Economic Growth Modelling in West Nusa Tenggara Using Bayesian Spatial Model Approach

Siti Soraya, Baiq Candra Herawati, Habib Ratu Perwira Negara

Abstract


Economic growth is a measure of the welfare of the people in an area. Economic movement is characterized by the number of goods and services produced. The high amount of goods produced and the services used are of course strongly influenced by the amount of available capital, the labor involved, and the level of technology used. The measuring instrument or a reflection of economic growth is the Gross Regional Domestic Product (GRDP). The purpose of this study is to model economic growth in NTB in 2018. In this study, GRDP modeling was carried out using the Bayesian Spatial approach. Based on the results of testing the spatial dependency and spatial heterogeneity, it shows that there is a spatial dependence on the GRDP of districts / cities in NTB Province.. From the analysis conducted, it was found that  was positive and insignificant at the 10% level. The parameter estimation results show that the number of workers, the value of capital and the number of workers weighed are variables that have a significant effect on the model. Thus the GRDP of an area in West Nusa Tenggara is influenced by the number of workers, the value of capital and the total labor weight and the GRDP of other surrounding areas.

Keywords


Economic Growth; Bayesian; Heterogeneity; GRDP; Spatial.

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References


Alinaitwe, G. (2012). Determinants of Economic Growth in Africa with Emphasis on the Role of Financial Markets Using Bayesian Averaging Of Classical Estimates. 4(May), 93–114. http://hdl.handle.net/2381/10911

Asatryan, Z., & Feld, L. P. (2015). Revisiting the link between growth and federalism: A Bayesian model averaging approach. Journal of Comparative Economics, 43(3), 772–781. https://doi.org/10.1016/j.jce.2014.04.005

Bera, A. K., Doğan, O., Taşpınar, S., & Leiluo, Y. (2019). Robust LM tests for spatial dynamic panel data models. Regional Science and Urban Economics, 76(October 2017), 47–66. https://doi.org/10.1016/j.regsciurbeco.2018.08.001

Chica-Olmo, J., Salaheddine, S. H., & Moya-Fernández, P. (2020). Spatial relationship between economic growth and renewable energy consumption in 26 European countries. Energy Economics, 92, 104962. https://doi.org/10.1016/j.eneco.2020.104962

Eris, M. N., & Ulasan, B. (2013). Trade openness and economic growth: Bayesian model averaging estimate of cross-country growth regressions. Economic Modelling, 33, 867–883. https://doi.org/10.1016/j.econmod.2013.05.014

Gründler, K., & Potrafke, N. (2019). Corruption and economic growth: New empirical evidence. European Journal of Political Economy, 60(August), 101810. https://doi.org/10.1016/j.ejpoleco.2019.08.001

Han, X., Lee, L.-F., & Xu, X. (2020). Large Sample Properties of Bayesian Estimation of Spatial Econometric Models. Econometric Theory, 1–39. https://doi.org/10.1017/s0266466620000286

Kostov, P. (2013). Spatial Weighting Matrix Selection in Spatial Lag Econometric Model. Econometrics, 1(1), 20–30. https://doi.org/10.12966/e.05.01.2013

Li, B., & Wu, S. (2017). Effects of local and civil environmental regulation on green total factor productivity in China: A spatial Durbin econometric analysis. Journal of Cleaner Production, 153, 342–353. https://doi.org/10.1016/j.jclepro.2016.10.042

Li, J., & Li, S. (2020). Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model. Energy Policy, 140(March), 111425. https://doi.org/10.1016/j.enpol.2020.111425

Liu, H., & Zhu, X. (2017). Joint modeling of multiple crimes: A Bayesian spatial approach. ISPRS International Journal of Geo-Information, 6(1). https://doi.org/10.3390/ijgi6010016

Ma, T., Hong, T., & Zhang, H. (2015). Tourism spatial spillover effects and urban economic growth. Journal of Business Research, 68(1), 74–80. https://doi.org/10.1016/j.jbusres.2014.05.005

Marie Therese S. Sario. (2018). A Spatial Econometric Model for Houshold Electricity Consumption in the Philippines. Sereal Untuk, 51(1), 51.

Mustaqim, Setiawan, Suhartono, & Ulama, B. S. S. (2019). Labor absorption and the growth of agricultural output: A simultaneous spatial durbin panel data model perspective of fiscal decentralization’s impact in Indonesia. Journal of Advanced Research in Law and Economics, 10(4), 1182–1194. https://doi.org/10.14505/jarle.v10.4(42).18

Saeed, T. U., Nateghi, R., Hall, T., & Waldorf, B. S. (2020). Statistical Analysis of Area-wide Alcohol-related Driving Crashes: A Spatial Econometric Approach. Geographical Analysis, 52(3), 394–417. https://doi.org/10.1111/gean.12216

Sainsbury, D. (2020). Toward a dynamic capability theory of economic growth. Industrial and Corporate Change, 29(4), 1047–1065. https://doi.org/10.1093/icc/dtz054

Seya, H., Tsutsumi, M., & Yamagata, Y. (2012). Income convergence in Japan: A Bayesian spatial Durbin model approach. Economic Modelling, 29(1), 60–71. https://doi.org/10.1016/j.econmod.2010.10.022

Surya, B., Menne, F., Sabhan, H., Suriani, S., Abubakar, H., & Idris, M. (2021). Economic Growth, Increasing Productivity of SMEs, and Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1–37. https://doi.org/10.3390/joitmc7010020

Syam, R., Sukarna, & Nurmah. (2020). Analisis Tingkat Kesejahteraan Masyarakat di Provinsi Nusa Tenggara Barat Menggunakan Model Regresi Multivariat. Journal of Mathematics, Computations, and Statistics, 3(2), 97–108. https://doi.org/https://doi.org/10.35580/jmathcos.v3i2.19189

Xu, A., Zhang, C. H., & Ruther, M. (2020). Spatial dependence and spatial heterogeneity in the effects of immigration on home values and native flight in Louisville, Kentucky. Journal of Urban Affairs, 00(00), 1–23. https://doi.org/10.1080/07352166.2020.1761257

Yolanda, A. M., Yunitaningtyas, K., & Indahwati. (2019). Spatial Data Panel Analysis for Poverty in East Java Province 2012-2017. Journal of Physics: Conference Series, 1265(1). https://doi.org/10.1088/1742-6596/1265/1/012027




DOI: https://doi.org/10.31764/jtam.v5i1.3357

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