Performance evaluation of bilateral economic cooperation between Taiwan and partner countries under new southbound policy: past, present, and future
Purpose – in light of the Taiwan New Southbound Policy (NSP), this paper aims to evaluate the performance of bilateral cooperation between Taiwan and its economic partner countries in order to have a better understanding of the coherence of reciprocal relations in the past, present and future.
Research methodology – firstly, both individual forecasting models and combining forecasts were employed to predict the future values based on a period of thirty years (1990–2019). Secondly, the paper proposes non-convex DEA to detect non-convex characteristics of datasets where the volume of inputs and outputs were unevenly allocated in past years. Finally, a DEA window was applied to provide efficiency scores for decision-making units (DMUs) across a period of twelve years (2014–2025).
Findings – the results found that the efficiency of seven out of eight DMUs will improve in the coming years. With a stable performance in both scale and efficiency, Singapore is Taiwan’s most successful economic partner, followed by Malaysia. The NSP remained as a vital foreign policy in supporting Taiwan’s bilateral trade and outward foreign direct investment (OFDI).
Research limitations – more inputs and outputs are required in order to reflect the overall performance of the bilateral cooperation between two economies. Furthermore, more extended models are worth further investigation.
Practical implications – the forecasting values of exports and imports can be used in analysing Taiwan economy’s trade deficits. This study provides useful inputs for managers in allocating resources of inbound and outbound values, and reacting rightfully to the uncertain future.
Originality/Value – the paper not only contribute much more than previous ones by evaluating into the relationship between size of scale and efficiency of bilateral economies but also provide advices for policymakers in creating mechanisms that can facilitate the NSP’s sustainable development.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ahmad, F., Draz, M. U., & Yang, S. C. (2016). A novel study on OFDI and home country exports: Implications for the ASEAN region. Journal of Chinese Economic and Foreign Trade Studies, 9(2), 131–145. https://doi.org/10.1108/JCEFTS-06-2016-0016
Athanasopolous, G., Hyndman, R. J., Song, H., & Wu, D. C. (2010). The tourism forecasting competition. International Journal of Forecasting, 27(3), 822–844. https://doi.org/10.1016/j.ijforecast.2010.04.009
Assimakopolous, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521–530. https://doi.org/10.1016/S0169-2070(00)00066-2
Ajayi, O. V. (2019). Comparing multivariate models’ forecasts of inflation for BRICS and OPEC countries. Business, Management and Economics Engineering, 17(2), 152–172. https://doi.org/10.3846/bme.2019.10556
Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Quarterly, 20(4), 451–468. https://doi.org/10.1057/jors.1969.103
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis, forecasting and control. Holden Day, United State.
Bhasin, N., & Baul, J. (2016). Exports and outward FDI: Are they complements or substitutes? Evidence from Asia. Multinational Business Review, 24(1), 62–78. https://doi.org/10.1108/MBR-05-2015-0016
Banker, P. C., Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Bayaraa, B., Tarnoczi, T., & Fenyves, V. (2019). Measuring performance by integrating K-medoids with DEA: Mongolian case. Journal of Business Economics & Management, 20(6), 1238–1257. https://doi.org/10.3846/jbem.2019.11237
Çatık, A. N., & Karaçuka, M. (2012). A comparative analysis of alternative univariate time series models in forecasting Turkish inflation. Journal of Business Economics and Management, 13(2), 275–293. https://doi.org/10.3846/16111699.2011.620135
Camarero, M., Gómez-Herrera, E., & Tamarit, C. (2018). New evidence on trade and FDI: How large is the euro effect? Open Economies Review, 29, 451–467. https://doi.org/10.1007/s11079-018-9479-y
Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414. https://doi.org/10.2307/1913388
Chang, C. C., Ma, L. Y., Chen, L. C., Lee, S. H., & Lin, J. Y. (2017). Looking South: Taiwan’s approach to forging manufacturing partnership with Southeast Asian countries under the Southbound policy. Defense Strategy and Assessment Journal, 9, 110–141.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen, P. K. (2020). Taiwan’s people-centered New Southbound Policy and its impact on US–Taiwan relations. The Pacific Review, 33(5), 813–841. https://doi.org/10.1080/09512748.2019.1594349
Fildes, R., & Petropoulos, F. (2015). Simple versus complex selection rules for forecasting many time series. Journal of Business Research, 68(8), 1692–1701. https://doi.org/10.1016/j.jbusres.2015.03.028
Goh, S. K., Wong, K. N., & Tham, S. Y. (2013). Trade linkages of inward and outward FDI: Evidence from Malaysia. Economic Modelling, 35, 224–230. https://doi.org/10.1016/j.econmod.2013.06.035
Huang, K. B. (2018). Taiwan’s new Southbound Policy: Background, objectives, framework and limits. Revista UNISCI Journal, 8(46), 47–68.
Hsu, T. T. K. (2017). A review of Taiwan’s old and new go south policy: An economic perspective. Prospect Journal, 18, 63–87.
Kalirajan, K. (2007). Regional cooperation and bilateral trade flows: An empirical measurement of resistance. The International Trade Journal, 21, 85–107. https://doi.org/10.1080/08853900701266555
Kozlova, A., & Miečinskienė, A. (2016). The research on interface between Lithuanian direct investment abroad and foreign trade flows. Business, Management and Economics Engineering, 14(1), 136–151. https://doi.org/10.3846/bme.2016.321
Lin, H. L., Hsiao, Y. C., & Lin, E. S. (2015). Do different types of FDI strategies spur productivity and innovation capability growth? Evidence from Taiwanese manufacturing firms. Journal of Business Economics & Management, 16(3), 599–620. https://doi.org/10.3846/16111699.2012.732957
Li, C. Y., Lai, A. C., Wang, Z. A., & Hsu, Y. C. (2019). The preliminary effectiveness of bilateral trade in China’s belt and road initiatives: a structural break approach. Applied Economics, 51(35), 3890–3905. https://doi.org/10.1080/00036846.2019.1584387
Lee, R. C., & Sun, G. (2019). Economic relationship between Taiwan and ASEAN, and the implications of the new Southbound Policy. Defense Strategy and Assessment Journal, 9, 65–89.
Kourentzes, N., Barrow, D., & Petropoulos, F. (2019). Another look at forecast selection and combination: Evidence from forecast pooling. International Journal of Production Economics, 209, 226–235. https://doi.org/10.1016/j.ijpe.2018.05.019
Ouenniche, J., & Tone, K. (2017). An out-of-sample evaluation framework for DEA with application in bankruptcy prediction. Annals of Operations Research, 254, 235–250. https://doi.org/10.1007/s10479-017-2431-5
Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Judgmental selection of forecasting models. Journal of Operations Management, 60(1), 34–46. https://doi.org/10.1016/j.jom.2018.05.005
Tone, K., & Tsutsui, M. T. (2015). How to deal with non-convex frontiers in data envelopment analysis. Journal of Optimization Theory and Applications, 166, 1002–1028. https://doi.org/10.1007/s10957-014-0626-3
Tone, K. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3–4), 145–156. https://doi.org/10.1016/j.omega.2009.07.003
Thomson, M. E., Pollock, A. C., Onkal, D., & Gonul, M. S. (2019). Combining forecasts: Performance and coherence. International Journal of Forecast, 35(2), 474–484. https://doi.org/10.1016/j.ijforecast.2018.10.006
Thomakos, D. D., & Guerard, J. B. (2004). Naïve, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance. International Journal of Forecast, 20, 53–67. https://doi.org/10.1016/S0169-2070(03)00010-4
Svetakov, I., & Kourentzes, N. (2018). Complex exponential smoothing for seasonal time series (Working Paper). Lancaster University.
Svetunkov, I., & Petropoulos, F. (2018). Old dog, new tricks: A modelling view of simple moving averages. International Journal of Production Research, 56(18), 6034–6047. https://doi.org/10.1080/00207543.2017.1380326
Yang, A. H., & Chiang, J. H. C. (2019). Enabling human values in foreign policy: The transformation of Taiwan’s new southbound policy. Journal of Human Values, 25(2), 75–86. https://doi.org/10.1177/0971685819826707