10/04/2020
(work-in-progress)
GEP China,
University of Nottingham Ningbo.
1

Wide array of contributions to the literature on FDI in China. While we’ve learnt a lot yet we identify some issues:
 Spatial relationship quite rare in FDI studies in general (Coughlin and Segev, 2000;
Hong et al., 2008; Chen, 2009)
 omission of spatial interdependence of FDI > may lead to biased estimates > fail to capture third-country effects (see Yeaple , 2003; Ekholm et al., 2007; Blonigen, et al. 2007). Thus, neighbourhood effects & spillovers neglected in most previous studies.
 Inter-city spillovers understudied


Most work have used provincial data or combination of ‘firm-provincial data’.

Spatial studies focus on ‘inter-provincial’ spillovers + Spillovers better captured in smaller areas
Two notable studies using city-level data (He, 2002) and firm-city (Head and Ries, 1996): both ignore spatial elements
 Regional differences: limited consideration given due to use of provincial data.
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

1) How important are inter-city spillovers for FDI? {Is FDI in one city promoted by
FDI/Market Potential in surrounding cities or at the expense?};
2) Is the influence the same for - i) the Hinterland and the Eastern regions.
 Use city-level data to examine relationship between FDI and neighbouring market potential & the spatial lag of FDI (two major sources identified by literature). {These may also be important to tell us about FDI motives (see Blonigen et al. 2007; Ledyaeva, 2009).}
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 Potential insights from this work?
 uncover insights into the importance of inter-city spillovers.
 Since 2000 regional policy, to reduce regional disparity, has banked on ‘cheap labour costs’ and an ‘even spread of resources’ under the Great Western Development Strategy
(GWDS) and Central China Rising Strategy (CCRS) to prop up the Hinterland. However, these policies are now deemed to be failures. The focus now has shifted towards developing ‘key cities’ to form ‘city clusters’. Our results may tell us about importance of spillovers in the hinterland and can be seen as relevant to the recent policy change.
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 Step 1: City level analysis





Data: 200 Chinese cities for 9 years (1999-2007) [Hinterland Cities: 105 + Eastern: 95]
 Selection of cities based on Head and Ries (1995) of choosing cities that attracted minimum no. of FDI
 China City Statistical Yearbook (various issues)
Ad Hoc solution for endogeneity used lagged explanatory variables
Spatial weights (W) constructed similar to Madariaga and Poncet (2007)
Carried spatial diagnostic tests: spatial lag model supported; spatial dependence for surrounding market potential and spatial FDI
Estimated using: OLS; Spatial OLS; Spatial 2SLS; Spatial ML
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Variables
FDI
Market Size
Unit Labour Costs
Education
Infrastructure
Agglomeration
Definitions
Log of ‘Real Realised FDI’ at the city level.
Log of ‘real GDP per capita’ at the city level
Log of ‘average wages to labour productivity’
Log of share of students enrolment at the third level in city’s population
Log of length of highways per sq km
Log of HOOVER coefficient
Policy Ordinal variable with values 0, 1, and 2.
0 = no special preference policies (SEZ, ETDZ, FTZ, EPZ); 1 = at least one; 2 = 2 or more.
Surrounding Market Potential Distance weighted Log of ‘real GDP per capita’ of neighbouring cities
Spatial lag of FDI Distance weighted spatial lag of dependent variable
Hypotheses
+
-
+
+
+/-
+
+
+/-
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Estimation results of the whole China (Whole Sample)
Market Size
Unit
Costs
Education
Labour
Infrastructure
Agglomeration
Policy
Traditional OLS
0.304
(0.080) ***
-0.242
(0.047)***
0.265
(0.044)***
0.530
(0.075)***
-0.109
(0.081)
0.933
(0.054)***
0.660
(0.106)***
Surrounding-
Market
Potential
Spatially
Weighted FDI
Constant
Spatial OLS
0.242
(0.077)***
-0.135
(0.043)***
0.353
(0.039)***
0.205
(0.074)***
-0.083
(0.076)
0.847
(0.051)***
-0.287
(0.154)*
Spatial 2SLS
0.233
(0.078)***
-0.120
(0.043)***
0.366
(0.040)***
0.157
(0.083)*
-0.079
(0.076)
0.835
(0.052)***
-0.427
(0.186)**
Spatial ML
0.501
(0.084)***
-0.137
(0.041)***
0.387
(0.038)***
0.215
(0.061)***
-0.217
(0.071)***
0.683
(0.051)***
-0.321
(0.085)***
1.819
(1.082)*
0.4950
0.707
(0.067)***
4.762
(1.151)***
0.5724
0.812
(0.081)***
5.200
(1.280)***
0.5707
0.774
(0.052)***
2.201
(0.880)**
R 2 /
Log Likelihood
F Test /Wald χ 2
Observations
238.96***
1525
242.99***
1525
2012.47***
1525
-2547.0952
1186.42***
1521
10/04/2020 7

Estimation results of the eastern cities(Eastern)
Market Size
Unit
Costs
Education
Labour
Infrastructure
Agglomeration
Policy
Surrounding-
Market Potential
Spatially
Weighted FDI
Constant
Traditional OLS
0.387
(0.141)***
-0.188
(0.054)***
0.264
(0.051)***
0.314
(0.090)***
0.139
(0.101)
0.727
(0.061)***
0.249
(0.115)**
5.566
(1.394)***
0.5836
Spatial OLS
0.348
(0.122)***
-0.133
(0.050)***
0.294
(0.046)***
0.202
(0.081)**
0.044
(0.095)
0.708
(0.058)***
-0.517
(0.123)***
0.554
(0.069)***
7.631
(1.243)***
0.6205
Spatial 2SLS
0.338
(0.117)***
-0.118
(0.050)***
0.302
(0.046)***
0.173
(0.081)**
0.019
(0.097)
0.703
(0.060)***
-0.715
(0.165)***
0.697
(0.098)***
8.164
(1.281)***
0.6180
R 2 /
Log Likelihood
F Test /Wald
χ 2
Observations
145.95***
733
150.73***
733
1217.42***
733
-1021.2591
751.85***
732
Spatial ML
0.547
(0.110)***
-0.135
(0.049)***
0.244
(0.043)***
0.254
(0.081)***
0.011
(0.094)
0.675
(0.058)***
-0.162
(0.096)*
0.352
(0.056)***
3.966
(1.073)***
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Estimation results of the hinterland cities (Hinterland)
Traditional OLS Spatial OLS Spatial 2SLS Spatial ML
Market Size
Unit
Costs
Education
Labour
Infrastructure
Agglomeration
Policy
Surrounding-
Market Potential
Spatially
Weighted FDI
Constant
R 2 /
Log Likelihood
F Test /Wald
χ 2
Observations
0.075
(0.072)
-0.274
(0.065)***
0.456
(0.069)***
0.458
(0.089)***
-0.366
(0.124)***
0.685
(0.164)***
0.573
(0.164)***
5.035
(1.790)***
0.3079
65.95
792
0.098
(0.083)
-0.169
(0.063)***
0.483
(0.065)***
0.146
(0.088)*
-0.206
(0.122)*
0.800
(0.121)***
-0.030
(0.181)
0.660
(0.085)***
4.479
(1.871)***
0.3988
66.36
792
0.094
(0.079)
-0.185
(0.064)***
0.479
(0.064)***
0.192
(0.095)**
-0.230
(0.120)*
0.783
(0.121)***
0.060
(0.166)
0.562
(0.112)***
4.562
(1.740)***
0.3968
507.55
792
0.482
(0.149)***
-0.132
(0.057)**
0.447
(0.067)***
0.067
(0.077)
-0.198
(0.112)*
0.855
(0.111)***
0.137
(0.128)
0.896
(0.062)***
-2.425
(2.041)
-1421.0893
523.893
789
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• How important are inter-city spillovers for FDI?
•
Important to an extent but differences across regions & macro/micro view
•
City-results only: SMP weak and –ve; Spatial FDI +ve sig. (elements regional differences)
•
Firm-City results: SMP +ve (Overall & East) and insig.
(Hinterland) ; Spatial FDI +ve
(Overall & Hinterland) and -ve (East)
•
Explanations: 1) Datasets/Methods; 2) FDI motives

 Dynamic Spatial Panel Data for city-level analysis: ‘FDI in Space and Time ’
 Explore further disaggregated location choice of MNEs

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Firm-level analysis pursued with larger sample and other multinomial logit techniques
Paths: New Economic Geography + Spatial Agglomeration and Fiscal Competition
+ Environmental Spillovers
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 Work by Blonigen et al. (2007) and (later adapted by) Ledyaeva (2009) and Hong et al. (2008) have used the following classification:
FDI Motivation
Pure horizontal
Pure vertical
Regional trade platform -
-
0
Sign of Spatial
Lag Variable
Complex strategy with agglomeration +
0
0
Sign of Surrounding Market
Potential Variable
+/-/0
(depends on local protectionism and sig. of neighbourhood effects)
+/-/0
(depends on local protectionism and sig. of neighbourhood effects)
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Figure 1: Regional Disparity of FDI inflows in China
*data of the first half year is used in 2009
12

FDI, 1999-2007 (100 million yuan)
10/04/2020
(432.176,1088.31]
(371.808,432.176]
(62.9982,371.808]
[0,62.9982]
Data Source: China Statistical Yearbook
Figure 2: Spatial Disparity of FDI inflows in China
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Average Wages, 1999-2007
Unit labour costs, 1999-2007
(20554.4,27368]
(14894.8,20554.4]
(12909.6,14894.8]
[11073.8,12909.6]
Data Source: China Statistical Yearbook
(.157533,1.77606]
(.10157,.157533]
(.069974,.10157]
[.05194,.069974]
Data Source: China Statistical Yearbook
Figure 3: Spatial Disparity of Labour Costs in China
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We follow Madariaga and Poncet (2007) in the construction of our spatial weights:
 We choose a spatial weighting matrix W that depends exclusively on the geographical distance d ij between cities i and j since the exogeneity of distance is unambiguous.
 Distance-based weights are defined as follows:

 d ij is the distance in kilometres between cities i and j. The distance 1,624 km is the cut-off parameter above which interactions are assumed to be negligible.
This distance is chosen such that each city interacts with at least one other Chinese city. This cut-off parameter is important since there must be a limit to the range of spatial dependence allowed by the spatial weights matrix (Abreu et al., 2005)
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employment for sector j in China employment for sector j in city i
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Total employment in China
Total employment in city i
Employment Data for 10 sectors: Manufacturing, Real Estate, Primary,
Utilities, Construction, Transportation, Wholesale & Retail, Education &
Health, Finance & Business Services, Other
16

 Abreu, M., De Groot, H. L. F., and Florax, R. J. G. M. (2005), Space and
Growth: A Survey of Empirical Evidence and Methods. Région et
Développement 21, 13-44.
 Blonigen, B.A., Davies, R.B., Waddell, G.R., and Naughton, H.T. (2007) FDI in space: Spatial Autoregressive Relationship in Foreign Direct Investment.
European Economic Review 51 (2007) 1303-1325.
 Chen, Y-J. (2009) Agglomeration and Location of Foreign Direct
Investment: The Case of China. China Economic Review 20 (2009), 549-557.
 Coughlin, C. and Segev, E. (2000) Foreign Direct Investment in China: A spatial Econometric Study. The World Economy 23 (1), 1-23.
 Crozet, M., Mayer, T. and Muchielli et al. (2004) How do firms agglomerate?
A study of FDI in France. Regional Science and Urban Economics vol. 34,
27-54
 Ekholm, K., Forslid, R., Markusen, J.R. (2007) Export-Platform Foreign
Direct Investment. Journal of European Economic Association, Vol. 5, No. 4,
776-795
 He, C. (2002) Information costs, agglomeration economies and the location of foreign direct investment in China. Regional Studies 36, 1029-1036
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
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Head, K. and Ries, J. (1996) Inter-City Competition for Foreign Investment:
Static and Dynamic Effects of China’s Incentive Areas. Journal of Urban
Economics 40, 38-60.
Hong, E., Sun, L-X., and Li, T. (2008) Location of Foreign Direct Investment in China: A Spatial Dynamic Panel Data Analysis by Country of Origin.
Discussion Paper 86, The Centre for Financial & Management Studies,
University of London.
Ledyaeva, S. (2009) Spatial Econometric Analysis of Foreign Direct
Investment Determinants in Russian Regions. The World Economy, Vol.32,
Issue 4, 643-666.
Madariaga, N. and Poncet, S. (2007) FDI in Chinese Cities: Spillovers and
Impact on Growth. The World Economy, Vol.30, Issue 5, 837-862.
Yeaple, S.R. (2003) The Complex Integration Strategies of Multinationals and Cross Country Dependencies in the Structure of Foreign Direct
Investment. Journal of International Economics 60 (2), 293-314.
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