class: center, middle, inverse, title-slide # Input-Output Efficiency and Regional Convergence Clusters: ## Evidence from the Provinces of Indonesia 1990-2010 ### Carlos Mendez<br /><a href="https://carlos-mendez.rbind.io" class="uri">https://carlos-mendez.rbind.io</a><br /><br />Associate Professor<br />Graduate School of International Development<br />Nagoya University, JAPAN ### Prepared for the 56th Annual Meeting of the Japan Section of the RSAI <br /><br />[ Slides available at: <a href="http://bit.ly/rsai2019japan" class="uri">http://bit.ly/rsai2019japan</a> ] --- ## 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) --- class: middle ## 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**: Technical efficiency policy should be focalized at the cluster level --- class: middle # Outline of this presentation 1. **Global convergence "on average":** Using classical summary measures - Sigma convergence - Beta 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 --- class: center, middle # (1) Global convergence "on average" **Using classical summary measures** Beta convergence Sigma convergence --- class: middle, center ## Sigma convergence  --- class: middle,center ## Beta convergence  --- class: center, middle # (2) Let's go beyond the average **Regional heterogeneity still matters** Distribution dynamics framework Distributional convergence --- class: middle # 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** --- class: middle, center ## The distribution dynamics framework  --- class: middle, center # (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 --- class: middle, center ## Overall efficiency: Two convergence clusters  --- class: middle, center ## Pure technical efficiency: Two convergence clusters  --- class: middle, center ## Scale efficiency: One convergence cluster  --- # 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? --- class: center, middle # Thank you very much for your attention https://carlos-mendez.rbind.io Slides available at: http://bit.ly/rsai2019japan Paper available at: http://bit.ly/jrsai2019p *** This research project was supported by JSPS KAKENHI Grant Number 19K13669