The Effect of Human Capital Flow on FDI Technological Advances :

An Empirical Study Based on Absorptive Capacity

Li Ping Unit: School of Economics, Shandong University of Technology Address: No.12 ZhangZhou RD, Shandong University of Technology, Zibo City, P.R.China Post number: 255049 E-mail: lpkx@sdut.edu.cn

Xu Jiayun Unit: School of Economics, Shandong University of Technology Address: No.12 ZhangZhou RD, Shandong University of Technology, Zibo City, P.R.China Post number: 255049 E-mail: xujiayun321@163.com

Abstract Human capital flow is an important factor that affecting the “absorptive capacity” of host countries, it is of great significance to strengthen the host countries’ absorptive capacity of FDI technology spillovers. Taking China for example, this paper made an empirical analysis about the effect of human capital flows on FDI technological advances, we selected the number of foreign students and the number of students that return home, as well as the employment rate of foreign Enterprise as proxy indicators of human capital flows, using data envelopment analysis (DEA) to measure the technological progress of China. The empirical results showed that: FDI and human capital flows, combined with technological advances were positively correlated, but the positive effect was not significant. China should improve the quality and enhance the overall level of human capital flows, in order to create a superior environment of qualified personnel for FDI technology spillover.

Key words Human Capital Flows, FDI Spillover, Absorptive Capacity

I. INTRODUCTION The new theory of economic growth suggests that,

technological progress is the driving force of economic growth. Subject to their own capacity of innovation constraints, FDI technology diffusion is an important source of technological progress in developing countries. The degree of FDI technology spillovers is closely related to the absorptive capacity of host countries.

By sorting out the literature at home and abroad, we find that the situation of host country’s human capital flows is a very important aspect of FDI technological absorptive capacity, which can restrain or enlarge the technology spillover effect of FDI by staff mobility between TNCs and local enterprises, multinational companies and personnel training input (cost savings), competition among enterprises and other channels. Rational and orderly flow of human capital can provide a good environment for FDI in the host country to improve the host

country’s technological absorptive capacity and promote FDI technology spillovers.

Technology spillover and knowledge dissemination with talent mobility as the carrier is becoming an important channel for developing countries’ enterprises to improve their ability to innovate. However, previous studies are mostly qualitative research, few articles about the production and implementation mechanism of human capital spillover effects are from a quantitative point. This study will incorporate the domestic human capital flows of host countries into one of the main factors of FDI technology absorptive capacity, to further improve the research of technological absorptive capacity of the host country in enhancing the FDI spillover effects, and it is of great practical significance for better interpretation of differences in FDI spillover effects.

II. MODEL SETTINGS AND MEASUREMENT OF VARIABLES

A. Model Settings Based on the basic econometric model of international

R&D spillover given by Coe and Helpman in 1995, and in order to test effects of human capital flows’ technological progress, we use the commonly used cross-item testing methods to test its effects. We take on the values of all variables, in order to eliminate the Heteroscedasticity, the model is as follows:

0 1 2ln ln * lnl fdiA H S RDβ β β γ= + + + (1) Where fdiS states the capital stock by means of FDI, RD

states the domestic R&D stock of the host country, TFP is total factor productivity, ln A states technological level, lH states human capital flow, which includes international human capital flow ( 1lH ), domestic human capital flow ( 2lH ) and an integrated variable nw ( 1 2*l lH H ). The final models are as follows：

10 1 2ln ln * lnlA H FDI RDβ β β γ= + + + (2)

978-1-4244-5326-9/10/$26.00 ©2010 IEEE

20 1 2ln ln * lnlA H FDI RDβ β β γ= + + + (3)

0 1 2ln ln * lnA nw FDI RDβ β β γ= + + + (4)

B. Measurement of Variables • Estimates of TFP: This paper used DEA-based

Malmquist index method proposed by FAR et al. (1994) to estimate the total factor productivity change in China. All samples include 30 provinces and cities(Chongqing included in Sichuan Province, excluding Hong Kong, Macao and Taiwan regions) from the year 1985 to 2007. Relevant data is available from China Statistical Yearbook, 55 years of statistical data compilation of New China and so on.

• Measurement of H : 1lH : The number of foreign students attracted by China and returned students divided by the number of people educated in the corresponding year. 2lH : The ratio of labors that have easy access to foreign advanced technology, these labors played an important role in the spread of technology. The ratio of the employed population in foreign-invested enterprises of China (including Hong Kong, Macao and Taiwan-funded enterprises) and the total number of employed population. NW equal to the product of 1lH and 2lH .

• Measurement of fdiS : We select eight developed countries that have more actual foreign direct investment in China during 1985-2007, and use LP method to calculate the stock of imported FDI R&D spillovers:

10

1

jtf fdi dt jt

j jt

FDIS S

GDP−

=

= ∗∑ (5)

where jtFDI represent the actual foreign direct investment into China from country j in the year of t , jtGDP represent country j ’s gross domestic product in the local currency price of the year, with the year of 1985 as the base period, converted into a unified price of U.S. dollars that year according to ppp exchange rates.

djtS represent country j ’s domestic R&D stock. The

amount of FDI inflows into China over the years are from the Statistical Yearbook of China's foreign trade and China Statistical Yearbook; Foreign GDP are from IMF World Economic Outlook Database, 2009; national R&D investment data are from OECD’s website The OECD Factbook 2009.

• We adopt the method of Li Ping(2006) to measure RD stock of China.

III. EMPIRICAL RESULTS AND ANALYSIS Most of time series have non-stationary phenomenon, a

simple regression may generate “false return”, so we will test the stability of time series and the mutual cointegration

relationship between them by the ADF unit root test and Johansen Cointegration test.

A. Smooth Test We test the stability of time series by the ADF unit root

test, results are as follows:

Table 1 ADF Stationarity Test Results Variable ADF test value Test Forms

(c，t，k) Conclusion

LNA -0.94 (c,0,4) Stable

LNRD 0.22 (c,0,4) Unstable

1( * )l fdiLN h S -0.96 (c,0,4) Unstable

2( * )l fdiLN h S -1.05 (c,0,4) Unstable

( * )fdiLN nw S -1.05 (c,0,4) Unstable

LNRDΔ -3.24 (c,0,4) Stable

1( * )l fdiLN h SΔ -3.4 (c,0,4) Stable

2( * )l fdiLN h SΔ -1.21 (c,0,4) Stable

( * )fdiLN nw SΔ -4.77 (c,1,4) Stable

Note: The test form (C, T, K) represent unit root test equation, including the constant term, time trends

and lag order number, 0 means it does not include time trend. Δ meansfirst order difference.

B. Cointegration Test We use Johansen Cointegration test to determine the

cointegration relationship between the non-stationary series. Test results are as follows:

Table 2 Variables Cointegration Test Results Hypothesis r = 0 r ≤1 r ≤2 the number of

Cointegration

Model 2 31.552** 17.345** 7.515** 3

Model 3 50.308** 24.513** 5.156** 3

Model 4 50.308** 24.513** 5.156** 3

Note: r indicades the number of cointegration relations, the data in the table are Eigenvalue trace statistic of the co-integration test, ** indicates rejecting the null hypothesis at the significance of 5% .

C. Regression Analysis Through the above test, the regression equation becomes:

0 1 2ln ln * lnfdiD A D H S D RDγ γ γ λ= + + + (6) To avoid self-relevance, this paper uses GLS method to

estimate the above models, the regression results are in the following table:

Table 3 Regression Results Variable Model 2 Model 3 Model 4

lnD RD -0.149（-1.75*） -0.66（-1.83*） -0.66（-1.83*）

1ln *l fdiD h S 0.238（2.43**）

2ln *l fdiD h S 0.37（2.28**）

ln * fdiD nw S 0.39（2.28**）

F statistic 2.86 9.83 9.83

2R 0.24 0.65 0.65

D.W. 2.45 2.2 2.17

Note: The values in brackets are t-test statistics for the variable, * indicates rejecting the original hypothesis at the significance of 10% , *** indicates rejecting the original hypothesis at the significance of 5% , *** indicates rejecting the original hypothesis at the significance of 1% .

From the regression results of model 2 and 3 in the above table, we can see that the cross-term coefficient of Indicators of human capital flows and FDI technology spillovers are significantly positive at the significance of 5%, indicating that China’s current human capital flow has a significant positive effect on FDI technology spillovers. Foreign employees are the groups most closely between domestic and FDI in China. With the accumulation of experience and the updation of ideas, a significant portion of them may choose to return to domestic companies as executives or create their own businesses. Therefore, how to guide and encourage the employment of Chinese workers in foreign-funded enterprises to enter their own businesses or start their own businesses is another important way to improve the technology absorptive capacity and to promote economic growth of China.

In model 4, the coefficient of the cross-term is positive, and the comprehensive index nw ’s role of technological progress is greater than their individual role in promoting the host country’s technological advances, indicating that the integrated variable nw has a significant role in promoting the effects of FDI technological advances. Overall, that is, the current situation of China’s human capital flow increased the absorptive capacity of China.

IV. CONCLUSIONS AND RECOMMENDATIONS This paper selected three proxy indicators of human capital

flow and take advantage of China’s statistical data from the year 1985 to 2007. Through the empirical analysis, we obtained the following conclusions: The introduction of foreign capital in China should focus its technical spillover effect which can be reached by enhancing China’s technological absorptive capacity; In order to enhance China’s technological absorptive capacity: From the perspective of human capital, we should increase investment in education, enhance the human capital stock, particularly we should emphasis on attracting human capital flows from abroad to domestic, and from foreign enterprises to domestic enterprises.

A. Positive Effect Human capital flow has positive effect for China in

enhancing its absorptive capacity of FDI technology spillovers. As the world’s largest developing country, from the time of reform and opening up, with the rapid development of China’s economy and culture, China’s total number of students studying abroad has reached 1.36 million; 90 years after the beginning of the 20th century, the number of returnees has been increasing rapidly in China, and the return rate has reached 60%. At the same time, the number of foreign students also increased, the size of foreign students coming to China in recent years expand with more than 30% annual growth rate.

On the one hand, such kind of international talent exchange will help China attract more FDI, which can enlarge the possibility of FDI technology spillovers. On the other hand, international human capital flow has a positive effect on enhancing China’s absorption ability of FDI technology

spillovers. International talent exchange can lay a solid talent foundation for China’s high-tech progress, and it can linked to the formation of a large community of people, where alumni associations and other social networking can communicate freely in this community, they can discuss the exchange of information on domestic economic and social opportunities and various venture capital. All in all, these communities can provide help and support for the domestic government or enterprises to a certain extent through a variety of ways, and improve China’s level of human capital, enhance our absorptive capacity of FDI technology spillovers.

B. Status China’s current human capital flow system is still not

perfect, the potential positive effect of human capital flow on technological advances hasn’t been fully played, and it is importment to realize that it still need to develop. Human capital flows are the most direct and most important aspects in the mechanism of FDI spillover, it is the most productive and efficient way to change the knowledge stock of employees in FDI enterprise into productivity. However, the movement of persons in this area is relatively closed, So:

First, Popular domestic enterprises and collective enterprises should become efficiency from internal, improve their working environment, in order to provide a good foundation for human resources. Secondly, we should vigorously improve China’s labor market and increase information transparency. The employment mechanisms of foreign-invested enterprises is relatively flexible, entry and exit barriers are relatively small, but there exists the phenomenon of “market absence” in many domestic enterprises, which can be due to the severe information asymmetry, the high search cost between employers and employees.

C. Recommendations To solve this problem: First, we should establish and

perfect the relevant intermediary, facilitating both sides’ information exchange. Second, improve network and reduce search costs. Through the above measures, it can make a part of skilled workers, senior managers and technical employees do contributions for domestic enterprises by their knowledge and experience acquired from their original FDI enterprises, and finally create technology spillovers.

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