OSIPP Discussion Paper : DP-2013-E-004-Rev

Are Investment Promotion Agencies Doing the Thing Right?
Evidence from China
January 31, 2015
Bin

Ni

PhD Candidate, The Graduate School of Economics, Osaka University【Keyword】China, investment promotion agency, firm-level data, sample selection【Abstract】This paper tries to verify how the establishment of investment promotion agencies(IPAs) affects the decisions of foreign firms regarding their subsequent investment by combining
firm-level data from the World Bank’s Enterprise Survey with city-level information on (IPAs) in
China. After correcting for potential endogeneity problems, the result confirms the IPAs’ role inpromoting incremental foreign direct investment (FDI) into China. Furthermore, it is shown thatHong Kong, Macau, and Taiwan (HMT) firms are less sensitive to IPAs’ efforts in making further
investment than are non-HMT foreign firms. In addition, IPAs are found to be more successful inenhancing foreign firms’ investment in high-tech than in low-tech industries.
【JEL Classification】F21, F23

This paper was previously entitled “What Determines Inward FDI in China?—An empirical study
using firm-level data”. I would like to thank Professor Otsuki for his inspiring advice.

1

I. Introduction
The past three decades have witnessed a considerable expansion in inward foreign direct
investment (FDI) into China 1, in tandem with China’s fast economic growth. Apart from afavorable investment environment, the strategic efforts made by the Chinese government arethought to have played an important role in inviting FDI, one being the establishment ofinvestment promotion agencies. The first China Investment Promotion Agency (CIPA) wasestablished in the 1980s to facilitate the promotion of foreign investment. Unlike special economic
zones (SEZs), which use tax incentive to invite FDI, IPAs are local organizations aimed at
assisting the investment activities of foreign investors, including both incumbents and newcomers.
Even though a few empirical studies have attempted to examine the relationship between
government policy and FDI in the context of China (Dean et al., 2009; Wang, 2013), none has
focused on the impact of IPAs.
Theoretically, IPAs are expected to raise FDI because compared with developed countries,
developing countries lack detailed information on the prevailing business environment, and thecost of acquiring information is quite high (Harding and Javorcik, 2007). Several studies haveempirically investigated IPAs’ influence on FDI in countries other than China. Morisset (2003) andMorisset and Andrews-Johnson (2004) use country-level data to indicate that the presence of IPAs
exerts a positive influence, a hypothesis supported by Charlton and Davis (2006) together with
Harding and Javorcik (2007). However, no research has used micro-level data, though such data
can control firm heterogeneity and better capture the direct effect of how foreign firms react toincentives provided by IPAs.
This study contributes to the previous literature in several ways. To the best of my knowledge, itis the first paper to use micro-level data to analyze IPAs’ effect on attracting FDI in China2. Rather
than focusing on the location choice of new entrants or on the amount of “greenfield” investment,
this paper takes a different perspective by examining the so-called “agglomeration effect”3. Using
advanced methodologies of instrumental variable and a sample selection model to alleviate thepotential endogeneity problem of IPAs, the approach reveals that the impact of IPAs on attracting
FDI is positive. The results also indicate that Hong Kong, Macau and Taiwan (HMT) firms are notas sensitive to the presence of IPAs in investment decision-making as for other non-HMT foreign
firms, and IPAs have different impacts between low-tech and high-tech industries.
II. Estimation Strategy and Data
Estimation strategy
The baseline empirical specification in the firm-level analysis takes the following reduced form:

ln FDI ict = α + β1 IPAct + βi xit + β c wct + gt + ε ict(1)

The dependent variable is the log of new fixed assets for firm i in city c at time t. I only focusedon firms with foreign ownership confirmed by the actual foreign share in capital. Since 86 per centof IPAs target investors that are already present in the host country (UNCTAD, 2001), I chose tofocus on the incremental FDI, i.e. the investment of the existing investors, in this paper. IPA is a1

By year 2011 inward FDI in China had reached US$712 billion, second only to that of the United States (World Invest Report, 2012).
Amiti and Javorcik (2008), Liu et al. (2010) have applied firm-level data to investigate the determinants of entry of foreign investment in China.
3
Agglomeration effect indicates that existing FDI attracts further FDI. Head et al. (1995), Head and Ries (1996) and Guimaraes et al. (2000) all
verify this effect.
22

dummy variable that specifies whether the city has an IPA at time t4. Since there existed very few
newly established IPAs during the analysis period (2002–2004), it is assumed that the city had an
IPA either before or after 2004 when conducting the estimation 5 . xit is a vector of firm
characteristics that might affect firm i’s decision-making on subsequent investment. To beconsistent with previous firm-level research on the micro determinants of FDI, sales revenue, wage
rate, and research and development (R&D) expenditure are included as control variables.
Following Devereux and Rachel (1998), tax rate is also included. Export customs clearance time isadded as a proxy for trade barriers.
wct represents a vector of city characteristics, including non-policy characteristics and policy
incentives. As for policy incentives other than IPAs, Cheng and Kwan (2000) prove the positive
influence of the creation of SEZs6 on attracting FDI. Foreign firms located in certain SEZs, which
had long been established before the establishment of IPAs, enjoy various treatment levels
concerning tax reduction. It is likely that foreign firms attempt to establish their subsidiaries inareas with tax incentives7. This factor is controlled by extending the concept of the SEZ to create a
new variable of the Investment Incentive Zone (IIZ), following Cho and Tung (1998). The action
accords with the focus on each city instead of the entire province and follows the same
methodology used to construct the IPA dummy. Year dummy gt is added to control the changeover time. The error term captures all the unobserved elements that might affect the analysis.
Econometric Concerns and Solutions
The use of a random effects model alone is not a sufficient solution to the potential endogeneity
problems that arise from two sources. The first source is reverse causality. However, it appears to
be a minor concern in the model used here because the dependent variable is incremental FDI at
the firm level, and it is unlikely to affect a city’s decision to establish an IPA. Still, thispossibility is addressed later for the sake of robustness check8.
The second source of the endogeneity problem comes from the possibility that IPAs might“self-select” into some cities. This kind of bias can also be summarized as one of the omitted
variable problems, which requires great care. Apart from the four special municipalities directly
under the control of the central government (Shanghai, Beijing, Tianjin, and Chongqing), thedevelopment of IPAs has been uneven across China. Judging from the fact that most IPAs are
clustered along the coastal areas (mainly South and East China) or located in the capital city ofeach province, I find that IPAs are more likely to be established in areas with relatively higher
GDP per capita or with a better business environment. The criteria used to select cities for IPA
establishment is not publicized. Thus, if unobserved city characteristics affect the criteria, they arecorrelated with foreign firms’ investment decisions, and the estimation of the IPA coefficient islikely to be biased.
To address this second type of endogeneity, I employed a treatment-effect model. If a foreign
firm is located in a city that has IPAs, it is defined as “treated”, otherwise as “control”. I modelled
the establishment of IPA as a function of a vector of city level characteristics, such as GDP per
capita, infrastructure investment, trade volume, etc. The investment decision of the firm can bemodeled as a two-step procedure. I omitted the subscripts for simplicity.
4
Since every province already has the provincial IPAs, this study focuses on city-level IPAs only. And according to CIPA’s definition, it isassumed that city IPAs have jurisdiction over all the foreign firms located in that city.
5
I also excluded IPAs set up during 2002-2004 for double check.
6
See Cheng and Kwan (2000) for full description of the Zone.
7
The new industry tax law of China has been applied since 2008. Thus the change of tax rate doesn’t affect my estimation.
8
As argued by Cheng and Kwan (2000), apart from IPAs, FDI inflow can also drive up wage rate, R&D, and sales of the firm. Thus I used system
GMM proposed by Blundell and Bond (1998) to correct for the possible estimation bias. The results remain consistent and are available uponrequest.

3

whereln FDI = α + βipa IPA + β X + ε(2)

IPA* = δ Y + u(3)⎧1, if IPA* > 0, which means the city has at least one IPA
IPA = ⎨
⎩0, otherwise

X in Equation 2 is supposed to capture all the firm-level characteristics that are assumed toinfluence the foreign firm’s investment. In addition, Y in Equation 3 includes the determinants of
IPA in the city level.
Different estimation methods are used depending on different assumptions on the relationshipbetween ε and u in the above equations. It is possible that the unobserved micro-level elements
are independent of the city-level factors that promote the construction of more IPAs, to be specific,
ε ⊥ u . If this assumption holds, then the consistent estimation of βipa can be obtained and I canapply corresponding estimation strategies such as control function9 or instrumental variable (IV)10
regression. See Wooldridge (2010) for a detailed discussion. Recent application of the control
function can be found in Petrin and Train (2010), and Liu et al. (2010). In contrast, it is also likelythat some cities’ cultural advantage (e.g., Shanghai’s close business ties with Japanese partners)
will lower the investment costs for some foreign firms, which can not be controlled using theinformation in the dataset. In the case when ε is correlated with u , the conditional mean
independence assumption is violated and the causal effect using the control function will be biased.
Thus I have to resort to other methods, such as the Heckman two-stage sample selection model. In
accordance with Wooldridge (2010, Chapter 21), I now assume that Equation 2 will take a moregeneralized form by allowing both observable and unobservable heterogeneity:ln FDI1 = α 1 + β1 X + ε1 , E(ε1 X) = 0
ln FDI 0 = α 0 + β 0 X + ε 0 , E(ε 0 X) = 0
ln FDI = IPA *(ln FDI1 − ln FDI 0 )

(4)
(5)
(6)

when u in Equation 3 is correlated with ε 1 and ε 0 above, it is shown that consistent estimation canbe achieved by conducting the following OLS11:φ (δ Y )φ (δ Y )E(ln FDI X, IPA) = f (IPA, X) + ρ1 ⋅ IPA ⋅+ ρ0 ⋅(1 − IPA)⋅(7)
Φ(δ Y )
1 − Φ(δ Y )where ρ1 is the correlation between u and ε 1 , ρ0 for u and ε 0 . φ (⋅) and Φ(⋅) are thedensity and cumulative normal distribution respectively, which can be obtained by running
Equation 3 using probit in the first step. By so doing the potential bias due to correlation between
ε and u (or selection-on-unobservables) can be alleviated. In practice I used the Heckman
selection model as the benchmark model and other options as robustness check12.

9
Control function regression relies on conditional mean independence (CMI) assumption, which refers to the exogenous impact of the “treatment”
on outcome lnFDI once firm characteristics X is controlled in this paper.
10
Here I am referring to the standard IV models such as two-stage least square (2SLS).
11
See Wooldridge (2010, P949).
12
I chose “ivtreatreg” when running the estimation, as suggested by Cerulli (2014). By changing the option of model type, it can fit into IV models
or Heckman selection model flexibly.

4

Data
The data on three types of variables examined in this paper—the ipadummy, firm characteristics,
and city-level factors—were collected from three main sources. A list of 141 city-level IPAs wasconstructed by combining two sources. To identify the several city-level IPAs not listed on the“Invest in China” website, I made reference to the information of the China Council forInternational Investment Promotion (hereafter referred as CCIIP)13.
Data regarding firm characteristics was taken from the China 2005 Investment Climate Surveyof World Bank. The survey reports three-year balanced panel data covering all manufacturingindustries for the period 2002–2004, while several variables remained constant throughout thisperiod, e.g., ownership share. The data was originally collected by administering a questionnaire to
12,400 firms in 123 cities throughout China, ranging from company information to to financing, tobe answered qualitatively or quantitatively. When it comes to the ownership of the firms, 990 are
HMT-invested, 1398 are other foreign-invested, and 365 are foreign–domestic shared firms.
“New fixed asset investment” is used to represent FDI volume; sales revenue is set to equal thesum of “core business” and “other business income”; R&D is defined as the annual value invested
in the R&D department while the wage rate is equal to the average wage of a permanent worker.
The tax rate equals the income tax/total sales revenue. I assume it is reasonable to have negative
figures for tax rate in the dataset because in China, if a foreign company operates at a loss in agiven year, the loss will be deducted from the total tax liability of the following year. All variables
are presented in the units of thousand yuan14 and “customs clearance time” measured by day isused as a proxy for trade time.
Table 1. Statistical Summary
VariablesN
Mean
ln_FDI
7963
9.804423
ipadummy36921
0.4427562
iizdummy36921
0.3701958
trade_time
36921
1.872284
ln_sale36857
10.71119ln_taxra
21514-3.167929
ln_wage_rate
36921-0.1160097
ln_rd
20256
5.960377
ln_frei
36721
8.646856
ln_city_gdp
36821
15.50882
ln_gdp_per
36821
9.352639
ln_trade_vol36821
14.52381
ln_infra_inv
36024
11.16436
ln_edu_exp
36326
10.54858
ln_ave_wage36426
9.305477
ln_post36326
9.837893
ln_tele36326
11.89652
ln_tot_save
36426
14.60622
Note: See Appendix Table A1 for the detailed description of the variables.

S.D.
2.179190.4967191
0.4828635
5.471339
2.050383
1.539815
0.4858685
2.609501
0.8609946
0.8553658
0.6857158
1.350243
1.528503
1.261787
0.4850426
0.9748189
1.184367
1.258158

Data on city-level factors was extracted from the Chinese Statistical Year Book. Idiosyncratic
city characteristics include: GDP per capita, average wage for workers, total annual savings, total
expenditure for postal mail and telecommunications, and expenditure on education, etc.

13

The CCIIP website does not list the establishment year for all city-level IPAs. However, as a city usually has either multiple IPAs or no IPAs, theavailable information is sufficient for me to determine whether the city had IPAs during 2002-2004.
14
The unit for wage rate is yuan. I divided it by 1000 to make it consistent with others.

5Meanwhile the IIZ dummy was constructed based on the information collected from the “Invest in
China Agency” website. Table 1 presents the statistical summary of the main variables.
III.

Estimation Results

Table 2.

The amount of FDI (logarithm) by year
Control (ipadummy=0)
Year
No. of firms
Mean
2002
1001
8.988336
2003
1002
9.187138
2004
1002
9.302308
Source: Enterprise Survey, World Bank.

Treated (ipadummy=1)
No. of firms
Mean
1651
10.05577
1652
10.21541
1655
10.31477

Mean-difference
1.067434
1.028272
1.012462

The simple statistical analysis can give us some intuition concerning the potential impact of IPAs.
As shown in Table 2, when a comparison of incremental investment is conducted between
IPA-supervised foreign firms and non-IPA-supervised firms, it is easy to identify the difference.
Even though the investment of the control group also witnesses a stable annual growth, the averageamount of investment of the treated group is higher than that of the comparison group.
Table 3.

Estimation Result(1)
Baseline
VARIABLES
lnFDI
ipadummy
0.262**(0.119)trade_time
0.00201(0.00622)
ln_sale0.646***(0.0287)ln_tax_rate-0.00834(0.0133)
ln_wage_rate-0.0207(0.0534)
ln_rd
0.0714***(0.0152)
ρ1(2)
Baseline
lnFDI
0.316**(0.133)0.00169(0.00600)
0.642***(0.0287)-0.0126(0.0130)-0.0379(0.0536)0.0744***(0.0154)(3)
IV (OLS)
lnFDI
1.349***(0.206)0.0116(0.00851)
0.946***(0.0281)0.00133(0.0296)-0.324***(0.0703)
0.0524***(0.0192)ρ0(4)
IV (2SLS)
lnFDI
1.094***(0.171)
0.0221**(0.0107)0.908***(0.0287)
0.0191(0.0309)-0.322***(0.0717)0.0591***(0.0202)(5)
HeckmanlnFDI
0.765***(0.224)0.0213**(0.00968)
0.849***(0.0289)-0.000665(0.0308)-0.218***(0.0745)0.0339*(0.0204)-0.0911(0.189)0.599***(0.129)City
characteristics
No
Yes
Random Effects
Yes
Yes
Observations
2,808
2,771
2,771
2,771
2,573
Notes: Baseline city characteristics include iizdummy, freight, gdp_perca, infra_invest, edu_exp, average_wage.
For column (3)(4)(5), the first stage city characteristics are the same as the ones used in baseline estimation.
Year dummies are included. Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

The estimation results using Equation 1 confirm the aforementioned hypothesis. In Table 3, thefirst column shows the results of RE model without city characteristics. ipadummy is positively
significant and increases the firm’s investment by 26%. This verifies the role of IPAs in attracting
more FDI into the region, which is consistent with previous literature. Meanwhile tax_rate andwage_rate have the expected sign, though they are not statistically convincing. The positive signsof Sales and R&D indicate that these variables positively affect existing foreign firms’ decision on
their subsequent investment. The second column shows the results when city-specific factors are
6

added as control variables ( γ ct in Equation 1). ipadummy remains positively significant15, though
the magnitude increases by more than 5%. The remainder of the control variables shows a similartendency as in the first set of specifications.
Columns (3) and (4) show the results using IV methods. Both apply to probit ipadummy oncovariates Y, as in Equation 3. It shows that the coefficient of ipadummy varies by 25% but its
positive significance remains. The reliability of this finding is strengthened by the result of using
the Heckman two-step selection model, as presented in the 5th column—even when potential bias
is controlled, IPAs still, to a great extent, increase the investment amounts of the existing foreign
firms. Compared with the baseline result, all three models lead to an upward estimation of thecoefficient for ipadummy despite the fluctuation, which indicates that the unobserved city factors(those entering the selection equation) might be negatively correlated with foreign firms’
investment decision. To confirm the existence of the selection bias, I calculated average treatment
effect (ATE), ATE on the treated (ATET), and ATE on the nontreated (ATENT) for (3), (4) and(5) respectively16. In this paper, ATE refers to the impact of IPAs on all foreign firms while ATET
measures the impact of IPAs on foreign firms that located in the cities with IPAs.
As Fig. 1 indicates, all three models follow a similar distribution of treatment effect. ATE in allcases are approximately subject to normal distribution and the range is in between ATET and
ATENT. This predicts that the untreated foreign firms would have invested more, had they been
assisted by IPAs.
Fig. 1.

Distribution of ATE, ATET and ATENT for different models

15

I tried various combinations of the city variables. For example, I replaced gdp_percapia with city_gdp, tele_expenditure with post_expenditure
and add total_savings. All these do not change the significance of IPA, thought the coefficient of IPA varies from 30%-32%.
16
ATET=E[(lnFDI1i-lnFDI0i)|IPAi=1].

7

After running the Heckman model, I tested the hypothesis of no correlation between unobserved
city and firm characteristics by examining the null H 0 : ρ1 = ρ0 = 0. The result rejects the null,
implying the appropriateness of using this specification 17 . Next I performed the simple
difference-in-mean test by putting the ATE results in all models together (Table 4). Based on theconsistent results, the conclusion can be drawn that IPAs do promote foreign firms to invest more.
Table 4. T test for ATE
Variable
ttest
ipadummy
1.03***
Note: * p<0.05; ** p<0.01; *** p<0.001

IV.

probit_ols
1.35***

probit_2sls
1.09***

heckit0.76***

Further Issues

Does the origin of FDI matter?
According to National Bureau of Statistics of China, HMT firms have been the leading investors in
China since the opening-up reform, with these regions comprising more than half of China’s
inward FDI during 1990-2004. Because of this skewed allocation of FDI, I would like to take a
step further to examine whether IPAs have different impact on investors of different origin. To dothis, samples were divided into HMT and non-HMT subgroups, and Equation 1 was re-estimatedfor each group. Such type of categorization has been used in previous studies (Lin et al., 2009; Itoet al., 2012). The results are presented in Table 5. IPAs clearly play a positive role in attracting
non-HMT foreign firms to invest more in China, whereas their impact on HMT firms is not as
obvious. This indicates that non-HMT foreign firms benefit more from IPAs’ assistance since they
have less access to local information than HMT firms do (such as quality of suppliers).
Table 5.

Comparison between HMT and Non-HMT firms
HMT group
Non-HMT group
Model
OLS
Heckman
OLS
Heckman
VARIABLES
lnFDI
lnFDI
lnFDI
lnFDI
ipadummy
0.210
0.508
0.355*
0.834***(0.189)(0.354)(0.181)(0.281)
trade_time-0.00512
0.01890.008770.0250*(0.00520)(0.0136)(0.00960)(0.0144)ln_sale0.619***
0.895***
0.656***
0.823***(0.0507)(0.0455)(0.0351)(0.0389)ln_tax_rate-0.0304
0.00150-0.00627-0.0227(0.0231)(0.0469)(0.0153)(0.0422)ln_wage_rate-0.00703-0.425***-0.0471-0.0952(0.0828)(0.126)(0.0654)(0.0964)
ln_rd
0.127***
0.101***
0.0478***-0.00297(0.0265)(0.0312)(0.0181)(0.0270)
Observations
859
856
1,912
1,645
Notes: City chracteristics include iizdummy, freight, gdp_perca, edu_exp, infra_invest, average_wage.
Random effect option is used for OLS estimation. Year dummies are included.
Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

Does the industry matter?
Liu and Daly (2011) explore the determinants of FDI inflow into different industries, namely
high-tech and low-tech industries. The motivation behind their study is the great increase in FDI in
the manufacturing industry in reaction to China’s Open Door policy. In their paper, it shows that17
Since ivtreatreg does not provide post-estimation test option when “IV” model is chosen, I used ivreg2 to re-estimate and run endogeneity test.
Durbin-Wu-Hausman statistics show that ipadummy is not exogenous.

8between 1997 and 2008, FDI in the manufacturing industry represented 63.2 per cent of all utilized
FDI in China and reached US $49.89 billion in 2008. Since China is undergoing the transition froman agriculturally centered society to an industrialized country, the desired sector for FDI inflow has
been shifting from traditional low-tech industries to high-tech manufacturing industries. As
realized by the Chinese government, technology advancement is an inevitable factor in a country’s
economic growth. Thus it would be interesting to determine whether government policy has had its
intended effect.
Table 6.

Comparison between high-tech and low-tech firms
High-techLow-tech
Model
OLS
Heckman
OLS
Heckman
VARIABLES
lnFDI
lnFDI
lnFDI
lnFDI
0.408**
1.101***
0.264-0.0817
ipadummy(0.180)(0.273)(0.221)(0.467)0.00871
0.0281**-0.00914
0.00669
trade_time(0.00773)(0.0127)(0.00701)(0.0171)0.640***
0.814***
0.646***
0.869***
ln_sale(0.0366)(0.0347)(0.0487)(0.0614)-0.00320-0.0238-0.0253-0.0629ln_tax_rate(0.0150)(0.0374)(0.0248)(0.0658)-0.0697-0.203**0.0297-0.0511ln_wage_rate(0.0563)(0.0916)(0.137)(0.192)0.0752***0.01730.0773***0.122***
ln_rd(0.0214)(0.0248)(0.0205)(0.0425)
1,731
1,399
1,040
1,014
Observations
Notes: City chracteristics include iizdummy, freight, gdp_perca, edu_exp, infra_invest, average_wage.
RE option is included in OLS estimation. Year dummies are included. Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

According to Holz (2013), the sectoral classification in China has undergone four periods ofreform. In consideration of the period of estimation, GB2002 (guobiao in Chinese, representing thestandard regulated in year 2002) was used for conducting further analysis. After all 30 industries
have been classified as either low-tech or high-tech industries based on the classification in Liu andDaly (2011), regression was performed on each subgroup in order to determine the impact of thepresence of IPAs.
The estimation results in Table 6 provide strong evidence that IPAs are more successful inencouraging high-tech foreign firms to increase the amount of their investment, which indicates thepotential “screening” function of IPAs to invite foreign investors with higher quality. This is inaccordance with the finding in UNCTAD (2001, P20). Nevertheless, future study should take a
step further to look into the specific industries which are “preferred” to others, as well as the reason
behind this phenomena.
V. Conclusion
The aim of this paper is to address the research gap regarding the empirical study of how agovernment policy, namely IPAs, can affect inward FDI into China. Despite data limitations, I
attempted different methodologies to correct for the potential econometric problem, and showedthat the presence of IPAs has a significantly positive effect on attracting incremental FDI into
China. This indicates that IPAs, as an FDI-inviting policy tool, do fulfill their expected
responsibilities. Consistent with previous literature, other factors such as sales volume, R&D,
wage rate, and tax rate are also found to be important in the decision-making process of foreign
investors.
9

The results also confirm that IPAs play a more important role in encouraging non-HMT foreigncompanies than HMT firms to invest in China. Owing to the advantage of location, HMT firms
are familiar with the business patterns in mainland China and have easier access to resource
information, which thus increases the ease with which they make investment decisions, regardlessof the assistance of IPAs. As such, policies regarding IPAs and similar entities have only slight
impact on HMT firms. Based on this finding and on a consideration of the close connection
between HMT firms and China, as well as HMT firms’ great influence on the mainland in termsof employment, technological exchange, and even cultural communication, the Chinese
government should develop more creative policy tools that will allow it to work more closely with
its HMT partners.
Lastly, it is shown that IPAs are more efficient in increasing the investment of foreign firms in
high-tech industries compared with low-tech industries. One possible explanation for this is that
the Chinese government uses IPAs to screen FDI, as advanced technology is preferred to promoteindustrial productivity.
Due to insufficient information, neither the quality of IPAs nor the impact of firm heterogeneity
can be examined. Further study should thus be undertaken to determine the best means ofevaluating IPAs in terms of their qualities and functions.
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Appendix
Table A1. Description of the variables
Variable
Descriptiony (FDI)
new fixed assets for firm i (1000 yuan)
ipadummy
whether the city has ipa or not
iizdummy
whether the city is iiz or not
Wage Rateaverage wage rate for firm i (yuan)
Salesales revenue for firm i (1000 yuan)
Taxra
tax rate for firm i
RD
R&D expenditure for firm i (1000 yuan)
trade_time
customs clearance time for firm i (days)
gdp_percapita
gdp per capita of the city (yuan)
city_gdp
GDP of the city (10,000 yuan)
infrastructure investment value of the city (10,000 yuan)
infra_inv
frei
freight volume of the city (10,000 ton)
trade_volume
trade revenue of the city (10,000 yuan)
ave_wageaverage wage of the city (yuan)
tot_save
total savings of the city by the end of year (10,000 yuan)
education expenditure of the city (10,000 yuan)
edu_exp
post
total postal expenditure of the city (10,000 yuan)
tele
total telecommunication expenditure of the city (10,000 yuan)

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SourceEnterprise Survey
CCIIP
Invest in China
Enterprise Survey
Enterprise Survey
Enterprise Survey
Enterprise Survey
Enterprise Survey
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook
China Statistical Yearbook

Table A2. Cities with IPAs in China (by 2009)
Province
City
Province
City
Province
City
anhui
hefei
hebei
shijiazhuang
shandong
jinan
anhui
wuhu
hebei
tangshanshandong
qingdaobeijingbeijingheilongjiang
haerbing
shandong
weihaichongqingchongqinghenanzhengzhou
shandong
yantaifujianfuzhouhubei
hankoushanghaishanghaifujianxiamenhubei
qianjiang
shanxi1
taiyuangansu
lanzhouhubei
wuhan
shanxi3
xi’anguangdong
dongguan
hunanchangshasichuanchengduguangdongfoshanjiangsunanjingsichuanyibin
guangdong
guangzhou
jiangsunantong
tianjintianjinguangdonghuizhou
jiangsusuzhouxinjiang
wulumuqi
guangdongshenzhenjiangsutaicang
xizanglasa
guangdongzhanjiang
jiangsulianyungang
yunnankunmingguangdongzhuhaijiangxi
nanchangzhejiang
cixi
guangxi
nanningjilinchangchunzhejianghangzhouguizhouguiyangliaoning
dalianzhejiang
ningbohainanhaikouliaoning
shenyangzhejiang
wenzhouhebei
baoding
neimenggu
huhehaotezhejiang
xiaoshanhebei
langfang
ningxiayinchuanhebei
qinhuangdao
qinghaixiningNotes: 58 cities in total. shanxi1 is the province in North China. The capital city is Tai Yuan. While shangxi3 is theprovince in North-West China. The capital city is Xi’an.

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