Determinants of FDI Location
in China using the Conditional
Logit Model
How to Resolve Regional Economic
Disparity in China
by
Doowon 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)
Real FDI
to China
Resistered Foreign
Firms at Year- end
700
300000
Real FDI to China
600
250000
Resistered Foreign
Firms at Year- end
500
200000
400
150000
300
100000
200
50000
100
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
0
2005
Source: China Statistical Yearbook, China City Statistical Yearbook
Status of FDI to China
Table 2-2, Upper 10 Countries in Real FDI Flows to China ( Unit: USD 10,000)
1995
No
Country
FDI
2000
Share(%)
Country
FDI
2005
Share(%)
Country
FDI
Share(%)
1
Hong 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
6
Korea 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
Total
6,032,459
100.00
Source: China Statistical Yearbook
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Status of FDI to China
Figure 2-4, Real FDI Flows to China by Region (Unit: USD 10,000)
1,400,000
1,200,000
1,000,000
70,000
60,000
50,000
1995
2000
2005
800,000
600,000
400,000
200,000
0
Source: China City Statistical Yearbook
Figure 2-5, Number of Registered Foreign Firms by Region at Year-end
1995
2000
2005
40,000
30,000
20,000
10,000
0
Source: China Statistical Yearbook
Status of FDI to China
Figure 2-6, Results of Cluster Analysis of FDI to China by Region(1992~2005)
Real FDI Flows
Source: China City Statistical Yearbook
Number of Registered Foreign Firms
Source: China 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 infrastructure, low wage and good governance.
Model

Panel analysis

OLS regression using the data that explains the regional characteristics
from 1992 to 2005.

Housman test
H 0 : Random Effect is significant
H1 : Fixed Effect is significant
P  value of Housman statistics  0.05 then Random Effect result is chosen.
P  value of Housman statistics  0.05 then Fixed Effect result is chosen.
Model

Conditional Logit Model


This model was introduced by McFadden(1974).
 ij
Let’s assume that the profit
of foreign ifirm obtained from undertaking FDI
to regionj is defined as;
n
 ij  a0  X sja uij
s
(1)
s 1
X sj ( s  1, n)
is unknown parameters,
j ( j  1, m)
the characteristics of region
as

is the variables describing
.
We can get the below equation (2) from (1).
n
 ij  exp(ln  ij )  a0 exp( as ln X sj )uij
s 1
(2)
Model

Conditional Logit Model

j
Let’s define the probability of undertaking FDI to region
by foreign
i firm
as;
n
pij 
exp( as ln X sj )
m
s 1
n
 exp( a
k 1

s 1
s
ln X sk )
Wij
i undertakes
j
When a foreign firm
’s FDI to region
undertaking FDI to region
is described as;
j
m
P   Pij
i

Wij
j 1
, the probability of
(4)
Log Likelihood function
L  ln( P)

(3)
as (s  1, n)
We should estimate the parameter
(5)
which maximize the equation (5).
Data
Table 5-1, Variable Definition
Variable
Description
FDI
Dependant Variable
FDI flows
NRFF
Number of registered foreign firms by region at Year-end
PCGDP
GDP per capita
AFDI
Accumulation of FDI flows from 1992
INF
Explanatory Variable
Density of Road and Railway, (Road＋Rail)/Surface
CONSUM
WAGE
Annual consumption per capita
Annual Income per capita
EDU
Number of graduate from University
Table 5-2, Correlations
PCGDP
AFDI
INF
CONSUM
WAGE
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
EDU
1.0000
Data
Table 5-3, Summary Statistics
Variables
Average
Standard Error
Min
Max
175,797.554
289,511.405
8.000
1,587,527.000
406
7,242.985
10,579.468
9.000
60,597.000
406
101.622
87.488
5.510
637.975
406
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
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
153.278
111.477
5.510
637.975
168
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
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
65.159
33.117
17.583
202.309
238
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
34,839.466
33,906.376
1,540.000
198,709.000
238
FDI
NRFF
PCGDP
Entire
AFDI
PCGDP
Coastal Area
AFDI
PCGDP
Hinterlands
AFDI
EDU
Observations
Empirical Results
Table 5-4, Result of Panel Analysis
FDI
PCGDP
AFDI
INF
CONSUM
WAGE
EDU
48.061
(2.174)
0.057
(12.554)
24.013
(1.749)
188.564
(1.821)
-129.955
(-3.125)
1.647
(7.204)
(1992~2005, Fixed Effect )
NRFF
**
***
*
**
***
***
3.343
(5.615)
6.60E-04
(5.427)
***
***
0.051
(0.139)
16.142
(5.787)
-7.926
(-7.077)
***
***
1.21E-03
(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]
Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.
Empirical Results
Table 5-5-A, Dummy Estimates of Fixed Effect Panel Analysis
(Defendant Variable: FDI Flows )
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
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
D6(Liaoning)
*
**
***
***
Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.
*
***
**
**
Empirical Results
Table 5-5-B, Dummy Estimates of Fixed Effect Panel Analysis
(Defendant Variable: Number of Registered Foreign Firms by Region)
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
***
***
Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.
*
***
Empirical Results
Table5-6, Results of Conditional logit Analysis ( Yearly)
(Dependent Variable: Number of Registered Foreign Firms by Region)
1995
PCGDP
AFDI
INF
CONSUM
WAGE
EDU
22.758
(10.635)
1.65E-06
(19.709)
1.170
(3.889)
0.014
(9.681)
-0.017
(-12.589)
4.77E-06
(1.754)
1998
***
***
***
***
***
*
10.584
(9.635)
5.44E-07
(21.997)
2.937
(9.882)
2.64E-03
(4.243)
-5.73E-03
(-9.876)
1.19E-05
(6.619)
2000
***
***
***
***
***
***
15.318
(10.631)
3.10E-07
(19.751)
2.033
(7.391)
5.91E-03
(6.880)
-6.65E-03
(-9.754)
8.73E-06
(4.750)
2003
***
***
***
***
***
***
6.655
(5.649)
1.84E-07
(20.253)
0.142
(2.723)
2005
***
***
**
6.821
(8.362)
1.36E-07
(20.425)
0.136
(2.701)
5.94E-04
2.64E-03
(0.937)
(5.918)
-7.97E-04
(-1.875)
1.62E-05
(11.435)
*
***
-1.68E-03
(-6.324)
9.61E-06
(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
Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.
***
***
**
***
***
***
Empirical Results
Table5-7, Results of Conditional logit Analysis
( Coastal Area, 1995~2005)
Whole of Coastal
PCGDP
AFDI
INF
CONSUM
WAGE
EDU
4.597
(20.828)
1.46E-07
(43.353)
***
***
Huabei
19.057
(17.038)
4.57E-07
(13.284)
Huadong
***
***
3.906
(7.023)
5.63E-08
(4.097)
0.020
0.017
-0.982
(0.746)
(0.466)
(-5.731)
3.40E-03
(27.653)
-2.16E-03
(-38.203)
6.42E-06
(16.080)
***
***
***
9.07E-04
(1.911)
-3.42E-03
(-12.864)
7.61E-06
(5.820)
*
***
***
1.00E-03
(8.928)
-3.31E-03
(-12.357)
Huanan
***
***
***
***
***
15.277
(14.548)
3.58E-08
(3.163)
2.63618
(12.419)
4.40E-03
(20.014)
-4.73E-03
(-41.669)
1.05E-06
3.43E-05
(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
Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.
***
**
***
***
***
***
Empirical Results
Table5-8, Results of Conditional logit Analysis
( Hinterland, 1995~2005)
Whole of Hinterland
PCGDP
AFDI
INF
CONSUM
WAGE
EDU
7.299
(6.423)
-5.20E-07
(-4.247)
0.741
(3.940)
2.73E-03
(6.924)
-2.84E-03
(-17.576)
2.83E-05
(21.544)
***
***
***
***
***
***
Middle
12.506
(5.464)
4.44E-07
(2.577)
0.813
(2.370)
West
***
**
**
-0.753
(-0.191)
-5.40E-07
(-1.945)
2.136
(3.523)
-3.73E-04
5.65E-03
(-0.667)
(6.789)
-2.35E-03
(-9.633)
1.54E-05
(6.343)
***
***
-3.20E-03
(-12.155)
2.88E-05
(11.198)
Log Likelihood
-20204.5
-12039.2
-5495.51
Mcffaden rate
0.0388
0.0154
0.0955
Observations
4018
2661
1357
*
***
***
***
***
Note: Shown in parenthesis are t-statistics. *, ** and *** indicate 10%, 5%, 1% significant level.
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 ar
ea such as Guangdong, Zhejiang, Jiangsu and Shanghai, while it hits the bottom in hinterlan
d 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!