Sains Malaysiana 51(11)(2022): 3795-3806

http://doi.org/10.17576/jsm-2022-5111-23

Spatial Quantile Autoregressive Model: Case Study of Income Inequality in Indonesia

(Model Autoregresif Kuantil Reruang: Suatu Kajian Kes Ketaksamaan Pendapatan di Indonesia)

EVELLIN DEWI LUSIANA^1,2*, HENNY PRAMOEDYO³ & BARIANTO NURASRI SUDARMAWAN⁴

¹Department of Aquatic Resource Management, Faculty of Fisheries and Marine Science, Universitas Brawijaya, Malang, 65145, Indonesia

²Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Brawijaya Malang, 65145, Indonesia

³Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Malang, 65145, Indonesia

⁴Faculty of Economics, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, 65145, Indonesia

Received: 4 December 2021/Accepted: 19 July 2022

Abstract

Substantial economic development in Indonesia has dramatically increased inequality in the last decade. This issue will hinder the country’s long-term economic development as well as creating socioeconomic instability and violence. This study analysed the effects of macroeconomic factors such as gross regional domestic product, investment, unemployment rate, and labour-force participation, on Indonesian provinces’ inequality. Since the economic development in Indonesia is mostly concentrated on Java Island, a spatial based analysis was appropriate. In addition, we also considered a method that enabled a specific level of inequality modelling, since previous studies used a mean-based analysis. Therefore, we proposed a spatial quantile autoregressive (SQAR) technique. The results showed that the Gini index of Indonesian provinces had a significant positive spatial autocorrelation (SA). Regions with similar Gini index values tended to cluster together. In addition, local analysis of the SA showed Java Island as a region that was characterized by high inequality, while Sumatra and Kalimantan Island were not. By contrast, the SQAR model suggested that there were various effects of macroeconomic factors on inequality at different levels of quantile. As a consequence, distinct approaches to handling inequality should be taken for provinces with low, medium, and high Gini index values.

Keywords: Gini index; Moran’s I; quantile regression; spatial connectivity

Abstrak

Pembangunan ekonomi yang besar di Indonesia telah meningkatkan ketidaksamaan secara mendadak dalam dekad yang lalu. Isu ini akan menghalang pembangunan ekonomi jangka panjang negara serta mewujudkan ketidakstabilan sosioekonomi dan keganasan. Kajian ini menganalisis kesan faktor makroekonomi seperti keluaran dalam negara serantau kasar, pelaburan, kadar pengangguran dan penyertaan tenaga buruh terhadap ketidaksamaan wilayah Indonesia. Memandangkan pembangunan ekonomi di Indonesia kebanyakannya tertumpu di Pulau Jawa, analisis berasaskan reruang adalah sesuai. Di samping itu, kami juga mempertimbangkan kaedah yang membolehkan pemodelan ketaksamaan tahap tertentu, memandangkan kajian terdahulu menggunakan analisis berasaskan min. Oleh itu, kami mencadangkan teknik autoregresif kuantil reruang (SQAR). Keputusan menunjukkan bahawa indeks Gini Wilayah Indonesia mempunyai autokorelasi reruang (SA) positif yang signifikan. Kawasan yang mempunyai nilai indeks Gini yang serupa cenderung berkumpul bersama. Di samping itu, analisis tempatan SA mendedahkan Pulau Jawa sebagai wilayah yang dicirikan oleh ketidaksamaan yang tinggi, manakala Pulau Sumatera dan Kalimantan tidak. Sebaliknya, model SQAR mencadangkan bahawa terdapat pelbagai kesan faktor makroekonomi terhadap ketidaksamaan pada tahap kuantil yang berbeza. Akibatnya, pendekatan berbeza untuk mengendalikan ketidaksamaan harus diambil untuk wilayah yang mempunyai nilai indeks Gini rendah, sederhana dan tinggi.

Kata kunci: Indeks Gini; Moran's I; perhubungan reruang; regresi kuantil

REFERENCES

Adrian, A. 2019. Empowerment strategies of micro, small, medium enterprises (MSMEs) To improve Indonesia export performance. International Journal of Economics, Business and Accounting Research (IJEBAR) 2(04): 50-60. DOI: 10.29040/ijebar.v2i04.222

Akita, T., Kurniawan, P.A. & Miyata, S. 2011. Structural changes and regional income inequality in Indonesia: A bidimensional decomposition analysis. Asian Economic Journal 25(1): 55-77. DOI: 10.1111/j.1467-8381.2011.02053.x

Alesina, A. & Rodrik, D. 1994. Distributive politics and economic growth. The Quarterly Journal of Economics 109(2): 465-490. DOI: 10.2307/2118470

Angrist, J., Chernozhukov, V. & Fernández-Val, I. 2006. Quantile regression under misspecification, with an application to the U.S. wage structure. Econometrica 74(2): 539-563.

Anselin, L. 1995. Local Indicators of Spatial Association - LISA. Geographical Analysis. 27(2): 93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

Anselin, L. 1988. Spatial Econometrics: Methods and Models. Dordrecht, The Netherlands: Kluwer Academic Publishers.

Bakar, A.A., Hamdan, R. & Sani, N.S. 2020. Ensemble learning for multidimensional poverty classification. Sains Malaysiana 49(2): 447-459. DOI: 10.17576/jsm-2020-4902-24

Balisacan, A.M., Pernia, E.M. & Asra, A. 2003. Revisiting Growth and Poverty Reduction in Indonesia: What do Subnational Data Show? DOI: 10.1080/0007491032000142782

Bénabou, R. 1996. Inequality and Growth. V. 11. MIT Press. DOI: 10.1086/654291.

BKPM. 2021. Realisasi Investasi di Indonesia Sepanjang Tahun 2020. https://www.bkpm.go.id/id/publikasi/siaran-pers/readmore/2413001/68601. Accessed on 2 June 2021.

Buchinsky, M. 1994. Changes in U.S. wage structure 1963-1987: Applications of quantile regression. Econometrica 62: 405-458.

Chernozhukov, V. & Hansen, C. 2006. Instrumental quantile regression inference for structural and treatment effect models. Journal of Econometrics 132(2): 491-525. DOI: https://doi.org/10.1016/j.jeconom.2005.02.009

Chunying, Z. 2011. A quantile regression analysis on the relations between foreign direct investment and technological innovation in China. In 2011 International Conference of Information Technology, Computer Engineering and Management Sciences. Volume 4. pp. 38-41. DOI: 10.1109/ICM.2011.233

Cliff, A.C. & Ord, J.K. 1973. Spatial Autocorrelation. London: Pion Limited.

Constantine, C. 2017. Economic structures, institutions and economic performance. Journal of Economic Structures 6(1): 2. DOI: 10.1186/s40008-017-0063-1

Dabla-Norris, E., Kochhar, K., Suphaphiphat, N., Ricka, F. & Tsounta, E. 2015. Causes and Consequences of Income Inequality: A Global Perspective. DOI: 10.5089/9781513555188.006

Dorodjatoen, A.M.H. 2019. DOI: 10.26182/5cca430b16437

Dudani, S.A. 1976. The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics. SMC-6(4): 325-327. DOI: 10.1109/TSMC.1976.5408784.

Fitzenberger, B. 1997. Computational aspects of censored quantile regression. In 3rd International Data Analysis based on L- 1-Statistical Procedures and Related Methods. V. 31. Hayword, California: IMS Lecture Notes Series. pp. 171-186.

Galiani, S. & Titiunik, R. 2005. Changes in the Panamanian wage structure: A quantile regression analysis. Económica L1(1-2): 3-27.

Gardeazabal, J. & Ugidos, A. 2005. Gender wage discrimination at quantiles. Journal of Population Economics 18(1): 165-179.

Girma, S. & Görg, H. 2003. Foreign direct investment, spillovers and absorptive capacity: Evidence from quantile regressions. IIIS Discussion Paper No. 1; GEP Working Paper No. 2002/14. DOI: 10.2139/ssrn.410742.

Hijrawadi, S.N. & Adrian. 2019. Migration pattern of industrial workers in Bekasi Regency. Jurnal SPATIAL Wahana Komunikasi dan Informasi Geografi 19(1): 25-31. DOI: 10.21009/spatial.191.04

Kaya, E. 2017. Quantile Regression and the Gender Wage Gap: Is There a Glass Ceiling in the Turkish Labor Market? Cardiff Economic Working Papers E2017/5, Cardiff University, Cardiff Business School, Economics Section.

Keefer, P. & Knack, S. 2002. Polarization, politics and property rights: Links between inequality and growth. Public Choice 111(1): 127-154. DOI: 10.1023/A:1015168000336

Khoirunurrofik, K. 2017. Trends and determinants of the geographical distribution of economic activities: Evidence from Indonesian manufacturing. Journal of Indonesian Applied Economics 7(1): 18-47. DOI: 10.21776/ub.jiae.2017.007.01.2

Kim, T.H. & Muller, C. 2004. Two-stage quantile regression when the first stage is based on quantile regression. The Econometrics Journal 7(1): 218-231.

Koenker, R. & Hallock, K. 2001. Quantile regressions. Journal of Economic Perspectives 15: 143-156.

Lee, L.F. 2002. Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models. Econometric Theory 18: 252-277.

Lesage, J.P. 1997. Bayesian estimation of spatial autoregressive models. International Regional Science Review 20(1-2): 113-129. DOI: 10.1177/016001769702000107

Liu, X. & Saraiva, P. 2015. GMM estimation of SAR models with endogenous regressors. Regional Science and Urban Economics 55: 68-79. DOI: https://doi.org/10.1016/j.regsciurbeco.2015.09.002

Majid, M.H.A. & Ibrahim, K. 2021. Composite pareto distributions for modelling household income distribution in Malaysia. Sains Malaysiana 50(7): 2047-2058. DOI: 10.17576/jsm-2021-5007-19

Malikov, E., Sun, Y. & Hite, D. 2019. (Under)Mining local residential property values: A semiparametric spatial quantile autoregression. Journal of Applied Econometrics 34(1): 82-109. DOI: https://doi.org/10.1002/jae.2655

Naim, M., Rasidi, M., Sahani, M., Ali, M., Choy, E.A., Hafiz, M. & Abstrak, R. 2013. Application of geographical information system for spatial-temporal mapping: A case study of dengue cases in Seremban, Negeri Sembilan, Malaysia. Sains Malaysiana 42(8): 1073-1080.

Nazara, S. 2010. Pemerataan antardaerah sebagai tantangan utama transformasi struktural pembangunan ekonomi Indonesia masa depan. Jurnal Ekonomi dan Pembangunan Indonesia 11(1): 83-98. DOI: 10.21002/jepi.v11i1.183

Nugraha, A. & Prayitno, G. 2020. Regional disparity in western and eastern Indonesia. International Journal of Economics and Business Administration VIII(Issue 4): 101-110. DOI: 10.35808/ijeba/572

Rodriguez, N. & Chowdhury, A. 2013. Are there policy lessons from the so-called Southeast Asian Model? ASEAN Journal of Economics, Management and Accounting 1(June): 1-12.

SAAPE. 2019. Growing Inequality in South Asia South Asia Inequality Report 2019.

Santoso, Y.I. 2021. Ekonomi 2020 Masih Terpusat di Pulau Jawa, Ini Kata Ekonom CORE. https://nasional.kontan.co.id/news/ekonomi-2020-masih-terpusat-di-pulau-jawa-ini-kata-ekonom-core. Accessed on 2 June 2021.

Skoufias, E. 2001. Changes in the regional inequality and social welfare in Indonesia from 1996 to 1999. Journal of International Development 13(1): 73-91. DOI: 10.1002/jid.750

Sukwika, T. 2018. Peran pembangunan infrastruktur terhadap ketimpangan ekonomi antarwilayah di Indonesia. Jurnal Wilayah dan Lingkungan 6(2): 115. DOI: 10.14710/jwl.6.2.115-130

Sulistiyono, S.T. & Rochwulaningsih, Y. 2013. Contest for hegemony: The dynamics of inland and maritime cultures relations in the history of Java Island, Indonesia. Journal of Marine and Island Cultures 2(2): 115-127. DOI: https://doi.org/10.1016/j.imic.2013.10.002

Suryahadi, A., Yumna, A., Raya, U.R. & Marbun, D. 2010. Review of Government’s Poverty Reduction Strategies, Policies, and Programs in Indonesia. https://smeru.or.id/en/publication/review-government%E2%80%99s-poverty-reduction-strategies-policies-and-programs-indonesia

Tansel, A., Keskin, H.I. & Ozdemir, Z.A. 2020. Public-private sector wage gap by gender in Egypt: Evidence from quantile regression on panel data, 1998-2018. World Development 135: 105060. DOI: 10.1016/j.worlddev.2020.1

Trzpiot, G. & Orwat-Acedanska, A. 2016. Spatial quantile regression in analysis of healthy life years in the European Union countries. Comparative Economic Research 19(5). DOI: 10.1515/cer-2016-0044

Upton, G.J.G. & Fingleton, B. 1985. Spatial Data Analysis by Example. New York: Wiley.

Wei, Y.D. 2015. Spatiality of regional inequality. Applied Geography 61: 1-10. https://doi.org/10.1016/j.apgeog.2015.03.013

Wicaksono, E., Amir, H. & Nugroho, A. 2017. The Source of Income Inequality in Indonesia: A Regression-Based Inequality Decomposition. https://www.adb.org/publications/sources-income-inequality-indonesia

Williamson, J.G. 1965. Regional inequality and the process of national development, development and cultural change. Journal of Chemical Information and Modeling 13(4): 1-84.

Zhang, J., Lu, Q., Guan, L. & Wang, X. 2021. Analysis of factors influencing energy efficiency based on spatial quantile autoregression: Evidence from the panel data in China. Energies 14(2): 504. DOI: 10.3390/en14020504

Zhuang, J., Kanbur, R. & Rhee, C. 2014. Rising Inequality in Asia and Policy Implications. DOI: 10.2139/ssrn.2399298

^*Corresponding author; email: evellinlusiana@ub.ac.id