The Effect of OFDI Reverse Technology Spillovers from ASEAN on China's Economic Growth

Abstract

Based on the panel data of Chinese outward foreign direct investment(OFDI) to 26 Association of Southeast Asian Nations(ASEAN)countries from 2003 to 2010,Using Integration, error correction model, impulse response function and the GLS method,this paper analyzes the effect of OFDI reverse technology spillovers(RTS) from the ASEAN on China's economic growth,and finds:(1)whether from short-term or long-term perspective,the role of RTS on promoting China's economic growth is proved,but the effect size is less than FDI;(2)The FDI from ASEAN can promote China's OFDI to ASEAN countries, however the OFDI cannot significantly promote FDI to increase;(3)In the long run, the effect gap between OFDI and FDI is narrowing, especiallyafter the economic crisis this trend is more obvious;(4) Developed countries have a greater impact on China's economy than developing countries. Introduction To get OFDI reverse overflow is a very important reasonfor China's foreign direct investment (GAO Wen Ling 2012).In other words, through foreign direct investment, multinational enterprises can be close to the state-of-the-art R & D resources in host country, and then get technology spillover from the host country to the home country, this kind of effect is called reverse spillovers. Duning (1994) Brunovan Pottelset al (2011) , Nigel Driffield & James HLove (2009, 2012) , Zouyu Juan (2012) , Yin et al (2013), all did a special study about this from different perspectives. However, the above research isonly about the impacts of the OFDI on China’s trade (including import and export)or GDP, on the one hand, does not distinguish between the unique role of the different economies, on the other hand, directly using OFDI data cannot reflect the size of the reverse technology spillovers. In this paper, We first use the OFDI reverse technology spillover model corrected from BK model to measure ASEAN's OFDI reverse overflow size,and then test the impact of this reverse overflow on short-term and long-term economic growth in China, and finally give policy recommendations. OFDI Reverse Technology Spillover Correction Model The modified reverse technology spillover model used in this article is based on the BK (Bitzer & Kerekes, 2008) model, as shown in Figure 1: 2nd International Conference on Science and Social Research (ICSSR 2013) © 2013. The authors Published by Atlantis Press 118 i i ij i j j j OFDI S b G G k = Figure 1. OFDI reverse technology Spillover model Which i OFDI is the foreign direct investment stock of an investment country; j k isthe investment stock of domestic fixed assets in investment country; means efficiency coefficient of reverse technology overflow which is introduced in the dashed box, and measured by “bilateral trade volume” and “technology distance” between and of the two countries; is the home country's absorption capacity; is the knowledge ability of the host country which is calculated by energy exports, number of scientific papers, patent applications and the high-tech exports of the host country. Model Specification and Data Selection Consider the logarithmic form of extended Cobb-Douglas production function to measure the impact of FDI and OFDI technology spillover on economic growth, as follows: (1) Among them: 1) Y represents output, measured by annual real GDP (billion) ,adjusted to 2000 constant prices; 2) L means labor level, using the “employed persons in urban and rural areas (tend of the year)” indicatorin China Statistical Yearbook ,million units; 3)K is fixed capital stock, measured by the sustainable archiving method, seeing (2). (2) Which is annual investment, adjusted to 2000 constant prices; is the depreciation rate, setting at 9.6%. 4)FDI: china received the direct investment flows from the respective countries, units of ten thousand U.S. dollars.5)OFDI: the OFDI reverse technology spillover effect size calculated in accordance with the above amendment model;What’s more, China's imports are set to the control variables and dummy variables are constructed to reflect the type of the host economy, such as the developed countries are set to 1, and 0 otherwise. Empirical test of ASEAN First make sure the data through variable unit root test, and granger causality analysis also proved that there is a strong causal relationship between variables, then measure the specific coefficient of the variables by regression analysis. A.Error correction model and the GLS model First to generate first order difference of the variables using Eviews software, and then to do regression analysis, the error correction model meaning short-term effects can be established: ln 0.801 ln 0.296 ln 0.165 ln 0.041 ln (4.922) (4.669) (1.984) (2.344) 0.290 ln 0.537 0.001 (21.423) (14.254) (14.864) GDP K L FDI OFDI t X u Δ = Δ + Δ + Δ + Δ = + Δ − −