Accepted Manuscript
A general equilibrium analysis of FDI growth in Chinese services
sectors
María C. Latorre, Hidemichi Yonezawa, Jing Zhou
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DOI:
Reference:
S1043-951X(17)30121-9
doi: 10.1016/j.chieco.2017.09.002
CHIECO 1103
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China Economic Review
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9 September 2016
27 July 2017
1 September 2017
Please cite this article as: María C. Latorre, Hidemichi Yonezawa, Jing Zhou , A general
equilibrium analysis of FDI growth in Chinese services sectors, China Economic Review
(2017), doi: 10.1016/j.chieco.2017.09.002
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A general equilibrium analysis of FDI growth in Chinese services sectors
María C. Latorre (Universidad Complutense de Madrid)
Hidemichi Yonezawa (ETH Zurich)
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Jing Zhou (Xiangtan University)
Abstract
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This paper analyzes one of the features of the Chinese economic transition, namely, the impact
of foreign direct investment (FDI) accruing to advanced services sectors. To that aim we use an
innovative computable general equilibrium (CGE) model that includes, in a multi-regional
setting, foreign multinationals operating in monopolistic competition. The model is based on
data that split the world economy in 2016 into 11 regions (China - US - EU27 - Great Britain other advanced economies - India - Japan - South East Asia - Latin America - Middle East - Sub
Saharan Africa) and 21 sectors. We provide quantitative evidence on several characteristics of
the 21 sectors in China, EU27 and the US, as well as other data on the role of China in the
global stage, including its evolution since 2004. Several scenarios focusing on the increase of
FDI inflows in services, because of the reduction of its FDI barriers, are simulated deriving short
and long run results. We find that the impact of more foreign multinationals in services is
positive for China but smaller than the one that had been obtained in other previous studies
on FDI in manufactures. This is due to the still limited role of services in the Chinese economy
and to a crowding out effect that domestic firms experience after the entry of foreign
multinationals. On the whole the impact is, however, slightly positive for China, because
manufactures benefit from the entry of foreign services multinationals. The rest of regions are
unaffected or benefit very slightly, due to the fact that services production is less export
oriented and more devoted to private consumption than in the case of manufactures.
However, their manufacturing sectors are slightly harmed by the stronger Chinese
competition. Many of them manage to more than offset this latter trend through higher
exports or FDI in services directed to China.
JEL codes: C68, F14, F15, F17, F21
Keywords: Multinationals, CGE, monopolistic competition, fragmentation, vertical integration,
consumption-oriented growth.
Corresponding author: María C. Latorre. Facultad de Estudios Estadísticos, Universidad Complutense de
Madrid, Avda. Puerta de Hierro s/n, Ciudad Universitaria, 28040-Madrid. Tel. (34) 91 394 3990 Fax: (34)
91 394 4024 Email: mmunozla@ucm.es
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A general equilibrium analysis of FDI growth in Chinese services sectors
Abstract
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This paper analyzes one of the features of the Chinese economic transition, namely, the impact
of foreign direct investment (FDI) accruing to advanced services sectors. To that aim we use an
innovative computable general equilibrium (CGE) model that includes, in a multi-regional
setting, foreign multinationals operating in monopolistic competition. The model is based on
data that split the world economy in 2016 into 11 regions (China - US - EU27 - Great Britain other advanced economies - India - Japan - South East Asia - Latin America - Middle East - Sub
Saharan Africa) and 21 sectors. We provide quantitative evidence on several characteristics of
the 21 sectors in China, EU27 and the US, as well as other data on the role of China in the
global stage, including its evolution since 2004. Several scenarios focusing on the increase of
FDI inflows in services, because of the reduction of its FDI barriers, are simulated deriving short
and long run results. We find that the impact of more foreign multinationals in services is
positive for China but smaller than the one that had been obtained in other previous studies
on FDI in manufactures. This is due to the still limited role of services in the Chinese economy
and to a crowding out effect that domestic firms experience after the entry of foreign
multinationals. On the whole the impact is, however, slightly positive for China, because
manufactures benefit from the entry of foreign services multinationals. The rest of regions are
unaffected or benefit very slightly, due to the fact that services production is less export
oriented and more devoted to private consumption than in the case of manufactures.
However, their manufacturing sectors are slightly harmed by the stronger Chinese
competition. Many of them manage to more than offset this latter trend through higher
exports or FDI in services directed to China.
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JEL codes: C68, F14, F15, F17, F21
Keywords: Multinationals, CGE, monopolistic competition, fragmentation, vertical integration,
consumption-oriented growth.
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1. Introduction
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Despite the recent lower rates of economic growth in China, FDI inflows have not ceased to
increase and are becoming more oriented to services. The latest World Investment Report
(2016, p. 45) asserts: “In China, inflows [in 2015] rose by 6 per cent to $136 billion and
continued to shift towards services, which accounted for a new record of 61 per cent of FDI.
Inflows to the sector expanded by 17 per cent, while FDI into manufacturing stagnated”.
Furthermore, according to the OECD (2017), the 2015 edition of the “Catalogue for the
Guidance of Foreign Investment Industries”, which governs FDI, is aimed at liberalizing some
services sectors. The OECD (2017) also highlights that the China’s 13th Five-Year-Plan (20162020) aims at raising the share of services in GDP1. In addition, due to the US withdrawal from
the Trans-Pacific Partnership (TPP), China will probably strengthen its position as a destination
of FDI in services. Against this background, in this paper, we analyze the impact of the increase
of FDI in Chinese services sectors. We cover the effects for China but also for other regions of
the world. To that aim, we use a computable general equilibrium (CGE) model of the world
economy split in 11 regions (China - US - EU27 - Great Britain - other advanced economies India - Japan - South East Asia - Latin America - Middle East – Sub Saharan Africa) and 21
sectors for the year 2016.
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A general equilibrium analysis has several advantages. It is based on a robust theoretical
framework and uses a broad set of real micro and macroeconomic data. Thus, the results
cover several micro and macroeconomic variables, including, sectoral production, exports,
imports and prices, together with GDP, wages, capital remuneration, welfare, aggregate
exports, aggregate imports and the Consumer Price Index, respectively. The model includes
the interactions between goods and factor markets, as well as the supply and demand side of
the economy in a unified framework. It seems intuitive that there must be some relationships
across all these different variables and possible perspectives in the economy in order to
properly estimate the impact of FDI. However, the literatures analyzing them tend to be
disconnected. In the international business literature, the analysis focuses often within the
multinational. This idea underlies the comments of Meyer (2004: 260-1): “international
business research has been largely looking into the multinational, rather than ‘looking out’
from multinationals to the societies in which they are operating” and that “One of the
challenges is to tie the partial views discussed in different literatures together to allow
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China is opening up its services sectors step by step. After successful pilot-run in a few cities,
liberalization will be carried out across different regions. Some sectors like retail and wholesale,
education, construction, computer services and logistics have already a relatively low barrier for foreign
investment. Banking, insurance, healthcare, air and maritime transport, telecoms and broadcasting are
under way of liberalization. Due to the significance of real estate to Chinese economy, the authority is
cautious about its liberalization. China hopes to restructure its economy and regards as valuable the role
of services in economic growth. It has made great efforts in services negotiations in Doha, GATS and
WTO, as well as in Trade in Services Agreement (TISA).
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comprehensive assessments” (Meyer, 2004: 261). CGE models belong more to the economic
literature (Shoven and Whalley, 1984; Latorre et al., 2009).
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Our model incorporates the real data on different types of costs and production destination
across the sectors and regions we consider. This microeconomic detail contrasts with the
approach of a few more stylized multi-country general equilibrium models which explicitly
consider FDI (such as, Arkolakis et al., 2015; Arita et al. 2014; Burstein and Monge-Naranjo,
2009; McGrattan and Prescott, 2009; Ramondo, 2013; Ramondo and Rogríguez-Clare, 2013).
These latter studies do not offer any impact across sectors in the economy, since their analyses
have relied in most cases on aggregates of manufacturing sectors, which we disaggregate,
while we also cover services and indeed, the entire economy. Our model further incorporates
an innovative feature, which is the introduction of three characteristics that to the best of our
knowledge are not available in any previous CGE model: multiregional framework,
monopolistic imperfect competition and foreign multinationals. Very few CGE models have
considered the presence of FDI flows and foreign multinationals. Tarr (2012) and Latorre
(2009) review the few available ones. Furthermore, even less CGE models have looked at the
impact of FDI going to services.
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Zhou and Latorre (2014a; 2014b; 2015) have focused on the impact of FDI in Chinese
manufacturing sectors. In those papers, we find that FDI inflows generally raise GDP, wages,
national income and welfare considerably in China. However, the magnitude of the impact
depends on the particular sector to which FDI accrues. In particular, the results depend on
whether it is an export or private consumption oriented sector and on whether it relies more
heavily on imported or on domestic intermediates. FDI in manufacturing usually raises exports,
which tends to crowd out other foreign competitors but raise the demand for imported
intermediates coming from other Asian suppliers. Regions with a GDP structure and export
specialization similar to the one of China are crowded out by tougher Chinese competition
after the arrival of FDI. By contrast, regions that exhibit a structure of production and exports,
which complements the one of China in the global value chain, get better off after FDI inflows
in China. Other studies have also found a close relationship between FDI inflows and the
Chinese impressive growth rates achieved in the last decades (e.g., Kim et al., 2003, Whalley
and Xin, 2010). Furthermore, FDI seems to play an important role in Chinese foreign trade
(e.g., Dean et al., 2009). Koopman et al. (2014) have shown that only around 40-50% of the
value added embodied in Chinese exports is created in China, the rest being imported from
Japan, Korea, Taiwan, Hong Kong and the US. In the case of some electronics devices the share
of foreign content in exports even rises to 80%. However, as we have already noted, the focus
of all these previous analyses has been primarily on the impact of FDI in manufacturing.
Interestingly, the impact of FDI in services may be different than the one in manufacturing due
to several reasons. First, because services’ production is less devoted to the external market
than that of goods, their effects work differently. As we shall see below, one important
characteristic of services sectors is that they are more oriented to domestic activities than to
exports. This contrasts, sharply, with manufacturing sectors, whose products cross borders
more easily than services. This trend will reinforce the change in the former Chinese exportoriented FDI and growth strategy. It seems that FDI in services will contribute to the shift from
the Chinese export-led growth to a consumption-led growth pattern.
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Second, Konan and Maskus (2006) and Konan and Kim (2004) derive for developing
economies, that the relative poor condition of domestic services with respect to services in
developed countries, results in high costs and unproductive input services. Since many of these
services, such as, finance, communications and business services provide key intermediates for
all sectors of the economy, better foreign intermediates would contribute to overall
productivity. Latorre and Yonezawa (2017) and Latorre et al., (2015) also obtain this result in
the context of developed economies in their analysis of the potential impact of the
Transatlantic Trade and Investment Partnership (TTIP). In this case, foreign intermediates
provided by the affiliates of multinationals provide a wider variety of inputs that raise exports
in manufacturing and contribute to overall GDP and welfare growth.
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Third, services liberalization brings about gains to the economy that are more evenly
distributed across sectors and factors owners than in the case of goods. Indeed, goods’
liberalization through tariff reductions results in the specialization of an economy in a few
sectors in which abundant factors benefit disproportionately (Konan and Maskus, 2006; Konan
and Kim, 2004). Latorre et al. (2009) and Latorre (2013; 2012) have also found these
concentration effects after the entry of FDI in manufacturing sectors in contrast with FDI
accruing to services sectors. Such redistribution impacts are absent or milder after services
liberalization because their contribution in the form of better intermediates for all sectors
tends to raise production and factors’ income in a more balanced way.
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Fourth, barriers to foreign services are different than the ones experienced by FDI in goods or
trade in goods. Many cross border services’ transactions do not involve tangible products so
that customs and port clearance procedures are generally less relevant for services. Konan and
Van Assche (2007) delve into this important aspect. They derive that the success of services
liberalization is related to issues of market structure and domestic regulation. One key
resulting issue is the quality of the technology of the foreign firms, in particular, whether they
are more efficient than the incumbent firms. Unless the former is more efficient than the
latter, there will not be beneficial productivity effects due to the use of better intermediates
across sectors. Another important issue is to promote through pro-competitive regulatory
reforms, that the new entrants will not collude but compete with incumbents. This should
ensure that prices of intermediates and final goods will go down since there is more
competition.
In our present analysis, we simulate the increase of FDI inflows going to services after the
reductions of the (mostly regulatory) barriers they face in China. This matches the current
policy orientation of the government regarding the attraction of FDI in services. It is also
consistent with the rising importance of FDI in services versus that of manufacturing. The
particular services we analyze are water transport, air transport, communications, finance,
insurance and business services. The existent barriers in those sectors are rather high, when
we compare them with the ones faced by multinationals in other large regions such as the
EU27 or the US. We estimate such impact in the short and long run on China and also derive
their effects for the rest of regions considered in our CGE model.
The rest of the paper is organized as follows. In section 2 we put the Chinese economy in the
world context and discuss several of its important characteristics relying on detailed sectoral
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data. Section 3 explains the model, data and simulations. Section 4 presents the outcomes for
China of several scenarios of increases of FDI inflows. Within the results section we also
perform a sensitivity analysis. A final section of summary and conclusions closes the paper.
2. China in the world economy
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In this section we offer an overview of several characteristics of the Chinese economy. We
begin with the data on the GDP and exports’ composition in the years 2004 and 20162. Table 1
offers the weight of each sector in total GDP and exports in China and in the world economy as
a whole. In addition, the last column of each year presents the weight of China in the world
economy. The rows display the 21 sectors of the model, together with some summarizing
figures of “All manufactures” (which includes other primary and construction) and “All
services”. Appendix one offers sectors’ definitions and their conversions across several
classifications.
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The weight of Chinese GDP in the world economy has risen from 5.0% in 2004 to 13.3% in
2016. As shown at the bottom of the last columns for each year, the weight is higher in
manufacturing sectors turning from 7.0 to 17.7 from 2004 till 2016, than in services where the
figures are of 3.3. and 9.6, respectively. In 2016, China still exhibits a very low share of services
sectors in its GDP (of 45.7%), not just compared with advanced economies but also with
respect to the world average (which is of 63.3%). This indicates there is a natural process
ahead of turning more and more into a services economy while development unfolds. The
other side of the coin is that the weight of manufactures in the Chinese economy (41.9%) is
still very high compared to world standards (31.4%)3.
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Chinese overall exports have risen from 8.8% in 2004 of total world exports to 12.8% in 2016.
Chinese trade is dominated by manufacturing goods, which constitute 93.9% of its total trade
in 2016. In general, world trade is mostly of manufactures (81.5% in 2016) and has not
changed much in the latest years, since it was 81% in 2004. However, we can see that this
pattern is considerably more marked in China4. The share of Chinese manufacturing trade in
the world for all manufactures rises from 10.1% in 2004 to 14.8% in 2016. For some sectors,
such as textiles (43.6%), electronics (35.6%), other manufactures (31.2%) the Chinese shares in
world trade are impressive, well beyond the importance of those sectors within the Chinese
We cover the longest period available in the latest release of the GTAP9A Database from May 2016.
Note that the GTAP database uses variables measured in nominal prices from the World Bank “Word
Development Indicators”, it would be an immense effort to deflate so many variables across sectors for
the world economy. In that sense, it is underestimating the actual shift to services that China has
already experienced according to other sources in which services already account for 50.5% of GDP in
2015, while manufacturing including construction, mining and utilities accounts for 40.5% (KPMG, 2016;
OECD, 2017). There is a trade-off between accuracy in the data and being able to put the Chinese
economy in a world context. Issues like re-exports are dealt carefully by the GTAP team, which use
United Nations COMTRADE for their calculations.
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In some regions, such as Great Britain (32.6%), India (28.1) and EU27 (22.0%) trade in services is above
the world average. This will be important for our results later on.
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export structure. Indeed, they account for 15.9%, 22.8% and 6.5% in total Chinese exports,
respectively.
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Table 2 further looks at the sectoral detail by analyzing the destination of imports accruing to
China, the EU27 and the US and the destination of production in those regions. In other words,
the table presents the percentage of imports and production that go to private consumption
or are intermediates for further processing in each sector5. We can see that in the three
regions most of the trade is of intermediates. This resembles the intense process of
fragmentation present in the world economy (Koopman et al, 2014). However, this trend is
even more pronounced in China, whose shares of intermediate imports are much larger than
in the US and EU27. The Asiatic giant also stands out due to the very low share of imports that
are related to private consumption. China has exhibited very high rates of savings for years,
which goes hand in hand with lower rates of private and public consumption6. It is important
to note that imports from services sectors tend to be more oriented to private consumption in
the three regions than imports from manufacturing.
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Table 2 also presents the destination of production across sectors. Although most of the
production is devoted to intermediates for further processing, in China this tendency is more
accentuated. Again, as happened with imports, the production devoted to private
consumption is in China, in general, much smaller than in the US and EU. The EU27 stands out
as a very active exporter, who exhibits enormous shares of exports. China has reduced its
export intensity in 2016, compared to the data from 2004. This contrasts with the trend in the
EU27 which has increased it for the same period. In all regions the share of production
devoted to exports is much more reduced in services’ sectors than in manufacturing. This
trend is even more pronounced in China. Further, as happened with imports, services sectors
production tends to be more oriented to private consumption than the one from
manufactures.
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All in all, Table 2 shows that China has its own peculiarities. In 2016 its production and imports
are very oriented to intermediates’ production and to exports. China is indeed the “factory of
the world”. On the other hand, we see that the nature of services’ sectors differs considerably
from manufacturing sectors. Services are more oriented to private consumption and less
oriented to exports.
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Figure 1 shows data related to the level of protection from the operations of foreign
multinationals in advanced services sectors in China, the EU27 and the US. In particular, the
numbers are the costs (in percentage of total costs) of undertaking all procedures necessary to
operate in the host economies7. As can be seen, Chinese protection is really high compared to
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The shares related to investment and public consumption are omitted in Table 2 to facilitate the
analysis of the main patterns. In the case of production the share devoted to exports is also included.
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We have deeply analyzed the consequences of these patterns for the impact of FDI in several
manufacturing sectors in Zhou and Latorre (2014a; 2014b; 2015).
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Technically these costs are called “Ad valorem equivalents (AVEs)”, which quantify existing restrictions
to the operations of foreign multinationals providing an estimation of their corresponding percentage
cost equivalence. These restrictions may take the form of red tape, accumulation of procedures,
payments, time to complete formalities. . . etc. The larger the AVEs the more costly it is for firms to
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the other two regions. We know the government is trying to open up these and other
advanced manufacturing sectors for private investment (OECD, 2017; KPMG, 2016). As a
consequence, these barriers should go down, thus, bringing about important costs reductions
for foreign multinationals. This is exactly the approach that we model in order to estimate the
impact of FDI in services8.
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The more sizeable the reductions in barriers are, the stronger the cost savings for potential
entrant firms9. This is the way in which FDI is endogenized in our model. Note that these
barriers, on the one hand, hinder competition and provide rents for incumbent firms. On the
other hand, they also may cause inefficiencies (bureaucracy, red tape…etc.) which imply a
waste of resources10. In addition, the larger the reductions in costs the stronger the pressure
for lower prices of intermediates provided by foreign firms. In other words, if foreign entrants
experience larger costs savings, due to the liberalization of barriers, more of them will accrue
to services sectors thus increasing competition and lowering prices of the goods they provide.
To analyze the channel by which other sectors benefit from the provision of foreign
intermediates, we need to look at what is the weight in total costs per sector of the use of
intermediates coming from advanced services sectors. These are the data shown in Table 3.
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Table 3 presents the cost of intermediates coming from advanced services sectors per unit of
production. Advanced services sectors are the ones that have foreign multinationals, namely,
water transport, air transport, communication, finance, insurance and business services. For
example, the value of 2.3% in Chinese agriculture means that from the total costs of
production in agriculture only 2.3% are related to payments to providers of intermediates
coming from advanced services sectors. We again put Chinese data in perspective with respect
to the EU27 and to the US. Intermediates coming from business services sectors play a smaller
role in China than in the EU27 and in the US. Only a few exceptions appear in water and air
transport, which are two very small sectors.
operate in a particular sector. Jafari and Tarr (2015) explain the different sources consulted to
econometrically derive the AVEs. The data are publicly available.
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Unfortunately, there are no estimations of these barriers to FDI in manufacturing sectors, so other
approaches are necessary to model its impact in these sectors. For example, Latorre and Hosoe (2016)
model FDI in manufacturing in China using detailed microeconomic information in a dynamic context.
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We will later see that we assume reductions in barriers to FDI of 25% and 50%. These costs’ reductions
are modelled as iceberg trade costs reductions. In the algebraic description of the model (Appendix 3)
the iceberg costs are for simplicity, included in the price PY of equation (10).
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We assume that 60% of the costs of the barriers are efficiency losses, while 40% create rents. Due to
the lack of other data, we rely on the study of Francois et al. (2013) for this assumption. Their
estimations are for the same sectors and are based on business surveys, consultations with regulators
and sector experts as well as on literature reviews, but their sources come mainly from the US and the
EU. Konan and Maskus (2016) show that eliminating barriers that create inefficiencies brings about
more sizeable GDP and welfare increases than the same percentage elimination of barriers that create
rents. In the latter case incumbent firms in the host economy gain some rent from the barriers, while in
the former case nobody gains from the barriers.
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3. The model, data and simulations
The main data we use is the latest available GTAP database version 9 (Narayanan et al., 2016).
This is a database for the world economy which offers information for 140 regions (most of
which are the countries) and 57 sectors. Using GEMPACK or GAMS software the data can be
aggregated to different regions and sectoral configurations. The GTAP 9 covers the years 2004,
2007 and 2011. We have taken the data from 2004 and 2011, but have updated the latest one
using GDP growth rates from 2012 till 2015 using the IMF world economic outlook (IMF, 2016).
Thus our final year is 201611.
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The data on barriers to foreign multinationals come from the publicly available database
created at the World Bank (Jafari and Tarr, 2015). We model two different conservative cuts of
50% and 25%. In the literature of FDI in services summarized in Tarr (2012) usually 50% cuts
have been simulated, while Konan and Van Assche (2007), Konan and Maskus (2006) , as well
as Konan and Kim (2004) eliminate the full barriers, which, additionally, tend to be larger than
the ones we find for China. The share in sales of foreign multinationals in advanced services
sectors across the different regions are from the US International Trade Commission Database
(Fukui and Lakatos, 2012)12.
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As noted before, we use a CGE model which has several innovative features. In particular, the
simultaneous consideration of three characteristics: monopolistic competition, a multiregional
framework and foreign multinationals in advanced services sectors had not previously been
achieved to the best of our knowledge13.
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Our model extends the multiregional framework of Balistreri et al. (2015) by including the
imperfect competition setting in the sectors with foreign multinationals and a long run steady
state simulation which goes beyond its short run impact. Balistreri et al. (2015) had, in turn,
extended the small open economy framework of Balistreri and Tarr (2011), and of other single
country models, such as Rutherford and Tarr (2008) and Latorre (2016). As already noted, only
a few CGE models include the operations of multinationals. For a long time, this has only be
possible in a single country framework (e.g., Konan and Van Assche, 2007; Konan and Maskus,
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This procedure does not update the entire production structure for the single countries and economic
regions we analyze, since in a multiregional setting we need a harmonized dataset. However, it takes
into account important changes in the size of the regions during the period 2011-2015, according to IMF
data. This seems of relevance given the differences across regions. China and India exhibit the largest
growth with a 33% and 29.6%, respectively, in that period. They are followed by Southeast Asia and SubSaharan Africa with 19%. The smallest increase is the one experienced in EU27 (without the UK) with a
2.3% increase in GDP for the entire period. In addition, Chinese Input-Output tables are only available
every five years. The latest one was for the year 2012 and was used to construct the GTAP9 database,
thus, the next one is expected to reflect the 2017 data.
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These data probably underestimate the current shares in sales of foreign multinationals, coming from
different areas of the globe, operating in China. However, we have not found any other source to proxy
their actual sales.
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We use this model for the analysis of the Transatlantic Trade and Investment Partnership (TTIP) with a
different regional configuration, benchmark year and set of simulations in Latorre and Yonezawa (2017),
which is still an unpublished paper. Recently, Olekseyuk (2016) has also introduced FDI in a framework
of imperfectly competition, but FDI is modelled only in the region in which the analysis focuses, namely,
Ukraine, while it is absent in the rest of regions in the model. Most notably, Oleseyuk’s model includes
heterogeneous firms in manufacturing in her multiregional framework.
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2006; Konan and Kim, 2004; Latorre et al., 2009; Latorre 2012; Latorre 2013). However, when a
multiregional framework is used, we can fully incorporate the foreign economies and keep
track of the impact on foreign countries, as well as the impact on China14. Also, the effects of
FDI creation and diversion among different host countries can be traced, much in the same
way as trade diversion or creation cannot be captured in a single country model but only in a
multi-regional setting (e.g., Ortiz and Latorre, 2017).
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The model has three types of sectors: 1) Manufacturing sectors with economies of scale and
monopolistic competition (chemicals, electronics, automobiles, textiles and other
manufacturing); 2) Advanced services sectors with foreign multinationals, which also have
economies of scale and monopolistic competition (water transport, air transport,
communication, finance, insurance and business services); 3) Perfect competition, which
consist of the rest of manufacturing and services sectors, that have not been included in the
previous groups, as well as agriculture. We follow the literature, especially Francois et al.
(2013), in defining which sectors should be modelled under perfect or imperfect competition.
However, the issue is not exempt from difficulties. In a latter paper analyzing the same issues
as Francois et al. 2013, Egger et al. (2015, pp. 551-2) derive a taxonomy based on trade
elasticities in which large (small) negative values imply a more (less) competitive sector. While
their data suggest that Motor vehicles, Other machinery and Energy production exhibit
monopolistic competition and economies of scale, elasticities’ values for the rest of sectors
look more similar, which makes their classification complicated15.
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In the perfect competition sectors firms produce with constant returns to scale. Products differ
according to their country of origin. In other words, an Armington (1969) specification is used
so that each region in the model produces a specific variety, which is an imperfect substitute
for varieties coming from other regions. This Armington assumption grasps the empirical
evidence that countries trade different varieties of the same good or service.
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The imperfect competition sectors are modelled as Dixit-Stiglitz monopolistic competition,
following Krugman (1980) and Helpman and Krugman (1985). This implies that a growing
number of varieties of a product (either through more domestic firms, exports or a higher
number of foreign affiliates) leads to potential increases in both consumers’ welfare and
producers’ productivity. The latter effect arises because with more firms and higher
competition the intermediates they produce become cheaper, which helps to save costs in
production for the firms using them. There is empirical evidence of FDI in services leading to
To the extent that the change in the foreign economies does affect Chinese economy (through the
change in trade of these foreign countries and China), even if we were only interested in China,
incorporating a multi-regional framework is of relevance.
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Furthermore, our sectors’ conversion from GTAP is not exactly the same as in Egger et al. (2015) and
Francois et al. (2013). In addition, other problems arise. For example, real estate and health services fit
well in an oligopoly structure, since only few giant companies dominate the supply, while the barriers to
foreign capital are still high. Our simulation tries to evaluate the impact of lower barriers in business
sectors, which comprises real estate and the housing market. But business sectors, as reflected in
Appendix 1 also comprises other sectors, such as, legal and accounting activities, engineering activities,
advertising and market research, etc., which tend to be better proxied by monopolistic competition. In
fact, following business services liberalization, the supply would be larger and households would benefit
from lower prices due to the entry of more competitors. Although, it is also true that in the case of real
estate there is a very strict limit in their expansion arising from the supply of land.
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higher productivity in other sectors of the economy using panel data and controlling for the
endogeneity of FDI (e.g., Fernandes and Paunov, 2012; Arnold et al., 2008). These results have
been well established more generally in the literature (e.g., Broda and Weinstein, 2006 and
Goldberg et al., 2009).
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In each region there is a representative consumer whose income stems from all factors’
remunerations. She fully spends her income in private consumption, which is the component
of final expenditure that adjusts in the model. Public spending remains constant in real terms
to avoid the distortions that its variation would bring about. So does investment, except in the
long run formulation in which the capital stock adjusts in order to maintain the initial rate of
return of capital. The intuition behind this is that in the long run firms can adjust their capital
stocks across sectors in the economy. Technically, for this steady state formulation we follow
the approach of Balistreri et al. (2016).
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The model includes a rich description of different types of costs with their corresponding taxes
across sectors, as well as abundant information on trade patters with different tariffs by
sector. Our model differentiates the impact of FDI flows according to the services’ sector to
which they accrue, which is in accordance with the fact that the effects of multinationals vary
across sectors (e.g., Smarzinska, 2004). We also include the impact of profit repatriation that is
assumed to be 50% in all the results we analyze. This issue is of importance according to
previous evidence (Konan and Van Aschee, 2007; Latorre et al., 2009; Gómez-Plana and
Latorre, 2014).
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Appendix 3 presents the algebraic description of the model.
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4. Results
4. 1 Impact on the Chinese economy
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Table 4 displays in its rows the impact on the main macroeconomic variables, namely, welfare
(measured as Hicksian equivalence of consumption), GDP, wages, capital remuneration,
aggregate exports, aggregate imports and the Consumer Price Index (CPI). The results are
available for the short run (2-3 years, approximately), as well as for the long run impact (more
than 4 years after the shock). The columns present cuts by 50% and 25% in the costs related to
the barriers faced by the foreign firms in advanced services sectors. The figures should be
interpreted as annual percentage changes in the years coming after the barriers have been
reduced.
The entry of foreign multinationals in advanced services sectors would increase Chinese
welfare by nearly one third of a percentage point (0.31%) annually in the short run, if barriers
are reduced by 50%. There would be a mild increase in wages and capital remuneration. The
GDP would also go up slightly. Aggregate imports and exports would rise somewhat more
heavily. The CPI would go down benefitting consumers. To understand these results we can
think that the entry of foreign multinationals brings more economic activity, thus, raising
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wages and in some cases, as here, also the capital remuneration16. This, in turn, increases GDP
and private consumption, to which our measure of welfare is related. The increases in
production related to the presence of the foreign multinationals have an impact on foreign
trade. Multinationals tend to rely more on imported intermediates for their production than
national firms do17.
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In the long run, the capital stock expands such that the capital return stays the same. The
capital stock expansion contributes to the positive effect on the economy although it comes
with the increase in investment spending18. This setting of the long run results in a small
increase relative to the short run. The only exception would be welfare, whose percentage
increase is smaller than in the short run. Even though capital remuneration and wages would
increase more heavily in the long run, the resulting larger national income would be allocated
to a smaller extent to private consumption (a trend that would be welfare enhancing) than to
investment. This implies smaller welfare gains compared to the ones exhibited by the rest of
macroeconomic aggregates, when moving from the short run to the long run.
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All in all, the entry of more foreign multinationals would bring a positive, but rather limited,
impact on the Chinese economy. To put these results in perspective we can compare them
with the ones obtained for developing countries such as Tunisia and Egypt in the analyses of
Konan and Kim (2004), Konan and Maskus (2006) and Konan and Van Assche (2007). These
authors derive much larger positive impacts than the ones we obtain for China. This can be
explained by the already alluded to fact that their barriers are larger than ours and they
assume they would be fully eliminated. They, however, assume different climates of
competition (either oligopolistic or perfect competition), which makes the comparison more
complicated. We can also contrasts our results with the ones derived for the US and the EU28
in Latorre and Yonezawa (2017) in the same climate of imperfect competition. This is
interesting given the fact that, as noted above, the barriers to FDI are considerably lower in the
EU28 and US than in China. A 0.36% (0.25%) and 0.43% (0.37%) welfare (GDP) increase are
obtained in the long run for a 25% reduction of the FDI barriers for the EU28 and the US,
respectively.
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We believe two important forces make the impact of FDI in China smaller in the comparison
versus the EU 28 and the US. First, the weight of services in the Chinese economy is much
more reduced than in the TTIP partners, as we saw above in the data. Furthermore, we also
saw, that the role of services sectors as suppliers of intermediates for downstream sectors was
In other studies of the impact of FDI in China, such as Zhou and Latorre (2015) and Zhou and Latorre
(2014a, 2014b), we have obtained that FDI would reduce capital remuneration. In contrast with the
models used in those papers, this one has imperfect competition and economies of scale.
17
This is confirmed by the study of Latorre and Hosoe (2016), who explicitly analyze the differences in
costs of Japanese multinationals versus domestic firms across manufacturing sectors in China using very
detailed data. It is also confirmed in Latorre (2013, 2014), who also looks at costs differences using a rich
dataset to analyze multinationals in the Czech Republic.
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Following the capital accumulation equation with steady state condition, we can derive that the
percentage change in capital stock equals the percentage change in investment spending. See Balistreri
et al. (2016) for more details.
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also more limited in China than in the EU27 and in the US. This necessarily limits several
positive mechanisms usually assigned to the availability of a larger number of providers (i.e.,
foreign multinationals) specialized in the production of advanced services. Second, FDI in China
brings about a crowding out effect that will reduce production in services sectors. Let us turn
to its analysis by looking in more detail to the sectoral outcomes.
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Table 5 presents the evolution of output, exports, imports and prices in a structure analogous
to the one of the aggregate outcomes. We now display the detail for all sectors. The mild
reduction in output of services sectors (“all services”) reflects the presence of a crowding out
effect in the advanced services (i.e., the ones with FDI) after the entry of multinationals.
Because multinationals benefit in many of them from very sizeable savings in costs, they
become very competitive and drive out of business some Chinese firms19. In communications,
in which the initial barriers are much lower than in the rest, this crowding out effect is not
present. The lower the reduction in the FDI barriers, the smaller this crowding effect and,
accordingly, the reduction of output in services sectors. However, this trend in services
releases labor that goes to manufacturing sectors. Because the intermediates coming from
services have become cheaper, manufacturing sectors are now more competitive (their prices
also go down) and can produce more, thereby increasing the demand for labor. Indeed, not
only services exports increase but also the ones from manufacturing sectors. By contrast,
manufacturing sectors now reduce the imports they need. Because their products are
comparatively cheaper they source more domestically, whereas more multinationals coming
to services sectors carry with them more imports. The reduction of prices that foreign
multinationals carry with them is more sizeable the larger the reduction in the barriers. This is
crucial for the competitiveness of manufacturing sectors, which constitute the bulk of Chinese
exports. That is why the larger the cut in the barriers to FDI, the better for overall
macroeconomic outcomes. Overall exports of the Chinese economy are stronger the more
sizeable the cut in barriers and so is its total output, as can be seen, in the last row of the table.
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In the long run the outcomes in China for all sectors tend to be slightly better than in the short
run. The fall (increase) of output experienced in services (manufacturing) sectors is smaller
(larger) than in the short run. However, the differences are small since the crowding out effect
is still present and most manufacturing sectors behave very similarly.
4.2 Impact on the rest of regions
Recall that our model has split the world economy into 10 more regions apart from China20. In
Table 6 we present the same aggregate outcomes we have analyzed before but now for all the
regions. To keep the analysis (and number of tables) manageable we focus on the short and
long run impact of the 50% reductions in costs related to FDI barriers. These are the ones that
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The percentage reductions in the number of domestic firms are limited, however. Their most sizeable
reduction appears in Air transport for the 50% cuts in costs. In this scenario, the number of domestic
firms would go down by around -4.5% both in the long and short run, but for smaller cuts they fall by
less than -2.0%. For the rest of sectors, the reductions are always below -2.0% and generally below 1.0%.
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Appendix two has the country composition of each of the regions.
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are more likely to affect the rest of regions, since they bring about the heaviest impact on
China. We keep the results that have already been presented for the Chinese economy (in
Table 4) to facilitate the comparison.
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It is interesting that we find no evidence of negative effects for the rest of regions when more
multinationals from the services sectors come to China. This contrasts heavily with our
previous results derived from the impact of FDI going to Chinese manufacturing sectors (Zhou
and Latorre, 2014a, 2014b, 2015). What could explain these differences between FDI accruing
to manufacturing and the ones accruing to services? On the one hand, services’ sector
themselves are less oriented to exports, as we saw, in our data above. Accordingly the
mechanism of transmission of the shock occurring in China to other economies is much weaker
than in manufacturing sectors. On the other hand, the increases we have derived in exports
from manufacturing sectors are rather modest, compared to the situation in which
multinationals were going to manufacturing sectors directly. For example, in Zhou and Latorre
(2014a, 2014b, 2015) we model multinationals going to the same sector of Electronics
modelled here, but the benchmark is 2007 in that case. Electronics is by far the sector that is
more globalized across manufacturing sectors with important international networks of
production. After the entry of FDI its exports go up by around 30%, while in the results we
obtain here they increase less than 1%. In Zhou and Latorre (2014b, 2015) we further analyze
the impact of FDI going to the same Machinery sector we have modelled now. For this sector,
exports went up by 19.6%, due to the also important international networks present in it.
Following these important increases in exports, we had derived that the rest of regions (which
were no more than six in those analyses) were crowded out from world markets due to the
fierce Chinese competition. Some of the regions did indeed reduce their GDP and wages in
accordance with other analysis undertaken from an econometric perspective focusing on
manufacturing sectors (e.g., Pierce and Schott, 2016 and Autor et al., 2013).
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In Latorre and Yonezawa (2017) we find that after the TTIP other areas could be slightly
crowded out, with the notable exception of China. However, the bulk of the analysis includes
not only the impact of barriers to FDI but also to trade. Note that the TTIP regions together
account for a larger share of trade and GDP than China alone does. However, when we confine
the analysis to the impact of FDI in services sectors after the TTIP (i.e., excluding barriers to
trade), we do not obtain any remarkable evidence of negative impacts for outsides of the TTIP.
If we look at the sectoral details of all of the regions we find the following. As reflected in Table
7 most manufacturing sectors tend to reduce production very slightly after the entry of
multinationals in Chinese services. By contrast, they increase also only marginally, their
production in services sectors. This contrasts with the evolution in China, that as can be seen is
just the contrary. Services sectors are reducing production moderately due to the crowding
out effect, while manufacturing mildly increase it. These trends imply that China is substituting
imports with domestic production in manufacturing and increasing imports, related to the
services’ affiliates in services sectors. The consequence of this is that the rest of regions are
reducing their manufacturing exports to China and increasing their services ones. These are
indeed the patterns reflected in the exports what we also see in Table 7. Comparing output
and exports, we can see that the evolution of output reflects that of exports.
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These results imply that we find a mild crowding out effect of manufacturing production in the
rest of regions after the entry of FDI in services in China. The Asiatic giant weighs much in
manufacturing trade and even small increases in its exports convey and effect for other areas
of the world. On the other hand, the increase of FDI in services will carry with it exports from
other regions to China in these sectors. Since China is more an exception in being still so
specialized in manufacturing, the positive trend in services in the rest of regions compensates
the reduction in exports in manufacturing21.
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4.3 Sensitivity analysis
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That is why at the macroeconomic level regions look to a great extent unaffected. However,
looking at the outcome of GDP (back in Table 8), Great Britain stands out with an impact that
in the short run is closed to the one in China. This has to do with the important specialization
in advanced services in this economy (reflected in Tables 1 and 2). As Table 8 shows, it derives
important increases in its capital remuneration which are related to the fact that its
multinationals will increase heavily their revenue in China. India benefits from an important
increase in aggregate exports. The EU27 is a mixture of the pattern of Great Britain and India
since it benefits from the side of more FDI in services coupled with more exports in those
sectors. The US by contrast does not gain much, despite its important specialization in services
exports. This is because the US is a rather closed economy and its production in services (as
appears in Table 4) is not much export oriented. Other advanced economies, which has
important Chinese partners such as Korea and Hong Kong, does not have such as specialization
in services exports as the one of Great Britain, India, the EU27 or the US. However, it benefits
more from its proximity to China insofar as its trade ties are strongest with China across all the
regions we consider.
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In this section, we examine the effects of different specifications for parameter values, mainly
the elasticities in the model. Following Harrison et al. (1993), we vary each of the parameters
one by one, while we keep the rest of parameters at the initial values. We again display the
results for the short-run version of a 50% cut in costs in the model, since we have found the
same pattern in the long-run version. We also focus on the welfare impact because the trends
were the same for GDP as well. The results are in Table 8, and the first row shows the result in
our central setting (“reference”), which is the one we have analyzed so far.
We see that the results in the central setting are quite robust with respect to all the
parameters except two. The most influencing one is the supply elasticity with respect to the
price of output of firms in the sectors with scale economies (namely, service sectors and
manufacturing). When this elasticity is high, the production of the sectors will not be so quickly
constrained by the increased cost of the specific capital required for the firm expansion. Since
the elasticities in service sectors are higher than manufacturing sectors, the change in these
elasticities are strongly related to the FDI reform (although in this paper we consider only the
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Although Table 7 displays only the short term results, we have also analyzed the long run ones. The
latter exhibit the same patterns as the former, with only a slightly more beneficial impact for exports
and production across all regions. This is consistent with the macroeconomic results in Table 6.
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FDI reform, we could think of the reduction of other types of trade costs, such as non-tariff
barriers faced by trade), and the benefit of the FDI reform is increased significantly. In
contrast, when we choose the low value of this supply elasticity, the welfare impact is not
changed much. This is because the central value of this elasticity (calculated based on the
empirical studies) does constrain the responsiveness already and further restriction does not
affect much.
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The other influencing assumption is the share of profit repatriation. In our central setting, it is
assumed that 50% of the profit of FDI firms is taken back to the source country. It could be
surprising that the increase in this share to 75% improves the welfare in China because FDI
firms in China now take more profit back to their countries. However, the reasoning is that
because of the higher share of profit repatriation, there is larger incentive to expand FDI,
which leads to the larger expansion of FDI and the positive impact on the welfare in China.
Such a positive impact outweighs the negative impact of higher profit repatriation.
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5. Summary and conclusions
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China is transitioning from an economy led by investment and exports, to one driven by
consumption and innovations, with a growing share of services. China is the “factory of the
world” due to its dominance in manufacturing, specifically in the lower and middle stage of
production chain. Our data show that China is in 2016 still remarkably specialized in
manufacturing production. The share of services in GDP is not only very far from the one in
advanced economies but also compared to the world average. This highlights that there is a lot
of scope for “a shift to services”, which is a natural process as economies develop. The data
also point to the fact that services sectors are of very different nature compared to
manufactures. The former are more oriented to the provision of private consumption than the
latter. Also, in comparison with manufacturing, they are less export oriented. This implies that
more productive capital in the form of FDI would enhance the push exerted by other forces to
a more consumption oriented economy, with less importance of exports and investment
ratios. For 2016 our data show that Chinese imports and production are still very oriented to
the provision of intermediates for further processing. In a comparison with the US and the
EU27 for that year, the share of imports and production that is devoted to private
consumption is remarkably low in China.
In China, advanced services sectors (water transport, air transport, communication, finance,
insurance and business services) are remarkably more protected from foreign competition
than other regions such as the US and the EU27. The Chinese government seems committed to
lower those barriers (OECD, 2017; KPMG, 2016) and FDI has been increasing heavily in
services, more than in manufacturing, in the last years according to UNCTAD (2016). In our
analysis we find that because the initial barriers are so high, foreign multinationals would
benefit from sizeable cost savings. This would deeply increase their competitiveness, which
would crowd out a small percentage of national firms operating in those advanced services
sectors. Production would go down, indeed, in advanced services sectors with the only
exception of communication in which the initial barriers are the smallest and far from the
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existing ones in the rest of sectors. The services sectors that do not have multinationals, such
as personal services and other services would, however, increase their production.
Despite the process of crowding out, the entry of foreign multinationals in services brings
about a reduction in their prices which is beneficial for manufacturing sectors. This is because
services provide intermediates to manufacturing sectors which will therefore increase their
export competitiveness. Due to the still very high share of manufactures in Chinese GDP, this
positive trend more than compensates the reduction in the services sector to which
multinationals accrue, resulting in an overall positive impact for the Chinese economy.
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We examine different levels of reductions in the costs related to the barriers that foreign
multinationals encounter (50% and 25%) deriving the impact for the short and long run.
Despite the fact that more crowding out takes place in services sectors, the larger the cuts in
the barriers are, the more positive the overall impact is. This is because the reduction in prices
of intermediates and final goods is also more sizeable with relatively large (i.e., with greater
percentage) cuts. In the long run, a process of domestic capital accumulation simultaneously
occurs to the entry of foreign multinationals. As a consequence, the entire process is slightly
more positive in this setting. All in all, a small positive increase in wages, capital remuneration
and GDP at the aggregate level takes place. Welfare and foreign trade also increase, while the
CPI goes down, benefitting consumers.
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Regarding the impact for the other regions of the world, we obtain, that they remain nearly
unaffected or benefit very slightly. No region loses. This contrasts sharply with other previous
analysis focusing on FDI going to manufacturing sectors, in which other areas would be
displaced in their exports due to the fierce Chinese competition (e.g., Zhou and Latorre, 2014a,
2014b, 2015), resulting in wages reductions. Other papers using different methodologies have
also derived a negative impact from FDI going to manufacturing in China (e.g., Pierce and
Schott, 2016).
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Due to the lower export orientation of services sector compared to manufactures and to their
still limited role in the Chinese economy the impact of FDI in services is considerably
dampened for China itself and for the rest of areas in the world, compared to FDI in
manufacturing.
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The results have been derived using an innovative CGE model, which simultaneously includes
three features relevant for this analysis. These features are, first, the presence of foreign
multinationals, which very few CGEs consider. Second, the inclusion of monopolistic
competition in the sectors with foreign multinationals and FDI, previously modelled in other
CGEs. However, we have not seen any other model combing the two previous characteristics
with the third one, namely, the multi-regional setting.
To conclude, in our view, several policy recommendations emerge from this analysis. First,
there is a possible crowding out effect for domestic firms in services sectors after the entry of
foreign multinationals. But the provision of high quality and cheap intermediates that
multinationals facilitate benefits overall production and GDP in China. China would move up
the value chain by shifting out-of-dated services firms, thus reinvigorating services sectors and
improving manufacturing competitiveness and efficiency. In this respect, the impact of FDI in
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services contrasts with the one in manufactures. The latter sectors do not play such an
important role for the provision of intermediates across most sectors in the economy. In any
case, the authorities have the chance to implement more pro-competitive or protectionist
regulation in order to shape these outcomes on resulting competition after the entry of
multinationals.
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Second, multinationals in services (and also in manufactures) benefit consumers’ welfare
expanding the variety and quality of choices to which households have access. The rising living
standards of Chinese households urges this higher quality and wider choices of services, which
services’ liberalization can enhance.
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Third, the larger the cuts in existing barriers to the operations of foreign multinationals are,
the more intense the attraction of FDI and increase in the number of firms and varieties are. In
the light of previous studies, we have been conservative in the percentage cost reductions of
barriers that we assume. Had we assume larger ones, the positive impact would have been
more sizeable. Note also, that in the case of services, these barriers tend to be more related to
domestic regulation than in the case of goods, due to the general intangible nature of the
former. Thus again, FDI in services contrasts with that of manufactures. In the case of FDI in
goods, many effects are more directly related to foreign trade protection or openness.
Liberalization regulation in services will be more oriented to beyond border reforms.
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Fourth, the data we have used assume that the use of intermediates coming from advanced
services sectors across manufacturing sectors is rather limited (compared to their use in the EU
and the US). This matches the fact that the role of services is still underdeveloped in the
Chinese economy. We believe these data probably underestimate the real figures. Thus, the
positive impact from FDI in services could be considerably larger.
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Fifth, our analysis includes the effects of profit repatriation. But our sensitivity analysis derives
that allowing the repatriation of a larger share of the rents of foreign multinationals
incentivizes FDI inflows more than in the case of lower shares, resulting in more positive
outcomes for China. This contrasts with other previous studies suggesting that more profit
repatriation could be harmful for host economies.
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With the rise in labor cost and higher stands in environment, some industries are divesting
from China to South Asia. Thus, the Chinese economy experiences big challenges and should
upgrade its economic structure. We have seen this is possible through the provision of more
varieties of services’ intermediates and goods for consumption that foreign affiliates could
provide. This would further improve its manufacturing competitiveness and would also be
good for sustainable economic development which is in danger due to extensive reliance mode
on manufacturing in overall Chinese production. China will probably be the factory of the
world for a long time but FDI in services will promote high-quality manufactures.
Acknowledgements
XXXXXXXX gratefully acknowledges that this research has been conducted thanks to the
financial support of Real Colegio Complutense at Harvard University and as a Research Fellow
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of both the Center for International Development (CID) at the Harvard Kennedy School and of
Real Colegio Complutense. She also gratefully acknowledges the financial support from the
Spanish Ministry of Economy and Competitiveness (Projects: ECO2016-78422-R and ECO201341317-R). XXXXXXX is grateful for the financial support from the Educational Commission of
Hunan Province of China (No. 16B267) and from Chinese Social Funds (No. 12CJL047). They
want to thank Edward Balistreri, David Tarr and Antonio G. Gómez-Plana for their very helpful
comments and suggestions.
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22
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Table 1. GDP and export structure in China and the world in 2004 and 2016
4.
2
6.
2
1.
2
0.
3
1.
9
4.
5
0.
5
4.
8
2.
3
29
.3
39
1.2
13.5
2.0
3.7
3.0
7.4
2.5
9.3
1.3
5.8
0.6
5.9
1.3
10.4
3.3
7.5
1.5
6.5
17.Finance
18.Insuranc
e
19.Business
services
20.Personal
services
21.Other
services
All
14.1
4.9
0.4
13.5
0.5
2.9
2.3
4.2
5.2
4.4
1.3
11.
1
1.8
3.1
43.
6
28.
3.7
2.2
3.4
7.0
9.8
2.9
13.0
1.3
28.7
1.8
12.3
3.3
18.8
2.7
1.2
3.
7
7.
8
0.
4
0.
2
1.
9
6.
0
0.
4
5.
7
2.
4
28
.7
41
24.4
18.6
0.6
14.1
1.2
27.5
3.2
20.2
1.7
29.0
6.5
15.8
0.4
13.2
0.4
7.0
2.3
10.9
5.2
15.4
1.2
4.8
9.9
7.7
2.8
41.
1
31.
11.0
9.3
17.7
23
PT
3.9
5.0
SC
2.6
30.5
Ch
in
a
1.
2
1.
1
2.
5
18
.3
3.
5
8.
1
6.
6
1.
9
1.
6
26
.7
14
.7
NU
6.9
AC
7.Metals
8.Motor
vehicles
9.Other
transport
10.Electroni
cs
11.Other
machinery
12.Other
manufactur
es
13.Construc
tion
14.Water
Transport
15.Air
Transport
16.Commun
ications
3.0
5.4
MA
6.Chemicals
19.8
2016
W
orl %CHN/
d
World
D
4.Textiles
5.Wood and
paper
3.9
Ch
in
a
12
.3
3.
7
2.
8
2.
8
1.
7
4.
7
5.
1
1.
7
0.
6
2.
5
4.
9
PT
E
3.Food
2004
W
orl %CHN/
d
World
CE
1.Agricultur
e
2.Other
primary
Ch
in
a
15
.3
4.
0
2.
0
3.
2
1.
5
4.
4
4.
6
1.
4
0.
7
2.
7
4.
9
Export structure
2004
2016
W
Ch W
orl %CHN in orl %CHN
d /world a
d /world
1.
2.3
4.8
0 2.6
4.7
0. 13.
9.0
1.1
3
6
0.3
2.
3.8
5.7
0 4.0
6.5
15
5.6
28.7
.9 4.7
43.6
3.
3.2
9.7
8 2.4
20.5
13.
10 15.
4
5.4
.8
5
9.0
7.
6.3
9.4
5 7.6
12.8
2.
7.6
2.3
1 5.8
4.7
2.
2.7
5.4
3 2.5
11.4
12.
22
3
19.3
.8 8.2
35.6
13.
19 13.
9
9.3
.4
9
18.0
RI
GDP structure
6.
8
0.
2
0.
2
0.
4
0.
2
0.
1
0.
1
0.
8
0.
5
4.
4
92
2.9
20.8
0.5
4.5
0.7
2.0
1.8
1.8
0.4
3.3
0.8
1.3
0.7
1.7
4.6
1.6
1.2
3.6
6.5
81.
6.0
10.1
6.
5
0.
3
0.
1
0.
3
0.
1
0.
1
0.
1
1.
2
0.
4
2.
8
93
2.7
31.2
0.6
7.1
0.6
1.3
1.6
2.4
0.4
4.3
1.1
1.4
0.7
2.4
4.8
3.3
1.0
4.8
5.8
81.
6.3
14.8
ACCEPTED MANUSCRIPT
manufactur
es
.9
7
44
.8
5.
0
67.
4
10
0
.9
4
45
.7
13
.3
63.
3
10
0
.1
0
.9
16.
5.
7
3.5
2
10
12
%World
5.0
13.3
0.0
8.8
.8
W
CH orl %CHN CH
N
d
/world N
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
3.3
9.6
6.
6
8.
8
AC
CE
PT
E
D
MA
NU
SC
RI
PT
All services
24
5
15.
9
10
0.0
W
orl
d
4.2
12.8
%CHN
/world
ACCEPTED MANUSCRIPT
Table 2. Imports by demand type and demand use of production in China, EU27 and US (2016,
in percentage).
4.Textiles
5.Wood
and paper
7.Metals
8.Motor
vehicles
AC
9.Other
transport
CE
PT
E
6.Chemica
ls
10.Electro
nics
11.Other
machinery
12.Other
manufact
ures
13.Constr
uction
14.Water
PT
RI
SC
NU
3.Food
Denand use of production
Private
consumptio Intermedia
n
tes
Exports
E
E
C E U C U U C U U
H U S H 2 S H 2 S
N 27 A N 7 A N 7 A
2
1 6 6 6
2 2
4. 14 4. 9. 1. 3. 1. 3. 2.
7 .8 8 9 3 2 2 2 0
9 6 9
3
0. 1. 0. 8. 8. 2. 1. 0. 7.
7 2 0 3 0 6 0 7 3
4
5 5 2 3
2
1. 45 4. 5. 9. 7. 3. 4. 8.
9 .7 7 0 8 0 1 3 2
1
4 6 2 4 2 3
3. 35 6. 1. 7. 4. 5. 6. 7.
6 .9 5 1 3 6 3 4 1
1 8 6 7 1 2
4. 12 5. 3. 0. 0. 1. 6. 6.
5 .8 8 2 1 1 6 6 1
1 8 4 5
4 2
6. 9. 8. 5. 4. 9. 8. 5. 1.
2 3 8 4 4 2 4 4 9
9 5 8
3 1
0. 1. 1. 1. 7. 2. 6. 8. 4.
3 8 5 3 6 1 4 2 6
2 4 1 2
5 1
9. 15 8. 6. 9. 3. 5. 8. 9.
9 .5 9 2 2 9 4 9 1
1 2 3 3 1 4 3
6. 5. 1. 8. 5. 2. 9. 8. 3.
8 8 8 0 4 0 3 7 8
4 2 5 4 5 1
3. 8. 6. 9. 8. 3. 2. 4. 7.
3 2 6 1 6 6 0 0 1
4 2 3 1 5 2
2. 4. 8. 9. 8. 5. 8. 3. 6.
5 7 6 0 5 6 3 7 8
1 8 5 6 1 2 1
2. 22 8. 3. 1. 3. 2. 3. 3.
1 .9 9 2 6 3 7 6 3
2 2
0. 2. 0. 5. 5. 7. 0. 2. 0.
0 3 0 9 5 6 3 4 4
4. 3. 1 8 7 7 1. 2 3.
MA
2.Other
primary
D
1.Agricult
ure
Impors by demand type
Private
consumptio Intermediat
n
es
E
C E U C U
H U S H 2 U
N 27 A N 7 SA
3 9 6
3. 33 8. 5. 5. 61
5 .4 6 7 7 .4
9 9 10
0. 2. 0. 9. 8. 0.
1 0 0 9 0 0
3
5 6 4
5. 56 4. 4. 3. 45
1 .7 2 9 3 .8
1
7 8 3
4. 62 2. 5. 7. 27
6 .8 4 3 1 .1
2 9 8
5. 14 2. 3. 3. 67
2 .6 7 9 8 .8
2 9 8
5. 16 7. 4. 1. 72
4 .4 0 6 0 .5
9 9
0. 1. 3. 8. 6. 94
2 7 3 7 1 .8
1
3 5 4
0. 37 8. 1. 6. 26
0 .9 6 0 0 .8
1 3 4
8. 10 2. 6. 9. 52
2 .5 2 8 4 .7
2 8 5
5. 16 0. 6. 6. 54
0 .7 3 4 9 .0
1 6 6
2. 9. 4. 2. 1. 44
8 5 7 4 2 .6
6 8 5
4. 40 8. 9. 7. 30
5 .0 0 7 9 .0
2
0. 1. 0. 6. 0. 9.
0 6 4 2 5 3
7. 13 1. 8 8 98
25
1.Agricult
ure
2.Other
primary
3.Food
4.Textiles
5.Wood
and paper
6.Chemica
ls
7.Metals
8.Motor
vehicles
9.Other
transport
10.Electro
nics
11.Other
machinery
12.Other
manufact
ures
13.Constr
uction
14.Water
5
15.Air
Transport
6.
8
3
2.
4
1
1.
3
4
7.
5
33
.0
2.
0
1
9.
8
2
4.
5
4
4.
8
1.
6
4
3.
2
2
1.
8
16
.7
16.Comm
unications
17.Financ
e
18.Insuran
ce
19.Busines
s services
20.Person
al services
29
.5
18
.2
47
.1
51
.7
3.
7
5
8.
9
3
0.
2
3.
8
9
1.
5
6
7.
6
8
7.
0
5
2.
5
6
7.
8
4
9.
5
6
5.
2
6.
6
6
7.
0
7
0.
5
8
1.
8
5
2.
8
7
2.
5
3
5.
6
4
4.
9
.4
1
2
4.
5
98
.0
5.
8
3
3.
9
1
1.
8
4
7.
3
13
.4
4.
9
4
7.
9
4
0.
9
5
7.
2
1.
6
4
3.
6
2
3.
2
3.
2
80
.2
68
.0
55
.2
94
.3
41
.1
28
.8
20
.2
49
.8
48
.3
5.
7
7
4.
2
5
2.
2
9.
9
8
4.
2
6
4.
9
8
6.
2
4
9.
9
6
4.
7
4
7.
0
4
7.
7
0
Transport
8.
6
2.
9
4
8.
4
1
2.
8
15.Air
Transport
1.
1
8.
4
2.
4
16.Comm
unications
0.
3
17.Financ
e
3.
7
18.Insuran
ce
2.
3
8.
5
1
8.
3
1
0.
5
3.
3
PT
.2
3.
2
3
8.
2
6
2.
8
7
1.
3
3
1.
9
7
4.
0
3
1.
0
2
5.
0
5.
0
19.Busines
s services
2.
0
9.
4
2.
6
20.Person
al services
SC
3
NU
Transport
RI
ACCEPTED MANUSCRIPT
8.
0
7
9.
2
4
7.
5
5
3.
4
3
9.
1
8
4.
7
2
2.
9
2
1.
5
0
2.
8
AC
CE
PT
E
D
MA
21.Other
43
69
37
1. 4. 1.
services
.9
.0
.0
3 4 3
All
manufact
ures
All
services
%World
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
26
21.Other
services
ACCEPTED MANUSCRIPT
Table 3. Percentages in total costs of intermediates coming from advanced services sectors (in
percentage in 2016)
Intermediates from advanced services
CHN
EU27
USA
2.3
7.1
13.7
11.2
11.5
7.6
2.9
11.4
8.8
4.1
10.1
8.4
3.8
11.9
6.3
4.0
9.4
6.0
4.3
9.3
6.3
5.0
10.2
5.6
4.4
12.6
6.9
6.9
16.8
11.3
5.1
11.5
7.1
AC
CE
PT
E
D
MA
NU
SC
RI
PT
1.Agriculture
2.Other primary
3.Food
4.Textiles
5.Wood and paper
6.Chemicals
7.Metals
8.Motor vehicles
9.Other transport
10.Electronics
11.Other machinery
12.Other
manufactures
4.5
10.7
8.1
13.Construction
5.1
11.2
11.3
14.Water Transport
22.4
19.3
28.1
15.Air Transport
19.1
13.9
13.9
16.Communications
24.0
36.2
33.5
17.Finance
24.5
48.0
28.7
18.Insurance
43.0
60.8
45.4
19.Business services
19.3
30.7
19.0
20.Personal services
6.7
14.2
19.7
21.Other services
10.6
13.3
13.3
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
27
ACCEPTED MANUSCRIPT
Table 4. Short term and long term impact of reductions in FDI barriers on main aggregate
variables in China (% change with respect to the initial data)
0.19
1.12
0.85
-0.28
0.09
0.50
0.39
-0.13
RI
0.03
0.47
0.36
-0.12
AC
CE
PT
E
D
MA
NU
SC
Welfare
GDP
Wages
Capital
remuneration
0.06
Exports
1.06
Imports
0.79
CPI
-0.26
Source: Authors’ estimations.
Long run
50%
25%
0.24
0.11
0.19
0.09
0.17
0.08
PT
Short run
50%
25%
0.31
0.14
0.09
0.04
0.09
0.04
28
ACCEPTED MANUSCRIPT
Table 5. Short term and long term impact of 50% reductions in FDI barriers on output, exports, imports and prices in China (% change with respect to the
initial data)
Output
Short run
Long run
50% 25% 50% 25%
Exports
Short run
Long run
50% 25% 50% 25%
Imports
T
P
Short run
50%
25%
Long run
50%
25%
0.17
1.Agriculture
0.37 0.17 0.39 0.18 0.82 0.37 0.83 0.37
0.14
0.06
2.Other primary
1.07 0.48 1.28 0.56 2.76 1.21 3.22 1.41
-0.13
-0.05
3.Food
0.34 0.15 0.33 0.15 1.04 0.46 1.10 0.49
0.02
4.Textiles
0.47 0.21 0.50 0.22 0.56 0.25 0.58 0.26
5.Wood and paper
I
R
0.08
-0.06
0.01
SC
-0.02
0.00
0.12
0.06
0.15
0.07
0.37 0.17 0.48 0.21 1.15 0.51 1.23 0.55
-0.17
-0.07
-0.09
-0.04
6.Chemicals
0.55 0.25 0.64 0.28 0.98 0.44 1.02 0.45
-0.20
-0.08
-0.13
-0.05
7.Metals
0.54 0.24 0.73 0.32 1.37 0.61 1.56 0.69
-0.06
-0.02
0.05
0.02
8.Motor vehicles
0.39 0.17 0.54 0.24 1.23 0.55 1.23 0.55
-0.64
-0.28
-0.45
-0.20
9.Other transport
0.50 0.23 0.68 0.30 1.79 0.79 1.92 0.85
-0.55
-0.24
-0.42
-0.18
10.Electronics
0.72 0.32 0.78 0.35 0.76 0.34 0.77 0.35
0.09
0.04
0.19
0.09
11.Other machinery
12.Other
manufactures
13.Construction
0.66 0.29 0.85 0.38 1.59 0.70 1.74 0.77
-0.25
-0.11
-0.13
-0.05
0.41 0.18 0.60 0.27 1.20 0.53 1.19 0.53
0.06 0.03 0.31 0.14 0.90 0.40 0.95 0.43
-0.67
-0.14
-0.30
-0.06
-0.42
0.10
-0.19
0.05
D
E
U
N
A
M
PT
E
C
C
A
29
-0.14
Prices
Short run
Long run
50% 25% 50% 25%
0.14 0.06 0.13 0.06
0.27 0.12 0.32 0.14
0.18 0.08 0.20 0.09
0.02 0.01 0.01 0.00
0.20 0.09 0.22 0.10
0.07 0.03 0.06 0.03
0.21 0.09 0.24 0.11
0.15 0.07 0.12 0.05
0.22 0.10 0.24 0.10
0.04 0.02 0.03 0.01
0.23 0.10 0.25 0.11
0.12 0.05 0.09 0.04
-
ACCEPTED MANUSCRIPT
14.Water Transport
15.Air Transport
16.Communications
0.19 0.08 0.15 0.06 0.95 0.43 1.01 0.45
4.40 1.89 4.36 1.87 1.04 0.64 1.10 0.66
13.73
32.90
14.69
32.94
14.71
0.97 0.43 1.09 0.48
1.33
0.70
1.27
1.22 0.54 1.30 0.57
10.71
1.68 0.82 1.70 0.84
6.50
C
S
U
T
P
I
R
0.67 0.32 0.74 0.35
28.18
12.92
28.26
12.96
20.Personal services
0.26 0.12 0.28 0.13 1.06 0.47 1.09 0.49
0.00
0.00
0.02
0.01
21.Other services
0.20 0.09 0.23 0.11 1.12 0.50 1.20 0.54
-0.12
-0.05
-0.11
-0.05
All manufactures
0.48 0.21 0.61 0.27 1.07 0.48 1.13 0.50
0.06 0.02 0.02 0.00 1.02 0.47 1.09 0.50
-0.16
-0.07
-0.09
-0.04
8.71
3.99
8.74
4.00
0.79
0.36
0.85
0.39
All services
0.02
0.01
0.18
0.40
30.41
19.Business services
18.Insurance
0.05
0.04
0.56
0.92
13.71
0.02
0.09
0.56
1.01
17.Finance
0.01
0.04
0.18
0.44
30.36
D
E
M
N
A
T
P
E
C
C
Total
0.32 0.14 0.42 0.19 1.06 0.47 1.13 0.50
Source: Authors’ estimations.
A
30
0.21
0.31
0.70
0.31
0.42
0.57
0.37
0.25
0.27
0.16
0.40
0.25
0.67
5.19
10.73
5.20
3.08
6.49
3.08
0.09
0.14
0.32
0.14
0.19
0.27
0.16
0.11
0.12
0.07
0.18
0.11
0.22
0.34
0.71
0.35
0.46
0.58
0.38
0.26
0.29
0.17
0.42
0.26
0.10
0.15
0.33
0.16
0.20
0.27
0.17
0.11
0.13
0.07
0.19
0.12
ACCEPTED MANUSCRIPT
Table 6. Short term and long term impact of reductions in FDI barriers on main aggregate variables across regions (% change with respect to the initial data)
Welfare
GDP
Wages
Capital
remuneration
Exports
Imports
CPI
Short
run
Long
run
Short
run
Long
run
Short
run
Long
run
Short
run
Long
run
Short
run
Long
run
Short
run
Long
run
Short
run
Long
run
CHN
EU27
GBR
USA
IND
JPN
LAC
MEN
OAC
SEA
SSA
0.31
0.08
0.10
0.02
0.06
0.02
0.04
0.06
0.08
0.02
0.24
0.10
0.12
0.02
0.07
0.03
0.05
0.08
0.08
0.03
0.09
0.04
0.08
0.01
0.04
0.01
0.02
0.03
0.04
0.04
0.01
0.19
0.07
0.09
0.02
0.05
0.02
0.03
0.03
0.05
0.06
0.02
0.09
0.02
0.03
0.01
0.07
0.00
C
S
U
I
R
T
P
0.07
0.01
0.03
0.03
0.02
0.01
0.17
0.05
0.05
0.01
0.08
0.02
0.03
0.03
0.04
0.02
0.06
0.07
0.16
0.03
M
0.01
0.03
0.03
0.03
0.03
0.06
0.06
0.01
0.19
0.10
0.19
0.03
0.04
0.04
0.05
0.03
0.06
0.08
0.02
1.06
0.19
0.07
0.13
0.48
0.03
0.05
0.10
0.14
0.07
0.10
C
C
T
P
E
D
E
N
A
0.06
1.12
0.21
0.09
0.14
0.50
0.04
0.06
0.10
0.15
0.09
0.11
0.79
0.10
0.06
0.05
0.04
0.03
0.05
0.07
0.01
0.03
0.03
0.85
0.12
0.08
0.05
0.05
0.04
0.06
0.07
0.03
0.05
0.04
-0.26
0.02
0.04
num
0.08
-0.02
0.01
0.02
0.02
0.01
0.01
-0.28
0.01
0.04
num
0.08
-0.01
0.01
0.01
0.02
0.01
0.00
A
31
ACCEPTED MANUSCRIPT
Source: Authors’ estimations.
Note: LAC stands for Latin America, OAC for other advanced countries, SEA for Southeast Asia, SSA for Sub-Saharan Africa and MEN for Middle-East and
North of Africa. Appendix 2 has the country composition of each of the regions.
T
P
I
R
C
S
U
N
A
D
E
M
T
P
E
C
C
A
32
ACCEPTED MANUSCRIPT
Table 7. Short term impact of 50% reductions in FDI barriers on output and exports across regions (% change with respect to the initial data)
Ouptut
CHN
1.Agriculture
2.Other
primary
3.Food
0.37
1.07
0.34
EU27
0.04
-0.10
0.05
GBR
0.02
-0.31
0.05
USA
0.03
0.02
0.02
IND
0.02
JPN
Exports
-0.55
0.02
LAC
0.00
0.10
0.02
0.02
-0.07
4.Textiles
0.47
-0.03
-0.07
-0.02
-0.11
-0.02
5.Wood and
paper
0.37
-0.01
-0.06
-0.02
-0.08
6.Chemicals
0.55
-0.02
-0.08
0.00
7.Metals
0.54
-0.15
-0.30
8.Motor
vehicles
9.Other
0.39
0.50
-0.05
-0.11
-0.10
-0.26
0.03
MEN
0.04
-0.06
0.05
D
E
OAC
0.01
-0.13
SEA
M
-0.09
0.04
-0.03
-0.03
-0.05
-0.03
-0.02
PT
-0.03
-0.02
-0.07
-0.07
-0.15
-0.01
-0.03
-0.02
-0.07
-0.06
-0.08
-0.28
-0.06
-0.15
-0.11
-0.20
-0.14
-0.01
-0.04
-0.10
-0.23
0.00
0.02
-0.03
-0.09
-0.02
-0.06
-0.05
-0.13
-0.02
-0.06
E
C
C
A
33
C
H
N
0.
03
0.
02
0.
82
0.
03
0.
03
0.
02
0.
01
0.
13
0.
01
-
1.
04
EU
27
2.
76
0.
56
1.
15
0.
98
1.
37
1.
23
1.
GB
R
0.
11
0.
53
0.
12
0.
16
0.
32
0.
13
0.
40
0.
20
-
T
P
I
R
0.
00
0.
20
C
S
U
0.04
N
A
0.03
SS
A
0.
01
0.
09
0.
17
0.
09
0.
29
0.
20
-
US
A
0.
04
0.
06
0.
06
0.
04
0.
07
0.
01
0.
15
0.
03
-
IN
D
0.
37
1.
03
0.
37
0.
39
0.
55
0.
19
0.
61
0.
29
-
JP
N
LA
C
M
EN
0.
14
0.
01
0.
09
0.
00
0.
10
0.
02
0.
08
0.
21
0.
07
0.
24
0.
04
-
0.
00
0.
02
0.
14
0.
09
0.
21
0.
07
0.
14
0.
09
0.
09
0.
03
0.
09
0.
01
0.
0.
03
-
O
AC
0.
02
0.
15
0.
03
0.
07
0.
26
0.
12
0.
22
0.
09
-
SE
A
0.
02
0.
13
0.
01
SS
A
0.
08
0.
03
0.
00
0.
17
0.
12
0.
20
0.
05
0.
06
0.
01
0.
15
0.
02
-
0.
04
0.
0.
10
ACCEPTED MANUSCRIPT
transport
10.Electronic
s
11.Other
machinery
12.Other
manufacture
s
13.Constructi
on
14.Water
Transport
15.Air
Transport
16.Communi
cations
0.72
-0.25
-0.24
-0.30
0.18
0.41
0.59
0.43
0.
00
0.
08
0.
34
PT
0.03
0.05
0.07
0.06
0.
03
0.
97
0.
11
0.
44
0.
26
0.
54
0.
24
0.
16
0.
03
0.
16
0.
07
1.
22
1.
68
0.
67
1.
06
0.
21
1.
07
1.
96
0.
01
0.
10
0.
18
0.
95
0.
11
-0.34
-0.08
-0.15
-0.15
-0.16
-0.12
0.66
-0.21
-0.34
-0.12
-0.28
-0.13
-0.21
-0.13
-0.32
-0.24
0.41
-0.07
-0.11
-0.11
-0.16
-0.06
-0.06
-0.03
-0.10
-0.08
0.06
0.01
0.01
0.00
0.01
0.00
0.00
0.00
0.00
-0.19
-0.01
-0.06
0.03
-0.11
0.01
0.04
0.15
-4.40
0.52
0.03
0.14
0.54
0.25
0.02
0.06
0.05
0.02
0.01
E
C
0.01
D
E
79
0.
90
0.
95
1.
04
0.
25
0.
26
0.
39
0.
18
0.
09
0.
14
1.
66
0.00
0.04
0.13
17.Finance
-0.09
0.07
0.01
0.02
0.01
0.01
0.02
0.06
0.05
0.05
18.Insurance
19.Business
services
-0.56
0.15
0.05
0.04
0.26
0.04
0.09
0.16
0.15
0.09
-1.01
0.14
0.14
0.06
0.83
0.02
0.09
0.22
0.16
0.17
0.
02
0.
05
0.
08
0.26
0.06
0.07
0.02
-0.13
0.02
0.03
0.05
0.05
0.06
0.
02
20.Personal
services
C
A
34
0.
76
1.
59
1.
20
0.
11
0.
34
0.
24
0.
03
0.
61
0.
33
0.
70
0.
40
0.
32
0.
05
3.
48
0.
08
0.
05
2.
72
2.
18
0.
26
T
P
I
R
C
S
U
N
A
M
0.
13
0.
13
0.
10
0.
07
0.
02
3.
32
1.
33
0.
41
0.
49
0.
65
1.
20
0.
09
02
0.
19
0.
20
0.
24
0.
04
0.
07
1.
78
2.
10
0.
19
0.
14
0.
40
0.
15
0.
07
1.
06
3.
26
0.
07
0.
11
0.
25
0.
09
0.
02
1.
22
1.
65
01
0.
04
0.
43
1.
77
0.
15
0.
15
0.
32
0.
14
0.
02
0.
28
1.
47
0.
26
0.
12
0.
10
0.
21
0.
10
0.
15
0.
12
1.
86
1.
11
0.
41
1.
95
1.
36
1.
05
1.
12
1.
75
0.
35
0.
53
1.
14
0.
47
1.
36
1.
99
0.
09
0.
02
0.
00
0.
34
1.
33
1.
74
0.
05
0.
03
0.
03
0.
06
0.
16
0.
06
0.
13
0.
15
0.
05
0.
00
0.
81
1.
60
ACCEPTED MANUSCRIPT
21.Other
services
All
manufacture
s
0.20
0.04
0.05
0.01
0.01
0.01
0.02
0.02
0.03
0.03
0.48
-0.06
-0.11
-0.04
-0.13
-0.03
-0.04
-0.04
-0.10
-0.05
All services
-0.06
0.07
0.06
0.02
0.09
0.01
0.03
0.05
0.07
0.06
Total
0.32
0.01
0.00
0.00
-0.03
-0.01
0.00
0.00
-0.01
-0.01
IND
JPN
Short Short
run
run
LAC
Short
run
MEN
Short
run
OAC SEA
Short Short
run
run
CHN EU27 GBR USA
Short Short Short Short
run
run
run
run
Source: Authors’ estimations.
Note: See note on Table 6.
D
E
T
P
E
C
C
A
35
0.
16
0.
30
0.
41
0.
07
0.
05
0.
10
0.
55
0.
13
0.
38
0.
40
1.
54
0.
48
T
P
I
R
C
S
U
N
A
M
0. 1.
00 12
0. 1.
03 07
0. 1.
02 02
0. 1.
00 06
SS
A
Short
run
0.
07
0.
21
0.
84
0.
19
0.
04
0.
08
0.
62
0.
03
0.
02
0.
12
0.
60
0.
05
0.
03
0.
10
0.
82
0.
10
0.
13
0.
19
0.
87
0.
14
0.
00
0.
11
0.
54
0.
07
0.
01
0.
04
0.
67
0.
10
ACCEPTED MANUSCRIPT
Table 8. Sensitivity analysis: Short run impact on welfare of 50 % cuts in FDI barriers
Reference
25% Profit
repatriation
75% Profit
repatriation
σ (D, M) (high)
σ (D, M) (low)
σ (M, M) (high)
σ (M, M) (low)
σ (qi, qj) (goods, high)
σ (qi, qj) (goods, low)
θm (i) (high)
θm (i) (low)
σ (A1,…,An)
(alternative)
ε (fi) (high)
ε (fi) (low)
σ (qi, qj) (services,
high)
σ (qi, qj) (services,
low)
σ (va, bs) (high)
σ (va, bs) (low)
σ (L,K) (high)
σ (L,K) (low)
θr (i) (high)
CHN
0.31
EU27
0.08
GBR
0.10
USA
0.02
IND
0.06
JPN
0.02
LAC
0.04
MEN
0.06
OAC
0.08
0.21
0.07
0.11
0.02
0.06
0.02
0.03
0.05
0.08
0.07
0.02
0.79
0.31
0.30
0.32
0.29
0.27
0.40
0.32
0.30
0.10
0.08
0.07
0.08
0.07
0.07
0.08
0.08
0.07
0.10
0.10
0.11
0.10
0.11
0.10
0.11
0.11
0.10
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.08
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.08
0.08
0.08
0.08
0.07
0.08
0.08
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.02
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.30
0.24
1.47
0.08
0.05
0.14
0.11
0.09
0.10
0.02
0.02
0.02
0.02
0.02
0.02
0.04
0.02
0.05
0.06
0.04
0.09
0.08
0.06
0.07
0.07
0.06
0.08
0.02
0.01
0.02
0.08
T
P
E
0.06
0.06
0.04
0.11
0.02
0.06
0.02
0.04
0.06
0.08
0.07
0.02
0.07
0.10
0.06
0.07
0.08
0.11
0.10
0.11
0.10
0.10
0.10
0.13
0.02
0.02
0.02
0.02
0.02
0.03
0.06
0.06
0.06
0.06
0.06
0.10
0.02
0.02
0.02
0.02
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.05
0.06
0.06
0.06
0.06
0.06
0.08
0.08
0.09
0.07
0.08
0.08
0.12
0.07
0.08
0.06
0.07
0.07
0.09
0.02
0.02
0.02
0.02
0.02
0.04
C
C
0.36
0.28
0.45
0.17
0.31
0.31
0.41
A
D
E
N
A
M
36
T
P
I
R
C
S
U
SEA
0.07
SSA
0.02
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θr (i) (low)
0.22
Source: Authors’ estimations.
σ (D, M)
σ (M, M)
θm (i)
σ (A1,…,An)
σ (qi, qj) (goods)
σ (qi, qj) (services)
ε (fi)
σ (va, bs)
σ (L,K)
θr (i)
0.05
0.08
0.01
0.03
0.02
0.03
0.04
0.04
0.05
0.01
Armington elasticity of substitution between imports and domestic goods in CRTS sectors
Armington elasticity of substitution between imports from different regions in CRTS sectors
Share of value added in multinational firms in sector i due to specialized primary factor imports in the benchmark
Elasticity of substitution in intermediate production between composite Armington aggregate goods
Elasticity of substitution between firm varieties in imperfectly competitive goods sectors
Elasticity of substitution between firm varieties in imperfectly competitive services sectors
Elasticity of supply with respect to price of output in national firms and multinationals in services
Elasticity of substitution between value-added and business services
Elasticity of substitution between primary factors of production in value added
Spillover of FDI barrier cut on exporters
T
P
I
R
C
S
U
N
A
D
E
M
T
P
E
C
C
A
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Figure 1. Costs related to FDI barriers (% of total costs)
00
10
20
30
40
50
60
70
80
T
P
14.Water Transport
I
R
C
S
U
15.Air Transport
N
A
16.Communications
D
E
17.Finance
T
P
E
C
C
18.Insurance
19.Business services
M
A
Source: Jafari and Tarr (2014).
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CHN
EUR
USA
ACCEPTED MANUSCRIPT
CE
PT
E
D
MA
NU
SC
RI
We offer an overview of the changing role of China in the world from 2004 to 2016
We analyze the impact of FDI inflows accruing to services sectors in China
We use an innovative computable general equilibrium (CGE) model
The impact of FDI in services contrasts sharply with the one of FDI in manufactures
The effects are slightly positive for China and the rest of regions
AC
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PT
Highlights
39