Do Agglomeration Externalities Drive Regional Wages? Evidence from Indonesian Microdata
DOI:
https://doi.org/10.15408/etk.v25i2.46777Keywords:
urbanization economies, localization economies, wage disparities, labor characteristics, two-stage least squaresAbstract
Research Originality: This research provides novelty by employing nighttime light (NTL) intensity as an alternative proxy for local density and, for the first time, by adopting a geological instrumental variable (clay content) to address endogeneity in Indonesia.
Research Objectives: This study aims to analyze the determinants of regional wage disparities in Indonesia, with particular focus on the role of agglomeration externalities.
Research Methods: This study utilized microdata from BPS-Statistics Indonesia and big data from 2014 to 2024. The analysis employed the instrumental variable two-stage least squares (IV-2SLS) approach to address endogeneity in the local density variable.
Empirical Result: The IV-2SLS estimates indicate that specialization has a positive and significant effect on regional wages, consistent with MAR externality theory, whereas diversity and competition have significant negative effects. Local density, proxied by both population density and NTL intensity, exerts a positive and significant effect on regional wages, with the NTL-based estimate yielding a higher elasticity, reflecting its superior capacity to capture actual urban economic density in Indonesia.
Implications: These findings suggest that regional development policies should promote sectoral specialization based on each region's comparative advantage and support urban densification through infrastructure investment and secondary city development to reduce regional wage disparities in Indonesia.
JEL Classification: J31, R12, C26
Downloads
References
Akbar, F. (2022). Gender Wage Gap: Evidence from Employment in Informal Sector. The Journal of Indonesia Sustainable Development Planning, 3(2), 104–117. https://doi.org/10.46456/jisdep.v3i2.301
Barufi, A. M. B., Haddad, E. A., & Nijkamp, P. (2023). Urban agglomeration, city size, and spatial density effects on wage flexibility: New evidence on the wage curve in Brazil. Regional Science Policy & Practice, 15(9), 1998–2026. https://doi.org/10.1111/rsp3.12669
Beltrán Tapia, F. J., Díez-Minguela, A., & Martinez-Galarraga, J. (2018). Tracing the Evolution of Agglomeration Economies: Spain, 1860–1991. The Journal of Economic History, 78(1), 81–117. https://doi.org/10.1017/S0022050718000086
BPS. (2024). Statistical Service Information System (SILASTIK). https://silastik.bps.go.id/
BPS. (2025). Indonesian Labor Market Indicators in 2025 (Vol. 16). Badan Pusat Statistik.
Capello, R. (2014). Proximity and regional innovation processes: is there space for new reflections? In Regional Development and Proximity Relations. Edward Elgar Publishing. https://doi.org/10.4337/9781781002896.00012
Catherine, S., Ebrahimian, M., Sraer, D., & Thesmar, D. (2022). Robustness Checks in Structural Analysis. https://doi.org/10.3386/w30443
Chauvin, J. P., Glaeser, E., Ma, Y., & Tobio, K. (2017). What is different about urbanization in rich and poor countries? Cities in Brazil, China, India and the United States. Journal of Urban Economics, 98, 17–49. https://doi.org/10.1016/j.jue.2016.05.003
Chen, A., Dai, T., & Partridge, M. D. (2021). Agglomeration and firm wage inequality: Evidence from China. Journal of Regional Science, 61(2), 352–386. https://doi.org/10.1111/jors.12512
Chen, L., Hasan, R., & Jiang, Y. (2022). Urban Agglomeration and Firm Innovation: Evidence from Asia. The World Bank Economic Review, 36(2), 533–558. https://doi.org/10.1093/wber/lhab022
Chumaidiyah, E., Dewantoro, M. D. R., Fauzi, P. M., & Kamil, A. A. (2023). Selection of Industrial Sites Using a Web-Based Geographical Information System to Minimize Risks: A Case Study in West Java, Indonesia. Sustainability, 15(22), 16034. https://doi.org/10.3390/su152216034
Combes, P.-P., & Gobillon, L. (2015). The Empirics of Agglomeration Economies (pp. 247–348). https://doi.org/10.1016/B978-0-444-59517-1.00005-2
Earth Observation Group. (2026). Nighttime light annual composites. https://eogdata.mines.edu/nighttime_light/annual/
Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C., & Taneja, J. (2021). Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
Geospatial Information Agency. (2026). Indonesia Geospatial Portal. https://tanahair.indonesia.go.id/portal-web/
Groot, S., de Groot, H. L. F., & Smit, M. J. (2014). Regional Wage Differences in The Netherlands: Micro Evidence on Agglomeration Externalities. Journal of Regional Science, 54(3), 503–523. https://doi.org/10.1111/jors.12070
Grover, A., Lall, S., & Timmis, J. (2023). Agglomeration economies in developing countries: A meta-analysis. Regional Science and Urban Economics, 101, 103901. https://doi.org/10.1016/j.regsciurbeco.2023.103901
Hengl, T. (2018). Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Zenodo. https://zenodo.org/records/1476855
Hu, S., Xiang, W., & Wan, Y. (2026). Digital Financial Inclusion and Economic Growth: Multi-Dimensional Evidence from Coverage, Depth, and Digitisation. Journal of Risk and Financial Management, 19(4), 284. https://doi.org/10.3390/jrfm19040284
Jacobs, J. (2016). The economy of cities. Vintage.
Kemenko Perekonomian. (2021). Report on the Study of the Impact of the Covid-19 Pandemic on Employment in Indonesia.
Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics, 133(1), 97–126. https://doi.org/10.1016/j.jeconom.2005.02.011
Maghfirah, M., & Samosir, O. B. (2022). Wages of Workers Spatial Analysis in Indonesia Region 2019. Proceedings of The International Conference on Data Science and Official Statistics, 2021(1), 708–716. https://doi.org/10.34123/icdsos.v2021i1.66
Özgüzel, C. (2023). Agglomeration effects in a developing economy: evidence from Turkey. Journal of Economic Geography, 23(4), 823–846. https://doi.org/10.1093/jeg/lbac035
Prasertsoong, N., & Puttanapong, N. (2022). Regional Wage Differences and Agglomeration Externalities: Micro Evidence from Thai Manufacturing Workers. Economies, 10(12). https://doi.org/10.3390/economies10120319
Putra, A. A., Hasibuan, H. S., Tambunan, R. P., & Lautetu, L. M. (2024). Integration of the Sustainable Development Goals into a Regional Development Plan in Indonesia. Sustainability, 16(23), 10235. https://doi.org/10.3390/su162310235
Ranta, M., & Ylinen, M. (2024). Employee benefits and company performance: Evidence from a high-dimensional machine learning model. Management Accounting Research, 64, 100876. https://doi.org/10.1016/j.mar.2023.100876
Ridhwan, M. M. (2021). Spatial wage differentials and agglomeration externalities: Evidence from Indonesian microdata. Economic Analysis and Policy, 71, 573–591. https://doi.org/10.1016/j.eap.2021.06.013
Rismaya, E., & Yusuf, A. A. (2026). Persistence of Labor Market Disparities in Indonesia: An Analysis of Regional Productivity and Wage Gaps, 2018–2023. Journal of Society and Development, 6(1), 14–22. https://doi.org/10.57032/jsd.v6i1.339
Rotman, A., & Mandel, H. (2023). Gender-Specific Wage Structure and the Gender Wage Gap in the U.S. Labor Market. Social Indicators Research, 165(2), 585–606. https://doi.org/10.1007/s11205-022-03030-4
Sangkasem, K., & Puttanapong, N. (2022). Analysis of spatial inequality using DMSP‐OLS nighttime‐light satellite imageries: A case study of Thailand. Regional Science Policy & Practice, 14(4), 828–849. https://doi.org/10.1111/rsp3.12386
Small, D. S., Tan, Z., Ramsahai, R. R., Lorch, S. A., & Brookhart, M. A. (2017). Instrumental Variable Estimation with a Stochastic Monotonicity Assumption. Statistical Science, 32(4). https://doi.org/10.1214/17-STS623
Sun, H. (2026). The impact of vocational skills training on earnings: Evidence from China. International Review of Economics & Finance, 107, 105079. https://doi.org/10.1016/j.iref.2026.105079
Tabash, M. I., Elsantil, Y., Hamadi, A., & Drachal, K. (2024). Globalization and Income Inequality in Developing Economies: A Comprehensive Analysis. Economies, 12(1), 23. https://doi.org/10.3390/economies12010023
Tan, M., Li, X., Li, S., Xin, L., Wang, X., Li, Q., Li, W., Li, Y., & Xiang, W. (2018). Modeling population density based on nighttime light images and land use data in China. Applied Geography, 90, 239–247. https://doi.org/10.1016/j.apgeog.2017.12.012
Umair, M., Ahmad, W., Hussain, B., Antohi, V. M., Fortea, C., & Zlati, M. L. (2024). The Role of Labor Force, Physical Capital, and Energy Consumption in Shaping Agricultural and Industrial Output in Pakistan. Sustainability, 16(17), 7425. https://doi.org/10.3390/su16177425
Yang, Z., Hong, Y., Guo, Q., Yu, X., & Zhao, M. (2022). The Impact of Topographic Relief on Population and Economy in the Southern Anhui Mountainous Area, China. Sustainability, 14(21), 14332. https://doi.org/10.3390/su142114332
Published
Issue
Section
License
Copyright (c) 2026 Icha Wahyu Kusuma Ningrum, Ribut Nurul Tri Wahyuni, Arya Candra Kusuma
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
