Changing Dynamics of Foreign Direct Investment in China ’ s Automotive Industry

China’s automotive industry has developed dramatically in recent years as more and more major multinational corporations (MNCs) in this industry began to invest in China. Most of these investments have developed in the form of joint-ventures with Chinese state owned enterprises (SOEs). This paper contributes to the current literature by studying the effect of foreign direct investment (FDI) on the productivity of the automotive industry in China using panel data during the 1999 –2008 period. Channels through which FDI may directly and indirectly affect the productivity are investigated using pooled ordinary least squares model (POLS) and fixed effects model (FES) to estimate the influence of FDI on productivity in the automotive industry. The results suggest that FDI plays a negative role in this industry and suggests that there is a need for Chinese government to modify its policies and practices in order to improve the productivity of such a key industry in the Chinese economy.


Introduction
Automobile industry has been the main driver of the intensification of technological changes in the 19 th century (Womack, Jones, and Roos, 1990).
More importantly, however, in recent years, automobile industry has been one of the most important heritors of Foreign Direct Investment (FDI), especially in emerging markets.The importance of automotive industry is very well accepted in the field of international business as it contributes to the economic development of any region where it is established.This is mostly due the fact that when established it creates millions of direct and indirect manufacturing employment, and hence generates growth of related upstream and downstream industries.In the United States, for example, the automotive industry and its related industries comprise 10 % of the GDP (Maxton and Wormald, 2004).In the developing countries, a burgeoning domestic auto industry is a key contributing factor of the industrialization process.This is especially true in the case of China.
However, industrial development In 2009, China produced more than 13 million vehicles, which was equivalent to 18 % of the total world production, and thus became the largest automotive producer surpassing the US and Japan (Chang, 2010).
According to previous literature, FDI plays an important role in the development of China's automotive industry.In theory, FDI promotes the host country's industrial productivity through the following: 1) the development of new products and processes; 2) the demonstrationimitation effect; and 3) the linkages effect and the worker training effect (Romer, 1990;Grossman and Helpman, 1991;Markusen and Venables, 1999).
However, previous literature have also suggested that at times the industrial productivity in a host country may not benefit from FDI because of technology diffusion restrictions imposed by MNCs, particularly those with affiliations in the host countries that decrease the linkage effects or keep the skills and the know-how secret (Teece, 1977;Das, 1987;Caves, 1996).To

Backward and Forward
Linkages: A Backward linkage is the linkage between MNCs and suppliers, while a forward linkage occurs between the MNCs and their customers and the companies that buy their products (Rodriguez-Clare, 1996).Backward linkages may help local suppliers promote their productivity by providing technical and information assistance (Belderbos, Capannelli and Fukao, 2001;Javorcik, 2004).In forward linkages local distributors and downstream firms can benefit from the MNC's knowledge to access higher-quality and/or lowerpriced products.

III. Model, Data and Methodology
We employ the widely adopted Cobb-Douglas production function model to test the relationship and the link between productivity and FDI.
Since changes in technology add value (Romer, 1990;Grossman and Helpman, 1991;Barro and Sala-i-Martin, 1995)   Based on the adopted production function, the following hypotheses are postulated: H

V. Conclusion
This paper focuses on the effects of FDI on the productivity of the

F-Testing
Hypothesis: In the regression output, the probability of F-statistic=0, so we can reject at 1% level.Therefore, the overall fit of the equation is statistically significant at 1% level.

Hypothesis Testing
1. Test the sign and significance of Ln(L) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slop coefficient of Ln(L) is positive as we expected.The P-value is 0.3403 for one tail, which is insignificant at 1%, 5% and 10% level.Therefore, we cannot reject at all levels.
2. Test the sign and significance of Ln(K) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(K) is negative as we unexpected.The P-value is 0.1613 for one tail, which is insignificant at 1%, 5% and 10% level.Thus, we cannot reject at all levels.
3. Test the sign and significance of Ln(H) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(H) is negative as we unexpected.The P-value is 0.015 for one tail, which is insignificant at 1% level of confidence, however is significant at 5% and 10% level of confidence.Therefore, we cannot reject at all levels.
4. Test the sign and significance of Ln(R) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(R) is positive as we expected.The P-value is 0.00125 for one tail, which is significant at 1%, 5% and 10% level.As a result, we can reject at all levels.
5. Test the sign and significance of Ln(F) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(F) is negative as we unexpected.The P-value is 0.0167 for one tail, which is insignificant at 1% confident level but 5% and 10% level.Therefore, we cannot reject at all levels.
6. Test the sign and significance of Ln(S) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(S) is positive as we expected.The P-value is 0.3725 for one tail, which is insignificant at 1%, 5% and 10% level.As a result, we cannot reject at all levels.
7. Test the sign and significance of Ln(G) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(G) is positive we expected.The P-value is 0.0158 for one tail, which is insignificant at 1% level but significant at 5% and 10% level.Therefore, we cannot reject at 1% level but we can reject at 5% and 10% level.
8. Test the sign and significance of Ln(E) at the 1%, 5% and 10% level.

Hypothesis: ≤0, >0
The slope coefficient of Ln(E) is positive as we expected.The P-value is 0 for one tail, which is significant at 1%, 5% and 10% level.Thus, we can reject at all levels.Therefore, it should belong to the equation.

Irrelevant Variables and Omitted Variables Testing Ln(L)
To sum up, the variable Ln(K) should belong to this equation.Therefore, it should belong to the equation.

Testing Ln(H)
To sum up, the variable Ln(F) should belong to this equation.

Testing Ln(S)
is not a new phenomenon in China.The Chinese auto industry developed rapidly after economic reform and a policy of openness to business were implemented with the open door policy since 1978.Yet, despite the economic reforms and increased openness, a large quantity of automobiles was still imported to satisfy the domestic market demand.In the beginning, FDI entered into China through joint ventures and the first joint venture in China's automotive industry was established between the Shanghai Auto Factory and the German Volkswagen in 1985.Since then, analysis is required.Hence, the purpose of our empirical investigation is to estimate the effects of FDI on the productivity of the Chinese automotive industry during the period of 1999-2008.Specifically, we examine the channels through which FDI may affect the productivity of the auto industry and whether the interaction between FDI and human capital can influence the FDI-productivity link.The paper is organized as follows: in Section II, a literature review is presented.on the FDIproductivity links, there are five interrelated modes through which FDI may impact a host country's productivity directly and indirectly (Caves, 1996; Markusen and Venables, 1999).The direct effect of FDI is defined as the impact on the productivity of firms that results from receiving FDI.The introduction of capital, new products, ideas and practices, new management skills lead to direct transfers of technology.The establishment of R&D centers is also considered a direct effect of FDI.The indirect effect of FDI, however, is the influence that a MNC's presence has on the productivity of local firms in the form of spillovers from foreign firms to local ones.In other words, what MNCs attempt to keep as proprietary knowledge and technology, will eventually result in indirect transfers of technology (Blomström and Persson, 1994).For example, backward and forward linkages, training effects, Here, new technologies can be introduced with the presence of FDI in the form of new ideas, products and procedures.New skills to operate the technologies are introduced and developed by FDI(Das, 1987; Grossman   and Helpman, 1991).Furthermore, a host country's stock of ideas can be augmented by those new ideas brought by MNCs, thus innovation is stimulated.
Where: Y (productivity) is taken as the current value-added in each subsectors of China's automotive industry.L (input of labor) is measured by the total number of employees in each sub-sector.K (Domestic capital stock) is defined by the current value of total domestic capital formation in each subsector.This suggested definition is in line with previous research, which assumes that FDI leads to increases on the domestic stock of capital and production capacity (According to Egger and Pfaffermayr, 2001).H (Human capital) is measured by the ratio of the number of technical staff to the annual average number of employees in each industry sub-sector.Human capital demonstrates the level of skill or education of employees.effects from FDI) is measured by the current value of FDI stock in each sub-sector.Since FDI transfers capital, technology and management skills to their affiliates in host country, the greater value the foreign investment inflows will lead to the higher productivity.S (Spillovers of FDI) is proxied by the ratio of output by foreign-invested enterprises in the sub-sectors of China's automotive industry to each sub-sector's total output.G (Absorptive Capacity) is measured by the product of each subsector's human capital and FDI stock (Size) is measured by the ratio of the total value of industrial output in each sub-sector to the number of firms in each sub-sector.Firm size stands for the economies of scale since it is an important factor that affects the productivity in the automotive industry.
The results from the FES model display that domestic technological efforts Ln(R), absorptive capacity Ln (G) and firm size Ln(E) are positive as expected.Ln(R) and Ln(E) are statistically significant at a 1 % level and Ln(G) is statistically significant at a 5 % level.The coefficient for Ln(R) is positive and statistically significant at the 1 % level, indicating that R&D positively affects the productivity in China's automotive industry.The magnitude of Ln(R) may mean that when other variables are kept constant, a 1% increase in R&D increases productivity by 0.268 %.The coefficient for Ln (G) is positive and statistically significant at the 5 % level, showing that the absorptive capability positively affects productivity in China's automotive industry and that domestic human capital plays a role in capturing the benefits from FDI.In addition, The magnitude of Ln(G) indicates that when other variables are kept constant, a 1% increase in absorptive capability will raise productivity by 2.018744 percent.The magnitude of the coefficient Ln (E) indicates that when other variables are kept constant, a 1% increase economy of scale will raise productivity by 1.108 %.The coefficient for Ln (E) is positive and statistically significant at the 1 % level, demonstrating that economy of scale positively affects productivity in China's automotive industry.This is an important finding and contribution to the emerging markets literature.On the other hand and surprisingly, foreign direct investment Ln (F) and human capital Ln (H) are negative and statistically significant.Input of labor Ln (L) and spillover in FDI Ln(S) are positive as expected;

FDI
082*Ln(L it )-0.2162*Ln(K it )-2.047*Ln(H it )+0.2683*Ln(R it )-1.9853*Ln(F it )+ 0.0264*Ln(S it )+2.0187*Ln(G it )+ 1.1076*Ln (E it ) 2. T-test:The P-value of Ln(S) for one tail is 0.3725, which is significant at 5% and 10% level.Thus, it should belong to the equation.3. Adjusted R-squared: the increased slightly from 0.9813 to 0.9817.It indicates that Ln(S) should be an irrelevant variable.4. Bias: with Ln(S) removed, some of the coefficients changed significantly.Therefore, it should belong to the equation.To sum up, the variable Ln(S) should belong to this equation.
. T-test: The P-value of Ln(H) for one tail is 0.015, which is significant at 5% level.Thus, it should belong to the equation.3.Adjusted R-squared: the decreased slightly from 0.9813 to 0.9792.It indicates that Ln(H) should be a relevant variable.4.Bias: with Ln(H) removed, most of the coefficients changed significantly.Therefore, it should belong to the equation.To sum up, the variable Ln(H) should belong to this equation. 2 Dependent Variable: LN(?Y) Method: Pooled Least Squares Date: 03/20/11 Time: 00:10 Sample: 1999 2008 Included observations: 10 Number of cross-sections used: 5 Total panel (balanced) observations: 50 1. Theory: as hypothesis 6 mentioned, this variable is sound theoretically.