Foreign direct investment performance drivers at the country level: a robust compromise multi-criteria decision-making approach
Abstract
This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.
Keyword : multiple criteria analysis, FDI, robust analysis, social welfare, economic development, socio-demographic drivers
How to Cite
Wanke, P., Tan, Y., Antunes, J., & Emrouznejad, A. (2024). Foreign direct investment performance drivers at the country level: a robust compromise multi-criteria decision-making approach. Technological and Economic Development of Economy, 30(1), 148–174. https://doi.org/10.3846/tede.2024.19532
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hadi-Vencheh, A., Tan, Y., Wanke, P., & Loghmanian, S. M. (2021). Air pollution assessment in China: A novel group multiple criteria decision making model under uncertain information. Sustainability, 13(4), Article 1686. https://doi.org/10.3390/su13041686
Hsieh, T. Y., Lu, S. T., & Tzeng, G. H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management, 22(7), 573–584. https://doi.org/10.1016/j.ijproman.2004.01.002
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Jørgensen, B. (1997). The theory of dispersion models. Chapman and Hall.
Keršulienė, V., & Turskis, Z. (2011). Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy, 17(4), 645–666. https://doi.org/10.3846/20294913.2011.635718
Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (Swara). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12
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Ly, A., Esperanca, J., & Davcik, N. S. (2018). What drives foreign direct investment: The role of language, geographical distance, information flows and technological similarity. Journal of Business Research, 88, 111–122. https://doi.org/10.1016/j.jbusres.2018.03.007
Lyles, M., Li, D., & Yan, H. (2014). Chinese outward foreign direct investment performance: The role of learning. Management and Organization Review, 10(3), 411–437. https://doi.org/10.1017/S1740877600004381
Makino, S., Beamish, P. W., & Zhao, N. B. (2004). The characteristics and performance of Japanese FDI in less developed and developed countries. Journal of World Business, 39(4), 377–392. https://doi.org/10.1016/j.jwb.2004.08.009
Manjappa, D. H., & Mahesha, M. (2008). Productivity performance of selected capital-intensive and labor-intensive industries in India during reform period: An empirical analysis. ICFAI Journal of Industrial Economics, 5, 57–65.
Maredza, A., Wanke, P., Antunes, J., Pimenta, R., & Tan, Y. (2022). Social welfare and bank performance: Evidence from a stochastic neural hybrid MCDM approach. Journal of Economic Studies, 49(7), 1137–1158. https://doi.org/10.1108/JES-05-2021-0236
McDonald, J., & Moffitt, R. (1980). The uses of Tobit analysis. The Review of Economics and Statistics, 62(2), 318–321. https://doi.org/10.2307/1924766
McKinsey & Company. (2015). Women matter: An Asian perspective. https://www.mckinsey.com/sg/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/women%20matter/women_matter_asia_final.pdf
Nitsch, D., Beamish, P., & Makino, S. (1995). Characteristics and performance of Japanese foreign direct investment in Europe. European Management Journal, 13(3), 276–285. https://doi.org/10.1016/0263-2373(95)00018-G
Nitsch, D., Beamish, P., & Makino, S. (1996). Entry mode and performance of Japanese FDI in Western Europe. MIR: Management International Review, 36(1), 27–43.
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with out ranking methods. European Journal of Operational Research, 178(2), 514–529. https://doi.org/10.1016/j.ejor.2006.01.020
Pangarkar, N., & Lim, H. (2003). Performance of foreign direct investment from Singapore. International Business Review, 12(5), 601–624. https://doi.org/10.1016/S0969-5931(03)00078-7
Paramati, S. R., Alam, M. S., Hammondeh, S., & Hafeez, K. (2021). Long-run relationship between R&D investment and environmental sustainability: Evidence from the European Union member countries. International Journal of Finance and Economics, 26(4), 5775–5792. https://doi.org/10.1002/ijfe.2093
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