Spatial quantile autoregressive model: case study of income inequality in Indonesia

Lusiana, Evellin Dewi, Pramoedyo, Henny and Sudarmawan, Barianto Nurasri ORCID: https://orcid.org/0000-0002-9209-8687 (2022) Spatial quantile autoregressive model: case study of income inequality in Indonesia. Sains Malaysiana, 51 (11). pp. 3795-3806. ISSN 01266039

Preview

Text 12497.pdf - Published Version Download (895kB) | Preview

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.

Item Type:	Journal Article
Keywords:	Gini index; Moran’s I; quantile regression; spatial connectivity
Subjects:	14 ECONOMICS > 1401 Economic Theory > 140102 Macroeconomic Theory 14 ECONOMICS > 1403 Econometrics > 140302 Econometric and Statistical Methods
Divisions:	Faculty of Economics > Department of Islamic Banking
Depositing User:	Barianto Nurasri Sudarmawan
Date Deposited:	11 Jan 2023 09:06

Downloads

Origin of downloads

Actions (login required)

View Item