Public Policy, Private Preferences, and the Japanese Trade Pattern
© Institute for International Economics.
As non-tariff 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 which integrates data on factor endowments, observable protection in traditional and non-traditional forms, and the keiretsu. It extends existing research in two principal ways. First, 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. Second, 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.
I would like to thank Masahiro Kawai, Robert Lawrence, Gary Saxonhouse, 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 assistance; Robert Lawrence and Kazuo Ueda kindly shared their data on keiretsu.
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. 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, and it is unclear whether they operate by raising import prices like tariffs or by restricting import quantities like quotas. As 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.
The usual procedure followed in this literature is to estimate econometrically a model of international trade, and 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 conclusions 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. The keiretsu are networks of affiliated firms. They typically may have long-standing financial, managerial, and product market 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, and possibly a 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 information exchange, coordination, and monitoring, outweigh the implicit costs of maintaining in-group preferences. Discrimination 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 the subsidiaries of Japanese firms used far less open procurement 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, exports, 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.
In addition, three studies examined this question econometrically in a single country, cross-industry framework, while another examined firm-level data. 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. In particular, the models may not fully account for comparative advantage. If keiretsu variables are positively correlated with the missing comparative advantage variables, then the effects of the omitted variables 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 investigate whether the existence of non-traditional trade barriers or the keiretsu can help explain the puzzle. This paper does this in two ways. First, 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. Second, the results of the indirect method are externally validated by confronting 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. This model is then estimated for a cross-national data set. In the suceeding section the Japanese residuals of the cross-national regressions are regressed against various measures of public policy and industrial structure. The paper concludes with some summary comments and observations.
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 endowment similarity, identical homothetic preferences, etc.) to generate a reduced form representation of a country's trade pattern based on available technology and its relative factor endowments. A country's output (Q), is produced from a factor use matrix (A), and a set of endowments (V):
World output can be described similarly:
Under the assumption of identical homothetic utility functions and factor price equalization, each country consumes each variety of the commodities in the same proportion:
where s is the country's share of world output, and s is defined as (4) s=(Yi-Bi)/Yw 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 production and consumption
or, by back substituting,
Unfortunately, as Leamer (1984) notes it is "wildly optimistic" to expect to be able to estimate this model directly. The 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. Instead, researchers have estimated reduced forms where data on industry net exports are regressed on national factor endowment data:
(6) Tij = SkbikVkj + uij
Tij = net exports of commodity i by country j,
Vkj = endowments of resource k of country j,
bik = coefficients indicating the impact on net exports of commodity i of an increase in the kth endowment, and
uij = a disturbance term.
Results obtained in previous studies suggest that the 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. (So, for example, labor might be measured with multiplicative error if either workers' intrinsic productivity varied across countries, or alternatively, if hours worked varied.) Similarly, the endowment data may be subject to additive error, if for example, the services of very long-lived assets (physical infrastructure, for instance) are undercounted in capital stock estimates.
The regression model in equation 6, can thus be generalized in a straightforward manner to
(7) Tij = a + Skbik(gkjVkj+dkj) + uij,
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 one and the additive measurement error term is set equal to zero (a la equation 6); model 2 in 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 (the dkj's) are estimated and the multiplicative error terms are set equal to one; and model 4, the most general model, in which both types of error are permitted (i.e. 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. It is the interpretation of these parameters which is problematic. One can only discern whether unexplained variations in trade performance are due to differences 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) then the effects of protection and unusually productive factors is indistinguishable. Indeed, the estimated measurement error parameters will wipe out precisely the effects of trade policy that one is trying to detect.
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, which permits the estimation of gross imports as a function of technology, relative endowments, and country size. Assume 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 (i.e. its share of rest-of-world production):
(8)M = s(Qw-Q)
or, by back substituting,
M = sA-1(Vw-V).
Recalling that s=(Yi-Bi)/Yw, dividing both sides of (8) by the numerator yields an lhs expression entirely in factor endowments and world income (which is constant across countries):
(9)Mi/(Yi-Bi) = (1/Yw)A-1(Vw-V).
On this basis, an equation similar to (6) is estimated:
(10) Mij/(Yi-Bi) = SkbikVkj/Yw + uij.
Likewise a set of four nested models reflecting differing assumptions about factor endowment measurement error can be constructed: model 1 (equation 10) in which no measurement error is assumed; model 2 in which multiplicative error is permitted; model 3 in which additive error is permitted; and model 4 in which both types of error are permitted.
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 land, pasture land, forest land, coal, oil, and minerals), and the c.i.f./f.o.b. ratio which was used as a proxy for transport costs. The country sample included thirty countries for which complete trade and endowment data sets could be constructed for the years 1968 and 1988. Documentation of data sources is contained in the Appendix.
Lagrange multiplier tests (Breusch and Pagan, 1979) were used to test for heteroscedasticity; where concern was warranted White's heteroscedastic-consistent covariance matrix estimator was used, otherwise ordinary least squares estimates are reported (White, 1980). 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 five percent level of significance in all (368) cases.
Studentized residuals were calculated for each observation, and for each regression specification the mean of each country's studentized residual was calculated. 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 interpretation in the case of the import regressions.
Table 1 lists the countries with significant means statistics 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 five percent level or higher. They were: Hong Kong, positive (i.e. higher than expected imports), models 1, 2, and 3; and Singapore, positive, models 1 and 2. Since the city-states were clearly outliers, the model was re-estimated excluding these observations, and then re-estimated on 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 is does not adequately capture the nonlinear relationship between national income and trade openness. In response the regressions were re-estimated with unscaled imports replacing scaled imports as the dependent variable. In this case a number of countries have significant means of their studentized 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 multiplicative error models 2 and 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 unbelievable these estimates are, equally bizarre results were obtained in previous attempts to estimate endogenous multiplicative measurement error terms by Bowen, Leamer, and Sveikauskas (1987) and Saxonhouse (1989). Sadly, it appears that our ability to estimate such factor productivity differences remains quite primitive.
In summary, if the scaled import model is rejected on the basis of misspecification, and models 2 and 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 3, the more general of the two remaining specifications, the only mean significant at the one percent level.
Public Policy and Private Preferences
The regressions reported thus far have controlled for the influence of factor endowments on the pattern of comparative advantage. The studentized residuals thus represent the deviations of actual from predicted trade flows which 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. In particular, if trade policies and the keiretsu restrict imports, then these variables should be positively correlated with the studentized residuals 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 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 phytosanitary regulations (S&P REG); production and or price controls (the two policies were perfectly collinear); state trading (STAT TRAD); discriminatory internal taxation; health and safety regulations; prior confirmation, notification, or approval requirements; voluntary export restraints applied to imports (VER IN); voluntary export restraints applied to exports (VER OUT); discriminatory standards, testing, and certification requirements (STNDS); and direct subsidies (ASST). This list, while not comprehensive (it omits administrative guidance, for instance), 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, way.
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. Consequently, results using three different sources of data on industry sales shares accounted for by horizontal (HKEI) and vertical (VKEI) keiretsu (Dodwell Marketing Consultants (1986), Toyo-Keizai (1994), and Keizai-Chousa-Kyoukai (1993)) are reported as an informal check on robustness.
The Japanese studentized residuals (from the non-rejected models 1 and 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.
The results reported for each keiretsu classification system are generally quite similar. In four of the six net export regressions, both the horizontal (HKEI) and 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 exports, once comparative advantage is taken into account.
Tariffs are negatively associated with net exports in all six cases, however. This is unexpected as tariffs, by suppressing imports, would raise net exports. There are at least two possible explanations for this. First, if products are differentiated, tariffs will increase the demand for home goods and discourage exports.
Second, the tendency of studentized residuals in both the net export and import equations to be negative, and the significant negative correlation between some of the more obvious trade policy variables and studentized residuals from the net export equations suggests an alternative interpretation. During the 1970s and 1980s Japan was increasingly the target of discriminatory protection by its trade partners. 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 exports. 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.
Indeed, 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. Similarly, 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) was negative and significant in five of six cases, as were the sanitary regulation (S&PREG) (in one case) and the technical standards (STNDS) nontariff barrier variables (in two cases).
The scaled import and unscaled import regressions are reported in Tables 4 and 5, respectively. 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. However, in one case in Table 5 the coefficient has an unexpected positive sign.
In contrast to the net export regressions, the tariff coefficients either have the expected negative sign, or else are statistically insignificant. Likewise, the quota variable is negative and significant as expected in one case, and insignificant otherwise. The results for the other nontariff barrier variables are less robust.
In summary, in 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 the 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 regulation, VER, and subsidy variables generally take their expected signs. The dummies for state trading and technical standards take unexpected signs more often than not.
This paper has extended previous research in two ways: first by nesting alternative cross-national models of comparative advantage to identify the possible sources of the divergent conclusions reached by previous studies, and 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 multiplicative and additive measurement error were estimated. The estimates of multiplicative error terms were implausible, as in previous attempts to estimate endogenous factor quality terms. 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 significantly different from zero at the one percent level.
These residuals were then regressed against policy variables to see if 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. First, it would be highly desirable to improve our analysis of cross-country productivity differences. Second, it would be desirable to improve the quality of data on nontariff barriers.
Lastly, this study (and others) 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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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. The 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 percent.
Human capital was calculated by multiplying the economically active labor force by the Psacharopoulos index of per capita educational capital. The 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, and 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, 1973, Returns to Education, Jossey-Bass, San Francisco.
Data on land endowments come from the Food and Agricultural Organisation (FAO), Production Yearbook.
The coal endowment was measured by domestic production in thousands of metric tons and comes from 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 thirteen minerals; the 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 twenty minerals [excluding oil, natural gas, and coal] by value of world output in 1984 and then dropping whose 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.
Lastly, the c.i.f./f.o.b. data come from the IMF, IFS Trade Supplement.
In some cases, data for Taiwan were unavailable from these sources, and 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 Japan, November 1990, Geneva.