Determinants of FDI Location in China using the Conditional Logit Model

1. Determinants of FDI Location in China using the Conditional Logit Model How to Resolve Regional Economic Disparity in China by Doowon Lee & Song Lim

2. Introduction • Purpose More than 70 percent of FDI into China are concentrated in coastal area. In this paper, we analyze the differences in the determinants of FDI into china between the coastal area and hinterlands, and find ways to diffuse FDI from costal area into hinterlands. • Method Panel Analysis & Conditional Logit Model.

3. Status of FDI to China Figure 2-1, FDI Flows to China ( Unit: USD 100,000,000) Source: China Statistical Yearbook, China City Statistical Yearbook

4. Status of FDI to China Table 2-2, Upper 10 Countries in Real FDI Flows to China ( Unit: USD 10,000) Source: China Statistical Yearbook

5. Status of FDI to China Figure 2-4, Real FDI Flows to China by Region(Unit: USD 10,000) Source: China City Statistical Yearbook Figure 2-5, Number of Registered Foreign Firms by Region at Year-end Source: China Statistical Yearbook

6. Status of FDI to China Figure 2-6, Results of Cluster Analysis of FDI to China by Region(1992~2005) Real FDI Flows Number of Registered Foreign Firms Source: China City Statistical Yearbook Source: China Statistical Yearbook

7. Introduction Chinese Map

8. Previous Studies • Panel analysis • Changsu Lee(2003) analyzed the determinants of FDI location in China by Korean enterprises and compared it with those by the world’s total enterprises. Results shows that the main factors of FDI are investor friendly FDI policies, human resource and lower factor cost. • Shenghua Li(2005) showed that GDP per capita, total trade volume, human resource and investment in fixed assets are positive in determining FDI from the world, while high wage and land prices are negative. Dependent variable was the real FDI flows to China from 1990 to 2002. • Myeonggi Jeong(2005) revealed that GDP size, average annual wage, human resource, infra-structure and regional nearness are attractive to foreign firms. • Ichiro Iwasaki & Keiko Suganuma(2004) analyzed the determinants of FDI in global level to Russia from 1996 to 2003. Average of annual temperature, mineral reserves in the region and market GRP and investor friendly FDI policies were shown as the important determinants of FDI location.

9. Previous Studies • Conditional Logit Analysis • Douglas P. Woodward(1992) analyzed Japanese manufacturing FDI to America from1979 to 1985, They found that market size, agglomerated manufacturing, labor productivity, level of education were positive determinants. Also, the labor union, density of blacks, unemployment, rate of poverty were negative. • Keith Head, John Ries, Deborah Swenson(1995) analyzed Japanese manufacturing FDI to America from 1979 to 1987. They found that the most important factors were agglomeration of Japanese KEIRETS companies. • Ryouhei Wakasugi(1997) analyzed the determinants of FDI location of Japanese enterprises to east Asia, and compared it with those of the global firms. Results showed that the rate of economic growth was positive to decision to undertake FDI, and high wage was negative. • Syujiro Urata, Hiroki Kawai(1999) examined the determinants of Japanese manufacturing FDI to developing countries, and compared it with those of developed countries. The important factors to attract FDI were the size of local market, good infra-structure, low wage and good governance.

10. Model • Panel analysis • OLS regression using the data that explains the regional characteristics from 1992 to 2005. • Housman test

11. Model • Conditional Logit Model • This model was introduced by McFadden(1974). • Let’s assume that the profit of foreign firm obtained from undertaking FDI to region is defined as; (1) is unknown parameters, is the variables describing the characteristics of region . • We can get the below equation (2) from (1). (2)

12. Model • Conditional Logit Model • Let’s define the probability of undertaking FDI to region by foreign firm as; (3) • When a foreign firm undertakes ’s FDI to region , the probability of undertaking FDI to region is described as; (4) • Log Likelihood function (5) • We should estimate the parameter which maximize the equation (5).

13. Data Table 5-1, Variable Definition Table 5-2, Correlations

14. Data Table 5-3, Summary Statistics

15. Empirical Results Table 5-4, Result of Panel Analysis(1992~2005, Fixed Effect ) Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.

16. Empirical Results Table 5-5-A, Dummy Estimates of Fixed Effect Panel Analysis (Defendant Variable: FDI Flows ) Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.

17. Empirical Results Table 5-5-B, Dummy Estimates of Fixed Effect Panel Analysis (Defendant Variable: Number of Registered Foreign Firmsby Region) Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.

18. Empirical Results Table5-6, Results of Conditional logit Analysis ( Yearly) (Dependent Variable: Number of Registered Foreign Firms by Region) Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.

19. Empirical Results Table5-7, Results of Conditional logit Analysis ( Coastal Area, 1995~2005) Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.

20. Empirical Results Table5-8, Results of Conditional logit Analysis ( Hinterland, 1995~2005) Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.

21. Conclusions • There are significant differences in determinant of FDI locations between coastal area and hinterlands. 1, Empirical results by panel analysis show that estimated coefficients for dummy variables for coastal areas are much higher than those for hinterlands. Especially, it is top in coastal area such asGuangdong, Zhejiang, Jiangsu and Shanghai, while it hits the bottom in hinterland such as Heilongjiang, Sichuan, Shanxi. 2, Conditional analysis shows thatforeign firms are more picky (sensitive) in selecting their FDI locations when they invest into hinterlands than into coastal area. • Foreign firms are sensitive to the agglomeration of FDIin coastal area while they do not evaluate it as determinant of investment in hinterland. • They focus on the market size in the coastal area. The bigger estimate for coefficient of consumption in coastal area proves this point. • High wage is more negative to foreign firms in hinterland than coastal area. • Infra-structure such as roads and railway in coastal area is not as important as those in hinterlands. • They value the importance of human-resource more in hinterland than that of coastal area. It reflects the fact that there is not enough number of highly educated or highly skilled human resource in hinterland.

22. Conclusions • Differences of estimators between coastal area and hinterland show us; 1, It is very difficult to diffuse the FDI from coastal area to hinterland, this difficulty will make the disparity of economic development between these two areas even more permanent. 2, Therefore, it is necessary to make hinterlands be attractive to foreign FDI • Economic size such as GDP per capita, consumption and the agglomeration of FDI are important determinations of FDI location, but it is difficult to improve them in the short term. • Low labor cost is truly attractive to foreign firms, but it conflicts to regional economic development. 3, Regional governments in hinterland should focus on improving the investment environment through investment in infra-structure and human-resource.

23. Thank you!