OSIPP Discussion Paper: DP-2007-E-008

Regional Integration, Sectoral Adjustments andNatural Groupings in East Asia
September 28, 2007
Hiro Lee
Professor, Osaka School of International Public Policy, Osaka University

Dominique van der Mensbrugghe
Senior Economist, The World Bank
[Keywords] Regional integration; FTA; RCA; East Asia; CGE model[JEL codes] F13, F15[Abstract] Although East Asian countries were relatively inactive in signing free trade
agreements (FTAs) until the end of 1990s, a number of FTAs involving East Asian
countries have been signed since the turn of the century. Because sectoral interests canexert significant influence on policy negotiations, the sectoral results would beparticularly important for political economy considerations. The objective of this study
is to compare welfare gains and sectoral adjustments resulting from various FTA
scenarios in East Asia using a dynamic global computable general equilibrium (CGE)model. The RCA rankings of commodities with various FTA scenarios and those with theglobal trade liberalization are correlated to examine how “natural” each grouping would
be. The results suggest that the ASEAN+3 FTA, with relatively large welfare gains andsmall structural adjustments, could be a facilitating intermediate step towards global
free trade. Some of the smaller FTAs, such as the ASEAN-China and ASEAN-Korea
FTAs, would result in large structural adjustments for ASEAN countries.

We are grateful for helpful comments on earlier versions to Fukunari Kimura, Jong-Wha Lee,
Kuo-Ching Lin, and the participants at the 2007 Far Eastern Meeting of the EconometricSociety, Taipei, 11-13 July 2007, and the seminar at National Taiwan University. Lee gratefully
acknowledges financial support from Japan Society for the Promotion of Science, grants-in-aid
for basic research (B), No. 19330050. The authors are solely responsible for any remaining
errors. Corresponding author: Hiro Lee, OSIPP, Osaka University, 1-31 Machikaneyama-cho,
Toyonaka, Osaka 560-0043, Japan. Email: hlee@osipp.osaka-u.ac.jp

11. Introduction
In the past decade the number of free trade agreements (FTAs) has proliferated
rapidly. Other than ASEAN Free Trade Agreement (AFTA), East Asian countries wererelatively inactive in signing FTAs until the end of 1990s. Since 2001, however, a number
of FTAs involving East Asian countries have been implemented or signed, including
Singapore-New Zealand (2001), Japan-Singapore (2002), Singapore-Australia (2003),
Singapore-U.S. (2004), Korea-Chile (2004), ASEAN-China (2005), Japan-Mexico (2005),
Thailand-Australia (2005), Korea-Singapore (2006), and ASEAN-Korea (2006).1 Korea
and the United Stated signed an FTA in June 2007, but it remains to be seen whether the
U.S. Congress and the Korean National Assembly would ratify the bilateral pact. A large
number of FTAs are currently being negotiated in East Asia, including ASEAN-Japan,
China-Australia, and Japan-Korea. The ASEAN+3 group, consisting of the ASEAN
countries, China, Japan, and Korea, has provided an effective mechanism for greater
cooperation and gradual regional economic integration in East Asia. The trends innegotiating for new FTAs are likely to grow in East Asia.2
Whether the growth of FTAs has a positive or negative impact on multilateral tradeliberalization under the World Trade Organization (WTO) has been debated intensely (e.g.,
Krueger, 1999; Panagariya, 2000; Lloyd and MacLaren, 2004). 3 Since the outcome
depends upon the choice of the objective function (e.g. economic welfare versus politicalobjective), the height and structure of initial trade barriers and other variables,4 it would be
helpful to examine the actual record. The earlier studies (e.g., World Trade Organization,

1 The ASEAN-Korea FTA signed in May 2006 was initially limited to trade in goods, and some

agricultural products including rice was left out. Thailand decided not to sign the FTA because of the
exclusion of rice.
2 See Kawai (2004), Feridhanusetyawan (2005), and Lee and Park (2005) for more detailed discussion

on the proliferation of FTAs in East Asia.
3 Proponents for regional integration argue that FTAs encourage member countries to liberalize beyond
the level committed by multilateral negotiations and that they are an effective way to promoteeconomic integration (e.g., Kahler, 1995; Dee, 2007). In addition, FTAs are likely to induce dynamic
effects that might contribute to member countries’ growth through the accumulation of physical andhuman capital, productivity growth, and accelerated domestic reforms (e.g., Ethier, 1998; Fukase andWinters, 2003). Opponents worry that the proliferation of FTAs is likely to undermine the multilateral
trading system and that beneficiaries of FTAs might form a political lobby to deter further multilateralliberalization (e.g., Bhagwati, 1995; Srinivasan, 1998a, 1998b; Panagariya, 1999a).
4 See, for example, Panagariya (1999b), Schiff and Winters (2003), and Lloyd and MacLaren (2004)

for reviews on this issue.

2

1995; World Bank, 2000) suggest that the proliferation of regional integration agreements(RIAs) did not delay multilateral liberalization. However, Lloyd and MacLaren (2004)
point out that after the WTO Ministerial Conference in Cancún in September 2003, theUnited States and the EU’s priority has shifted to the completion of bilateral and regional
trade negotiations. Thus, the relationship between the growth of RIAs and multilateralliberalization might have changed.
A number of studies have quantified the effects of various FTAs in East Asia using
a computable general equilibrium (CGE) model (e.g., Brown et al., 2003; Dee, 2007; Leeet al., 2004; Park, 2006; Scollay and Gilbert, 2001; Urata and Kiyota, 2003; Zhai, 2006).
These studies generally find: (1) most of the FTA members’ real income increases while atleast some of non-members’ real income declines; (2) the larger the economic size of theFTA, the larger the aggregate gain to the members; (3) if FTAs are confined to only tariff
liberalization for merchandize trade, the welfare gains are limited and some of themembers are more likely to become worse off; (4) when FTAs include services tradeliberalization and trade facilitation, the gain to each member increases and the aggregate
loss to non-members decreases; (5) the impact of FTAs becomes greater when the model
incorporates increasing returns to scale and imperfect competition in at least some of theindustries; and (6) the effects of FTAs tend to become more beneficial with the model
incorporating investment and growth effects.
While the aggregate welfare effect of FTAs is certainly important, the sectoral
impact could be of an even greater concern to policy makers. This is because regional
integration might lead to a sharp contraction of output and employment in highly protected
sectors. For example, the agricultural sectors in Japan and Korea are highly subsidized
and shielded against foreign imports by significant tariffs and nontariff barriers. In anumber of developing members, tariffs, industrial policy, and other government policies
protect certain manufacturing industries from foreign competition. Since trade policy isoften formulated from the bottom up, a modern view of national interest, such as thatbased on trade reciprocity, might encounter conflicts with established domestic interests.
Using a dynamic global CGE model, we evaluate the effects of various free trade
agreements involving East Asian countries, emphasizing sectoral adjustments and changes
in the pattern of trade resulting from the formation of FTAs. The next section gives an3

overview of the model. Section 3 provides a brief description of the baseline and policy
scenarios, followed by assessments of computational results in section 4. The final section
summarizes the main policy conclusions.
2. Overview of the Model
The model used in this study, known as the LINKAGE model, is a dynamic global
CGE model developed by van der Mensbrugghe (2005). It spans the period 2001-2015 andall sectors are assumed to be perfectly competitive and operate under constant returns toscale.5 Production in each sector is modeled by a series of nested constant elasticity of
substitution (CES) production functions, which are intended to represent the different
substitution and complementarity relations across the various inputs in each sector. Labor
can have three different skill levels: unskilled, skilled, and highly skilled. The first two aresubstitutable and combined in a CES aggregation function as a single labor bundle. Highly
skilled labor is combined with capital to form a physical plus human capital bundle.
Dynamics in this model is recursive. Population and labor supply growth areexogenous. Capital accumulation is based on past savings and investment. In each period
the supply of primary factors is generally predetermined. The supply of land is assumed to
be sensitive to the contemporaneous price of land, however. Land is assumed to bepartially mobile across agricultural sectors. Thus rates of return are sector-specific, but
sectoral land supply reacts to changes in relative rates of return. Some of the natural
resource sectors also have a sector-specific factor whose contemporaneous supply is pricesensitive. The model incorporates a vintage structure for capital that allows for adjustment
costs. New capital is assumed to be perfectly mobile across sectors, whereas installed
capital is only partially mobile. All else equal, countries with higher savings rates will
have more ‘flexible’ capital since it is assumed that substitution elasticities are higher withnew capital than with installed capital. Labor within each skill category is perfectly mobile
across sectors.

5 The assumption of constant returns to scale is a simplification and generally biases downwards thegains from trade reform because expansion of trade provides scale efficiencies. The introduction ofscale economies raises a number of important issues, each of which could significantly modify the
results, but we prefer to leave out of the current study. They include the lack of data on the minimum
efficient scale and the specification of market structure (e.g., Cournot versus Bertrand competition), thenumber of firms, conjectural variations, and whether there is free entry and exit.

4Factor income accrues to a single representative household, which finances
government expenditures (through direct and indirect taxes) and investment (throughdomestic savings). Domestic savings may be augmented or diminished by a net capital
flow. In the current version of the model, the latter is exogenous in any given time period
for each region, thereby generating a fixed current account balance. Ex ante shocks to thecurrent account—e.g., a reduction in trade barriers—induces a change in the real exchange
rate. Government fiscal balances are also fixed in each time period, and the equilibrating
mechanism is lump-sum taxes on the representative household. For example, a reduction
in tariff revenue is compensated by an increase in household direct taxation.
The basic assumption on trade is that imports originating in different regions areimperfect substitutes (known as the Armington assumption). The model uses a nested
demand structure. Aggregate domestic absorption by sector is allocated between domestic
goods and a single composite import good. The latter is then allocated across region oforigin to determine the bilateral trade flows on a sectoral basis. An analogous dual-nested
structure is used to allocate domestic production between domestic and export markets
using constant elasticity of transformation functions.
The model has four trade prices incorporating four separate instruments. First,
producers receive price PE for exported goods. Second, the FOB price, WPE, includes
domestic export taxes or subsidies. Third, the CIF price, WPM, includes the direct costs ofport-to-port shipping, represented by the ad valorem wedge ζ, as well as a non-monetaryor frictional cost,6 represented by the iceberg parameter λ. Thus the relationship between
the FOB price and the CIF price is given by
WPM r ,r ',i = (1 + ζ r ,r ',i ) WPE r ,r ',i λ r ,r ',i(1)where subscripts r, r', and i denote exporting region/country, importing region/country,
and commodity, respectively. Finally, the domestic price of imports, PM, is equal to the
CIF price, WPM, plus the ad valorem tariff (or tariff-equivalent) rate. In our model, an
increase in λ r ,r ',i represents a reduction in trade-related risk, lower administrative barriers

6 This type of cost is referred to as ‘iceberg’ transport cost, developed by Samuelson (1952) based on aconcept developed earlier by von Thünen. More recently, these have been used in work by Helpman
and Krugman (1985) and Fujita, Krugman, and Venables (1999).

5

to trade (e.g., customs procedures) and/or a fall in technical barrier (e.g., mutual
recognition of product standards). In other words, trade facilitation would increase thevalue of λ r ,r ',i .
Foreign capital flows (e.g., foreign direct investment: FDI) are exogenous in thecurrent version. We acknowledge that endogenous treatment of FDI is very importantwhen we discuss the implications of FTAs because trade and FDI are closely linked. It has
been shown that allowing for capital to flow to countries with relatively high rates ofreturn could significantly raise the gains from trade reform. In our preliminary
investigation on the impact of APEC trade and investment liberalization, Lee and van der
Mensbrugghe (2005) find that an addition of FDI liberalization to trade liberalization
would significantly boost the gains to most of the APEC economies.
Most of the data used in the model come from the GTAP database, version 6,
which provides 2001 data on input-output, value added, final demand, bilateral trade, tax
and subsidy data for 87 regions and 57 sectors.7 For the purpose of the present study, thedatabase is aggregated into 10 regions and 26 sectors as shown in Table 1.
Table 1
Regional and sectoral aggregation
A. Regional aggregation
Country/region

Corresponding economies/regions in the GTAP database

China
Japan
Korea
TaiwanASEAN

China and Hong Kong
Japan
Korea
TaiwanIndonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, rest ofSoutheast Asia
Australia and New ZealandUnited States, Canada, Mexico
Central America and the Caribbean, South America
Austria, Belgium, Denmark, Finland, France, Germany, Great Britain,
Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden,
plus the new member countries since 2004
All the other economies/regions

Australasia
North America
Latin America
EU

Rest of world

7 Dimaranan (2006) gives detailed descriptions of the GTAP database, version 6.

6

Table 1 (continued)
B. Sectoral aggregation
SectorCorresponding commodities/sectors in the GTAP database

Rice
Other grains
Vegetables and fruits
Other crops
Livestock
Natural resources
Fossil fuelFood products
TextilesApparelLeather
Wood products
Paper products
Petroleum products
Chemical products
Mineral products
Iron and steel
Nonferrous metal
Metal products
Machinery
Electronic equipment
Motor vehicles
Other transport equip.
Other manufactures
Trade and transport
Services

Paddy rice, processed rice
Wheat, cereal grains n.e.s.
Vegetables and fruits
Oil seeds, sugar cane and sugar beet, plant-based fibers, crops n.e.s.
Bovine cattle, sheep and goats, animal products n.e.s.
forestry, minerals
Coal, oil, gas
Fishing, food products, beverages and tobacco products
TextilesWearing apparel
Leather products
Wood products
Paper products and publishing
Petroleum and coal products
Chemical, rubber and plastic products
Non-metallic mineral products
Iron and steel
Nonferrous metal
Fabricated metal products
Machinery and equipment
Electronic equipment
Motor vehicles and parts
Other transportation equipment
Manufactures n.e.s.
Trade, sea transport, air transport, transport n.e.s.
Construction, public utilities, communication, financial services,other services

Source: GTAP database, version 6.

3. The Baseline and Policy Scenarios
3.1

The Baseline Scenario

To evaluate alternative FTA scenarios in East Asia, we first establish a baseline,
which shows the path of each economy in the absence of any FTAs over the period 20012015. In the baseline, several key variables, including GDP growth rates, population andlabor supply, are predetermined by the exogenous assumptions. Projections of real GDP,
population and labor supply are broadly consistent with the World Bank’s long-term
forecast. We assume that the trade and transport margin declines by 1 percent per annumin every country. The dynamics are calibrated in each country/region by imposing theassumption of a balanced growth path. This implies that the ratio between labor and the7capital/fixed-factor bundle (in efficiency units) is held constant over time.8 When policy
scenarios are simulated, the growth of capital is endogenously determined by the savinginvestment relation.
Several assumptions underline the calibration of productivity. Agricultural
productivity is fixed in the baseline using results from recent empirical studies. Sectoral
productivity in non-agricultural sectors is composed of three components: a uniform
economywide factor that is calibrated to achieve the given GDP target, a sector-specificfactor related to openness, and a constant shifter. The sector-specific factor intended tocapture openness-sensitive changes in productivity, χi,t, is given byη⎛ Ei ,t ⎞ i⎟
χ i ,t = φ i ,t ⎜⎜
⎟
X
i
,
t
⎝
⎠

(2)

where Ei,t is exports of commodity i, Xi,t is output of commodity i, φi,t is a shift parameter,and ηi is the elasticity of productivity with respect to openness. φi,t is calibrated in thebaseline scenario so that the trade-sensitive portion of sectoral productivity is some shareof total productivity.9
There are four types of taxes in the model: commodity taxes, production taxes,
income taxes and trade taxes (consisting of tariffs and export taxes). Commodity andproduction taxes are held constant throughout the simulation period. The marginal income
tax rate adjusts to maintain a given government budget surplus or deficit. For the final
implementation of the Uruguay Round, the tariff and export tax rates are lowered during
the 2001-2005 period. The Agreement on Textiles and Clothing (ATC) that have phased
out import quotas on textiles and apparel are also incorporated in the baseline. Finally,
China and Taiwan’s WTO accession and the resulting reductions in their tariff rates arealso included.

8 This involves computing in each period a measure of Harrod-neutral technical progress in thecapital/fixed-factor bundle as a residual, given that the growth of the labor force (in efficiency units) is
pre-determined. This is a standard calibration procedure in dynamic CGE modeling.
9 Three main channels have been identified linking openness with productivity: imports of technology-

laden intermediate inputs (for example fertilizers in agriculture), imports of capital goods, and export
market penetration (with the requirement to produce to a higher standard than at home to be able topenetrate new markets; expanding foreign markets can also lead to scale economies). Much empirical
work is ongoing trying to identify the extent to which each one of these channels operates. At a macrolevel, there are to some extent observationally equivalent to the extent that current account balances are
more or less exogenous. de Melo and Robinson (1990) and Dessus et al. (1999) take an approachsimilar to ours. Das et al. (2001) have explored some firm-level characteristics of export supply
response.

83.2

Policy Scenarios

To evaluate sectoral adjustments and changes in the pattern of trade resulting fromprospective free trade agreements in East Asia, the following seven policy scenarios are
considered:
1) ASEAN-China FTA: Free trade among the ASEAN countries and China10
2) ASEAN-Japan FTA: Free trade among the ASEAN countries and Japan3) ASEAN-Korea FTA: Free trade among the ASEAN countries and Korea
4) China-Japan-Korea FTA: Free trade among China, Japan and Korea
5) ASEAN+3: Free trade among the ASEAN countries, China, Japan and Korea
6) ASEAN-EU: Free trade among the ASEAN and EU member countries
7) Global trade liberalization (GTL): Complete abolition of import tariffs and export
subsidies
While the likelihood of actually completing the above trade liberalization or FTAs withina reasonable time horizon differs significantly across scenarios, it is worth examining eachof them. Scenario 1 is under implementation as ASEAN countries and China signed aframework agreement in 2002 to establish the FTA for trade in goods by 2010 for China
and ASEAN-6 and by 2015 for newer ASEAN member states.11 Scenario 2 that excludes
sensitive sectors is likely to be realized as ASEAN and Japan signed an agreement on theComprehensive Economic Partnership (CEP) in October 2003 and the negotiation is
expected to be concluded by the end of 2007.12 An FTA between ASEAN and Korea(scenario 3), initially limited to trade in goods and excluding some agricultural products
such as rice, was signed in May 2006. The proposal for China-Japan-Korea FTA (scenario4) has been considered by the governments of the three countries (Wong et al., 2004), and

10 Throughout the paper Hong Kong is included in China, as indicated in Table 1.
11 ASEAN-6 refers to the original ASEAN members; i.e., Brunei, Indonesia, Malaysia, Philippines,

Singapore, and Thailand. Newer ASEAN member states are Cambodia, Lao PDR, Myanmar, andVietnam.
12 Yamazawa and Hiratsuka (2003) provides an overview of ASEAN-Japan Comprehensive Economic

Partnership.

9

a joint research on economic cooperation among these countries has been undertaken by
the Development Research Center of the State Council of China, the National Institute for
Research Advancement of Japan, and the Korea Institute for International Economic
Policy. Although negotiations for an FTA among the economies of ASEAN+3 have notyet begun, we include scenario 5 because a number of studies have examined the possible
effects of such an arrangement (e.g., Brown et al., 2003; Lee and Park, 2005). In addition,in May 2007 the ASEAN countries and the EU agreed to start negotiations on acomprehensive trade agreement. Finally, we have the global trade liberalization (GTL)
scenario so that the effects of the FTA scenarios can be compared with those of the global
scenario.
In all FTA experiments, we gradually remove bilateral tariffs and export subsidiesof the relevant sectors among the member countries over the 2007-2012 period. We set theelasticity of productivity with respect to openness, ηi, to 0.5 in agricultural sectors and to
1.0 in all other sectors. We assume that frictional trade costs (e.g. administrative barriers
and trade-related risk) would be reduced by 2.5 percent in all FTA scenarios and the GTL
scenario.13

4. Results
4.1

Effects on Welfare
The welfare results for the seven policy scenarios, as deviations in equivalentvariations (EV) from the baseline in 2015, are summarized in Table 2. The GTL scenario(scenario 7) is the most attractive for all countries and regions. To be realistic, however,the WTO process is fraught with uncertainty about the scope, depth, and timeliness ofmultilateral commitments to abolish trade barriers. Although the Doha Round started in
2001, there have been disagreements between developed countries and developing
countries on a number of issues, particularly on the extent of reductions in agricultural
subsidies in the United States and the EU and tariff caps. As a result, the negotiations of

13 Keuschnigg and Kohler (2000) and Madsen and Sorensen (2002) use a 5 percent reduction in realcosts of trade between the EU-15 and Central and East European countries. We use a smaller reductionin these costs because the reductions in technical barriers are expected to be negligible for FTAs in East
Asia as well as for GTL compared with EU enlargement.

10the current round have been deadlocked a number of times.14 This kind of uncertainty has
been an important impetus to regional agreements, particularly those between small
groups of nations who find consensus, implementation and monitoring easier.

Table 2
Effects on welfare (deviations in equivalent variations from the baseline in 2015)
Scenario
Country/region(1)
ASEANChina(2)
ASEANJapan

(3)
(4)
(5)
ASEANChinaASEAN
Korea Japan-Korea plus 3(6)
ASEANEU(7)GTL

A. Absolute deviations (US$ billion in 2001 prices)
China
26.8
-2.9
Japan
-1.7
19.7
Korea
-1.1
-0.6
Taiwan
-1.1
-0.7
ASEAN
27.2
15.3
Australia/New Zealand
-0.2
-0.3
North America
-0.3
-0.9
Latin America
0.0
-0.3
EU
-1.9
-0.8
Rest of the world
-1.8
-1.6

-2.5
-0.9
9.1
-0.4
11.8
-0.2
-0.4
-0.6
-0.4
-1.6

58.7
29.8
16.8
-1.5
-3.8
-0.5
-1.8
-1.1
-0.7
-2.0

64.9
29.7
16.2
-2.6
23.5
-0.8
-2.8
-1.3
-2.5
-4.5

-2.3
-0.5
-0.6
-0.5
9.9
-0.2
-0.3
-0.9
88.0
-7.1

69.7
48.2
23.7
12.1
22.6
11.4
130.8
36.1
76.2
108.1

East Asia total
World total

50.0
45.7

30.8
26.8

17.1
13.9

100.0
93.8

131.7
119.7

6.0
85.5

176.3
538.8

B. Percent deviations
China
Japan
Korea
TaiwanASEAN
Australia/New Zealand
North America
Latin America
EU
Rest of the world

1.1
0.0
-0.2
-0.3
2.8
0.0
0.0
0.0
0.0
0.0

-0.1
0.5
-0.1
-0.2
1.6
-0.1
0.0
0.0
0.0
0.0

-0.1
0.0
1.4
-0.1
1.2
0.0
0.0
0.0
0.0
0.0

2.4
0.7
2.5
-0.4
-0.4
-0.1
0.0
-0.1
0.0
-0.1

2.7
0.7
2.4
-0.6
2.5
-0.2
0.0
-0.1
0.0
-0.1

-0.1
0.0
-0.1
-0.1
1.0
0.0
0.0
-0.1
1.1
-0.2

2.9
1.2
3.5
2.9
2.4
2.3
0.9
2.2
1.0
2.8

East Asia total
World total

0.6
0.1

0.4
0.1

0.2
0.0

1.2
0.3

1.6
0.3

0.1
0.2

2.1
1.5

In the ASEAN-China FTA (scenario 1), EV of ASEAN increases by 2.8 percent,whereas EV of China increases by a smaller percentage (1.1%), compared with thebaseline values in 2015. This largely results from two factors: (1) the share of ASEAN’s

14 See, for example, Langhammer (2004) for causes and triggers of the setback at the WTO Ministerial

Conference in Cancún in September 2003.

11

exports to China is significantly larger than the share of China’s exports to ASEAN, and(2) the exports to output ratio is substantially higher for ASEAN countries. The welfare
effects on non-member countries are negligible except for Korea and Taiwan, which
experience 0.2 and 0.3 percent declines in their EVs. When ASEAN and Japan form an
FTA (scenario 2), ASEAN’s EV increases by 1.6 percent while Japan’s EV increases byjust 0.5 percent in 2015. Welfare of other East Asian countries (China, Korea, andTaiwan) declines very slightly. In the ASEAN-Korea FTA (scenario 3), the corresponding
changes for ASEAN and Korea’s EV are 1.2 and 1.4 percent.
When a free trade area is formed among China, Japan, and Korea (scenario 4),
China and Korea are expected to accrue relatively large welfare gains (2.4% and 2.5%,
respectively) while Japan’s welfare is expected to rise by 0.7 percent. This is largely
because (1) China has relatively high pre-FTA tariffs, 15 and (2) Korea will havepreferential accesses to the large Chinese and Japanese markets and its exports to China
and Japan, which already constitute large shares of Korea’s total exports, will increase
dramatically. Under the trilateral FTA, China, Korea and Japan respectively obtain 84, 71
and 62 percent of the GTL’s benefits. Thus, the China-Japan-Korea FTA could be a very
attractive stepping stone to globalization for the three countries although large politicalobstacles must be surmounted to achieve such an FTA.
Under the ASEAN+3 FTA scenario (scenario 5), the welfare of all members
increases although the welfare gain for Japan and Korea are somewhat smaller than under
the trilateral FTA. These small losses are more than offset by the ASEAN and China’s
gains and East Asia as a whole is expected to gain $131.7 billion (1.6%) in 2015,
compared with its $176.3 billion (2.1%) gain under the GTL. In other words, East Asia
will be able to attain three-quarters of GTL’s benefits from the ASEAN+3 FTA.
If free trade between ASEAN and the EU is realized (scenario 6), ASEAN is
expected to realize a 1.0 percent gain in its welfare, which is surprisingly smaller than thegain expected from an FTA with China, Japan, or Korea. The EU’s welfare is predicted toincrease by 1.1 percent. One of the reasons for a relatively small welfare gain for ASEAN
is that its trade with East Asian countries, particularly with China, is projected to grow

15 The countries with high pre-FTA tariffs generally gain proportionally more than those with low

tariffs because of larger gains to their consumers resulting from greater reductions in import prices.

12significantly faster than that with the EU during the simulation period. For example, by2015 ASEAN’s exports are predicted to increase by $81 billion under the ASEAN-China
FTA scenario, but they are predicted to increase by only $40 billion under the ASEAN-EUFTA scenario (compared with the baseline exports for ASEAN).
4.2

Effects on Sectoral Output
While the aggregate welfare and trade results are of interest in themselves, themost useful results are at the industry level, where structural adjustments and resource
reallocations occur in response to policy changes. Because sectoral interests can exert
significant influence on policy negotiations, the sectoral results would be most importantfor political economy considerations. In this section we examine the effects of alternative
policy scenarios on sectoral output.
Tables 3-6 summarize output adjustments for the 26 sectors in China, Japan, Korea
and ASEAN countries under the policy scenarios in which each country/region is aparticipant. Sectoral adjustments are expressed in percent deviations from the baseline forthe year 2015. They are large in most of the agricultural and food sectors for East Asia ingeneral and Japan and Korea in particular because of high trade barriers in these twocountries.
China’s agricultural and food sectors expand under the China-Japan-Korea FTA
and ASEAN+3 FTA scenarios (Table 3). In particular, the percentage increases in output
of other crops are over 150 percent relative to the baseline in 2015 under these two
scenarios although in absolute values, these increases are smaller than rice, other grains,vegetables and fruits, and food products.
Among the manufacturing sectors, output of other transportation equipment
increases 15-16 percent when the ASEAN countries are included in the FTA. Output of
the apparel and leather sectors increases moderately when Japan is an FTA member, but
the increase is small relative to the GTL scenario because China’s exports of theseproducts to North American and the EU are much larger. The motor vehicle sector

13contracts when Japan and Korea are FTA members, but the extent of contraction isrelatively small compared with the GTL scenario.16

Table 3China’s sectoral output adjustments under alternative scenarios(percent deviations from the baseline for the year 2015)
Scenario
Commodity/sector(1)
ASEANChina(4)
ChinaJapan-Korea

(5)
ASEAN
plus 3

(7)
GTL

Rice
Other grains
Vegetables and fruits
Other crops
Livestock
Natural resources
Fossil fuelFood products
TextilesApparelLeather
Wood products
Paper products
Petroleum products
Chemical products
Mineral products
Iron and steel
Nonferrous metal
Metal products
Machinery
Electronic equipment
Motor vehicles
Other transport equip.
Other manufactures
Trade and transport
Services

0.4
0.3
0.2
-0.2
0.8
-0.4
0.3
1.4
0.9
-0.2
2.4
-1.5
0.1
2.4
-2.2
0.0
0.7
0.1
0.3
0.4
2.0
2.0
16.0
-0.5
0.0
0.3

26.7
27.0
5.5
156.9
3.1
-0.9
0.2
7.0
-0.6
5.0
1.4
0.8
-0.3
-1.0
-0.9
-0.5
-2.0
-2.1
-0.7
-1.2
1.2
-4.4
0.3
-0.5
0.1
0.4

19.8
26.7
5.3
157.6
3.3
-1.1
0.4
7.3
-0.2
3.9
3.0
-0.7
-0.1
1.2
-2.7
-0.5
-1.4
-2.0
-0.5
-1.0
1.9
-4.8
15.3
-0.6
0.1
0.4

17.1
-50.9
4.9
2.4
5.4
-2.5
-4.4
3.9
11.4
20.7
17.9
1.9
-0.1
-3.2
-3.1
0.1
-3.5
-6.5
1.8
-2.6
0.9
-13.9
13.9
0.0
0.3
0.2

Japan’s rice sector contracts significantly under all four scenarios (Table 4). The
contractions of other agricultural and food products, particularly other grains, are muchsmaller under the three FTA scenarios than under the GTL scenario. Under the ChinaJapan-Korea FTA and ASEAN+3 FTA scenarios, Japan’s textile sector expands by 19-21
16 Under the China-Japan-Korea FTA and ASEAN+3 FTA scenarios, large increases in China’s

imports of motor vehicles from Japan and Korea are partially offset by reductions in its imports from
North America and the EU.

14

percent, largely driven by sharp increases in its exports to China. By contrast, the appareland leather sectors respectively contract by 7-8 percent and 13-15 percent, caused by large
increases in its imports from China and to a much lesser extent from increases in its
imports from other member countries. Under the ASEAN+3 FTA scenario, output of steel
increases by 4 percent, about the same percentage increase as the GTL scenario largely
because about three-quarters of Japan’s steel exports are shipped to other East Asian
countries.

Table 4
Japan’s sectoral output adjustments under alternative scenarios(percent deviations from the baseline for the year 2015)
Scenario
Commodity/sector(2)
ASEANJapan(4)
ChinaJapan-Korea

(5)
ASEAN
plus 3

(7)
GTL

Rice
Other grains
Vegetables and fruits
Other crops
Livestock
Natural resources
Fossil fuelFood products
TextilesApparelLeather
Wood products
Paper products
Petroleum products
Chemical products
Mineral products
Iron and steel
Nonferrous metal
Metal products
Machinery
Electronic equipment
Motor vehicles
Other transport equip.
Other manufactures
Trade and transport
Services

-53.2
0.8
0.5
0.3
-0.9
-0.2
-2.2
-2.4
2.9
-0.7
-3.8
-1.1
0.0
0.1
0.6
0.5
3.1
1.3
1.1
-0.1
-0.8
4.4
0.8
0.7
0.0
0.2

-58.0
-33.0
-0.1
-2.1
2.5
-1.0
-3.7
-0.5
20.6
-7.8
-12.7
-2.6
0.0
-0.3
1.9
2.3
1.6
1.8
0.3
2.5
-1.4
-1.3
-3.5
-0.7
-0.1
0.2

-58.5
-34.1
-0.8
-2.7
0.8
-0.2
-3.8
-2.9
18.8
-7.4
-14.8
-2.2
0.0
-0.5
1.4
2.5
4.0
2.9
1.2
2.2
-2.5
2.2
-5.0
0.2
0.0
0.2

-65.7
-87.3
-5.6
-5.8
-6.4
-0.2
-7.9
-14.5
10.2
-7.1
-22.0
-0.9
-0.6
-3.5
1.1
2.3
3.9
-0.9
1.8
0.9
-0.7
14.0
7.6
0.1
0.4
0.4

15

A small contraction in Japan’s motor vehicle industry under the China-JapanKorea FTA scenario needs to be interpreted with caution. Large increases in its exports to
China and Korea increase the price of Japanese automobiles in our model, reducing its
exports to nonmember countries as well as the domestic demand. However, in practice theautomakers are unlikely to raise the price at least in the short run because they usually try
to maintain the market shares. Thus, output of motor vehicles in Japan is likely to increase
even under this scenario.

Table 5
Korea’s sectoral output adjustments under alternative scenarios(percent deviations from the baseline for the year 2015)
Scenario
Commodity/sector(3)
ASEANKorea(4)ChinaJapan-Korea

(5)
ASEAN
plus 3

(7)
GTL

Rice
Other grains
Vegetables and fruits
Other crops
Livestock
Natural resources
Fossil fuelFood products
TextilesApparelLeather
Wood products
Paper products
Petroleum products
Chemical products
Mineral products
Iron and steel
Nonferrous metal
Metal products
Machinery
Electronic equipment
Motor vehicles
Other transport equip.
Other manufactures
Trade and transport
Services

-41.0
-19.7
4.5
7.5
20.0
-4.2
-2.9
14.6
5.6
-1.4
25.5
-5.7
-2.0
2.8
-0.8
-2.6
0.3
0.3
-0.4
-3.3
-3.8
21.3
6.4
2.7
0.7
-0.2

-54.8
-47.3
-20.0
-30.9
36.7
-8.1
-0.3
22.5
21.0
5.0
100.2
-3.3
1.6
17.0
6.1
-1.4
-3.7
-1.4
-1.8
-4.9
-0.6
-1.1
-12.2
10.5
1.0
-0.2

-55.1
-49.8
-19.7
-30.2
35.0
-7.9
-1.9
20.8
23.0
5.6
90.4
-5.9
0.6
14.0
2.1
-2.1
-2.3
0.6
-1.6
-5.1
-2.7
11.7
-15.5
12.7
1.1
-0.3

-55.7
-91.9
-17.3
-68.4
31.1
-14.8
-14.4
16.1
25.8
4.6
68.4
-4.3
-3.6
17.6
-1.8
-7.5
-5.7
-10.3
-2.4
-8.3
-3.4
30.0
22.3
2.8
3.0
-0.2

Like Japan, Korea will experience large output adjustments in the agricultural
sectors under all four scenarios (Table 5). In particular, output of other gains (mainly

16wheat) is predicted to contract by almost 70 percent. Production of rice is also expected tofall by 41-56 percent, and output of vegetables and fruits and other crops will contract
significantly under the China-Japan-Korea FTA, ASEAN+3 FTA, and GTL scenarios.17
Among the manufacturing sectors, output of textiles, leather, petroleum products,
and other manufactures would increase. Although the motor vehicle industry would
expand under the ASEAN-Korea FTA and GTL scenarios, it is expected to contract under
the China-Japan-Korea FTA and ASEAN+3 FTA scenarios largely because Korea’s
imports of motor vehicles from Japan increase substantially.
Under the FTA scenarios in which ASEAN countries are members, the sectoral
adjustments differ significantly across the scenarios (Table 6). For example, the textile,
apparel, and leather industries expand by 13-29 percent under the ASEAN-EU FTA
scenario, whereas these industries either increase only slightly or contract in the other four
FTA scenarios. This is mainly because ASEAN countries have comparative advantage inthese products over the EU, but not over China. In addition, the protection rates on theseproducts in ASEAN were high relative to those in Japan and Korea in the base year (2001).
Output of chemicals and machinery expands substantially when China is a memberof the FTA, and output of motor vehicles and parts contracts noticeably in all scenarios
except the ASEAN-China FTA case.

17 In practice, however, Japan and Korea are extremely unlikely to remove tariffs on imports of rice,wheat, and a number of other agricultural products. Thus, the magnitudes of sectoral adjustments are
expected to be smaller than those reported in Tables 3-6. Lee et al. (2004) provide several FTA
scenarios where trade barriers on agricultural products and processed food are assumed to remain fixed.
They confirm that welfare gains to the FTA member countries become significantly smaller in the absenceof agricultural liberalization.

17

Table 6
ASEAN’s sectoral output adjustments under alternative scenarios(percent deviations from the baseline for the year 2015)
Scenario
Commodity/sector(1)
ASEANChina(2)
ASEANJapan(3)
ASEANKorea

(5)
ASEAN
plus 3(6)ASEANEU

(7)
GTL

Rice
Other grains
Vegetables and fruits
Other crops
Livestock
Natural resources
Fossil fuelFood products
TextilesApparelLeather
Wood products
Paper products
Petroleum products
Chemical products
Mineral products
Iron and steel
Nonferrous metal
Metal products
Machinery
Electronic equipment
Motor vehicles
Other transport equip.
Other manufactures
Trade and transport
Services

-1.2
-10.5
6.7
11.1
1.6
-5.0
-3.7
3.0
-0.8
-1.1
-9.7
-7.1
-1.3
2.0
22.8
0.8
0.9
-3.2
0.4
11.2
1.4
9.2
-8.2
-3.8
-0.5
-0.2

24.7
-0.5
-3.5
9.3
4.4
-3.3
-1.5
11.7
0.3
3.3
0.9
-1.6
-2.1
0.9
-0.8
-2.4
-7.4
-3.6
-0.6
4.6
-0.5
-13.7
11.3
-1.4
-0.6
-0.3

7.1
30.1
-3.8
9.1
0.9
-2.3
-0.2
3.9
0.6
1.9
-2.8
-2.8
-0.6
0.5
2.1
0.9
-0.2
-1.4
1.9
5.4
-0.7
-7.9
12.4
-0.8
-0.6
-0.2

7.1
-2.6
3.6
9.4
3.9
-3.8
-2.1
9.3
-4.8
2.3
-8.2
-2.2
-2.2
-0.3
17.7
-0.8
-7.2
-4.0
-1.6
10.8
1.7
-18.4
-6.0
-3.2
-0.6
-0.3

7.5
-5.4
-3.1
13.4
3.2
-4.7
-3.5
7.4
13.0
15.3
29.0
-4.6
-3.2
-0.5
0.2
-2.4
-4.1
-7.0
-1.4
0.6
-4.1
-6.0
12.0
-0.6
-0.5
-0.3

13.9
-16.2
3.6
-2.2
9.7
-8.2
-6.0
21.1
6.5
24.3
7.5
-5.0
-3.4
-0.4
12.0
-1.7
-10.8
-13.7
-3.8
5.1
-4.9
-20.4
-9.3
-7.8
-0.5
-0.7

4.3

Changes in the Pattern of Trade
In this section we examine the effects of alternative FTA scenarios on the patternof East Asian trade. Specifically, we compute the indices of revealed comparative advantage(RCA), developed by Balassa (1965), and correlate RCA rankings of commodities withvarious FTA scenarios and those with the global trade liberalization scenario to examine how“natural” the groupings would be.
RCA is defined as

18

RCA =

E r ,i ∑ E r , i

∑E
r

r

r ,i

∑∑ E
r

(3)
r ,i

i

where Er,i is country (region) r’s exports of commodity i. In other words, RCA is definedas the share of commodity i in country r’s total exports relative to the commodity’s sharein total world exports. When the RCA index is greater than one, commodity i is more
important in country r’s exports than it is in total world exports, implying that the country
has a comparative advantage in the commodity.

Table 7
Revealed comparative advantage indices for China, Japan, Korea and ASEAN in 2001
Commodity/sector

China

Japan

Korea

ASEAN

Rice
Other grains
Vegetables and fruits
Other crops
Livestock
Natural resources
Fossil fuelFood products
TextilesApparelLeather
Wood products
Paper products
Petroleum products
Chemical products
Mineral products
Iron and steel
Nonferrous metal
Metal products
Machinery
Electronic equipment
Motor vehicles
Other transport equip.
Other manufactures
Trade and transport
Services

1.21
0.37
0.65
0.46
1.05
0.35
0.18
0.44
1.94
3.77
5.20
1.46
0.40
0.47
0.52
1.12
0.32
0.39
1.37
0.86
1.26
0.07
0.42
3.43
2.17
0.46

1.87
0.00
0.01
0.04
0.09
0.05
0.00
0.12
0.72
0.05
0.05
0.06
0.27
0.15
0.91
0.97
1.58
0.59
0.75
1.76
1.76
2.33
1.25
0.71
0.56
0.43

0.09
0.00
0.23
0.17
0.08
0.04
0.00
0.26
2.69
0.84
0.99
0.12
0.57
1.72
1.01
0.54
1.61
0.69
0.98
0.78
2.39
1.15
1.83
0.58
0.55
0.56

4.48
0.06
0.66
1.53
0.35
1.39
0.87
1.18
0.97
1.49
1.44
1.69
0.64
1.01
0.69
0.69
0.27
0.55
0.45
0.58
3.07
0.11
0.24
0.75
0.80
0.77

Source: GTAP database, version 6.

Table 7 provides the RCA indices for the 26 sectors in China, Japan, Korea and
ASEAN in 2001. In general, the larger the RCA index for a given commodity, the higher
is the ranking of the product by comparative advantage (Kreinin and Plummer, 1994a,b).

19

However, the RCA index may be distorted by tariffs, nontariff barriers, subsidies andother policies. For example, RCA in Japan’s rice sector is equal to 1.87, but this number ishighly distorted because rice is one of the least competitive sectors in Japan.
In China the commodities/sectors with the five highest RCA index values in 2001
are leather (5.20), apparel (3.77), other manufactures (3.43), trade and transport (2.17),
and textiles (1.94). In Japan they are motor vehicles (2.33), machinery (1.76), electronicequipment (1.76), iron and steel (1.58), and other transport equipment (1.25). In Korea thecommodities with high RCA rankings are textiles (2.69), electronic equipment (2.39),
other transport equipment (1.83), petroleum products (1.72), and iron and steel (1.61),
whereas in ASEAN they are rice (4.48), electronic equipment (3.07), wood products (1.69),
other crops (1.53), apparel (1.49), and leather (1.44).
To examine how “natural” each FTA grouping might be, we first compute the RCA
indices under the GTL scenario for the year 2015. Second, we calculate the RCA indicesfor each member country of FTA with respect to the bloc it joins, rather than the world asa whole, for the six FTA scenarios in 2015. Third, we compute the Spearman rankcorrelation coefficients for China, Japan, Korea, ASEAN and the EU between each FTA
scenario and the GTL scenario in 2015.18 The results are summarized in Table 8. It should
be noted that our definition of natural trading bloc is one where each member country has arelatively high Spearman rank correlation coefficient between the particular trading bloc
and global trade liberalization. This should contrasted with a more conventional definitionof natural trading partners where a pair of countries have similar size, GDP per capita, areneighboring, and share a common border and/or a common language (e.g., Frankel et al.,1995).
For China and ASEAN the correlation coefficients of RCA rankings between any
particular FTA and GTL differ considerably across the FTA scenarios. On the contrary,for Japan and Korea the coefficients are 0.83-0.91 and 0.71-0.85, respectively, suggestingthat RCA rankings between any given FTA and GTL are similar for the two countries. For
example, for Japan motor vehicles, machinery, and iron and steel are ranked in the top 3,

18 We also computed the Spearman rank correlation coefficients between each FTA scenario and thebaseline scenario in 2015 for these countries, but the results were qualitatively similar. This is because
we assume partial trade liberalization on imports of all products from all trading partners in the baseline.

20

while agricultural products and fossil fuel are ranked at or near the bottom of the rankings,
under all three FTA scenarios in which Japan is a member as well as under GTL. By
contrast, China has a very low coefficient between the China-Japan-Korea FTA and GTL.
This is mainly caused by large increases in China’s exports of other grains, other crops
and fossil fuel to Japan and Korea, thereby significantly raising its RCA rankings of thesecommodities with respect to the trilateral bloc. However, China’s RCA indices of thesecommodities under GTL are at the bottom of the rankings. Thus, for China large increases
in exports of agricultural products and energy under the China-Japan-Korea FTA are
undesirable because they result in sectoral adjustments that are quite different from those
expected from GTL.

Table 8Spearman rank correlation coefficients between each FTA scenario
and global trade liberalization in 2015
Scenario
Country/region
China
Japan
Korea
ASEAN
EU

(1)
ASEANChina(2)
ASEANJapan(3)
ASEANKorea(4)ChinaJapan-Korea

(5)
ASEAN
plus 3(6)ASEANEU

0.60
–
–
0.24
–

–
0.91
–
0.45
–

–
–
0.85
0.21
–

0.04
0.83
0.71
–
–

0.54
0.86
0.82
0.41
–

–
–
–
0.77
0.56

ASEAN has low Spearman correlation coefficients between the FTA with either
China or Korea and GTL for two important reasons. First, electronic equipment has thehighest RCA index among all commodity groups in ASEAN under GTL. However, its
RCA ranking falls to 10-15th in the ASEAN-China and ASEAN-Korea FTA scenarios.
Second, apparel and leather’s RCA indices are near the top of the rankings under GTL, but
their rankings fall significantly under the bilateral FTA with China or Korea.
Kreinin and Plummer (1994b) suggest that a natural FTA is one in which theSpearman correlation coefficients are high for all member countries. Among the six FTA
scenarios considered in this study, the ASEAN-EU FTA may be considered relatively
natural as the Spearman correlation coefficients are greater than 0.5 for both ASEAN and21

the EU. Among the five alternative East Asian FTAs, the ASEAN-Japan and theASEAN+3 FTAs cause relatively small adjustments in the rankings of industries
compared with the rankings of industries under GTL.
5. Concluding Remarks
In this paper, we have examined the effects of alternative FTA scenarios in East
Asia on sectoral adjustments and the pattern of trade. Our findings indicate that the ChinaJapan-Korea FTA and the ASEAN+3 FTA would bring about relatively large welfare
gains to all member countries. However, a tradeoff exists between welfare gains and costs
associated with structural adjustments. For China, a low Spearman rank correlation
coefficient between the China-Japan-Korea FTA and global trade liberalization suggeststhat it would need to undergo considerable structural adjustments under the trilateral FTA.
The average correlation coefficient of the member countries’ RCA rankings between aparticular FTA and GTL is significantly greater for the ASEAN-Japan and the ASEAN+3
FTAs. Thus, it appears that the ASEAN+3 FTA would be the most attractive FTA among
the alternative FTAs considered in this study, with relatively large welfare gains and small
structural adjustments.

22

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25

Appendix: Model Equations

A.1.

The Neo-Classical Model in Comparative Static ModeIn the equations describing the model specification, the following indices are
frequently employed. In general, the regional and time indices are omitted unless needed
for clarification. The base sectoral, labor and regional indices are specific to the GTAP
data set. The other indices are specific to the model specification.
i

Sectoral index j is used as an alias for i.

llLabor skill (representing skilled, unskilled, and highly skilled labor).

l

A subset of ll, which excludes highly skilled labor.

f

An index for other domestic final demand agents (government and investment).

r

Regional index. r' is used as an alias for r.

v

Capital vintage.

ttime index.

Other labels for important subsets of sectors are the following:
Index

Subset label

i
i
i
i
i
i
i
i

Description

cr
lv
ag
ip
e
ft
fd
ik

Crops sectors (user-determined)
Livestock sectors (user-determined)
Agricultural sectors (the union of the crop and livestock sectors)
Non-agricultural products (user-determined)
Energy sectors (user-determined)
Fertilizer sectors (user-determined)
Feed sectors (user-determined)
Sectors including in the calibrating productivity (user-determined)

Production Technology
Crop Production(1)

NDcr =

∑α

nd

cr ,v

σ crp ,v −1 ⎛ PXvcr ,v ⎞( ATcr )v(2)

VAcr ,v = α va cr ,v ( ATcr )σ crp , v −1

σ crp ,v⎜⎜⎟⎟
⎝ PNDcr ⎠⎛ PXv cr ,v ⎞⎜⎟⎜ PVA ⎟
cr , v ⎠⎝XPvcr ,v

σ crp , v

XPv cr ,v

26

(3)

PXvcr ,v =(4)

PX cr =[]

1 / (1−σ cr , v )pp1
α nd cr ,v (PNDcr )1−σ cr ,v + α va cr ,v (PVAcr ,v )1−σ cr ,vATcr

XPv cr ,v∑ XPp

PXv cr ,v

crv

σ crv ,v

(5)

⎛ PVAcr ,v ⎞⎟⎟ALcr = ∑ α crl ,v ⎜⎜v⎝ AWcr ⎠(6)⎛ PVAcr ,v ⎞⎜⎟HKTEFcr ,v = α crhktef,v ⎜⎟PHKTEF
cr ,v ⎠⎝

(7)

PVAcr ,v = α crl ,v ( AWcr )[VAcr ,v( )⎛ AWcr ⎞⎜⎟⎜W ⎟⎝ l ,cr ⎠(9)1−σ cr ⎤⎡⎛ Wl ,cr ⎞ld⎥
AWcr = ∑ ⎢α l ,cr ⎜⎜ l ⎟⎟⎥λl ⎢⎝ l ,cr ⎠
⎣
⎦

(10)

fert cr = ∑ α(l1 / 1−σ cr⎛ PHKTEFcr ,v ⎞
⎜⎜
⎟⎟
Pfert
cr
⎝
⎠

HKTEFcr ,v

(11)

(12)

PHKTEFcr ,v = α(13)⎛ PHKTEcr ,v ⎞⎟XEpcr ,v = α cre ,v ⎜⎜⎟⎝ PEpcr ,v ⎠(14)

⎛ PHKTEcr ,v ⎞⎟HKTcr ,v = α crhkt,v ⎜⎜⎟PHKT
cr ,v ⎠⎝

(15)

PHKTEcr ,v = α cre ,v (PEpcr ,v )(16)

LdHSk ,cr = ∑ α crh ,v λlHSk ,cr(Pfertcr )HKTEFcr ,v

)

+α

hkte
cr ,v

(PHKTE )1−σ crf , v

cr ,v

](

1 / 1−σ crf , v

σ cre ,v

HKTEcr ,v
σ cre , ve1−σ cr,v(v

σ crf ,v

1−σ crf , v[)

σ crf , v

⎛ PHKTEFcr ,v ⎞⎜⎟HKTEcr ,v = α crhkte
,v ⎜
⎟
PHKTE
cr ,v ⎠
⎝

[

)

ALcr

l

fert
cr ,v

v
1 / 1−σ cr
,v

l
σ cr

Ldl ,cr = α lld,cr λll ,cr

v

](

v
1−σ cr
,v

(8)

fert
cr ,v

VAcr ,v+ α crhktef,v (PHKTEFcr ,v )v
1−σ cr
,v

l
−1
σ cr

σ crv ,v

σ crh ,v −1

HKTEcr ,v+ α crhkt,v (PHKTcr ,v )e1−σ cr,v⎛ PHKTcr ,v ⎞
⎜
⎟
⎜ W
⎟
⎝ HSk ,cr ⎠27

σ crh , v

HKTcr ,v](e
1 / 1−σ cr
,v

)

)

σ cre ,v(17)⎛ PHKTcr ,v ⎞⎟KTcr ,v = α crkt ,v ⎜⎜⎟PKT
cr ,v ⎠⎝

(18)

1−σ cr , v
⎡
⎤
⎛
⎞
h
W
1−σ cr
,
HSk
cr
kt
h
,v ⎥
⎢
⎜
⎟
PHKTcr ,v = α cr ,v ⎜ l+ α cr ,v (PKTcr ,v )⎢⎥λHSk ,cr ⎟⎠⎝⎣⎦HKTcr ,vh

( )⎛ PKTcr ,v ⎞
⎜
⎟
⎜ R
⎟
⎝ cr ,v ⎠( )⎛ PKTcr ,v ⎞⎜⎜⎟⎟⎝ PTcr ⎠(19)

Kvcrd ,v = α crk ,v λkcr ,v(20)

Tcrd = ∑ α crt ,v λtcr

σ crk ,v −1

σ crk ,v −1

v

(21)

( )

Fcrd = ∑ α crf ,v λcrf

σ crk , v −1v(h1 / 1−σ cr,v

σ crk , v

KTcr ,v

σ crk , v

⎛ PKTcr ,v ⎞⎜⎜⎟⎟⎝ PFcr ⎠KTcr ,v
σ crk , v

KTcr ,vkk
1−σ cr ,v
1−σ cr1−σ cr
,v
,v ⎤
⎡
⎛
⎞
R
⎛
⎞
⎛
⎞
PT
PF
,
cr
v
k
t
f
cr
cr
⎢
⎥
⎜
⎟
PKTcr ,v = α cr ,v ⎜ k ⎟+ α cr ,v ⎜⎜ t ⎟⎟+ α cr ,v ⎜⎜ f ⎟⎟
⎢
⎥
λ
λ
λ
⎝ cr ⎠⎝ cr ⎠⎝ cr ,v ⎠
⎣
⎦
k

(22)

)

(

(σ −1) ⎛⎜)⎞Pfertcr
⎟
Ap
⎜ 1+τ
⎟
PA
,
ft
cr
ft
⎝
⎠

ft
cr

(23)

XAp ft ,cr = α ftft,cr λ ftft ,cr(24)

1−σ cr ⎤
⎡
Ap
⎡
⎤
1
PA
+
τ
ft
cr
ft
,
⎥
Pfert cr = ⎢∑ α ftft,cr ⎢
⎥
ft
⎢ ft
⎥
λ
ft ,cr
⎣⎢
⎦⎥
⎣
⎦

(25)

XApe ,cr = ∑ α eep,cr ,v λep
e ,cr

(

(

)

)

( )

v

σ crep, v −1

ft

(

(

(

)

fert cr1 / 1−σ crft

⎛ PEpcr ,v ⎞⎜⎟⎜ 1 + τ Ap PA ⎟
e ,cr
e ⎠
⎝

)

σ crep,v)(26)

1−σ cr ,v ⎤⎡⎛ 1 + τ eAp,cr PAe ⎞ep⎥⎟
PEpcr ,v = ⎢∑ α e ,cr ,v ⎜⎜
ep
⎟
⎢ e
⎥
λ
,
e
cr
⎝
⎠
⎣
⎦

(27)

XApnnft ,cr = annft ,cr NDcr

(28)

Ap
PNDcr = ∑ annft ,cr 1 + τ nnft
,cr PAnnft(σ crft

ep

)

nnft

28

(

ep
1 / 1−σ cr,v

XEpcr ,v)(k
1 / 1−σ cr,v)Livestock Production(29)

NDlv = ∑ α

nd
lv ,v

( ATlv )σ lvp , v −1v

⎛ PXvlv ,v ⎞
⎜⎜
⎟⎟
PND
lv ⎠
⎝

⎛ PXvlv ,v ⎞⎜⎟⎜ PVA ⎟
lv ,v ⎠⎝

σ lvp ,v

XPvlv ,v

σ lvp , v

(30)

VAlv ,v = α lvva,v ( ATlv )(31)

1 / (1−σ lvp , v )1
1−σ lvp ,v1−σ lvp , v
ndva
PXvlv ,v =α lv ,v (PNDlv )+ α lv ,v (PVAlv ,v )
ATlv

(32)

PX lv =

σ lvp ,v −1

XPvlv ,v[]

XPvlv ,v

∑ XP

PXvlv ,v

lv

v

(33)

KTELlv ,v = α lvktel,v VAlv ,v(34)

TFDlv ,v = α lvtfd,vVAlv ,v(35)

PVAf lv ,v = α lvktel,v PKTELlv ,v + α lvtfd,v PTFDlv ,v(36)⎛ PTFDlv ,v ⎞⎜⎟⎟feed lv = ∑ α lvfeed,v ⎜v⎝ Pfeed lv ⎠(37)

Tlvd = ∑ α lvt ,v λtlv

( )σ lvf ,v −1v

σ lvf ,v

TFDlv ,v

⎛ PTFDlv ,v ⎞⎜⎜⎟⎟⎝ PTlv ⎠σ lvf ,v

TFDlv ,v(38)

1−σ lv ,v ⎤⎡1−σ lvf ,vfeedt ⎛ PTlv ⎞⎥+ α lv ,v ⎜⎜ t ⎟⎟PTFDlv ,v = ⎢α lv ,v (Pfeedlv )λ⎢⎥⎝ lv ⎠⎣⎦(39)⎛ PKTELlv ,v ⎞⎟⎟ALlv = ∑ α lvl ,v ⎜⎜v⎝ AWlv ⎠(40)⎛ PKTELlv ,v ⎞⎟⎜HKTElv ,v = α lvhkte
,v ⎜
⎟
PHKTE
lv
,
v
⎠
⎝

(41)

PKTELlv ,v = α lvl ,v ( AWlv )

(42)

( )

f

[

Ldl ,lv = α lld,lv λll ,lv

σ lvl −1(

1 / 1−σ lvf , v)

σ lvv ,v

KTELlv ,v1−σ lvv ,v⎛ AWlv ⎞
⎟
⎜
⎜W ⎟
,
l
lv
⎠
⎝

σ lvv ,v

KTELlv ,v+ α lvhkte,v (PHKTElv )

σ lvl

ALlv

29

1−σ lvv , v](

1 / 1−σ lvv ,v

)

(43)

1−σ cr ⎤⎡⎛ Wl ,cr ⎞ld⎥⎢
AWcr = ∑ α l ,cr ⎜⎜ l ⎟⎟⎥⎢λl
⎝ l ,cr ⎠
⎦
⎣

(44)

⎛ PHKTElv ,v ⎞⎟XEplv ,v = α lve ,v ⎜⎜
⎟
PEp
lv
,
v
⎠
⎝

(45)

⎛ PHKTElv ,v ⎞⎟HKTlv ,v = α lvhkt,v ⎜⎜⎟PHKT
lv ,v ⎠⎝

(46)

PKTElv ,v = α(47)

LdHSk ,lv = ∑ α lvh ,v λlHSk ,lv(

l
1 / 1−σ cr

l

[

e
lv ,v

(PEp )σ lve ,v

HKTElv ,vσ lve , v

KTElv ,v

1−σ lve , v

lv ,v

(

)

)

σ lvh , v −1

v

+α

hkt
lv ,v

(PHKT )

1−σ lve ,vlv ,v⎛ PHKTlv ,v ⎞
⎜
⎟
⎜ W
⎟
⎝ HSk ,lv ⎠σ lvh , v

HKTlv ,v

σ lvh , v

(48)

⎛ PHKTlv ,v ⎞⎟KTlv ,v = α lvkt,v ⎜⎜
⎟
PKT
lv
,
v
⎝
⎠

(49)

1−σ lv ,v⎤⎡⎛ WHSk ,lv ⎞1−σ lvh ,v ⎥hkt⎢⎟
PHKTlv ,v = α lv ,v ⎜⎜ l
+ α lv ,v (PKTlv ,v )⎥⎢λHSk ,lv ⎟⎠⎝⎦⎣HKTlv ,v
h

( )

(50)

Kvlvd ,v = α lvk ,v λlvk ,v(51)

F = ∑α
d
lv

f
lv ,v

(λ )

σ lvk ,v −1

f σ lv ,v −1
lvkv⎛ PKTlv ,v ⎞
⎟
⎜
⎟
⎜ R
lv
,
v
⎠
⎝

KTlv ,v

σ lvk ,v

⎛ PKTlv ,v ⎞
⎜⎜
⎟⎟
PF
lv ⎠
⎝

KTlv ,v

1−σ lv ,v1−σ lvk ,v ⎤
⎡
⎞
⎛
R
⎛
⎞
PF
⎥
PKTlv ,v = ⎢α lvk ,v ⎜⎜ klv ,v ⎟⎟+ α lvf ,v ⎜⎜ f lv ⎟⎟⎥⎢λλ⎝ lv ⎠⎝ lv ,v ⎠⎦⎣((σ −1)⎛⎜

)

fd
lv

(

1 / 1−σ lvk ,v)σ lvfd⎞Pfeedlv
⎟
Ap
⎜ 1+τ
⎟
PA
,
fd
lv
fd
⎝
⎠

(53)

XAp fd ,lv = α fdfd,lv λ fdfd ,lv(54)

1−σ lv ⎤⎡⎡ 1 + τ fdAp,lv PA fd ⎤fd⎥⎢
Pfeedlv = ∑ α fd ,lv ⎢
⎥
fd
⎥
⎢ fd
λ
⎥⎦
⎢⎣
fd ,lv⎦⎣((

1 / 1−σ lvh ,v

σ lvk ,v

k

(52)

](

1 / 1−σ lve ,v

(

)

)

fd

30

(

feedlv

1 / 1−σ lvfd

)

)

)

(55)

( )

XApe ,lv = ∑ α eep,lv ,v λep
e ,lv

σ lvep,v −1v

σ lvep,v⎛ PEplv ,v ⎞⎟⎜⎜ 1 + τ Ap PA ⎟
e ,lv
e ⎠
⎝

(

)

(56)

1−σ lv ,v ⎤
⎡
Ap
⎛
⎞
(
1
)
PA
τ
+
e
lv
e
,
⎥
⎟
PEplv ,v = ⎢∑ α eep,lv ,v ⎜
ep
⎜
⎟
⎥
⎢ e
λ
ej
⎝
⎠
⎦
⎣

(57)

XApnnff ,lv = annfd ,lv NDlv

(58)

Ap
PNDlv = ∑ annfd ,lv 1 + τ nnfd
,lv PAnnfd(

1 / 1−σ lvep,v

ep(

XEplv ,v

)

)

nnfd

Non-Agricultural Production(59)

NDip = ∑α nd (AT )(σip ,v

σ ipp ,vp
ip , v −1

ip)⎛⎜ PXvip ,v ⎞⎟v(

σ ipp ,v)⎛⎜ PXvip ,v ⎞⎟⎜ PVA ⎟
ip ,v ⎠⎝

(60)

VAip ,v = α ipva,v (ATip )(61)

PXvip ,v =(62)

PX ip XPip = ∑ PXvip ,v XPvip ,v

σ ipp ,v −1[

XPvip ,v

⎜ PND ⎟
ip ⎠⎝XPvip ,v]

1 / (1−σ ip ,v )
1
1−σ p
1−σ p
α ipnd,v PNDip ip ,v + α ipva,v PVAip ,v ip ,v
ATip
p

v

σ ipv

(63)

⎛ PVAip ,v ⎞⎟ VAip ,v
ALip = ∑ α ipl ,v ⎜
⎜
⎟
AW
v
ip ⎠
⎝

(64)

⎛ PVAip ,v ⎞⎜⎟HKTEip ,v = α iphkte
,v ⎜
⎟
PHKTE
ip ,v ⎠⎝

σ ipv ,v

(65)[

VAip ,vljv
PVAip ,v = α iphkte,v PHKTEip ,v + α ip ,v ( AWip )
1−σ v

( )

σ ipl −1

⎛ AWip ⎞⎟⎜⎜W ⎟⎝ l ,ip ⎠1−σ ipv ,v

σ ipl

(66)

Ldl ,ip = α lld,ip λll ,ip(67)

1−σ ip⎡⎛ Wl ,ip ⎞ ⎤ld ⎜⎢AWip = ∑ α l ,ip l ⎟ ⎥⎜λ ⎟ ⎥l ⎢⎝ l ,ip ⎠ ⎦
⎣
l

ALip

(

1 / 1−σ ipl

)

31

](

1 / 1−σ ipv ,v)

σ ipe ,v⎛ PHKTEip ,v ⎞
⎜
⎟
⎜ PEp
⎟
ip ,v
⎝
⎠

(68)

XEpip ,v = α(69)⎛ PHKTEip ,v ⎞⎟HKTip ,v = α iphkt,v ⎜⎜ PHKT ⎟ip ,v ⎠⎝(70)

PHKTEip ,v = α ipe ,v (PEpip ,v )(71)

LdHSk ,ip = ∑ α iph ,v λlHSk ,ip

e
ip ,v

[

HKTEip ,v
σ ipe ,v

HKTEip ,v+ α iphkt,v (PHKTip ,v )1−σ ipe ,v()

σ iph ,v −1

v1−σ ipe ,v](

1 / 1−σ ipe ,v)

σ iph ,v

⎛ PHKTip ,v ⎞
⎜
⎟
⎜ W
⎟
⎝ HSk ,ip ⎠HKTip ,v

σ iph ,v

(72)

⎛ PHKTip ,v ⎞⎟KTip ,v = α ipkt,v ⎜⎜ PKT ⎟
ip
,
v
⎝
⎠

(73)

1−σ ip ,v⎤⎡⎛⎞W
1−σ iph ,v ⎥
HSk
ip
,
h ⎜
kt
⎢
⎟
PHKTip ,v = α ip ,v l
+ α ip ,v (PKTip ,v )⎜λ⎟⎥⎢HSk ,ip ⎠
⎝
⎦
⎣

(74)

Fipd = ∑ α ipf ,v λip

HKTip ,v
h

( )

( )

⎛ PKTip ,v ⎞⎟⎜⎜ PT ⎟
ip ⎠⎝( )⎛ PKTip ,v ⎞
⎟
⎜
⎟
⎜ R
ip
,
v
⎠
⎝

v

(75)

Tipd = ∑ α ipt ,v λtip

σ ipk ,v −1vkk σ ip ,v −1)

σ ipk ,v

⎛ PKTip ,v ⎞⎜⎟⎜ PF ⎟
ip⎝⎠kf σ ip ,v −1(

1 / 1−σ iph ,v

KTip ,v

σ ipk ,v

KTip ,v

σ ipk ,v

(76)

Kvipd ,v = α ipk ,v λip(77)

1−σ ip ,v
1−σ ip ,v
1−σ ip ,v ⎤
⎡
⎛
⎞
⎛
⎞
⎛
⎞
PF
PT
R
ip
ip
ip
v
,
⎥
+ α ipt ,v ⎜ t ⎟PKTip ,v = ⎢α ipf ,v ⎜ f ⎟+ α ipk ,v ⎜ k ⎟
⎜ λ ⎟
⎜ λ ⎟
⎜λ ⎟
⎥
⎢
⎝ ip ⎠⎝ ip ⎠⎝ ip ,v ⎠
⎦
⎣

(78)

XApnf ,ip = α nf ,ip NDip

(79)

PNDip = ∑ anf ,ip 1 + τ nfAp,ip PAnf

KTip ,v

k

(

k

k

)

nf

(80)

( )

XApe ,ip = ∑ α eep,ip ,v λepe ,ipv

σ ipep,v −1⎛ PEpip ,v ⎞⎜⎟⎜ 1 + τ Ap PA ⎟
e ,ip
e ⎠
⎝

(

)

32

σ ipep,v

XEpip ,v(

1 / 1−σ ipk ,v

)

(81)

(

)

1−σ ip ,v ⎤⎡⎛ 1 + τ eAp,ip PAe ⎞
ep ⎜
⎥
⎢
⎟
PEpip ,v = ∑ α e ,ip ,v
ep
⎜
⎟
⎥
⎢ e
λ
e ,ip
⎝
⎠
⎦
⎣
ep

(

1 / 1−σ ipep,v)Market structure(82)(

PPi = PX i (1 + π i ) 1 + τ ip)

Income Distribution(83)

TY = ∑ NPTi Ti di

(84)

FY = ∑ PFi Fi di

(85)

LYll = ∑ NWll ,i Ldll ,i
i

(86)

KY =

∑∑ NR Kv + ∑π PX XP
v

(87)

d
i ,v

i ,v

i

i

i

i

i

YH h = ϕ ht TY + ϕ hf FY + ∑ ϕ hl ,ll LYll + ϕ hk KY − DeprYh + PGDP TRGh
ll

(88)

DeprYh = ϕ hk δ f PGDP K(89)

Ydh = 1 − χ kκ hh YH h()

Final Demand
(90)

(

)

Yh* = Yd h − ∑ 1 + τ jAc,h PA j Poph θ j ,hj

μ i ,hYh*Ac(1 + τ i,h ) PAi(91)

XAci ,h = Poph θ i ,h +(92)

S hh = Yd h − ∑ 1 + τ iAc,h PAi XAci ,h

(

)

i

(93)

∑ (1 + τ )PA XAc
CPI =∑ (1 + τ )PA XAc
Ac
i ,h

i

Ac
i ,h , 0

i ,0

i ,h

i

h

i ,h

i

(94)

XAf i , f = aif, f FD f

(95)

PFD f = ∑ aif, f 1 + τ iAf, f PAi

(

)

i

33

TradeImport Specification
(96)

XAi =

∑ XAp + ∑ XAc + ∑ XAf
i, j

i ,h

j

(97)

(98)

(99)

(100)

(101)

⎧
⎪ XDid
⎪
⎨
⎪
⎪ PDi
⎩

h

⎛ PA ⎞= β ⎜⎜ i ⎟⎟⎝ PDi ⎠⎧⎪ PAi⎨⎪⎩ XAi

σ im

d
i

XAi

= PAi

⎧
⎪ XMTi⎪⎨⎪⎪ PMTi⎩⎛ PAi ⎞⎟⎟= β ⎜⎜
PMT
i ⎠
⎝

if

σm <∞

if

σm =∞

σ im

m
i

XAi

= PAi
= β id PDi[

1−σ im= XDid+⎧⎪WTFrd',r ,i⎪⎪⎨⎪⎪ PM r′,r ,i⎪⎩⎧⎪ PMTr ,i⎪⎨⎪⎪ XMTr ,i
⎩

i, f

f

+ β im PMTi

if

σm <∞

if

σm =∞

1−σ im] ( ) if σ < ∞1 / 1−σ im

m

XMTi

⎛ PMTr ,i ⎞⎟= β rw',r ,i ⎜⎜⎟⎝ PM r ',r ,i ⎠

if

σm =∞

σ rw,i

XMTr ,i= PMTr ,i⎡1−σ w ⎤= ⎢∑ β rw',r ,i (PM r ',r ,i ) r ,i ⎥
⎣ r'
⎦

if

σw <∞

if

σw =∞

(

1 / 1−σ rw,i= ∑ WTFrd',r ,i

)
if

σw <∞

if

σw =∞

Export Specification

(102)

⎧
⎪ XDis
⎪
⎨
⎪
⎪ PDi
⎩

⎛ PDi ⎞⎟⎟
= γ id ⎜⎜⎝ PPi ⎠

σ ix

( XPi − XMgi ) if σ ix < ∞

= PPi

if

34

σ ix = ∞(103)

(104)

(105)

(106)

⎧
⎪ ESi
⎪
⎨
⎪
⎪ PPEi⎩⎛ PPEi ⎞⎟⎟= γ ⎜⎜⎝ PPi ⎠σ ixei

( XPi − XMg i ) if

σ ix < ∞if

σ ix = ∞

= PPi
=

[γ PD

=

XDis + ESi + XMg i⎧⎪WTFrs,r ',i⎪⎪⎨⎪⎪ PEr ,r ',i⎪⎩⎛ PEr ,r ',i ⎞⎟= γ rw,r ',i ⎜⎜
⎟
PPE
r
i
,
⎝
⎠

⎧
⎪ PPEr ,i⎪⎨⎪⎪ ES r ,i⎩⎡1+σ z ⎤= ⎢∑ γ rw,r ',i (PEr ,r ',i ) r ,i ⎥
⎦
⎣ r'

⎧
⎪ PPi
⎨
⎪⎩ XPidi

1+σ ixi+ γ ie PPEi

1+σ ix] ( ) if σ < ∞1 / 1+σ ix

x
i

if

σ ix = ∞σ rz,iES r ,i= PPEr ,i=if

σ iz < ∞if

σ iz = ∞(1 / 1+σ rz,i

)

∑WTF

s
r ,r ',iif

σ iz < ∞if

σ iz = ∞r'

Trade Prices(107)

WPEr ,r ',i = (1 + τ re,r ',i ) PEr ,r ',i(108)

WPM r ,r ',i = 1 + ζ rt ,r ',i WPEr ,r ',i / λwr ,r ',i(109)

PM r ,r ',i = (1 + τ rm,r ',i )WPM r ,r ',i()

Demand for International Trade and Transport Services(110)

WPMg WXMg =

∑∑∑ ζ
r

r'

t
d
r , r ',i WPE r , r ',i WTFr , r ',iiAllocation of the Demand for International Trade and Transport Services across Regions
TT ⎛ WPMg ⎞r ⎜⎜ APMg ⎟⎟
r ⎠
⎝

σ TT

(111)

AXMg r = α

(112)

⎡
1−σ TT ⎤WPMg = ⎢ α rTT APMg r
⎥
⎣ r
⎦

∑

WXMg

(

1 / 1−σ TT)

35

Local Supply for Trade and Transport Services(113)

XMg r ,i = a rMg,i AXMg r(114)

APMg r =∑ a PP
Mg
r ,i

r ,i

i

Goods Market Equilibrium(115)

XDrs,i = XDrd,i(116)

WTFrd,r ',i = λwr ,r ',iWTFrs,r ',iDomestic Closure∑τ (1 + π )PX XP + ∑ χ κ YH
+ ∑∑τ PA XAp+ ∑∑τ PA XAc+ ∑∑τ
PA XAf+ ∑∑τ WPM WTF+ ∑∑τ
PE WTF
+ ∑∑τ
NW L
+ ∑∑τ
NR Kv + ∑τ NPT T
=

YGr

p
r ,i

r ,i

r ,i

h
r

r ,i

i

(117)

r ,i

h

r ', r ,i

(118)

Ac
r ,i , h

r ,i

i

d
r ', r ,ier , r ',iidr ,ll ,ir ,ll ,i

ll

i

r'
k
r ,i ,vi

r ,i ,vv

S g = YG − PFDGov FDGov − ∑ PGDP TRGh
h

(119)

RS g = S g / PGDP

(120)

FDGov = χ Gov RGDPMP(121)

PFDInv ,r* FDInv ,r* = ∑ S hh,r* + DeprYhr* + S rg* + P S rf*

[

]

h

(122)

∑ ∑ ∑WPE
P=∑ ∑ ∑WPEr∈OECD r ' i∈Manur∈OECD r ' i∈Manu

WTFr ,r ',i , 0

r ,r ',i

WTFr ,r ',i ,0r ,r ',i , 0

Factor Markets
Labor Markets

(123)

(124)

ω llL
⎧
s
l ⎛ TWll ⎞⎪ Lll = χ ll ⎜⎟⎪⎝ PABS ⎠⎨⎪⎪TWll = PABS TWll , 0⎩∑Ldll , jif

0 ≤ ω llL < ∞ifω llL = ∞Af
r ,i , fr ,i , hir'lr ,ll ,iir ,i , jjm
r ',r ,i

i

r

h

Ap
r ,i , j

i

h
r ,h

= Lsllj

36

r ,r ',idr ,i ,vr ,ifs
r ,r ',i

t
r ,i

r ,i

d
r ,i

r ,i , f(125)

Wll , j = Φ ll , jTWll(126)

Wll , j = 1 + τ lll , j NWll , j()

Land Market

(127)

ηT
⎧
T ⎛ PTLnd ⎞⎪TLnd = χ ⎜⎟⎪⎝ PABS ⎠⎨⎪⎪ PTLnd = PABS PTLnd 0⎩

if0 ≤ ηT < ∞if η T = ∞

1 / (1+ω )⎧⎡1+ω t ⎤T
⎪ PTLnd = ⎢∑ γ i PTi ⎥
if
⎪
⎦
⎣ i
⎨
⎪
d
if
⎪TLnd = ∑ Ti
i
⎩
T

(128)

ω
⎧
PTi ⎞
s
T⎛
⎪Ti = γ i ⎜⎟ TLnd⎪⎝ PTLnd ⎠⎨⎪⎪ PTi = PTLnd⎩

0 ≤ ωT < ∞ωT = ∞

T

(129)

(130)

Ti d = Ti s

(131)

PTi = 1 + τ it NPTi(

if

0 ≤ ωT < ∞ifωT = ∞)

Sector-specific Factors

(132)

(133)

ωF
⎧
s
F ⎛ PFi ⎞⎪ Fi = χ i ⎜⎟⎪⎝ PABS ⎠⎨⎪⎪ PFi = PABS PFi , 0⎩

if0≤ωF < ∞ifωF = ∞

Fi s = Fi d

Capital Market in a Single Vintage Frameworkω⎧sk ⎛ Ri ⎞⎪ KS i = γ i ⎜ ⎟ K s⎪⎝ TR ⎠⎨⎪⎪ Ri = TR
⎩
K

(134)

if

0 ≤ωK < ∞if

ωK = ∞

37

1 / (1+ω )⎧K ⎤⎡⎪TR = ⎢∑ γ ik (Ri )1+ω ⎥
if
⎪
⎣ i
⎦
⎨
⎪
d
s
if
⎪∑ K i = K
⎩ i
K

(135)

(136)

∑ K = KSdi

0 ≤ωK < ∞

ωK = ∞sii

Capital Market Equilibrium in a Multiple Vintage Framework

Kvid,v
XPvi ,v(137)

χ iv,v =(138)

K i0,t (RRi ,t ) i ≤ χ iv,Old XPi

(139)

∑∑ Kv

ηk

d
i ,v ,tiand RRi ,t ≤ 1= K tsv(140)

NRi ,Old ,t = RRi ,tTRt(141)

NRi , New,t = TRt(142)

Ri ,v ,t = 1 + τ ik,v ,t NRi ,v ,t()

Allocation of Output across Vintages(143)

XPi = ∑ XPviv
v

(144)

XPvi ,Old ,t = K i0,t (RRi ,t ) i / χ iv,Old ,tηkAggregate Capital Stock in a Recursive Dynamic Framework
(145)

(

) FD
(1 + γ ) − (1 − δ ) FD
K = (1 − δ ) K +FDInv ,t = 1 + γ I

nInv ,t − n

I

(146)

(147)

n

n

n

t

t −n

Inv ,t −nγ I +δ

K 0s
K =
Kt
K0
s
t

38

Other Equations and Definitions⎡∑ ⎢∑ (1 + τ=

RGDPMPr

i

(148)

⎣

i

r ,i , 0h∑∑ (WPE+)PAAc
r ,i ,h , 0⎤
XAcr ,i ,h + ∑ 1 + τ rAf,i , f , 0 PAr ,i ,0 XAf r ,i , f ⎥
f
⎦

(

)

)

WTFrs,r ',i − WPM r ',r ,i , 0WTFrd',r ,i + APMg r ,0 AXMg r

r ,r ',i , 0

r'

(149)

⎤
⎡
RGDP = ∑ ATi ⎢λti NPTi , 0Ti d + λif PFi ,0 Fi d + ∑ λlll ,i NWll ,i ,0 Ldll ,i + ∑ λik,v NRi ,v , 0 Kvid,v ⎥
ll
i
v
⎣
⎦

(150)

PGDP RGDP =∑ [YH + DeprY − PGDP TRG ] − ∑π PX XP
h

h

h

h

i

i

∑ PA XAPABS =∑ PA XA
i

(151)

i

i

i

i

i,0

i

i

(152)

∑∑WPE
i

A.2.

WTFrs,r ',i + APMg r AXMg r + P S f = ∑∑ WPM r ',r ,iWTFrd',r ,ir ,r ',ir'iModel Dynamics

Endogenous Dynamic Equations() RGDPMP= (1 + γ + χ + π ) λ
n

(153)

RGDPMPt = 1 + g y(154)

λlll ,ik ,t(155)⎛ ES ⎞
χ ip,t = φi p,t ⎜⎜ i ,t ⎟⎟⎝ XPi ,t ⎠(156)

χ ikp ,t = α ikp ,t (γ tl + χ ikp ,t + π ik ,t )

(157)

p
p
s
λlll ,ink ,t = 1 + χ ink.t + 1 − α ink .t γ ink .t

(158)

k
p
p
s
λink
,v ,t = 1 + χ ink ,t + 1 − α ink ,t γ ink ,t

(159)

p
p
s
λtink ,t = 1 + χ ink,t + 1 − α ink ,t γ ink ,t

(160)

f
p
p
s
λink
,t = 1 + χ ink ,t + 1 − α ink ,t γ ink ,t

l
t

p
ik ,t

t −n

n

ik ,t

l
ll ,ik ,t −n

ηip

[

(

)

]λ

[

(

)

]λ

n

l
ll ,ink ,t − nnlink ,v ,t − n

[

(

)

]λ

[

(

)

]λ

n

n

t
ink ,t − nfink ,t − n39r'

Exogenous Dynamic Equations

(

) Pop
n

(161)

Popt = 1 + g Pop(162)

χ tL = (1 + g L ) χ tL−n(163)

χ tT = (1 + g T ) χ tT−n(164)

χ iF,t = (1 + g F ) χ iF,t −n(165)

K i0,t = ∑ (1 − δ ) Kvid,v ,t −n

t −n

n

n

n

n

v

(166)

λtip ,t = (1 + γ tt ) λtip ,t −n(167)

λipf ,t = (1 + γ t f ) λipf ,t −n

(168)

e
ep
λep
e , j ,t = (1 + γ e , j ,t ) λe , j ,t

n

n

n

A.3 Definitions of Variables
Production Variables
Crops
ND
VA
PXv
PX
PP

Demand for aggregate non-energy non-fertilizer intermediate demand
Demand for value added+energy+ fertilizer bundle
Unit cost of production by vintage
Average unit cost of production
Producer pricer x crr x cr x v
r x cr x v
r x crr x cr

AL
HKTEF
PVA

Demand for aggregate labor (x/ ‘highly skilled’)
Demand for capital+energy+fertilizer+land bundle
Price of value added+energy+fertilizer bundler x crr x cr x v
r x cr x v

LdAW

Demand for labor (x/ ‘highly skilled’)Aggregate sectoral wage (x/ ‘highly skilled’)r x cr x l
r x crHKTE
Demand for capital+energy+land bundle
Fert
Demand for fertilizer
PHKTEF Price of capital+energy+fertilizer+land bundler x cr x v
r x crr x cr x v

XEp
HKT
PHKTE

Demand for aggregate energy bundle
Demand for bundle of capital plus land
Price of capital+energy+land bundler x cr x v
r x cr x v
r x cr x v

LHsk
KT
PHKT

Demand ‘highly’ skilled labor
Demand for bundle of capital plus land
Price of capital (human and physical)+land bundler x crr x cr x v
r x cr x v

40

Td
Fd
Kvd
PKT

Demand for land
Demand for sector-specific factor
Demand for capital (by vintage)
Price for bundle of capital plus landr x crr x crr x cr x v
r x cr x v

XAp
PND
PEp
Pfert

Demand for (Armington) intermediate goods
Price of aggregate non-energy intermediate goods
Price of aggregate energy bundle
Price for fertilizerr x cr x j
r x crr x cr x v
r x cr

ND
VA
PXv
PX
PP

Demand for aggregate non-energy non-feed intermediate demand
Demand for value added+energy+feed bundle
Unit cost of production by vintage
Average unit cost of production
Producer pricer x lvr x lv x v
r x lv x v
r x lvr x lv

TFD
KTEL
PVA

Demand for land-feed bundle
Demand for capital-energy-labor composite good
Price of value added+energy+feed bundler x lv x v
r x lv x v
r x lv x v

Td
Feed
PTFD

Demand for land
Demand for feed
Price of land feed bundler x lvr x lvr x lv x v

AL
HKTE
PKTEL

Demand for aggregate labor (x/ ‘highly’ skilled)
Demand for capital-energy bundle
Price of labor-capital-energy bundler x lvr x lv x v
r x lv x v

Ld
AW

Demand for labor by skill (x/ ‘highly’ skilled)
Aggregate sectoral wage (x/ ‘highly’ skilled)r x lv x l
r x lv

XEp
HKT
PHKTE

Demand for aggregate energy bundle
Demand for bundle of capital and other factors
Price of capital+energy bundler x lv x v
r x lv x v
r x lv x v

LHsk
KT
PHKT

Demand ‘highly’ skilled labor
Demand for bundle of capital plus other factors
Price of capital (human and physical)+other bundler x lvr x lv x v
r x lv x v

Fd
Kvd
PKT

Demand for sector-specific factor
Demand for capital (by vintage)
Price for bundle of capital plus landr x lvr x lv x v
r x lv x v

XAp
PND
PEp
Pfeed

Demand for (Armington) intermediate goods
Price of aggregate non-energy intermediate goods
Price of aggregate energy bundle
Price of feedr x lv x j
r x lvr x lv x v
r x lv

Livestock

Non-agricultural sectors
ND
VA
PXv
PX
PP

Demand for aggregate non-energy intermediate demand
Demand for value added+energy bundle
Unit cost of production by vintage
Average unit cost of production
Producer price41

r x ipr x ip x v
r x ip x v
r x ipr x ip

AL
HKTE
PVA

Demand for aggregate labor (x/ ‘highly’ skilled)
Demand for capital+energy bundle
Price of value added+energy bundler x ipr x ip x v
r x ip x v

LdAW

Demand for labor by skill (x/ ‘highly’ skilled)
Aggregate sectoral wage (x/ ‘highly’ skilled)r x ip x lr x ip

XEp
HKT
PHKTE

Demand for aggregate energy bundle
Demand for bundle of capital plus other resources
Price of capital+energy bundler x ip x v
r x ip x v
r x ip x v

LHsk
KT
PHKT

Demand ‘highly’ skilled labor
Demand for bundle of capital plus other factors
Price of capital (human and physical)+other bundler x ipr x ip x v
r x ip x v

Td
Fd
Kvd
PKT

Demand for land
Demand for sector-specific resources
Demand for capital (by vintage)
Price for bundle of capital plus other resourcesr x ipr x ipr x ip x v
r x ip x v

XAp
PND
PEp

Demand for (Armington) intermediate goods
Price of aggregate non-energy intermediate goods
Price of aggregate energy bundler x ip x j
r x ipr x ip x v

Income Variables
TY
FY
LY
KY
YH
DY
Yd

Aggregate land remuneration
Aggregate sector-specific factor remuneration
Aggregate labor remuneration (by skill)
Aggregate capital remuneration
Gross household income
Depreciation allowance
Disposable household incomerrr x ll
r
rxh
rxh
rxh

Final Demand Variables
Y*
XAc
Sh
CPI

Supernumerary income
Household (Armington) demand for goods and services
Household saving
Consumer price index

rxh
rxixh
rxh
rxh

XAf
PFD

Other final (Armington) demand for goods and services
Aggregate price index for other final demand

rxixf
rxf

Trade Variables
XA
XD
XMT
PA

Aggregate Armington demand
Domestic demand for domestic productiona
Domestic demand for aggregate imports
Armington price

rxi
rxi
rxi
rxi

WTF
PMT

Trade flow matrixb
Price of aggregate imports

rxrxi
rxi

42

PD
ES
XP

Price of domestic goods sold locallyAggregate supply of exports
Aggregate domestic output

rxi
rxi
rxi

WPE
PPE

Determination of bilateral (world) export prices
Price of aggregate exports

rxrxi
rxi

WXMg
AXMg
WPMg
XMg
APMg

Volume of world demand for international trade and transport services
Regional supply of international trade and transport services
Aggregate world price of international trade and transport services
Regional sectoral demand for goods and services related to trade
Regional supply price of international trade and transport services

1
r
1
rxi
r

Domestic Closure Variables
YG
Sg
RSg
FDGov

Aggregate government revenue
Government saving (or deficit)
Real government saving (or deficit)
Aggregate volume of government expenditures on goods and services

r
r
r
r

FDInv
P

Aggregate volume of investment expenditures on goods and servicesc
Price index of OECD exports

r-1
1

Factor Market Variables
Ls
TW
W
NW

Aggregate labor supply
Economy-wide equilibrium wage
Sector-specific wage
After tax wager x llr x llr x ll x i
r x ll x i

TLnd
PTLnd
Ts
PT
NPT

Aggregate land supply
Economy-wide land price
Sectoral land supply
Sectoral-specific land price
After tax land price

r
r
rxi
rxi
rxi

Fs
PF

Supply of sector-specific factors
Price of sector-specific factor

rxi
rxi

KSs
TR
R
NR
RR

Supply of sectoral capitald
Economy-wide rental rate
Sector and vintage specific rental rate
Sector and vintage specific rental rate after tax
Relative price of Old to New capitale

rxi
r
rxixv
rxixv
rxi

χv

Capital-output ratiof
Output by vintage

rxi
rxixv

Rate of real investment growth
Aggregate capital stock (non-normalized)
Aggregate capital stock (normalized)

r
r
r

XPv

γI
K
Ks

43

Other Variables
RGDPMP Real GDP at market price
RGDP
Real GDP at factor cost
PGDP
GDP deflator (at factor cost)
PABS
Price index of aggregate domestic absorption

r
r
r
r

gy

λ
χp
φp

Growth rate of real GDP (at factor cost)gLabor productivity factor
Trade-sensitive productivity shifter
Productivity shifter calibration parameterhrr x ll x ik
rxi
r x ik

λl
λk
λt
λf

Exogenous labor productivity factor
Exogenous capital productivity factor
Exogenous land productivity factor
Exogenous sector-specific factor productivity factorr x ll x ink
r x ink x v
r x inkr x inkl44