The driver of global agricultural value chains: Evidence from 6 ASEAN Countries

Ariyo Dharma Pahla Irhamna, Muhammad Firdaus, Bustanul Arifin, Anny Ratnawati

Abstract

This study investigates the drivers of global agricultural value chain (GAVC) participation in six ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Employing panel geographically weighted regression, the paper analyzes the spatial heterogeneity of factors influencing global agricultural value chain participation. Our findings reveal significant variations in the impact of tariffs, FDI, agricultural land area, inland waters, and trade balance across countries. Tariffs emerge as a crucial factor in enhancing competitiveness in Indonesia, Singapore, and Thailand, aligning with trade theory. FDI positively influences Malaysia's global agricultural value chain participation, emphasizing attracting foreign investment. Agricultural land area plays a pivotal role in Indonesia and Thailand, highlighting the significance of resource endowments. Inland waters contribute significantly to agriculture in the Philippines, while their impact in Indonesia suggests potential inefficiencies in water management. Trade balance in food products positively affects global agricultural value chain involvement in Thailand and Vietnam. These findings underscore the need for tailored policies to address the unique characteristics of each ASEAN country. Future research should explore the long-term implications of these factors and consider broader socio-economic and environmental contexts. 

Keywords

ASEAN; Geographically Weighted Panel Regressions; Global Agricultural Value Chain

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References

[1]Gereffi G, Humphrey J, Sturgeon T. The governance of global value chains. Rev Int Polit Econ 2005;12:78–104. https://doi.org/10.1080/09692290500049805.

[2]Lim S. Global Agricultural Value Chains and Structural Transformation. Cambridge, MA: 2021. https://doi.org/10.3386/w29194.

[3]FAO, IFAD, UNICEF, WFP, WHO. In Brief to The State of Food Security and Nutrition in the World 2024. 2024. https://doi.org/10.4060/cd1276en.

[4]Balié J, Del Prete D, Magrini E, Montalbano P, Nenci S. Does Trade Policy Impact Food and Agriculture Global Value Chain Participation of Sub‐Saharan African Countries? Am J Agric Econ 2019;101:773–89. https://doi.org/10.1093/ajae/aay091.

[5]IPPC. Point of Departure and Key Concepts. Climate Change 2022 – Impacts, Adaptation and Vulnerability, Cambridge University Press; 2023, p. 121–96. https://doi.org/10.1017/9781009325844.003.

[6]OECD. Global value chains in agriculture and food: A synthesis of OECD analysis. 2020. https://doi.org/10.1787/6e3993fa-en.

[7]Dalheimer B, Bellemare MF, Lim S. Global Agricultural Value Chains and Food Prices. 2023.

[8]Montalbano P, Nenci S. Does global value chain participation and positioning in the agriculture and food sectors affect economic performance? A global assessment. Food Policy 2022;108:102235. https://doi.org/10.1016/j.foodpol.2022.102235.

[9]Beck A, Lim S, Taglioni D. Understanding firm networks in global agricultural value chains. Food Policy 2024;127:102689. https://doi.org/10.1016/j.foodpol.2024.102689.

[10]ASEAN FOOD SECURITY INFORMATION SYSTEM (AFSIS). ASEAN Agricultural Commodity Outlook. Bangkok: 2023.

[11]ASEAN. Role of Agriculture, Forestry, and Fishing Industry in ASEAN Economy. ASEAN Statistical Brief 2024.

[12]World Bank. Agricultural raw materials exports (% of merchandise exports) 2024. https://datacatalog.worldbank.org/public-licenses#cc-by (accessed January 7, 2025).

[13]Awokuse T, Lim S, Santeramo F, Steinbach S. Robust policy frameworks for strengthening the resilience and sustainability of agri-food global value chains. Food Policy 2024;127:102714. https://doi.org/10.1016/j.foodpol.2024.102714.

[14]Wang J-K, Kim O. Land Reform in Taiwan, 1950-1961: Effects on Agriculture and Structural Change 2024. https://doi.org/10.2139/ssrn.4951831.

[15]Carter C, Steinbach S. The Impact of Retaliatory Tariffs on Agricultural and Food Trade. Cambridge, MA: 2020. https://doi.org/10.3386/w27147.

[16]Hoekman B. Agricultural Tariffs or Subsidies: Which Are More Important for Developing Economies? World Bank Econ Rev 2004;18:175–204. https://doi.org/10.1093/wber/lhh037.

[17]Dunning JH. Toward an Eclectic Theory of International Production: Some Empirical Tests. J Int Bus Stud 1980;11:9–31. https://doi.org/10.1057/palgrave.jibs.8490593.

[18]Blomström M, Kokko A, Mucchielli J-L. The Economics of Foreign Direct Investment Incentives. Foreign Direct Investment in the Real and Financial Sector of Industrial Countries, Berlin, Heidelberg: Springer Berlin Heidelberg; 2003, p. 37–60. https://doi.org/10.1007/978-3-540-24736-4_3.

[19]Cheng M, Wu J, Li C, Jia Y, Xia X. Tele-connection of global agricultural land network: Incorporating complex network approach with multi-regional input-output analysis. Land Use Policy 2023;125:106464. https://doi.org/10.1016/j.landusepol.2022.106464.

[20]Chen GQ, Han MY. Global supply chain of arable land use: Production-based and consumption-based trade imbalance. Land Use Policy 2015;49:118–30. https://doi.org/10.1016/j.landusepol.2015.07.023.

[21]Han M, Chen G. Global arable land transfers embodied in Mainland China’s foreign trade. Land Use Policy 2018;70:521–34. https://doi.org/10.1016/j.landusepol.2017.07.022.

[22]Chen B, Han MY, Peng K, Zhou SL, Shao L, Wu XF, et al. Global land-water nexus: Agricultural land and freshwater use embodied in worldwide supply chains. Science of The Total Environment 2018;613–614:931–43. https://doi.org/10.1016/j.scitotenv.2017.09.138.

[23]Fang D, Cai Q, Wu F, Chen B, Zhang L. Modified linkage analysis for water-land nexus driven by interregional trade. J Clean Prod 2022;353:131547. https://doi.org/10.1016/j.jclepro.2022.131547.

[24]Van den Broeck G, Van Hoyweghen K, Maertens M. Horticultural exports and food security in Senegal. Glob Food Sec 2018;17:162–71. https://doi.org/10.1016/j.gfs.2017.12.002.

[25]Fotheringham AS, Brunsdon C, Charlton ME. Geographically weighted regression: The analysis of spatially varying relationships. Chichester, UK: John Wiley & Sons; 2002.

[26]Wheeler D, Tiefelsdorf M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 2005;7:161–87. https://doi.org/10.1007/s10109-005-0155-6.

[27]Thissen M, Graaff T, Oort F. Competitive network positions in trade and structural economic growth: A geographically weighted regression analysis for European regions. Papers in Regional Science 2016;95:159–81. https://doi.org/10.1111/pirs.12224.

[28]Wooldridge JM. Introductory Econometrics: A Modern Approach. 5th ed. South-Western Cengage Learning; 2013.

[29]Gujarati DN. Basic Econometrics. McGraw Hill; 2003.

[30]LeSage J, Pace RK. Introduction to Spatial Econometrics. Chapman and Hall/CRC; 2009. https://doi.org/10.1201/9781420064254.

[31]Franzese RJ, Hays JC. Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data. Political Analysis 2007;15:140–64. https://doi.org/10.1093/pan/mpm005.

[32]Anselin L. Spatial Econometrics: Methods and Models. Springer Netherlands; 1988.

[33]Thang TT, Pham TSH, Barnes BR. Spatial Spillover Effects from Foreign Direct Investment in Vietnam. J Dev Stud 2016;52:1431–45. https://doi.org/10.1080/00220388.2016.1166205.

[34]Alaerts GJ. Adaptive policy implementation: Process and impact of Indonesia’s national irrigation reform 1999–2018. World Dev 2020;129:104880. https://doi.org/10.1016/j.worlddev.2020.104880.

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