REVEALING NOVEL INSIGHTS ON ECONOMIC STRUCTURE FROM A SPATIAL PERSPECTIVE: EMPIRICAL FINDINGS FROM VIETNAM

Bac Truong Cong

doi:10.2298/IJGI240305012C

Authors

Bac Truong Cong University of Economics and Law, Faculty of Economics, Ho Chi Minh City; Vietnam National University, Ho Chi Minh City https://orcid.org/0000-0002-2218-0597

DOI:

https://doi.org/10.2298/IJGI240305012C

Keywords:

economic structure, spatial autocorrelation, Moran’s I, LISA, Vietnam

Abstract

Economic structure plays an essential role in distributing resources and shaping the development trend of a country. Although it has become a topic of interest for scholars, most studies focus on analyzing factors affecting the structural transformation process but ignore the correlation in economic structure between localities. This study explores this correlation through the case of a country undergoing a remarkable economic restructuring process—Vietnam. Based on the data from 2010 to 2019, the Moran’s index (I) is used to assess the level of spatial correlation in the economic structure of localities and the Local Indicator of Spatial Autocorrelation (LISA) is analyzed to determine the specific locations where local spatial correlation occurs. Research results show that the economic structure of localities is unevenly distributed across geographical space. In addition, there exists a spatial autocorrelation phenomenon in localities' economic structure for two sectors—agriculture and industry. At the same time, there is no evidence to show this for the service sector. This discovery confirms the necessity of incorporating spatial factors in research related to economic structure to avoid inaccurate conclusions. From a business perspective, based on the findings of this research, companies can assess the level of competition, risks, as well as business partnership opportunities in different areas, and make appropriate investment decisions. The research results might also serve government agencies regarding planning and functional zoning and formulating and implementing development and economic restructuring policies for various regions.

Article metrics

References

Aguilar, A. G., & Hernandez, J. (2016). Metropolitan Change and Uneven Distribution of Urban Sub‐Centres in Mexico City, 1989–2009. Bulletin of Latin American Research, 35(2), 191–209. https://doi.org/10.1111/blar.12407

Aguilar, A. G., Ward, P. M., & Smith Sr, C. B. (2003). Globalization, regional development, and mega-city expansion in Latin America: analyzing Mexico City’s peri-urban hinterland. Cities, 20(1), 3–21. https://doi.org/10.1016/S0264-2751(02)00092-6

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

Assunção, R. M., & Reis, E. A. (1999). A new proposal to adjust Moran's I for population density. Statistics in Medicine, 18(16), 2147–2162. https://doi.org/10.1002/(sici)1097-0258(19990830)18:16%3C2147::aid-sim179%3E3.0.co;2-i

Aung, N., & Mar, T. T. (2019). Rural-Urban Interaction in Rural Development of Peri-Urban Areas in Yangon Region, Myanmar: A Case Study of Hlegu Township. In H. James (Ed.), Population, Development, and the Environment (pp. 113–136). Palgrave Macmillan. https://doi.org/10.1007/978-981-13-2101-6_8

Bac, T. C., Van, T., & Long, T. T. (2022). Understanding Structural Transformation from a Regional Linkage Perspective: Evidence from Vietnam. Asian Economic and Financial Review, 12(8), 673–685. https://doi.org/10.55493/5002.v12i8.4585

Boudeville, J. R. (1966). Problems of regional economic planning. Edinburgh University Press.

Bustos, P., Caprettini, B., & Ponticelli, J. (2016). Agricultural productivity and structural transformation: Evidence from Brazil. American Economic Review, 106(6), 1320–1365. http://doi.org/10.1257/aer.20131061

Christaller, W. (1933). Die zentralen Orte in Süddeutschland [Central Places in Southern Germany]. Gustav Fischer Verlag.

Cliff, A. D., & Ord, K. (1970). Spatial Autocorrelation: A Review of Existing and New Measures with Applications. Economic Geography, 46, 269–292. https://doi.org/10.2307/143144

Cliff, A. D., & Ord, J. K. (1973). Spatial Autocorrelation. Pion.

Cimino, O., Vassallo, M., Henke, R., & Vanni, F. (2021). Income Diversification Strategies of Italian Peri-Urban Farms: A Structural Equation Modeling Approach. Land, 10(8), Article 790. http://dx.doi.org/10.3390/land10080790

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

da Silva, E. G., & Teixeira, A. A. (2014). Introduction: Structural change, competitiveness and industrial policy. In A. Teixeira, E. Silva, & R. Mamede (Eds.), Structural Change, Competitiveness and Industrial Policy (pp. 3–9). Routledge.

Duranton, G., & Puga, D. (2004). Micro-foundations of urban agglomeration economies. In J. V. Henderson & J.-F. Thisse (Eds.), Handbook of Regional and Urban Economics: Vol. 4. Cities and Geography (pp. 2063–2117). Elsevier. https://doi.org/10.1016/S1574-0080(04)80005-1

Ellison, G., Glaeser, E. L., & Kerr, W. R. (2010). What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns. American Economic Review, 100(3), 1195–1213. https://doi.org/10.1257/aer.100.3.1195

Fafchamps, M., & Shilpi, F. (2003). The spatial division of labour in Nepal. The Journal of Development Studies, 39(6), 23–66. https://doi.org/10.1080/00220380312331293577

Finney, M. M., & Kohlhase, J. E. (2008). The effect of urbanization on labor turnover. Journal of Regional Science, 48(2), 311–328. https://doi.org/10.1111/j.1467-9787.2008.00553.x

Friedmann, J. (1966). Regional Development Policy: A Case Study of Venezuela. MIT Press.

General Statistics Office. (2019). Statistical Yearbook of Viet Nam 2019. https://www.gso.gov.vn/en/data-and-statistics/2020/09/statistical-yearbook-2019/

Geniaux, G., & Martinetti, D. (2018). A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Regional Science and Urban Economics, 72, 74–85. https://doi.org/10.1016/j.regsciurbeco.2017.04.001

Getis, A. (2008). A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective. Geographical Analysis, 40(3), 297–309. https://doi.org/10.1111/j.1538-4632.2008.00727.x

Getis, A. (2009). Spatial Autocorrelation. In M. M. Fischer & A. Getis (Eds.), Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications (pp. 255–278). Springer. https://doi.org/10.1007/978-3-642-03647-7_14

Glaeser, E. L. (Ed.). (2010). Agglomeration Economics. University of Chicago Press. http://dx.doi.org/10.7208/chicago/9780226297927.001.0001

Global Administrative Areas. (2022). GADM database of Global Administrative Areas (Version is 4.1) [Data set]. https://gadm.org/data.html

Haining, R., Law, J., & Griffith, D. (2009). Modelling small area counts in the presence of overdispersion and spatial autocorrelation. Computational Statistics & Data Analysis, 53(8), 2923–2937. http://dx.doi.org/10.1016/j.csda.2008.08.014

Hassan, M. M., Alenezi, M. S., & Good, R. Z. (2020). Spatial pattern analysis of manufacturing industries in Keraniganj, Dhaka, Bangladesh. GeoJournal, 85(1), 269–283. https://doi.org/10.1007/s10708-018-9961-5

Jung, P. H., Thill, J. C., & Issel, M. (2019). Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data. Geographical Analysis, 51(3), 354–380. https://doi.org/10.1111/gean.12177

Kannan, E., Balamurugan, G., & Narayanan, S. (2021). Spatial economic analysis of agricultural land use changes: a case of peri-urban Bangalore, India. Journal of the Asia Pacific Economy, 26(1), 34–50. https://doi.org/10.1080/13547860.2020.1717285

Kies, U., Mrosek, T., & Schulte, A. (2009). Spatial analysis of regional industrial clusters in the German forest sector. International Forestry Review, 11(1), 38–51. https://doi.org/10.1505/ifor.11.1.38

Li, L., Jiang, Z., Duan, N., Dong, W., Hu, K., & Sun, W. (2012). An approach to optimize police patrol activities based on the spatial pattern of crime hotspots. In G. Xiong, Z. Liu, X.-W. Liu, F. Zhu, D. Shen (Eds.), Service Science, Management, and Engineering: Theory and Applications (pp. 141–163). https://doi.org/10.1016/B978-0-12-397037-4.00008-9

Lichstein, J. W., Simons, T. R., Shriner, S. A., & Franzreb, K. E. (2002). Spatial autocorrelation and autoregressive models in ecology. Ecological Monographs, 72(3), 445–463. https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/0012-9615%282002%29072%5B0445%3ASAAAMI%5D2.0.CO%3B2

Lin, J. Y. (2019). Structural change and poverty elimination. China Agricultural Economic Review, 11(3), 452–459. http://doi.org/10.1108/CAER-08-2018-0169

Loayza, N. V., & Raddatz, C. (2010). The composition of growth matters for poverty alleviation. Journal of Development Economics, 93(1), 137–151. http://dx.doi.org/10.1016/j.jdeveco.2009.03.008

Lokantara, I. G. W., & Amo, F. M. (2021). Causing Factors of Urban Sprawl Symptoms in Peri-Urban Areas of Singaraja City, Bali Province. Journal of Regional and Rural Development Planning (Jurnal Perencanaan Pembangunan Wilayah dan Perdesaan), 5(3), 134–142. http://dx.doi.org/10.29244/jp2wd.2021.5.3.134-142

Lösch, A. (1940). Die räumliche Ordnung der Wirtschaft: Eine Untersuchung über Standort, Wirtschaftsgebiete und internationalen Handel [The spatial order of the economy: An investigation on location, economic areas and international trade]. Gustav Fischer Verlag.

McMillan, M., & Headey, D. (2014). Introduction – Understanding Structural Transformation in Africa. World Development, 63, 1–10. http://dx.doi.org/10.1016/j.worlddev.2014.02.007

Meraner, M., Pölling, B., & Finger, R. (2018). Diversification in peri-urban agriculture: A case study in the Ruhr metropolitan region. Journal of Land Use Science, 13(3), 284–300. http://dx.doi.org/10.1080/1747423X.2018.1529830

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.2307/2332142

Mondal, D., & Banerjee, A. (2021). Exploring peri-urban dynamism in India: Evidence from Kolkata Metropolis. Journal of Urban Management, 10(4), 382–392. http://dx.doi.org/10.1016/j.jum.2021.06.004

Naumov, I. V., & Trynov, A. V. (2019). Modeling the Investment Attractiveness of the Types of Economic Activities in the Region with the Use of the Matrix of Financial Flows. Economic and Social Changes: Facts, Trends, Forecast, 12(4), 53–66. http://dx.doi.org/10.15838/esc.2019.4.64.4

Nilsson, P. (2019). Spatial spillovers and households’ involvement in the non-farm sector: evidence from rural Rwanda. Regional Studies, 53(5), 731–740. https://doi.org/10.1080/00343404.2018.1482415

Oden, N. (1995). Adjusting Moran's I for population density. Statistics in Medicine, 14(1), 17–26. https://doi.org/10.1002/sim.4780140104

Patel, S. K., Verma, P., & Shankar Singh, G. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 191(9), 1–17. http://dx.doi.org/10.1007/s10661-019-7736-1

Perroux, F. (1955). Note sur la notion de "pôle de croissance" [A note on the notion of growth pole]. Économie Appliquée, 1–2, 307–320. https://www.persee.fr/doc/ecoap_0013-0494_1955_num_8_1_2522

Rodrik, D. (2016). Premature deindustrialization. Journal of Economic Growth, 21, 1–33. http://doi.org/10.1007/s10887-015-9122-3

Roh, Y. S., Kim, K.-S., & Lee, K.-H. (2023). An analysis of location types and spatial patterns in the Korean food manufacturing industry. Cogent Food & Agriculture, 9(1), Article 2213942. https://doi.org/10.1080/23311932.2023.2213942

Rosenthal, S. S., & Strange, W. C. (2001). The Determinants of Agglomeration. Journal of Urban Economics, 50(2), 191–229. https://doi.org/10.1006/juec.2001.2230

Safiullin, A. R., & Gubaidullina, A. I. (2018). Approach to risk and investment attractiveness estimation for regional economic activity of Russia. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 9(5), 455–467. http://TUENGR.COM/V09/455.pdf

Sharma, A., & Chandrasekhar, S. (2014). Growth of the urban shadow, spatial distribution of economic activities, and commuting by workers in rural and urban India. World Development, 61, 154–166. http://dx.doi.org/10.1016/j.worlddev.2014.04.003

Szirmai, A. (2012). Industrialization as an engine of growth in developing countries, 1950–2005. Structural Change and Economic Dynamics, 23(4), 406–420. https://doi.org/10.1016/j.strueco.2011.01.005

Tian, L. (2015). Land use dynamics driven by rural industrialization and land finance in the peri-urban areas of China: “The examples of Jiangyin and Shunde”. Land Use Policy, 45, 117–127. http://dx.doi.org/10.1016/j.landusepol.2015.01.006

Timmer, M. P., de Vries, G. J. & de Vries, K. (2015). Patterns of structural change in developing countries. In J. Weiss & M. Tribe (Eds.), Handbook of Industry and Development (pp. 65–83). Routledge. https://doi.org/10.4324/9780203387061

Truong Cong, B. (2021a). The impact of metropolises’ characteristics on provincial economic structure transformation: evidence from Vietnam. Cogent Economics & Finance, 9(1), Article 1937849. https://doi.org/10.1080/23322039.2021.1937849

Truong Cong, B. (2021b). The agglomeration of business types at the regional level in Vietnam. Science & Technology Development Journal: Economics- Law & Management, 6(1), 2101–2114. https://doi.org/10.32508/stdjelm.v6i1.826

Truong Cong, B. (2022). Revisiting Rural Economic Structural Transformation from the Viewpoint of Regional Linkages. Economy of Regions, 18(2), 312–323. https://doi.org/10.17059/ekon.reg.2022-2-1

Truong Cong, B., Tran, V., & Tran Thanh, L. (2023). Impact of metropolises’ competitiveness characteristics on structural transformation of provinces in Vietnam: A spatial approach. Journal of Competitiveness, 15(1), 1–19. https://doi.org/10.7441/joc.2023.01.01

van Aswegen, M., & Retief, F. P. (2020). The role of economic sectoral structure as a policy mechanism towards more resilient peripheral regions: The case of South Africa. GeoJournal, 86(6), 2823–2843. http://dx.doi.org/10.1007/s10708-020-10233-0

Von Thünen, J. H. (1842). Der isolirte Staat in Beziehung auf Landwirthschaft und Nationalökonomie: Teil 1. Untersuchungen über den Einfluß, den die Getreidepreise, der Reichthum des Bodens und die Abgaben auf den Ackerbau ausüben [The Isolated State in Relation to Agriculture and Political Economy: Vol. 1. Investigations into the influence of grain prices, land wealth and taxes on agriculture]. Leopold.

Waldhör, T. (1996). The Spatial Autocorrelation Coefficient Moran's I Under Heteroscedasticity. Statistics in Medicine, 15(7–9), 887–892. https://doi.org/10.1002/(SICI)1097-0258(19960415)15:7/9%3C887::AID-SIM257%3E3.0.CO;2-E

Wang, W., Zhang, J., & Su, F. (2018). An index-based spatial evaluation model of exploitative intensity: A case study of coastal zone in Vietnam. Journal of Geographical Sciences, 28(3), 291–305. https://doi.org/10.1007/s11442-018-1473-1

Weber, A. (1909). Über den Standort der Industrien: Erster Teil. Reine Theorie des Standorts [Location of Industries: Vol. 1. The pure theory of location]. JCB Mohr (Paul Siebeck).

Westerholt, R. (2022). Exploratory Statistical Analysis of Spatial Structures in Urban Datasets. In J. Gurr, R. Parr, & D. Hardt (Eds.), Metropolitan Research: Methods and Approaches (pp. 37–61). Bielefeld. https://doi.org/10.14361/9783839463109-003

Xue, B., Xiao, X., & Li, J. (2020). Identification method and empirical study of urban industrial spatial relationship based on POI big data: a case of Shenyang City, China. Geography and Sustainability, 1(2), 152–162. http://dx.doi.org/10.1016/j.geosus.2020.06.003

Zhou, X., & Lin, H. (2008). Moran's I. In S. Shekhar & H. Xiong (Eds.), Encyclopedia of GIS. Springer. https://doi.org/10.1007/978-0-387-35973-1_817