Promotion or Liberalization: The Effect of Targeted Investment
Policies on FDI Inflows∗
Mitsuo Inada
Miyazaki Municipal University

Abstract
This study empirically examines two Foreign Direct Investment (FDI) policies (targeted promotion andselective liberalization) and quantifies which of the two targeted measure is more effective to attract FDI inflows and to stimulate the activities of Multinational Enterprises (MNEs). We further ask whether policies
for promoting FDI inflows should encourage entrants to set up new operations, or whether policies should
encourage incumbents to stretch out existing operations. We implement a difference-in-differences estimation that measures the effects of FDI policies by comparing affected and non-affected industries before andafter the change of FDI policies in compliance with the Chinese WTO accession. Using aggregated dataon foreign equity and sales by MNEs’ foreign affiliates in China over the 1999-2007, the results show that
selective liberalization has a positive impact on FDI inflows especially from incumbents, while targeted
promotion does not. The results suggest that selective liberalization is preferable to targeted promotion.

Keywords: Investment policies, FDI flows, Targeted Promotion, Selective liberalization, China——————————————————————–∗

The author is grateful for helpful comments and suggestions by Alan Deardorff, Naoto Jinji, Eiichi

Tomiura, Masayuki Morikawa, Tadashi Ito, Isao Kamata, Ayumu Tanaka, and Discussion Paper seminar participants at RIETI. This research is supported by the JSPS Grants-in-Aid for Scientific Research
15K03428 and 19H01481.

1

1Introduction

Investment policies have been an important tool for attracting foreign direct investment (FDI). For example,
FDI contributes to productivity growth in the recipient countries by generating export(Aitken, Hanson,
and Harrison, 1997) or R&D spillovers (Branstetter, 2006), and by stimulating contact between local firms
and multinational enterprises (MNEs) (Javorcik, 2004). Policymakers have thus been open to the foreign
affiliates of MNEs; they have recently taken a step further, trying to combine investment policies with
development strategies. These policies have begun to receive attention in the economics literature (Harding
and Javorcik, 2011, 2012; Lu, Tao, and Zhu, 2017). However, previous studies show that investment policies
positively affect FDI inflows in developing countries but have no such effect in developed countries. Thereis no consensus yet in the literature regarding how investment policies affect FDI inflows.
To broaden our understanding of investment policies, we examine and compare the impact of two
different types of recent policies: targeted promotion and selective liberalization. Investment promotion
provides investors with a set of favorable conditions, such as tax incentives and investment facilitation
services. Tax incentives are designed to maintain profits, and facilitation services simplify administrative
procedures or provide information on the investment climate through Investment Promotion Agencies(IPAs).1 Investment liberalization consists of opening domestic markets to foreign affiliates and limiting
policy space for government intervention, and it has been used to attract foreign advanced technology andinnovation activities. These promotions aim to compensate for FDI’s additional fixed costs that arise frommarket failures, such as insufficient public goods provision, a lack of information on the investment climate,
and the existence of positive externalities from FDI to local firms.
Policymakers in recipient economies have recently taken a step further, trying to combine these investment policies—common promotion and liberalization—with their industrial policy. A common feature ofindustrial policy is the intention to maximize positive spillovers from the activities of MNEs by stimulating
the diffusion of knowledge and technology and by promoting linkages with domestic suppliers (UNCTAD,
2018). To this end, targeted investment policies—targeted promotion and selective liberalization—are consistent with the industrial policy. These targeted policies do not provide general investors with preferential
treatment via tax incentives or via opening domestic markets to foreign affiliates. Rather, they provide
these benefits to specific investors that bring advanced technology and engage in innovation activities like
R&D; they further target investment in export-oriented sectors and adoption of key technology in man1 Special Economic Zones (SEZs) offer not only tax incentives and facilitation services but also infrastructure and customs
procedures aid.

2

ufacturing supply chains. Although these targeted efforts are important not only for achieving industrial
growth but also for contributing to sustainable growth, to the best of our knowledge, these two measures
have not been compared in the literature. Hence, this study examines which targeted measure is more
effective in attracting FDI inflows and in stimulating MNE activities.
Table 1 presents the extent to which targeted investment tools are used by the countries in each grouping.
The table shows that targeted tools are used more commonly in developing countries. Looking at eachgrouping, selective liberalization are chiefly used, while targeted promotion are secondarily used. However,comparing the groupings, both entry and investment promotions are commonly applied in developing
economies, but not in developed economies. Thus, when examining how investment policies affect FDI
inflows, these two tools need to be compared.
In this study, we further shed light on the implicit assumption underlying investment policies, namely,that the aim of the policy is to attract new entrants. By contrast, the standard heterogeneous firmmodel (Helpman, Melitz, and Yeaple, 2004) assumes that the entrants investing in foreign countries are
firms with productivity levels above the cutoff at which firms have positive operating profits from sales
in foreign markets. Thus, in practice, incumbents are larger firms with productivity levels above thecutoff but new entrants may be smaller firms with productivity levels below the cutoff. Then, we further
investigate whether policies for promoting FDI inflows, and thereby, achieving sustainable development,
should encourage entrants to set up new operations, or whether they should encourage incumbents to
expand existing operations.
We control for simultaneity between FDI inflows and industrial growth by focusing on FDI policy thatassigns treatment and control groups to affected industries. Bias arises when we cannot distinguish whether
growing industries are selected to attract FDI inflows or whether the industries selected for promotion of
FDI generate industrial growth. To avoid this potential bias, we specifically implement a difference-indifferences (DD) estimator that measures the effects of FDI policies by comparing the affected and nonaffected industries before and after the change of FDI policies in 2002. This was the year of the initial
regime shift on FDI in China in compliance with its World Trade Organization (WTO) accession protocol,
and, therefore, foreign investors had no reason to expect the magnitude of the ultimate regulation change.
Further, they did not have full information on which industries would have their restrictions lifted until
half a month or less before the regulation change took effect. We thus regard this regime shift with respect
to FDI as varying significantly among different industries.2
2

To the best of our knowledge, the entire list was first published in International Trade News on March 15, 2002.

3

There is an increasing number of studies exploiting the relaxation of FDI regulations following China’s
WTO accession (Inada, 2013, Lu, Tao, and Zhu, 2017, Lu, Sugita, and Zhu, 2017, Hsu et al., 2018,
Liang, Lovely, and Zhang, 2019). They mainly shed light on the impact of the regulation change on the
productivity of domestic firms in the same industry.3 Relative to these studies, our contribution is that we
examine FDI inflows in more detail. Lu, Tao, and Zhu’s (2017) analysis is solely based on the relaxationof FDI regulations. In contrast, we classify this relaxation into two types of investment policies. Then,
first and most importantly, we quantify the effect of the different types of investment policies. Second,
we assess the effect of these different types on FDI inflows in addition to the extensive and the intensive
margin.
This study complements the international trade literature on targeted policies. Most studies examine
the impact of targeted investment promotion on FDI inflows and the activities of MNEs. For example,
targeted tax incentives for FDI are associated with linkages from FDI to local firms (Du, Harrison, andJefferson, 2014), and, in developing countries, targeted facilitation leads to higher FDI flows (Harding andJavorcik, 2011) and exporting higher-quality goods, but, in China, it only leads to additional investment
by foreign affiliates (Ni, Todo, and Inui, 2017).4 Azémar and Desbordes (2010), the study most similar toours examines and compares the impact of entry-cost subsidies and labor market deregulation. However,few studies examine the effect of targeted policies; the number of previous studies examining the impactof selective liberalization on FDI inflows is still limited, and selective liberalization has not been evaluatedin comparison with targeted promotion.
The remainder of this paper is divided into four sections. Section 2 develops a brief conceptual framework for analyzing investment policies. Section 3 describes the dataset and provides a preliminary graphicalanalysis. Section 4 provides empirical evidence showing that targeted policies affect the intensive marginof FDI and that targeted promotion and selective liberalization affect the activities of MNEs differently.
Section 5 concludes the paper.

2

Conceptual framework

Investment policies have been incorporated as subsidies into the standard heterogeneous firm model oftrade. Specifically, Chor (2009) argue that, although a subsidy to either MNEs’ fixed or variable costs
3

Unlike these studies, Inada and Guo (2016) examine the effect of the regulation change on the productivity of domestic
firms in the home economy.
4 There is an increasing number of studies investigating the effect of spatially targeted program (creation of SEZs) on FDI
inflows and export-oriented investment (Wang, 2013, Schminke and Van Biesebroeck, 2013, Alder, Shao, and Zilibotti, 2016,
Chen, Poncet, and Xiong, 2017, and Lu, Wang, and Zhu, 2018).

4

of operation generates a welfare improvement through the selection effect, a variable-cost subsidy createsa larger welfare improvement than a fixed-cost subsidy. Using the model in Chor (2009), we present theconceptual framework to guide our empirical study.5
Consider a cutoff productivity level above which exporters have positive operating profits from FDI(see Helpman, Melitz, and Yeaple, 2004; Chor, 2009). In the model of the last two subsidies, there are twocountries, Home and Foreign, labeled H and F , respectively. First, consider the use of a fixed-cost subsidy(0 < sf < 1) by the Foreign government, which lowers the FDI cutoff:

a1−ε
I,j =

sf,j (fI,j − fX,j )wH,j

BF,j wF1−ε − (τj wH )1−ε

!
,

(1)

where j denotes the industry, aI,j is the FDI cutoff, fI,j is the fixed cost of FDI, fX,j is the fixed cost ofexporting, wj is the effective unit wage costs, ε is the elasticity of substitution in demand between any
two goods, τj is the trade cost, and BF,j is the demand parameter. The cutoff shows that the fixed costof FDI decreases from (fI,j − fX,j )wH to sf,j (fI,j − fX,j )wH . Thus, the subsidy enables only the mostproductive firms that were initially servicing the foreign market via exports to switch to FDI, thereby
creating a welfare gain from the selection effect.
In practice, the amount of sf,j is related to industry characteristics. For example, this subsidy could
open domestic markets to foreign affiliates in export-oriented sectors, or allow the adoption of key technology in manufacturing supply chains. Therefore, we claim that a fixed-cost subsidy can be used selectively
by the Foreign government to attract Home firms that contribute to Foreign’s industrial growth. This isselective liberalization.
Second, consider the use of a variable-cost subsidy (0 < sv < 1) by the Foreign government, which
reduces MNEs’ effective unit wage costs:

a1−ε
I,j =



(fI,j − fX,j )wH,j
BF,j [{(1 − sv,j )wF }1−ε − (τj wH )1−ε ]


,

(2)

This cutoff shows that the variable-cost for MNEs decreases from wF,j to (1 − sv,j )wF,j . The subsidy
generates the above-mentioned selection effect by lowering the cutoff (as 1 − ε < 0). Moreover, there isan additional production effect that reduces the effective price markup of MNEs. In the standard-type
5 There is another stream of literature on the optimal policies for attracting FDI through the real options approach. See
Pennings (2000, 2005), Yu, Chang, and Fan (2007), and Tian (2018). A similar argument to Chor (2009) was made by
Tian (2018), who argues that a tax rate reduction (or a variable-cost subsidy) is preferable, in terms of option values, to an
investment-cost subsidy (or a fixed-cost subsidy) regardless of the FDI timing.

5

model of heterogeneous firms by Melitz (2003), this effect raises the output levels of all overseas affiliates
and increases consumption gains. Thus, a variable-cost subsidy could create a larger welfare improvement
than a fixed-cost subsidy.
Further, in practice, the amount of sv,j is related to industry characteristics. For example, a set offavorable conditions such as tax incentives are provided to maximize positive spillovers from the activities
of MNEs by promoting linkages with domestic suppliers (Du, Harrison, and Jefferson, 2014). Therefore,
we claim a the variable-cost subsidy that substantially lowers unit wage costs can be used selectively. Thisis targeted promotion.

3

Data and graphical analysis

3.1

Data

3.1.1

Industry-city level unbalanced panel data

The main data are based on aggregate data from the Annual Survey of Industrial Firms (ASIF), administered by the National Bureau of Statistics of China, for the period from 1999 to 2007. We focus on
foreign-owned enterprises and enterprises from Hong Kong, Taiwan, and Macao, namely, their joint ventures and wholly-owned enterprises. We aggregate our sample firms by dividing them into incumbents orentrants, based on the ownership code reported in the ASIF data. For our study, we aggregate the firmlevel financial information to an ASIF industry-city level. Thus, we use unbalanced panel data consistingof 424 four-digit industries and 416 prefecture-level cities.
For the purpose of controlling regional factors affecting the fixed and variable costs of FDI, we include
city characteristics such as wages; per capita industrial output; per capita number of secondary school
students; highway density; per capita post and telecommunications; and per capita loans by, and deposits
in, financial institutions.6
3.1.2

Data on Chinese FDI policies

To obtain information about Chinese FDI policies, following Lu, Tao, and Zhu (2017), we compare the 1997
and 2002 version of the Catalogue for the Guidance of Foreign Investment Industries. This comparison
allows us to examine three types of investment policies:• FDI was promoted (hereinafter, the products benefiting under this policy are referred to as (FDI)
promoted products). For example, high-tech chemical fibers, such as “carbon fibers,” were listed
6 The data source is the Urban Statistical Yearbook of China (various issues) and the China Statistical Yearbook for Regional
Economy (various issues).

6

in the permitted category in the 1997 Catalogue, but listed in the supported category in the 2002
Catalogue. In the supported category, foreign investors receive preferential tax rates that substantiallylower the variable costs of MNEs.7 Thus, we call this categorical shift targeted promotion.
• FDI was liberalized (hereinafter, the products benefiting under this policy are referred to as (FDI)
liberalized products). For example, home appliances, such as “washing machines,” were listed in
the restricted category in the 1997 Catalogue, but listed in the permitted category in the 2002
Catalogue. In the restricted category, foreign investors are subject to the foreign ownership cap,
but in the permitted category, the cap is removed; consequently, foreign investors receive benefits
from the reduction in the documentation cost of an application for approval to establish subsidiaries
from the regulatory authority. Table 3 displays, for each product category, the regulatory authority
granting the approval to establish foreign subsidiaries. It is clear that for products in the supported
or permitted category, approval can be applied for through a lower regulatory authority than for
products in the restricted category. Thus, we treat this categorical shift as selective liberalization.
• FDI was both promoted and liberalized. For example, “assembled vehicles and motorcycles” waslisted in the restricted category in the 1997 Catalogue, but listed in the supported category in the2002 Catalogue. We designate such products as combined (policy) products.
We follow the process outlined in Lu, Tao, and Zhu (2017) to aggregate the changes in FDI policies fromthe Catalogue product level to the ASIF industry level. Table 2 shows the shift in the product categories
from the 1998 to the 2002 version of the Catalogue. The process results in five possible scenarios:
1. (FDI) Promoted Industries: For the Catalogue products in four-digit industries, there was either apromotion in FDI policy or no change in FDI policy. For example, in Table 2, 63 products were listed
in the permitted category in the 1997 Catalogue, but listed in the supported category in the 2002
Catalogue. Thus, we refer to such industries as promoted industries.
2. (FDI) Liberalized Industries: For the Catalogue products in four-digit industries, there was either aliberalization in FDI policy or no change in FDI policy. For example, in Table 2, 38 products werelisted in the restricted category in the 1997 Catalogue, but listed in the permitted category in the2002 Catalogue. Thus, we refer to such industries as liberalized industries.
7 According to Article 73 of the Rules for the Implementation of the Income Tax Law of the People’s Republic of China forEnterprises with Foreign Investment and Foreign Enterprises, foreign firms in the supported category receive a 15% corporate
tax rate.

73. (FDI) Combined Industries: For the Catalogue products in four-digit industries, there was an improvement (i.e., both a promotion and a liberalization) in FDI policy or no change in FDI policy. For
example, in Table 2, 17 products were listed in the restricted category in the 1997 Catalogue, butlisted in the supported category in the 2002 Catalogue. Thus, we refer to such industries as combined
industries.
4. (FDI) Discouraged Industries: For the Catalogue products in four-digit industries, there was either
a deterioration in FDI policy or no change in FDI policy. For example, in Table 2, 4 products in
the 1997 Catalogue was listed in the permitted category, but listed in the restricted category in the2002 Catalogue. In addition, 28 products in the 1997 Catalogue was listed in the supported category,but listed in the permitted category in the 2002 Catalogue. Thus, we refer to such industries as
discouraged industries.
5. No-change Industries: For the Catalogue products in a four digit industry, there was no change in
FDI policies between 1997 and 2002. Thus, no-change industries are the principal diagonal elements
in Table 2.
Unlike in Lu, Tao, and Zhu (2017), (FDI) encouraged industries in this study consist of promoted andliberalized industries. In our regression analysis, the treatment group is promoted or liberalized industries
and the control group is no-change industries. Following Lu, Tao, and Zhu (2017), FDI-discouraged
industries are excluded from the analysis. Moreover, to distinguish between the effect of FDI promotion
and liberalization, we control for the combined industries. The results (available upon request) remainrobust when we include the combined industries in the promoted or liberalized industries.
Finally, investment policies are applied to both entrants and incumbents. Incumbents in the supported
category can benefit from corporate tax exemption if they make an additional investment satisfying one of
the following: (i) the capital amount exceeds USD 60 million, or (ii) the capital amount increases by 50%,and exceeds USD 15 million. Likewise, entrants can benefit from a reduction in the documentation cost of
establishment when they expand their existing operations.

3.2

Estimation specification

Based on the empirical implications from (1) and (2), consider the following DD specification that tests
for the influence of investment policies affecting FDI inflows:8Log yjct = αj + βc + γt +2007
X

δt Liberalizedj × Y eart +t=2000

+

2007
X

2007
X

ζt P romotedj × Y eart(3)

t=2000ηt Combinedj × Y eart + X 0 ct λ + ujct ,t=2000where j, c, and t denote the four-digit industry, prefecture-level city, and year, respectively. yjct represents
the ownership, number of foreign affiliates, and performance of foreign affiliates (e.g., sales) of industry j
in year t. λj , λc , and λt are the industry, city, and year fixed effects, respectively. Liberalizedj indicates
whether industry j includes liberalized products; P romotedj indicates whether industry j includes promoted products, and Combinedj indicates whether industry j includes combined products. Y eart is a year
dummy, Xct is a vector of time-varying city characteristics to isolate the effect of investment policies, andujct is the error term. The standard errors are clustered by four-digit industry to adjust for possible serial
correlation and heteroskedasticity, following the suggestion in Bertrand, Duflo, and Mullainathan (2004).
The summary statistics of key variables are shown in Table 4.

3.3

Identifying assumption and graphical analysis

3.3.1

Identifying assumption

The key identifying assumption here is that the trend of FDI in China would be the same in both industries
in the absence of FDI policies. The common trend assumption can be applied to the conditional expectation:
E[y0jct |j, c, t, Xct ] = αj + βc + γt + X 0 ct λ.

(4)y0jct denotes an outcome in the pre-treatment period (i.e., t < 2002). This equation indicates that encouraged industries can differ from no-change industries only in the fixed effects and in all the other controls.
In the graphical analysis below, we check whether FDI policies induce a deviation from this common trend.
3.3.2

Graphical analysis

Figure 1 shows the growth trend in foreign equity (left panel) and foreign affiliate sales (right panel) over
2000–2007. Both exhibit a similar trend, although it is driven by the growth in encouraged industries,
especially incumbents in encouraged industries. Their growth accounts for 66% of the total foreign equity
growth and 80% of the total sales growth.
Panel A in Figure 2 presents the trend in foreign affiliates’ average foreign equity (left panel) and averagesales (right panel) for the encouraged and no-change industries over 1999–2007. Prior to the treatment
year, the trajectories of the two types of industries are very similar. However, after the treatment year ,9the trajectories of the encouraged industries deviate from those of the no-change industries. Panel B in
Figure 2 shows the trend in foreign affiliates’ average foreign equity (left panel) and average sales (rightpanel) for the promoted, liberalized, and no-change industries over 1999–2007. The trajectories are similar
to those for the encouraged and no-change industries even when the encouraged industries are divided into
promoted and liberalized industries. These results indicate that the trends in the treatment and control
groups are parallel in the pre-treatment period but divergent in the post-treatment period.
Figure 3 displays the trend in foreign affiliates’ (left panel) and domestically-owned firms’ (right panel)effective corporate tax rates for the encouraged and no-change industries over 1999–2007. Following thesuggestion by Du, Harrison, and Jefferson (2014), we calculated these effective tax rates using reported theincome taxes paid as a share of total profits. The trajectories in the left panel are considerably parallel inthe pre-treatment period and divergent in the post-treatment period. In the post-treatment periods, tax
subsidies for FDI are indeed effective, but the magnitude of the effect is substantially small. The effective
tax rate for FDI decreased from 18.9% in 2002 to 17.3% in 2004. This is because the tax subsidy had beenprevalent in the pre-treatment period, irrespective of whether the industries were affected by investment
policies. Foreign affiliates received a 15% tax subsidy whereas domestically-owned firms faced a statutory
tax rate of 33%. Comparing the left and right panels, throughout the period, the tax rates of foreign
affiliates are approximately 10% lower than those of domestically-owned firms.

4

Empirical results

Table 5 reports the DD estimation results using foreign equity by foreign affiliates (column 1) and theirincumbents (column 2), and number of foreign affiliates (column 3) as dependent variables. The first column
in Table 5 shows that the Liberalizedj × Y eart interactions are positive and statistically significant in the
post-WTO period, whereas the P romotedj × Y eart interactions are negative and statistically insignificant.
In Figure 4, we plot the set of estimated coefficients from the DD estimation of the first column of Table 5
for Liberalizedj × Y eart (column (a) in panel A) and P romotedj × Y eart (column (b) in panel A), showing
the differences in foreign equity between the encouraged and no-change industries over time. Althoughthese results are consistent with the findings in the literature (e.g., Lu, Tao, and Zhu, 2017) that FDI
policy induces inflows of FDI after China’s WTO accession, selective liberalization has a positive impact
on FDI inflows while targeted promotion does not.
The second column in Table 5 shows that interactions Liberalizedj ×Y eart are consistently positive andstatistically significant in the post-WTO period, whereas the interactions P romotedj × Y eart are negative10

and statistically insignificant, suggesting that selective liberalization has a positive impact on FDI inflows,
especially for incumbents.
The third column in Table 5 shows that both Liberalizedj × Y eart and P romotedj × Y eart are statistically insignificant. To summarize, these results suggest that FDI policies have indeed had a positive
impact on the intensive margin of FDI, but less so on the extensive margin.
To evaluate the magnitude of the effect, we focus on the estimates of the post-WTO period from thefirst column in Table 5. The estimate of 0.697 on the fourth line implies that selective liberalization raises
FDI inflows in an industry by 69.7%. This magnitude is comparable to the DD estimates in the literature(e.g., 0.4118 in Charlton and Davis, 2007; 0.770 by Harding and Javorcik, 2011). The estimated effects are
incremental if we compare 2004 to 2007, at 67.2% and 72.4%, respectively. This gradual increase is also
comparable to the findings of Harding and Javorcik, 2011. These results suggest selective liberalization
has a persistent and incremental effect on the FDI inflows in the post-WTO period, possibly because timeis needed for the effects of liberalization to appear.
The DD estimates, reported in Table 6, measure the effect of FDI policies on foreign affiliate sales. The
first column in Table 5 shows that both Liberalizedj × Y eart and P romotedj × Y eart are positive andstatistically significant in the post-WTO period, although targeted promotion becomes significant startingfrom 2004. In Figure 4, we again plot the set of estimated coefficients from the DD estimation in the firstcolumn in Table 6 for Liberalizedj × Y eart (column (a) in panel B) and P romotedj × Y eart (column (b) ofpanel B). Unlike the results in Table 5, these results suggest that both selective liberalization and targeted
promotion have a positive impact on foreign affiliate sales.
The second column in Table 5 shows that the Liberalizedj × Y eart interactions are positive and statistically significant in the post-WTO period, whereas the P romotedj × Y eart interactions are positive but
statistically insignificant, suggesting that attracted FDI in the liberalized industries encourage incumbents
to expand their existing operations.
We further investigate whether investment policies work as a catalyst to attract specific investors toexport-oriented sectors or to innovation activities. Presumably, the presence and activities of MNEs could
generate positive spillovers by diffusing information about the international market (Aitken, Hanson, andHarrison, 1997), or by diffusing technological information via innovation activities (Jaffe, 1986 and Cheungand Lin, 2004).
We look at the number of exporters and export sales as measures of the export performance of theattracted MNEs. The first column of Table 7 shows that both Liberalizedj ×Y eart and P romotedj ×Y eart11are statistically insignificant, suggesting again that FDI policies do not have any effect on the extensive
margin of FDI.
The second column in Table 7 shows that the P romotedj × Y eart interactions are positive and statistically significant in the post-WTO period, whereas the Liberalizedj × Y eart interactions are statisticallyinsignificant, suggesting that targeted promotion induces larger exporting sales.
We also look at the number of firms introducing a new product, and new product revenues as proxies
for firms’ innovation activities, following the suggestion by Zhang (2015) and Lu, Tao, and Zhu (2017) thata new product can reflect the output of R&D investment. The regression results are shown in columns 3-4
in Table 7. The third column in Table 7 shows that both Liberalizedj × Y eart and P romotedj × Y eartare statistically insignificant, although Liberalizedj × Y ear2003 is significant but only at the 10% level.
Overall, we find a consistent result; that is, FDI policies do not affect the extensive margin of FDI.
The fourth column in Table 7 shows the P romotedj × Y eart interactions are positive and statisticallysignificant in the post-WTO period, whereas the Liberalizedj ×Y eart interactions are statistically insignificant, suggesting that the attracted FDI in the liberalized industries increases their innovative activities.

5

Policy implication

We identified the positive impact of selective liberalization on FDI inflows and innovation activities. Thisfinding have straightforward implications for policymakers. For policymakers, this implies that targeted
efforts are important for absorbing advanced technology. The policymakers could promote inward FDI by
opening domestic markets to specific investors that bring advanced technology and engage in innovation
activities. For example, Japanese authorities have recently promoted innovation activities by implementing
the Act on Special Measures for Productivity Improvement. The aim of the act is to relax regulations on
innovation activities. This special measures could allow the adoption of key technology in manufacturing
supply chains. The results of this study provide support for the policy.
Second, my finding suggests that selective liberalization have a positive impact on the intensive marginof FDI. The Chinese government stimulated incumbents to expand existing operations by relaxing FDI
regulations. Again we look at the estimates of the post-WTO period from the second column in Table
5. The estimate of 0.692 on the fourth line implies that selective liberalization raises FDI inflows in anindustry by 69.2%. This magnitude is comparable to the DD estimates in the first column in Table 5.
The estimated effects are also incremental if we compare 2004 to 2007, at 69.2% and 82.9%, respectively.
Thus, our results indicates that policies for promoting FDI inflows, and thereby, achieving sustainable12

development should encourage incumbents to expand existing operations.
Third, policymakers should continue to make effort to simplify administrative procedures or provide
information on the investment climate through targeted promotion. The estimation results in Table 7 showthat targeted promotion have positive effects on export activities. Policymakers could attract investment
in export-oriented sectors by promoting specific investors with preferential treatment via tax incentives,
facilitation services, or providing information on the investment climate through Investment Promotion
Agencies (IPAs).

6

Conclusion

This study investigated the different investment policies most commonly used both in developed and developing countries. Our difference-in-differences estimates demonstrate that selective liberalization is more
effective in attracting FDI inflows than targeted promotion and that the attracted FDI in the liberalized
industries encourages incumbents to expand their existing operations. To summarize, these FDI policies
have a positive impact on the intensive margin of FDI, but less so on the extensive margin.
One major caveat of these results is that they are not consistent with the theoretical prediction thata variable-cost subsidy could generate a larger welfare improvement than a fixed-cost subsidy. However,
it is plausible that variable-cost subsidies, that is, tax subsidies for FDI, are indeed effective, but themagnitude of their effect is substantially small. Moreover, despite this caveat, it is likely that targeted
promotion induces an additional production effect on MNEs’ activities.
In addition, we find that targeted promotion has a positive impact on export activities, whereas selective
liberalization has a positive impact on the innovation activities of MNEs. These results suggest thattargeted investment policies can indeed work in tandem with industrial policy that intends to maximize
positive spillovers from the activities of MNEs by stimulating the diffusion of knowledge and technology.
However, different policies have different effect.
Finally, it is worth indicating that our findings broaden our understanding of investment policies indeveloping countries and also probably in developed countries. This study suggests that selective liberalization may be an effective policy option for stimulating industrial growth.

13

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Table 1: Targeted investment policy tools by economic grouping between 2010 and 2018 (number of tools)

All tools
World
Developed economies
Developing economies

481
116
365

Targetedpromotion84
1074

Selective
liberalization
339
102
237

Specialtreatment
58
4
54

Source: Calculated by the author from United Nations Conference on Trade and Development (UNCTAD) investment
policy hub.

Table 2: Product category transitions from 1998 to 2002

1998

Supported
Permitted
Restricted
Prohibited
Total

Supported

Permitted

2002
Restricted

Prohibited

Total

131
63
17
0
211

28
—
38
0
—

0
4
31
0
35

0
0
0
5
5

159
—
86
5

Source: Calculated by the author from data published by the Gazette of the State Council of the People’s Republic ofChina, No. 40, January 1998, and No. 3, January 2003.

Table 3: Regulatory authorities for approval of establishment of foreign subsidiaries (Unit: million USD)
Investment amountMore than 500
From 300 to 500
From 100 to 300
From 50 to 100
Less than 50

Supported and Permitted products
the State Council, National DRC
National DRC

Restricted products

Local DRC

National DRC
Provincial DRC

the State Council
National DRC

Source: Summarized by the author based on information published in the Gazette of the State Council of the People’s
Republic of China, No. 20, July 2005.
Note: DRC is an abbreviation for the Development and Reform Commission, which is in charge of China’s macroeconomicplanning.

17

Table 4: Summary statistics
Variables
Log of foreign equity
Log of incumbents’ foreign equityObs.
85,93985,939

Mean
9.10
8.71

Std. dev.
2.80
3.29

Log of number of firms
Log of sales
Log of incumbents’ sales

85,93985,93985,939

0.53
11.12
10.64

0.80
1.71
2.80

Log of number of exporters
Log of export sales
Log of number of firms with new productLog of new product sales

85,93985,93973,33373,333

0.64
6.32
0.27
1.47

0.70
5.30
0.51
3.66

Note: The sample period covers 1999–2007. New product sales in the data set are missing in 2004, a census year. Summary
statistics are provided separately for foreign affiliates and their incumbents. This table reports the number of observations,and the means and standard deviations for the following variables: foreign equity, sales, export sales, and domestic sales.

18

Table 5: Main results
Foreign affiliates
Log of foreign equity(1)

Incumbents
Log of foreign equity(2)

Foreign affiliates
Log of number of firms(3)

Liberalized×Year2000

0.054(0.183)0.038(0.185)-0.049∗∗(0.024)

Liberalized×Year2001

0.024(0.154)0.005(0.160)-0.006(0.024)

Liberalized×Year2002

0.221(0.189)0.150(0.221)

0.004(0.039)Liberalized×Year2003

0.697∗∗(0.344)0.692∗(0.352)0.061(0.070)Liberalized×Year2004

0.672∗(0.343)0.739∗∗(0.329)0.065(0.067)

Liberalized×Year2005

0.775∗∗(0.332)0.768∗∗(0.342)0.078(0.070)Liberalized×Year2006

0.781∗∗(0.335)0.829∗∗∗(0.316)0.081(0.070)Liberalized×Year2007

0.724∗∗(0.351)0.696∗∗(0.343)0.056(0.074)

Promoted×Year2000

0.057(0.112)0.044(0.114)0.020(0.016)

Promoted×Year2001-0.039(0.139)-0.047(0.141)-0.017(0.018)Promoted×Year2002-0.194(0.139)-0.210(0.145)-0.022(0.025)

Promoted×Year2003-0.049(0.201)-0.143(0.210)0.015(0.060)

Promoted×Year2004-0.029(0.199)-0.199(0.204)0.017(0.061)

Promoted×Year2005-0.070(0.203)-0.181(0.213)0.026(0.062)

Promoted×Year2006-0.049(0.201)-0.158(0.206)0.038(0.063)

Promoted×Year2007-0.050(0.202)-0.132(0.213)0.022(0.063)

Combined×Year Dummies
Industry fixed effects
City fixed effects
Year fixed effects
Time-varying city controls
R2
N

Yes
Yes
Yes
Yes
Yes
0.220
85,939

Yes
Yes
Yes
Yes
Yes
0.215
85,939

Yes
Yes
Yes
Yes
Yes
0.366
85,939Dependent variable

Note: Observations on foreign affiliates and their incumbents are aggregated at the industry-municipality-year level.
Difference-in-differences are the differences in changes between the encouraged (promoted or liberalized) and no-change
industries. Time-varying city controls include wages; per capita industrial output; per capita number of secondary school
students; highway density; per capita post and telecommunications; and per capita loans by, and deposits in, financial
institutions. Robust standard errors are reported in parentheses, clustered at the four-digit industry level. ***, **, and *indicate significance at the 1%, 5%, and 10% levels, respectively.

19

Table 6: Foreign affiliate sales
Dependent variable:
Log sales

Foreign affiliates(1)

Incumbents(2)

Liberalized×Year2000

0.059(0.067)0.034(0.069)Liberalized×Year2001

0.003(0.080)-0.022(0.084)

Liberalized×Year2002

0.142∗(0.073)0.035(0.106)

Liberalized×Year2003

0.546∗∗(0.229)0.588∗∗(0.248)Liberalized×Year2004

0.553∗∗(0.233)0.651∗∗∗(0.225)

Liberalized×Year2005

0.539∗∗(0.216)

0.614∗∗∗(0.228)Liberalized×Year2006

0.590∗∗(0.234)0.725∗∗∗(0.220)Liberalized×Year2007

0.522∗∗(0.218)0.596∗∗∗(0.227)

Promoted×Year2000

0.068(0.065)0.060(0.068)Promoted×Year2001

0.052(0.076)0.054(0.079)Promoted×Year2002

0.041(0.067)0.000(0.066)

Promoted×Year2003

0.225(0.146)0.108(0.172)

Promoted×Year2004

0.283∗(0.149)0.066(0.171)

Promoted×Year2005

0.263∗(0.144)0.084(0.179)Promoted×Year2006

0.346∗∗(0.149)0.200(0.179)Promoted×Year2007

0.314∗∗(0.146)0.133(0.186)

Combined×Year Dummies
Industry fixed effects
City fixed effects
Year fixed effects
Time-varying city controls
R2
N

Yes
Yes
Yes
Yes
Yes
0.282
85,939

Yes
Yes
Yes
Yes
Yes
0.168
85,939Note: Observations on foreign affiliates and their incumbents are aggregated at the industry-municipality-year level.
Difference-in-differences are the differences in changes between the encouraged (promoted or liberalized) and no-change
industries. Time-varying city controls include wages; per capita industrial output; per capita number of secondary school
students; highway density; per capita post and telecommunications; and per capita loans by, and deposits in, financial
institutions. Robust standard errors are reported in parentheses, clustered at the four-digit industry level. ***, ** , and *indicate significance at the 1%, 5%, and 10% levels, respectively.

20

Table 7: Other measures of foreign affiliate performance
Foreign Affiliates
Log number of exporters

Log export sales

(1)

(2)

Foreign Affiliates
Log number of firms
with new product(3)

Liberalized×Year2000-0.021(0.023)0.010(0.256)0.013(0.016)0.168(0.200)Liberalized×Year2001-0.020(0.025)-0.238(0.232)0.020(0.014)0.259(0.183)

Liberalized×Year2002-0.014(0.028)-0.131(0.246)0.000(0.012)-0.019(0.155)

Liberalized×Year2003-0.018(0.056)-0.165(0.420)0.042∗(0.024)0.775∗∗(0.344)

Liberalized×Year2004-0.022(0.048)-0.283(0.371)––––

Liberalized×Year2005-0.010(0.045)-0.265(0.347)0.036(0.024)0.744∗∗(0.372)

Liberalized×Year2006

0.003(0.047)-0.170(0.371)

0.041(0.027)0.760∗∗(0.378)Liberalized×Year2007-0.002(0.046)-0.107(0.365)0.032(0.026)0.660∗(0.372)

Promoted×Year2000

0.002(0.014)-0.019(0.150)-0.001(0.011)

0.007(0.135)

Promoted×Year2001

0.002(0.016)0.213(0.170)-0.013(0.013)-0.126(0.175)

Promoted×Year2002

0.005(0.024)0.108(0.213)-0.001(0.014)-0.004(0.185)

Promoted×Year2003

0.048(0.042)0.764∗∗(0.371)-0.002(0.016)0.057(0.225)

Promoted×Year2004

0.039(0.044)0.734∗(0.380)––––

Promoted×Year2005

0.053(0.043)0.799∗∗(0.390)0.008(0.017)0.126(0.233)

Promoted×Year2006

0.069(0.044)

0.992∗∗(0.403)0.012(0.018)0.193(0.236)

Promoted×Year2007

0.056(0.042)0.746∗(0.387)0.004(0.019)0.059(0.238)Combined×Year Dummies
Industry fixed effects
City fixed effects
Year fixed effects
Time-varying city controls
R2
N

Yes
Yes
Yes
Yes
Yes
0.397
85,939

Yes
Yes
Yes
Yes
Yes
0.328
85,939

Yes
Yes
Yes
Yes
Yes
0.167
73,333

Yes
Yes
Yes
Yes
Yes
0.161
73,333

Dependent variable

Log new product sales(4)

Note: All observations are at the industry-municipality-year level. New product sales in the data set are missing in 2004,
which was a census year. Difference-in-differences are the differences in changes between the encouraged (promoted orliberalized) and no-change industries. Time varying city controls include wages; per capita industrial output; per capita
number of secondary school students; highway density; per capita post and telecommunications; and per capita loans by,
and deposits in, financial institutions. Robust standard errors are reported in parentheses, clustered at the four-digit
industry level. ***, ** , and * indicate significance at the 1%, 5%, and 10% levels, respectively.

21

Figure 1: Trends in foreign affiliate sales and foreign equity over 2000–2007 (Unit: billion yuan)
Source: Calculated by ASIF.

22

Figure 2: Comparison of average trends in foreign affiliate sales and foreign equity between FDI-encouraged
and no-change industries over 1999–2007 (Unit: billion yuan)
Source: Calculated by ASIF.

Figure 3: Comparison of effective tax for foreign affiliates and domestic firms between FDI-encouraged andno-change industries over 1999–2007 (Unit: percent)Note: In this figure, domestic firms are defined as currently operating firms registered as non-foreign affiliates.
Source: Calculated by ASIF.

23

Figure 4: Estimated coefficients of the interactions between encouraged industries and year dummies
Note: The solid line is the trend of the foreign affiliate sales difference between liberalized/promoted industries (treatmentgroup) and no-change industries (control group) for the specification (1) in Table 5 (panel A) and (2) in Table 5 (panel B).
The dashed lines represent the 90% confidence interval of the estimated effect.

24