The Chinese growth model transition: A general equilibrium analysis of
the shift of FDI inflows from manufacturing to services
María C. Latorre (Universidad Complutense de Madrid)
Hidemichi Yonezawa (ETH Zurich)
Jing Zhou (Xiangtan University)
Abstract
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-SubSaharan
Africa) and 21 sectors. We provide quantitative evidence on several characteristics of the 21
sectors in China and the rest of regions, as well as other data on the role of China in the world
stage, including its evolution since 2004. Several scenarios of FDI going to services are
simulated deriving short and long run results. We find that the impact of more foreign
multinationals going to services contrast sharply with the one obtained in previous studies
from 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
areas 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: [email protected]
1. Introduction
China is in the midst of a fundamental transition with several remarkable characteristics. It is
moving from an investment-intensive, export-led model of growth, to a consumption- and
innovation-driven one. The services’ sectors are becoming increasingly more important, while
manufacturing is losing weight. China is experiencing a rapid urbanization rate and consumers
are becoming increasingly sophisticated, demanding high-quality, customized products. There
is an impressive rise of e-commerce. The government actively promotes initiatives to tackle
the issues of overcapacity and industrial transformation, entrepreneurship and innovation. It is
trying to streamline administrative procedures and remove barriers to business creation and
expansion, as well as opening up of sectors previously off limits to private sector investment. It
is also incentivizing outward foreign direct investment in the sectors considered strategic. All
this is happening with slower GDP growth, rising costs and more uncertain RMB exchange rate
environment (KPMG, 2016).
In this paper, we focus on one of the aspects of this transition, namely, the role of FDI. Despite
the recent lower rates of growth in China, FDI inflows have not ceased to increase and are
becoming more oriented to services and advanced manufacturing sectors. We analyze this
trend looking at the impact on China and also on other big areas of the world. Many studies
have found a closer 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%.
This change of pattern of Chinese FDI inflows is not unique. In general, FDI world flows are
experiencing the same trend. As countries develop, increasing the share of services’ in GDP, it
is logical that investment activities should also become more related to services. However, the
weight of services in the GDP of China is still small compared to the world average, as we shall
see. So, in some sense, the importance of services FDI is more marked given the country’s
stage of development.
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 AsiaLatin America-Middle East-SubSaharan Africa) and 21 sectors for the year 2016. A general
equilibrium analysis has several advantages. It is based in 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 literature strands analyzing them tend to be disconnected. In the
2
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 MNE, rather than ‘looking out’ from MNEs 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 comprehensive assessments” (Meyer, 2004: 261). CGE
models belong more to the Economics literature (Shoven and Whalley, 1984; Latorre et al.,
2009).
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. Data on manufacturing sectors have
been more readily available, while the ones on services are more recent. However, 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 export-oriented FDI and growth strategy. It seems that FDI in services will, thus
reinforce the shift from the Chinese export-led growth to a consumption-led growth pattern.
Our model incorporates the real data on different type 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 simultaneous characteristics that to
the best of our knowledge are not available in previous CGE models. These three
characteristics are a multiregional framework, monopolistic imperfect competition and foreign
multinationals.
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
data. Section 3 explains the model, data and simulations. Section 4 presents the outcomes for
China of several scenarios of increases of FDI inflows in the short and long run. Within the
results section we also analyze the effects for the rest of regions in the model and perform a
sensitivity analysis. A final section of summary and conclusions closes the paper.
2. China in the world economy
In this section we offer an overview of several characteristics of the Chinese economy,
presenting their differences across sectors. We further put them in perspective with respect to
other big areas of the world.
3
We begin with the data on the GDP composition in the years 2004 and 2016 1 that appear in
Table 1. Basically, it offers the weight of each sector in the total GDP across each of the regions
considered. The columns present those different regions with two more final columns. One is
for the structure of GDP in the entire world economy and another one, for the weight of China
in the GDP of 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”. The row at the very bottom offers the weight of the total GDP
of each of the regions considered in the world GDP. Appendix one offers sectors’ definitions
and their conversions across several classifications, while Appendix two has the country
composition of each of the regions.
Looking at the last row of Table 1 we see that 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 compare to world standards
(31.4%) 2.
Table 2 offers data on exports in a structure analogous to that of Table 1. Its last row shows
that 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 the figure was 81% in 2004. However, we can see that
this pattern is considerably more marked in China. For some areas, such as the Great Britain
(32.6%), India (28.1), EU27 (22.0%) trade in services is above the world average. This will be
important for our results later on. The last column of each year shows the growth of the share
of Chinese manufacturing trade in the world, which for all manufacturing 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 export structure. Indeed, they account for
15.9%, 22.8% and 6.5% in total Chinese exports, respectively.
1
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 Bankd “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).
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.
2
4
In Table 3 we analyze some data on costs structure in China. To keep the information
manageable we put it in perspective with respect to the EU27 and to the US. Again sectors
appear in the rows. The first group of columns displays the share of value added (i.e., the sum
of capital and labor remuneration) per unit of production. The second group of columns
presents the evolution of that share between 2004 and 2016. The values in those columns are
the difference in percentage points between the share in 2016 and the share in 2004 (i.e., if
they go up the share of value added has risen in the period and vice versa). The third group of
columns shows that the share of costs that goes to labor (i.e., labor remunerations which
basically is the number of workers multiplied by their wages) per unit of production. The next
group of columns displays the difference in labor remunerations between 2016 and 2004 in a
manner analogous to the difference in value added we have just explained. The last group of
columns shows the cost of intermediates coming from advanced services sectors per unit of
production3. For example, the value of 2.3% in 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.
In general, one unit of production generates a lower value added share in China than in the US
and EU27. This trend is very clear across manufacturing sectors, while there are a few
exceptions in services. The trend from the period 2004 till 2016 has been of an increase in the
value added created in manufacturing activities in China, contrasting with that of the EU, in
which in many sectors the share of value added has gone down. For the US the evolution of VA
is mixed in manufacturing, with some sectors increasing or diminishing their value added
shares. The lower generation of value added is related to lower labor remuneration (always
compared to the US and EU27), which is also very clear in manufacturing sectors and not
always the case in services. However, during the period 2004 till 2016 labor remuneration has
increased across many sectors in China. Finally, intermediates coming from business services
sectors play a smaller role in China than in other areas, such as EU27 and the US. Only a few
exceptions appear in water and air transport, which are two very small sectors.
Table 4 further looks at the sectoral detail by analyzing the destination of imports accruing to
China, the EU27 and the US. In other words, the table presents the percentage of imports in
each sector that go to private consumption, intermediates for further processing, investment
or public spending. We can see that in the main three areas most of the trade is of
intermediates. This resembles the intense process of fragmentation present in the world
economy. 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 consumption 4. It is important to note that imports from services sectors
tend to be more oriented to both private and public consumption in the three areas
considered than imports from manufacturing.
3
Advanced services sectors are the ones that have foreign MNEs, namely, water transport, air transport,
communication, finance, insurance and business services.
4
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).
5
In Table 5 we look at the destination of production with a structure similar to the one of Table
4, except that we now add another final destination of production, namely, exports. Although
most of the production is devoted to intermediates for further processing, in China this
tendency is more accentuated. China devotes larger shares of its production to intermediates
than the US and EU27 do. 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 contrary applies
to investment, to which China applies more sizeable percentages of its production compared
to EU27 and US. The EU27 stands out as a very active exporter, who exhibits enormous shares
for 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. For all of
the areas considered we can see that the shares 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.
All in all, these two latter tables show that China has its own peculiarities. In 2016 its
production and imports are very oriented to intermediates’ production, to investment and to
exports. China is indeed the “factor 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.
Figure 1 shows data related to the level of protection from the operations of foreign MNEs 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 economies 5. As can be seen, Chinese protection is really high compared to the other two
regions. We know the government is trying to open up these and other advanced
manufacturing sectors for private investment (KPMG, 2016). As a consequence, these barriers
should go down, thus, bringing about important costs reductions for foreign MNEs. This is
exactly the approach that we model in order to estimate the impact of FDI in services 6.
3. The model, data and simulations
The main data we use is the latest available GTAP database version 9A (Narayanan et al.,
2016). This is a database for the world economy which offers information for 140 regions
(most of the countries) and 57 sectors. Using Gempack or GAMS software the data can be
aggregated to different regions and sectoral configurations. The GTAP 9A covers the years
2004, 2007 and 2011. We have taken the data from 2004 and 2011, but have updated the
5
Technically these costs are called “Ad valorem equivalents (AVEs)”, which quantify existing restrictions
to the operations of foreign MNEs 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 operate in a
particular sector. Jafari and Tarr (2014) explain the different sources consulted to econometrically derive
the AVEs. The data are publicly available.
6
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.
6
latest one using GDP growth rates from 2012 till 2015 using the IMF world economic outlook
from April 2016 (IMF, 2016). Thus our final year is 2016.
The data on barriers to foreign MNEs come from the publicly available database created at the
World Bank (Jafari and Tarr, 2014). We model three different cuts of 50%, 25% and 10%. In the
literature of FDI in services summarized in Tarr (2012) usually 50% cuts have been simulated.
We also try lower percentage reductions since higher ones do not seem reasonable. 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).
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 MNEs in advanced services’ sectors had not previously been achieved
to the best of our knowledge 7.
Our model extends the multiregional framework of Balistreri et al. (2015) by including the
imperfect competition setting in the sectors with foreign MNEs. These authors had in turn
extended other single country models which had FDI in services such as Rutherford and Tarr
(2008) and Latorre (2016). We further include a long run steady state simulation, while
Balisteri et al. (2015) had concentrated on the short run impact.
The model has three type of sectors: 1) Manufacturing sectors with economies of scale and
monopolistic competition (chemicals, electronics, automoviles, textiles and other
manufacturing); 2) Advanced services sectors with foreign MNEs, 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.
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 and 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.
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
7
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 (2016),
which is still an unpublished paper. Recently, Oleseyuk (2015) 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 its multiregional framework.
7
production for the firms using them. There is empirical evidence of FDI in services leading to
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).
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
steady state formulation in which the capital stock adjusts in order to maintain the initial rate
of return of capital. For the steady state formulation we follow the approach of Balistreri et al.
(2016).
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 (Latorre et al., 2009; Gómez-Plana and Latorre, 2014).
Appendix 3 presents the algebraic description of the model.
4. Results
4. 1 Impact on the Chinese economy
Table 6 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 successively smaller cuts in the costs
related to the barriers faced by the foreign firms in advanced services sectors of 50%, 25% and
10%. The figures should be interpreted as annual percentage changes in the years coming after
the barriers have been reduced.
The entry of foreign MNEs 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 wages and in some
8
cases, as here, also the capital remuneration 8. 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 do 9.
In the long run, the capital stock increases in the economy as a response to the upward
pressure on the capital return, while 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 spending.3 This setting of the long run
results in the small increase relative to the short run. The only exception would be welfare,
whose percentage increase is smaller than in the short run. Due to the fact that the Chinese
economy exhibits very high investment rates and very low consumption rates, the negative
welfare impact of the increase in investment spending is significant. 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.
All in all, the entry of more foreign MNEs would bring a positive, but rather limited, impact on
the Chinese economy. The larger the cut in the barriers, the stronger the impact is, for reasons
that will become clear below. To put these results in perspective we can compare them with
the ones derived for the US and the EU28 in Latorre and Yonezawa (2016). 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.
We believe two important forces make the impact of FDI in China smaller. 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 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 MNEs) 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.
8
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.
9
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.
3
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.
9
Table 7 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 as well as
some summarizing figures for “all manufacturing”, “all services” and the “total” for the 21
sectors. The latter are displayed in the rows at the bottom of the table. 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 MNEs. Because MNEs benefit in
many of them from very sizeable savings in costs, they become very competitive and drive out
of business some Chinese firms 10. 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 is 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.
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 China. In
table 8 we present the same aggregate outcomes we have analyzed before 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 are more
likely to affect the rest of regions, since they bring about the heaviest impact on China. We
keep the effects that have already been presented for the Chinese economy to facilitate the
comparison.
It is interesting that we find no evidence of negative effects for the rest of regions when more
MNEs from the services sectors come to China. This contrasts heavily with our previous results
10
The percentage reductions in the number of domestic firms are limited. 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%.
10
derived from the impact of FDI going to Chinese manufacturing sectors (Latorre and Zhou,
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 MNEs are
going to manufacturing sectors directly. For example, in Zhou and Latorre (2014a, 2014b,
2015) we model MNEs 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).
In Latorre and Yonezawa (2016) we find that after the TTIP other areas could be slightly
crowded, 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 areas 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 regions outside the TTIP
area.
If we look at the sectoral details of all of the regions we find the following. As reflected in Table
9 most manufacturing sectors tend to reduce production very slightly after the entry of MNEs
in Chinese services. By contrast, they increase also very little, 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 Table 10. Comparing the Tables of output and exports, we can see that
output reflects the evolution of exports.
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
11
specialized in manufacturing, the positive trend in services in the rest of regions compensates
the reduction in exports in manufacturing.
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 MNEs 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 appear sin Table 5) 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.
4.3 Sensitivity analysis
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 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 11, and the first row shows the result in our
central setting (“reference”).
We see that the results in the central setting are quite robust with respect to all the
parameters except two parameters. 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 FDI reform, we could think of the reduction of other types of trade costs,
such as non-tariff barriers), 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.
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 are 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
12
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.
5. Summary and conclusions
China is transitioning from an economy led by investment and exports, to one driven by
consumption and innovations, with a growing share of services. We have shown empirical
evidence of how in the period from 2004 till 2016 China has been increasing the value added
generated in many manufacturing sectors. This contrasts with the trend in the EU27 (i.e.,
excluding Great Britain), in which value added per unit of production has decreased across
many manufacturing and services sectors. However, still in 2016, China generates less value
added per unit of production in manufacturing than the US and the EU27 do. In services, this
pattern is, however, not that clear.
The low value added generation is mainly related to lower labor remuneration in China
compared to the one in the EU27 and in the US. But, again, this pattern is clearer in
manufacturing sectors than in services. The data also show that in the period from 2004 till
2016, the share of labor remuneration in China has been increasing across many sectors, which
matches the well-known tendency that wages have been increasing in China.
Our data have also shown that China is in 2016 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 “the 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 labor intensive and more oriented to the provision of private consumption
than the latter. Also, in comparison with manufacturing, they are less export and investment
oriented. This implies that the shift to services that China is experiencing enhances 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. China is indeed the “world factory”. 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. Further, the investment effort
surpasses the one of the two other areas.
In China, advanced services sectors (water transport, air transport, communication, finance,
insurance and business services) are remarkably more protected from foreign competition
than other areas such as the US and the EU27. The Chinese government seems committed to
lower those barriers. What would be the impact of FDI inflows going to advanced services
sectors? In our analysis we find that because the initial barriers are so high, even with small
cuts, foreign multinationals would benefit from sizeable cost savings. This would heavily
13
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 existing ones in the rest of sectors. The services sectors that do not
have MNEs, such as personal services and other services would, however, increase their
production.
Despite the process of crowding out, the entry of foreign MNEs 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 MNEs accrue.
We examine different levels of reductions in the costs related to the barriers that foreign
MNEs encounter (50%, 25% and 10%) 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, because the reduction in prices is also more sizeable than with lower cuts, the
overall impacts is more positive with greater percentage cuts. In the long run, a process of
domestic capital accumulation simultaneously occurs to the entry of foreign MNEs. 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.
Regarding the impact for the other areas 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., Latorre and Zhou, 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).
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.
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
MNEs, which very few CGEs model. Second, the inclusion of monopolistic competition in the
sectors with foreign MNEs 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.
14
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17
Table 1. GDP structure of the world and the different regions in 2004 and 2016
CHN
1.Agriculture
15.3
2.Other primary
4.0
3.Food
2.0
4.Textiles
3.2
5.Wood and paper
1.5
6.Chemicals
4.4
7.Metals
4.6
8.Motor vehicles
1.4
9.Other transport
0.7
10.Electronics
2.7
11.Other machinery
4.9
12.Other manufactures 4.2
13.Construction
6.2
14.Water Transport
1.2
15.Air Transport
0.3
16.Communications
1.9
17.Finance
4.5
18.Insurance
0.5
19.Business services
4.8
20.Personal services
2.3
21.Other services
29.3
All manufactures
39.9
All services
44.8
%World
5.0
EUR
3.5
0.5
3.0
1.3
2.4
3.3
2.7
1.6
0.5
1.0
4.0
1.7
7.1
0.3
0.4
2.5
4.0
1.0
15.5
3.5
40.0
29.2
67.2
22.8
GBR
0.7
1.5
2.8
0.9
2.1
2.3
1.8
1.1
0.7
0.8
2.8
1.3
6.0
0.4
0.5
3.0
2.9
1.4
15.8
3.4
47.5
24.3
75.0
4.9
USA
1.2
0.9
1.9
0.7
2.5
2.7
1.9
1.0
0.8
0.6
3.7
0.9
7.1
0.3
0.6
2.3
7.8
1.9
10.5
3.2
47.5
24.8
74.1
29.8
JPN
1.3
0.1
2.2
0.4
1.3
2.3
2.7
1.6
0.3
2.1
2.8
1.1
5.5
0.5
0.2
2.3
4.2
1.5
11.6
3.3
52.7
22.4
76.3
11.0
IND
17.5
2.5
2.9
2.0
0.7
2.7
2.7
0.6
0.4
0.2
1.8
2.6
7.4
0.7
0.2
1.7
5.2
1.1
4.9
0.3
41.7
26.5
55.9
1.9
LAC
6.1
5.2
4.6
1.7
1.6
3.5
2.2
1.4
0.3
1.3
1.9
1.6
5.9
0.3
0.3
2.5
3.4
0.9
7.8
4.2
43.2
31.3
62.6
5.6
2004
OAC SEA
2.5 12.7
3.7 7.9
1.9 4.6
1.0 3.7
2.0 2.1
3.2 4.4
2.8 1.8
1.3 1.0
0.6 0.6
2.9 4.7
2.8 2.3
1.4 1.8
5.6 5.0
0.5 0.9
0.6 0.7
2.3 1.6
5.2 2.5
1.3 0.6
11.5 3.3
2.5 2.3
44.2 35.5
29.3 39.9
68.2 47.4
9.5 2.2
SSA
16.5
12.5
5.1
1.5
1.4
2.0
3.0
0.7
0.3
0.3
1.1
1.5
3.9
0.3
0.5
2.1
1.9
2.6
7.8
2.6
32.4
33.2
50.3
1.4
MEN
6.3
21.3
3.4
2.0
0.9
3.4
2.8
0.9
0.3
0.7
1.6
1.7
5.6
0.8
0.6
1.5
4.6
0.4
5.0
1.6
34.7
44.7
49.1
5.7
World %CHN/World CHN EUR GBR
3.9
19.8
12.3 2.8 1.1
3.0
6.9
3.7 0.7 2.1
2.6
3.9
2.8 3.3 2.7
1.2
13.5
2.8 1.2 0.9
2.0
3.7
1.7 2.3 2.0
3.0
7.4
4.7 3.7 2.5
2.5
9.3
5.1 2.9 1.5
1.3
5.8
1.7 1.6 1.0
0.6
5.9
0.6 0.6 0.9
1.3
10.4
2.5 0.8 0.5
3.3
7.5
4.9 4.3 2.6
1.5
14.1
3.7 1.7 1.2
6.5
4.9
7.8 6.7 5.2
0.4
13.5
0.4 0.4 0.5
0.5
2.9
0.2 0.4 0.4
2.3
4.2
1.9 2.6 3.4
5.2
4.4
6.0 4.1 4.1
1.3
1.8
0.4 1.2 1.6
11.1
2.2
5.7 15.7 15.7
3.1
3.7
2.4 3.4 3.3
43.6
3.4
28.7 39.5 46.9
28.7
7.0
41.9 29.9 23.0
67.4
3.3
45.7 67.3 75.9
100
5.0
13.3 16.8 3.2
USA
1.4
1.7
1.9
0.7
2.4
2.8
1.9
0.9
0.8
0.5
3.5
0.8
6.1
0.3
0.5
2.4
8.0
1.9
10.5
3.3
47.8
24.0
74.7
22.3
JPN
1.4
0.1
2.3
0.3
1.3
2.3
2.5
1.5
0.3
1.6
3.1
1.0
4.9
0.6
0.2
2.4
4.3
1.6
11.3
3.4
53.7
21.2
77.4
7.2
IND
17.7
2.1
2.4
2.2
0.7
2.5
2.2
0.5
0.3
0.2
1.7
2.5
9.2
0.6
0.1
1.3
4.5
1.0
5.5
0.1
42.6
26.5
55.8
3.5
2016
LAC OAC
6.2 2.2
5.2 5.2
4.3 1.9
1.5 0.7
1.6 1.7
3.6 3.0
2.4 2.8
1.4 1.1
0.4 0.7
1.3 2.5
1.6 2.9
1.6 1.3
6.6 5.9
0.2 0.6
0.3 0.6
2.7 2.3
3.7 5.3
1.0 1.3
8.0 12.3
3.7 2.5
42.9 43.3
31.3 29.7
62.5 68.2
8.2 10.0
SEA
13.7
8.2
5.3
2.6
1.6
4.6
2.0
1.0
0.6
1.9
1.7
1.8
6.8
0.8
0.5
1.6
2.8
0.6
3.4
2.4
36.1
38.1
48.2
3.7
SSA
19.9
14.1
4.9
1.1
1.1
1.5
2.7
0.6
0.6
0.4
1.2
1.2
5.4
0.2
0.4
1.9
2.1
2.1
6.0
2.3
30.3
34.7
45.4
2.3
MEN
5.3
24.0
3.5
1.4
0.8
3.3
2.6
0.8
0.3
0.6
1.8
1.8
6.9
0.6
0.5
1.6
3.6
0.3
5.2
1.5
33.7
47.8
47.0
9.4
World%CHN/World
5.4
30.5
5.0
9.8
2.9
13.0
1.3
28.7
1.8
12.3
3.3
18.8
2.7
24.4
1.2
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
11.0
41.1
9.3
31.4
17.7
63.3
9.6
100
13.3
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
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.
18
Table 2. Export structure of the world and the different regions in 2004 and 2016
CHN
1.Agriculture
1.2
2.Other primary
1.1
3.Food
2.5
4.Textiles
18.3
5.Wood and paper
3.5
6.Chemicals
8.1
7.Metals
6.6
8.Motor vehicles
1.9
9.Other transport
1.6
10.Electronics
26.7
11.Other machinery
14.7
12.Other manufactures 6.8
13.Construction
0.2
14.Water Transport
0.2
15.Air Transport
0.4
16.Communications
0.2
17.Finance
0.1
18.Insurance
0.1
19.Business services
0.8
20.Personal services
0.5
21.Other services
4.4
All manufactures
92.1
All services
6.6
%world
8.8
EUR
1.2
0.7
4.9
4.1
4.2
16.0
5.4
10.7
3.2
6.3
18.6
3.2
0.7
1.3
2.6
0.5
0.5
1.1
5.8
1.5
7.3
78.1
20.7
20.1
GBR
0.7
4.6
4.0
2.4
2.4
16.7
5.0
8.5
2.3
7.1
12.6
2.2
0.2
0.7
3.3
0.9
4.1
1.7
13.3
1.9
5.6
67.9
31.4
5.7
USA
4.0
1.0
3.2
2.1
3.1
16.5
4.3
7.9
6.2
10.8
17.9
2.3
0.3
0.0
1.8
0.1
1.1
1.0
5.8
2.2
8.1
75.7
20.2
13.5
JPN
0.1
0.1
0.4
1.4
0.7
11.9
6.2
19.3
2.7
19.6
26.2
2.1
1.0
0.5
1.2
0.1
0.5
0.2
1.9
0.4
3.5
91.5
8.4
8.2
IND
3.5
4.1
5.0
15.9
0.8
15.8
7.6
2.0
0.8
0.9
4.6
13.7
0.4
0.8
0.4
1.0
0.3
0.9
16.1
0.1
5.2
71.8
24.7
1.5
LAC
7.4
16.9
9.1
5.6
3.5
8.0
8.3
7.7
1.6
7.7
10.0
1.6
0.2
1.0
1.1
0.6
0.4
0.4
2.3
1.1
5.5
80.2
12.4
6.3
2004
OAC SEA
1.8 2.5
7.2 7.4
3.5 7.0
3.9 12.1
4.2 4.6
13.6 8.8
7.7 3.4
7.4 1.5
2.4 0.7
15.2 29.9
12.4 7.3
2.3 2.7
0.3 0.4
0.7 0.7
1.6 2.1
0.4 0.6
1.0 0.3
0.9 0.3
4.8 1.7
0.9 1.0
7.9 5.0
80.2 85.8
18.0 11.8
18.2 6.0
SSA
8.8
44.6
4.3
2.8
2.2
3.4
12.0
2.5
1.1
0.3
2.4
1.0
0.2
0.6
2.1
0.6
0.4
0.6
3.3
1.2
5.7
76.8
14.4
2.2
2016
MEN World %CHN/world CHN EUR GBR USA JPN IND LAC OAC SEA SSA
1.7
2.3
4.8
1.0 1.4 0.8 5.0 0.2 3.8 9.3 2.1 2.9 6.4
47.1 9.0
1.1
0.3 1.0 3.9 1.5 0.1 3.9 22.0 11.2 11.8 58.7
1.5
3.8
5.7
2.0 5.1 4.1 4.1 0.5 5.4 10.2 3.2 9.6 2.4
6.1
5.6
28.7
15.9 2.8 1.9 1.2 1.2 9.9 2.4 2.3 10.7 1.0
0.9
3.2
9.7
3.8 3.2 2.0 2.7 0.9 0.6 2.4 2.2 3.3 0.9
15.4 13.4
5.4
10.8 18.0 18.4 21.9 15.2 21.5 8.0 16.4 12.5 2.9
7.4
6.3
9.4
7.5 6.8 6.4 6.3 9.9 5.5 10.6 10.0 4.8 13.6
1.2
7.6
2.3
2.1 9.8 7.6 6.5 17.6 2.3 7.0 5.0 2.1 1.7
0.7
2.7
5.4
2.3 3.4 4.0 5.2 2.6 1.4 1.1 2.9 0.6 0.3
0.9 12.3
19.3
22.8 3.5 3.2 5.3 10.2 1.5 6.1 11.1 15.6 0.1
1.7 13.9
9.3
19.4 19.0 12.2 17.1 30.2 4.8 7.8 13.8 8.1 1.2
1.4
2.9
20.8
6.5 2.7 2.3 2.1 2.3 11.0 0.9 1.8 2.3 0.7
0.4
0.5
4.5
0.3 1.2 0.4 0.4 1.3 0.3 0.2 0.6 0.5 0.2
0.7
0.7
2.0
0.1 1.1 0.5 0.0 0.4 0.6 1.1 0.6 1.0 0.4
1.4
1.8
1.8
0.3 2.7 3.0 1.7 0.9 0.5 1.1 1.5 2.4 1.7
0.6
0.4
3.3
0.1 0.5 0.8 0.2 0.1 1.0 0.5 0.3 0.7 0.5
0.4
0.8
1.3
0.1 0.9 5.8 2.1 0.6 1.0 0.4 1.7 0.3 0.4
0.4
0.7
1.7
0.1 1.0 1.4 1.1 0.3 0.9 0.5 0.8 0.4 0.4
2.8
4.6
1.6
1.2 6.6 14.0 5.5 2.0 19.0 3.1 5.1 2.2 1.4
0.8
1.2
3.6
0.4 1.4 1.7 2.0 0.3 0.3 0.8 0.8 1.3 0.8
6.5
6.5
6.0
2.8 7.7 5.5 8.0 3.4 4.9 4.7 6.6 6.7 4.3
84.8 81.0
10.1
93.9 76.6 66.6 74.4 91.8 68.1 78.5 80.6 82.0 83.8
13.6 16.7
3.5
5.2 22.0 32.6 20.6 8.0 28.1 12.2 17.3 15.1 9.8
9.5 100.0
8.8
12.8 17.9 3.9 12.0 5.9 2.6 6.6 16.9 6.1 2.8
MEN
1.1
57.6
1.2
2.8
0.6
15.5
4.8
1.0
0.4
0.5
1.5
0.8
0.4
0.5
1.4
0.6
0.5
0.3
2.8
0.5
5.0
87.2
11.7
12.5
World %CHN/world
2.6
4.7
13.6
0.3
4.0
6.5
4.7
43.6
2.4
20.5
15.5
9.0
7.6
12.8
5.8
4.7
2.5
11.4
8.2
35.6
13.9
18.0
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
6.3
81.5
14.8
15.9
4.2
100.0
12.8
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
Note: see note on Table 1.
19
Table 3. Percentages in total costs of value added, labor remuneration and intermediates coming from advanced services sectors (in percentage in 2016) and variations for
the period 2004-2016 (in p.p.)
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
13.Construction
14.Water Transport
15.Air Transport
16.Communications
17.Finance
18.Insurance
19.Business services
20.Personal services
21.Other services
CHN
63.2
48.9
17.5
18.5
21.2
15.1
17.9
18.0
21.8
19.0
19.2
29.2
27.5
23.3
19.6
44.9
52.3
36.8
36.1
54.0
53.7
2016 (%)
EUR
54.0
53.5
29.8
32.1
35.4
21.6
31.9
22.0
25.2
26.9
34.0
35.5
42.2
12.4
14.7
44.4
45.4
32.3
47.1
57.0
57.6
Value added
Difference 2004-2016 (p.p)
USA
CHN
EUR
USA
41.7
4.0
-3.7
-16.0
66.5
1.9
1.5
14.5
30.5
4.5
0.8
1.5
31.4
0.5
0.5
-0.2
43.8
2.2
0.0
8.4
22.9
-0.3
-3.3
-2.0
35.5
-0.8
-1.4
2.2
22.2
1.8
0.2
0.4
41.7
0.0
-0.2
16.3
12.9
3.8
0.1
-13.9
45.7
0.2
-0.4
11.2
43.3
0.9
-0.9
7.0
51.0
-0.2
-0.1
8.8
36.7
-11.9
-1.5
22.8
24.7
0.4
-3.4
6.6
46.0
-1.1
0.1
1.6
64.3
-14.3
-0.7
18.1
47.7
9.4
0.1
15.5
63.6
-5.1
-0.4
16.1
36.0
1.1
-0.4
-21.5
65.1
-1.3
-0.9
6.5
CHN
37.8
18.4
8.7
11.7
12.8
7.1
8.3
10.2
15.1
12.3
11.3
13.3
20.7
13.6
13.5
21.2
28.8
31.1
24.7
39.9
33.8
Labor remuneration
Intermediates from advanced services
2016 (%)
Difference 2004-2016 (p.p)
2016 (%)
EUR
USA
CHN
EUR
USA
CHN
EUR
USA
31.8
15.5
3.2
-2.7
-19.0
2.3
7.1
13.7
17.7
15.6
3.4
-0.2
-2.3
11.2
11.5
7.6
15.3
18.6
2.4
0.3
3.6
2.9
11.4
8.8
21.4
24.9
-0.5
0.6
4.1
4.1
10.1
8.4
20.9
32.5
2.3
0.1
11.6
3.8
11.9
6.3
11.6
13.6
-0.3
-1.6
0.4
4.0
9.4
6.0
20.9
28.2
-1.0
-0.8
6.4
4.3
9.3
6.3
15.2
17.2
2.2
0.0
2.1
5.0
10.2
5.6
18.9
34.5
2.5
0.0
15.6
4.4
12.6
6.9
15.5
8.9
3.0
0.2
-6.3
6.9
16.8
11.3
23.9
35.7
0.8
-0.1
11.7
5.1
11.5
7.1
22.1
33.5
2.2
-0.3
11.0
4.5
10.7
8.1
24.0
44.8
0.5
0.2
21.0
5.1
11.2
11.3
6.4
29.4
-9.4
-1.1
21.8
22.4
19.3
28.1
11.7
20.3
0.5
-2.6
6.0
19.1
13.9
13.9
29.0
27.6
0.0
0.2
-1.2
24.0
36.2
33.5
32.5
58.6
-7.9
-0.2
25.9
24.5
48.0
28.7
25.0
43.8
6.7
0.1
18.9
43.0
60.8
45.4
30.6
58.5
-3.2
0.1
28.1
19.3
30.7
19.0
30.2
27.2
0.7
0.2
-2.8
6.7
14.2
19.7
33.0
43.5
-0.1
-0.1
10.5
10.6
13.3
13.3
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
20
Table 5. Imports in China, EU28 and US by demand type (2016, in percentage)
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
13.Construction
14.Water Transport
15.Air Transport
16.Communications
17.Finance
18.Insurance
19.Business services
20.Personal services
21.Other services
Private consumption
EUR
USA
CHN
38.6
3.5
33.4
0.0
2.0
0.1
56.7
54.2
35.1
72.4
14.6
62.8
22.7
5.2
14.6
16.4
27.0
5.4
0.2
1.7
3.3
38.6
10.0
37.9
10.5
12.2
8.2
20.3
5.0
16.7
9.5
14.7
2.8
40.0
68.0
4.5
1.6
0.4
0.0
7.3
13.2
1.5
2.0
6.8
33.0
32.4
29.5
19.8
11.3
18.2
24.5
44.8
47.5
47.1
16.7
3.7
1.6
43.2
51.7
58.9
30.2
21.8
43.9
CHN
95.7
99.9
64.9
85.3
93.9
94.6
98.7
51.0
36.8
86.4
62.4
89.7
6.2
83.8
91.5
67.6
87.0
52.5
67.8
49.5
65.2
Intermediates
EUR
65.7
98.0
43.3
37.1
83.8
81.0
96.1
46.0
49.4
56.9
61.2
57.9
20.5
86.6
67.0
70.5
81.8
52.8
72.5
35.6
44.9
USA
61.4
100.0
45.8
27.1
67.8
72.5
94.8
26.8
52.7
54.0
44.6
30.0
9.3
98.4
98.0
80.2
68.0
55.2
94.3
41.1
69.0
CHN
0.7
0.0
0.0
0.0
0.8
0.0
1.1
38.9
55.0
8.6
34.8
5.8
93.8
5.3
0.5
0.0
0.0
0.0
24.6
0.0
4.7
Investment
EUR
0.7
0.0
0.0
0.1
1.6
0.1
2.2
16.0
40.0
26.3
29.1
2.1
77.8
0.0
0.0
0.0
0.0
0.0
9.6
3.5
2.6
USA
0.0
0.0
0.0
0.5
9.5
0.2
1.9
34.5
35.1
25.6
40.6
2.0
90.3
0.0
0.0
0.0
7.5
0.0
2.0
0.0
0.4
Public consumption
EUR
USA
CHN
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
2.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.1
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.1
3.7
1.2
0.0
0.0
0.0
0.0
0.0
1.7
0.0
0.0
0.0
0.0
0.1
6.1
1.2
0.0
7.3
9.1
0.0
0.5
8.3
8.6
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
21
Table 6. Demand use of production in China, EU28 and US by demand type (2016, in percentage)
Private consumption
CHN
EUR
USA
1.Agriculture
24.7
14.8
14.8
2.Other primary
0.7
1.2
0.0
3.Food
41.9
45.7
54.7
4.Textiles
13.6
35.9
46.5
5.Wood and paper
4.5
12.8
15.8
6.Chemicals
6.2
9.3
18.8
7.Metals
0.3
1.8
1.5
8.Motor vehicles
9.9
15.5
28.9
9.Other transport
6.8
5.8
11.8
10.Electronics
3.3
8.2
6.6
11.Other machinery
2.5
4.7
8.6
12.Other manufactures
2.1
22.9
18.9
13.Construction
0.0
2.3
0.0
14.Water Transport
4.1
3.2
14.5
15.Air Transport
5.8
13.4
4.9
16.Communications
33.9
28.8
47.9
17.Finance
11.8
20.2
40.9
18.Insurance
47.3
49.8
57.2
19.Business services
1.6
3.2
5.7
20.Personal services
43.6
48.3
74.2
21.Other services
23.2
37.0
52.2
Intermediates
CHN
EUR
USA
69.9
61.3
63.2
98.3
68.0
92.6
55.0
29.8
37.0
61.1
27.3
44.6
83.2
60.1
70.1
85.4
44.4
59.2
91.3
57.6
82.1
46.2
19.2
23.9
28.0
35.4
32.0
49.1
28.6
53.6
49.0
28.5
35.6
83.2
51.6
63.3
5.9
25.5
27.6
89.9
73.2
78.0
84.2
38.2
79.2
64.9
62.8
47.5
86.2
71.3
53.4
49.9
31.9
39.1
64.7
74.0
84.7
47.0
31.0
22.9
47.7
25.0
21.5
CHN
4.2
0.0
0.0
0.0
0.7
0.0
2.0
38.5
45.8
5.7
30.2
2.0
93.8
3.0
0.4
0.0
0.0
0.0
25.2
0.0
2.6
Investment
EUR
USA
0.8
0.0
0.1
0.0
0.0
0.0
0.1
1.5
0.5
8.0
0.0
0.1
2.4
1.9
6.3
28.1
10.2
22.4
9.2
22.7
13.1
29.1
1.9
4.5
69.7
71.9
0.1
4.5
0.0
3.2
0.0
2.2
0.0
2.4
0.0
0.0
10.9
4.6
3.3
0.0
0.3
2.1
Public consumption
CHN
EUR
USA
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.2
0.3
0.0
0.0
0.0
0.0
0.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
0.6
0.0
1.0
0.0
0.0
0.0
0.0
0.0
1.7
0.0
0.0
0.0
0.0
0.0
6.2
1.4
0.0
7.4
8.0
0.2
25.3
33.3
22.9
CHN
1.2
1.0
3.1
25.3
11.6
8.4
6.4
5.4
19.3
42.0
18.3
12.7
0.3
1.0
8.6
1.1
0.3
2.8
2.3
2.0
1.3
Exports
EUR
USA
23.2
22.0
30.7
7.3
24.3
8.2
36.4
7.1
26.6
6.1
45.4
21.9
38.2
14.6
58.9
19.1
48.7
33.8
54.0
17.1
53.7
26.8
23.6
13.3
2.4
0.4
22.9
3.0
48.4
12.8
8.4
2.4
8.5
3.3
18.3
3.7
10.5
5.0
9.4
2.6
4.4
1.3
Source: Authors’ estimations based on Narayanan et al. (2016) and IMF (2016).
22
Table 6. Short term and long term impact of reductions in FDI barriers on main aggregate variables in China (% change with respect to the initial data)
Welfare
GDP
Wages
Capital remuneration
Exports
Imports
CPI
Short run
50%
25%
0.31
0.14
0.09
0.04
0.09
0.04
0.06
0.03
1.06
0.47
0.79
0.36
-0.26
-0.12
10%
0.05
0.02
0.02
0.01
0.18
0.14
-0.05
Long run
50%
25%
0.24
0.11
0.19
0.09
0.17
0.08
0.19
0.09
1.12
0.50
0.85
0.39
-0.28
-0.13
10%
0.04
0.03
0.03
0.03
0.19
0.15
-0.05
Source: Authors’ estimations.
23
Table 7. 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)
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
13.Construction
14.Water Transport
15.Air Transport
16.Communications
17.Finance
18.Insurance
19.Business services
20.Personal services
21.Other services
All manufactures
All services
Total
Output
Short run
Long run
50% 25% 10% 50% 25% 10%
0.37 0.17 0.06 0.39 0.18 0.07
1.07 0.48 0.18 1.28 0.56 0.21
0.34 0.15 0.06 0.33 0.15 0.06
0.47 0.21 0.08 0.50 0.22 0.08
0.37 0.17 0.06 0.48 0.21 0.08
0.55 0.25 0.09 0.64 0.28 0.11
0.54 0.24 0.09 0.73 0.32 0.12
0.39 0.17 0.07 0.54 0.24 0.09
0.50 0.23 0.09 0.68 0.30 0.11
0.72 0.32 0.12 0.78 0.35 0.13
0.66 0.29 0.11 0.85 0.38 0.14
0.41 0.18 0.07 0.60 0.27 0.10
0.06 0.03 0.01 0.31 0.14 0.05
-0.19 -0.08 -0.03 -0.15 -0.06 -0.02
-4.40 -1.89 -0.69 -4.36 -1.87 -0.68
0.02 0.01 0.00 0.05 0.02 0.01
-0.09 -0.04 -0.01 -0.04 -0.01 0.00
-0.56 -0.18 -0.05 -0.56 -0.18 -0.05
-1.01 -0.44 -0.16 -0.92 -0.40 -0.15
0.26 0.12 0.05 0.28 0.13 0.05
0.20 0.09 0.03 0.23 0.11 0.04
0.48 0.21 0.08 0.61 0.27 0.10
-0.06 -0.02 -0.01 -0.02 0.00 0.00
0.32 0.14 0.06 0.42 0.19 0.07
Exports
Short run
Long run
50% 25% 10% 50% 25% 10%
0.82 0.37 0.14 0.83 0.37 0.14
2.76 1.21 0.45 3.22 1.41 0.52
1.04 0.46 0.17 1.10 0.49 0.18
0.56 0.25 0.09 0.58 0.26 0.10
1.15 0.51 0.19 1.23 0.55 0.21
0.98 0.44 0.16 1.02 0.45 0.17
1.37 0.61 0.23 1.56 0.69 0.26
1.23 0.55 0.20 1.23 0.55 0.20
1.79 0.79 0.30 1.92 0.85 0.32
0.76 0.34 0.13 0.77 0.35 0.13
1.59 0.70 0.26 1.74 0.77 0.29
1.20 0.53 0.20 1.19 0.53 0.20
0.90 0.40 0.15 0.95 0.43 0.16
0.95 0.43 0.16 1.01 0.45 0.17
1.04 0.64 0.27 1.10 0.66 0.28
0.97 0.43 0.16 1.09 0.48 0.18
1.22 0.54 0.20 1.30 0.57 0.21
1.68 0.82 0.33 1.70 0.84 0.34
0.67 0.32 0.13 0.74 0.35 0.14
1.06 0.47 0.18 1.09 0.49 0.18
1.12 0.50 0.19 1.20 0.54 0.20
1.07 0.48 0.18 1.13 0.50 0.19
1.02 0.47 0.18 1.09 0.50 0.19
1.06 0.47 0.18 1.13 0.50 0.19
Imports
Short run
Long run
50% 25% 10% 50% 25% 10%
0.14 0.06 0.03 0.17 0.08 0.03
-0.13 -0.05 -0.02 -0.14 -0.06 -0.02
0.02 0.01 0.00 -0.02 0.00 0.00
0.12 0.06 0.02 0.15 0.07 0.03
-0.17 -0.07 -0.03 -0.09 -0.04 -0.01
-0.20 -0.08 -0.03 -0.13 -0.05 -0.02
-0.06 -0.02 -0.01 0.05 0.02 0.01
-0.64 -0.28 -0.10 -0.45 -0.20 -0.07
-0.55 -0.24 -0.09 -0.42 -0.18 -0.07
0.09 0.04 0.02 0.19 0.09 0.03
-0.25 -0.11 -0.04 -0.13 -0.05 -0.02
-0.67 -0.30 -0.11 -0.42 -0.19 -0.07
-0.14 -0.06 -0.02 0.10 0.05 0.02
30.36 13.71 5.17 30.41 13.73 5.18
32.90 14.69 5.51 32.94 14.71 5.51
1.33 0.70 0.29 1.27 0.67 0.28
10.71 5.19 2.04 10.73 5.20 2.04
6.50 3.08 1.19 6.49 3.08 1.19
28.18 12.92 4.91 28.26 12.96 4.92
0.00 0.00 0.00 0.02 0.01 0.00
-0.12 -0.05 -0.02 -0.11 -0.05 -0.02
-0.16 -0.07 -0.02 -0.09 -0.04 -0.01
8.71 3.99 1.51 8.74 4.00 1.52
0.79 0.36 0.14 0.85 0.39 0.15
Prices
Short run
Long run
50% 25% 10% 50% 25% 10%
-0.14 -0.06 -0.02 -0.13 -0.06 -0.02
-0.27 -0.12 -0.04 -0.32 -0.14 -0.05
-0.18 -0.08 -0.03 -0.20 -0.09 -0.03
-0.02 -0.01 0.00 -0.01 0.00 0.00
-0.20 -0.09 -0.03 -0.22 -0.10 -0.04
-0.07 -0.03 -0.01 -0.06 -0.03 -0.01
-0.21 -0.09 -0.03 -0.24 -0.11 -0.04
-0.15 -0.07 -0.02 -0.12 -0.05 -0.02
-0.22 -0.10 -0.04 -0.24 -0.10 -0.04
-0.04 -0.02 -0.01 -0.03 -0.01 -0.01
-0.23 -0.10 -0.04 -0.25 -0.11 -0.04
-0.12 -0.05 -0.02 -0.09 -0.04 -0.01
-0.21 -0.09 -0.04 -0.22 -0.10 -0.04
-0.31 -0.14 -0.05 -0.34 -0.15 -0.06
-0.70 -0.32 -0.12 -0.71 -0.33 -0.13
-0.31 -0.14 -0.05 -0.35 -0.16 -0.06
-0.42 -0.19 -0.07 -0.46 -0.20 -0.07
-0.57 -0.27 -0.11 -0.58 -0.27 -0.11
-0.37 -0.16 -0.06 -0.38 -0.17 -0.06
-0.25 -0.11 -0.04 -0.26 -0.11 -0.04
-0.27 -0.12 -0.04 -0.29 -0.13 -0.05
-0.16 -0.07 -0.03 -0.17 -0.07 -0.03
-0.40 -0.18 -0.07 -0.42 -0.19 -0.07
-0.25 -0.11 -0.04 -0.26 -0.12 -0.04
Source: Authors’ estimations.
24
Table 8. Short term and long term impact of reductions in FDI barriers on main aggregate variables across regions (% change with respect to the initial data)
Short run
Long run
Short run
GDP
Long run
Short run
Wages
Long run
Short run
Capital rem
Long run
Short run
Exports
Long run
Short run
Imports
Long run
Short run
CPI
Long run
Welfare
CHN
0.31
0.24
0.09
0.19
0.09
0.17
0.06
0.19
1.06
1.12
0.79
0.85
-0.26
-0.28
EUR
0.08
0.10
0.04
0.07
0.02
0.05
0.07
0.10
0.19
0.21
0.10
0.12
0.02
0.01
GBR
0.10
0.12
0.08
0.09
0.03
0.05
0.16
0.19
0.07
0.09
0.06
0.08
0.04
0.04
USA
0.02
0.02
0.01
0.02
0.01
0.01
0.03
0.03
0.13
0.14
0.05
0.05
num
num
IND
0.06
0.07
0.04
0.05
0.07
0.08
0.03
0.04
0.48
0.50
0.04
0.05
0.08
0.08
JPN
0.02
0.03
0.01
0.02
0.00
0.01
0.03
0.04
0.03
0.04
0.03
0.04
-0.02
-0.01
LAC
0.04
0.05
0.02
0.03
0.01
0.02
0.03
0.05
0.05
0.06
0.05
0.06
0.01
0.01
MEN
0.06
0.06
0.03
0.03
0.03
0.03
0.03
0.03
0.10
0.10
0.07
0.07
0.02
0.01
OAC
0.08
0.08
0.04
0.05
0.03
0.03
0.06
0.06
0.14
0.15
0.01
0.03
0.02
0.02
SEA
0.07
0.08
0.04
0.06
0.02
0.04
0.06
0.08
0.07
0.09
0.03
0.05
0.01
0.01
SSA
0.02
0.03
0.01
0.02
0.01
0.02
0.01
0.02
0.10
0.11
0.03
0.04
0.01
0.00
Source: Authors’ estimations.
Note: See note on Table 1.
25
Table 9. Short term and long term impact of 50% reductions in FDI barriers on output across regions (% change with respect to the initial data)
CHN
SR
LR
1.Agriculture
0.37
0.39
2.Other primary
1.07
1.28
3.Food
0.34
0.33
4.Textiles
0.47
0.50
5.Wood and paper
0.37
0.48
6.Chemicals
0.55
0.64
7.Metals
0.54
0.73
8.Motor vehicles
0.39
0.54
9.Other transport
0.50
0.68
10.Electronics
0.72
0.78
11.Other machinery
0.66
0.85
12.Other manufactures 0.41
0.60
13.Construction
0.06
0.31
14.Water Transport
-0.19 -0.15
15.Air Transport
-4.40 -4.36
16.Communications
0.02
0.05
17.Finance
-0.09 -0.04
18.Insurance
-0.56 -0.56
19.Business services
-1.01 -0.92
20.Personal services
0.26
0.28
21.Other services
0.20
0.23
All manufactures
0.48
0.61
All services
-0.06 -0.02
Total
0.32
0.42
EUR
SR
LR
0.04
0.07
-0.10 -0.05
0.05
0.08
-0.03 -0.01
-0.01 0.01
-0.02 0.01
-0.15 -0.12
-0.05 -0.01
-0.11 -0.08
-0.25 -0.19
-0.21 -0.19
-0.07 -0.03
0.01
0.06
-0.01 0.02
0.52
0.54
0.06
0.09
0.07
0.10
0.15
0.17
0.14
0.17
0.06
0.08
0.04
0.05
-0.06 -0.03
0.07
0.09
0.01
0.04
GBR
SR
LR
0.02
0.04
-0.31 -0.19
0.05
0.07
-0.07 -0.05
-0.06 -0.04
-0.08 -0.07
-0.30 -0.29
-0.10 -0.08
-0.26 -0.24
-0.24 -0.21
-0.34 -0.32
-0.11 -0.08
0.01
0.05
-0.06 -0.04
0.03
0.04
0.05
0.07
0.01
0.02
0.05
0.06
0.14
0.16
0.07
0.08
0.05
0.06
-0.11 -0.08
0.06
0.08
0.00
0.02
USA
SR
LR
0.03
0.03
0.02
0.01
0.02
0.03
-0.02 -0.02
-0.02 -0.01
0.00
0.00
-0.08 -0.08
-0.01 0.01
-0.04 -0.04
-0.30 -0.26
-0.12 -0.12
-0.11 -0.09
0.00
0.01
0.03
0.03
0.14
0.15
0.02
0.03
0.02
0.03
0.04
0.04
0.06
0.06
0.02
0.02
0.01
0.01
-0.04 -0.04
0.02
0.02
0.00
0.00
IND
SR
LR
0.02
0.02
-0.55 -0.52
0.00
0.01
-0.11 -0.10
-0.08 -0.07
-0.15 -0.14
-0.28 -0.26
-0.10 -0.08
-0.23 -0.22
-0.34 -0.31
-0.28 -0.26
-0.16 -0.13
0.01
0.04
-0.11 -0.09
0.54
0.54
0.01
0.02
0.01
0.02
0.26
0.27
0.83
0.83
-0.13 -0.13
0.01
0.02
-0.13 -0.11
0.09
0.09
-0.03 -0.02
JPN
SR
LR
0.02
0.03
0.10
0.07
0.02
0.02
-0.02 -0.01
-0.02 -0.01
-0.01 0.00
-0.06 -0.06
0.00
0.01
0.02
0.02
-0.08 -0.06
-0.13 -0.13
-0.06 -0.04
0.00
0.02
0.01
0.02
0.25
0.26
0.01
0.02
0.01
0.01
0.04
0.05
0.02
0.02
0.02
0.03
0.01
0.01
-0.03 -0.03
0.01
0.02
-0.01 0.00
LAC
SR
LR
0.02
0.03
-0.07 -0.06
0.03
0.04
-0.03 -0.02
-0.03 -0.02
-0.03 -0.02
-0.15 -0.14
-0.03 -0.02
-0.09 -0.08
-0.15 -0.12
-0.21 -0.20
-0.06 -0.04
0.00
0.02
0.04
0.05
0.18
0.18
0.03
0.04
0.02
0.03
0.09
0.10
0.09
0.09
0.03
0.04
0.02
0.02
-0.04 -0.03
0.03
0.03
0.00
0.00
MEN
SR
LR
0.04
0.05
-0.06 -0.07
0.05
0.05
-0.03 -0.01
-0.02 -0.02
-0.02 -0.01
-0.11 -0.11
-0.02 -0.01
-0.06 -0.06
-0.15 -0.13
-0.13 -0.13
-0.03 -0.02
0.00
0.00
0.15
0.15
0.41
0.41
0.05
0.05
0.06
0.06
0.16
0.17
0.22
0.22
0.05
0.05
0.02
0.02
-0.04 -0.03
0.05
0.05
0.00
0.01
OAC
SR
LR
0.01
0.02
-0.13 -0.14
0.03
0.03
-0.05 -0.04
-0.07 -0.07
-0.07 -0.06
-0.20 -0.20
-0.05 -0.04
-0.13 -0.12
-0.16 -0.14
-0.32 -0.30
-0.10 -0.09
0.00
0.02
0.04
0.06
0.59
0.60
0.07
0.07
0.05
0.06
0.15
0.15
0.16
0.17
0.05
0.06
0.03
0.03
-0.10 -0.09
0.07
0.07
-0.01 0.00
SEA
SR
LR
0.04
0.05
-0.09 -0.05
0.04
0.05
-0.03 -0.02
-0.07 -0.06
-0.06 -0.04
-0.14 -0.12
-0.02 0.00
-0.06 -0.04
-0.12 -0.09
-0.24 -0.21
-0.08 -0.05
0.00
0.03
0.13
0.15
0.43
0.44
0.06
0.07
0.05
0.05
0.09
0.10
0.17
0.18
0.06
0.07
0.03
0.04
-0.05 -0.03
0.06
0.07
-0.01 0.01
SSA
SR
LR
0.03
0.03
-0.02 -0.01
0.03
0.03
-0.03 -0.02
-0.02 -0.02
-0.01 0.00
-0.13 -0.12
-0.01 0.00
-0.13 -0.12
-0.13 -0.11
-0.10 -0.10
-0.07 -0.05
0.00
0.01
0.08
0.08
0.34
0.34
0.03
0.03
0.02
0.03
0.05
0.05
0.08
0.08
0.02
0.02
0.00
0.01
-0.03 -0.02
0.02
0.03
0.00
0.01
Source: Authors’ estimations.
Note: See note on Table 1.
26
Table 10. Short term and long term impact of 50% reductions in FDI barriers on exports across regions (% change with respect to the initial data)
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
13.Construction
14.Water Transport
15.Air Transport
16.Communications
17.Finance
18.Insurance
19.Business services
20.Personal services
21.Other services
All manufactures
All services
Total
CHN
SR
LR
0.82
0.83
2.76
3.22
1.04
1.10
0.56
0.58
1.15
1.23
0.98
1.02
1.37
1.56
1.23
1.23
1.79
1.92
0.76
0.77
1.59
1.74
1.20
1.19
0.90
0.95
0.95
1.01
1.04
1.10
0.97
1.09
1.22
1.30
1.68
1.70
0.67
0.74
1.06
1.09
1.12
1.20
1.07
1.13
1.02
1.09
1.06
1.12
EUR
SR
LR
0.00
0.01
-0.20 -0.14
0.01
0.03
-0.09 -0.09
-0.17 -0.15
-0.09 -0.07
-0.29 -0.29
-0.20 -0.14
-0.25 -0.23
-0.26 -0.22
-0.39 -0.37
-0.18 -0.15
-0.09 -0.05
0.14
0.17
1.66
1.68
0.11
0.14
0.21
0.23
1.07
1.10
1.96
1.99
-0.01 0.02
-0.07 -0.06
-0.21 -0.19
0.84
0.86
0.19
0.21
GBR
SR
LR
-0.11 -0.08
-0.53 -0.32
-0.12 -0.10
-0.16 -0.15
-0.32 -0.30
-0.13 -0.11
-0.40 -0.39
-0.20 -0.18
-0.44 -0.42
-0.26 -0.23
-0.54 -0.52
-0.24 -0.22
-0.16 -0.12
0.03
0.04
0.16
0.17
-0.07 -0.06
0.10
0.11
0.18
0.19
0.95
0.97
-0.11 -0.09
-0.16 -0.14
-0.30 -0.27
0.41
0.43
0.07
0.09
USA
SR
LR
0.04
0.06
0.06
0.04
0.06
0.06
0.04
0.05
-0.07 -0.07
-0.01 0.00
-0.15 -0.17
-0.03 -0.01
-0.11 -0.10
-0.34 -0.28
-0.24 -0.24
-0.03 0.00
0.02
0.03
3.32
3.33
1.33
1.34
0.41
0.39
0.49
0.49
0.65
0.65
1.20
1.20
0.09
0.09
0.05
0.05
-0.10 -0.09
0.55
0.55
0.13
0.14
IND
SR
LR
-0.37 -0.36
-1.03 -1.01
-0.37 -0.36
-0.39 -0.37
-0.55 -0.55
-0.19 -0.18
-0.61 -0.58
-0.29 -0.27
-0.61 -0.58
-0.33 -0.30
-0.70 -0.67
-0.40 -0.38
-0.32 -0.29
0.05
0.05
3.48
3.49
-0.08 -0.08
-0.05 -0.04
2.72
2.72
2.18
2.18
-0.26 -0.25
-0.38 -0.37
-0.40 -0.39
1.54
1.55
0.48
0.50
JPN
SR
LR
0.14
0.15
0.07
0.00
0.14
0.13
0.09
0.10
-0.09 -0.08
-0.03 -0.02
-0.09 -0.09
-0.01 0.00
0.02
0.02
-0.06 -0.03
-0.16 -0.15
-0.06 -0.02
0.04
0.08
0.43
0.43
1.77
1.77
0.26
0.25
0.12
0.12
1.86
1.85
1.11
1.11
0.09
0.09
0.04
0.04
-0.08 -0.06
0.62
0.62
0.03
0.04
LAC
SR
LR
0.01
0.03
-0.09 -0.08
0.02
0.03
-0.08 -0.06
-0.21 -0.19
-0.07 -0.06
-0.24 -0.22
-0.04 -0.03
-0.15 -0.12
-0.15 -0.13
-0.32 -0.32
-0.14 -0.12
-0.02 0.01
0.28
0.28
1.47
1.48
0.12
0.12
0.41
0.41
1.95
1.95
1.36
1.37
0.02
0.02
-0.02 -0.01
-0.12 -0.11
0.60
0.60
0.05
0.06
MEN
SR
LR
0.00
0.03
-0.10 -0.11
0.00
0.01
-0.02 0.01
-0.14 -0.11
-0.09 -0.07
-0.21 -0.19
0.03
0.06
-0.19 -0.16
-0.20 -0.14
-0.24 -0.22
-0.04 0.01
-0.07 -0.01
1.78
1.79
2.10
2.12
0.10
0.11
1.05
1.07
1.12
1.12
1.75
1.77
0.00
0.02
-0.03 -0.02
-0.10 -0.10
0.82
0.83
0.10
0.10
OAC
SR
LR
-0.02 -0.01
-0.15 -0.17
-0.03 -0.04
-0.07 -0.06
-0.26 -0.26
-0.12 -0.11
-0.22 -0.22
-0.09 -0.08
-0.19 -0.19
-0.14 -0.11
-0.40 -0.37
-0.15 -0.12
-0.07 -0.05
1.06
1.06
3.26
3.27
0.21
0.20
0.34
0.34
1.33
1.32
1.74
1.75
-0.05 -0.05
-0.13 -0.14
-0.19 -0.18
0.87
0.87
0.14
0.15
SEA
SR
LR
-0.02 -0.03
-0.13 -0.08
-0.01 -0.02
0.00
0.01
-0.17 -0.16
-0.12 -0.10
-0.20 -0.18
0.02
0.03
-0.07 -0.03
-0.11 -0.08
-0.25 -0.21
-0.09 -0.05
-0.02 0.02
1.22
1.23
1.65
1.66
0.10
0.11
0.35
0.36
0.53
0.53
1.14
1.15
0.03
0.04
0.00
0.00
-0.11 -0.08
0.54
0.55
0.07
0.09
SSA
SR
LR
0.08
0.09
-0.03 -0.02
0.10
0.10
0.05
0.07
-0.06 -0.05
-0.01 0.01
-0.15 -0.13
0.04
0.05
0.01
0.05
-0.13 -0.10
-0.15 -0.13
-0.05 -0.01
0.00
0.04
0.81
0.82
1.60
1.61
0.15
0.15
0.47
0.47
1.36
1.36
1.99
2.00
0.03
0.03
0.01
0.01
-0.04 -0.03
0.67
0.68
0.10
0.11
Source: Authors’ estimations.
Note: See note on Table 1.
27
Table 11. Sensitivity analysis: Short run impact on welfare of 50 % cuts in FDI barriers
Reference
θr (i) (25%)
θr (i) (75%)
25% Profit repatiation
75% Profit repatiation
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)
CHN
0.31
0.22
0.41
0.21
0.79
0.31
0.30
0.32
0.29
0.27
0.40
0.32
0.30
0.30
0.24
1.47
0.36
0.28
0.45
0.17
0.31
0.31
EUR
0.08
0.05
0.11
0.07
0.10
0.08
0.07
0.08
0.07
0.07
0.08
0.08
0.07
0.08
0.05
0.14
0.08
0.07
0.10
0.06
0.07
0.08
GBR
0.10
0.08
0.13
0.11
0.10
0.10
0.11
0.10
0.11
0.10
0.11
0.11
0.10
0.11
0.09
0.10
0.11
0.10
0.11
0.10
0.10
0.10
USA
0.02
0.01
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
IND
0.06
0.03
0.10
0.06
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.04
0.06
0.06
0.06
0.06
0.06
0.06
JPN
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
LAC
0.04
0.03
0.05
0.03
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.02
0.05
0.04
0.04
0.04
0.04
0.04
0.04
MEN
0.06
0.04
0.08
0.05
0.08
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.04
0.09
0.06
0.06
0.06
0.06
0.06
0.06
OAC
0.08
0.04
0.12
0.08
0.07
0.07
0.08
0.08
0.08
0.08
0.07
0.08
0.08
0.08
0.06
0.07
0.08
0.08
0.09
0.07
0.08
0.08
SEA
0.07
0.05
0.09
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.06
0.08
0.07
0.07
0.08
0.06
0.07
0.07
SSA
0.02
0.01
0.04
0.02
0.02
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
Source: Authors’ estimations.
Parameters definitions
θr (i)
Shares of rents in services sectors captured by domestic agents
σ (D, M)
Armington elasticity of substitution between imports and domestic goods in CRTS sectors
σ (M, M)
Armington elasticity of substitution between imports from different regions in CRTS sectors
θm (i)
Share of value added in multinational firms in sector i due to specialized primary factor imports in the benchmark
σ (A1,…,An)
Elasticity of substitution in intermediate production between composite Armington aggregate goods
σ (qi, qj) (goods)
Elasticity of substitution between firm varieties in imperfectly competitive goods sectors
σ (qi, qj) (services) Elasticity of substitution between firm varieties in imperfectly competitive services sectors
ε (fi)
Elasticity of supply with respect to price of output in national firms and multinationals in services
σ (va, bs)
Elasticity of substitution between value-added and business services
σ (L,K)
Elasticity of substitution between primary factors of production in value added
28
Figure 1. Costs related to FDI barriers (% of total costs)
00
10
20
30
40
50
60
70
80
14.Water Transport
15.Air Transport
16.Communications
CHN
EUR
USA
17.Finance
18.Insurance
19.Business services
Source: Jafari and Tarr (2014).
29
Appendix 1. Mapping of model sectors to Nace Rev 2, Isic Rev 3.1 and GTAP classifications
Sectors
1.Agriculture
2.Other primary
Nace Rev 2
A Agriculture, forestry and fishery products
B Mining and quarrying
C10 Manufacture of food products
3.Food
C11 Manufacture of beverages
C12 Manufacture of tobacco products
C13 Manufacture of textiles
4.Textiles
C14 Manufacture of wearing apparel
C15 Manufacture of leather and related products
C16 Manufacture of wood and of products of wood, cork, straw and plaiting materials
5.Wood and paper
C17 Manufacture of paper and paper products
C18 Printing and reproduction of recorded media
C19 Manufacture of coke and refined petroleum products
C20 Manufacture of chemicals and chemical products
6.Chemicals
C21 Manufacture of basic pharmaceutical products and pharmaceutical preparations
C22 Manufacture of rubber and plastic products
12.Other manufactures C23 Manufacture of other non-metallic mineral products
C24 Manufacture of basic metals
7.Metals
C25 Manufacture of fabricated metal products, except machinery and equipment
C26 Manufacture of computer, electronic and optical products
10.Electronics
C27 Manufacture of electrical equipment
11.Other machinery
C28 Manufacture of machinery and equipment n.e.c.
C29 Manufacture of motor vehicles, trailers and semi-trailers
8. Motor vehicles
9.Other transport
C30 Manufacture of other transport equipment
C31 Manufacture of furniture
12.Other manufactures
C32 Other manufacturing
11.Other machinery
C33 Repair and installation of machinery and equipment
D Electricity, gas, steam and air conditioning supply
D35 Electricity, gas, steam and air conditioning supply
E Water supply; sewerage, waste management and remediation activities
21.Other services
E36 Water collection, treatment and supply
E37 Sewerage
E38 Waste collection, treatment and disposal activities; materials recovery
E39 Remediation activities and other waste management services
F Construction
F41 Construction of buildings
13.Construction
F42 Civil engineering
F43 Specialised construction activities
G Wholesale and retail trade; repair of motor vehicles and motorcycles
G45 Wholesale and retail trade and repair of motor vehicles and motorcycles
21.Other services
G454 Sale, maintenance and repair of motorcycles and related parts and accessories
G47 Retail trade, except of motor vehicles and motorcycles
H49 Land transport and transport via pipelines
14.Water Transport
H50 Water transport
15.Air Transport
H51 Air transport
21.Other services
H52 Warehousing and support activities for transportation
16.Communications
H53 Postal and courier activities
I55 Accommodation
21.Other services
I56 Food and beverage service activities
19.Business services
J582 Software publishing
J59 Motion picture, video and television programme production, sound recording
20.Personal services
J60 Programming and broadcasting activities
16.Communications
J61 Telecommunications
J62 Computer programming, consultancy and related activities
19.Business services
J63 Information service activities
17.Finance
K64 Financial service activities, except insurance and pension funding
18.Insurance
K65 Insurance, reinsurance and pension funding, except compulsory social security
17.Finance
K66 Activities auxiliary to financial services and insurance activities
L68 Real estate activities
M69 Legal and accounting activities
M70 Activities of head offices; management consultancy activities
M71 Architectural and engineering activities; technical testing and analysis
M72 Scientific research and development
19.Business services
M73 Advertising and market research
M74 Other professional, scientific and technical activities
M75 Veterinary activities
N77 Rental and leasing activities
N78 Employment activities
21.Other services
N79 Travel agency, tour operator reservation service and related activities
N80 Security and investigation activities
19.Business services
N81 Services to buildings and landscape activities
N82 Office administrative, office support and other business support activities
O - Public administration and defence; compulsory social security
21.Other services
P - Education
Q - Human health and social work activities
R - Arts, entertainment and recreation
S - Other services activities
20.Personal services
S95 Repair of computers and personal and household goods
T - Activities of households as employers; undifferentiated goods and services
Isic Rev 3.1
GTAP
ISIC 01-05
ISIC 10-14
1-14
15-18
ISIC 15-16
19-26
ISIC 17-19
27-29
ISIC 20-22
30-31
ISIC 24-25
32,33
ISIC 23, 26
34,39,42
ISIC 27-28
35,36,37
ISIC 30, 32
40
ISIC 29, 31, 33
ISIC 34
ISIC 35
41
38
39
ISIC 23, 26
34,39,42
ISIC 29, 31, 33
41
ISIC 40,41,50,51,52,63,75,80,85,90
43,44,45,47
48,56,57
ISIC 45
46
ISIC 40,41,50,51,52,63,75,80,85,90
43,44,45,47
48,56,57
ISIC 61
ISIC 62
ISIC 40,41,50,51,52,63,75,80,85,90
ISIC 70-74
ISIC 40,41,50,51,52,63,75,80,85,90
ISIC 40,41,50,51,52,63,75,80,85,90
ISIC 91-93
ISIC 91-93
ISIC 70-74
ISIC 91-93
ISIC 65,67
ISIC 66
ISIC 65,67
49
50
43,44,45,47 48,56,5
51
43,44,45,47
48,56,57
54
55
51
54
52
53
52
ISIC 91-93
54
ISIC 40,41,50,51,52,63,75,80,85,90
43,44,45,47 48,56,5
ISIC 91-93
54
ISIC 40,41,50,51,52,63,75,80,85,90
43,44,45,47
48,56,57
ISIC 91-93
55
30
Appendix 2. Mapping of model regions
Latin America and the
Caribbean
(Latin America, LAC)
Antigua and Barbuda
Argentina
The Bahamas
Barbados
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Dominica
Dominican Republic
Ecuador
El Salvador
Grenada
Guatemala
Guyana
Haiti
Honduras
Jamaica
Mexico
Nicaragua
Panama
Paraguay
Peru
St. Kitts and Nevis
St. Lucia
St.Vincent & the Grenadines
Suriname
Trinidad and Tobago
Uruguay
Venezuela
Middle East, North Africa,
Afghanistan and Pakistan
(Middle-East, MEN)
Afghanistan
Algeria
Armenia
Azerbaijan
Bahrain
Belarus
Djibouti
Egypt
Georgia
Iran
Iraq
Jordan
Kazakhstan
Kyrgyzstan
Kuwait
Lebanon
Libya
Mauritania
Morocco
Oman
Pakistan
Qatar
Russia
Rest of Eastern Europe
Rest of Former Soviet Union
Saudi Arabia
1
Sudan
2
Syria
Tunisia
Turkey
Ukraine
United Arab Emirates
Yemen
Sub-Saharan Africa
(Sub-Saharan A., SSA)
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cabo Verde
Central African Republic
Chad
Comoros
Dem. Rep. of the Congo
Republic of Congo
Côte d'Ivoire
Equatorial Guinea
Eritrea
Ethiopia
Gabon
The Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
São Tomé and Príncipe
Senegal
Seychelles
Sierra Leone
South Africa
South Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Emerging and Developing Asia Other advanced countries
(Southeast Asia, SEA)
Bangladesh
Bhutan
Brunei Darussalam
Cambodia
Fiji
Indonesia
Kiribati
Lao P.D.R.
Malaysia
Maldives
Marshall Islands
Micronesia
Mongolia
Myanmar
Nepal
Palau
Papua New Guinea
Philippines
Samoa
Solomon Islands
Sri Lanka
Thailand
Timor-Leste
Tonga
Tuvalu
Vanuatu
Vietnam
OAC
Hong Kong SAR
Iceland
Israel
Korea
New Zealand
Norway
Singapore
San Marino
Switzerland
Taiwan Province of China
31