class: center, middle, inverse, title-slide # Regional Convergence and Spatial Dependence across Subnational Regions in ASEAN: ## Evidence from Satellite Nighttime Light Data ### 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<br /> JAPAN ### Prepared for the 2020 (57th) Annual Meeting of the Japan Section of the Regional Science Association International (JSRSAI) <br /><br />[ Slides and paper available at: <a href="https://quarcs-lab.org/research" class="uri">https://quarcs-lab.org/research</a> ] --- class: center, middle <style type="text/css"> .highlight-last-item > ul > li, .highlight-last-item > ol > li { opacity: 0.5; } .highlight-last-item > ul > li:last-of-type, .highlight-last-item > ol > li:last-of-type { opacity: 1; } </style> ### Joint work with Felipe Santos-Marquez (Nagoya University) --- class: middle, center # A summary of the paper in 2 slides... --- class: ## Motivation: - **Economic integration is a central priority** of the development agenda of the Association of Southeast Asian Nations (ASEAN) -- - **Lack of subnational convergence studies** covering multiple countries in ASEAN - Lack of comparable subnational data -- - **Satellite nighttime light data are increasingly used** for evaluating the performance of economies in which official statics are non‐existent, limited, or non‐comparable. -- ## Research objective: Use a novel luminosity‐based measure of GDP per capita to **study regional convergence and spatial dependence** across subnational regions of ASEAN -- ## Methods: - **Sigma and beta convergence** frameworks (Barro and Sala-i-Martin, 1992) - **Global and local spatial dependence** indicators (Anselin et al., 2007) -- ## Data: - **Luminosity-based GDP per capita** covering **9 ASEAN countries** and **274 subnational regions** over the **1998-2012 period** (Lessmann and Seidel 2017) --- class: middle, highlight-last-item ## Main Results: 1. Almost 60 percent of the differences in **(official) GDP per capita can be predicted by this luminosity‐based measure** of GDP -- 2. Although there is an overall (average) process of regional convergence, regional **inequality within most countries has not significantly decreased**. -- 3. **Increasing spatial dependence over time** and **stable spatial clusters (hotspots and coldspots)** that are located across multiple national boundaries -- --- class: middle # Outline of this presentation 1. Overview of the data 2. Measuring regional integration - Beta and sigma convergence - Global and local spatial dependence 3. Main results of the paper - Almost 60 percent of the differences in **(official) GDP per capita can be predicted by this luminosity‐based measure** of GDP - Although there is an overall (average) process of regional convergence, regional **inequality within most countries has not significantly decreased**. - **Increasing spatial dependence over time** and **stable spatial clusters (hotspots and coldspots)** that are located across multiple national boundaries <br /> <br /> [ Slides and paper available at: https://quarcs-lab.org/research ] --- class: center, middle # (1) Overview of the data --- class: middle # Luminosity-based GDP per capita ### Seminal work of Lessmann and Seidel (2017): - A new dataset to study regional inequality across subnational regions of multiple countries. - Pure luminosity data vs Luminosity-based GDP - Luminosity-based GDP = F(Pure luminosity, National GDP, other controls) -- ### Sample Coverage for ASEAN: - 9 countries (Singapore not included) - 274 first-level subnational regions - 1998-2012 period --- class: center, middle # (2) Measuring regional integration Luminosity data Regional convergence analysis Spatial dependence analysis --- class: middle, center # How do we study regional integration?  --- class: middle, center # (3) Main results Almost 60 percent of the differences in **(official) GDP per capita can be predicted by this luminosity‐based measure** of GDP Although there is an overall (average) process of regional convergence, regional **inequality within most countries has not significantly decreased**. **Increasing spatial dependence over time** and **stable spatial clusters (hotspots and coldspots)** that are located across multiple national boundaries --- class: middle, center ## Luminosity alone only explains 40% of the official GDP differences  --- class: middle, center ## Luminosity-based GDP has more explanatory power: Almost 60%pi  --- class: middle, center # Sigma convergence ## Overall, subnational disparities have been reducing over time  --- class: middle, center ## Within serveral countries, the reduction of disparities is NOT statistically significant  Disparities across the subnational regions of Myanmar are **actually increasing!** --- class: middle, center # Beta convergence ## Overall, poor subnational regions are catching up  --- class: middle, center ## Within serveral countries, the cathching-up process is NOT statistically significant  Within countries, **the speed of convergence is very slow** --- class: middle, center ## Spatial dependence has been increasing over time  --- class: middle, center # Local spatial dependence ## Highly stable spatial clusters (Hotspots and coldspots)  Spatial clusters **cross multiple national boundaries** --- class: # Concluding Remarks ## Main Results: 1. Almost 60 percent of the differences in **(official) GDP per capita can be predicted by this luminosity‐based measure** of GDP -- 2. Although there is an overall (average) process of regional convergence, regional **inequality within most countries has not significantly decreased**. -- 3. **Increasing spatial dependence over time** and **stable spatial clusters (hotspots and coldspots)** that are located across multiple national boundaries -- ## Implications, discussion, and further research - A new disaggregated (subnational) approach to **evaluate the integration process of ASEAN**. - A similar approach (convergence and spatial dependence) is used to monitor the integration and cohesion of the European Union -- - Spatial clusters help us identify **regions facing similar challenges and opportunities** - Call for **better coordination and cooperation within clusters** -- - Next step: Formally **integrate spatial dependence modeling with regional convergence modeling** - Spatial convergence models, spatial hierarchical models, and geographically weighted regressions --- class: center, middle # 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 --- # References - Barro, R., & Sala‐i‐Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223– 251. - Henderson JV, Storeygard A, Weil DN (2012) Measuring economic growth from outer space. American economic review 102(2):994–1028 - Lessmann C, Seidel A (2017) Regional inequality, convergence, and its determinants – A view from outer space. European Economic Review 92(November 2016):110–132 - Mendez C, Santos F (2020) Regional Convergence and Spatial Dependence across Subnational Regions in ASEAN: Evidence from Satellite Nighttime Light Data. Regional Science Policy and Practice, Forthcoming, DOI https://doi.org/10.1111/rsp3.12335 - Mveyange A (2018) Measuring and Explaining Patterns of Spatial Income Inequality from Outer Space Evidence from Africa. World Bank Working Paper 8484(June)