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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Notes
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.
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.
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.
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.
For the purposes of the present article, the terms“decision-making units” and “production units” are used interchangeably.
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.
Optimal flexible bandwidths are derived from the the minimization of the asymptotic mean integrated square error (AMISE).
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).
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.
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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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DOI: https://doi.org/10.1007/s41685-020-00144-w