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PISSN 2508-1640 EISSN 2508-1667 an open access journal
East Asian Economic Review vol. 21, no.4 (December 2017) 343-384
http://dx.doi.org/10.11644/KIEP.EAER.2017.21.4.334
ⓒ Korea Institute for International Economic Policy
Quantifying the Comprehensive and Progressive Agreement for
Trans-Pacific Partnership*†
Dan Ciuriak Ciuriak Consulting Inc.
C.D. Howe Institute
Centre for International Governance Innovation (CIGI)
BKP Development Research & Consulting GmbH
Jingliang Xiao Infinite-Sum Modeling Inc.
Ali Dadkhah Ciuriak Consulting Inc.
We assess the outcomes for the negotiating parties in the Trans-Pacific Partnership if the
remaining eleven parties go ahead with the agreement as negotiated without the United
States, as compared to the outcomes under the original twelve-member agreement signed in
October 2016. We find that the eleven-party agreement, now renamed as the Comprehensive
and Progressive Agreement for Trans-Pacific Partnership (CPTPP), is a much smaller
deal than the twelve-party one, but that some parties do better without the United States
in the deal, in particular those in the Western Hemisphere – Canada, Mexico, Chile, and
Peru. For the politically relevant medium term, the United States stands to be less well-
off outside the TPP than inside. Since provisional deals can be in place for a long time,
the results of this study suggest that the eleven parties are better off to implement the
CPTPP, leaving aside the controversial governance elements, the implications of which
for national interests are unclear and which, in any event, may be substantially affected
by parallel bilateral negotiations between individual CPTPP parties and the United States.
Keywords: Trans-Pacific Partnership, TPP, CPTPP, United States, CGE Modelling
JEL Classification: F02, F13, F15
* Acknowledgements: The present study updates the results for the TPP11 reported in Dade
et al. (2017) and the results for the TPP12 reported in Ciuriak et al. (2016a, 2016b) for the
treatment of the services impacts by adopting more recent estimates of the height of barriers
ID
ID
ID
344 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
I. INTRODUCTION †
Following the withdrawal by the Trump Administration of the United States
from the Trans-Pacific Partnership (TPP) agreement, the other eleven TPP parties
(“the Eleven”) entered into negotiations to implement the agreement largely as
negotiated, but without the United States. Agreement on the core elements of a
revised TPP – renamed the Comprehensive Progressive Agreement for Trans-Pacific
Partnership (CPTPP) – was reached on the margins of the Asia-Pacific Economic
Cooperation (APEC) summit in Da Nang in November 2017.
The original TPP aspired to rewrite the ground rules of international commerce
for the 21st Century, modelled on the US economic governance regime. The CPTPP
preserves the original text of the TPP agreed in October 2016, but suspends a
number of provisions and leaves open a number of specific issues to be resolved.
The suspended provisions impact on the CPTPP regime in the following areas:
Express shipments;
Investment (in particular, the investor-state dispute settlement or ISDS
mechanism);
The intellectual property (IP) property rights regime, in particular measures
covering, inter alia:
▪ patentable subject matter,
▪ patent term restoration,
▪ protection of undisclosed data for pharmaceutical approvals,
▪ extended term of protection for data used to developed biologic medicines,
▪ technological protection measures (TPMs) and rights management information
(RMI), and
▪ copyright extension;
Resolution of telecommunications disputes.
to services trade in the Asia Pacific for both the TPP12 and the CPTPP simulations. Thanks
are due to two anonymous reviewers for helpful comments and suggestions and also to
Natassia Ciuriak for editing the manuscript. The views expressed are those of the study team,
as is responsibility for any and all errors of fact or interpretation.
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 345
ⓒ 2017 East Asian Economic Review
The issues that remain outstanding for further negotiations (with the CPTPP
Member requiring modifications in parentheses) include:
▪ State-owned enterprises (Malaysia);
▪ Services and investment non-conforming measures (Brunei Darussalam);
▪ Dispute settlement trade sanctions (Vietnam); and
▪ Cultural exception (Canada)
This paper assesses the quantitative implications of the CPTPP going forward.
The CPTPP policy shock consists of the liberalization commitments made by the
parties in the original TPP for tariffs and non-tariff barriers (NTBs) in goods and
services and foreign direct investment (FDI). These commitments are evaluated
against the OECD’s Trade Facilitation Index (TFI), Services Trade Restrictiveness
Index (STRI), and Foreign Direct Investment Restrictiveness Index (FDIR) for
goods trade, cross-border services trade, and investment, respectively. For goods
trade, we take into account the impacts of rules of origin (ROOs) in terms of a less-
than-full rate of preferences utilization, but assume a high rate of utilization to reflect
a key TPP outcome, namely ROOs regionalization. For services trade, we take
account of the value of binding commitments. The services estimates now constitute
an upper bound depending on the extent of derogations from the final TPP text
originally negotiated, if and when the CPTPP is implemented.
The CPTPP is simulated on a dynamic version of the Global Trade Analysis
Project’s (GTAP) computable general equilibrium (CGE) model that incorporates
FDI by introducing a foreign-owned representative firm into each GTAP region-
sector. FDI responds in tandem with domestic investment to changes in expected
rates of return (RORs) in each region-sector due to trade liberalization and reductions
in NTBs facing investment. To bring out the relative contribution of the CPTPP’s
various quantifiable elements, we simulate the shocks on a sequential basis for each
policy measure, such that the marginal effect of each set of measures is brought out.
The rest of this paper is organized as follows: section 2 sets out some basic
background on the TPP economies; section 3 provides an overview of the quantitative
modeling; section 4 describes the policy shock; section 5 sets out the results; and
section 6 provides a discussion and draws conclusions.
346 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
II. BACKGROUND
1. The TPP Economies … and the United States
The original twelve TPP signatories were Australia, Brunei, Canada, Chile, Japan,
Malaysia, Mexico, New Zealand, Peru, Singapore, the United States, and Vietnam.
They are all APEC members and have a combined population of close to 825
million, a combined GDP of just under US$30 trillion,1 imports of goods of over
US$4.7 trillion,2 and imports of commercial services of approximately US$1.1
trillion.3 The CPTPP generates about one-third of the total GDP generated by the
TPP12, accounts for about 60% of the population of the TPP12 region, and has an
average per capita income about one-third of the US level, measured at market
exchange rates.
Table 1. Income and Population, Estimated 2017, CPTPP and the United States
GDP GDP Per Capita Population
Current
US$ Millions
PPP
US$ Millions Current US$ PPP US$ Millions
Australia 1,390,150 1,235,297 56,135 49,882 24.8
Brunei Darussalam 11,963 32,913 27,893 76,743 0.4
Canada 1,529,760 1,682,503 42,225 46,441 36.2
Chile 263,206 452,095 14,315 24,588 18.4
Japan 4,884,489 5,405,072 38,550 42,659 126.7
Malaysia 309,858 926,081 9,660 28,871 32.1
Mexico 1,142,453 2,406,087 9,249 19,480 123.5
New Zealand 200,837 185,748 41,629 38,502 4.8
Peru 210,013 424,639 6,598 13,342 31.8
Singapore 305,757 513,744 53,880 90,531 5.7
Vietnam 215,963 643,902 2,306 6,876 93.6
CPTPP 10,464,449 13,908,081 21,010 27,923 498
Memo: United States 19,362,129 19,362,129 59,495 59,495 325.4
TPP12 29,826,578 33,270,210 36,218 40,400 823.5
Source: IMF (October 2017).
1 Estimated 2017 population and GDP from IMF (October 2017). 2 Merchandise imports, 2015 from ITC (2015). 3 Services imports, 2015 from WTO (n.d.).
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 347
ⓒ 2017 East Asian Economic Review
A similar calculus applies in terms of the CPTPP’s role as a market for imports
and a source of outward FDI. As shown in Table 2, the CPTPP accounts for 14.7%
of global goods imports, almost 13% of global services imports, and 14.3% of
global goods and services imports combined. Table 3 shows that the CPTPP
accounts for about 14.4% of global FDI stocks, inward and outward, and is
comprised, on net terms, of outward investors.
Table 2. Global Imports, CPTPP Parties and the United States, 2015, Current US$ Millions
Total Goods Total Services Combined Total
Goods and Services
Imports
from the
World
Imports
from
CPTPP
Parties
Imports
from the
World
Imports
from
CPTPP
Parties
Imports
from the
World
Imports
from
CPTPP
Parties
Australia 200,766 42,622 54,622 13,773 255,388 56,395
Brunei 3,229 1,471 22,245 25,474 1,471
Canada 419,152 47,844 96,270 7,270 515,422 55,114
Chile 63,038 7,476 13,444 76,482 7,476
Japan 625,568 105,221 175,641 22,019 801,209 127,240
Malaysia 176,175 46,424 40,044 216,219 46,424
Mexico 395,232 42,908 32,057 427,289 42,908
NewZealand 36,528 10,772 11,680 5,716 48,208 16,488
Peru 38,105 5,567 7,963 46,068 5,567
Singapore 296,888 62,480 143,469 14,375 440,357 76,855
Vietnam 165,776 28,122 15,501 181,277 28,122
CPTPP Total 2,420,457 400,907 612,936 63,153 3,033,393 464,060
United States 2,306,822 856,546 490,614 2,797,436 856,546
TPP12 Total 4,727,279 NA 1,103,550 NA 5,830,829 NA
World Total 16,473,391 4,729,460 21,202,851
CPTPP Share of
World Total 14.69% 12.96% 14.31%
Source: International Trade Centre (2015) for goods trade and WTO (n.d.) for services trade.
348 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
Table 3. Inward and Outward Investment, CPTPP Parties and the United States,
2015, Current US$ Millions
Stocks Flows
Inward Outward Inward Outward
Australia 537,351 396,431 22,264 -16,739
Brunei 6,061 2645 173 508
Canada 756,038 1,078,333 48,643 67,182
Chile 207,827 87,415 20,176 15,513
Japan 170,698 1,226,554 -2,250 128,654
Malaysia 117,644 136,892 11,121 9,899
Mexico 419,956 151,924 30,285 8,072
New Zealand 66,056 17,262 -986 214
Peru 86,114 2,815 6,861 127
Singapore 978,411 625,259 65,262 35,485
Vietnam 102,791 8590 11,800 1,100
CPTPP Total 3,448,947 3,734,120 213,349 250,015
United States 5,587,969 5,982,787 379,894 299,969
TPP12 Total 9,036,916 9,716,907 593,243 549,984
World Total 24,983,214 25,044,916 1,762,155 1,474,242
CPTPP Share of World 13.81% 14.91% 12.11% 16.96%
Note: Negative figures for outward flows reflect net disinvestment from abroad.
Source: UNCTAD (2016: Annex Tables 1 and 2).
III. FRAMEWORK FOR QUANTITATIVE ANALYSIS
1. The GTAP-FDI Model
To model the TPP, we use a recursive dynamic version of the standard GTAP
CGE model, adapted to incorporate FDI (Ciuriak and Xiao, 2014, provide a
description of the way FDI is incorporated in the model).
CGE models integrate a number of accounts to provide a complete description
of an economy:
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 349
ⓒ 2017 East Asian Economic Review
▪ The standard national income and expenditure accounts;
▪ A breakdown of industry by sector that reflects inter-sectoral input-output
links, which take into account internationally-sourced intermediate goods and
services (in all, the GTAP dataset allows for the representation of up to 57
sectors, 43 of which are goods);
▪ A production function for each sector that combines sector-specific inputs of
capital, skilled and unskilled labour, and intermediate inputs; and
▪ A trade account that models the international linkages for each sector of the
economy.
The model generates results for national account aggregates, industry output and
prices, factor inputs and prices, and trade flows. For a technical description of the
GTAP model, see Hertel (1997); for a discussion of the degree of confidence in
CGE estimates, see Hertel et al. (2003).
On the production side, the model evaluates efficiency gains from the reallocation
of factors of production across sectors. In the first stage (“nest”), land, labour (skilled
and unskilled), and capital substitute for one another to generate domestic value-
added by sector; intermediate inputs, which include imported inputs, substitute for
domestic value-added in the second stage.
Given that we use a dynamic model, both labour and capital respond to changes
in factor returns. Labour responds to changes in the wage rate according to an
estimated long-run elasticity equal to one. Capital supply responds to changes in the
ROR on capital; the investment response is based on the Monash capital model
(Dixon and Rimmer, 1998). Both labour and capital are mobile across all sectors
within a country. Capital is also mobile internationally.
On the demand side, an aggregate Cobb-Douglas utility function allocates
expenditures to private consumption, government spending, and savings so as to
maximize per capita aggregate utility. Following a shock, such as the CPTPP, the
changes in consumption are allocated across these three aggregates based on their
income shares in each region.
Private household demand responds to changes in prices and income. This latter
effect reflects the fact that consumption of particular types of goods, such as luxury
goods, increases more with higher income than does consumption of other goods,
350 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
such as staple food products.4 Notably, changes in trade protection result in changes
not only in the prices of intermediate production goods, but also in the prices of
consumer goods, which induces demand responses.
The trade module assumes imperfect substitution based on product differentiation
across regions. The key parameter determining the scale of impacts on trade from
a tariff shock is the elasticity of substitution – a high substitution elasticity generates
relatively large trade impacts for a given size of tariff shock. Note that the GTAP
sectors reflect relatively large aggregates of individual products; accordingly,
substitution elasticities are lower than they would be for product categories that are
defined more narrowly and, thus, are more substitutable for each other.
Economic welfare is based on equivalent variation: the lump sum payment at
pre-shock prices without the shock that leaves households as well off as in the post-
shock economy.
We use a perfect competition specification of the GTAP model. Some models
incorporate imperfect competition for industrial goods sectors, introducing price
mark-ups that represent monopolistic pure profits in equilibrium. These price mark-
ups are reduced by intensified competition under trade liberalization, generating
additional welfare gains.5 Several recent models incorporate heterogeneous firms
features, which generate productivity gains from reallocation of market shares to
more productive firms under trade liberalization.6 As it is problematic to combine
all these features in one model while retaining a reasonable degree of product and
regional disaggregation, no single modelling exercise can be considered definitive;
a suite of studies is required to hone in on the likely impacts (see, e.g., Narayanan
et al., 2015).
2. Implementation
We use the recently-updated GTAP V9 database with a base year of 2011. For
the simulations, we adopt a 33-product group aggregation, featuring 11 agricultural
4 Household demand is modelled using a Constant Difference of Elasticities function, which
captures the fact that the structure of household demand changes as income increases (i.e.,
in technical terms, it is “non-homothetic”). 5 See Roson, 2006, for a review of the issues raised by this methodology. 6 These include Zhai (2008), Dixon et al. (2013), Balistreri and Rutherford (2013), Oyamada
(2013), and Itakura and Oyamada (2013). See Roson and Oyamada (2014) for a review.
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 351
ⓒ 2017 East Asian Economic Review
and food sectors, 4 other primary sectors, 10 industrial sectors, and 8 services sectors.
The regional disaggregation used for the model features 40 economies and/or regions
designed to model the various mega-regional trade agreements. We report the
results for the CPTPP economies, the United States, China, India, Korea, Taiwan,
Other APEC, the EU28, and the rest of the world (ROW). Tables 4 and 5 provide
the breakdown for sectors and regions.
Table 4. Sectors in the Modelling Framework
Agriculture and Food Forestry, Fishing,
Mining
Industry and
Manufacturing Services
Rice Forestry Textiles and Apparel Construction
Wheat and Cereals Fishing Leather Products Trade
Fruit and Vegetables Fossil Fuels Wood Products Transport
Oil Seeds and Vegetable Oils Mineral Products Chemicals, Rubber,
and Plastics Communication
Sugar Metals and Metal Products Financial Services
Dairy Automotive Business Services
Beef Transport Equipment Recreation
Pork and Poultry Electronic Equipment Other Services
Other Agriculture Machinery and Equipment
Food Products Other Manufactures
Beverages and Tobacco
Source: Compiled by the authors.
Table 5. Regions in the Modelling Framework
TPP Other RCEP TTIP/Other TISA TFTA and ROW
Australia Indonesia EU28 Ethiopia
Canada Philippines Norway Kenya
Chile Thailand Switzerland Mozambique
Japan Rest of Southeast Asia Other EFTA (Iceland and Liechtenstein) Tanzania
Malaysia China Israel Uganda
Mexico Korea Pakistan Rwanda
New Zealand India Turkey Rest of East Africa
Peru Hong Kong SACU
Singapore Taiwan Other TFTA
United States Colombia ROW
Vietnam Central America (Costa Rica and Panama)
Other South America (Paraguay and Uruguay)
Note: Brunei is part of Rest of Southeast Asia.
Source: Compiled by the authors.
352 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
The CPTPP is assumed to be implemented in 2018, the same as was assumed
for the TPP12 in Ciuriak et al. (2016b). We first simulate the GTAP database
forward to 2035, using GTAP dynamic database tools, which draw on available
macroeconomic data (IMF World Economic Outlook for the near term and projections
from Fouré et al., 2012, for the out years).
The policy shocks – tariff reductions, the effect of ROOs on preference utilization,
NTBs on services, and NTBs on investment – are implemented on this projected
base in a dynamic process whereby changes in the ROR on capital induce investment
and changes in wage rates induce labour force participation changes. The shocks
are simulated sequentially, allowing us to identify the impacts by policy measure.
The results reported are changes relative to the baseline at 2018, 2025, and 2035.
The 2035 results may be interpreted as a permanent change in the level of trade
and economic output, once full equilibrium has been restored following the policy
shocks.
3. Closures
In CGE simulations, the number of endogenous variables is limited; the others
must be set exogenously by assumption, thus defining the “closure” of the model.
CGE models can be simulated with various alternative closures; the choice influences
the results significantly.7
Under the GTAP model’s default microeconomic closure, the factor endowments
(i.e., the total supply of labour, both skilled and unskilled, as well as of capital and
land) are fixed; factor prices (i.e., wages and returns to capital and land) adjust to
restore full employment of the factors of production in the post-shock equilibrium.8
Under alternative microeconomic closures that are sometimes used, the returns to
capital or to labour can be fixed and the supply of capital and/or labour then adjusts
7 Ciuriak and Chen (2008), modeling the Canada-Korea FTA, find GDP impacts vary from
0.064%, when labour and capital supply both fixed, to 0.268%, where both capital and
labour supply are flexible. 8 This is sometimes described as reflecting a medium-term time horizon in which labour
supply is “sticky.”
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 353
ⓒ 2017 East Asian Economic Review
to restore equilibrium.9 Each of these closure rules makes an extreme assumption
about the supply of labour and/or capital: it is either perfectly elastic or perfectly
inelastic. The reality is likely to be somewhere in between.
In the GTAP-FDI model, investment adjusts to changes in the ROR; similarly,
we allow labour supply to adjust to changes in wages. As a result, the TPP generates
“endowment” effects: that is, the supply of labour and capital changes based on
changes in returns to labour and capital. For both labour and capital, the supply
elasticity is set at one; for labour supply, this assumption is based on estimates of
long-run labour supply from the literature;10 for capital supply, the assumption is
based on regressions of the investment response to a change in ROR using firm-
level data.
As regards GTAP’s macroeconomic closures, two approaches are available. First,
the current account can be fixed, which assumes that the external balance is
determined entirely by domestic investment-savings dynamics. When trade policy
shocks result in unbalanced changes in imports and exports, the original trade
balance is restored by implicit exchange rate adjustments. Alternatively, the current
account can be allowed to adjust to the trade shock. The change in the current account
must then be offset by equivalent changes in capital flows. In reality, unbalanced trade
impacts are likely to have both effects: induce subsequent exchange rate adjustments
and offset capital flows. The choice of macroeconomic closure can have significant
implications for the model outcomes.11 Given the active role of FDI in our model,
we necessarily adopt the closure where the current account adjusts.
9 The closure rule in which the ROR to capital is fixed is sometimes described as reflecting
longer-run “steady-state” growth conditions. For an example of the use of the labour market
closure rule, under which the wage rate is fixed, see Francois and Baughman (2005). 10 See Evers et al. (2008) for a meta-analysis of the labour supply elasticity literature; this
study concludes the elasticity is about 0.1 for men and 0.6 for women, or about 0.3 on
average. Ham and Reilly (2013) find statistically-significant inter-temporal labour supply
elasticities of 0.89 with the Panel Study of Income Dynamics dataset and 1.0 with the
Consumer Expenditure Survey dataset. The CPTPP is likely to be implemented in a
condition of economic slack, hence supporting the assumed moderately higher elasticity. 11 E.g., Gilbert (2004), modelling the Korea-US FTA, finds that the fixed current account
simulation reduces welfare gains for Korea to 3/5 the level of the simulation with a flexible
current account and marginally (by 5%) for the United States.
354 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
IV. THE CPTPP POLICY SHOCK
1. Tariffs
Tariff reduction/elimination is based on the original TPP schedules and technical
summaries released by the parties. The shocks follow those in Ciuriak et al. (2016b).
There are several general points to be borne in mind.
First, the precise extent to which the CPTPP liberalization schedules improve
upon existing free trade agreement (FTA) commitments could not be taken fully
into account in this analysis due to resource constraints. Significant improvements
that have been flagged by governments in their technical summaries are incorporated
– for example, the CPTPP improves upon the market access commitments made by
Japan on beef to countries with which it has existing FTAs that provide lesser
market access (Australia, Mexico, and Peru). Otherwise, we do not attempt to identify
marginal additional improvements under the CPTPP compared to existing agreements.
The CPTPP does clean up the spaghetti bowl of FTAs in the Asia-Pacific to some
extent, but our simulations do not fully capture this; this is largely housekeeping,
however, and should not materially impact the assessment.
Second, as regards the time path of the liberalization schedules, the CPTPP’s
schedules are highly complex and differentiated by individual products and countries,
which makes it impractical to attempt to capture the phase-outs in detail. We review
the tariff elimination schedules to identify the overall timeframes for phase-outs
applied for different product groups and construct stylized straight-line elimination
schedules accordingly. We note that, at the high level of aggregation at which CGE
simulations are run and given the changing composition of trade, especially in the
later stages of the implementation period, the trade weights for the individual tariff
lines will, in any event, change (and probably quite significantly). We provide read-
outs of the impacts at years 8 (2025) and 18 (2035) of the CPTPP implementation
period; these are, in our view, reasonable estimates of the medium- and longer-
term impacts.
Third, we do not take into account the trailing bits of liberalization that extend
beyond 2035. Changing economic conditions make impact estimates that far in the
future highly uncertain and such commitments are of limited relevance to either
policy or business.
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 355
ⓒ 2017 East Asian Economic Review
Fourth, as regards the value of the managed trade concessions in the agricultural
sector, we assume full quota utilization with physical quantities converted to values
based on unit values in trade in the relevant product groups. Given uncertainties
about quota utilization and the fluctuations in unit prices from year to year and
across countries, these impact estimates are subject to some degree of uncertainty.
2. Preference Utilization
Preferences for industrial products are not fully utilized due to ROOs compliance
costs. We assume that agricultural products face negligible ROOs costs and are
mostly traded by large agri-business firms with adequate administrative capacity.
Accordingly, we assume 100% preference utilization and impose no charge for this
use. For textiles and clothing and autos, we assume a high utilization rate of 90%
due to the size of the tariff savings and the likelihood that supply chains would be
adjusted to take full advantage of the CPTPP (there is evidence that factories were
already being shifted into Vietnam to take advantage of the TPP for exports to the
United States). For other industrial sectors, we assume 80% preference utilization
to reflect the regionalization of ROOs, a significant negotiating achievement. We
phase in the utilization rate from 60% in the first year by 5% per year to reflect
adjustment to the regime.
We incorporate no charge for utilizing preferences into the simulations, since
the assumption of preference underutilization based on empirical evidence concerning
observed utilization rates already includes the trade effects of ROOs costs. We
consider that the Armington specification of the model, which allows for differing
unit costs of traded goods, already addresses the welfare costs of trade diversion
(in terms of sourcing imports from higher-cost TPP-region sources).
3. Goods Sector NTBs
The overall assessment of the TPP’s impact on goods trade NTBs in Ciuriak et
al. (2016b) was that it was below the level that is meaningful for a macroeconomic
analysis, particularly given the advances made in the WTO’s Trade Facilitation
Agreement. We incorporate no general goods trade facilitation shock for the CPTPP
analysis
356 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
4. Services Sector NTBs
We develop the liberalization shock for services NTBs by coding the CPTPP
against the cross-border services trade components of the CPTPP parties’ STRI
developed by the OECD (Geloso Grosso et al., 2015). We also take into account
the extent of squeezing “water” out of the bindings in the General Agreement on
Trade in Services (GATS) by comparing CPTPP bindings to the parties’ scores in
the corresponding GATS Trade Restrictiveness Index (GTRI) developed by
Miroudot and Pertel (2015) and/or in existing bilateral FTAs.
In developing the CPTPP policy shock for services, we proceed as follows:
▪ NTBs, as quantified by gravity-model-based analysis, implicitly reflect both
the effect of actual restrictions and of “water”, as measured by the difference
between the GTRI and the STRI (that is, the difference between bound
commitments and applied practice).
▪ On the basis of regression analysis of the effect of bindings (Ciuriak and
Lysenko, 2016), we assume that actual market restrictions, as measured by
the STRI, have twice the restrictive power as an equivalent amount of “water”.
▪ Accordingly, we adopt the following simple formula: Total NTB = α(STRI +
0.5*Water), where α is a coefficient that scales the index-based measure to
the ad valorem equivalent (AVE) of a country’s sector-specific NTBs developed
for GTAP sectors by Fontagné et al. (2016). We assume that only 25% of
these measured AVEs correspond to the barriers to services trade itemized in
the OECD’s STRI/GTRI framework and thus actionable under the CPTPP.
This assumption is consistent with the general conclusion obtained from the
Berden et al. (2009) survey of NTBs goods and services, that 50% could in
principle be removed i.e., that they were “actionable”; and the Francois et al.
(2013) assessment that an ambitious FTA could reduce trans-Atlantic barriers
by 50% of actionable barriers (i.e., by 25% of the total observed barriers).
5. Barriers to FDI
For FDI, we build in a liberalization shock based on cross-referencing the CPTPP’s
measures to the OECD’s FDIR index for CPTPP members. Given the presence of
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 357
ⓒ 2017 East Asian Economic Review
numerous bilateral investment agreements within the region, the marginal impact
of new bindings attributable to the CPTPP is not likely to be of major significance
and a specific quantification of the value of bindings was not included.
6. Other Issues
We do not explicitly model the impact of IP measures, for several reasons. First,
IP measures work very differently than trade liberalization. Where trade liberalization
increases competition and reduces prices, increased IP protection does the opposite.12
The benefit from IP protection is increased research and development and increased
innovation, which are manifest in additional product varieties. The conventional
modelling framework for FTA analysis is not equipped to analyze IP issues, as it
does not reflect the impact of IP protection on asset values (Ciuriak, 2017). The
impact on any individual economy of increased IP protection is thus an open
empirical question. Innovation could be inhibited in some jurisdictions depending
on whether disincentives outweigh incentives (Ciuriak and Curtis, 2015). From a
financial flow perspective, the direct benefits of increased IP protection in the
CPTPP would be heavily skewed to the countries with the largest stocks of IP (e.g.,
Japan). Taking these flows and the enhanced values of the companies’ intangible
assets (and hence their market capitalization) into account could materially impact
the distributional impact of the CPTPP across the various parties.
Government procurement is also not modelled. Since most procurement is done
through commercial presence (“Modality 2” in government procurement; see
Cernat and Kutlina-Dimitrova, 2015), rather than on a cross-border basis (“Modality
1”) and since Modality 2 already benefits fully from national treatment rules under
WTO commitments, the CPTPP’s impact here is likely to be small in any event.
Accordingly, unlike for IP, the failure to explicitly model procurement will not
materially affect the overall CPTPP impacts.
12 For a discussion of the interaction between trade rules and innovation, see Ciuriak and
Curtis (2015).
358 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
7. Summary of Shocks Between CPTPP Parties
Table 6 summarizes the tariff shock. The table may be read as follows: the
countries listed in the columns face the percentage tariff reduction offered by the
countries in the top row. Thus, for example, Australia faces a reduction of the
weighted average tariff by Canada of 0.293%. This low figures reflects the fact
that 75.6% of Canada’s applied most-favoured nation (MFN) tariff lines are
already at zero, the main areas where Canada liberalizes for the most part exclude
the high-tariff sectors (dairy and poultry), and Canada imports little from Australia
in sectors where tariff cuts are significant (automotive and textiles and apparel).
As can be seen, two of the parties – Chile which has an FTA with with every other
CPTPP member, and Singapore, which operates under effectively unilateral free
trade for goods – have no tariff shock. Few of the bilateral relationships feature
significant tariff reductions.
Table 6. Summary of the Trade-Weighted Tariff Shock, by Dyad
AUS BRU CAN CHL JAP MLY MEX NZ PER SGP VN
AUS 0 0 -0.293 0 0 0 -1.893 0 -0.436 0 0
BRU 0 0 0 0 0 0 -1.817 0 0 0 0
CAN -1.201 -0.020 0 0 -1.449 -0.853 0 -0.049 0 0 -2.293
CHL 0 0 0 0 0 0 0 0 0 0 0
JAP 0 0 -2.175 0 0 0 -2.710 -3.523 -1.901 0 0
MLY 0 0 -0.747 0 0 0 -3.650 0 -1.772 0 0
MEX -3.667 -0.019 0 0 0 -2.828 0 -1.679 0 0 -2.656
NZ 0 0 -5.858 0 -3.574 0 -11.993 0 -0.255 0 0
PER -0.619 -0.005 0 0 0 -1.188 0 -0.720 0 0 -1.678
SGP 0 0 -0.003 0 0 0 -2.599 0 0 0 0
VN 0 0 -5.240 0 0 0 -14.020 0 -3.756 0 0
Source: Calculations by the study team. Note that the Australia-Peru FTA announced at the APEC
meetings in Da Nang in November 2017 is not reflected and the liberalization pursuant to that
FTA is treated as due to the CPTPP.
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Table 7 summarizes the services liberalization shock.
Table 7. Summary of the Services Shock: % Change in Trade-Weighted TCE, by Dyad
AUS BRU CAN CHL JAP MLY MEX NZ PER SGP VN
AUS 0 0.0035 0.3207 0.0020 0.0180 0.0018 0.0526 0 0.0041 0.0135 0.0009
BRU 0.0000 0 0.0805 0.2175 0.0000 0.0799 0.0582 0.0001 0.1666 0.0002 0.0051
CAN 0.6194 0.7771 0 0.0638 0.3825 0.7475 0.0024 0.2533 0.0426 1.2046 0.7592
CHL 0.0172 0.5909 0.0412 0 0.0295 0.3029 0.0057 0.3211 0.0014 0.5702 0.0650
JAP 0.0011 0.0645 0.4521 0.1723 0 0.0468 0.0146 0.4214 0.1188 0.0126 0.0358
MLY 0.0006 0.0120 0.2738 0.3197 0.0001 0 0.0657 0.0013 0.1412 0.0001 0.0145
MEX 0.1663 0.8962 0.0032 0.0196 0.0569 0.6916 0 0.2131 0.0202 1.4451 0.7525
NZ 0 0.0289 0.3526 0.2282 0.2637 0.0086 0.0470 0 0.1023 0.0017 0.0070
PER 0.0577 1.1070 0.0083 0.0023 0.0486 1.7438 0.0157 0.3368 0 0.8612 0.2214
SGP 0 0 1.0823 1.4133 0.1144 0 0.2362 0.0337 0.9980 0 0
VN 0.0002 0.0024 0.2330 0.1868 0.0001 0.0224 0.0523 0.0018 0.1220 0.0003 0
Source: Calculations by the study team.
This table provides the change in the trade cost equivalent (TCE) derived by
multiplying the percentage reduction in the region-sector NTBs times the
corresponding region sector AVE measure of services trade barriers. This TCE
reduction is expressed as a trade technology (AMS) shock in the GTAP modelling
framework.
Table 8 provides the weighted average shock to the “phantom tax” in each
CPTPP region.
Table 8. Percentage Reduction in the Phantom Tax on FDI by CPTPP Region
CPTPP Party Phantom Tax on FDI Reduction
Australia 12.8%
Brunei 4.6%
Canada 1.3%
Chile 27.3%
Japan 5.6%
Malaysia 5.1%
Mexico 16.7%
New Zealand 27.8%
Peru 51.1%
Singapore 13.9%
Vietnam 6.1%
Source: Calculations by the study team.
360 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
V. RESULTS
1. Trade Impacts
The CPTPP generates 2.40% in additional intra-regional exports, only two-fifths
of the TPP12’s impact in level terms (US$17.34 billion at 2017 prices for the
CPTPP vs. US$56.3 billion for the TPP12), but larger in percentage terms. The
larger percentage gain reflects the removal of the large baseline level of US trade
with the other TPP parties. Taking into account trade deflection, total exports of
CPTPP parties to the world rise by 0.22% (about US$12.27 billion at 2017 prices).
As a trade deal, the CPTPP improves upon the TPP12 for the Eastern Pacific
parties (Mexico, Canada, Peru, and Chile), as these countries avoid erosion of
existing preferences in the US market, while they pick up market share in the
Western Pacific from the United States. It also improves upon the TPP12 for
Singapore, which avoids preference erosion in its Asian markets from US export
gains in those markets. Apart from the United States, which flips from gains to
losses under the CPTPP, Vietnam and Japan see the biggest discount of gains,
because they stood to gain the most in the US market under the TPP12. Third
parties are less negatively hit by the CPTPP than by the TPP12. The EU28, Other
APEC, and China experience the largest reduction of negative impact.
Table 9. Trade Impacts: Exports to TPP Partners and to the World, 2035
TPP12 CPTPP
2017 US$ Millions % Change 2017 US$ Millions % Change
Exports to TPP Parties
Australia -270 -0.22 115 0.15
Canada 2,043 0.46 2,560 4.88
Chile -61 -0.14 -23 -0.09
Japan 10,950 4.73 4,323 3.40
Malaysia 4,636 2.97 1,985 1.66
Mexico 1,587 0.44 1,548 3.12
New Zealand 1,814 5.98 1,638 6.56
Peru -9 0.01 80 0.46
Singapore 654 0.43 652 0.50
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 361
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Table 9. Continued
TPP12 CPTPP
2017 US$ Millions % Change 2017 US$ Millions % Change
Vietnam 17,640 15.20 4,507 6.83
United States 17,376 1.85 -3,129 -0.32
China -1,203 -0.05 -132 -0.01
India -1,187 -0.46 -109 -0.11
Korea 368 0.15 47 0.03
Taiwan 472 0.37 136 0.16
Other APEC -1,585 -0.32 -183 -0.06
EU28 -3,003 -0.29 -894 -0.18
ROW -2,712 -0.26 -358 -0.07
Memo: TPP/CPTPP 56,297 2.07 17,334 2.40
Exports to the World
Australia 136 0.08 359 0.08
Canada 1,554 0.20 1,771 0.22
Chile -55 0.00 4 0.01
Japan 6,969 0.51 2,028 0.12
Malaysia 4,969 0.96 1,183 0.18
Mexico 1,936 0.39 2,547 0.47
New Zealand 891 0.73 656 0.42
Peru 33 0.08 109 0.12
Singapore 1,070 0.22 1,099 0.22
Vietnam 13,097 3.63 2,576 0.66
United States 12,678 0.41 -1,434 -0.03
China -1,785 -0.02 -539 -0.01
India -408 0.01 -45 0.00
Korea -24 0.00 15 0.00
Taiwan -21 -0.01 24 0.00
Other APEC -1,240 -0.06 -219 -0.02
EU28 -3,035 -0.01 -617 0.00
ROW -2,391 -0.04 -198 -0.01
Memo: TPP/CPTPP 43,182 0.52 12,270 0.22
Note: TPP totals do not include Brunei. Further, the original model data, which are in USD at 2011
prices, are converted to 2017 USD using the change in the US GDP deflator over the period
(10.25% over the period in IMF, October 2017).
Exports are valued using GTAP code VXW, a valuation which includes transport margins, while
the corresponding sum of sectoral exports in Table 13 are measured by the GTAP code VXWD,
which does not include transport margins, as these are not allocated by sector. The difference in
the two concepts is marginal.
Source: Calculations by the authors. The TPP12 estimates are revised versions of the results reported in
Ciuriak et al. (2016a, 2016b), based on updates to the estimates of the height of services NTBs.
362 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
Table 10. Trade Impacts: Imports from TPP Partners and from the World, 2035
TPP12 CPTPP
2017 US$ Millions % Change 2017 US$ Millions % Change
Imports from TPP Parties
Australia 818 0.52 1,149 1.22
Canada 2,741 0.66 3,957 5.37
Chile -21 -0.06 30 0.16
Japan 9,964 2.82 2,168 1.03
Malaysia 7,367 4.50 686 0.56
Mexico 5,025 1.57 8,409 19.19
New Zealand 1,254 4.11 711 3.19
Peru 293 1.04 363 2.98
Singapore 50 0.02 190 0.16
Vietnam 6,004 8.13 663 1.10
United States 25,823 2.36 -791 -0.07
China -3,633 -0.26 -1,433 -0.14
India -569 -0.28 -168 -0.13
Korea -929 -0.30 -353 -0.17
Taiwan -374 -0.28 -172 -0.19
Other APEC -1,471 -0.37 -560 -0.19
EU28 -3,637 -0.38 -1,052 -0.24
ROW -2,461 -0.34 -666 -0.21
Memo: TPP/CPTPP 59,164 2.08 18,262 2.39
Imports from the World
Australia 15 0.01 287 0.06
Canada 1,555 0.23 1,850 0.29
Chile -72 -0.04 0 0.00
Japan 8,193 0.59 2,407 0.18
Malaysia 5,354 1.07 1,155 0.24
Mexico 2,189 0.42 2,974 0.58
New Zealand 986 1.34 733 0.99
Peru 38 0.04 121 0.13
Singapore 516 0.11 514 0.10
Vietnam 15,687 4.32 3,175 0.88
United States 13,922 0.42 -1,770 -0.05
China -3,073 -0.07 -612 -0.02
India -1,109 -0.08 -142 -0.02
Korea -143 -0.02 11 0.00
Taiwan -24 -0.02 25 0.00
Other APEC -1,517 -0.11 -252 -0.03
EU28 -4,060 -0.03 -734 -0.01
ROW -3,235 -0.06 -328 -0.01
Memo: TPP/CPTPP 48,276 0.59 13,149 0.28
Note: Imports are valued using GTAP code VSW, a valuation which includes transport margins while
the corresponding sum of sectoral imports in Table 13 are measured by the GTAP code VIWS,
which does not include transport margins as these are not allocated by sector. The difference in the
two concepts is marginal. See also notes to Table 9.
Source: Calculations by the authors.
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2. Impacts on GDP and Economic Welfare
For the CPTPP as a group, the simulations suggest that real GDP will rise by
about 0.075% generating economic welfare benefits of about US$13.47 billion by
2035. These gains are smaller in absolute terms, but about the same in percentage
terms, compared to the gains under the TPP12. It must be mentioned here that the
difference in welfare effects does not take into account any differences in the non-
quantified measures – including in particular IP – that might emerge under a
provisional CPTPP compared to the full package of the TPP12 as negotiated.
Table 11. GDP and Economic Welfare Impacts of the TPP and CPTPP
TPP12 CPTPP
Real GDP
(%)
Welfare
US$ Millions
Real GDP
(%)
Welfare
US$ Millions
Australia 0.003 -61 0.017 379
Canada 0.068 1,759 0.082 2,233
Chile -0.019 -109 0.007 30
Japan 0.138 10,148 0.040 3,265
Malaysia 0.715 3,893 0.127 972
Mexico 0.084 1,174 0.156 2,642
New Zealand 0.488 1,515 0.366 1,217
Peru 0.005 -30 0.024 65
Singapore 0.173 833 0.199 948
Vietnam 2.354 7,297 0.481 1,725
United States 0.038 8,375 -0.008 -1,959
China -0.024 -6,155 -0.003 -964
India -0.033 -2,216 -0.008 -493
Korea -0.037 -992 -0.006 -180
Taiwan -0.027 -175 -0.005 -41
Other APEC -0.046 -1,817 -0.008 -363
EU28 -0.019 -4,438 -0.004 -1,004
Rest of World -0.029 -3,574 -0.004 -292
Memo: TPP/CPTPP 0.098 34,794 0.075 13,474
Note: Welfare is measured as equivalent variation. See also notes to Table 9.
Source: Calculations by the authors.
364 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
We observe that the GDP percentage gain, in real terms, is about one-quarter the
size of the two-way trade percentage gain in real terms, which is a reasonable ratio
in light of earlier literature on this issue (the rule of thumb suggests a ratio of
around 20%). However, the welfare gain is large in value terms relative to the total
trade gain (welfare gains of US$13.5 billion vs. gains in terms of two-way trade of
about US$25.4 billion); this reflects terms of trade improvements for the CPTPP.
Given the size of the CPTPP region relative to the world, a non-negligible impact
on terms of trade is plausible. Overall, the simulation results generate broadly
reasonable ratios.
The impact on real GDP and welfare follows the pattern of trade impacts, with
Australia, Mexico, Canada, Chile, and Peru improving their outcomes in the CPTPP
compared to the TPP12. The United States has a relatively large flip on welfare
going from +US$8.4 billion under the TPP12 to almost –US$2 billion under the
CPTPP, consistent with the flip on real GDP from 0.038% to -0.008%.
3. Sources of the Impacts
Table 12 provides a decomposition of the impacts in 2035 by policy: tariff
reduction and ROOs; reduction of services NTBs; and easing of FDI restrictions.
For the CPTPP, the major gains in welfare come from tariff reduction net of ROOs
costs (about US$1.4 billion), supplemented by services liberalization (about
US$1.8 billion), and FDI liberalization (about US$1.6 billion). Most of the gains
in services and FDI are attributable to the binding of existing market access and,
thus, due to a reduction of uncertainty.
One of the notable features of FDI liberalization is that the reallocation of capital
to more profitable applications within the CPTPP frees up capital for net investment
in third parties. The model simulation suggests all regions would in fact benefit
from the FDI liberalization measures (this is a feature present in both the CPTPP
and TPP12).
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 365
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Table 12. Decomposition of CPTPP Impacts by Policy, Cumulated Change in 2035
Tariff/
ROOs
Services
NTBs
FDI
NTBs Total
Tariff/
ROOs
Services
NTBs
FDI
NTBs Total
Real GDP % Change Welfare, 2017 US$ Millions
Australia 0.003 0.003 0.01 0.017 13 71 294 379
Canada 0.061 0.016 0.005 0.082 1,656 437 140 2,233
Chile -0.007 0.009 0.005 0.007 -36 43 23 30
Japan 0.034 0.004 0.003 0.04 2,835 269 161 3,265
Malaysia 0.088 0.019 0.02 0.127 704 117 150 972
Mexico 0.142 0.007 0.007 0.156 2,369 150 122 2,642
New Zealand 0.332 0.019 0.015 0.366 1,119 52 46 1,217
Peru 0.019 0.004 0.001 0.024 41 16 7 65
Singapore 0.005 0.058 0.137 0.199 52 223 674 948
Vietnam 0.458 0.021 0.003 0.481 1,650 59 15 1,725
United States -0.008 -0.001 0.001 -0.008 -1,953 -126 121 -1,959
China -0.005 0 0.002 -0.003 -1,228 -42 305 -964
India -0.009 -0.001 0.002 -0.008 -541 -64 112 -493
Korea -0.007 -0.001 0.002 -0.006 -202 -18 40 -180
Taiwan -0.007 -0.001 0.002 -0.005 -43 -5 7 -41
O/APEC -0.012 0 0.004 -0.008 -477 -11 125 -363
EU28 -0.004 -0.001 0.001 -0.004 -1,059 -145 199 -1,004
ROW -0.005 0 0.002 -0.004 -769 -38 516 -292
CPTPP 0.057 0.009 0.009 0.075 10,404 1,438 1,633 13,474
Source: Calculations by the authors.
4. Sectoral Impacts
Table 13 sets out the CPTPP sectoral impacts. In terms of intra-TPP exports,
automotive products (US$3.6 billion) stand out in the case of goods exports, and
business services (US$576 million) in the case of services exports. The large gains
that Vietnam stood to make in textiles and apparel under the TPP12 through
enhanced access to the US market are washed out in the CPTPP. However, after
automotive products, textiles and apparel (US$3.2 billion) see the largest gains in
intra-TPP exports. Other sectors that will palpably feel an intra-TPP expansion of
exports include machinery and equipment (US$1.8 billion) and leather products
(US$1.6 billion). In the agri-foods area, beef (US$891 million), processed foods
(US$715 million), and fruit and vegetables (US$267 million) make notable gains.
366 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
Table 13. CPTPP Regional Sectoral Impacts 2035
Change Over Baseline in
US$ Millions
Percentage
Change
Intra-
TPP
Exports
TPP
Exports
to
World
TPP
Imports
from
World
Intra-
TPP
Exports
TPP
Exports
to
World
TPP
Imports
from
World
1 Rice -1 -40 7 -0.12 -0.69 0.17
2 Wheat and Cereals 32 -11 13 0.63 -0.04 0.05
3 Fruit and Vegetables 267 176 5 8.22 0.58 0.02
4 Oilseeds and Vegetable Oils 36 -58 67 0.48 -0.10 0.17
5 Sugar 0 -2 11 0.11 -0.06 0.17
6 Dairy 38 -64 128 1.10 -0.17 0.45
7 Beef 891 549 516 18.00 2.30 4.06
8 Pork and Poultry 1 -16 12 0.14 -0.13 0.29
9 Other Agriculture 9 3 21 0.97 0.06 0.35
10 Forestry 195 94 788 0.19 0.02 0.12
11 Fishing 70 43 169 0.19 0.01 0.13
12 Fossil Fuels 828 630 591 18.28 3.51 3.91
13 Mineral Products 158 122 26 5.06 1.39 0.12
14 Food Products 715 595 487 4.12 0.78 0.48
15 Beverages and Tobacco 104 98 94 2.66 0.51 0.34
16 Textiles and Apparel 3,159 2,941 2,110 18.11 2.83 1.19
17 Leather Products 1,595 1,380 832 35.10 3.98 2.64
18 Wood Products 414 168 385 1.72 0.12 0.28
19 CRP 1,070 607 1,275 1.66 0.13 0.24
20 Metal Products 624 260 714 0.99 0.06 0.19
21 Automotive 3,608 3,378 1,462 6.79 0.78 0.49
22 Transport Equipment 140 -13 123 1.54 -0.02 0.11
23 Electronic Equipment 284 -114 593 0.30 -0.02 0.13
24 Machinery and Equipment 1,786 688 1,373 1.81 0.10 0.19
25 Other Manufacturing 160 14 165 3.20 0.04 0.26
26 Other Services -1 -75 98 -0.02 -0.17 0.17
27 Construction 87 52 87 2.44 0.18 0.29
28 Trade 104 63 143 0.93 0.08 0.17
29 Transportation Services 139 81 217 0.69 0.06 0.13
30 Communications 24 1 36 1.28 0.01 0.18
31 Financial Services 221 207 173 4.19 0.30 0.24
32 Business Services 576 490 357 2.45 0.34 0.22
33 Recreation Services -4 -56 75 -0.06 -0.12 0.15
Total 17,334 12,197 13,149 2.44 0.26 0.28
Source: Calculations by the authors. See also notes to Table 9 and 10.
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 367
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VI. DISCUSSION AND CONCLUSIONS
The original TPP12 agreement promised to be a relatively modest deal when
evaluated in traditional terms of trade, jobs, and growth. As noted in Ciuriak et al.
(2016b), this reflected a number of factors:
▪ Apart from sensitive sectors, tariffs are already low in Asia-Pacific trade.
▪ Sensitive sectors successfully resisted significant liberalization.
▪ Preferences will not be fully utilized and utilization of preferences generates
administrative costs for firms, which detract from the trade gains.
▪ The TPP as negotiated had little impact on goods trade costs, as it did not
improve upon the WTO Trade Facilitation Agreement and had few sector-
specific facilitating measures.
▪ Services market access was minimally impacted by the terms of the agreement
– the TPP’s main role was to improve upon bindings under the GATS – and
then only by half the membership.
▪ FDI in most sectors is welcomed by all countries to start with and there is an
extensive existing web of bilateral investment treaties in place, many of which
already feature such mechanisms as ISDS.
The CPTPP promises still lower gains due to the withdrawal of the biggest
economy among the twelve original signatories. Nonetheless, it does promise gains:
for the CPTPP as a group, the simulations suggest that real GDP will rise by about
0.075% generating economic welfare benefits by close to US$13.5 billion by 2035.
Moreover, the CPTPP would improve upon the current trade regime prevailing in
the Asia-Pacific region, not least because it would go a long way to clearing up the
“spaghetti bowl” of existing bilateral agreements in the region.
The United States forfeits, in the first instance, the gains it stood to make under
the TPP12 and incurs losses from preference erosion. This is not likely to be the
final bottom line for the United States and the CPTPP. The Trump Administration
has indicated its preference for one-on-one negotiations, where it holds the whip hand,
being the larger economy with the larger market. The provisional implementation
of a CPTPP would undoubtedly be followed by a US announcement of its intent
to open bilateral trade negotiations with at least some of the Eleven.
368 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
Whether countries would take up the offer, and whether the United States would
in reality improve upon the TPP12 outcome through bilateral negotiations, are
open questions. In the latter regard, it is important to note that the United States
already negotiated the TPP12 on a bilateral basis – including, for example, the
ROOs for the automotive sector in the TPP12, which have been rejected by the
Trump Administration and which were negotiated bilaterally between the United
States and Japan and presented to the rest of the membership as a fait accompli.
This point is underscored by the fact that all trade deals, regional or multilateral,
ultimately involve one-on-one bargaining among the main parties on the key points
– for example, China’s accession to the WTO involved a one-on-one negotiation
between China and the United States, where the latter held all the cards. In any
event, replicating or improving upon the TPP12 outcome would involve significant
time and resources and the CPTPP could serve as the basis for Asia-Pacific trade
for a considerable window in time.
For the remaining CPTPP parties, the implications of going ahead without the
United States vary. For the four parties in the Americas (Canada, Mexico, Chile,
and Peru), US withdrawal actually promises, in the first instance, to expand the
gains from the CPTPP. This reflects the fact all four have FTAs in force with the
United States and do not experience preference erosion in their key US markets
under the CPTPP, while making additional preferential trade gains at US expense
in Asian markets. Within the CPTPP group, no country stands to benefit more from
US withdrawal than Mexico. It increases its welfare gain from about US$1,138
million to about about US$2.6 billion. Third parties also benefit from US
withdrawal from the TPP since they experience less erosion of their competitive
position in both the US and the CPTPP markets.
How to handle the automotive sector under a CPTPP would be a major issue,
however. The TPP12 featured a significant lowering of the overall amount of
regional value content (RVC) required for an automotive product to qualify for
TPP preferences compared to the North American Free Trade Agreement (NAFTA)
standard of 62.5% for automobiles and light trucks. As noted, this measure was
agreed in a bilateral between the United States and Japan and whether it would
make sense to apply in a CPTPP context, given the Trump Administration’s
rejection of this negotiated outcome and indications that higher RVC would be
demanded, is thus a wide-open question. Deciding a provisional regime for automotive
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 369
ⓒ 2017 East Asian Economic Review
ROOs would be one of the major elements of negotiating implementation of the
CPTPP.
An important caveat concerning reliance on the results reported here for policy
considerations is that the available quantitative tools are inadequate to assess the
impacts of the TPP as an instrument of systemic regulation and asset value protection
for the knowledge-based economy (KBE). A KBE works on different principles
than the industrial economy. It is based on amassing asset portfolios and data and
exploiting the associated rents, not on moving inventory. The network externalities
and “winner-take-most” economics of the KBE raise difficult and different questions
about competition than those that are contemplated in the CPTPP governance
model, which extends comprehensive protection to established assets and thus
established market positions, including by creating a general freedom to operate
that allows firms to optimize their mode of engagement with the global economy.
The wealth effects of the CPTPP are likely to be heavily skewed to the countries
with the largest stocks of intangible assets to protect.
A second important caveat is that, in many of the controversial governance areas,
the situation is unsettled. The CPTPP’s ISDS mechanism is clearly inferior to that
which was developed by the European Union and Canada for their Comprehensive
Economic and Trade Agreement (CETA) – and the CETA mechanism itself is still
not carved in stone. Further, recent US Supreme Court decisions affecting the US
regime for IP protection (e.g., in TC Heartlands vs. Kraft Foods over forum shopping
for IP litigation, amongst others) have potentially far-reaching implications for the
governance regime in the United States, to which the TPP should now catch up.
And a suitable governance regime for data flows, which would enable the cloud
business model, but prevent and discipline social and political engineering by
private interests, has yet to be developed. All of these are fluid issues, all remain
highly controversial, and all will be the subject of intense negotiations with the
United States in all of its bilateral negotiations going forward, as well as in the
WTO discussions on e-commerce in the wake of the 11th Ministerial Conference
in Buenos Aires. Importantly in this regard, it is valid to question the extent to which
it is wise for the CPTPP parties to carve in stone any specific regime in these areas
amongst themselves.
As regards the overall scale of the impacts reported in this study, there is inevitably
some degree of sensitivity to the assumptions underlying the simulations. Generally,
trade impacts are higher with larger trade elasticities (specifically the elasticities
370 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
of substitution); and the real GDP gains are higher for a given trade shock the
stronger the supply side response of the economy to the incentives created by trade
liberalization. For the CPTPP simulation, the real GDP impacts across all regions
average about 27% of the size of the change in real two-way trade. This is broadly
in line with historical experience regarding the productivity gains generated by
increased openness (the standard rule of thumb suggests a ratio of about 20%). The
simulations rely on the standard GTAP elasticities of substitution, which were recently
updated and represent an internationally recognized benchmark for empirical analysis.
Nonetheless, given that CGE simulations of trade agreements typically generate
lower estimates of bilateral trade gains than reported in gravity model studies, the
trade and real GDP impacts may be considered to be conservative estimates.
Bearing in mind these caveats, this study concludes that, for the politically relevant
medium term, the United States stands to be less well-off without the TPP12 than
with the CPTPP in force. Measured using traditional metrics, this expected discount
is small, but not insignificant for many US stakeholders. An alternative perspective
on the impact of modern trade agreements that emphasizes their impact on asset
prices – treating the TPP as an “asset value protection agreement” rather than an
FTA – suggests the expected discount for the United States might be substantially
greater (Ciuriak, 2017). For the eleven CPTPP parties, the study suggests that the
gains remain significant and in some cases greater than under the TPP12.
Accordingly, if there is a real option for the Eleven to suspend the controversial
issues while proceeding with the conventional trade liberalization agenda on a
provisional basis, the Eleven should seize it.
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 371
ⓒ 2017 East Asian Economic Review
APPENDIX:
TECHNICAL DESCRIPTION OF THE GTAP-FDI MODEL
The distinguishing feature of the GTAP-FDI model is that there are two investors
for each sector and each region: one domestic investor and one foreign investor.
By contrast, the GTAP database has only one composite investor for each region-
sector. To disaggregate the original GTAP investment-related variables, we proceed
as follows.
Based on firm-level data from Standard & Poor’s Capital IQ database, we assign
listed firms to GTAP sectors. We extract information on gross operating surplus,
rate of return and depreciation rate for earch firms in the sample. We then generate
initial estimates of the capital stock, the value of depreciation and the level of
investment through the equations described in Figures A1 and A2. These values
are then aggregated to construct corresponding values for a representative firm
corresponding to the GTAP sector classification.
These initial levels are not, however, consistent with the levels of capital,
depreciation and investment for the aggregate single representative firm in the
GTAP database. To preserve the consistency of the GTAP database, we scale the
initial estimates of the values of the capital stock, depreciation and investment for
the domestic and foreign-owned representative firms such that the sum is consistent
with the values in the GTAP database.
The second step is to disaggregate each GTAP sector (the representative firm)
into domestic-owned and foreign-owned firms. We firstly utilize the FDI and FAS
data (Lakatos et al., 2011) to estimate the share of foreign owned capital in each
GTAP sector in each region. For the input structure of the domestic and foreign-
owned firms, we use the FDIR (FDI restrictiveness index) data from OECD and
build a gravity model to estimate the potential growth in FDI when removing FDIR.
Combined with the investment theory in the model, we estimate the phantom tax
and its corresponding income (payment on capital) of the two types of firms. For
the rest of the input structures of the two types of firms we assume they have the
same technology with the original representative firm. Then we use these shares to
split the original representative firm into domestic and foreign owned representative
firm.
372 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
These procedures are described in Figure A1. The derivation of the equations in
Figure A1 is given in Figure A2.
Figure A1. The Procedure in Creating Investment Matrices
Scale to GTAP
aggregate value,
VDEP(r)
Scale to GTAP
aggregate value,
VKB(r)
FDIR j,r Initial estimates of capital
stocks
𝐾𝑗,𝑟 = 𝐺𝑂𝑆𝑗,𝑟
(𝑅𝑂𝑅𝑗,𝑟 +𝐷𝑗,𝑟 )
Initial estimates of investment
𝐼𝑗,𝑟 ൫𝐾_𝐺𝑗,𝑟 + 𝐷𝑗,𝑟 ൯ 𝐺𝑂𝑆𝑗,𝑟
(𝑅𝑂𝑅𝑗,𝑟 + 𝐷𝑗,𝑟 )
Final investment estimates
VINV2j,r
Final start- of-year
capital stock estimates
VKB2j,r
Final depreciation
estimates
VDEP2j,r
Final end- of-year capital stock
estimates
VKE2j,r
Initial estimates of depreciation
VD j,r = D j,r K j,r
aggregated value,
REGINV(r)
Scale to GTAP
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 373
ⓒ 2017 East Asian Economic Review
Figure A2. Derivation of the Equations in Figure 1
The value of investment in each sector can be inferred from the capital accumulation
formula:
(E1.1)
where and are the values of the sector’s capital stock at the beginning and the
end of the year, respectively;
is the sector’s depreciation rate; and
is the value of investment in the sector during the year.
From (E1.1) we have:
(E1.2)
where k is the growth rate of capital stock in the sector, .
If the values of , and D are known, I can be calculated directly from (E1.2).
While plausible official statistics are available for D, this is not the case for the opening
and closing capital stocks. Accordingly, we use available data on gross operating
surplus (GOS) by sector and the return on capital by sector to derive sector-specific
investment.
The net rate of return on capital is given by:
(E1.3)
where is the net rate of return on the sector’s capital in the period; is the gross
operating surplus (or capital rental) in the sector. From (E1.3), we have:
(E1.4)
From (E1.2) and (E1.3) we have:
(E1.5)
1 0 (1 )K K D I
0 K
1 K
D
I
0 ( _ )I K K G D
1
0
_ 1 K
K G K
0 K _K G
0
GOS ROR D
K
ROR GOS
0 ( )
GOS K
ROR D
( _ )K G D GOS I
ROR D
374 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
To construct the foreign affiliate sales (FAS) matrix ( ) we start with the
FAS data in Lakatos et al. (2011). This FAS dataset has three dimensions: sectors,
host countries and source countries. We sum across source countries to get the
overall sales of foreign affiliates in sector j and host country h. Then, we divide
this by the total domestic sales of products in host country h ( ) to estimate
the penetration rates of foreign firms (FAS/TS) in each sector and region. This
penetration ratio is then used to allocate sales in the GTAP database to the
representative domestic and foreign-owned firms.
Similarly, the FDI matrix ( ) sourced from Fukui and Lakatos (2012) also
has three dimensions. We sum across source countries, then divide by the capital
stock matrix ( ) to estimate the percentage of capital stock (FDI/VKB) that
is owned by foreign investors in each sector and region.
Fukui and Lakatos (2012) argue that FAS data provide better information about
the operations of foreign affiliates than data on international flow of funds. So, to
integrate the FAS dataset, we apply the following regression model to estimate the
FDI/VKB ratios:
(E2.1)
where
is the 1197x1 matrix representing the ratios of FDI to VKB across
21 regions and 57 sectors;
is the 1197x1 matrix representing the ratios of FAS to TS across 21
regions and 57 sectors;
captures country-specific factors;
captures industry factors; and
, , , are the regression coefficients.
We take the average of the fitted ratio from (E2.1) and the actual FDI/VKB
ratios (excluding outliers) to get the final estimated share of capital stock that is
,
s
j r FAS
,j r TS
,
s
j r FDI
, 2
j r VKB
0 1 2 3 _ _FDI VKB FAS TS countrydummy industrydummy
_FDI VKB
_FAS TS
countrydummy
industrydummy
0
1
2
3
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owned by foreign investors. Then we use these ratios to split the capital stock
matrix ( ) into the origin-specific capital stock matrix ( ).
For the investment matrix ( ) we assume that firms owned by domestic
and foreign investors have the same depreciation rate. Provisionally, we also assume
they have the same capital growth rate, although this is a restrictive assumption
since we know that foreign investment has been growing more rapidly than overall
investment. Using Equation (E1.2) in section 1, , we calculate
the origin-specific investment matrix.
Next, we apply the concept of a “phantom tax” to break down the gross operating
surplus of a given region-sector into gross operating surplus for domestic and foreign
owned capital ( ) as shown in Figure A3. A phantom tax restricts entry of
FDI but does not result in the collection of revenue. Intuitively, the phantom tax
restricts the entry of FDI notwithstanding higher returns to foreign capital. With
the removal of the phantom tax, foreign capital has an incentive to take advantage
of the higher returns by increasing investment, thus expanding the FDI stock.
We start with the FDI restrictiveness matrix, then quantify the effect on FDI of
the restrictions-that is we determine by how much the share of FDI in the region-
sector capital stock would increase if we remove all barriers. We then use these
results to estimate the phantom tax applying to FDI in each region-sector. We assume
that the phantom tax creates a wedge between the rates of return of domestic versus
foreign-owned capital; this allows us to then derive the gross operating surplus
matrix ( ).
To estimate by how much the FDI stock will increase if we remove all FDI barriers,
we use the following gravity-like econometric specification:
(E2.2)
where
, 2
j r VKB
, 2
s
j r VKB
, 2
s
j r VINV
0 ( _ )I K K G D
,
s
j r GOS
,
s
j r GOS
, , 0 1 , 2 3 4 5
6 , 7 , 8 9 , 2 1 2
j h s j h s h s row
j h j h h j h
FDI GDP GDP GDPPC GDPPC GDP
SK U FDIR TOI TOI
376 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
is the log of FDI stocks in sector j, host country h, and source country s;
is the log of output of sector j, in host country h;
is the log of GDP of source country s;
is the log of GDP per capita of host country h;
is the log of GDP per capita of source country s;
is the log of GDP of the rest of the world;
is the ratio of skilled to unskilled workers of host country h, relative
to that of source country s;
is the FDI restrictiveness index in sector j, host country h;
is the trade openness index in host country h (i.e., imports plus export,
divided by GDP);
is the trade openness index of sector j in host country h; and
are the regression coefficients.
In this econometric exercise, we use all the 44 regions in the OECD dataset, and
extract the data of the corresponding 44 regions from the full GTAP v8 database.
So the sample size is 110,352 (i.e., 57*44*44). The regression results show that
the elasticity of FDI stocks with respect to the FDI restrictiveness index ( ) is
equal to -2.2 (see Appendix 2).
For example, if Australia remove all the barriers of FDI in electricity sector (FDI
restrictiveness index =0.175), it would lead to a 38.5% increase in the FDI stock
in that sector.
In calibrating the model, we assume that the initial phantom tax rates have been
set so as to equalize the after-tax return from reallocating a unit of capital from
domestic producers to foreign producers, while simultaneously collecting no net
revenue. That is, for given values for , , , and , set
and 13 such that:
13 To simplify the notation here, we omit the subscripts denoting sectors and regions.
, ,j h s FDI
,j h GDP
s GDP
h GDPPC
s GDPPC
row GDP
, 2
j h SK U
,j h FDIR
1 h
TOI
, 2
j h TOI
0 9 ~
7
row R
dom R
row GOS
dom GOS
row T
dom T
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 377
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(E2.3)
(E2.4)
(E2.5)
(E2.6)
(E2.7)
(E2.8)
where
dom
R and are the gross returns on domestic and foreign capital;
and are equal to 1 minus the phantom tax τ, thus 1- τdom and 1-τrow
respectively;
and are the values of gross capital rental payments on
domestic and foreign capital;
and are the values of capital stock owned by domestic and
foreign investors; and
and are the ratios of domestic and foreign GOS to the overall GOS in
the sector.
By solving (E2.3) and (E2.4), we have
(E2.9)
We substitute (E2.5) and (E2.6) into (E2.9) to get
( ) /( ) 1 row row dom dom
R T R T
(1 ) (1 ) 0 dom dom row row
GOS T GOS T
/ row row row
R GOS VKB
/ dom dom dom
R GOS VKB
/( ) row row row dom
S GOS GOS GOS
/( ) dom dom row dom
S GOS GOS GOS
row R
dom T
row T
dom GOS
row GOS
dom VKB
row VKB
dom S
row S
1 ( )
row row dom row
dom
R T S S
R
378 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
(E2.10)
(E2.11)
From above, we calculate the and . Once we know the level
of the phantom tax (τrow), we can derive the share of share of foreign and domestic GOS from (E2.10). Given the total sectoral GOS in the GTAP database, the origin-
specific GOS matrix ( ) can thus be determined.
Next, to derive the expression linking a change in the level of the phantom tax
(τrow) to a change in FDI, consistent with the calculation where the change in FDI was linked to a change in the FDI restrictiveness index.
Proceeding by way of example, in the previous section we determined that full
elimination of FDI restrictions would result in a 38.5% increase in FDI in Australia’s
electricity sector. Assuming an elasticity of capital supply with respect to the rate
of tax of 0.3 on a provisional basis,14 this implies the elimination of FDI restrictions
is equivalent to an effective tax cut of about 128% (=38.5%/0.3). With this
information in hand, the system of equations above generates both the phantom tax
and the respective RORs for the domestic and foreign-owned sectors (recall: the
wedge in RORs between domestic and foreign-owned capital emerges due to the
presence of restrictions on FDI).
In the Australian electricity sector, the real gross return on foreign capital is
calculated to be about 19%, and on domestic capital about 7.8%. These estimates
are consistent with a phantom tax on foreign capital of about 56% and a negative
phantom tax (i.e., a subsidy) of 6.6% on domestic capital.
To see this, recall that
Thus, (.19 * (1-0.562))/(.078 * (1+.066)) = 1
14 This estimate is drawn from the MONASH model.
( )
row row
row dom row
VKB S
VKB VKB T
1 dom row
S S
dom VKB
row VKB
,
s
j r GOS
( ) /( ) 1 row row dom dom
R T R T
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 379
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The elimination of the phantom tax implies that the expression goes
from .438 (= 1-0.562) to 1.0; this is an increase of 128% (=(1/0.438)-1, expressed
as a percent).
Finally, from the VKB determined above, the foreign-owned Australian electricity
capital stock is about 3,500mn USD. Given the 19% gross rate of return for
foreign-owned capital determined by the procedure above, we obtain the GOS for
foreign-owned capital of about 665mn USD.
The Investment Function
In the Monash investment function, the growth rate of capital (and hence the
level of investment) is determined by investors’ willingness to supply increased
capital to industry j in region r ( ), which in turn depends on changes in the
expected rate of return for capital in that sector and region. Assuming that investors
are cautious, any shock to the rate of return in a given sector and region is, however,
eliminated only gradually. This results in similar treatment of investment as in
models that incorporate costs of adjustment that are positively related to the level
of investment in a given year (based on, e.g., construction /installation costs of
capital suppliers). The MONASH model, however, instead of relying on increasing
adjustment costs as the mechanism to limit investment, incorporates investor
perceptions of risk for this purpose. Thus, following the MONASH model, we
assume that investors are willing to support capital growth in industry j in region r
in year t to move above the historically normal rate of capital growth for this sector-
region, only if they expect the rate of return to be above the sector-region’s
historically normal level (see Dixon and Rimmer, 1998 for a discussion). Accordingly,
the supply of capital in the GTAP-M3 model can be described by the following
equations:
row T
, _
j r K G
1
,
, 0
,
_ 1 j r
j r
j r
K K G
K
, , , , , ,
, , , ,
,
(1/ ) *[ln( _ _ _ ) ln( _ _
_ ) ln( _ _ _ ) ln( _ _
_ )]
j r j r j r j r j r j r
j r j r j r j r
j r
EROR RORN C K G K G MIN K G MAX
K G TREND K K G MIN K G MAX
TREND K
380 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
where
are the growths of capital stock of sector j in region r;
and are the capital stocks in end and start of period of sector j in
region r;
are the expected rates of return of sector j in region r;
are the historically normal rates of return of sector j in region r;
are the minimum possible rates of growth of capital equal to
the negative of the depreciation rate in each sector j in region r;
are the maximum feasible rates of capital growth;
are the historically normal capital growth rates of sector j in
region r; and
are positive parameters.
Figure A3 shows the diagrammatical relationship between the growth in capital
stock (K_G) and the expected rate of return (EROR). Once we know the expected
rates of return, the growth of capital stocks can be found.
Figure A3. Capital-supply Function in MONASH
, _
j r K G
1
,j r K
0
,j r K
,j r EROR
,j r RORN
, _ _
j r K G MIN
, _ _
j r K G MAX
, _
j r TREND K
,j r C
EROR
RORN
K_G_MIN TREND_K K_G_MAX K_GR
Quantifying the Comprehensive and Progressive Agreement for Trans-Pacific Partnership 381
ⓒ 2017 East Asian Economic Review
This equation may be understood as follows. For industry j in region r to attract
sufficient investment in year t to achieve its trend capital growth rate, ,
the expected rate of return must equal the normal rate of return .
If the expected rate of return is higher (lower) than the normal rate, investment into
that section-region will exceed (fall short of) the amount needed to sustain capital
growth at the historically observed trend.
Also we know the following dynamic linkage between capital stock and investment:
,
where D is the depreciation rate of the physical capital stock. Thus we can derive
the investment of sector j in region r.
Regression Results for FDI on FDI Restrictiveness Index
OLS, using observations 1-110352 (n = 88952)
Missing or incomplete observations dropped: 21400
Dependent variable: l_FDIS
Coefficient Std. error t-ratio p-value
constant -66.545 3.714 -17.920 0.0000 ***
l_GPIH 1.038 0.005 230.400 0.0000 ***
l_GDPS 0.867 0.010 84.480 0.0000 ***
l_GPPH 1.359 0.017 78.490 0.0000 ***
l_GPPS 2.325 0.018 128.100 0.0000 ***
l_GPRW 0.377 0.204 1.849 0.0644 *
SK2U 0.412 0.054 7.630 0.0000 ***
FDIR -2.246 0.063 -35.460 0.0000 ***
TOI2 0.000 0.000 9.445 0.0000 ***
TOI1 0.935 0.030 31.510 0.0000 ***
, _
j r TREND K
,j r EROR
,j r RORN
1 0
, , , , (1 )
j r j r j r j r K K D I
382 Dan Ciuriak, Jingliang Xiao and Ali Dadkhah
ⓒ Korea Institute for International Economic Policy
Mean dependent variable -3.81135 S.D. dependent variable 5.529016
Sum squared residuals 1141196 S.E. of regression 3.582009
R-squared 0.580324 Adjusted R-squared 0.580282
F(9, 88942) 13665.38 P-value(F) 0
Log-likelihood -239708 Akaike criterion 479436.8
Schwarz criterion 479530.7 Hannan-Quinn 479465.5
Log-likelihood for FDIS = 99319
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First version received on 1 August 2017
Peer-reviewed version received on 13 September 2017
Final version accepted on 26 December 2017
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