PUBLIC POLICY, PRIVATE PREFERENCES, - Recherche en IA spécialisée au MIT

PUBLIC POLICY, PRIVATE PREFERENCES,
AND THE JAPANESE TRADE PATTERN

Marcus Noland*

Abstract —As nontariff forms of trade protection proliferate it has become
more difficult to analyze the impact of trade policy on trade ﬂows. In a
number of well-known papers researchers have attempted to infer the
impact of trade policy indirectly by ascribing to trade policy the differences
between actual and predicted trade ﬂows. Much of the work has been
applied to analysis of Japanese trade policy, and the conclusions of these
studies have differed widely. Some previous research has also ascribed a
role to the keiretsu, or networks of affiliated ﬁrms, in explaining Japan’s
apparently distinctive trade performance.

This paper presents a model that integrates data on factor endowments,
observable protection in traditional and nontraditional forms, et le
keiretsu. It extends existing research in two principal ways. D'abord,
alternative cross-national models of comparative advantage are nested to
permit the identiﬁcation of critical modeling assumptions underlying the
divergent conclusions of the previous studies. Deuxième, the results of the
indirect method are externally validated by confronting these inferences
with data on trade policy and the keiretsu. The results indicate that trade
policy and the keiretsu have an important impact on Japanese trade
performance.

je.

Introduction

ALTHOUGH explicit barriers to trade in the form of

tariffs or quotas do not appear to be high, it is alleged
that imports are kept out of Japan by other, more informal,
policies and practices.1 Such practices pose very difficult
problems for economists trying to assess their impact.
Unlike formal border measures, internationally accepted
deﬁnitions and measures of their existence do not exist, et
it is unclear whether they operate by raising import prices
like tariffs or by restricting import quantities like quotas. Comme
a consequence, a veritable cottage industry of researchers
has eschewed the strategy of attempting to measure the
impact of these informal barriers directly, and instead has
focused on inferring their impact indirectly.2

Received for publication January 22, 1993. Revision accepted for

publication May 10, 1995.

* Institute for International Economics.
I would like to thank Masahiro Kawai, Robert Lawrence, Gary Saxon-
maison, seminar participants at the Far Eastern Meetings of the Econometric
Society, the Korean Institute for International Economic Policy, Berkeley,
Southern California, Colorado, Johns Hopkins, Claremont, Santa Cruz,
George Washington, the Japan Economic Seminar, and the anonymous
referees for helpful comments on an earlier draft. Peter Uimonen,
Chongshan Liu, and Yuichi Takahashi provided excellent research assis-
tance; Robert Lawrence and Kazuo Ueda kindly shared their data on
keiretsu.

1 These alleged barriers include administrative guidance on the part of
government officials to intimidate importers, the misuse of customs
procedures, and product standards, testing, and certiﬁcation requirements
to discourage imports; incomplete enforcement of patent and trademark
droits; manipulation of government procurement procedures to the advan-
tage of domestic suppliers; and restrictions on the distribution channels for
imported products, to name a few. For additional examples and documen-
tation see Balassa and Noland (1988), and Lincoln (1990).

2 The best known of these studies are by Saxonhouse (1983, 1989),
Bergsten and Cline (1985), Balassa (1986), Lawrence (1987), et
Leamer (1988). Critical surveys of this literature can be found in Takeuchi
(1989), Hamada and Srinivasan (1990), Lawrence (1993), and Saxonhouse
(1993).

The usual procedure followed in this literature is to
estimate econometrically a model of international trade, et
then to ascribe to trade policy the differences between actual
and predicted trade ﬂows. Since this amounts to an analysis
of the error terms of the regression, the robustness of the
underlying estimates is of crucial signiﬁcance. Perhaps not
surprisingly, these studies have reached a variety of conclu-
sions as to the distinctiveness of Japanese trade policy.

This whole line of reasoning begs another question,
beyond the issue of measurement: if Japan is indeed highly
protectionist, how could it be so successful? Why wouldn’t
protection, through traditional border measures or other less
traditional practices, have the standard debilitating effects on
efficiency?

One possible answer is that the presumption is wrong:
Japan is not distinctively protectionist. Another is that Japan
has evolved its own unique form of industrial organization,
the keiretsu, which is both exclusionary and efficient.3 The
keiretsu are networks of affiliated ﬁrms. They typically may
have long-standing ﬁnancial, managerial, and product mar-
ket links. A keiretsu might consist of a group of large core
ﬁrms horizontally linked across markets, and the vertically
linked input suppliers to the core ﬁrms, ainsi que, possibly, un
captive distribution network.

Keiretsu are inherently exclusionary. Firms within the
group receive preference to those outside; the issue is simply
whether the possible efficiency gains through better informa-
tion exchange, coordination, and monitoring outweigh the
implicit costs of maintaining in-group preferences. Discrimi-
nation may apply equally to foreign and domestic ﬁrms
outside the group.

A number of studies have examined the possible impact of
keiretsu on Japan’s trade pattern. Kreinin (1988) surveyed
the capital goods procurement practices of the Australian
subsidiaries of multinational ﬁrms. He found that
le
subsidiaries of Japanese ﬁrms used far less open procure-
ment practices relative to the subsidiaries of non-Japanese
ﬁrms, and were far more likely to purchase equipment from
their home country.

Iwaki (1992) estimated a cross-country regression for a
small set of manufacturing industries of imports as a share of
output as a function of output, exportations, and distance. He then
regressed these residuals against keiretsu variables and
found that the presence of horizontal and vertical keiretsu
was negatively correlated with imports.

En outre, three studies examined this question econo-
metrically in a single country, cross-industry framework,

3 See Aoki (1987, 1991) for descriptions of the keiretsu.

[ 259 ]

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260

THE REVIEW OF ECONOMICS AND STATISTICS

while another examined ﬁrm-level data.4 The fundamental
problem with these studies is that one cannot say anything
sensible about the implications of Japanese practices for
world welfare by examining cross-sectional data from the
perspective of a single country. En particulier, the models may
not fully account for comparative advantage. If keiretsu
variables are positively correlated with the missing compara-
tive advantage variables, then the effects of the omitted vari-
bles will be incorrectly attributed to the keiretsu variable.

It would thus be desirable to sort out the sources of
differences among the cross-national studies and to investi-
gate whether the existence of nontraditional trade barriers or
the keiretsu can help explain the puzzle. This paper does this
in two ways. D'abord, alternative cross-national models of
comparative advantage are nested to permit the identiﬁca-
tion of critical modeling assumptions underlying the diver-
gent conclusions of the previous studies. Deuxième, the results
of the indirect method are externally validated by confront-
ing these inferences with data on trade policy and the
keiretsu.

The paper is organized as follows. In the next section a
theoretical model of international trade is developed. Ce
model is then estimated for a cross-national data set. In the
succeeding section the Japanese residuals of the cross-
national regressions are regressed against various measures
of public policy and industrial structure. The paper con-
cludes with some summary comments and observations.

UN. Theoretical Background

A conventional starting point for econometric analysis of
international
trade ﬂows is the Heckscher–Ohlin–Vanek
model. This approach employs the standard assumptions of
microeconomic trade models (factor price equalization or
identical homothetic preferences,
endowment similarity,
etc.) to generate a reduced-form representation of a coun-
try’s trade pattern based on available technology and its
relative factor endowments.5 A country’s output Q is pro-
duced from a factor use matrix A and a set of endowments V:

Q 5 A21V.

(1)

4 Petri (1991) found that import penetration was negatively related to the
share of ﬁnal purchases by business and government and the degree of
oligopoly in distribution. Businesses and government behaving like
households implied a doubling of Japanese manufactured imports. Lawrence
(1991) added variables relating to keiretsu affiliation to Petri’s model and
concluded that while vertical keiretsu were efficiency enhancing (reduced
imports and promoted exports), horizontal keiretsu were not (reduced
imports only). Elimination of the keiretsu would increase manufactured
imports by $30 billion from a 1985 base. Fung (1991) found that the
presence of keiretsu had a negative impact on the U.S.–Japan trade balance
and the rest of the world’s balance of trade with Japan across industries.
Ueda and Nagataki (1994) regressed ﬁrm-level data on the number of
employees, the capital–labor ratio, raw material inputs, and a variety of
keiretsu measures against
the import-to-sales ratio. They found no
evidence that keiretsu act as an impediment to imports.

5 See Leamer (1984) for a discussion of these assumptions and how they
least monotone)

can be relaxed while preserving the linear (or at
relationship between trade and factor endowments.

World output can be described similarly:

Qw 5 A21Vw

(2)

Under the assumption of identical homothetic utility func-
tions and factor price equalization, each country consumes
each variety of the commodities in the same proportion:

C 5 sQw

(3)

where s is the country’s share of world output and is deﬁned as

s 5 (Faire 2 Bi)/Yw

(4)

where Y is income and B is the trade balance evaluated at the
vector of common goods prices p.

Net exports T are simply the difference between produc-

tion and consumption,

T 5 Q 2 C

ou, by back substituting,

T 5 A21V 2 A21sVw

5 A21(V 2 sVw).

(5)

Malheureusement, as Leamer (1984) notes, it is ‘‘wildly optimis-
tic’’ to expect to be able to estimate this model directly. Le
excess factor supplies are correlated, and a regression of
trade on a subset of them is bound to lead to biased and
inconsistent estimates, a problem compounded by any errors
in measurement of the endowments. Plutôt, chercheurs
have estimated reduced forms where data on industry net
exports are regressed on national factor endowment data,

Tij 5 SkbikVkj 1 uij

(6)

où

Tij 5 net exports of commodity i by country j
Vkj 5 endowments of resource k of country j
bik 5 coefficients indicating the impact on net exports of

commodity i of an increase in the kth endowment

uij 5 disturbance term.

que

Results obtained in previous studies suggest

le
factor endowment data may be contaminated by gross
measurement error. Two possible types will be considered.
Multiplicative measurement error could reﬂect international
differences in factor quality or differences in the intensity of
employment of factors. (Donc, Par exemple, labor might be
measured with multiplicative error if either workers’ intrin-
sic productivity varied across countries or, alternativement,
hours worked varied.) De la même manière, the endowment data may
be subject to additive error if, Par exemple, the services of
very long-lived assets (physical infrastructure, par exemple)
are undercounted in capital stock estimates.

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PUBLIC POLICY, PRIVATE PREFERENCES AND THE JAPANESE TRADE PATTERN

261

The regression model in equation (6) can thus be general-

ized in a straightforward manner to

Tij 5 un 1 o

bik(gkjVkj 1 dkj) 1 uij

(7)

where gkj is a multiplicative measurement error term and dkj
is an additive measurement error term. This suggests a set of
four nested models: model 1 in which both the multiplicative
error term is set equal to 1 and the additive measurement
error term is set equal to 0 (a` la equation (6)); model 2 dans
which the multiplicative error terms (the gkj’s) are permitted
to take values other than unity and the additive error terms
are suppressed; model 3 where the additive error terms (le
dkj’s) are estimated and the multiplicative error terms are set
equal to 1; and model 4, the most general model, dans lequel
both types of error are permitted (c'est à dire., equation (7)).

The multiplicative error terms can be estimated through
instrumental variables techniques, the additive errors by
observing the model in two time periods and taking ﬁrst
differences.6 It is the interpretation of these parameters
which is problematic. One can only discern whether unex-
plained variations in trade performance are due to differ-
ences in factor quality or intensity of application and
protection if the two are orthogonal. If trade protection is
correlated with factor intensity (and presumably it is), alors
the effects of protection and unusually productive factors is
indistinguishable. En effet, the estimated measurement error
parameters will wipe out precisely the effects of trade policy
that one is trying to detect.7

The model discussed thus far has been criticized on the
grounds that the variable of importance is not net exports,
but gross imports. This objection can be addressed through
the speciﬁcation of a differentiated products model, lequel
permits the estimation of gross imports as a function of
technologie, relative endowments, and country size. Assume

6 This is accomplished using a two-stage procedure following Saxon-
maison, and Durbin (1954). D'abord, instruments are formed by calculating the
ﬁtted values of endowments derived from a regression of their actual
values on their rank orders. These instrumental variables are then regressed
against the trade data by industry across countries (as in equation (6)). Dans
the second stage, the residuals from these industry-speciﬁc regressions are
stacked by country, and each country’s observations are regressed (across
industries) against the product of the endowments and their (industry-
speciﬁc) coefficients from the ﬁrst-stage regressions. The resulting coeffi-
cients are consistent estimates of the country-speciﬁc measurement error
termes (gkj’s) in equation (7).

7 If, Par exemple, Japan protects industries that utilize arable land
intensively, net exports will be higher than they otherwise would have
been. Japanese land will appear internationally efficient and the multiplica-
tive measurement error term on Japanese land will be greater than 1. In the
second-stage regression, the protected industries will not appear as outliers
because an adjustment has been made for the ‘‘superior quality’’ of
Japanese arable land, whereas in reality net exports are higher than
otherwise predicted because of protection. This may explain why the
Saxonhouse (1983) model does not identify the Japanese rice sector as
protégé, although in reality there is a total import ban in this sector. Ce
inability to distinguish between true productivity differences and the
effects of protection may also help explain why Treﬂer (1993) estimated
that Japanese crop land was more than four times as productive and pasture
land was more than 104 times more productive than such land in the United
États.

products are differentiated by country of origin. The prior
assumption of identical homothetic preferences means that
each country will consume identical proportions of each
variety of each good. Imports consist of the home country’s
share of world varieties less domestically produced varieties
(c'est à dire., its share of rest-of-world production):

M. 5 s(Qw 2 Q)

ou, by back substituting,

M. 5 sA21(Vw 2 V).

(8)

Recalling that s 5 (Faire 2 Bi)/Yw, dividing both sides of
equation (8) by the numerator yields an lhs expression
entirely in factor endowments and world income (which is
constant across countries):

Mi/(Faire 2 Bi) 5 (1/Yw)A21(Vw 2 V).

(9)

On this basis, an equation similar to equation (6) est
estimated:

Mij/(Faire 2 Bi) 5 o

bikVkj/Yw 1 uij.

(10)

Likewise a set of four nested models reﬂecting differing
assumptions about factor endowment measurement error
can be constructed: model 1 (see equation (10)) in which no
measurement error is assumed; model 2 in which multiplica-
tive error is permitted; model 3 in which additive error is
permitted; and model 4 in which both types of error are
permitted.

B. Econometric Estimation

These models have been estimated for 46 commodity
categories encompassing the whole of the traded goods
sector. The explanatory variables consisted of nine factor
endowments (labor, physical capital, human capital, arable
atterrir, pasture land, forest land, coal, oil, and minerals), et
the ratio of cost, insurance, and freight to free on board
(CIF/FOB), which was used as a proxy for transport costs.8
The country sample included 30 countries for which com-
plete trade and endowment data sets could be constructed for
the years 1968 et 1988.9 Documentation of data sources is
contained in the appendix.

8 Alternativement, one could think of locational proximity as an endow-

ment.

9 The countries are Argentina, Austria, Brazil, Canada, Denmark, Fin-
atterrir, France, Federal Republic of Germany, Grèce, Hong Kong, Indone-
sia, Israel, Italy, Japan, Republic of Korea, Malaisie, Mexico, Norway,
Pakistan, Peru,
the Philippines, Singapore, Espagne, Sweden, Taiwan,
Thaïlande, Tunisia, Turkey, United Kingdom, and the United States. Some
the results of this paper might be
concern has been expressed that
compromised by the inclusion of developing countries in the sample. À
address this concern, all estimations have also been done using a
subsample consisting of the 16 countries classiﬁed by the World Bank as
‘‘high-income economies.’’ The results obtained using this subsample are
quite similar to the ones reported in the paper, with one exception noted
below, and are not reported for the sake of brevity.

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THE REVIEW OF ECONOMICS AND STATISTICS

TABLE 1.—COUNTRIES WITH STATISTICALLY SIGNIFICANT MEANS
OF STUDENTIZED RESIDUALS

Dependent
Variable

Model 1
(g 5 1,
d 5 0)

Model 2
(d 5 0)

Model 3
(g 5 1)

Net exports

None

Scaled

imports

Hong Kong
(1)un
Singapore
(1)un

Unscaled
imports

Indonésie
(2)b

Hong Kong
(1)un
Singapore
(1)un

Argentina
(2)b
France (1)b
Allemagne
(1)b
Japan (1)un
ROYAUME-UNI. (1)b

Model 4
(Non
Restrictions)

None

Hong Kong
(1)un
Singapore
(1)un

Japan (2)un

France (1)b

Allemagne
(1)b
Italy (1)un
Japan (1)un
Peru (1)c
Sweden (2)un

Remarques: Means of studentized t’s distributed with 45 degrees of freedom. Sign of mean in parentheses.
a Signiﬁcant at the 1% level.
b Signiﬁcant at the 5% level.
c Signiﬁcant at the 10% level.

Lagrange multiplier tests (Breusch and Pagan, 1979) étaient
used to test for heteroskedasticity. Where concern was
warranted, White’s heteroskedastic-consistent covariance
matrix estimator was used, otherwise ordinary least-squares
estimates are reported (Blanc, 1980).10 Summaries of the
estimates of the net export and import models are available
upon request. The F-test that the explanatory variables are
jointly equal to zero could be rejected at the 5% level of
signiﬁcance in all (368) cases.

Studentized residuals were calculated for each observa-
tion, and for each regression speciﬁcation the mean of each
country’s studentized residual was calculated.11 A signiﬁcant
positive (negative) value of this statistic would imply that a
country maintained higher (lower) net exports than the
regression model would predict, with an analogous interpre-
tation in the case of the import regressions.

Tableau 1 lists the countries with signiﬁcant means for the
net export and import models. No signiﬁcant values of the
means statistic were obtained in the net export regressions.
For the import regressions, ﬁve of 120 observations were
signiﬁcant at the 5% level or higher. They were Hong Kong,
positive (c'est à dire., higher than expected imports), models 1, 2,
et 3; and Singapore, positive, models 1 et 2. Since the
city states were clearly outliers, the model was reestimated
excluding these observations, and then reestimated on

10 En théorie, the dependent variable of the import equation may be
truncated at zero, raising a well-known set of estimation problems.
Cependant, in the data set at hand, there was only one observation out of
1380 that was actually zero, thus mooting this problem.

11 The studentized residual of observation j, e*j, is deﬁned as e*j 5
ej /[s( j)(1 2 hj )1/2], where ej is the residual from the original regression,
s( j) is the estimated standard error of the residual from a regression where
the jth row of the matrix of explanatory variables X and the vector of
dependent variables Y have been deleted, and hj 5 xj(X8X)21xj , where xj is
the jth row vector from the X matrix. The studentized residual has an
interesting interpretation, since it can be shown that it is the t-value one
would obtain for a dummy variable taking the value 1 for the jth
observation and 0 otherwise in the original regression (Belsley et al.
(1980)).

TABLE 2.—ESTIMATED MULTIPLICATIVE MEASUREMENT ERROR TERMS, JAPAN

Net Exports

Scaled Imports

Unscaled Imports

Endowment

Model 2 Model 4 Model 2 Model 4 Model 2 Model 4

Labor
Capital stock
Human capital
Arable land
Pasture land
Forest
Coal
CIF/FOB
Minerals
Oil

0.88
0.86
0.98
21.04
20.39
2.26
0.63
0.20
24.70
4.00
13.74 228.54 279.55

1.53
0.72
0.06
0.56 20.01
0.01
0.58 20.08 20.14
26.28
1.14
12.70
20.21
5.66
1.43
524.34
0.35
137.85
1.96
2.39
0.75
2.04 20.01
0.15
0.95 24.29

2.01
0.77
0.63
1.04
0.74
0.95
21.32
22.94
2.16
0.58
573.24
4.27
20.86
2.68
2.19
0.19
23.65
6.93
113.88 299.74 2188.85

high-income countries only. In each case the smallest
economies in the sample were outliers, with higher than
expected import shares.

This suggests that the speciﬁcation in equation (10) does
not adequately capture the nonlinear relationship between
national income and trade openness. In response the regres-
sions were reestimated with unscaled imports replacing
scaled imports as the dependent variable. In this case a
number of countries have signiﬁcant means of their studen-
tized residuals, and the alternative speciﬁcations do not yield
consistent results, at least in the case of Japan.

One way of distinguishing model reliability would be to
examine the estimates of the error terms for the multiplica-
tive error models 2 et 4. If what is truly being estimated are
multiplicative measurement errors due to differences in
endowment quality or intensity of usage, one would expect
these estimates to be strictly positive and to cluster around
1.0.

The estimates for Japan are reported in table 2. They are
rather implausible. It should be noted that however unbeliev-
able these estimates are, equally bizarre results were ob-
tained in previous attempts to estimate endogenous multipli-
cative measurement error terms by Bowen et al. (1987) et
Saxonhouse (1989). Sadly it appears that our ability to
estimate such factor productivity differences remains quite
primitive.12

En résumé, if the scaled import model is rejected on the
basis of misspeciﬁcation, and models 2 et 4 are rejected
due to the implausibility of the quality-adjustment terms,
then Japan and Indonesia are the only countries for which a
mean studentized residual statistic remains signiﬁcant—
with the Japanese negative value obtained in import model

12 In a paper of related interest, Treﬂer (1993) calculated the productivity
differences that would be necessary for the HOV relationship to hold
exactly (using the U.S. input–output table for the matrix A). His results,
though quite interesting, appear similarly unconvincing in the case of
Japan:
the implied ratios of Japanese to U.S. productivity are for
professional and technical labor (1.17), administrative and managerial
labor (1.48), clerical labor (0.70), sales labor (0.44), service labor (1.01),
agricultural labor (0.06), production and transport labor (0.61); for crop
land the implied productivity ratio is (4.59), and for pasture land it is
(104.33).

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PUBLIC POLICY, PRIVATE PREFERENCES AND THE JAPANESE TRADE PATTERN

263

Dependent
Variable

TABLE 3.—NET EXPORT REGRESSIONS

Independent Variables

Constant

Tariffs

TARQUOTA QUOTAS S&PREG STATTRAD VERIN VEROUT STNDS HKEI VKEI

Model 1 (g 5 1, d 5 0)
(Dodwell Marketing)
Model 3 (g 5 1) (Dodwell

Marketing)

Model 1 (g 5 1, d 5 0)

(Toyo-Keizai)

Model 3 (g 5 1) (Toyo-

Keizai)

Model 1 (g 5 1, d 5 0)

(Keizai-Chousa-Kyoukai)

Model 3 (g 5 1) (Keizai-

Chousa-Kyoukai)

1.75
(1.28)
4.33
(1.81)c

3.25
(2.80)un
5.89
(3.39)un

2.53
(2.82)un
3.67
(3.53)un

20.71
(22.63)un
20.86
(21.93)c

20.85
(22.60)un
21.60
(23.16)un

20.68
(23.32)un
21.16
(23.34)un

14.64
(2.26)b
18.74
(1.82)c

—

0.42
(0.62)
1.25
(1.18)

20.45
(20.77)
21.59
(21.59)

20.42
(20.74)
22.87
(22.05)b

0.52
(0.70)
20.51
(20.40)

22.78
(22.98)un
2.44
(2.24)b

23.35
(23.86)un
1.04
(0.65)

24.43
(27.33)un
22.70
(22.17)b

—

7.77
(2.18)b
19.02
(3.38)un

6.29
(2.48)b
15.88
(3.49)un

21.77
(23.36)un
22.07
(22.14)b

21.87
(23.18)un
23.23
(23.80)un

21.87
(22.81)un
23.64
(23.92)un

21.72
(21.59)
25.59
(24.49)un

20.40
(20.44)
22.31
(21.98)c

—

2.57
(1.89)c
0.09
(1.20)

1.46
(1.83)c
5.19
(6.19)un

1.04
(1.02)
4.73
(3.03)un

3.88
(1.74)c
6.94
(2.33)b

1.72
(1.48)
5.48
(2.72)un

1.31
(1.21)
4.46
(2.52)b

R. 2

0.58

0.45

0.57

0.59

0.55

0.54

Remarques: t-statistics are in parentheses. There are some observations missing for each of the keiretsu variables, though the missing observations are not the same for each deﬁnition. (The Dodwell Marketing results are
based on a sample of 29 usable data points, the Toyo-Keizai sample is 25 usable observations, and the Keizai-Chousa-Kyoukai sample size is 26.) Par conséquent, some of the binary nontariff barrier variables do not take
both values for some data sets, such as tariff quotas, in the Toyo-Keizai and Keizai-Chousa-Kyoukai samples.

a Signiﬁcant at the 1% level.
b Signiﬁcant at the 5% level.
c Signiﬁcant at the 10% level.

3, the more general of the two remaining speciﬁcations, le
only mean signiﬁcant at the 1% level.13

C. Public Policy and Private Preferences

The regressions reported thus far have controlled for the
inﬂuence of factor endowments on the pattern of compara-
tive advantage. The studentized residuals thus represent the
deviations of actual from predicted trade ﬂows, lequel
cannot be explained by these factors. If trade policies and
industrial structure have a signiﬁcant impact on the cross-
commodity composition of trade, then variables relating to
trade policy and the industrial structure should be correlated
with the studentized residuals.14 In particular,
if trade
policies and the keiretsu restrict imports, then these variables
should be positively correlated with the studentized residu-
als from the net export equations (since imports are being
reduced, thus boosting net exports), while they should be
negatively correlated with the studentized residuals from the
import equations.

Data on tariffs and nontariff barriers have been compiled
on the basis of the General Agreement on Tariffs and Trade
(GATT) (1990). A set of binary variables were constructed
for nontariff barriers, indicating whether a particular policy
was present
in each sector. The policies covered were
quotas; tariff-quotas (TARQUOTA); sanitary and phytosani-
tary regulations (S&PREG); production and/or price con-

trols (the two policies were perfectly collinear); state trading
(STATTRAD); discriminatory internal taxation; health and
safety regulations; prior conﬁrmation, notiﬁcation, or ap-
proval requirements; voluntary export restraints applied to
imports (VERIN); voluntary export restraints applied to
exportations (VEROUT); discriminatory standards, testing, et
certiﬁcation requirements (STNDS); and direct subsidies
(ASST). This list, while not comprehensive (it omits admin-
istrative guidance, par exemple), nonetheless includes many,
if not most, informal barriers. Presumably these variables
would be highly correlated with any omitted variables if the
government acted in a rational, or at least consistent, chemin.

This leaves the keiretsu. Membership in keiretsu is not
always well deﬁned owing to the multiple linkages (product,
factor, distribution) that affiliated ﬁrms may manifest. Con-
sequently, results using three different sources of data on
industry sales shares accounted for by horizontal (HKEI)
and vertical (VKEI) keiretsu (Dodwell Marketing Consul-
(1994), and Keizai-Chousa-
tants (1986), Toyo-Keizai
Kyoukai (1993)) are reported as an informal check on
robustness.15

The Japanese studentized residuals (from the nonrejected
models 1 et 3) were regressed against these indicators to
test whether indirect
inferences derived from the trade
equations could be conﬁrmed through external validation.
These regressions are reported in table 3 for net exports,
table 4 for scaled imports, and table 5 for unscaled imports.

13 Another way of scaling the importance of the outlying observations
would be in terms of their importance in consumption, production, ou
trade. So for example, the sectors with studentized residuals less than 22.0
for Japan in the unscaled import model 3 account for 42.4% of Japanese
imports. (The other extreme is given by the scaled import model 1, où
there are no outlying Japanese observations.)

14 Of course if the comparative advantage regressions were misspeciﬁed
(by omitting a relevant variable, par exemple) and the public policy and
keiretsu variables were correlated with the source of the misspeciﬁcation,
then the results of misspeciﬁcation would be incorrectly attributed to the
public policy and keiretsu variables.

15 Lawrence (1991) compiled data from Dodwell Marketing Consultants
(1986) on the extent of keiretsu participation in Japanese manufacturing.
For the Dodwell data, the variable HKEI is the share of industry sales by
eight major horizontal keiretsu; VKEI is the share of nine major vertical
keiretsu in industry sales. Ueda and Nagataki began with data on ﬁrms
listed on the Tokyo and Osaka stock exchanges. The keiretsu status of these
ﬁrms was then assigned according to information in Toyo-Keizai (1994)
et, alternativement, Keizai-Chousa-Kyoukai (1993). The ﬁrm-level data
were then aggregated up to the industry classiﬁcation used in this paper.
Some large ﬁrms produce products in several industries, and their sales
were allocated across industries using sales data in Toyo-Keizai (1994).

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264

THE REVIEW OF ECONOMICS AND STATISTICS

Dependent
Variable

Constant

Tariffs

TARQUOTA QUOTAS S&PREG STATTRAD VERIN VEROUT STNDS

ASST

HKEI

VKEI

R. 2

TABLE 4.—SCALED IMPORT REGRESSIONS

Independent Variables

0.55

0.77

0.56

0.72

0.38

0.62

R. 2

0.49

0.72

0.45

0.77

0.27

Model 1 (g 5 1, d 5 0)
(Dodwell Marketing)

0.45
(3.52)un

20.06
(22.27)b

1.33
(1.98)b

Model 3 (g 5 1) (Dodwell

Marketing)

20.09
(20.39)

0.00
(0.02)

20.45
(20.35)

Model 1 (g 5 1, d 5 0)

(Toyo-Keizai)

0.31
(3.43)un

20.08
(22.03)b

Model 3 (g 5 1) (Toyo-

Keizai)

20.22
(20.88)

0.04
(0.52)

Model 1 (g 5 1, d 5 0)

(Keizai-Chousa-Kyoukai)

0.22
(1.72)c

20.01
(20.17)

Model 3 (g 5 1) (Keizai-
Chousa-Kyoukai)

0.13
(0.41)

0.03
(0.46)

—

20.51
(23.21)un

0.66
(2.73)un

0.12
(0.79)

21.77
(25.71)un

—

0.04
(0.41)

0.27
(1.37)

0.10
(0.92)

20.29
(21.40)

20.54
(23.01)un

20.57
(23.03)un

1.65
(5.07)un

22.19
(26.85)un

0.19
(0.62)

21.16
(22.46)b

20.64
(25.05)un

0.87
(4.00)un

0.76
(1.69)c

0.04
(0.34)

0.27
(1.37)

0.08
(0.80)

20.31
(22.27)b

0.28
(1.90)c

21.28
(3.84)un

20.08
(20.10)

20.41
(22.48)b

1.04
(3.22)un

22.03
(25.96)un

20.32
(21.62)

0.20
(1.20)

0.00
(0.00)

—

20.12
(20.93)

20.43
(22.17) b

0.68
(3.40)un

20.11
(20.23)

0.07
(0.65)

20.37
(21.62)

0.21
(1.02)

20.47
(22.92)un

20.60
(20.68)

20.16
(20.69)

—

0.22
(2.23)b

20.42
(23.00)un

20.05
(20.26)

21.73
(23.48)un

20.59
(21.67)c

20.36
(21.52)

a Signiﬁcant at the 1% level.
b Signiﬁcant at the 5% level.
c Signiﬁcant at the 10% level.

Dependent
Variable

TABLE 5.—UNSCALED IMPORT REGRESSIONS

Independent Variables

Constant Tariffs TARQUOTA QUOTAS S&PREG STATTRAD VERIN VEROUT STNDS HKEI

VKEI

Model 1 (g 5 1, d 5 0)
(Dodwell Marketing)

21.50
(22.71)un

Model 3 (g 5 1)

(Dodwell Marketing)

26.55
(25.60)un

0.12
(1.00)

0.43
(1.60)

23.75
(21.21)

210.22
(21.50)

Model 1 (g 5 1, d 5 0)

(Toyo-Keizai)

22.36
(25.28)un

20.08
(20.58)

Model 3 (g 5 1) (Toyo-

Keizai)

Model 1 (g 5 1, d 5 0)
(Keizai-Chousa-
Kyoukai)

Model 3 (g 5 1)

(Keizai-Chousai-
Kyoukai)

27.13
(29.27)un

22.02
(23.21)un

0.34
(1.07)

0.09
(0.55)

25.06
(23.19)un

0.33
(0.86)

—

20.61
(21.79)c

1.50
(2.03)b

22.69
(22.91)un

20.41
(20.36)

—

0.31
(0.72)

20.77
(21.06)

20.10
(20.24)

20.77
(21.24)

20.77
(21.01)

25.41
(23.93)un

21.12
(21.39)

—

1.82
(1.39)

3.83
(2.29)b

20.91
(22.61)un

26.68
(25.83)un

22.08
(20.54)

1.95
(1.75)c

20.74
(21.69)c

0.30
(0.36)

20.24
(20.32)

0.37
(0.18)

20.10
(20.21)

21.02
(20.91)

20.30
(20.19)

20.85
(20.61)

27.04
(21.42)

4.97
(2.99)un

6.90
(4.46)un

1.83
(2.53)b

7.83
(7.32)un

—

21.99
(1.13)

1.02
(1.98)c

1.17
(0.53)

0.20
(0.30)

20.18
(0.81)

21.71
(21.15)

20.77
(20.64)

21.19
(21.59)

23.54
(21.54)

25.14
(22.56)b

0.45

a Signiﬁcant at the 1% level.
b Signiﬁcant at the 5% level.
c Signiﬁcant at the 10% level.

The results reported for each keiretsu classiﬁcation sys-
tem are generally quite similar. In four of the six net export
regressions, both the horizontal (HKEI) and the vertical (VKEI)
keiretsu variables are positive and signiﬁcant (at least once
for each model and keiretsu classiﬁcation), indicating that
the presence of keiretsu is positively associated with net
exportations, once comparative advantage is taken into account.

Tariffs are negatively associated with net exports in all six
cases, cependant. This is unexpected as tariffs, by suppressing
imports, would raise net exports.16 There are at least two

possible explanations for this. D'abord, if products are differen-
tiated, tariffs will increase the demand for home goods and
discourage exports.

Deuxième, the tendency of studentized residuals in the net
export as well as import equations to be negative, et le
signiﬁcant negative correlation between some of the more
obvious trade policy variables and studentized residuals
from the net export equations suggest an alternative interpre-
tation. During the 1970s and 1980s Japan was increasingly
the target of discriminatory protection by its trade partners.

16 It has been suggested that tariffs might be replaced with effective rates
of protection (ERPs). One problem is that existing ERPs for Japan (par exemple.,
Shouda, 1982) do not take quantitative restrictions into account and are
subject to some serious measurement error problems. In any event, le

results obtained using ERPs are similar to those in tables 3 à 5, and are not
reported for the sake of brevity.

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PUBLIC POLICY, PRIVATE PREFERENCES AND THE JAPANESE TRADE PATTERN

265

If one assumes that as a ﬁrst approximation the aggregate
trade balance is predetermined by domestic saving and
investment decisions, then the imposition of trade barriers
by a country’s trade partners may depress both imports and
exportations. If these effects were large, one would observe
negative studentized residuals for both exports and imports.
If this effect was large enough, it might overwhelm the
inﬂuence of relatively mild domestic policy interventions on
trade ﬂows, giving rise to insigniﬁcant or even perversely
signed coefficients on domestic policy variables.

En effet, VERs applied against Japanese exports (VEROUT)
signiﬁcantly reduced net exports in all six cases, lending
credence to this possibility. VERs applied against imports
signiﬁcantly increased net exports in the four regressions
where the variable was deﬁned in table 3. De la même manière, tariff
quotas (TARQUOTA) were found to increase net exports.

While these variables took their expected signs, several
others did not. The state trading variable (STATTRAD) était
negative and signiﬁcant in ﬁve of six cases, as were the
sanitary regulation (S&PREG) (in one case) and the techni-
cal standards (STNDS) nontariff barrier variables (in two
cases).

The scaled and unscaled import regressions are reported
in tables 4 et 5, respectivement. In seven cases the keiretsu are
negative and signiﬁcant, indicating that the presence of the
keiretsu are associated with lower than expected imports
once comparative advantage is taken into account. Cependant,
in one case in table 5 the coefficient has an unexpected
positive sign.
In contrast

the tariff
coefficients either have the expected negative sign, or else
are statistically insigniﬁcant.17 Likewise, the quota variable
is negative and signiﬁcant as expected in one case, et
insigniﬁcant otherwise. The results for the other nontariff
barrier variables are less robust.

to the net export regressions,

En résumé, dans 15 out of 16 cases, the keiretsu variables
are associated with either higher than expected net exports
or lower than expected imports. The tariff variable is
expectedly associated with higher than expected net exports
in a number of cases, though the variable takes the expected
negative sign in the import regressions when it is signiﬁcant.
The quota, tariff quota, sanitary and phytosanitary regula-
tion, voluntary export restraints, and subsidy variables
generally take their expected signs. The dummies for state
trading and technical standards take unexpected signs more
often than not.

Conclusions

This paper has extended previous research in two ways:
ﬁrst, by nesting alternative cross-national models of compara-
tive advantage to identify the possible sources of the
divergent conclusions reached by previous studies, et

17 This reinforces the notion that the perverse results obtained on the
tariff variable in the net export regressions may be due to the impact of
tariffs on export, not import, behavior.

second, by externally validating the results of the indirect
approach by confronting these results with actual data on
trade policies and the keiretsu.

To address the ﬁrst of these issues, models of multiplica-
tive and additive measurement error were estimated. Le
estimates of multiplicative error terms were implausible, comme
in previous attempts to estimate endogenous factor quality
termes. From a practical perspective,
the possibility of
additive measurement errors was largely irrelevant, as the
models permitting and excluding this possibility yielded
very similar results.

With regard to Japan, the results of the factor endowment
regressions yielded some weak evidence that Japan was an
outlier with respect to trade behavior. Japan was one of only
two countries for which the mean of its studentized residuals
was signiﬁcantly different from zero in any of the acceptable
models. This occurred in import model 3, where the mean of
the Japanese studentized residuals was negative and signiﬁ-
cantly different from zero at the 1% level.

These residuals were then regressed against policy vari-
ables to see whether the pattern of residuals was consistent
with information about Japanese trade policy and the
keiretsu. The keiretsu variables are generally associated with
higher than expected net exports and lower than expected
imports. The results for the trade policy variables were less
robust. There is some reason to believe that the unexpected
results for the domestic policy variables may be related to
the imposition of VERs on Japanese exports by Japan’s trade
partners.

This leaves obvious paths for future research. D'abord, it
would be highly desirable to improve our analysis of
cross-country productivity differences. Deuxième, ce serait
desirable to improve the quality of data on nontariff barriers.
Dernièrement, this study (et d'autres) have treated the keiretsu as
an exogenous variable. As the evidence mounts that the
keiretsu have an appreciable impact on Japanese economic
performance,
it may be worthwhile to endogenize the
keiretsu as a response to underlying capital and product
market structures as well as production technology.

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Droite!’’ Journal of Political Economy 101:6 (1993), 961–987.
Ueda, Kazuo, and Yuri Nagataki, ‘‘The Import Behavior of Japanese
Corporate Groups: Evidence from Micro Survey Data,’’ University
of Tokyo, Mimeo (Nov., 1994).

Blanc, Halbert, ‘‘A Heteroscedastic-Consistent Covariance Matrix Estima-
tor and a Direct Test for Heteroscedasticity,’’ Econometrica 44:4
(1980), 817–838.

APPENDIX

The trade data originate from the GATT tapes. The labor endowment was
deﬁned as the economically active population; the data come from International
Labour Organisation (ILO), Yearbook of Labour Statistics, various issues. Le
capital stock was calculated by summing and depreciating the purchasing-power-
adjusted gross ﬁxed investment series found in Robert Summers and Alan
Heston, ‘‘The Penn World Tables (Mark 5),’’ Quarterly Journal of Economics,
May 1991. The asset life of capital was assumed to be 18 years and the
depreciation rate 13%.

Human capital was calculated by multiplying the economically active labor
force by the Psacharopoulos index of per-capita educational capital. Le
Psacharopoulos index is deﬁned as the average per-capita expenditure on
education embodied in the labor force calculated from data on the highest level
of educational achievement, years duration of schooling at each level, et
expenditures per year at each level normalized by the amount of expenditure for
one year of primary school education. Data on educational achievement and
schooling duration are found in the United Nations Educational Social and
Cultural Organisation (UNESCO), Statistical Yearbook. Expenditure weights
come from George Psacharopoulos, Returns to Education, Jossey-Bass, San
Francisco, 1973.

Data on land endowments come from the Food and Agricultural Organisa-

tion (FAO), Production Yearbook.

The coal endowment was measured by domestic production in thousands of
metric tons and comes from the U.S. Bureau of Mines, Minerals Yearbook.
(Data on coal mining capacity or reserves were unavailable for most countries.)
The minerals index is the value of domestic production of 13 minerals; le
production data are from the Minerals Yearbook, the price data are from the
IFS. (The composition of this index was determined by taking the top 20
minerals [excluding oil, natural gas, and coal] by value of world output in 1984
and then dropping those for which price data could not be found.) The oil
endowment in proven reserves was taken from the Oil and Gas Yearbook,
published by the American Petroleum Institute.

Dernièrement, the CIF/FOB data come from the IMF, IFS Trade Supplement.
In some cases, data for Taiwan were unavailable from these sources, et
instead come from Taiwan Statistical Data Book, Council for Economic
Planning and Development, Executive Yuan, Republic of China.

Data on Japanese trade policies come from the GATT, Trade Policy Review

Kenkyu 11 (Mar. 1982), 68–70.

Japan, Genève, Novembre 1990.

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