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Regional efficiency convergence and efficiency clusters

Evidence from the provinces of Indonesia 1990–2010

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Abstract

Improving production efficiency at the regional level is often considered a means to reduce regional inequality. This article studies 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 article concludes highlighting the importance of monitoring and evaluating heterogeneous (beyond average) behaviour, multiple convergence clusters, and geographic proximity when formulating regional policies that aim to reduce regional inequality.

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Fig. 1

Source: Adapted from Quah (1993)

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Notes

  1. To reduce economic jargon, in the rest of this article, the term pure efficiency (PE) will be used to refer to the concept of pure efficiency that is used the DEA framework.

  2. In this article, the terms provinces or regions are used interchangeably. However, sometimes the former is used to emphasize an empirical construct and the latter is used to emphasize a more conceptual unit of analysis.

  3. Although the work of Mokoginta and Wijaya (2016) and Kataoka (2018) indicate conflicting results, their efficiency measures are not directly comparable. One potential factor behind these conflicting results, in addition to differences in time horizon and number of provinces, is that Mokoginta and Wijaya (2016) include government consumption as an input for the computation of efficiency scores.

  4. Political reforms in the late 1990s increased the number of provinces from 27 to 34. Thus, to avoid comparability problems, only 26 provinces are considered in the analysis. See footnote 5 of Kataoka (2018) for details about the aggregation of new and existing provinces.

  5. For the purposes of the present article, the terms“decision-making units” and “production units” are used interchangeably.

  6. Human capital is proxied by the average period of education, weighted by the educational attainment of the provincial labour force. Physical capital estimates are from Kataoka (2013) and they are based on the perpetual inventory method.

  7. However, not all provinces improved their efficiency level during this period. For instance, Aceh, Riau, East Java, and Papua reduced their overall efficiency (OE) score. See Table 2 of Kataoka (2018) for a detailed list of the provinces and their performance in each score over time.

  8. See Epstein et al. (2003), Magrini (2004, 1999), Bianco (2016) or Mendez (2018) for more comprehensive presentations of the distributional convergence framework.

  9. Optimal flexible bandwidths are derived from the the minimization of the asymptotic mean integrated square error (AMISE).

  10. Figures 2, 3 and 4 graphically illustrate the stochastic kernel (Eq. 7) from different yet complementary perspectives. Panels A and C are based on the numerator of Eq. 7 while Panels B and D are based on the entire ratio. Note that the commonality among all panels is that they all represent a bi-variate distribution. However, only Panels B and D represent a bi-variate conditional distribution (that is, Eq. 7).

  11. As in Menardi and Azzalini (2014), the word “core” cluster refers to a group of observations that are identified based on the higher level sets of a density function.

References

  • Akita T (1988) Regional development and income disparities in Indonesia. Asian Econ J 2(2):165–191

    Article  Google Scholar 

  • Arbia G, Basile R, Piras G (2006) Analyzing Intra-Distribution Dynamics: A Reappraisal. ERSA conference papers ERSA06p262, European Regional Science Association. https://ideas.repec.org/p/wiw/wiwrsa/ersa06p262.html. Accessed 22 Jan 2019

  • Azzalini A, Torelli N (2007) Clustering via nonparametric density estimation. Stat Comput 17(1):71–80

    Article  Google Scholar 

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092

    Article  Google Scholar 

  • Barro R, Sala-I-Martin X (1991) Convergence across states and regions. Brook Papers Econ Activity 1991:107–182

    Article  Google Scholar 

  • Barro R, Sala-I-Martin X (1992a) Convergence. J Polit Econ 100(2):223–251

    Article  Google Scholar 

  • Barro R, Sala-I-Martin X (1992b) Regional growth and migration: a Japan-United States comparison. J Jpn Int Econ 6(4):312–346

    Article  Google Scholar 

  • Bendesa IKG, Meydianawathi LG, Handra H, Djoni Hartono DP, Resosudarmo BP, Yusuf AA (2016) Tourism and sustainable regional development. Padjadjaran University Press, Bandung

    Google Scholar 

  • Bianco SD (2016) Going clubbing in the eighties: convergence in manufacturing sectors at a glance. Empir Econ 50(2):623–659. https://doi.org/10.1007/s00181-015-0940-0

    Article  Google Scholar 

  • Caselli F (2005) Accounting for cross-country income differences. Handb Econ Growth 1:679–741

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  Google Scholar 

  • Charnes A, Cooper WW, Li S (1989) Using data envelopment analysis to evaluate efficiency in the economic performance of chinese cities. Socio-Econ Plan Sci 23(6):325–344

    Article  Google Scholar 

  • Cole MA, Neumayer E (2003) The pitfalls of convergence analysis: is the income gap really widening? Appl Econ Lett 10(6):355–357

    Article  Google Scholar 

  • Dalgaard CJ, Vastrup J (2001) On the measurement of \(\sigma\)-convergence. Econ Lett 70(2):283–287

    Article  Google Scholar 

  • Epstein P, Howlett P, Schulze MS (2003) Distribution dynamics: stratification, polarization, and convergence among oecd economies, 1870–1992. Explor Econ Hist 40(1):78–97

    Article  Google Scholar 

  • Esmara H (1975) Regional income disparities. Bull Indones Econ Stud 11(1):41–57

    Article  Google Scholar 

  • Garcia JG, Soelistianingsih L (1998) Why do differences in provincial incomes persist in Indonesia? Bull Indones Econ Stud 34(1):95–120

    Article  Google Scholar 

  • Gunawan A, Mendez C, Santos-Marquez F (2019) Regional income disparities, distributional convergence, and spatial effects: evidence from Indonesia. MPRA Working Paper 95972, University Library of Munich, Germany. https://ideas.repec.org/p/pra/mprapa/97090.html. Accessed 10 Oct 2019

  • Halkos GE, Tzeremes NG (2010) Measuring regional economic efficiency: the case of greek prefectures. Ann Reg Sci 45(3):603–632

    Article  Google Scholar 

  • Hyndman R, Bashtannyk D, Grunwald G (1996) Estimating and visualizing conditional densities. J Comput Graph Stat 5(4):315–336

    Google Scholar 

  • Johnson PA (2005) A continuous state space approach to convergence by parts. Econ Lett 86(3):317–321

    Article  Google Scholar 

  • Kataoka M (2012) Economic growth and interregional resource allocation in indonesia. Stud Reg Sci 42(4):911–920

    Article  Google Scholar 

  • Kataoka M (2013) Capital stock estimates by province and interprovincial distribution in Indonesia. Asian Econ J 27(4):409–428

    Article  Google Scholar 

  • Kataoka M (2018) Inequality convergence in inefficiency and interprovincial income inequality in Indonesia for 1990–2010. Asia-Pac J Reg Sci 2(2):297–313

    Article  Google Scholar 

  • Kharisma B, Saleh S (2013) Convergence of income among provinces in Indonesia, 1984–2008: A panel data approach. J Indones Econ Bus 28(2):167–187

    Google Scholar 

  • Kurniawan H, de Groot HL, Mulder P (2019) Are poor provinces catching-up the rich provinces in Indonesia? Reg Sci Policy Pract 11(1):89–108

    Article  Google Scholar 

  • Magrini S (1999) The evolution of income disparities among the regions of the european union. Reg Sci Urban Econ 29(2):257–281

    Article  Google Scholar 

  • Magrini S (2004) Regional (di) convergence. In: Henderson VJ, Thisse J (Eds) Handbook of regional and urban economics, vol 4. Elsevier, pp 2741–2796

  • Magrini S (2009) Why should we analyse convergence using the distribution dynamics approach? Sci Reg 8(1):5–34

    Google Scholar 

  • Menardi G, Azzalini A (2014) An advancement in clustering via nonparametric density estimation. Stat Comput 24(5):753–767

    Article  Google Scholar 

  • Mendez C (2018) On the distribution dynamics of human development: Evidence from the metropolitan regions of Bolivia. Econ Bull 38(4):2467–2475. https://ideas.repec.org/a/ebl/ecbull/eb-18-00452.html. Accessed 10 Oct 2019

  • Mokoginta I, Wijaya M (2015) Measuring economic efficiency of provinces in Indonesia. Parahyangan Catholic University Working Paper

  • Mokoginta I, Wijaya M (2016) Measuring economic efficiency of provinces in Indonesia. In: Bendesa IKG, Meydianawathi LG, Handra H, Djoni Hartono DP, Resosudarmo B, Yusuf A (Eds) Tourism and sustainable regional development. Padjadjaran University Press, Bandung, pp 3–26

    Google Scholar 

  • Pittau MG, Zelli R, Massari R (2016) Evidence of convergence clubs using mixture models. Econ Rev 35(7):1317–1342

    Article  Google Scholar 

  • Quah D (1993) Galton’s fallacy and tests of the convergence hypothesis. Scand J Econ 95(4):427–443

    Article  Google Scholar 

  • Quah D (1996) Twin peaks: growth and convergence in models of distribution dynamics. Econ J 106(437):1045–1055

    Article  Google Scholar 

  • Quah D (1997) Empirics for growth and distribution: stratification, polarization, and convergence clubs. J Econ Growth 2(1):27–59

    Article  Google Scholar 

  • Ram R (2018) Comparison of cross-country measures of sigma-convergence in per-capita income, 1960–2010. Appl Econ Lett 25(14):1010–1014

    Article  Google Scholar 

  • Resosudarmo BP, Vidyattama Y (2006) Regional income disparity in Indonesia: A panel data analysis. ASEAN Economic Bulletin 23(1):31–44

  • Sakamoto H (2007) The dynamics of inter-provincial income distribution in Indonesia. The International Centre for the Study of East Asian Development Working Paper 2007-25

  • Sala-I-Martin XX (1996) Regional cohesion: evidence and theories of regional growth and convergence. Eur Econ Rev 40(6):1325–1352

    Article  Google Scholar 

  • Schaffer A, Simar L, Rauland J (2011) Decomposing regional efficiency. J Reg Sci 51(5):931–947

    Article  Google Scholar 

  • Stolp C (1990) Strengths and weaknesses of data envelopment analysis: an urban and regional perspective. Comput Environ Urban Syst 14(2):103–116

    Article  Google Scholar 

  • Tirtosuharto D (2010) The impact of fiscal decentralization and state allocative efficiency on regional growth in indonesia. J Int Commerce Econ Policy 1(02):287–307

    Article  Google Scholar 

  • Young AT, Higgins MJ, Levy D (2008) Sigma convergence versus beta convergence: evidence from US county-level data. J Money Credit Bank 40(5):1083–1093

    Article  Google Scholar 

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  1. Graduate School of International Development, Nagoya University, Furocho, Chikusa-ku, Nagoya, 464-8601, Japan

    Carlos Mendez

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Appendix

Appendix

Table 4 Efficiency scores and convergence clusters in 2010
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Mendez, C. Regional efficiency convergence and efficiency clusters. Asia-Pac J Reg Sci 4, 391–411 (2020). https://doi.org/10.1007/s41685-020-00144-w

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