribution dynamics framework

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  11. (3) Local convergence clusters
    Overall efficiency = Pure technical efficiency x Scale efficiency
    Overall efficiency: Two convergence clusters
    Pure technical efficiency: Two convergence clusters
    Scale efficiency: One convergence cluster

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  12. Overall e ciency: Two convergence clusters

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  13. Pure technical e ciency: Two convergence clusters

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  14. Scale e ciency: One convergence cluster

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  15. Spatial distribution of overall e ciency clusters

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  16. Spatial distribution of pure e ciency clusters

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  17. Concluding Remarks
    A happy ending "on average" :
    Differences in overall efficiency and its two determinants (pure technical efficiency and scale
    efficiency) have decreased over the 1990-2010 period.
    Global convergence on average
    Focus beyond the average :
    Regional differences are still important
    Multiple local convergence clubs:
    Overall efficiency: Two convergence clusters
    Pure technical efficiency: Two convergence clusters
    Scale efficiency: One convergence cluster
    Implications and further research
    Convergence clusters help us identify regions facing similar challenges
    Call for better coordination of regional policies at the cluster level
    What is the role of geographical neighbors in accelerating convergence?
    What alternative clustering frameworks could be implemented?

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  18. Thank you very much for your attention
    https://carlos-mendez.rbind.io
    Slides available at: http://bit.ly/rsai2019japan
    This research project was supported by JSPS KAKENHI Grant Number 19K13669

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