Have Digitalization and Credit Access Accelerated Growth Performance in East Java?
A Spatial Econometric and Google Trend Analysis
Abstract
This article evaluates the effect of digitalization and credit access in boosting regional growth performance across 38 districts in East Java over the 2010-2021 period. Using google trend analysis, we capture the pattern and spatial distribution of main explanatory variables, which are digitalization and credit access to support our empirical findings. Results show that overall the google trends’ portraits indicate adequate similarities in credit access, which declare the negative yet significant effect to growth. We also find interesting findings to be highlighted for internet access. Both google trends and empirical data show negative correlation between digitalization and credit access. From the standpoint of spatial spills-over effect, there is significant and positive spatial autocorrelation in internet access across districts in Indonesia. Applying spatial econometric model, two key factors appear to boost economic growth in the recovery period, which are digitalization and education-related variable. This article concludes that digitalization can not work itself. The inclusion of spills-over effect and spatial dependence across districts is needed to accelerate regional growth. Thus, from policy perspectives, our findings suggest that spatial-based policies by combining digitalization with human capital are more appropriate to boost growth performance in East Java.
References
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115.
Anselin, L., Sridharan, S., & Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: the discovery of interesting patterns. Social Indicators Research, 287-309.
Aprianoor, P., & Muktiali, M. (2015). Kajian Ketimpangan Wilayah di Provinsi Jawa Barat. Teknik PWK (Perencanaan Wilayah Kota), 4(4), 484-498.
Arbia, G., R. Basile., & Salvatore, M. (2003). Measuring spatial effects in parametric and non-parametric modelling of regional growth and convergence. In UNU/WIDER Project Meeting on Spatial Inequality in Development
Badan Pusat Statistik. (2021). E-Commerce 2021. BPS
Bai, C. E., Ma, H., & Pan, W. (2012). Spatial spillover and regional economic growth in China. China Economic Review, 23(4), 982-990.
Bernat, G.A. (1996) Does manufacturing matter? A spatial econometric view of Kaldor’s laws. Journal of Regional Science 36, 463–77
BPS Provinsi Jawa Timur. (2021). Statistik Kesejahteraan Rakyat Provinsi Jawa Timur Tahun 2021. BPS Jawa Timur
BPS Provinsi Jawa Timur. (2022). Produk Domestik Regional Bruto Provinsi Jawa Timur Menurut Lapangan Usaha Tahun 2017-2021. BPS Jawa Timur
BPS Provinsi Jawa Timur. (2022). Produk Domestik Regional Bruto Kabupaten/Kota Jawa Timur Menurut Lapangan Usaha Tahun 2017-2021. BPS Jawa Timur
Choi, H., & Varian, H. (2012). Predicting the Present with Google Trends. Economic, 88: 2–9.
Christy, F.E. (2020). Prediksi Angka Penggunaan E-Commerce di Indonesia 2024. Available in Prediksi Angka Pengguna E-commerce di Indonesia 2024 - Data - Tempo.co
Cravo, T. A., Becker, B., & Gourlay, A. (2015). Regional growth and SMEs in Brazil: a spatial panel approach. Regional Studies, 49(12), 1995-2016.
Cuaresma, J. C., Doppelhofer, G., & Feldkircher, M. (2014). The determinants of economic growth in European regions. Regional Studies, 48(1), 44-67.
Day, J., & Lewis, B. (2013). Beyond univariate measurement of spatial autocorrelation: disaggregated spillover effects for Indonesia. Annals of GIS, 19(3), 169-185.
Day, J., & Ellis, P. (2014). Urbanization for everyone: benefits of urbanization in Indonesia’s Rural regions. J Urban Plan Dev 140(3):04014006.
Duha, T., Fajriyah, N., Setiawan, W., & Dewi, E. (2022). Implementasi Teknologi Big Data di Era Digital. Jurnal Informatika, 1(1), 1-7.
Fahta, A. I. A., Moreni, A. F., & Anggita, A. (2022). The Impact of Digitalization to Accelerate Economic Performance During COVID-19 Pandemic. East Java Economic Journal, 6(2), 253-270.
Falentina, A. T., Resosudarmo, B. P., Darmawan, D., & Sulistyaningrum, E. (2021). Digitalisation and the performance of micro and small enterprises in Yogyakarta, Indonesia. Bulletin of Indonesian Economic Studies, 57(3), 343-369. DOI: 10.1080/00074918.2020.1803210
Fingleton, B., & López‐Bazo, E. (2006). Empirical growth models with spatial effects. Papers in regional science, 85(2), 177-198.
Funke, M. and Niebuhr, A. (2000). Spatial R&D Spillovers and Economic Growth–Evidence from West Germany, HWWA Discussion Paper No 98
Gartner. (2014). “IT Glossary Big Data”. Available in http://www.gartner.com/itglossary/big-data/.
Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography, 4, 175-205. https://doi.org/10.1007/s40980-015-0016-y
Islami, F.S. & Nugorho. (2018). Faktor-faktor yang Mempengaruhi Ketimpangan Wilayah di Provinsi Jawa Timur, Indonesia. Media Ekonomi dan Manajemen 33(1).
Jhingan, M.L. (1983). Ekonomi Pembangunan dan Perencanaan, Terjemah oleh D. Guritno. (2007). PT Rajagrafindo Persada, Jakarta.
Kondo, K. (2015). Spatial persistence of Japanese unemployment rates. Japan and the World Economy, 36, 113-122.
Kyswantoro, Y.F. (2016). Pemetaan Wilayah Sektor Unggulan di Provinsi Jawa Timur Melalui Analisis Input-Ouput Tahun 2013. FEB Universitas Brawijaya
LeSage, J. P., & Fischer, M. M. (2008). Spatial growth regressions: model specification, estimation and interpretation. Spatial Economic Analysis, 3(3), 275-304.
Lima, R. C. D. A., & Silveira Neto, R. D. M. (2016). Physical and human capital and Brazilian regional growth: a spatial econometric approach for the period 1970–2010. Regional Studies, 50(10), 1688-1701.
Magalhaes, A., Hewings, G., & Azzoni, C. (2005). Spatial Dependence and Regional Convergence in Brazil. Journal of Regional Research, 6, 5-20.
Mankiw, N. G. (2007). Makroekonomi, Edisi Keenam. Erlangga
McCulloch, N. A., & Suharnoko Sjahrir, B. (2008). Endowments, location or luck? Evaluating the determinants of sub-national growth in decentralized Indonesia. Evaluating the Determinants of Sub-National Growth in Decentralized Indonesia (November 1, 2008). World Bank Policy Research Working Paper, (4769).
Miranti, R. C., Yan, S. S., & Aginta, H. (2021). Regional Growth, Convergence, and Heterogeneity in Sumatera: Evidence From New Satellite Data. The 2nd Sumatranomics. Kantor Perwakilan Bank Indonesia Perwakilan Sumatera Utara
Mohl, P., & Hagen, T. (2010). Do EU structural funds promote regional growth? New evidence from various panel data approaches. Regional Science and Urban Economics, 40(5), 353-365.
Nooraeni, Purba, & Yudho. (2019). Using Google Trends Data an Initial Signal Indonesia Unemployment Rate.
Noto, G. H. (2016). Analisis Ketimpangan Wilayah Antar Kabupaten/Kota dan Faktor-faktor yang Mempengaruhi di Provinsi Jawa Timur. Universitas Brawijaya
Nurhayati, A., & Suryantini, A. (2015). Gambaran Umum Sektor Unggulan Dan Kontribusi Sektor Pertanian di Provinsi Jawa Timur (Olah Data Tabel Input-Output Provinsi Jawa Timur Tahun 2010). Agriekonomika, 4(2), 154-167.
Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The use of google trends in health care research: a systematic review. PloS one, 9(10), e109583. doi:10.1371/journal.pone.0109583
Patricia, J. (2021). Peramalan Laju Produk Domestik Bruto Indonesia dengan Data Google Trends Menggunakan Metode Neural Network dan eXtreme Gradient Boosting. Institut Teknologi Sepuluh Nopember.
Putra, A. P., & Wulandari, H. (2021, November). Mobile Positioning Data: Prediktor Produk Domestik Regional Bruto (PDRB) Pada Masa Pandemi. In Seminar Nasional Official Statistics, 2021(1), 1076-1082.
Qian, L., Fang, Q., & Lu, Z. (2020). Research on the synergy of green economy and digital economy in stimulus policies. Southwest Financ, 413, 3-13.
Ravallion, M. (2012). Why don’t we see poverty convergence?. American Economic Review, 102(1), 504-523.
Resende, G. M., de Carvalho, A. X. Y., Sakowski, P. A. M., & Cravo, T. A. (2016). Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models. The Annals of Regional Science, 56, 1-31.
Rey, S. J., & Montouri, B. D. (1999). US regional income convergence: a spatial econometric perspective. Regional studies, 33(2), 143-156. https://doi.org/10.1080/00343409950122945
Richardson, H. W. (1978). Regional and Urban Economics. London: Penguin Books Ltd. Tempo.
Rumayya, Wardaya W, Landiyanto EA .(2005). Club Convergence & Regional Spillovers In East Java. Regional Economic Development (November)
Solomon, E. M., & van Klyton, A. (2020). The impact of digital technology usage on economic growth in Africa. Utilities policy, 67, 101104.
Tian, L., Wang, H., & Chen, Y. (2010). Spatial externalities in China regional economic growth. China Economic Review, 21, S20–S31.
Torres-Preciado, V. H., Polanco-Gaytán, M., & Tinoco-Zermeño, M. Á. (2014). Technological innovation and regional economic growth in Mexico: a spatial perspective. The Annals of Regional Science, 52, 183-200.
UN Global Pulse. (2012). “Big Data for Development: Challenges and Opportunities”.
Vicente, M. R., Lopez-Menendez, & Perez. (2015). Forecasting unemployment with Internet Search Data: Does it help to Improve Predictions when Job Destruction is Skyrocketing? J. Technological Forecasting and Social Change, 132–139.
Wibisono, P., & Kuncoro, M. (2015). Growth Spillover Among Districts/Municipalities in East Java Province 2001-2013. Jurnal Ekonomi dan Pembangunan Indonesia, 16 (1).
Yordani, R., & Sugiarto. (2016). Clustering City/County in East Java Province Based On Sustainable Development Indicators 2012-2013. Jurnal Ekonomi Pembangunan, 24 (1).
Ying, L. G. (2003). Understanding China’s recent growth experience: A spatial econometric perspective. The Annals of Regional Science, 37, 613–628.
Copyright (c) 2023 Ragdad Cani Miranti, Deltha Airuzsh Lubis, Yongky Choirul Anam
This work is licensed under a Creative Commons Attribution 4.0 International License.
