Download Nuevos Desarrollos en Teoría del Comercio Internacional y su

Nuevos Desarrollos
en Teoría del Comercio Internacional
y su Análisis Empírico
Timothy J. Kehoe
University of Minnesota y Federal Reserve Bank of Minneapolis
Universitat de Barcelona
Mayo 2006
www.econ.umn.edu/~tkehoe/ub-06.html
Outline:
1. Standard theory (hybrid Heckscher-Ohlin/New Trade Theory)
does not well when matched with the data on the growth and
composition of trade.
2. Applied general equilibrium models that put the standard theory
to work do not well in predicting the impact of trade
liberalization experiences like NAFTA.
3. Much of the growth of trade after a trade liberalization
experience is growth on the extensive margin. Models need to
allow for corner solutions or fixed costs.
4. Modeling the fixed costs may explain why real exchange rate
data indicate that more arbitrage across countries that have a
strong bilateral trade relationship.
5. The major determinant of large macroeconomic fluctuations in
Mexico is fluctuations in total factor productivity. If trade
liberalization and the 1995 financial crisis have had large
macroeconomic effects, it is through their effects on TFP.
1. Standard theory (hybrid Heckscher-Ohlin/New Trade
Theory) does not well when matched with the data on the
growth and composition of trade.
In the 1980s and 1990s trade economists reached a consensus that
North-North trade — trade among rich countries — was driven by
forces captured by the New Trade Theory and North-South trade
— trade between rich countries and poor countries — was driven
by forces captured by Heckscher-Ohlin theory. (South-South trade
was negligible.)
A. V. Deardorff, “Testing Trade Theories and Predicting Trade
Flows,” in R. W. Jones and P. B. Kenen, editors, Handbook of
International Economics, volume l, North-Holland, 1984, 467-517.
J. Markusen, “Explaining the Volume of Trade: An Eclectic
Approach,” American Economic Review, 76 (1986), 1002-1011.
In fact, a calibrated version of this hybrid model does not
match the data.
R. Bergoeing and T. J. Kehoe, “Trade Theory and Trade Facts,”
Federal Reserve Bank of Minneapolis, 2003.
TRADE THEORY
Traditional trade theory — Ricardo, Heckscher-Ohlin — says
countries trade because they are different.
In 1990 by far the largest bilateral trade relation in the world was
U.S.-Canada. The largest two-digit SITC export of the United
States to Canada was 78 Road Vehicles. The largest two-digit
SITC export of Canada to the United States was 78 Road Vehicles.
The New Trade Theory — increasing returns, taste for variety,
monopolistic competition — explains how similar countries can
engage in a lot of intraindustry trade.
Helpman and Krugman (1985)
Markusen (1986)
TRADE THEORY AND TRADE FACTS
x Some recent trade facts
x A “New Trade Theory” model
x Accounting for the facts
x Intermediate goods?
x Policy?
How important is the quantitative failure of the New
Trade Theory?
Where should trade theory and applications go from
here?
SOME RECENT TRADE FACTS
x The ratio of trade to product has increased.
World trade/world GDP increased by 59.3 percent 1961-1990.
OECD-OECD trade/OECD GDP increased by 111.5 percent
1961-1990.
x Trade has become more concentrated among industrialized
countries
OECD-OECD trade/OECD-RW trade increased by 87.1 percent
1961-1990.
x Trade among industrialized countries is mostly intraindustry
trade
Grubel-Lloyd index for OECD-OECD trade in 1990 is 68.4.
Grubel-Lloyd index for OECD-RW trade in 1990 is 38.1.
OECD-OECD Trade / OECD GDP
0.12
0.10
0.08
0.06
0.04
0.02
0.00
1960
1963
1966
1969
1972
1975
year
1978
1981
1984
1987
1990
OECD-OECD Trade / OECD-RW Trade
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1960
1963
1966
1969
1972
1975
year
1978
1981
1984
1987
1990
Helpman and Krugman (1985):
“These....empirical weaknesses of conventional trade
theory...become understandable once economies of scale and
imperfect competition are introduced into our analysis.”
Markusen, Melvin, Kaempfer, and Maskus (1995):
“Thus, nonhomogeneous demand leads to a decrease in NorthSouth trade and to an increase in intraindustry trade among the
northern industrialized countries. These are the stylized facts that
were to be explained.”
Goal: To measure how much of the increase in the ratio of
trade to output in the OECD and of the concentration of world
trade among OECD countries can be accounted for by the
“New Trade Theory.”
PUNCHLINE
In a calibrated general equilibrium model,
the New Trade Theory cannot account for the
increase in the ratio of trade to output in the
OECD.
Back-of-the-envelope calculations:
Suppose that the world consists of the OECD and the only trade is
manufactures.
With Dixit-Stiglitz preferences, country j exports all of its
production of manufactures Ymj except for the fraction s j Y j / Y oe
that it retains for domestic consumption.
World imports:
M
World trade/GDP:
M
Y oe
World trade/GDP:
n
¦j
M Ymoe
Ymoe Y oe
j
j
1
s
Y
m.
1
1 ¦
n
j
oe
Y
j 2
m
s
(
)
.
oe
1
Y
M
Y oe
n
M Ymoe
Ymoe Y oe
1 ¦
n
j
oe
Y
j 2
m
s
(
)
.
oe
1
Y
1 ¦ j 1 ( s j ) 2 goes from 0.663 in 1961 to 0.827 in 1990.
Ymoe / Y oe goes from 0.295 in 1961 to 0.222 in 1990.
0.663 u 0.295 0.196 | 0.184 0.827 u 0.222 .
Effects cancel!
A “NEW TRADE THEORY” MODEL
Environment:
x Static: endowments of factors are exogenous
x 2 regions: OECD and rest of world
x 2 traded goods: homogeneous — primaries (CRS) and
differentiated — manufactures (IRS)
x 1 nontraded good — services (CRS)
x 2 factors: (effective) labor and capital
x Identical technologies and preferences (love for variety) across
regions
x Primaries are inferior to manufactures
We only consider merchandise trade in both the data and in
the model.
Key Features of the Model
Consumers' problem:
max
s.t.
E p (c pj J p )K E m ( ³ cmj ( z ) U dz p )K / U E s (csj J s )K 1
Dw
K
q p c pj ³ w qm ( z )cmj ( z )dz p qsj csj d r j k j w j h j .
D
Firms' problems
Primaries and Services: Standard CRS problems.
Ypj
T p ( K pj )D p ( H pj )1D p
Ys j
Ts ( K sj )D s ( H sj )1D s
Manufactures: Standard (Dixit-Stiglitz) monopolistically
competitive problem:
x Fixed cost.
Ym ( z )
max ª¬Tm K m ( z )Dm H m ( z )1Dm F ,0 º¼
x Firm z sets its price qm ( z ) to max profits given all of the
other prices.
n
Ym ( z ) ¦ j 1 Cmj ( z ) Cmrw ( z ) .
1
1K
Cmj ( z )
Em (r j K j w j H j q p J p N j qsj J s N j )
1
1U
U
1U
qm ( z ) ª ³ D w qm ( z c) dz cº
¬
¼
UK
U (1K )
(1U )
U
K
1K
'
U
K
1
ª
º
1U
1K 1K
'
Em « ³ w qm ( z c) dz c
» E s qs
D
¬
¼
x Every firm is uniquely associated with only one variety
(symmetry).
x Free entry.
[0, d w ] with d w finite and endogenously determined.
x Dw
K
1K
E p qp
1
1K
1
1K
Volume of Trade
Let s j be the share of country j , j
production of manufactures,
sj
³D
j
1,..., n, rw , in the world
Ym ( z )dz / ³D w Ym ( z )dz
Ymj / Ymw .
The imports by country j from the OECD are
M oej (1 s rw s j )Cmj
M oerw (1 s rw )Cmrw .
Total imports in the OECD from the other OECD countries are
n
M
oe
oe
n
j 2
rw
oe
M
(1
s
(
s
)
/(1
s
))
C
¦
¦
m .
j
oe
j 1
rw
j 1
OECD in 1990
Country
Australia
Austria
Belgium-Lux
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Share of GDP %
1.79
0.97
1.26
3.45
0.78
0.81
7.26
9.96
0.50
0.04
0.28
6.64
Country
Japan
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Share of GDP %
18.04
1.72
0.26
0.70
0.41
3.00
1.40
0.17
0.91
5.92
33.72
ACCOUNTING FOR THE FACTS
Compare the changes that the model predicts for 1961-1990 with
what actually took place.
Focus on key variables:
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manfacturing GDP/OECD GDP
Calibrate to 1990 data.
Backcast to 1961 by imposing changes in parameters:
relative sizes of countries in the OECD
populations
sectoral productivities
endowments
ACCOUNTING FOR THE FACTS
Benchmark 1990 OECD Data Set
(Billion U.S. dollars)
H ioe
K ioe
Yi oe
Cioe
Yi oe Cioe
Primaries Manufactures
228
2,884
441
775
669
3,659
862
3,466
-193
193
Services
8,644
3,497
12,141
12,141
0
Total
11,756
4,713
16,469
16,469
0
ACCOUNTING FOR THE FACTS
Benchmark 1990 Rest of the World Data Set
(Billion U.S. dollars)
Yi rw
Cirw
Yi rw Cirw
Primaries Manufactures
1,223
1,159
1,030
1,352
193
-193
Services
3,447
3,447
0
Total
5,829
5,829
0
ACCOUNTING FOR THE FACTS
x N oe
x
¦
i
854 , N rw
rw
Y
p ,m , s i
x Set q p qm ( z )
values).
xU
4, 428 .
¦
i
rw
C
p ,m , s i
qs
w
5,829 .
r
1 (quantities are 1990
1/1.2 (Morrison 1990, Martins, Scarpetta, and Pilat 1996).
x Normalize d w
100 .
x Calibrate H rw , K rw so that benchmark data set is an
equilibrium.
x Alternative calibrations of utility parameters J p , J s , and K .
OECD in 1961
Country
Austria
Belgium-Lux
Canada
Denmark
France
Germany
Greece
Iceland
Ireland
Italy
Share of GDP %
0.75
1.25
4.22
0.70
6.99
9.71
0.50
0.03
0.21
4.64
Country
Share of GDP %
Netherlands
1.37
Norway
0.60
Portugal
0.32
Spain
1.38
Sweden
1.62
Switzerland
1.07
Turkey
0.83
United Kingdom
8.08
United States
55.74
Numerical Experiments
Calculate equilibrium in 1961:
T p ,1961
T m ,1961
T s ,1961
N oe
T p ,1990
T m ,1990 /1.01429 , F1961
F1990 /1.01429
T s ,1990 /1.00529 (Echevarria 1997)
536, N rw
2,545
Numerical Experiments
oe
oe
rw
rw
Choose H1961
, K1961
, H1961
, K1961
so that
¦
¦
¦
¦
i
oe
oe
Y
/
N
1990
p ,m , s i ,1990
oe
i p ,m , s i ,1961
Y
i
oe
1961
/N
rw
rw
Y
/
N
1990
p ,m , s i ,1990
rw
i p ,m , s i ,1961
Y
rw
1961
/N
oe
oe
K1961
K1990
oe
oe
H1961
H1990
q p ,1961 (Yprw,1961 C prw,1961 )
¦
i p ,m , s
rw
i ,1961 i ,1961
q
Y
2.400
2.055
0.050
How Can the Model Work in Matching the Facts?
x The ratio of trade to product has increased:
The size distribution of countries has become more equal
(Helpman-Krugman).
x Trade has become more concentrated among industrialized
countries:
OECD countries have comparative advantage in manufactures,
while the RW has comparative advantage in primaries.
Because they are inferior to manufactures, primaries become
less important in trade as the world becomes richer
(Markusen).
How Can the Model Work in Matching the Facts?
x Trade among industrialized countries is largely intraindustry
trade:
OECD countries export manufactures. Because of taste for
variety, every country consumes some manufactures from
every other country (Dixit-Stiglitz).
x The different total factor productivity growth rates across
sectors imply that the price of manufactures relative to
primaries and services has fallen sharply between 1961 and
1990. If price elasticities of demand are not equal to one, a lot
can happen.
Experiment 1
Jp
Jp K
0
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1. Jp = 0, Js = 0, K = 0
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1961
1990
Change
0.053
0.844
0.295
0.112
1.579
0.222
111.5%
87.1%
24.6%
0.108
0.893
0.223
0.136
1.169
0.222
25.8%
30.9%
0.4%
Experiment 2
Jp = 169.5, Js = 314.7 to match consumption in RW in 1990,
K=0
1961
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
2. Jp = 169.5, Js = 314.7, K = 0
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1990
Change
0.053
0.844
0.295
0.112 111.5%
1.579 87.1%
0.222 24.6%
0.103
0.739
0.225
0.132
1.060
0.222
28.1%
43.6%
1.4%
Experiment 3
Jp = 169.5, Js = 314.7,
K = 0.559 to match growth in OECD-OECD Trade/OECD GDP
1961
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
3. Jp = 169.5, Js = 314.7, K = 0.559
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1990
Change
0.053
0.844
0.295
0.112 111.5%
1.579 87.1%
0.222 24.6%
0.063
0.738
0.137
0.132 111.5%
1.060 43.7 %
0.222 62.7%
Experiments 4 and 5
Jp = 169.5, Js = 314.7, reasonable values of K ( 0.5 t 1/(1 K ) t 0.1)
1961
1990
Change
Data
OECD-OECD Trade/OECD GDP
0.053
0.112
111.5%
OECD-OECD Trade/OECD-RW Trade
0.844
1.579
87.1%
OECD Manf GDP/OECD GDP
0.295
0.222
24.6%
4. Jp = 169.5, Js = 314.7, K = 1
OECD-OECD Trade/OECD GDP
0.118
0.132
11.7%
OECD-OECD Trade/OECD-RW Trade
0.739
1.060
43.5%
OECD Manf GDP/OECD GDP
0.259
0.222
14.1%
5. Jp = 169.5, Js = 314.7, K = 9
OECD-OECD Trade/OECD GDP
0.118
0.132
1.6%
OECD-OECD Trade/OECD-RW Trade
0.739
1.060
43.5%
OECD Manf GDP/OECD GDP
0.284
0.222
21.8%
Sensitivity Analysis:
Alternative Calibration Methodologies
x Alternative specifications of nonhomogeneity
x Gross imports calibration
x Alternative RW endowment calibration
x Alternative RW growth calibration
x Intermediate goods
INTERMEDIATE GOODS?
Ypj
j
j
ª X j ³ w X mp
º
(
z
)
dz
X
D
1
D
p
p
pp
sp
»
min «
, D
,
, T p K pj H pj amp
asp
«¬ a pp
»¼
Ym ( z )
Ys j
j
ª X j ( z ) ³ w X mm
( z , z ')dz ' X j ( z ) º
pm
«
, D
, sm , »
amm
asm »
min « a pm
«
»
Dm
1D m
F
«¬Tm K m ( z ) H m ( z ) »¼
j
ª X j ³ w X ms
º
j
(
z
)
dz
D
1
D
X
s
s
ps
min «
, D
, ss , Ts K sj H sj »
ams
ass
«¬ a ps
»¼
Results for Model with Intermediate Goods
1961
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
4. Jp = 307.8, Js = 262.2, K = 1
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
5. Jp = 307.8, Js = 262.2, K = 9
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1990
Change
0.053 0.112
0.844 1.579
0.295 0.222
111.5%
87.1%
24.6%
0.323 0.370
0.994 1.305
0.263 0.222
14.5%
31.3%
15.6%
0.337 0.370
0.933 1.305
0.307 0.222
9.7%
39.9%
27.5%
POLICY?
In a version of our model with n OECD countries, a manufacturing
sector, and a uniform ad valorem tariff W, the ratio of exports to
income is given by
M
Y
(n 1)C f
Y
n 1
n 1 (1 W )1/(1 U )
Fixing n to replicate the size distribution of national incomes in the
OECD, and setting U 1/1.2 , a fall in W from 0.45 to 0.05 produces
an increase in the ratio of trade to output as seen in the data.
World Trade / World GDP
0.90
0.80
0.70
rho=1/2.0
0.60
0.50
0.40
rho=1/1.5
0.30
rho=1/1.1
0.20
rho=1/1.2
0.10
0.00
0
0.1
0.2
0.3
0.4
0.5
tau
0.6
0.7
0.8
0.9
1
2. Applied general equilibrium models that put the standard
theory to work do not well in predicting the impact of trade
liberalization experiences like NAFTA.
Applied general equilibrium models were the only analytical game in
town when it came to analyzing the impact of NAFTA in 1992-1993.
Typical sort of model: Static applied general equilibrium model with
large number of industries and imperfect competition (Dixit-Stiglitz or
Eastman-Stykolt) and finite number of firms in some industries. In some
numerical experiments, new capital is placed in Mexico owned by
consumers in the rest of North America to account for capital flows.
Examples:
Brown-Deardorff-Stern model of Canada, Mexico, and the United States
Cox-Harris model of Canada
Sobarzo model of Mexico
T. J. Kehoe, “An Evaluation of the Performance of Applied
General Equilibrium Models of the Impact of NAFTA,” in T. J.
Kehoe, T. N. Srinivasan, and J. Whalley, editors, Frontiers in
Applied General Equilibrium Modeling: Essays in Honor of
Herbert Scarf, Cambridge University Press, 2005, 341-77.
Research Agenda:
x Compare results of numerical experiments of models with data.
x Determine what shocks — besides NAFTA policies — were
important.
x Construct a simple applied general equilibrium model and
perform experiments with alternative specifications to determine
what was wrong with the 1992-1993 models.
Applied GE Models Can Do a Good Job!
Spain: Kehoe-Polo-Sancho (1992) evaluation of the performance
of the Kehoe-Manresa-Noyola-Polo-Sancho-Serra MEGA model
of the Spanish economy: A Shoven-Whalley type model with
perfect competition, modified to allow government and trade
deficits and unemployment (Kehoe-Serra). Spain’s entry into the
European Community in 1986 was accompanied by a fiscal reform
that introduced a value-added tax (VAT) on consumption to
replace a complex range of indirect taxes, including a turnover tax
applied at every stage of the production process. What would
happen to tax revenues? Trade reform was of secondary
importance.
Canada-U.S.: Fox (1999) evaluation of the performance of the
Brown-Stern (1989) model of the 1989 Canada-U.S. FTA.
Other changes besides policy changes are important!
Changes in Consumer Prices in the Spanish Model
(Percent)
sector
food and nonalcoholic beverages
tobacco and alcoholic beverages
clothing
housing
household articles
medical services
transportation
recreation
other services
data
model
model
model
1985-1986 policy only shocks only policy&shocks
1.8
-2.3
4.0
1.7
3.9
2.5
3.1
5.8
2.1
5.6
0.9
6.6
-3.3
-2.2
-2.7
-4.8
0.1
2.2
0.7
2.9
-0.7
-4.8
0.6
-4.2
-4.0
2.6
-8.8
-6.2
-1.4
-1.3
1.5
0.1
2.9
1.1
1.7
2.8
weighted correlation with data
variance decomposition of change
-0.08
0.30
0.87
0.77
0.94
0.85
regression coefficient a
regression coefficient b
0.00
-0.08
0.00
0.54
0.00
0.67
Measures of Accuracy of Model Results
1. Weighted correlation coefficient.
2. Variance decomposition of the (weighted) variance of the
changes in the data:
vardec( y data , y model )
var ( y model )
.
model
data
model
var ( y
) var ( y y
)
3, 4. Estimated coefficients a and b from the (weighted)
regression
xidata
a bximodel ei .
Changes in Value of Gross Output/GDP in the Spanish Model (Percent)
sector
agriculture
energy
basic industry
machinery
automobile industry
food products
other manufacturing
construction
commerce
transportation
services
government services
data
1985-1986
-0.4
-20.3
-9.0
3.7
1.1
-1.8
0.5
5.7
6.6
-18.4
8.7
7.6
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
model
policy only
-1.1
-3.5
1.6
3.8
3.9
-2.4
-1.7
8.5
-3.6
-1.5
-1.1
3.4
model
model
shocks only policy&shocks
8.3
6.9
-29.4
-32.0
-1.8
-0.1
1.0
5.0
4.7
8.6
4.7
2.1
2.3
0.5
1.4
10.3
4.4
0.4
1.0
-0.7
5.8
4.5
0.9
4.3
0.16
0.11
0.80
0.73
0.77
0.71
-0.52
0.44
-0.52
0.75
-0.52
0.67
Changes in Trade/GDP
in the Spanish Model (Percent)
direction of exports
Spain to rest of E.C.
Spain to rest of world
rest of E.C. to Spain
rest of world to Spain
data
model
model
model
1985-1986 policy only shocks only policy&shocks
-6.7
-3.2
-4.9
-7.8
-33.2
-3.6
-6.1
-9.3
14.7
4.4
-3.9
0.6
-34.1
-1.8
-16.8
-17.7
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
0.69
0.02
0.77
0.17
0.90
0.24
-12.46
5.33
2.06
2.21
5.68
2.37
Changes in Composition of GDP in the Spanish Model (Percent of GDP)
variable
wages and salaries
business income
net indirect taxes and tariffs
data
model
1985-1986 policy only
-0.53
-0.87
-1.27
-1.63
1.80
2.50
model
shocks only
-0.02
0.45
-0.42
model
policy&shocks
-0.91
-1.24
2.15
correlation with data
variance decomposition of change
0.998
0.93
-0.94
0.04
0.99
0.96
regression coefficient a
regression coefficient b
private consumption
private investment
government consumption
government investment
exports
-imports
0.00
0.73
-1.23
1.81
-0.06
-0.06
-0.42
-0.03
0.00
-3.45
-0.51
-0.58
-0.38
-0.07
-0.69
2.23
0.00
0.85
-1.78
1.32
-0.44
-0.13
-1.07
2.10
correlation with data
variance decomposition of change
0.40
0.20
0.77
0.35
0.83
0.58
regression coefficient a
regression coefficient b
0.00
0.87
0.00
1.49
0.00
1.24
-0.81
1.09
-0.02
-0.06
-3.40
3.20
Public Finances in the Spanish Model
(Percent of GDP)
data
model
model
model
variable
1985-1986 policy only shocks only policy&shocks
indirect taxes and subsidies
2.38
3.32
-0.38
2.98
tariffs
-0.58
-0.82
-0.04
-0.83
social security payments
0.04
-0.19
-0.03
-0.22
direct taxes and transfers
-0.84
-0.66
0.93
0.26
government capital income
-0.13
-0.06
0.02
-0.04
correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
0.99
0.93
-0.70
0.08
0.92
0.86
-0.06
0.74
0.35
-1.82
-0.17
0.80
Models of NAFTA
Did Not Do a Good Job!
Ex-post evaluations of the performance of applied GE models are
essential if policy makers are to have confidence in the results
produced by this sort of model.
Just as importantly, they help make applied GE analysis a
scientific discipline in which there are well-defined puzzles and
clear successes and failures for alternative hypotheses.
Changes in Trade/GDP
in Brown-Deardorff-Stern Model (Percent)
variable
Canadian exports
Canadian imports
Mexican exports
Mexican imports
U.S. exports
U.S. imports
data
1988-1999
52.9
57.7
240.6
50.5
19.1
29.9
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
model
4.3
4.2
50.8
34.0
2.9
2.3
0.64
0.08
23.20
2.43
Changes in Canadian Exports/ GDP in the Brown-Deardorff-Stern Model (Percent)
sector
agriculture
mining and quarrying
food
textiles
clothing
leather products
footwear
wood products
furniture and fixtures
paper products
printing and publishing
chemicals
petroleum and products
rubber products
nonmetal mineral products
glass products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
miscellaneous manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to Mexico
1988–1999
model
122.5
3.1
-34.0
-0.3
89.3
2.2
268.2
-0.9
1544.3
1.3
443.0
1.4
517.0
3.7
232.6
4.7
3801.7
2.7
240.7
-4.3
6187.4
-2.0
37.1
-7.8
678.1
-8.5
647.4
-1.0
333.5
-1.8
264.4
-2.2
195.2
-15.0
38.4
-64.7
767.0
-10.0
376.8
-8.9
633.9
-26.2
305.8
-4.4
1404.5
-12.1
exports to United States
1988–1999
model
106.1
3.4
75.8
0.4
91.7
8.9
97.8
15.3
237.1
45.3
-14.4
11.3
32.8
28.3
36.5
0.1
282.6
12.5
113.7
-1.8
37.2
-1.6
109.4
-3.1
-42.5
0.5
113.4
9.5
20.5
1.2
74.5
30.4
92.1
12.9
34.7
18.5
102.2
15.2
28.9
3.3
88.6
14.5
30.7
10.7
100.0
-2.1
-0.91
0.003
-0.43
0.02
249.24
-15.48
79.20
-2.80
Changes in Mexican Exports/GDP in the Brown-Deardorff-Stern Model (Percent)
sector
agriculture
mining and quarrying
food
textiles
clothing
leather products
footwear
wood products
furniture and fixtures
paper products
printing and publishing
chemicals
petroleum and products
rubber products
nonmetal mineral products
glass products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
miscellaneous manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to Canada
1988–1999
model
-20.5
-4.1
-35.5
27.3
70.4
10.8
939.7
21.6
1847.0
19.2
1470.3
36.2
153.0
38.6
4387.6
15.0
4933.2
36.2
23.9
32.9
476.3
15.0
204.6
36.0
-10.6
32.9
2366.2
-6.7
1396.1
5.7
676.8
13.3
32.5
19.4
-35.4
138.1
610.4
41.9
570.6
17.3
1349.2
137.3
2303.4
3.3
379.4
61.1
exports to United States
1988–1999
model
-15.0
2.5
-22.9
26.9
9.4
7.5
832.3
11.8
829.6
18.6
618.3
11.7
111.1
4.6
145.6
-2.7
181.2
7.6
70.3
13.9
122.1
3.9
70.4
17.0
66.4
34.1
783.8
-5.3
222.3
3.7
469.8
32.3
40.9
30.8
111.2
156.5
477.2
26.8
123.6
18.5
744.9
178.0
349.0
6.2
181.5
43.2
0.19
0.01
0.71
0.04
120.32
2.07
38.13
3.87
Changes in U.S. Exports/GDP in the Brown-Deardorff-Stern Model (Percent)
sector
agriculture
mining and quarrying
food
textiles
clothing
leather products
footwear
wood products
furniture and fixtures
paper products
printing and publishing
chemicals
petroleum and products
rubber products
nonmetal mineral products
glass products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
miscellaneous manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to Canada
1988–1999
model
-24.1
5.1
-23.6
1.0
62.4
12.7
177.2
44.0
145.5
56.7
29.9
7.9
48.8
45.7
76.4
6.7
83.8
35.6
-20.5
18.9
50.8
3.9
49.8
21.8
-6.9
0.8
95.6
19.1
56.5
11.9
50.5
4.4
0.6
11.6
-20.7
-6.7
66.7
18.2
36.2
9.9
154.4
14.9
36.5
-4.6
117.3
11.5
exports to Mexico
1988–1999
model
6.5
7.9
-19.8
0.5
37.7
13.0
850.5
18.6
543.0
50.3
87.7
15.5
33.1
35.4
25.7
7.0
224.1
18.6
-41.9
-3.9
507.9
-1.1
61.5
-8.4
-41.1
-7.4
165.6
12.8
55.9
0.8
112.9
42.3
144.5
-2.8
-28.7
-55.1
301.4
5.4
350.8
-2.9
167.8
-10.9
290.3
9.9
362.3
-9.4
-0.01
0.14
37.27
-0.02
0.50
0.02
190.89
3.42
Changes in Canadian Trade/GDP
in Cox-Harris Model (Percent)
variable
total trade
trade with Mexico
trade with United States
data
1988-2000
57.2
280.0
76.2
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
model
10.0
52.2
20.0
0.99
0.52
38.40
1.93
Changes in Canadian Trade/GDP in the Cox-Harris Model (Percent)
sector
agriculture
forestry
fishing
mining
food, beverages, and tobacco
rubber and plastics
textiles and leather
wood and paper
steel and metal products
transportation equipment
machinery and appliances
nonmetallic minerals
refineries
chemicals and misc. manufactures
total exports
1988-2000
model
-13.7
-4.1
215.5
-11.5
81.5
-5.4
21.7
-7.0
50.9
18.6
194.4
24.5
201.1
108.8
31.9
7.3
30.2
19.5
66.3
3.5
112.9
57.1
102.7
31.8
20.3
-2.7
53.3
28.1
total imports
1988-2000
model
4.6
7.2
-21.5
7.1
107.3
9.5
32.1
4.0
60.0
3.8
87.7
13.8
24.6
18.2
97.3
7.2
52.2
10.0
29.7
3.0
65.0
13.3
3.6
7.3
5.1
1.5
92.5
10.4
weighted correlation with data
variance decomposition of change
0.49
0.32
0.85
0.08
41.85
0.81
22.00
3.55
regression coefficient a
regression coefficient b
What Do We Learn from these Evaluations?
The Spanish model seems to have been far more successful in
predicting the consequences of policy changes than the three
models of NAFTA, but
x Kehoe, Polo, and Sancho (KPS) knew the structure of their
model well enough to precisely identify the relationships
between the variables in their model with those in the data;
x KPS were able to use the model to carry out numerical exercises
to incorporate the impact of exogenous shocks.
KPS had an incentive to show their model in the best possible
light.
3. Much of the growth of trade after a trade liberalization
experience is growth on the extensive margin. Models need
to allow for corner solutions or fixed costs.
T. J. Kehoe and K. J. Ruhl, “How Important is the New Goods
Margin in International Trade?” Federal Reserve Bank of
Minneapolis, 2002.
K. J. Ruhl, “Solving the Elasticity Puzzle in International
Economics,” University of Texas at Austin, 2005.
What happens to the least-traded goods:
Over the business cycle?
During trade liberalization?
Indirect evidence on the extensive margin
4. Modeling the fixed costs may explain why real exchange rate
data indicate that more arbitrage across countries that have
a strong bilateral trade relationship.
C. M. Betts and T. J. Kehoe, “Real Exchange Rate Movements and
the Relative Price of Nontraded Goods,” University of Minnesota
and University of Southern California, 2003.
C. M. Betts and T. J. Kehoe, “U.S. Real Exchange Rate
Fluctuations and Relative Price Fluctuations,” University of
Minnesota and University of Southern California, 2003.
5. The major determinant of large macroeconomic fluctuations
in Mexico is fluctuations in total factor productivity. If
trade liberalization and the 1995 financial crisis have had
large macroeconomic effects, it is through their effects on
TFP.
R. Bergoeing, P. J. Kehoe, T. J. Kehoe, and R. Soto “A Decade
Lost and Found: Mexico and Chile in the 1980s,” Review of
Economic Dynamics, 5 (2002), 166-205.
T. J. Kehoe and E. C. Prescott, “Great Depressions of the
Twentieth Century,” Review of Economic Dynamics, 5 (2002), 118.
T. J. Kehoe and K. J. Ruhl, “Sudden Stops, Sectoral Reallocations,
and Productivity Drops,” University of Minnesota, 2006.
1. Standard theory (hybrid Heckscher-Ohlin/New Trade
Theory) does not well when matched with the data on the
growth and composition of trade.
In the 1980s and 1990s trade economists reached a consensus that
North-North trade — trade among rich countries — was driven by
forces captured by the New Trade Theory and North-South trade
— trade between rich countries and poor countries — was driven
by forces captured by Heckscher-Ohlin theory. (South-South trade
was negligible.)
A. V. Deardorff, “Testing Trade Theories and Predicting Trade
Flows,” in R. W. Jones and P. B. Kenen, editors, Handbook of
International Economics, volume l, North-Holland, 1984, 467-517.
J. Markusen, “Explaining the Volume of Trade: An Eclectic
Approach,” American Economic Review, 76 (1986), 1002-1011.
In fact, a calibrated version of this hybrid model does not
match the data.
R. Bergoeing and T. J. Kehoe, “Trade Theory and Trade Facts,”
Federal Reserve Bank of Minneapolis, 2003.
TRADE THEORY
Traditional trade theory — Ricardo, Heckscher-Ohlin — says
countries trade because they are different.
In 1990 by far the largest bilateral trade relation in the world was
U.S.-Canada. The largest two-digit SITC export of the United
States to Canada was 78 Road Vehicles. The largest two-digit
SITC export of Canada to the United States was 78 Road Vehicles.
The New Trade Theory — increasing returns, taste for variety,
monopolistic competition — explains how similar countries can
engage in a lot of intraindustry trade.
Helpman and Krugman (1985)
Markusen (1986)
TRADE THEORY AND TRADE FACTS
x Some recent trade facts
x A “New Trade Theory” model
x Accounting for the facts
x Intermediate goods?
x Policy?
How important is the quantitative failure of the New
Trade Theory?
Where should trade theory and applications go from
here?
SOME RECENT TRADE FACTS
x The ratio of trade to product has increased.
World trade/world GDP increased by 59.3 percent 1961-1990.
OECD-OECD trade/OECD GDP increased by 111.5 percent
1961-1990.
x Trade has become more concentrated among industrialized
countries
OECD-OECD trade/OECD-RW trade increased by 87.1 percent
1961-1990.
x Trade among industrialized countries is mostly intraindustry
trade
Grubel-Lloyd index for OECD-OECD trade in 1990 is 68.4.
Grubel-Lloyd index for OECD-RW trade in 1990 is 38.1.
OECD-OECD Trade / OECD GDP
0.12
0.10
0.08
0.06
0.04
0.02
0.00
1960
1963
1966
1969
1972
1975
year
1978
1981
1984
1987
1990
OECD-OECD Trade / OECD-RW Trade
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1960
1963
1966
1969
1972
1975
year
1978
1981
1984
1987
1990
Helpman and Krugman (1985):
“These....empirical weaknesses of conventional trade
theory...become understandable once economies of scale and
imperfect competition are introduced into our analysis.”
Markusen, Melvin, Kaempfer, and Maskus (1995):
“Thus, nonhomogeneous demand leads to a decrease in NorthSouth trade and to an increase in intraindustry trade among the
northern industrialized countries. These are the stylized facts that
were to be explained.”
Goal: To measure how much of the increase in the ratio of
trade to output in the OECD and of the concentration of world
trade among OECD countries can be accounted for by the
“New Trade Theory.”
PUNCHLINE
In a calibrated general equilibrium model,
the New Trade Theory cannot account for the
increase in the ratio of trade to output in the
OECD.
Back-of-the-envelope calculations:
Suppose that the world consists of the OECD and the only trade is
manufactures.
With Dixit-Stiglitz preferences, country j exports all of its
production of manufactures Ymj except for the fraction s j Y j / Y oe
that it retains for domestic consumption.
World imports:
M
World trade/GDP:
M
Y oe
World trade/GDP:
n
¦j
M Ymoe
Ymoe Y oe
j
j
1
s
Y
m.
1
1 ¦
n
j
oe
Y
j 2
m
s
(
)
.
oe
1
Y
M
Y oe
n
M Ymoe
Ymoe Y oe
1 ¦
n
j
oe
Y
j 2
m
s
(
)
.
oe
1
Y
1 ¦ j 1 ( s j ) 2 goes from 0.663 in 1961 to 0.827 in 1990.
Ymoe / Y oe goes from 0.295 in 1961 to 0.222 in 1990.
0.663 u 0.295 0.196 | 0.184 0.827 u 0.222 .
Effects cancel!
A “NEW TRADE THEORY” MODEL
Environment:
x Static: endowments of factors are exogenous
x 2 regions: OECD and rest of world
x 2 traded goods: homogeneous — primaries (CRS) and
differentiated — manufactures (IRS)
x 1 nontraded good — services (CRS)
x 2 factors: (effective) labor and capital
x Identical technologies and preferences (love for variety) across
regions
x Primaries are inferior to manufactures
We only consider merchandise trade in both the data and in
the model.
Key Features of the Model
Consumers' problem:
max
s.t.
E p (c pj J p )K E m ( ³ cmj ( z ) U dz p )K / U E s (csj J s )K 1
Dw
K
q p c pj ³ w qm ( z )cmj ( z )dz p qsj csj d r j k j w j h j .
D
Firms' problems
Primaries and Services: Standard CRS problems.
Ypj
T p ( K pj )D p ( H pj )1D p
Ys j
Ts ( K sj )D s ( H sj )1D s
Manufactures: Standard (Dixit-Stiglitz) monopolistically
competitive problem:
x Fixed cost.
Ym ( z )
max ª¬Tm K m ( z )Dm H m ( z )1Dm F ,0 º¼
x Firm z sets its price qm ( z ) to max profits given all of the
other prices.
n
Ym ( z ) ¦ j 1 Cmj ( z ) Cmrw ( z ) .
1
1K
Cmj ( z )
Em (r j K j w j H j q p J p N j qsj J s N j )
1
1U
U
1U
qm ( z ) ª ³ D w qm ( z c) dz cº
¬
¼
UK
U (1K )
(1U )
U
K
1K
'
U
K
1
ª
º
1U
1K 1K
'
Em « ³ w qm ( z c) dz c
» E s qs
D
¬
¼
x Every firm is uniquely associated with only one variety
(symmetry).
x Free entry.
[0, d w ] with d w finite and endogenously determined.
x Dw
K
1K
E p qp
1
1K
1
1K
Volume of Trade
Let s j be the share of country j , j
production of manufactures,
sj
³D
j
1,..., n, rw , in the world
Ym ( z )dz / ³D w Ym ( z )dz
Ymj / Ymw .
The imports by country j from the OECD are
M oej (1 s rw s j )Cmj
M oerw (1 s rw )Cmrw .
Total imports in the OECD from the other OECD countries are
n
M
oe
oe
n
j 2
rw
oe
M
(1
s
(
s
)
/(1
s
))
C
¦
¦
m .
j
oe
j 1
rw
j 1
OECD in 1990
Country
Australia
Austria
Belgium-Lux
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Share of GDP %
1.79
0.97
1.26
3.45
0.78
0.81
7.26
9.96
0.50
0.04
0.28
6.64
Country
Japan
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Share of GDP %
18.04
1.72
0.26
0.70
0.41
3.00
1.40
0.17
0.91
5.92
33.72
ACCOUNTING FOR THE FACTS
Compare the changes that the model predicts for 1961-1990 with
what actually took place.
Focus on key variables:
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manfacturing GDP/OECD GDP
Calibrate to 1990 data.
Backcast to 1961 by imposing changes in parameters:
relative sizes of countries in the OECD
populations
sectoral productivities
endowments
ACCOUNTING FOR THE FACTS
Benchmark 1990 OECD Data Set
(Billion U.S. dollars)
H ioe
K ioe
Yi oe
Cioe
Yi oe Cioe
Primaries Manufactures
228
2,884
441
775
669
3,659
862
3,466
-193
193
Services
8,644
3,497
12,141
12,141
0
Total
11,756
4,713
16,469
16,469
0
ACCOUNTING FOR THE FACTS
Benchmark 1990 Rest of the World Data Set
(Billion U.S. dollars)
Yi rw
Cirw
Yi rw Cirw
Primaries Manufactures
1,223
1,159
1,030
1,352
193
-193
Services
3,447
3,447
0
Total
5,829
5,829
0
ACCOUNTING FOR THE FACTS
x N oe
x
¦
i
854 , N rw
rw
Y
p ,m , s i
x Set q p qm ( z )
values).
xU
4, 428 .
¦
i
rw
C
p ,m , s i
qs
w
5,829 .
r
1 (quantities are 1990
1/1.2 (Morrison 1990, Martins, Scarpetta, and Pilat 1996).
x Normalize d w
100 .
x Calibrate H rw , K rw so that benchmark data set is an
equilibrium.
x Alternative calibrations of utility parameters J p , J s , and K .
OECD in 1961
Country
Austria
Belgium-Lux
Canada
Denmark
France
Germany
Greece
Iceland
Ireland
Italy
Share of GDP %
0.75
1.25
4.22
0.70
6.99
9.71
0.50
0.03
0.21
4.64
Country
Share of GDP %
Netherlands
1.37
Norway
0.60
Portugal
0.32
Spain
1.38
Sweden
1.62
Switzerland
1.07
Turkey
0.83
United Kingdom
8.08
United States
55.74
Numerical Experiments
Calculate equilibrium in 1961:
T p ,1961
T m ,1961
T s ,1961
N oe
T p ,1990
T m ,1990 /1.01429 , F1961
F1990 /1.01429
T s ,1990 /1.00529 (Echevarria 1997)
536, N rw
2,545
Numerical Experiments
oe
oe
rw
rw
Choose H1961
, K1961
, H1961
, K1961
so that
¦
¦
¦
¦
i
oe
oe
Y
/
N
1990
p ,m , s i ,1990
oe
i p ,m , s i ,1961
Y
i
oe
1961
/N
rw
rw
Y
/
N
1990
p ,m , s i ,1990
rw
i p ,m , s i ,1961
Y
rw
1961
/N
oe
oe
K1961
K1990
oe
oe
H1961
H1990
q p ,1961 (Yprw,1961 C prw,1961 )
¦
i p ,m , s
rw
i ,1961 i ,1961
q
Y
2.400
2.055
0.050
How Can the Model Work in Matching the Facts?
x The ratio of trade to product has increased:
The size distribution of countries has become more equal
(Helpman-Krugman).
x Trade has become more concentrated among industrialized
countries:
OECD countries have comparative advantage in manufactures,
while the RW has comparative advantage in primaries.
Because they are inferior to manufactures, primaries become
less important in trade as the world becomes richer
(Markusen).
How Can the Model Work in Matching the Facts?
x Trade among industrialized countries is largely intraindustry
trade:
OECD countries export manufactures. Because of taste for
variety, every country consumes some manufactures from
every other country (Dixit-Stiglitz).
x The different total factor productivity growth rates across
sectors imply that the price of manufactures relative to
primaries and services has fallen sharply between 1961 and
1990. If price elasticities of demand are not equal to one, a lot
can happen.
Experiment 1
Jp
Jp K
0
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1. Jp = 0, Js = 0, K = 0
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1961
1990
Change
0.053
0.844
0.295
0.112
1.579
0.222
111.5%
87.1%
24.6%
0.108
0.893
0.223
0.136
1.169
0.222
25.8%
30.9%
0.4%
Experiment 2
Jp = 169.5, Js = 314.7 to match consumption in RW in 1990,
K=0
1961
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
2. Jp = 169.5, Js = 314.7, K = 0
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1990
Change
0.053
0.844
0.295
0.112 111.5%
1.579 87.1%
0.222 24.6%
0.103
0.739
0.225
0.132
1.060
0.222
28.1%
43.6%
1.4%
Experiment 3
Jp = 169.5, Js = 314.7,
K = 0.559 to match growth in OECD-OECD Trade/OECD GDP
1961
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
3. Jp = 169.5, Js = 314.7, K = 0.559
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1990
Change
0.053
0.844
0.295
0.112 111.5%
1.579 87.1%
0.222 24.6%
0.063
0.738
0.137
0.132 111.5%
1.060 43.7 %
0.222 62.7%
Experiments 4 and 5
Jp = 169.5, Js = 314.7, reasonable values of K ( 0.5 t 1/(1 K ) t 0.1)
1961
1990
Change
Data
OECD-OECD Trade/OECD GDP
0.053
0.112
111.5%
OECD-OECD Trade/OECD-RW Trade
0.844
1.579
87.1%
OECD Manf GDP/OECD GDP
0.295
0.222
24.6%
4. Jp = 169.5, Js = 314.7, K = 1
OECD-OECD Trade/OECD GDP
0.118
0.132
11.7%
OECD-OECD Trade/OECD-RW Trade
0.739
1.060
43.5%
OECD Manf GDP/OECD GDP
0.259
0.222
14.1%
5. Jp = 169.5, Js = 314.7, K = 9
OECD-OECD Trade/OECD GDP
0.118
0.132
1.6%
OECD-OECD Trade/OECD-RW Trade
0.739
1.060
43.5%
OECD Manf GDP/OECD GDP
0.284
0.222
21.8%
Sensitivity Analysis:
Alternative Calibration Methodologies
x Alternative specifications of nonhomogeneity
x Gross imports calibration
x Alternative RW endowment calibration
x Alternative RW growth calibration
x Intermediate goods
INTERMEDIATE GOODS?
Ypj
j
j
ª X j ³ w X mp
º
(
z
)
dz
X
D
1
D
p
p
pp
sp
»
min «
, D
,
, T p K pj H pj amp
asp
«¬ a pp
»¼
Ym ( z )
Ys j
j
ª X j ( z ) ³ w X mm
( z , z ')dz ' X j ( z ) º
pm
«
, D
, sm , »
amm
asm »
min « a pm
«
»
Dm
1D m
F
«¬Tm K m ( z ) H m ( z ) »¼
j
ª X j ³ w X ms
º
j
(
z
)
dz
D
1
D
X
s
s
ps
min «
, D
, ss , Ts K sj H sj »
ams
ass
«¬ a ps
»¼
Results for Model with Intermediate Goods
1961
Data
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
4. Jp = 307.8, Js = 262.2, K = 1
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
5. Jp = 307.8, Js = 262.2, K = 9
OECD-OECD Trade/OECD GDP
OECD-OECD Trade/OECD-RW Trade
OECD Manf GDP/OECD GDP
1990
Change
0.053 0.112
0.844 1.579
0.295 0.222
111.5%
87.1%
24.6%
0.323 0.370
0.994 1.305
0.263 0.222
14.5%
31.3%
15.6%
0.337 0.370
0.933 1.305
0.307 0.222
9.7%
39.9%
27.5%
POLICY?
In a version of our model with n OECD countries, a manufacturing
sector, and a uniform ad valorem tariff W, the ratio of exports to
income is given by
M
Y
(n 1)C f
Y
n 1
n 1 (1 W )1/(1 U )
Fixing n to replicate the size distribution of national incomes in the
OECD, and setting U 1/1.2 , a fall in W from 0.45 to 0.05 produces
an increase in the ratio of trade to output as seen in the data.
World Trade / World GDP
0.90
0.80
0.70
rho=1/2.0
0.60
0.50
0.40
rho=1/1.5
0.30
rho=1/1.1
0.20
rho=1/1.2
0.10
0.00
0
0.1
0.2
0.3
0.4
0.5
tau
0.6
0.7
0.8
0.9
1
2. Applied general equilibrium models that put the standard
theory to work do not well in predicting the impact of trade
liberalization experiences like NAFTA.
Applied general equilibrium models were the only analytical game in
town when it came to analyzing the impact of NAFTA in 1992-1993.
Typical sort of model: Static applied general equilibrium model with
large number of industries and imperfect competition (Dixit-Stiglitz or
Eastman-Stykolt) and finite number of firms in some industries. In some
numerical experiments, new capital is placed in Mexico owned by
consumers in the rest of North America to account for capital flows.
Examples:
Brown-Deardorff-Stern model of Canada, Mexico, and the United States
Cox-Harris model of Canada
Sobarzo model of Mexico
T. J. Kehoe, “An Evaluation of the Performance of Applied
General Equilibrium Models of the Impact of NAFTA,” in T. J.
Kehoe, T. N. Srinivasan, and J. Whalley, editors, Frontiers in
Applied General Equilibrium Modeling: Essays in Honor of
Herbert Scarf, Cambridge University Press, 2005, 341-77.
Research Agenda:
x Compare results of numerical experiments of models with data.
x Determine what shocks — besides NAFTA policies — were
important.
x Construct a simple applied general equilibrium model and
perform experiments with alternative specifications to determine
what was wrong with the 1992-1993 models.
Applied GE Models Can Do a Good Job!
Spain: Kehoe-Polo-Sancho (1992) evaluation of the performance
of the Kehoe-Manresa-Noyola-Polo-Sancho-Serra MEGA model
of the Spanish economy: A Shoven-Whalley type model with
perfect competition, modified to allow government and trade
deficits and unemployment (Kehoe-Serra). Spain’s entry into the
European Community in 1986 was accompanied by a fiscal reform
that introduced a value-added tax (VAT) on consumption to
replace a complex range of indirect taxes, including a turnover tax
applied at every stage of the production process. What would
happen to tax revenues? Trade reform was of secondary
importance.
Canada-U.S.: Fox (1999) evaluation of the performance of the
Brown-Stern (1989) model of the 1989 Canada-U.S. FTA.
Other changes besides policy changes are important!
Changes in Consumer Prices in the Spanish Model
(Percent)
sector
food and nonalcoholic beverages
tobacco and alcoholic beverages
clothing
housing
household articles
medical services
transportation
recreation
other services
data
model
model
model
1985-1986 policy only shocks only policy&shocks
1.8
-2.3
4.0
1.7
3.9
2.5
3.1
5.8
2.1
5.6
0.9
6.6
-3.3
-2.2
-2.7
-4.8
0.1
2.2
0.7
2.9
-0.7
-4.8
0.6
-4.2
-4.0
2.6
-8.8
-6.2
-1.4
-1.3
1.5
0.1
2.9
1.1
1.7
2.8
weighted correlation with data
variance decomposition of change
-0.08
0.30
0.87
0.77
0.94
0.85
regression coefficient a
regression coefficient b
0.00
-0.08
0.00
0.54
0.00
0.67
Measures of Accuracy of Model Results
1. Weighted correlation coefficient.
2. Variance decomposition of the (weighted) variance of the
changes in the data:
vardec( y data , y model )
var ( y model )
.
model
data
model
var ( y
) var ( y y
)
3, 4. Estimated coefficients a and b from the (weighted)
regression
xidata
a bximodel ei .
Changes in Value of Gross Output/GDP in the Spanish Model (Percent)
sector
agriculture
energy
basic industry
machinery
automobile industry
food products
other manufacturing
construction
commerce
transportation
services
government services
data
1985-1986
-0.4
-20.3
-9.0
3.7
1.1
-1.8
0.5
5.7
6.6
-18.4
8.7
7.6
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
model
policy only
-1.1
-3.5
1.6
3.8
3.9
-2.4
-1.7
8.5
-3.6
-1.5
-1.1
3.4
model
model
shocks only policy&shocks
8.3
6.9
-29.4
-32.0
-1.8
-0.1
1.0
5.0
4.7
8.6
4.7
2.1
2.3
0.5
1.4
10.3
4.4
0.4
1.0
-0.7
5.8
4.5
0.9
4.3
0.16
0.11
0.80
0.73
0.77
0.71
-0.52
0.44
-0.52
0.75
-0.52
0.67
Changes in Trade/GDP
in the Spanish Model (Percent)
direction of exports
Spain to rest of E.C.
Spain to rest of world
rest of E.C. to Spain
rest of world to Spain
data
model
model
model
1985-1986 policy only shocks only policy&shocks
-6.7
-3.2
-4.9
-7.8
-33.2
-3.6
-6.1
-9.3
14.7
4.4
-3.9
0.6
-34.1
-1.8
-16.8
-17.7
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
0.69
0.02
0.77
0.17
0.90
0.24
-12.46
5.33
2.06
2.21
5.68
2.37
Changes in Composition of GDP in the Spanish Model (Percent of GDP)
variable
wages and salaries
business income
net indirect taxes and tariffs
data
model
1985-1986 policy only
-0.53
-0.87
-1.27
-1.63
1.80
2.50
model
shocks only
-0.02
0.45
-0.42
model
policy&shocks
-0.91
-1.24
2.15
correlation with data
variance decomposition of change
0.998
0.93
-0.94
0.04
0.99
0.96
regression coefficient a
regression coefficient b
private consumption
private investment
government consumption
government investment
exports
-imports
0.00
0.73
-1.23
1.81
-0.06
-0.06
-0.42
-0.03
0.00
-3.45
-0.51
-0.58
-0.38
-0.07
-0.69
2.23
0.00
0.85
-1.78
1.32
-0.44
-0.13
-1.07
2.10
correlation with data
variance decomposition of change
0.40
0.20
0.77
0.35
0.83
0.58
regression coefficient a
regression coefficient b
0.00
0.87
0.00
1.49
0.00
1.24
-0.81
1.09
-0.02
-0.06
-3.40
3.20
Public Finances in the Spanish Model
(Percent of GDP)
data
model
model
model
variable
1985-1986 policy only shocks only policy&shocks
indirect taxes and subsidies
2.38
3.32
-0.38
2.98
tariffs
-0.58
-0.82
-0.04
-0.83
social security payments
0.04
-0.19
-0.03
-0.22
direct taxes and transfers
-0.84
-0.66
0.93
0.26
government capital income
-0.13
-0.06
0.02
-0.04
correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
0.99
0.93
-0.70
0.08
0.92
0.86
-0.06
0.74
0.35
-1.82
-0.17
0.80
Models of NAFTA
Did Not Do a Good Job!
Ex-post evaluations of the performance of applied GE models are
essential if policy makers are to have confidence in the results
produced by this sort of model.
Just as importantly, they help make applied GE analysis a
scientific discipline in which there are well-defined puzzles and
clear successes and failures for alternative hypotheses.
Changes in Trade/GDP
in Brown-Deardorff-Stern Model (Percent)
variable
Canadian exports
Canadian imports
Mexican exports
Mexican imports
U.S. exports
U.S. imports
data
1988-1999
52.9
57.7
240.6
50.5
19.1
29.9
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
model
4.3
4.2
50.8
34.0
2.9
2.3
0.64
0.08
23.20
2.43
Changes in Canadian Exports/ GDP in the Brown-Deardorff-Stern Model (Percent)
sector
agriculture
mining and quarrying
food
textiles
clothing
leather products
footwear
wood products
furniture and fixtures
paper products
printing and publishing
chemicals
petroleum and products
rubber products
nonmetal mineral products
glass products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
miscellaneous manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to Mexico
1988–1999
model
122.5
3.1
-34.0
-0.3
89.3
2.2
268.2
-0.9
1544.3
1.3
443.0
1.4
517.0
3.7
232.6
4.7
3801.7
2.7
240.7
-4.3
6187.4
-2.0
37.1
-7.8
678.1
-8.5
647.4
-1.0
333.5
-1.8
264.4
-2.2
195.2
-15.0
38.4
-64.7
767.0
-10.0
376.8
-8.9
633.9
-26.2
305.8
-4.4
1404.5
-12.1
exports to United States
1988–1999
model
106.1
3.4
75.8
0.4
91.7
8.9
97.8
15.3
237.1
45.3
-14.4
11.3
32.8
28.3
36.5
0.1
282.6
12.5
113.7
-1.8
37.2
-1.6
109.4
-3.1
-42.5
0.5
113.4
9.5
20.5
1.2
74.5
30.4
92.1
12.9
34.7
18.5
102.2
15.2
28.9
3.3
88.6
14.5
30.7
10.7
100.0
-2.1
-0.91
0.003
-0.43
0.02
249.24
-15.48
79.20
-2.80
Changes in Mexican Exports/GDP in the Brown-Deardorff-Stern Model (Percent)
sector
agriculture
mining and quarrying
food
textiles
clothing
leather products
footwear
wood products
furniture and fixtures
paper products
printing and publishing
chemicals
petroleum and products
rubber products
nonmetal mineral products
glass products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
miscellaneous manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to Canada
1988–1999
model
-20.5
-4.1
-35.5
27.3
70.4
10.8
939.7
21.6
1847.0
19.2
1470.3
36.2
153.0
38.6
4387.6
15.0
4933.2
36.2
23.9
32.9
476.3
15.0
204.6
36.0
-10.6
32.9
2366.2
-6.7
1396.1
5.7
676.8
13.3
32.5
19.4
-35.4
138.1
610.4
41.9
570.6
17.3
1349.2
137.3
2303.4
3.3
379.4
61.1
exports to United States
1988–1999
model
-15.0
2.5
-22.9
26.9
9.4
7.5
832.3
11.8
829.6
18.6
618.3
11.7
111.1
4.6
145.6
-2.7
181.2
7.6
70.3
13.9
122.1
3.9
70.4
17.0
66.4
34.1
783.8
-5.3
222.3
3.7
469.8
32.3
40.9
30.8
111.2
156.5
477.2
26.8
123.6
18.5
744.9
178.0
349.0
6.2
181.5
43.2
0.19
0.01
0.71
0.04
120.32
2.07
38.13
3.87
Changes in U.S. Exports/GDP in the Brown-Deardorff-Stern Model (Percent)
sector
agriculture
mining and quarrying
food
textiles
clothing
leather products
footwear
wood products
furniture and fixtures
paper products
printing and publishing
chemicals
petroleum and products
rubber products
nonmetal mineral products
glass products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
miscellaneous manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to Canada
1988–1999
model
-24.1
5.1
-23.6
1.0
62.4
12.7
177.2
44.0
145.5
56.7
29.9
7.9
48.8
45.7
76.4
6.7
83.8
35.6
-20.5
18.9
50.8
3.9
49.8
21.8
-6.9
0.8
95.6
19.1
56.5
11.9
50.5
4.4
0.6
11.6
-20.7
-6.7
66.7
18.2
36.2
9.9
154.4
14.9
36.5
-4.6
117.3
11.5
exports to Mexico
1988–1999
model
6.5
7.9
-19.8
0.5
37.7
13.0
850.5
18.6
543.0
50.3
87.7
15.5
33.1
35.4
25.7
7.0
224.1
18.6
-41.9
-3.9
507.9
-1.1
61.5
-8.4
-41.1
-7.4
165.6
12.8
55.9
0.8
112.9
42.3
144.5
-2.8
-28.7
-55.1
301.4
5.4
350.8
-2.9
167.8
-10.9
290.3
9.9
362.3
-9.4
-0.01
0.14
37.27
-0.02
0.50
0.02
190.89
3.42
Changes in Canadian Trade/GDP
in Cox-Harris Model (Percent)
variable
total trade
trade with Mexico
trade with United States
data
1988-2000
57.2
280.0
76.2
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
model
10.0
52.2
20.0
0.99
0.52
38.40
1.93
Changes in Canadian Trade/GDP in the Cox-Harris Model (Percent)
sector
agriculture
forestry
fishing
mining
food, beverages, and tobacco
rubber and plastics
textiles and leather
wood and paper
steel and metal products
transportation equipment
machinery and appliances
nonmetallic minerals
refineries
chemicals and misc. manufactures
total exports
1988-2000
model
-13.7
-4.1
215.5
-11.5
81.5
-5.4
21.7
-7.0
50.9
18.6
194.4
24.5
201.1
108.8
31.9
7.3
30.2
19.5
66.3
3.5
112.9
57.1
102.7
31.8
20.3
-2.7
53.3
28.1
total imports
1988-2000
model
4.6
7.2
-21.5
7.1
107.3
9.5
32.1
4.0
60.0
3.8
87.7
13.8
24.6
18.2
97.3
7.2
52.2
10.0
29.7
3.0
65.0
13.3
3.6
7.3
5.1
1.5
92.5
10.4
weighted correlation with data
variance decomposition of change
0.49
0.32
0.85
0.08
41.85
0.81
22.00
3.55
regression coefficient a
regression coefficient b
Changes in Mexican Trade/GDP in the Sobarzo Model (Percent)
sector
agriculture
mining
petroleum
food
beverages
tobacco
textiles
wearing apparel
leather
wood
paper
chemicals
rubber
nonmetallic mineral products
iron and steel
nonferrous metals
metal products
nonelectrical machinery
electrical machinery
transportation equipment
other manufactures
weighted correlation with data
variance decomposition of change
regression coefficient a
regression coefficient b
exports to North America
1988–2000
model
-15.3
-11.1
-23.2
-17.0
-37.6
-19.5
5.2
-6.9
42.0
5.2
-42.3
2.8
534.1
1.9
2097.3
30.0
264.3
12.4
415.1
-8.5
12.8
-7.9
41.9
-4.4
479.0
12.8
37.5
-6.2
35.9
-4.9
-40.3
-9.8
469.5
-4.4
521.7
-7.4
3189.1
1.0
224.5
-5.0
975.1
-4.5
0.61
0.0004
imports from North America
1988–2000
model
-28.2
3.4
-50.7
13.2
65.9
-6.8
11.8
-5.0
216.0
-1.8
3957.1
-11.6
833.2
-1.2
832.9
4.5
621.0
-0.4
168.9
11.7
68.1
-4.7
71.8
-2.7
792.0
-0.1
226.5
10.9
40.3
17.7
101.2
9.8
478.7
9.5
129.0
20.7
749.1
9.6
368.0
11.2
183.6
4.2
0.23
0.002
495.08
30.77
174.52
5.35
What Do We Learn from these Evaluations?
The Spanish model seems to have been far more successful in
predicting the consequences of policy changes than the three
models of NAFTA, but
x Kehoe, Polo, and Sancho (KPS) knew the structure of their
model well enough to precisely identify the relationships
between the variables in their model with those in the data;
x KPS were able to use the model to carry out numerical exercises
to incorporate the impact of exogenous shocks.
KPS had an incentive to show their model in the best possible
light.
3. Much of the growth of trade after a trade liberalization
experience is growth on the extensive margin. Models need
to allow for corner solutions or fixed costs.
T. J. Kehoe and K. J. Ruhl, “How Important is the New Goods
Margin in International Trade?” Federal Reserve Bank of
Minneapolis, 2002.
K. J. Ruhl, “Solving the Elasticity Puzzle in International
Economics,” University of Texas at Austin, 2005.
What happens to the least-traded goods:
Over the business cycle?
During trade liberalization?
Indirect evidence on the extensive margin
How Does Trade Grow?
ʀ Intensive Margin: growth in goods already traded
ʀ Extensive Margin: trade in goods not traded before
The Extensive Margin
ʀ The Extensive Margin has recently gained attention
ʀ Models
ɿ Melitz (2003)
ɿ Alessandria and Choi (2003)
ɿ Ruhl (2004)
ʀ Empirically
ɿ Hummels and Klenow (2002)
ɿ Eaton, Kortum and Kramarz (2004)
What Happens to the Extensive Margin?
ʀ During trade liberalization?
ɿ Large changes in the extensive margin
ʀ Over the business cycle?
ɿ Little change in extensive margin
Evidence from Trade Agreements
ʀ Events
ɿ Greece’s Accession to the European Econ. Community - 1981
ɿ Portugal’s Accession to the European Community - 1986
ɿ Spain’s Accession to the European Community - 1986
ɿ U.S.-Canada Free Trade Agreement - 1989
ɿ North American Free Trade Agreement - 1994
ʀ Data
ʀ Four-digit SITC bilateral trade data (OECD)
ɿ 789 codes in revision 2
ʀ Indirect Evidence
Measure One
1. Rank codes from lowest value of exports to highest value of
exports based on average of first 3 years
2. Form sets of codes by cumulating exports: the first 742.9
codes make up 10 percent of exports; the next 24.1 codes
make up 10 percent of exports; and so on.
3. Calculate each set’s share of export value at the end of the
sample period.
Composition of Exports: Mexico to Canada
0.30
Fraction of 1989 Exports
0.25
0.20
0.15
736.6
28.8
10.3
5.3
1.8
1.9
1.5
0.8
1.4
0.8
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.10
0.05
0.00
Cummulative Fraction of 1989 Exports
Composition of Exports: Mexico to Canada
0.30
Fraction of 1999 Exports
0.25
736.6
0.20
1.4
28.8
0.15
10.3
1.5
0.10
1.9
5.3
0.05
0.8
1.8
0.8
0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cummulative Fraction of 1989 Exports
0.9
1.0
Composition of Exports: Greece to EEC
0.40
730.9
Fraction of 1986 Exports
0.35
0.30
0.25
0.20
15.3
0.15
6.6
0.10
22.6
4.7
2.5
0.05
2.3
1.8
0.7
0.8
1.4
0.9
0.9
1.0
0.00
0.1
0.2
0.3
0.4
0.5
0.6
Cummulative Fraction of 1976 Exports
Measure Two
1. Order codes as before.
2. Cumulate exports as before.
3. Follow the evolution of the first (least-traded) set’s share of
total exports before, during, and after the liberalization.
Exports: Mexico to Canada
Fraction of Total Export Value
0.30
0.20
0.10
0.00
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Exports: Canada to Mexico
0.35
Fraction of Total Export Value
0.30
0.25
0.20
0.15
0.10
0.05
0.00
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Exports: Greece to EEC
Fraction of Total Export Value
0.4
0.3
0.2
0.1
0.0
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
Trade Liberalization and the Extensive Margin
Period
1989-1999
1989-1999
1989-1999
1989-1999
1989-1999
1989-1999
1978-1986
1982-1987
1982-1987
Trade Flow
Mexico - U.S.
U.S. – Mexico
Mexico - Canada
Canada - Mexico
Canada - U.S.
U.S. – Canada
Greece to the EEC
Spain to the EC
Portugal to the EC
Share of Export Growth
0.153
0.118
0.231
0.307
0.162
0.130
0.371
0.128
0.147
Business Cycles and the Extensive Margin
ʀ Over same period, consider countries with stable policy
ɿ U.S. – Japan
ɿ U.S. – U.K.
ɿ U.S. – Germany
Composition of Exports: U.S. to Germany
0.30
Fraction of 1999 Exports
0.25
0.20
0.15
604.1
86.8
41.7
24.9
0.10
0.20
0.30
0.40
0.10
12.5
7.3
5.0
0.50
0.60
0.70
3.6
2.0
1.1
0.90
1.00
0.05
0.00
0.80
Cummulative Fraction of 1989 Exports
Exports: United States to Germany
Fraction of Total Export Value
0.30
0.25
0.20
0.15
0.10
U.S. Exports to Germany
0.05
0.00
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Exports: Germany and the United States
Fraction of Total Export Value
0.30
0.25
0.20
0.15
German Exports to U.S.
0.10
U.S. Exports to Germany
0.05
0.00
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Business Cycles and the Extensive Margin
Period
Trade Flow
Share of Export Growth
1989-1999
U.S. - U.K.
0.096
1989-1999
U.K. - U.S.
0.128
1989-1999
U.S. - Japan
0.130
1989-1999
Japan - U.S.
0.103
1989-1999
U.S. - Germany
0.104
1989-1999
Germany - U.S.
0.103
The Model
ʀ Countries: foreign and home
ʀ Continuum of goods:
yi x 1
li x ai x x  > 0,1@
ʀ Stand-in consumer in each country with labor Li .
ʀ Preferences:
1
U
³ log >c x @ dx
i
0
ʀ ad valorem tariffs: W i
Determination of Exports
ʀ x is exported by foreign if
ah x ! w f a f x 1 W h œ
ah x ! w f 1 W h a f x
ʀ x is exported by home if
wf
ah x a f x 1W f
ʀ x is not traded if
wf
ah x !
! w f 1 W f 1W h a f x
Dornbusch, Fisher, Samuelson (1977)
ʀ Order goods according to the relative unit costs.
ah x a f x
w f 1 W h wf
1W f
Foreign Exports
Not Traded
Home Exports
x
Dornbusch, Fisher, Samuelson (1977)
ʀ Order goods according to the relative unit costs.
ʀ Problems
ɿ Trade data is collected in aggregates.
ɿ Difficult to obtain data on relative unit costs.
ɿ Both countries may export the same aggregate.
Our Approach
ʀ SITC ordering: an aggregate is an interval in > 0,1@
ʀ Take J evenly spaced points in > 0,1@ .
ʀ Randomly assign log-productivities.
Dj
ª ah j º
log «
»
¬a f j ¼
D j u > D ,D @
ʀ Points not on the grid are filled in by linear interpolation.
Relative Productivity Curve
ʀ Steeper segments
ɿ less trade growth
ɿ more intra-industry trade
ʀ For a given J larger D imply steeper segments.
ʀ For a given D , larger J imply steeper segments.
ah x a f x
ah x a f x
Low D
High D
1 W h w f
wf
f
1 W x
f
1 W wf
1 W h w f
x
Model Solution
1. Choose J and D .
2. Draw a realization of the relative productivity curve.
3. Solve the model and compute extensive margin measures.
4. Repeat for 5000 simulations.
5. Calculate means over simulations.
Calibration
ʀ Parameters: D , J , L f Lh , SITC endpoints
ʀ Country size is measured by gross output of commodities.
ʀ Codes are ordered by their SITC number.
ʀ Code size is determined by its world export value.
sizek
MEX
US
EX WORLD
EX
,k
WORLD , k
MEX
US
EX
EX
¦ WORLD,k ¦ WORLD,k
k
k
ʀ J and D determined by aggregate trade growth
and Intra-industry trade
Composition of Exports: Mexico to U.S. 1989-1999
By Sets if Categories Based on Export Size
Two Standard
Deviations
Fraction of 1999 Exports
0.28
0.21
Data
0.14
Model
0.07
0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cummulative Fraction of 1989 Exports
0.9
1.0
Composition of Exports: Mexico to Canada 1989-1999
By Sets of Categories Based on Export Size
0.7
Two Standard
Deviations
Fraction of 1999 Exports
0.6
0.5
0.4
0.3
Data
0.2
Model
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cummulative Fraction of 1989 Exports
0.9
1.0
Model with Intensive and Extensive Margins
ʀ Same Environment
ʀ New Preferences
1
U
ª i
º
U
« ³ c x dx »
¬0
¼
1
U
V
1
1 U
ʀ Expenditure on Goods
ɿ Old Model
ci x pi x wi Li
c x p x
i
ɿ New Model
i
1V
§ p x ·
w L ¨¨
¸¸
i
© P ¹
i
i i
Composition of Exports: Mexico to U.S. 1989-1999
By Sets of Categories Based on Export Size
Fraction of 1999 Exports
0.28
Two Standard
Deviations
0.21
Data
0.14
Model
0.07
0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cummulative Fraction of 1989 Exports
0.9
1.0
Composition of Exports: Mexico to Canada 1989-1999
By Sets of Categories Based on Export Size
Fraction of 1999 Exports
0.60
Two Standard
Deviations
0.50
0.40
0.30
Data
0.20
Model
0.10
0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cummulative Fraction of 1989 Exports
0.9
1.0
Conclusions
1. The extensive margin is important.
ɿ Average increase in export share: 67%
ɿ Correct timing
2. Simple model can produce extensive margin growth.
ɿ Calibration uses aggregate production data.
Relative Productivity Parameters
Growth in Trade’s Share of Production
and Grubel-Lloyd Index
J
D
ʀ Grubel-Lloyd Index
GLUS
MEX
1
¦
¦
kSITC
US
MEX
EX MEX
EX US
MEX
US
ª
EX
EX
US
MEX º
¼
kSITC ¬
Calibration Values
Grubel-Lloyd
Index (1989)
MEX-US
MEX-CAN
MEX-US
MEX-CAN
.487
.147
Growth in
Trade/Production
(1989-1999)
201%
299%
D
J
.223
.208
3215
63
Relative Output
(1989)
.06
.66
Lf
Lh
.06
.66
Calibration Sensitivity
ʀ Ideal SITC Measure:
sizek
ykh ykf
f
h
y
y
¦ k ¦ k
k
k
ʀ Our Proxy:
sizek
EX wh ,k EX wf ,k
h
f
EX
EX
¦ w, k ¦ w, k
k
k
K. J. Ruhl, “Solving the Elasticity Puzzle in International
Economics,” University of Texas at Austin, 2005.
The “Armington” Elasticity
x Elasticity of substitution between domestic and foreign goods
x Crucial elasticity in international economic models
x International Real Business Cycle (IRBC) models:
ż Terms of trade volatility
ż Net exports and terms of trade co-movements
x Applied General Equilibrium (AGE) Trade models:
ż Trade response to tariff changes
The Elasticity Puzzle
x Time series (Business Cycles):
ż Estimates are low
ż Relative prices volatile
ż Quantities less volatile
x Panel studies (Trade agreement):
ż Estimates are high
ż Small change in tariffs (prices)
ż Large change in quantities
Time Series Estimates: Low Elasticity (1.5)
Study
Reinert and Roland Holst (1992)
Range
>0.1, 3.5@
Reinert and Shiells (1993)
>0.1, 1.5@
>0.2, 4.9@
Gallaway et al. (2003)
Trade Liberalization Estimates: High Elasticity (9.0)
Study
Clausing (2001)
Range
>8.9, 11.0@
Head and Reis (2001)
>7.9, 11.4@
> 4.0, 13.0@
Romalis (2002)
Why do the Estimates Differ?
x Time series – no liberalization:
ż Change in trade volume from goods already traded
ż Change mostly on the intensive margin
x Trade liberalization:
ż Change in intensive margin plus
ż New types of goods being traded
ż Change on the extensive margin
Modeling the Extensive Margin
x Model: extensive margin from export entry costs
x Empirical evidence of entry costs
ż Roberts and Tybout (1997)
ż Bernard and Wagner (2001)
ż Bernard and Jensen (2003)
ż Bernard, Jensen and Schott (2003)
The Effects of Entry Costs
x Business cycle shocks:
ż Small extensive margin effect
x Trade liberalization:
ż Big extensive margin effect
x Asymmetry creates different empirical elasticities
Model Overview
x Two countries: ^h, f `, with labor L
x Infinitely lived consumers
x No international borrowing/lending
x Continuum of traded goods plants in each country
ż Differentiated goods
ż Monopolistic competitors
ż Heterogeneous productivity
x Export entry costs
ż Differs across plants: second source of heterogeneity
x Non-traded good, competitive market: A
x Tariff on traded goods (iceberg): W
Uncertainty
x At date t , + possible events, Kt
1,..., +
x Each event is associated with a vector of productivity shocks:
zt
ª¬ zh Kt , z f Kt º¼
x First-order Markov process with transition matrix /
OKK c
pr Kt 1 K c Kt
K
Traded Good Plants
x Traded good technology:
y I , N zI l
x Plant heterogeneity I , N ż constant, idiosyncratic productivity: I
ż export entry cost: N
ż plant of type I , N x Q plants born each period with distribution F I , N x Fraction G of plants exogenously die each period
Timing
P hx I , N : plants of type I ,N who paid entry cost
P hd I ,N : plants of type I , N who have not paid entry cost
P
P
P hd
P hx
hd
, P hx , P fd , P fx Shock/
Production
Birth/
Death
Shock/
Production
Death
Stay: non-exporter
P hd c
Switch: exporter
Stay: exporter
P hxc
Consumers
max
J log C 1 J log A h
h
q ,ch L ,c f L s.t.
1
C
³
L, hh P ª
ºU
U
U
h
h
«
ch L dL c f L dL »
« h
»
h
L, f P ¬L,h P ¼
³
phh L chh L dL ³
³
L, hf P 1 W p hf L c hf L dL phA A
L 3h
Non-traded Good
max phA K , P A l
s.t. A
zh K l
Normalize wh 1, implying phA K , P zh K Traded Goods: Static Profit Maximization
S d phh , l ;I , N ,K , P max phh z K I l l
phh , l
s.t.
chh phh ;K , P z K I l
S x phf , l ;I , N ,K , P max phf z K I l l
phf , l
s.t.
chf phf ;K , P z K I l
Pricing rules:
p I , N ,K , P h
h
p I , N ,K , P f
h
1
UI z K Dynamic Choice: Export or Sell Domestically
x Exporter’s Value Function:
Vx I , N ,K , P d K , P S d I , N ,K , P S x I , N ,K , P 1 G E ¦Vx I , N ,K c, P c OKK c
s.t. P c=0 K ,P x d K , P = multiplier on budget constraint
Kc
x Non-exporter’s Value Function:
Vd I , N ,K , P max ^S d I , N ,K , P d K , P E 1 G ¦Vd I , N ,K c, P c OKK c ,
Kc
½
¬ªS d I , N ,K , P N ¼º d K , P E 1 G ¦Vx I , N ,K c, P c OKK c ¾
Kc
¿
s.t. P c 0 K , P Equilibrium
x Cutoff level of productivity for each value of the entry cost
x For a plant of type I , N If I t IˆN K , P export and sell domestically
If I IˆN K , P only sell domestically
x In Equilibrium
ż “Low” productivity/“high” entry cost plants sell domestic
ż “High” productivity/“low” entry cost plants also export
ż Similar to Melitz (2003)
Determining Cutoffs
x For the cutoff plant:
ż entry cost = discounted, expected value of exporting
x IˆN K , P is the level of productivity, I , that solves:
d K , P N
entry cost
ª
º
1 G E «¦Vx I , N ,K c, P c OKKc ¦Vd I , N ,K c, P c OKKc »
Kc
¬ Kc
¼
expected value of exporting
Costs and Benefits
Finding the Cutoff Producer
Value of Exporting:
Steady State
Entry Cost
Non-Exporters
Iˆss
Firm Productivity
Exporters
I Choosing Parameters
x Set V
1
1 U
2 and W
0.15
x Calibrate to the United States (1987) and a symmetric partner.
E
Parameters
Annual real interest rate (4%)
J
Share of manufactures in GDP (18%)
G
Annual loss of jobs from plant deaths as percentage
of employment (Davis et. al., 1996) (6%)
Other Parameters
x Distribution over new plants:
FN I 1
I
TI
FI N 1
TN
N N x N , I , Q , TI ,TN jointly determine:
ż Average plant size (12 employees)
ż Standard deviation of plant sizes (892)
ż Average exporting plant size (15 employees)
ż Standard deviation of exporting plant sizes (912)
ż Fraction of production that is exported (9%)
Plant Size Distribution:
All Plants
Plant Size Distribution:
Exporting Plants
1.0
1.0
Data
0.8
0.8
Model
0.6
0.6
0.4
0.4
Data
Model
0.2
0.2
0.0
0.0
1
10
100
Employees per Plant (log scale)
1000
10
100
Employees per Plant (log scale)
1000
Productivity Process
x Two shocks, low and high:
zi
1 H
zi
1 H
x Countries have symmetric processes with Markov Matrix
/i
ª O
1 O º
«
»
O ¼
¬1 O
x H : standard deviation of the U.S. Solow Residuals (1.0%)
x O : autocorrelation of the U.S. Solow Residuals (0.90)
How does Trade Liberalization Differ from Business Cycles?
x Trade liberalization
ż Permanent changes
ż Large magnitudes
x Business cycles
ż Persistent, but not permanent changes
ż Small magnitudes
Developing Intuition: Persistent vs. Permanent Shocks
x1% positive productivity shock in foreign country
ż Shock is persistent – autocorrelation of 0.90
x 1% decrease in tariffs
ż Change in tariffs is permanent
Costs and Benefits
Response to 1% Productivity Shock
Autocorrelation = 0.90
Value of Exporting:
1% Productivity Shock
Value of Exporting:
Steady State
Entry Cost
Iˆbc Iˆss
Firm Productivity
I Response to a 1% Foreign Productivity Shock
Increase in imports on intensive margin
=
1.89%
Increase in imports on extensive margin
=
0.16%
Total increase in imports
=
2.05%
Change in consumption of home goods
=
-0.10%
% Change Imports/Dom. Cons.
% Change Price
2.17
0.99
2.19
Response to 1% Permanent Decrease in Tariffs
Costs and Benefits
Value of Exporting:
1% Decrease in Tariffs
Value of Exporting:
1% Productivity Shock
Value of Exporting:
Steady State
Entry Cost
Iˆtar
Iˆbc Iˆss
Firm Productivity
I Response to a 1% Tariff Reduction
Increase in imports on intensive margin
=
1.42%
Increase in imports on extensive margin
=
3.04%
Total increase in imports
=
4.46%
Change in consumption of home goods
=
-0.33%
% Change Imports/Dom. Cons.
% Change Tariff
4.81
1.00
4.81
Quantitative Results
x Two experiments
x Trade liberalization
ż Eliminate 15% tariff
ż Compute elasticity across tariff regimes
x Time series regressions
ż Use model to generate simulated data
ż Estimate elasticity as in the literature
Trade Liberalization Elasticity
Variable
Entry Costs
No Entry Costs
(% change)
(% change)
Exports
87.1
30.5
Imports Dom. Cons.
93.0
32.2
Exporting Plants
37.7
0.0
Implied Elasticity
6.2
2.1
Elasticity in the Time Series
x Simulate: produce price/quantity time series
x Regress:
log C f ,t / Ch ,t D V log ph ,t / p f ,t H t
Parameter
Estimate
D
-0.015
(6.36e-04)
(standard error)
(standard error)
1.39
(0.06)
R- squared
0.30
V
Conclusion
x Gap between dynamic macro models and trade models
ż Partially closes the gap
ż Modeling firm behavior as motivated by the data
ż Step towards better modeling of trade policy
x Single model can account for the elasticity puzzle
ż Time series elasticity of 1.4
ż Trade liberalization elasticity of 6.2
4. Modeling the fixed costs may explain why real exchange rate
data indicate that more arbitrage across countries that have
a strong bilateral trade relationship.
C. M. Betts and T. J. Kehoe, “Real Exchange Rate Movements and
the Relative Price of Nontraded Goods,” University of Minnesota
and University of Southern California, 2003.
C. M. Betts and T. J. Kehoe, “U.S. Real Exchange Rate
Fluctuations and Relative Price Fluctuations,” University of
Minnesota and University of Southern California, 2003.
Argentina-U.S. Real Exchange Rate
1.00
0.80
rer
log(RER)
0.60
0.40
0.20
0.00
rerN
-0.20
-0.40
1986
1988
1990
1992
1994
year
1996
1998
2000
2002
U.S. BILATERAL REAL EXCHANGE RATES
AND RELATIVE PRICES OF NONTRADED GOODS
Germany-U.S. Real Exchange Rate
Monthly CPI/PPI
Canada-U.S. Real Exchange Rate
Monthly CPI/PPI
0.50
0.50
0.40
rer
0.30
0.30
0.20
0.20
log(RER)
log(RER)
0.40
0.10
0.00
-0.10
rerN
0.10
-0.10
-0.20
-0.20
-0.30
1980
-0.30
1980
1985
rerN
0.00
1990
year
1995
2000
rer
1985
1990
year
1995
2000
We investigate the empirical relation between
the U.S. bilateral real exchange rate with 5 of her largest trade
partners
and
the bilateral relative price of nontraded to traded goods.
We measure the relation by
simple correlation (comovements)
relative standard deviation (volatility)
variance decompositions (percent of fluctuations)
We find the relation depends crucially on
1. the price series used to measure the relative price of nontraded
goods
2. the choice of trade partner.
Specifically, the relation is stronger when
1. traded goods prices are measured using production site prices
— not final consumption price data
2. the trade intensity between the U.S. and a trade partner is larger
— links international relative price behavior and size of trade
flows
Traditional theory real exchange rate theory (Cassel, Pigou, and
many others)
dichotomy of goods into
x costlessly traded (arbitraged prices)
x entirely nontraded (domestic prices)
INAPPROPRIATE
PLAN OF DISCUSSION
x Methodology
x Data
x Results
x What We Learn
x Extended Results (paper 2)
x Theoretical Model (paper 3)
x Conclusions
METHODOLOGY
Traditional real exchange rate theory attributes all (or most) real
exchange rate movements to changes in the relative price of
nontraded goods.
Recent empirical analyses using variance decompositions argue
there is almost no role for relative price of nontraded goods in real
exchange rate movements. All movements are driven by
deviations from law of one price.
REAL EXCHANGE RATE MEASUREMENT
Bilateral real exchange rate between the United States and
country i :
RERi ,us
NERi ,us
Pus
Pi
NERi ,us : nominal exchange rate — country i currency units per
U.S. dollar
Pj : price deflator or index for the basket of goods consumed or
produced in country j , j us, i .
Traditional theory dichotomizes goods into
x costlessly traded (arbitraged prices)
x entirely nontraded (domestic prices)
Pj ( PjT , PjN )
Decompose
RERi ,us
§
PusT ·§ Pi T
¨ NERi ,us T ¸¨
Pi ¹ © Pi
©
PusT ·
Pus ¸¹
RERi ,us
RERiT,us u RERiN,us
where
RERiT,us
NERi ,us
PusT
Pi T
is the real exchange rate of traded goods — the component that
measures deviations from the law of one price and
T
T
§
·
§
·
P
P
N
i
us
RERi ,us
¨ P ( PT , P N ) ¸ ¨ P ( PT , P N ) ¸
© i i i
¹ © us us us ¹
is what we refer to as the (bilateral) relative price of nontraded (to
traded) goods.
In logarithms,
reri ,us
reriT,us reri ,Nus .
In the case where
Pj P , P
T
j
RERiN,us
N
j
T Jj
j
N 1J j
j
P P ,
§
· §
·
Pi T
PusT
¨ P ( PT , P N ) ¸ ¨ P ( PT , P N ) ¸
© i i i
¹ © us us us ¹
§
T
P
i
¨
¨ PT J i P N 1J i
i
© i
T
1J i
§ Pi ·
¨ PN ¸
© i ¹
·
¸
¸
¹
§
T
P
us
¨
¨ PT J us P N 1J us
us
© us
T
us
N
us
§P
¨P
©
1J us
·
¸
¹
·
¸
¸
¹
SUMMARY STATISTICS
Analyze the relation between reri ,us and reri ,Nus :
1. corr (reri ,us , reri ,Nus )
cov(reri ,us , reri ,Nus )
var (rer
i ,us
2.
N
i ,us
std (rer )
std (reri ,us )
N
i ,us
1/ 2
)var (rer ) N
i ,us
.
1/ 2
§ var (rer ) ·
¨
¸
var
(
rer
)
i ,us ¹
©
3. vardec(reri ,us , reri ,Nus )
var (reri ,Nus )
var (reri ,Nus ) var (reriT,us )
(Another possibility:
vardec 2 (reri ,us , reri ,Nus )
var (reri ,Nus ) cov(reri ,Nus , reriT,us )
var (reri ,us )
.)
DATA
Data on 5 of largest trade partners of the United States:
x Canada (1)
x Mexico (2)
x Japan (3)
x Germany (6)
x Korea (7).
These countries account for 53 percent of U.S. trade in 2000.
Construct measures of reri ,us :
Need aggregate price indices (and nominal exchange rates)
1. Deflator for Gross Output at production site for all goods and
services produced by a country (GO)
2. Consumer Price Index for entire basket of consumption goods
and services (CPI)
3. Personal Consumption Deflators for all personal consumption
expenditures (PCD).
(Another possibility: Deflator for Gross Domestic Product for all
goods and services produced by a country (GDP).)
Construct measures of reri ,Nus :
Need traded goods price measures
1. Deflator for GO of agriculture, mining, and manufacturing
2. Producer price index for entire basket of producer goods (PPI).
3. CPI for “traded” components of consumption basket - all goods
less food.
4. PCD for “traded” components of personal consumption
expenditures - commodities.
(Another possibility: Deflator for GDP of agriculture, mining, and
manufacturing.)
WHAT WE LEARN
x The frequency of the data does not matter.
x Detrending matters (theory should guide for the choice and the
explanation for why it matters).
x The size of bilateral trade relationship is crucial.
The larger is the trade relationship, the more closely related are
reri ,us and reri ,Nus .
x The data series used to measure the relative price of nontraded
goods reri ,Nus matters a lot.
good conceptually
sectoral GO deflators
PPIs
not good conceptually
CPI components
PCD components
(sectoral GDP deflators)
highly correlated
CPI components-PCD components
sectoral GOP deflators-PPIs (-sectoral GDP deflators)
widely available
PPIs
less widely available difficult to obtain
CPI components
sectoral GO deflators
PCD components
(sectoral GDP deflators)
SUGGESTION
MODIFY THE TRADITIONAL THEORY SO
THAT GOODS DIFFER BY DEGREE OF
TRADABILITY
TABLE 1
COMPARISON OF FREQUENCIES:
CANADA-U.S. REAL EXCHANGE RATE
PPI-CPI data 1980-2000
annual
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
annual
quarterly quarterly quarterly monthly
0.88
0.70
0.66
0.88
0.69
0.65
0.88
0.69
0.65
0.88
0.51
0.41
1 lag
4 lags
0.88
0.51
0.41
1 lag
4 lags
16 lags
0.87
0.51
0.41
1 lag
(1 year)
(4 years)
(1 quarter)
(1 year)
(4 years)
0.70
0.55
0.40
0.82
0.55
0.51
0.56
0.51
0.28
0.70
0.55
0.39
0.82
0.55
0.51
monthly
3 lags
(1 month) (1 quarter)
0.48
0.55
0.29
0.48
0.51
0.26
monthly monthly
12 lags
48 lags
(1 year)
(4 years)
0.67
0.55
0.37
0.82
0.55
0.50
log(RER)
log(RER)
log(RER)
0.30
0.20
0.10
0.00
-0.10
1985
1985
1985
rerN
Monthly CPI/PPI
rer
1990
year
rerN
Quarterly CPI/PPI
rer
1990
year
rerN
Annual CPI/PPI
rer
1990
1995
2000
2000
2000
1995
1995
CANADA-U.S. REAL EXCHANGE RATE
-0.20
1980
0.30
0.20
0.10
0.00
-0.10
-0.20
1980
0.30
0.20
0.10
0.00
-0.10
-0.20
1980
year
TABLE 2A
CANADA-U.S. REAL EXCHANGE RATE
Annual Data
GO
PPI-CPI
Components
Deflators
of CPI
1980-1998
1980-2000
1980-2000
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Components
of PCD
1980-2000
0.81
0.51
0.38
0.88
0.70
0.66
0.46
0.63
0.33
0.42
0.57
0.27
0.78
0.45
0.29
0.88
0.51
0.41
-0.43
0.17
0.02
-0.32
0.14
0.02
0.54
0.40
0.20
0.70
0.55
0.40
-0.07
0.20
0.09
-0.11
0.13
0.06
0.74
0.47
0.33
0.82
0.55
0.51
-0.19
0.16
0.12
-0.08
0.13
0.09
FIGURE 3A
CANADA-U.S. REAL EXCHANGE RATE
CPI / CPI components
0.40
0.40
0.20
0.20
log(RER)
log(RER)
GO deflators
rerN
0.00
rerN
0.00
rer
-0.20
-0.40
1980
rer
-0.20
-0.40
1985
1990
year
1995
2000
1980
1985
1990
year
1995
2000
TABLE 2B
GERMANY-U.S. REAL EXCHANGE RATE
Annual Data
GO
PPI-CPI
Components
Deflators
of CPI
1980-2000
1980-2000
1980-2000
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Components
of PCD
1980-2000
-0.55
0.25
0.04
-0.33
0.73
0.21
-0.05
0.15
0.02
-0.24
0.25
0.05
0.18
0.20
0.04
-0.15
0.13
0.02
0.24
0.12
0.01
0.37
0.10
0.01
0.16
0.13
0.03
-0.24
0.14
0.04
0.18
0.10
0.01
-0.02
0.07
0.01
0.24
0.21
0.07
0.02
0.12
0.10
0.31
0.10
0.01
0.49
0.09
0.02
FIGURE 3B
GERMANY-U.S. REAL EXCHANGE RATE
GO deflators
CPI / CPI components
0.40
0.40
rer
rer
0.20
rerN
log(RER)
log(RER)
0.20
0.00
rerN
0.00
-0.20
-0.20
-0.40
1980
-0.40
1980
1985
1990
year
1995
2000
1985
1990
year
1995
2000
TABLE 2C
JAPAN-U.S. REAL EXCHANGE RATE
Annual Data
GO
PPI-CPI
Components
Deflators
of CPI
1980-2000
1980-2000
1980-2000
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Components
of PCD
1990-2000
-0.33
0.14
0.02
0.92
0.27
0.11
-0.74
0.17
0.02
-0.60
0.13
0.01
0.47
0.12
0.02
0.95
0.16
0.03
-0.27
0.07
0.00
0.35
0.07
0.00
0.30
0.12
0.02
0.87
0.17
0.05
-0.32
0.09
0.01
0.13
0.07
0.01
0.52
0.12
0.02
0.95
0.16
0.05
-0.36
0.06
0.01
0.43
0.07
0.01
FIGURE 3C
JAPAN-U.S. REAL EXCHANGE RATE
GO deflators
CPI / CPI components
0.40
0.40
rer
rer
0.20
log(RER)
log(RER)
0.20
0.00
0.00
rerN
rerN
-0.20
-0.20
-0.40
1980
-0.40
1980
1985
1990
year
1995
2000
1985
1990
year
1995
2000
TABLE 2D
KOREA-U.S. REAL EXCHANGE RATE
Annual Data
GO
PPI-CPI
Components
Deflators
of CPI
1980-2000
1980-2000
1980-2000
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Components
of PCD
1980-2000
0.65
0.21
0.05
0.43
0.48
0.22
0.64
0.23
0.06
0.72
0.36
0.18
0.82
0.18
0.04
0.94
0.30
0.14
0.63
0.21
0.05
0.72
0.24
0.08
0.81
0.23
0.08
0.88
0.26
0.10
0.57
0.22
0.06
0.49
0.16
0.03
0.80
0.18
0.05
0.94
0.30
0.13
0.62
0.21
0.06
0.72
0.24
0.08
FIGURE 3D
KOREA-U.S. REAL EXCHANGE RATE
GO deflators
CPI / CPI components
0.40
0.40
rer
rerN
0.00
rerN
0.00
-0.20
-0.20
-0.40
1980
-0.40
1985
1990
year
1995
rer
0.20
log(RER)
log(RER)
0.20
2000
1980
1985
1990
year
1995
2000
TABLE 2E
MEXICO-U.S. REAL EXCHANGE RATE
Annual Data
GO
PPI-CPI
Components
Deflators
of CPI
1980-2000
1981-2000
1980-2000
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Components
of PCD
1980-2000
0.75
0.36
0.18
0.74
0.21
0.06
0.64
0.55
0.33
0.81
0.23
0.08
0.84
0.36
0.20
0.73
0.22
0.06
0.67
0.46
0.26
0.84
0.24
0.08
0.52
0.25
0.07
0.54
0.19
0.04
0.26
0.28
0.08
0.51
0.16
0.03
0.91
0.38
0.25
0.78
0.24
0.08
0.73
0.51
0.34
0.92
0.27
0.12
FIGURE 3E
MEX ICO-U.S. REAL EXCHANGE RATE
GO deflators
CPI / CPI components
0.40
0.40
rer
rer
0.00
0.20
log(RER)
log(RER)
0.20
rerN
0.00
rerN
-0.20
-0.20
-0.40
1980
-0.40
1985
1990
year
1995
2000
1980
1985
1990
year
1995
2000
TABLE 3
COMPARISON OF SERIES:
TRADE WEIGHTED AVERAGE
Annual Data
GO
PPI-CPI
Components
Deflators
of CPI
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Components
of PCD
0.44
0.36
0.21
0.73
0.48
0.33
0.23
0.45
0.22
0.27
0.36
0.15
0.68
0.32
0.18
0.77
0.32
0.20
0.00
0.22
0.08
0.23
0.15
0.03
0.47
0.27
0.11
0.63
0.33
0.19
0.02
0.19
0.06
0.14
0.13
0.04
0.70
0.34
0.21
0.78
0.34
0.25
0.10
0.23
0.14
0.37
0.16
0.08
TABLE 4
COMPARISON OF SERIES:
CORRELATIONS OF DIFFERENT MEASURES OF rer N
Annual Data
Levels
PPI-CPI-GO deflator
CPI components-GO deflator
PCD components-GO deflator
PCD components-CPI components
Detrended levels
CPI-PPI/GO deflator
CPI components/GO deflator
PCD components/GO deflator
PCD components/CPI components
1 year changes
CPI-PPI/GO deflator
CPI components/GO deflator
PCD components/GO deflator
PCD components/CPI components
4 year changes
CPI-PPI/GO deflator
CPI components/GO deflator
PCD components/GO deflator
PCD components/CPI components
Canada
Germany
Japan
Korea
Mexico
weighted
average
0.97
0.52
0.54
0.996
0.92
0.91
0.99
0.88
-0.61
0.85
0.91
0.94
0.09
0.76
0.02
0.19
0.70
0.54
0.95
0.68
0.52
0.64
0.72
0.84
0.96
-0.18
-0.12
0.92
0.54
0.83
0.88
0.90
0.37
0.61
0.81
0.40
0.89
0.71
0.77
0.88
0.71
0.82
0.96
0.86
0.74
0.37
0.47
0.80
0.88
-0.19
-0.14
0.79
0.48
0.73
0.65
0.60
0.28
0.44
0.79
0.48
0.88
0.52
0.59
0.53
0.56
0.73
0.86
0.75
0.64
0.29
0.41
0.69
0.98
0.04
0.03
0.87
0.56
0.89
0.97
0.92
0.47
0.54
0.79
0.19
0.91
0.69
0.75
0.91
0.81
0.86
0.97
0.89
0.80
0.46
0.54
0.75
TABLE 5
COMPARISON OF COUNTRIES: GROSS OUTPUT DEFLATORS
Annual Data
Canada
Germany
Japan
Korea
Importance of trade to country i
2000 bilateral trade/GDP
0.58
0.05
0.04
0.14
2000 bilateral trade/trade
0.82
0.08
0.26
0.20
Rank of U.S. as partner
1
3
1
1
Importance of trade to U.S.
2000 bilateral trade/U.S. GDP
0.04
0.01
0.02
0.01
2000 bilateral trade/U.S. trade
0.21
0.04
0.11
0.03
Rank of country i as partner
1
6
3
7
Levels
corr(rer,rer N )
0.81
-0.55
-0.33
0.65
N
std(rer ) / std(rer)
N
vardec(rer,rer )
Detrended levels
corr(rer,rer N )
N
std(rer ) / std(rer)
N
vardec(rer,rer )
1 year changes
corr(rer,rer N )
N
std(rer ) / std(rer)
N
vardec(rer,rer )
4 year changes
corr(rer,rer N )
N
std(rer ) / std(rer)
N
vardec(rer,rer )
Mexico
0.44
0.83
1
0.03
0.13
2
0.75
0.51
0.25
0.14
0.21
0.36
0.38
0.04
0.02
0.05
0.18
0.78
0.18
0.47
0.82
0.84
0.45
0.20
0.12
0.18
0.36
0.29
0.04
0.02
0.04
0.20
0.54
0.16
0.30
0.81
0.52
0.40
0.13
0.12
0.23
0.25
0.20
0.03
0.02
0.08
0.07
0.74
0.24
0.52
0.80
0.91
0.47
0.21
0.12
0.18
0.38
0.33
0.07
0.02
0.05
0.25
TABLE A
GROSS DOMESTIC PRODUCT DEFLATORS
Annual Data
Levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Detrended levels
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
1 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
4 year changes
corr(rer,rer N )
std(rer N ) / std(rer)
vardec(rer,rer N )
Canada
19801998
Germany
19802000
Japan
19802000
Korea
19802000
Mexico
19802000
weighted
0.80
0.90
0.69
-0.32
0.53
0.15
-0.69
0.22
0.03
0.00
0.26
0.06
0.74
0.50
0.33
0.34
0.59
0.38
0.63
0.75
0.48
0.19
0.27
0.07
-0.24
0.18
0.03
0.56
0.18
0.04
0.84
0.47
0.33
0.47
0.49
0.29
0.05
0.81
0.33
-0.14
0.18
0.05
-0.29
0.16
0.03
0.61
0.26
0.09
0.47
0.36
0.15
0.11
0.48
0.18
0.65
0.68
0.54
0.18
0.27
0.12
-0.16
0.16
0.03
0.56
0.19
0.06
0.91
0.47
0.40
0.51
0.46
0.34
average
TABLE B
COMPARISON OF GROSS DOMESTIC PRODUCT DEFLATORS
AND GROSS OUTPUT DEFLATORS
Annual Data
Canada
Germany
Japan
Korea
Mexico
1980-1998 1980-2000 1980-2000 1980-2000 1980-2000
weighted
average
Levels
corr(rer(GDP),rer(GO))
corr(rer N (GDP),rer N (GO))
std(rer(GDP)) / std(rer(GO))
std(rer N (GDP)) / std(rer N (GO))
0.99
0.96
1.27
2.23
0.99
0.97
1.11
1.68
0.997
0.89
1.10
1.74
0.94
0.86
1.34
1.66
0.98
0.97
1.09
1.52
0.99
0.94
1.18
1.87
0.98
0.88
1.07
1.77
0.88
0.80
0.94
1.51
0.995
0.69
1.06
1.57
0.99
0.86
1.22
1.22
0.99
0.98
1.11
1.45
0.98
0.86
1.08
1.59
0.99
0.87
1.04
2.13
0.99
0.48
1.05
1.50
0.99
0.57
1.05
1.49
0.99
0.76
1.19
1.35
0.99
0.92
1.09
1.58
0.99
0.78
1.06
1.76
0.99
0.96
1.01
1.47
0.998
0.85
1.07
1.75
0.997
0.71
1.06
1.49
0.99
0.90
1.20
1.28
0.99
0.99
1.14
1.42
0.99
0.91
1.07
1.47
Detrended levels
corr(rer(GDP),rer(GO))
corr(rer N (GDP),rer N (GO))
std(rer(GDP )) / std(rer(GO))
std(rer N (GDP)) / std(rer N (GO))
1 year changes
corr(rer(GDP),rer(GO))
corr(rer N (GDP),rer N (GO))
std(rer(GDP)) / std(rer(GO))
std(rer N (GDP)) / std(rer N (GO))
4 year changes
corr(rer(GDP),rer(GO))
corr(rer N (GDP),rer N (GO))
std(rer(GDP )) / std(rer(GO))
std(rer N (GDP)) / std(rer N (GO))
EXTENDED RESULTS
Examine sample of 50 countries and all possible 1225 bilateral real
exchange rates.
Use same methodology and summary statistics and CPI-PPI
measures of prices.
Examine robustness of results to
1. Presence of U.S. in bilateral trade partner pairs in the sample.
2. Presence of rich-country/poor-country bilateral trade pairs in
the sample.
3. Presence of high-inflation/low inflation bilateral trade pairs in
the sample.
Find that there is a substantive relation between reri ,us and reri ,Nus on
average.
The relation does not depend on these three factors (at least in the
manner one might expect).
Strength of the relation depends crucially on size of the trade
relationship between two trade partners.
Table I
COUNTRIES IN THE SAMPLE
Percent World Trade in 2000
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
Colombia
Costa Rica
Cyprus
Denmark
Egypt
El Salvador
Finland
France
Germany
Greece
0.39
1.01
1.04
2.75
0.90
3.98
0.27
0.18
0.10
0.06
0.73
0.19
0.06
0.64
5.04
8.10
0.35
Hong Kong (P.R.C.)
India
Indonesia
Ireland
Israel
Italy
Japan
Jordan
Korea
Luxembourg
Malaysia
Mexico
Netherlands
New Zealand
Norway
Pakistan
Panama
3.02
0.70
0.75
1.00
0.50
3.65
6.45
0.04
2.50
0.16
1.42
2.44
3.61
0.20
0.70
0.15
0.13
Peru
Philippines
Saudi Arabia
South Africa
Singapore
Spain
Sri Lanka
Sweden
Switzerland
Thailand
Trinidad and Tobago
Turkey
Uruguay
United Kingdom
United States
Venezuela
0.10
0.66
0.84
0.36
2.08
2.08
0.10
1.23
1.39
0.96
0.04
0.63
0.05
4.88
15.37
0.38
Table II
U.S. BILATERAL REAL EXCHANGE RATES
Weighted Means
income level
high
low
inflation
high
low
0.63
0.44
0.26
0.62
0.45
0.28
0.65
0.42
0.21
0.69
0.37
0.18
0.61
0.46
0.28
0.74
0.49
0.32
0.28
0.29
0.10
0.58
0.27
0.11
0.68
0.63
0.43
0.50
0.35
0.17
0.47
0.36
0.19
0.54
0.34
0.11
0.51
0.29
0.11
0.49
0.37
0.19
0.53
0.40
0.22
0.39
0.20
0.08
0.42
0.19
0.06
0.58
0.52
0.28
0.66 0.61
0.36 0.35
0.22 0.23
49
24
88.13 59.80
0.75
0.39
0.18
25
28.33
0.77
0.34
0.18
18
21.32
0.62
0.37
0.23
31
66.81
0.73
0.41
0.31
25
66.22
0.44
0.21
0.10
24
21.91
0.60
0.22
0.10
31
45.92
0.71
0.52
0.35
18
42.21
all
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
countries
percent of U.S. trade
trade intensity
high
low
std(rer)
high
low
Table III
INCOME LEVELS
ALL BILATERAL REAL EXCHANGE RATES
Weighted Means
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
bilateral pairs
percent of world trade
all
highhigh
highlow
lowlow
0.53
0.66
0.32
0.50
0.74
0.33
0.60
0.47
0.26
0.63
0.64
0.43
0.45
0.46
0.19
0.42
0.50
0.21
0.52
0.36
0.13
0.58
0.43
0.21
0.60
0.54
0.26
1225
71.88
0.57
0.59
0.27
300
49.80
0.65
0.40
0.19
625
19.78
0.70
0.51
0.33
300
2.30
Table IV
INFLATION LEVELS
ALL BILATERAL REAL EXCHANGE RATES
Weighted Means
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
bilateral pairs
percent of world trade
all
highhigh
highlow
lowlow
0.53
0.66
0.32
0.68
0.63
0.47
0.63
0.46
0.24
0.51
0.71
0.33
0.46
0.46
0.19
0.65
0.43
0.23
0.51
0.34
0.13
0.44
0.49
0.20
0.60
0.54
0.25
1225
71.88
0.76
0.53
0.39
153
0.81
0.69
0.40
0.20
576
13.11
0.58
0.57
0.26
496
57.96
Table V
TRADE INTENSITY
ALL BILATERAL REAL EXCHANGE RATES
Weighted Means
all
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
bilateral pairs
percent of world trade
trade intensity
high
low
0.53
0.66
0.32
0.62
0.71
0.36
0.46
0.62
0.28
0.46
0.46
0.19
0.49
0.56
0.24
0.42
0.37
0.14
0.60
0.54
0.25
1225
71.88
0.66
0.61
0.30
51
33.51
0.54
0.48
0.21
1174
38.37
Table VI
REAL EXCHANGE RATE VARIABILITY
ALL BILATERAL REAL EXCHANGE RATES
Weighted Means
std(rer)
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
bilateral pairs
percent of world trade
all
high
low
0.53
0.66
0.32
0.59
0.36
0.20
0.50
0.87
0.40
0.46
0.46
0.19
0.46
0.26
0.09
0.45
0.60
0.26
0.60
0.54
0.25
1225
71.88
0.60
0.29
0.13
863
29.55
0.60
0.71
0.33
362
42.33
Table VII
U.S. BILATERAL REAL EXCHANGE RATES
TRADING BLOC TRADE PARTNERS
Weighted Means
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
countries
percent of U.S. trade
all
EU
nonEU
NAFTA
non-NAFTA
nonNAFTA-nonEU
0.63
0.44
0.26
0.23
0.27
0.08
0.74
0.49
0.31
0.82
0.45
0.35
0.50
0.44
0.20
0.65
0.53
0.27
0.50
0.35
0.17
0.41
0.17
0.04
0.52
0.40
0.20
0.55
0.42
0.22
0.46
0.30
0.13
0.49
0.38
0.18
0.66
0.36
0.22
49
88.13
0.43
0.18
0.05
14
19.19
0.72
0.41
0.26
35
68.94
0.77
0.46
0.30
2
34.31
0.58
0.30
0.16
47
53.82
0.66
0.37
0.23
33
34.63
Table VIII
TRADING BLOC TRADE PARTNERS
ALL BILATERAL REAL EXCHANGE RATES
Weighted Means
levels
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
1 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
4 year lags
corr(rer, rerN)
std(rerN)/std(rer)
vardec(rer, rerN)
bilateral pairs
percent of world trade
EU&
NAFTA&
nonEU/
nonEU/
NAFTA&
nonEU/
nonNAFTA&
nonNAFTA NAFTA nonNAFTA
nonEU/
nonNAFTA
all
EU&
EU
EU&
NAFTA
0.53
0.66
0.32
0.40
1.05
0.40
0.25
0.28
0.09
0.47
0.59
0.29
0.82
0.44
0.34
0.65
0.53
0.27
0.61
0.56
0.36
0.46
0.46
0.19
0.43
0.70
0.27
0.42
0.17
0.04
0.37
0.34
0.13
0.55
0.42
0.22
0.50
0.39
0.19
0.47
0.40
0.17
0.60
0.54
0.25
1225
71.88
0.63
0.83
0.35
91
21.44
0.43
0.21
0.06
42
6.77
0.46
0.49
0.20
462
11.27
0.77
0.45
0.30
3
10.62
0.66
0.37
0.23
99
11.64
0.46
0.26
0.22
528
10.25
5. The major determinant of large macroeconomic fluctuations
in Mexico is fluctuations in total factor productivity. If
trade liberalization and the 1995 financial crisis have had
large macroeconomic effects, it is through their effects on
TFP.
R. Bergoeing, P. J. Kehoe, T. J. Kehoe, and R. Soto “A Decade
Lost and Found: Mexico and Chile in the 1980s,” Review of
Economic Dynamics, 5 (2002), 166-205.
T. J. Kehoe and E. C. Prescott, “Great Depressions of the
Twentieth Century,” Review of Economic Dynamics, 5 (2002), 118.
T. J. Kehoe and K. J. Ruhl, “Sudden Stops, Sectoral Reallocations,
and Productivity Drops,” University of Minnesota, 2006.
Grandes Depresiones
Timothy J. Kehoe
University of Minnesota y Federal Reserve Bank of Minneapolis
Edward C. Prescott
Federal Reserve Bank of Minneapolis y Arizona State University
Instituto Tecnológico Autónomo de México
Abril de 2006
www.econ.umn.edu/~tkehoe
Estados Unidos: PIB real por persona en edad de trabajar
índice (1900=100)
800
400
200
100
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
Canadá: PIB real por persona en edad de trabajar
índice (1900=100)
15.66
800
14.66
400
200
13.66
100
12.66
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
Italia: PIB real por persona en edad de trabajar
1600
índice (1900=100)
800
400
200
100
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
Nueva Zelanda: PIB real por person en edad de trabajar
800
índice (1900=100)
400
200
?
100
50
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
España: PIB real por persona en edad de trabajar
11.68
800
índice (1900=100)
400
10.68
200
9.68
100
8.68
50
7.68
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
Chile: PIB real por persona en edad de trabajar
9.308
800
índice (1900=100)
400
8.308
7.308
200
100
6.308
50
5.308
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
México: PIB real por persona en edad de trabajar
800
9.64
índice (1900=100)
400
8.64
200
7.64
100
6.64
50
5.64
1900
1910
1920
1930
1940
1950
año
1960
1970
1980
1990
2000
Grandes Depresiones del Siglo XX
Uso de contabilidad de crecimiento y de modelos de equilibrio
general aplicados para reexaminar episodios de grandes
depresiones:
Reino Unido (1920s y 1930s) — Cole y Ohanian
Canadá (1930s) — Amaral y MacGee
Francia (1930s) — Beaudry y Portier
Alemania (1930s) — Fisher y Hornstein
Italia (1930s) — Perri y Quadrini
Argentina (1970s y 1980s) — Kydland y Zarazaga
Chile y México (1980s) — Bergoeing, Kehoe, Kehoe y Soto
Japón (1990s) — Hayashi y Prescott
(Review of Economic Dynamics, enero 2002; versión revisada y
expandida por aparecer en un volumen de la Reserva Federal de
Minneapolis)
Grandes depresiones: metodología
Cole y Ohanian (1999), Kehoe y Prescott (2002)
Función de producción agregada:
Yt = At Kta L1t-a .
Cuando At = A0 g (1-a )t , el producto per cápita crece a la tasa
constante g - 1.
El crecimiento del producto se mide con respecto a su
tendencia.
•
El crecimiento de tendencia representa el crecimiento – a
una tasa suave en el tiempo – del conocimiento utilizable
para la producción.
•
Este conocimiento no es específico a los países.
•
Los países crecen a la misma tasa, g - 1, en trayectorias
equilibradas de crecimiento diferentes.
•
Los niveles difieren entre países porque las instituciones
son diferentes.
•
Cambios institucionales mueven a los países a trayectorias
de crecimiento equilibradas diferentes.
•
Adoptamos g - 1 como la tasa de crecimiento del líder
industrial – Estados Unidos: g = 1,02
Contabilidad de crecimiento
Yt : PIB real (cuentas nacionales)
X t : inversión real (cuentas nacionales)
Lt : horas trabajadas (encuestas laborales)
Construcción del stock de capital:
Kt +1 = (1 - d ) K t + X t
La productividad total de factores es el residuo:
At = Yt K ta L1t-a
d = 0,05
a = 0,30
Descomponiendo los cambios en el PIB
por persona en edad de trabajar
§ Yt ·
§ Kt ·
§ Lt ·
1
α
log ¨ ¸ =
log ( At ) +
log ¨ ¸ + log ¨ ¸
1−α
© Nt ¹ 1 − α
© Yt ¹
© Nt ¹
Las teorías de depresión tradicionales enfatizan la caída en el
stock de capital o en las horas trabajadas como los factores
más importantes para explicar las depresiones.
Contabilidad de crecimiento en los Estados Unidos
400
350
índice (1960=100)
300
250
Yt / N t
1
1D
t
200
A
Lt / N t
150
100
D
1D
Kt / Yt 50
0
1960
1965
1970
1975
1980
1985
1990
1995
2000
Contabilidad de crecimiento en España
400
1
1D
t
350
A
índice (1960=100)
300
Yt / N t
250
200
D
1D
Kt / Yt 150
100
Lt / N t
50
0
1960
1965
1970
1975
1980
1985
1990
1995
2000
México y Chile durante los años 80s
R. Bergoeing, P. J. Kehoe, T. J. Kehoe y R. Soto
Crisis similares durante 1981-1983
x más severa en Chile que en México
Recuperaciones diferentes
x más rápida en Chile que en México
¿Por qué patrones diferentes?
PIB real por persona en edad de trabajar (15-64)
sin tendencia de 2 por ciento por año
130
índice (1980=100)
120
Chile
110
100
90
80
México
70
60
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Crisis similares
Condiciones iniciales:
· abultada deuda externa
· apreciación del tipo de cambio real
· abultado déficit comercial
· problemas bancarios
Shocks:
· aumento en las tasas de interés externas
· caída en el precio del cobre y del petróleo
· falta de acceso a créditos externos
Teorías para recuperaciones diferentes
Teoría de Corbo y Fischer para la rápida recuperación de Chile
• La fuerte depreciación del tipo de cambio real y la caída en
los salarios reales generó crecimiento liderado por
exportaciones.
Teoría de Sachs para la lenta recuperación de México
• La deuda “pendiente” deprimió la inversión.
Teoría de las reformas estructurales
• Las reformas estructurales que implementó Chile durante los
años 70s se realizaron en México durante los años 80s o 90s.
Teoría de Corbo y Fischer para la rápida
recuperación de Chile
La fuerte depreciación del tipo de cambio real y la caída
en los salarios reales generó crecimiento liderado por
exportaciones.
¿Pero qué ocurrió en México?
Indice de salarios reales en manufacturas
150
140
índice (1980=100)
130
Chile
120
110
100
90
80
México
70
60
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Valor de las exportaciones
deflactadas por IPP de EEUU
700
índice (1980=100)
600
500
México
400
300
200
Chile
100
0
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Teoría de Sachs para la lenta
recuperación de México
Abultada deuda “pendiente” en México:
· La mayoría de los nuevos créditos eran necesarios
para pagar viejos créditos.
· Inversiones socialmente beneficiosas no se realizaban.
¿Pero qué ocurrió en Chile?
Deuda externa total como porcentaje del PIB
140
porcentaje del PIB
120
100
Chile
80
60
México
40
20
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Inversión como porcentaje del PIB
30
porcentaje del PIB
25
México
20
15
Chile
10
5
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Teoría de las reformas estructurales
En 1979 Chile había privatizado y reformado su sistema tributario,
su sistema bancario, su ley de quiebras y sus políticas de comercio
exterior.
México esperó hasta más tarde.
Recuperaciones diferentes:
· Chile aprovechó los beneficios de las reformas.
· México pagó los costos de las distorsiones.
¿Cómo podemos determinar qué reformas fueron cruciales?
· Afectaron las reformas la acumulación de insumos o su
eficiencia?
· ¿Cuál fue el momento de las reformas?
Contabilidad de crecimiento
Yt : PIB real (cuentas nacionales)
X t : inversión real (cuentas nacionales)
Lt : horas trabajadas (encuestas laborales)
Construcción del stock de capital:
Kt +1 = (1 - d ) K t + X t
La productividad total de factores es el residuo:
At = Yt K ta L1t-a
d = 0,05
a = 0,30
Descomponiendo los cambios en el PIB
por persona en edad de trabajar
§ Yt ·
§ Kt ·
§ Lt ·
1
α
log ¨ ¸ =
log ( At ) +
log ¨ ¸ + log ¨ ¸
1−α
© Nt ¹ 1 − α
© Yt ¹
© Nt ¹
Las teorías de depresión tradicionales enfatizan la caída en el
stock de capital o en las horas trabajadas como los factores
más importantes para explicar las depresiones.
Modelo de equilibrio general aplicado
dinámico
El consumidor representativo maximiza
¦
f
t 1980
E t ª«J log Ct (1J )log(hNt Lt ) º»
¬«
¼»
sujeto a
Ct Kt 1 Kt
donde Tt
wt Lt (1 W t )(rt G ) Kt Tt
W t (rt G ) Kt es la transferencia de ingreso tributario
Factibilidad:
Ct Kt 1 (1 G ) Kt
At KtD L1t D .
Calibración
Condiciones de primer orden:
1
C
t 1
E
C
1 (1 W )(r G )
t
t
t
J wt
1J
.
hNt Lt Ct
Utilizar datos de 1960-1980 para calibrar los parámetros:
E 0.98, W 1
J
Ct E Ct 1
(rt G )Ct 1
Ÿ W 0.45 en México, W 0.56 en Chile ;
Ct
Ÿ J 0.30 en México, J 0.28 en Chile .
Ct wt (hNt Lt )
Experimentos numéricos
Caso básico:
W t 0.45 en México, W t 0.56 en Chile , 1980-2000.
Reforma tributaria:
W t 0.45 en México, W t 0.56 en Chile , 1980-1988;
W t 0.12 en México, W t 0.12 en Chile , 1988-2000.
Productividad total de factores
sin tendencia de 1,4 por ciento por año
130
índice (1980=100)
120
110
Chile
100
90
80
70
60
1980
México
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Lecciones del proyecto Grandes Depresiones
• Los determinantes principales de las depresiones no son caídas en los
insumos de capital y trabajo — enfatizadas por la teorías tradicionales
de depresiones — sino caídas en la eficiencia, medida como
productividad total de factores (PTF), con que estos insumos son
usados.
• Shocks exógenos, como caídas en los términos de intercambio y
aumentos en las tasas de interés externas que afectaron a Chile y
México a comienzos de los años 80s, pueden causar una reducción en
la actividad económica de la magnitud típicamente asociada al ciclo
económico.
• Política gubernamental equivocada puede transformar esa reducción en
una caída severa y prolongada en la actividad económica bajo su
tendencia — una gran depresión.
Experimentos numéricos para México:
PIB real por persona en edad de trabajar
Caso base
Reforma tributaria
Y/N (sin tendencia)
Y/N (sin tendencia)
120
120
100
100
80
80
modelo
datos
datos
60
40
1980
60
modelo
1985
1990
1995
40
2000
1980
1985
1990
1995
2000
Experimentos numéricos para Chile:
PIB real por persona en edad de trabajar
Caso base
Reforma tributaria
Y/N (sin tendencia)
Y/N (sin tendencia)
140
140
120
120
modelo
100
100
datos
80
80
modelo
60
1980
1985
1990
1995
datos
60
2000
1980
1985
1990
1995
2000
¿Qué aprendemos de la contabilidad de crecimiento y
de los experimentos numéricos?
Casi toda la diferencia en las recuperaciones de México y Chile es
resultado de diferencias en las trayectorias de la productividad.
Las reformas tributarias son importantes para explicar algunos rasgos de
las recuperaciones, pero no sus diferencias.
Implicancias para el estudio de la teoría de las reformas estructurales:
· Las únicas reformas que podrían explicar la diferencia entre ambas
recuperaciones son aquéllas que se traducen principalmente en
diferencias en productividad, no las que lo hacen a través de
diferencias en el uso de los insumos.
· El momento de las reformas es crucial para que expliquen la
diferencia en el comportamiento económico.
Reformas fiscales
Chile:
• reformas tributarias: 1975, 1984
• reforma a la seguridad social: 1980
• excedentes fiscales
México:
• reformas tributarias 1980, 1985, 1987, 1989
• déficit fiscales
¡Importantes, pero no para explicar las
diferencias!
Reformas de política comercial
Chile: a fines de los años 70s
• todas las restricciones cuantitativas habían sido eliminadas
• aranceles uniformes en 10 por ciento
• aumento en los aranceles durante la crisis — aranceles bajo 10 por
ciento en 1991
México: a mediados de los años 80s
• 100 por ciento de la producción doméstica protegida por licencias de
importación
• barreras no arancelarias y tipos de cambio duales
Reformas masivas al comercio exterior en México durante 1987-1994,
culminando con el NAFTA
¡El momento parece incorrecto!
Privatización
Chile
• principales privatizaciones durante 1974-1979
México
• nacionalización masiva en 1982
° expropiación de bancos de compañías privadas
° el gobierno controlaba entre 60 y 80 por ciento del PIB
• privatizaciones principales a partir de 1989
¿El momento parece incorrecto?
Banca
Chile: 1982 y después
· gobierno se hizo cargo de los bancos que quebraron
· tasas de interés determinadas en el mercado
· reducción en los requerimientos de reservas.
México: 1982 y después
· nacionalización de todos los bancos
· gobierno fijó tasas de interés para depósitos bajas
· 75 por ciento de los créditos dirigidos al gobierno o por el gobierno.
Crédito privado como porcentaje del PIB
90
80
Chile
porcentaje del PIB
70
60
50
40
México
30
20
10
0
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Leyes de quiebra
Chile había reformado la administración de sus
procedimientos de quiebra en 1978. En 1982 reformó su
ley de quiebra asimilándola más a la imperante en
Estados Unidos.
México reformó sus procedimientos de quiebra de
manera similar sólo en el año 2000.
Quiebras comerciales en Chile
500
número por año
400
300
200
100
0
1980
1982
1984
1986
1988
1990
año
1992
1994
1996
1998
2000
Sudden Stops, Sectoral Reallocations,
and Real Exchange Rates
Timothy J. Kehoe
University of Minnesota and Federal Reserve Bank of Minneapolis
and
Kim J. Ruhl
University of Texas, Austin
Definition: sudden stops
An abrupt decline in capital inflows.
x Argentina, Mexico: 1994-1995
x Indonesia, Korea, Thailand: 1996-1998
x Germany, Sweden, Spain: 1992-1993
x Argentina 2000-2002
It is not speed that kills, it is the sudden stop.
— Bankers’ Adage (Dornbusch, Goldfajn, and Valdés, 1995)
Two lines of research
What causes sudden stops?
x large literature
x take effects – loss of output – as given
What are the effects of sudden stops? (our approach)
x fewer studies
x one sector models
x take sudden stop as given
Effects of sudden stops
Aggregate
x real exchange rate depreciation
x trade balance surplus
x decrease in GDP
x decrease in TFP
Sectoral Detail
x tradable good output falls less than nontradable
x labor reallocated to tradable good sector
x increase in pT p N
Example: Mexico 1994-1995
Opens to capital flows: late 1980s
x trade deficits
x real exchange rate appreciation
Sudden stop: 1994-1995
x trade surplus
x real exchange rate depreciation
x fall in GDP, TFP
End of sudden stop
x trade deficits
x real exchange rate appreciation
x recovery of GDP, TFP
Our model
Small open economy
x multisector: tradable, nontradable
x costly to adjust labor across sectors
Sudden stop
x tradable goods price increase, increase production
x capital and labor misallocated
x costs from moving labor
Model accounts for:
x real exchange rate
x labor allocation
x trade balance
Misses:
x GDP, TFP
Outline
1. Data: Mexico
2. Explanations
3. Model
4. Calibration
5. Results
Mexico: trade balance
4.0
percent of GDP (%)
2.0
0.0
-2.0
-4.0
-6.0
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: interest rates (dollar denominated debt)
25.0
15.0
10.0
nominal rate (stripped)
Dec 30, 1994 = 8.90%
5.0
Mexico premia
Nov 30, 1994 = 4.56%
10/26/95
07/18/95
04/09/95
12/30/94
09/21/94
06/13/94
03/05/94
11/25/93
08/17/93
05/09/93
0.0
01/29/93
percent per year
20.0
Mexico-U.S. real exchange rate
RERmex ,us
NERmex ,us
Pus
Pmex
x NERmex ,us : nominal exchange rate — pesos per dollar
x Pj : GDP price deflator in country
Decomposing real exchange rates
RERmex ,us
T
§
PusT ·§ Pmex
PusT ·
/
¨ NERmex ,us T ¸¨
¸
P
P
P
mex ¹© mex
us ¹
©
T
N
RERmex
u
RER
mex ,us
,us
Deviations from the law of one price:
T
RERmex
,us
NERmex ,us
PusT
T
Pmex
Relative price of nontradable to tradable goods:
T
T
§
·
§
·
P
P
N
mex
us
RERmex ,us
¨
¸/¨
¸
P
P
© mex ¹ © us ¹
Mexico-U.S. real exchange rate
0.3
log(RER)
0.2
rer
0.1
0.0
rer
N
-0.1
-0.2
1988
1990
1992
1994
1996
1998
2000
2002
Sectoral Reallocation
Mexico: sectoral value added
140
130
index (1994=100)
120
tradable goods
110
100
nontradable goods
90
80
70
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: traded good employment
share of total employment
0.40
0.38
0.36
Data
0.34
0.32
Trend
0.30
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: traded good employment, detrended
0.08
deviation from trend
0.06
0.04
0.02
0.00
-0.02
1988
1990
1992
1994
1996
1998
2000
2002
Growth accounting
Yt : real GDP (national income accounts)
X t : real investment (national income accounts)
Lt : hours worked (labor surveys)
Construct Capital Stocks:
K t 1
1 G Kt X t
Total factor productivity:
At
Yt K tD L1tD
Mexico: output and TFP
110
index (1994 = 100)
105
Y/L
100
95
TFP
90
1988
1990
1992
1994
1996
1998
2000
2002
Two theories
Output and TFP fall with a sudden stop because:
1. cost of imported intermediates increases
2. real frictions that waste/mismeasure output and inputs
x adjustment frictions
x variable capital utilization
x labor hoarding
1. Cost of imported intermediates increases
Mexico: terms of trade
115
index (1994 = 100)
110
105
100
95
1988
1990
1992
1994
1996
1998
2000
2002
1. Cost of imported intermediates increases
A deterioration in the terms of trade makes it expensive for an
economy to import intermediate goods.
International trade as production technology
x Exports are inputs, imports are outputs.
x decline in terms of trade § negative technology shock.
Can this negative “technology shock” account for the drop in TFP
during the crisis?
1. Cost of imported intermediates increases
A deterioration in the terms of trade makes it expensive for an
economy to import intermediate goods.
International trade as production technology
x Exports are inputs, imports are outputs.
x decline in terms of trade § negative technology shock.
Can this negative “technology shock” account for the drop in TFP
during the crisis?
No.
Standard national income accounting (SNA, NIPA)
implies that terms of trade shocks have no first-order
effects on real output (GDP, GNP)
A model with international trade
Final good
f ( A , m)
y
Exports: x
y
cx
Imported intermediate inputs: m, price p (terms of trade)
Balanced trade
pm
x
Real GDP (base year prices)
Y
c x p0 m
y p0 m
f (A, m) p0 m
Competitive economy solves
max f ( A, m) pm
m
f m ( A, m( p )) { p
mc( pˆ )
1
0
f mm ( A, m( pˆ ))
How does real GDP change with an increase in p — a
deterioration in the terms of trade (depreciation in the real
exchange rate)?
Y ( p ) { f ( A, m( p )) p0 m( p )
Y c( pˆ )
f m ( A, m( pˆ ))mc( pˆ ) p0 mc( pˆ ) ( pˆ p0 ) mc( pˆ )
pˆ | p0 Ÿ Y c( pˆ ) | 0
Alternative accounting concepts
x Diewert and Morrison (1974, 1986)
x Kohli (1983, 1996)
x U.S. Bureau of Economic Analysis (Command Basis GDP)
x United Nations Statistics Division (Gross National Income)
x GNP, GDP (SNA, NIPA) do not.
Alternative accounting concepts
x Diewert and Morrison (1974, 1986)
x Kohli (1983, 1996)
x U.S. Bureau of Economic Analysis (Command Basis GDP)
x United Nations Statistics Division (Gross National Income)
x GNP, GDP (SNA, NIPA) do not.
Terms of trade shocks are worse than you think!
2. Real frictions waste outputs: our model
Small open economy (Mexico)
Produces nontradable goods, y N , and tradable goods, yD
x use intermediates plus capital and labor
Composite tradable yTt
f yDt , mt Frictions:
x sector specific capital
x costly to move labor across sectors
Quantitative model
Consumers
\
§
·
U
U U
ª §c ·
º
§
·
¸
c
f
t¨
Tt
Nt
max ¦ t 0 E ¨ «H ¨ ¸ 1 H ¨
¸ » 1 ¸ /\
n
© nt ¹ »¼
¨ «¬ © t ¹
¸
©
¹
s.t.
pTt cTt pNt cNt qt iDt iNt bt 1 wt A t 1 rt bt rDt k Dt rNt k Nt Tt
cTt t 0 , cNt t 0 , at t A
b0 , k D 0 .k N 0 given
Here
A t is working-age population,
nt 0.5A t 0.5 popt is adult-equivalent population,
Production functions
Domestically produced traded good
D D 1D D
yDt = min ª¬ zTDt / aTD , z NDt / aND , AD k Dt
A Dt º¼ 4 D A Dt 1 , A Dt A Dt 1
where 4 D A Dt 1 , A Dt § A Dt A Dt 1 ·
= TD ¨
¸
A
Dt 1
©
¹
2
Nontraded good
D N 1D N
yNt = min ª¬ zTNt / aTN , z NNt / aNN , AN k Nt
A Nt º¼ 4 N A Nt 1 , A Nt A Nt 1
where 4 N A Nt 1 , A Nt § A Nt A Nt 1 ·
= TN ¨
¸
A
Nt 1
©
¹
2
Composite traded good (Armington aggregator)
1
]
yTt = M P xDt
1 P mt] ]
Investment good
iDt + iNt
J 1J
GzTIt
z NIt
k Dt 1
) (iDt / k Dt )k Dt 1 G k Dt
k Nt 1
) (iNt / k Nt )k Nt 1 G k Nt
) (i / k )
ª G 1K i / k K 1 K G / K º
¬
¼
Market clearing
Domestically produced traded good
xDt + xFt = yDt
Nontraded good
cNt z NIt z NDt z NNt
y Nt
Composite traded good
cTt zTIt zTDt zTNt
Labor market
A Dt A Nt
At
yTt
Balance of payments
pDt xFt 1 rt bt
mt bt 1
Foreign demand
xFt
Dt 1 W Ft pDt Transfer of tariff revenue
Tt
W Dt mt
1
1]
Sudden stop!
bt
bt 1 , t 1995,1996
x agents do not foresee sudden stop
x agents do foresee length of sudden stop
x domestic interest rate is endogenously determined
x interest payments on foreign debt made at r *
Calibration
Rest of world is U.S.
x 72% of imports to Mexico from U.S. (1988-2002)
x 60% of foreign direct investment from U.S. (1988-2002)
x r* 5% Ÿ E
Elasticities
x subs. tradable and nontradable cons.: 0.5 (Kravis, et al.)
x intertemporal elasticity: 0.5
x subs. domestic tradable and imports: 2.0
Labor Adjustment T D T N x labor shift from sudden stop: 6.5%
Calibration continued
Set G
0.06
Normalize prices in 1989 to 1
Mexico input-output table, 1989
x share parameters: aTD , aND , aTN , aNN , D D ,D N , H , P
x scale parameters: AD , AN , M , D
Input – Output Table, 1989
9.02
36.26
24.47
11.13
14.98
50.59
86.85
Nontradable
Total intermediate consumption
Employee compensation
9.76
36.00
21.29
19.42
28.44
43.71
29.18
65.44
65.00
52.49
76.96
0.00
11.90
23.03
0.00
0.00
14.98
0.00
64.40
114.99
0.00
93.58
180.43
65.00
Return to capital
Value added
Imports
13.58
34.87
14.98
21.42
65.13
0.00
35.00
100.00
14.98
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
35.00
100.00
14.98
Tariffs
Total Gross Output
0.00
86.85
0.00
93.58
0.00
180.42
0.00
76.96
0.00
23.03
0.00
14.98
0.00
114.99
0.00
295.41
Tradable
aTN
zTN ,1989
y N ,1989
Use Int.Tradables
GO Nontradable
w1989 { 1 Ÿ A D ,1989
9.76
93.58
21.29, A N ,1989
Total final
demand
Exports
27.24
Commodity
Total int.
demand
Tradable
Nontradable
Investment
Total Demand
Final Demand
Consumption
Input
0.10
43.71
No sudden stop
Model begins in 1988
x k D ,1988 k N ,1988 k1988
x A D ,1988 A N ,1988 A1988
x allow labor capital to costlessly adjust (anticipated)
Mexico opens to capital in 1990
Exogenous
x population growth
x initial capital stocks
No exogenous TFP growth!
No sudden stop dynamics
No sudden stop: capital stock
No sudden stop: trade balance
400
10.0
350
share of GDP (%)
index (1988 = 100)
5.0
300
250
200
0.0
-5.0
150
100
1988
1993
1998
2003
2008
2013
2018
2023
2028
-10.0
1988
1993
1998
2003
2008
2013
2018
2023
2028
Baseline Model
Model begins in 1988
x k D ,1988 k N ,1988 k1988
x A D ,1988 A N ,1988 A1988
x allow labor capital to costlessly adjust (anticipated)
Mexico opens to capital in 1990
Sudden stop hits in 1995
Mexico: trade balance
8.00
percent of gdp (%)
4.00
0.00
Data
-4.00
-8.00
-12.00
1988
Model
1990
1992
1994
1996
1998
2000
2002
Mexico: real exchange rates
0.5
0.3
log(RER)
Data
0.0
-0.3
Model
-0.5
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: nontraded/traded good prices
0.6
log(RERN)
0.4
0.2
Model
0.0
Data
-0.2
-0.4
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: sectoral value added
140
140
130
130
120
120
tradable goods
index (1994=100)
index (1994=100)
Mexico: sectoral value added
110
100
nontradable goods
100
nontradable goods
90
90
80
80
70
1988
1990
1992
1994
1996
model
1998
2000
2002
tradable goods
110
70
1988
1990
1992
1994
1996
data
1998
2000
2002
Mexico: traded good employment, detrended
0.08
deviation from trend
0.06
Model
0.04
0.02
Data
0.00
-0.02
-0.04
1988
1990
1992
1994
1996
1998
2000
2002
Real GDP
Yt
p
p
Dt0
yDt pTt0 zTDt pNt0 z NDt
Nt0
y Nt pTt0 zTNt pNt0 z NNt
Real Investment
pTt0 zTIt pNt0 z NIt
It
Real Capital Stock
K t 1
(1 G ) K t I t
Total Factor Productivity
TFPt
Yt
K tD L1tD
W
Dt
mt
Mexico: GDP per working age person
Mexico: Total Factor Productivity
115
110
110
index (1994 = 100)
index (1994=100)
105
105
Model
100
95
Data
Model
100
Data
95
90
85
1988
1990
1992
1994
1996
1998
2000
2002
90
1988
1990
1992
1994
1996
1998
2000
2002
Extension: country risk premia
Adjusted return on Mexican debt higher than U.S.
x time-varying country specific risk premia
Take premia as exogenous
*
*
r
V mex ,t x rmex
,t
Mexico: interest rates
75
percent per year
baseline model
50
risk premia model
25
data
0
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Mexico: trade balance
Mexico: traded good employment, detrended
0.08
8.00
0.06
deviation from trend
share of gdp (%)
4.00
0.00
data
-4.00
risk premia model
-8.00
risk premia model
0.04
0.02
data
0.00
-0.02
baseline model
baseline model
-12.00
1988
1990
1992
1994
1996
1998
2000
-0.04
1988
2002
Mexico: real exchange rates
1994
1996
1998
2000
2002
2000
2002
0.50
0.25
data
log(RERN)
log(RER)
1992
Mexico: nontraded/traded good prices
0.50
0.25
1990
0.00
-0.25
data
0.00
risk premia model
baseline model
-0.25
risk premia model
baseline model
-0.50
1988
1990
1992
1994
1996
1998
2000
2002
-0.50
1988
1990
1992
1994
1996
1998
Mexico: sectoral value added
Mexico: sectoral value added
140
140
tradable goods
130
130
risk premia model
120
110
baseline model
100
90
tradable goods
110
100
nontradable goods
90
nontradable goods
80
70
1988
index (1994=100)
index (1994=100)
120
80
1990
1992
1994
1996
model
1998
2000
2002
70
1988
1990
1992
1994
1996
data
1998
2000
2002
Mexico: GDP per working age person
Mexico: Total Factor Productivity
115
110
110
risk premia model
index (1994 = 100)
index (1994=100)
105
105
100
95
baseline model
100
risk premia model
95
data
data
90
baseline model
85
1988
1990
1992
1994
1996
1998
2000
2002
90
1988
1990
1992
1994
1996
1998
2000
2002
Extension: foresight
Agents know of sudden stop in 1994
x Agents do foresee length of sudden stop
x Domestic interest rate is endogenously determined
Take premia as exogenous
*
*
r
V mex ,t x rmex
,t
Mexico: trade balance
Mexico: traded good employment, detrended
0.08
8.00
surprise model
0.06
deviation from trend
percent (%)
4.00
0.00
data
-4.00
surprise model
0.04
data
0.02
0.00
-0.02
no surprise model
-8.00
-0.04
-12.00
1988
1990
1992
1994
1996
1998
2000
-0.06
1988
2002
Mexico: real exchange rates
0.50
0.25
0.25
no surprise model
log(RERN)
log(RER)
0.00
-0.25
-0.50
1988
1992
1994
1996
1992
1994
1996
1998
2000
2002
1998
no surprise model
surprise model
0.00
data
-0.25
surprise model
1990
1990
Mexico: nontraded/traded good prices
0.50
data
no surprise model
2000
2002
-0.50
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: sectoral value added
Mexico: sectoral value added
140
140
tradable goods
130
no surprise model
120
120
index (1994=100)
index (1994 = 100)
130
110
100
surprise model
90
nontradable goods
100
nontradable goods
90
80
70
1988
tradable goods
110
80
1990
1992
1994
1996
1998
2000
2002
70
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: GDP per working age person
Mexico: Total Factor Productivity
110
110
surprise model
105
index (1994 = 100)
index (1994 = 100)
105
100
no surprise model
95
data
no surprise model
100
surprise model
95
90
85
1988
data
1990
1992
1994
1996
1998
2000
2002
90
1988
1990
1992
1994
1996
1998
2000
2002
Accounting for GDP
x
Take TFP as exogenous
D D 1D D
yDt = min ª¬ zTDt / aTD , z NDt / aND , ADt k Dt
A Dt º¼
D N 1D N
y Nt = min ª¬ zTNt / aTN , z NNt / aNN , ANt k Nt
A Nt º¼
x
All else same
Mexico: total factor productivity
110
index (1994 = 100)
105
model
100
data
95
90
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: GDP per working age person
115
index (1994=100)
110
105
100
Data
95
Model
90
85
1988
1990
1992
1994
1996
1998
2000
2002
Mexico: sectoral value added
140
140
130
130
tradable goods
120
index (1994=100)
120
index (1994=100)
Mexico: sectoral value added
110
100
nontradable goods
90
100
nontradable goods
90
80
70
1988
tradable goods
110
80
1990
1992
1994
1996
model
1998
2000
2002
70
1988
1990
1992
1994
1996
data
1998
2000
2002
Mexico: trade balance
Mexico: traded good employment, detrended
0.08
8.00
0.06
deviation from trend
percent of gdp (%)
4.00
0.00
Data
-4.00
-8.00
-12.00
1988
Model
1990
1992
1994
Model
0.04
0.02
Data
0.00
-0.02
1996
1998
2000
-0.04
1988
2002
1990
Mexico: real exchange rates
1992
1994
1996
1998
2000
2002
2000
2002
Mexico: nontraded/traded good prices
0.5
0.6
0.4
0.3
log(RERN)
log(RER)
Data
0.0
0.2
Model
0.0
Data
-0.3
-0.2
Model
-0.5
1988
1990
1992
1994
1996
1998
2000
2002
-0.4
1988
1990
1992
1994
1996
1998
Further Work
1. Leisure choice
2. Nonconvex adjustment
Conclusions
1. Sudden stops affect sectors differently
x increase in pT p N
x tradable good output falls less than nontradable
x labor reallocated to tradable good sector
2. International prices cannot affect GDP/TFP
3. Multisector model accounts for
x real exchange rate
x labor allocation
x trade balance