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

Determinants of FDI Location in China using the Conditional Logit Model

How to Resolve Regional Economic Disparity in ChinabyDoowon Lee & Song Lim

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.

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

0

100

200

300

400

500

600

700

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Real FDIto China

0

50000

100000

150000

200000

250000

300000

Resistered ForeignFirms at Year- end

Real FDI to China

Resistered ForeignFirms at Year- end

Status of FDI to China

　 1995 2000 2005

No Country FDIShare(

%)Country FDI

Share(%)

Country FDIShare(

%)

1Hong Kong

2,040,183 42.39 Hong Kong

1,549,998 38.07 Hong Kong

1,794,879

29.75

2 Japan 511,332 10.62 USA 438,389 10.77 Virgin Is 902,167 14.96

3 Taiwan 316,516 6.58 Virgin Is 383,289 9.41 Japan 652,977 10.82

4 USA 313,466 6.51 Japan 291,585 7.16 Korea

Rep.516,834 8.57

5 Singapore 186,061 3.87 Taiwan 229,658 5.64 USA 306,123 5.07

6Korea

Rep.119,053 2.47 Singapore 217,220 5.34 Singapore 220,432 3.65

7 U.K 100,931 2.10 Korea

Rep.148,961 3.66 Taiwan 215,171 3.57

8 France 71,626 1.49 U.K 116,405 2.86 Cayman Is 194,754 3.23

9 Canada 61,966 1.29 German 104,149 2.56 Germany 153,004 2.54

10 Italy 54,780 1.14 France 85,316 2.10 Samoa 135,187 2.24

　 Others 1,037,355 21.55 Others 506,541 12.44 Others 940,931 15.60

　 Total 4,813,269 100.00 Total 4,071,481 100.00 Total6,032,45

9 100.00

Table 2-2, Upper 10 Countries in Real FDI Flows to China ( Unit: USD 10,000)

Source: China Statistical Yearbook

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


0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

Bei j

i ng

Tian

j in

Hebe

i

Shan

xi

I nne

r Mo

ngol

i a

Liao

ning

J il i

n

Hei l

ongj

i ang

Shan

ghai

J ian

gsu

Zhej

i ang

Anhu

i

Fuj i

an

J ian

gxi

Shan

dong

Hena

nHu

bei

Huna

n

Guan

gdon

g

Guan

gxi

Hai n

an

Sich

uan

Gui z

hou

Yunn

anXi

axi

Gans

u

Qing

hai

Ning

xia

Xinj

i ang

199520002005

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Bei j

i ng

Tian

j in

Hebe

i

Shan

xi

I nne

r Mo

ngol

i a

Liao

ning

J il i

n

Hei l

ongj

i ang

Shan

ghai

J ian

gsu

Zhej

i ang

Anhu

i

Fuj i

an

J ian

gxi

Shan

dong

Hena

nHu

bei

Huna

n

Guan

gdon

g

Guan

gxi

Hai n

an

Sich

uan

Gui z

hou

Yunn

anXi

axi

Gans

u

Qing

hai

Ning

xia

Xinj

i ang

199520002005

Status of FDI to ChinaFigure 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


Introduction

Chinese Map

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.

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.

Model

Panel analysis

OLS regression using the data that explains the regional characteristics

from 1992 to 2005. Housman test

0

1

:

:

H Random Effect is significant

H Fixed Effect is significant

0.05 .

0.05 .

P value of Housman statistics then Random Effect result is chosen

P value of Housman statistics then Fixed Effect result is chosen

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)

ij ij

01

s

na

ij sj ijs

a X u

sa ( 1, )sjX s n

( 1, )j j m

01

exp(ln ) exp( ln )n

ij ij s sj ijs

a a X u

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).

ij

1

1 1

exp( ln )

exp( ln )

n

s sjs

ij m n

s skk s

a Xp

a X

( 1, )sa s n

i ijW j

j

1

ij

mW

iji j

P P

ln( )L P

DataTable 5-1, Variable Definition

Variable Description

Dependant VariableFDI FDI flows

NRFF Number of registered foreign firms by region at Year-end

Explanatory Variable

PCGDP GDP per capita

AFDI Accumulation of FDI flows from 1992

INF Density of Road and Railway, (Road ＋ Rail)/Surface

CONSUM Annual consumption per capita

WAGE Annual Income per capita

EDU Number of graduate from University

Table 5-2, Correlations

　 PCGDP AFDI INF CONSUM WAGE EDU

PCGDP 1.0000 　　　　　

AFDI 0.5869 1.0000 　　　　

INF 0.5079 0.3222 1.0000 　　　

CONSUM 0.8741 0.6335 0.4327 1.0000 　　

WAGE 0.8696 0.5622 0.3976 0.9592 1.0000 　

EDU 0.4886 0.5409 0.2278 0.5782 0.5663 1.0000

Data

　 Variables Average Standard Error Min Max Observations

Entire

FDI 175,797.554 289,511.405 8.000 1,587,527.000 406

NRFF 7,242.985 10,579.468 9.000 60,597.000 406

PCGDP 101.622 87.488 5.510 637.975 406

AFDI 1,049,145.916 2,098,352.619 68.000 16,391,100.000 406

INF 344.711 393.620 16.486 6,882.087 406

CONSUM 572.501 253.078 217.857 1,706.700 406

WAGE 1,100.389 644.250 374.491 4,255.781 406

EDU 40,970.424 37,829.024 1,540.000 229,679.000 406

Coastal Area

FDI 368,764.036 368,007.147 17,156.000 1,587,527.000 168

NRFF 14,647.940 13,184.566 1,915.000 60,597.000 168

PCGDP 153.278 111.477 5.510 637.975 168

AFDI 2,232,932.518 2,854,691.555 17,156.000 16,391,100.000 168

INF 527.083 542.544 164.879 6,882.087 168

CONSUM 691.068 300.617 268.888 1,706.700 168

WAGE 1,316.107 792.943 432.039 4,255.781 168

EDU 49,655.946 41,345.937 1,742.000 229,679.000 168

Hinterlands

FDI 39,585.920 52,680.140 8.000 275,871.000 238

NRFF 2,015.958 1,571.122 9.000 6,229.000 238

PCGDP 65.159 33.117 17.583 202.309 238

AFDI 213,531.845 295,616.416 68.000 1,826,911.000 238

INF 215.979 130.893 16.486 538.397 238

CONSUM 488.807 169.622 217.857 929.963 238

WAGE 948.118 458.209 374.491 2,364.749 238

EDU 34,839.466 33,906.376 1,540.000 198,709.000 238

Table 5-3, Summary Statistics

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.

　 FDI NRFF

PCGDP48.061

**3.343

***(2.174) (5.615)

AFDI0.057

***6.60E-04

***(12.554) (5.427)

INF24.013

*0.051

　(1.749) (0.139)

CONSUM188.564

**16.142

***(1.821) (5.787)

WAGE-129.955

***-7.926

***(-3.125) (-7.077)

EDU1.647

***1.21E-03

　(7.204) (0.197)

Adjusted R-sq 0.92 0.96

Hausman Test of CHISQ(6) = 63.516 CHISQ(6) = 81.503

H0: RE vs. FE P-value = [0.0000] P-value = [0.0000]

Empirical Results

　 Estimator t-statistic 　　 Estimator t-statistic 　

D1(Beijing) -19995 -0.460 　 D16(Henan) -75056 -2.591 *

D2(Tenjin) 31863.2 0.851 　 D17(Hubei) -54794 -1.619 　

D3(Hebei) -45912 -1.506 　 D18(Hunan) -50169 -1.425 　

D4(Shanxi) -46406 -1.655 * D19(Guangdong) 566829 11.381 ***

D5(Inner mongolia) -17732 -0.624 　 D20(Guangxi) -26951 -0.769 　

D6(Liaoning) 67173.4 2.261 ** D21(Hainan) -3907.8 -0.120 　

D7(Jilin) -39289 -1.373 　 D22(Sichuan) -84279 -2.412 **

D8(Heilongjiang) -70563 -2.506 ** D23(Guizhou) -15998 -0.501 　

D9(Shanghai) 100605 2.064 ** D24(Yunnan) -32189 -0.941 　

D10(Jiangsu) 336635 10.518 *** D25(Xiaxi) -74118 -2.434 **

D11(Zhejiang) 78808.9 1.920 * D26(Gansu) -9245.2 -0.321 　

D12(Anhui) -40471 -1.320 　 D27(Qinghai) 32753.9 1.121 　

D13(Fujian) 145624 4.150 *** D28(Ningxia) 5926.53 0.204 　

D14(Jiangxi) -1872.3 -0.067 　 D29(Xinjiang) -26734 -0.920 　

D15(Shandong) 151697 4.747 *** 　　　　


Table 5-5-A, Dummy Estimates of Fixed Effect Panel Analysis

(Defendant Variable: FDI Flows )

Empirical Results

　 Estimator t-statistic 　　 Estimator t-statistic 　

D1(Beijing) -614.54 -0.525 　 D16(Henan) 348.718 0.447 　

D2(Tenjin) 1144.59 1.135 　 D17(Hubei) 48.0504 0.053 　

D3(Hebei) -260.75 -0.317 　 D18(Hunan) -1659.4 -1.750 *

D4(Shanxi) -1613.7 -2.137 ** D19(Guangdong) 39297.7 29.300 ***

D5(Inner mongolia) -2179.5 -2.847 *** D20(Guangxi) -472.71 -0.501 　

D6(Liaoning) 6540.39 8.175 *** D21(Hainan) 1554.93 1.771 *

D7(Jilin) -768.51 -0.997 　 D22(Sichuan) 1429.47 1.519 　

D8(Heilongjiang) -445.16 -0.587 　 D23(Guizhou) -1033.18 -1.201 　

D9(Shanghai) 4203.29 3.203 *** D24(Yunnan) -1260.3 -1.369 　

D10(Jiangsu) 14347.3 16.646 *** D25(Xiaxi) -696.648 -0.850 　

D11(Zhejiang) 3772.38 3.412 *** D26(Gansu) -364.447 -0.470 　

D12(Anhui) -726.76 -0.880 　 D27(Qinghai) -89.3398 -0.114 　

D13(Fujian) 8598.07 9.100 *** D28(Ningxia) -1197.82 -1.529 　

D14(Jiangxi) 52.7837 0.070 　 D29(Xinjiang) -2273.12 -2.906 ***

D15(Shandong) 8822.73 10.252 *** 　　　　

Table 5-5-B, Dummy Estimates of Fixed Effect Panel Analysis

(Defendant Variable: Number of Registered Foreign Firms by Region)


Empirical Results

　 1995 1998 2000 2003 2005

PCGDP22.758

***10.584

***15.318

***6.655

***6.821

***(10.635) (9.635) (10.631) (5.649) (8.362)

AFDI1.65E-06

***5.44E-07

***3.10E-07

***1.84E-07

***1.36E-07

***(19.709) (21.997) (19.751) (20.253) (20.425)

INF1.170

***2.937

***2.033

***0.142

**0.136

**(3.889) (9.882) (7.391) (2.723) (2.701)

CONSUM0.014

***2.64E-03

***5.91E-03

***5.94E-04 2.64E-03

***(9.681) (4.243) (6.880) (0.937) (5.918)

WAGE-0.017

***-5.73E-03

***-6.65E-03

***-7.97E-04

*-1.68E-03

***(-12.589) (-9.876) (-9.754) (-1.875) (-6.324)

EDU4.77E-06

*1.19E-05

***8.73E-06

***1.62E-05

***9.61E-06

***(1.754) (6.619) (4.750) (11.435) (11.953)

Log-Likelihood -6487.28 -6301.2 -5643.78 -6178.35 -7026.98

Mcffaden-rate 0.1742 0.1760 0.1711 0.1860 0.1974

Observations 2333 2271 2022 2254 2600


Table5-6, Results of Conditional logit Analysis ( Yearly)

(Dependent Variable: Number of Registered Foreign Firms by Region)

Empirical Results


Table5-7, Results of Conditional logit Analysis

( Coastal Area, 1995~2005)　 Whole of Coastal Huabei Huadong Huanan

PCGDP4.597

***19.057

***3.906

***15.277

***(20.828) (17.038) (7.023) (14.548)

AFDI1.46E-07

***4.57E-07

***5.63E-08

***3.58E-08

**(43.353) (13.284) (4.097) (3.163)

INF0.020 0.017 -0.982

***2.63618

***(0.746) (0.466) (-5.731) (12.419)

CONSUM3.40E-03

***9.07E-04

*1.00E-03

***4.40E-03

***(27.653) (1.911) (8.928) (20.014)

WAGE-2.16E-03

***-3.42E-03

***-3.31E-03

***-4.73E-03

***(-38.203) (-12.864) (-12.357) (-41.669)

EDU6.42E-06

***7.61E-06

***1.05E-06 3.43E-05

***(16.080) (5.820) (0.970) (22.805)

Log-Likelihood -97614.8 -15130.8 -29572.7 -29634.1

Mcffaden rate 0.0393 0.0164 0.0110 0.1143

Observations 20809 4065 7902 8842

Empirical Results


　 Whole of Hinterland Middle West

PCGDP7.299

***12.506

***-0.753

(6.423) (5.464) (-0.191)

AFDI-5.20E-07

***4.44E-07

**-5.40E-07

*(-4.247) (2.577) (-1.945)

INF0.741

***0.813

**2.136

***(3.940) (2.370) (3.523)

CONSUM2.73E-03

***-3.73E-04 5.65E-03

***(6.924) (-0.667) (6.789)

WAGE-2.84E-03

***-2.35E-03

***-3.20E-03

***(-17.576) (-9.633) (-12.155)

EDU2.83E-05

***1.54E-05

***2.88E-05

***(21.544) (6.343) (11.198)

Log Likelihood -20204.5 -12039.2 -5495.51

Mcffaden rate 0.0388 0.0154 0.0955

Observations 4018 2661 1357

Table5-8, Results of Conditional logit Analysis

( Hinterland, 1995~2005)

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 as Guangdong, Zhejiang, Jiangsu and Shanghai, while it hits the bottom in hinterland such as Heilongjiang, Sichuan, Shanxi.

2, Conditional analysis shows that foreign 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 FDI in 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.

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.

Thank you!

Documents

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