Exchange rate sensitivity of U.S. trade flows: evidence from industry data.

1. Introduction

In an effort to boost employment, a country could stimulate its exports and discourage its imports and thereby improve its trade balance. One policy that has received a great deal of attention in the literature is currency devaluation. By making exports cheaper and imports expensive, devaluation is said to improve the trade balance. The only condition required is that the sum of import and export demand price elasticities exceed unity (that condition, known as the Marshall-Lerner condition, is derived under the assumption of perfectly elastic supply of trade). Most previous studies that attempted to assess the Marshall-Lerner condition relied on price elasticities that were obtained by estimating aggregate import and export demand functions. These studies provided mixed conclusions as far as the effectiveness of devaluation or depreciation is concerned. Examples include Houthakker and Magee (1969), Khan (1974), Goldstein and Khan (1976, 1978), Wilson and Takacs (1979), Haynes and Stone (1983a, 1983b), Warner and Krienin (1983), Bahmani-Oskooee (1986, 1998), and Bahmani-Oskooee and Niroomand (1998). The mixed conclusion could be related to aggregation bias. When aggregate trade data are employed in import and export demand functions, significant price elasticity with one trading partner could be more than offset by an insignificant price elasticity with another trading partner, yielding an insignificant price elasticity.

Because of aggregation bias, another body of the literature has emerged in recent years that concentrates on using trade data at the bilateral level. Examples in this latter group include Rose and Yellen (1989), Cushman (1987, 1990), Summary (1989), Marquez (1990), Haynes, Hutchison, and Mikesell (1986), Eaton (1994), Bahmani-Oskooee and Brooks (1999), Nadenichek (2000), and Bahmani-Oskooee and Goswami (2004). Except Bahmani-Oskooee and Goswami (2004), all other studies in this second group have estimated bilateral trade elasticities between the United States and her major trading partners and concluded that the real bilateral exchange rate is a significant determinant of bilateral trade balance, at least in some cases. Bahmani-Oskooee and Goswami (2004), who considered the bilateral trade flows between Japan and her nine major trading partners, found that in most cases Japanese exports are not sensitive to the real bilateral exchange rate, but her imports are. These studies as well as those in the first group estimate price elasticities in demand under the assumption that supply is perfectly elastic and then evaluate the Marshall-Lerner condition involving own-price coefficients in demand. Thus, evidence in these studies is limited because it assumes perfectly elastic supply of trade for both exports and imports.

Although there is additional room to expand the literature in the second group by considering the experiences of countries other than the United States and Japan, in this paper we would like to open another avenue of research by investigating the impact of real depreciation of the dollar on imports and exports of 66 American industries, a disaggregation by industry rather than by country. Disaggregation by industry will avoid problems associated with petroleum imports, as does disaggregation by country (Rose and Yellen 1989). (1) For this purpose, in Section 2 we outline the import and export demand functions for each commodity group along with the estimation method. In Section 3, we present the empirical results. Section 4 provides our summary and conclusion. Data definitions and sources are cited in an appendix.

2. The Models and the Method

In formulating any import and export demand function, it is a common practice to relate the volume of imports and exports to a measure of income and relative prices. The main purpose is to obtain estimates of import and export demand elasticities so that we can better judge the effectiveness of currency devaluation in increasing a country's inpayments and reducing outpayments. One major limitation of these studies is that they have assumed a perfectly elastic supply. An exception is Haynes, Hutchison, and Mikesell (1986), who considered the bilateral trade between the United States and Japan by formulating the demand and supply equations. They then estimated not only the bilateral demand and supply models but also reduced-form models in which bilateral import and export values were directly related to real bilateral exchange rate in addition to other variables. The advantage of this direct method is that one could easily determine whether currency depreciation has favorable effects on a country's inpayments and outpayments. Furthermore, Bahmani-Oskooee and Goswami (2004), who considered Japan's experience with her nine largest trading partners, argued that because import and export prices are not available at the bilateral level, relating import and export values directly to real bilateral exchange rate is the only way to assess the impact of currency depreciation on inpayments and outpayments. At the industry level, because of lack of import and export prices, we also concentrate on nominal figures and try to investigate sensitivity of import and export values of each industry to a change in exchange rate. In doing so, we modify BahmaniOskooee and Goswami's (2004) models so that they conform to industry data. Thus, for each commodity group (or industry) we formulate the inpayments and outpayments functions by Equations 1 and 2 respectively (2):

Ln [VX.sub.i,t] = a + b Ln [Y.sub.w,t] + c Ln [RE.sub.t]+[[epsilon].sub.t] (1) Ln [VM.sub.i,t] = d + e Ln [Y.sub.us,t] + f Ln [RE.sub.1] + [[mu].sub.t], (2)

where [VX.sub.i,t] is industry i's exports (in dollars), which is assumed to depend on world income, [Y.sub.w], and real effective value of the dollar, RE. In Equation 2, [VM.sub.i,t] is the value of imports by industry i, which is assumed to depend on U.S. income, [Y.sub.US], and real effective value of the dollar. Although estimates of coefficients assigned to income variables in both equations are expected to be positive, estimate of c in Equation 1 is expected to be negative, and that of f in Equation 2 positive. These expected signs of c and fare based on the definition of RE, which is defined as number of units of foreign currencies per dollar. (3,4) Note that Equation 1 is a reduced-form equation that is derived from a supply-and-demand model in which the export supply of good i by the United States is perfectly elastic, whereas the rest of the world demand for good i depends on rest of the world income ([Ysub.w]) and the real effective exchange rate (RE). Similarly, the reduced form Equation 2 is derived from a supply and demand model in which the supply of good i by the rest of the world is assumed to be perfectly elastic, yet the demand by the United States for the same good is assumed to depend on the U.S. income ([Y.sub.US]) and the real effective exchange rate (RE). Thus, except the real exchange rate, other supply factors are excluded from the specifications that could render the empirical results somewhat biased.

Equations 1 and 2 are long-run relationships among the variables of interest. The advances in econometric literature dictate that in estimating the long-run relations we must incorporate the shortrun dynamics into the estimation procedure. This could be done by specifying Equations 1 and 2 in an error-correction modeling format. Following Pesaran, Shin, and Smith (2001) and their new method of Autoregressive Distributed Lag (ARDL) approach to cointegration analysis, we rewrite Equations 1 and 2 in an error-correction modeling format as in Equations 3 and 4:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

Note that Equations 3 and 4 differ from standard distributed lag models in that they include a linear combination of the lagged level of all variables, normally referred to as an error-correction term. The first step in estimating Equations 3 and 4 is to decide whether to retain the lagged level of variables in Equations 3 and 4. Pesaran, Shin, and Smith (2001) propose using the standard F-test with new critical values that they tabulate. The advantage of this approach is that there is no need for preunit root testing. Based on the assumption that all variables could be I(1) or I(0) or some I(1) and some I(0), Pesaran, Shin, and Smith (2001) provide an upper and a lower bound of critical values. If our calculated F-statistic turns out to be significant (higher than the upper bound), the lagged level variables are to be retained in Equations 3 and 4, which is an indication of cointegration among the variables. Once the first stage is settled, we estimate Equations 3 and 4 by employing a standard criterion to select the optimum lag length of each first differenced variable. Although the short-run effects of devaluation are judged by coefficient estimates of [DELTA]Ln [RE.sub.t-k], the long-run effects are judged by estimates of d and h that are normalized on the estimates of b and f in Equations 3 and 4, respectively.

3. Empirical Results

Monthly import and export data from 66 industries in the United States (SITC Commodity Groupings) over the January 1991-August 2002 period are employed in estimating error-correction models 3 and 4. The first step in applying Peseran, Shin, and Smith (2001) ARDL technique is to carry out the F-test to determine whether we are justified in retaining the lagged-level variables. As mentioned above, this amounts to testing the null hypothesis of no cointegration, i.e., b=c=d=0 in Equation 3 and f=g=h=0 in Equation 4 against the alternative of b [not equal to] c [not equal to] d [not equal to] 0 in Equation 3 and f [not equal to] g [not equal to] h [not equal to] 0 in Equation 4 by using the familiar F-test with new critical values. Bahmani-Oskooee and Brooks (1999) have demonstrated that the results of the F-test will be sensitive to the number of lags imposed on each first differenced variable. Following their approach, we performed the F-test as a preliminary exercise for different lag order. The results not reported, but available from the authors, revealed that there were more significant F-statistics at lower lags than at higher lags. For example, in both models at lag length of four, in 40 out of 66 cases, the calculated F-statistic was higher than its critical value of 3.80. However, when we shifted to 10 lags, the number of significant cases dropped to only 13. These results should be considered preliminary. Stronger results in favor of cointegration are to be presented once we rely on the optimum number of lags selected by the Akaike Information Criterion (AIC).

In the second stage, we employ the AIC criterion in selecting the optimum number of lags. Because of a large volume of results in this second stage, we restrict ourselves to reporting only the long-run coefficient estimates along with some diagnostics in Tables 1 (export demand) and 2 (import demand). (5)

First, we should indicate at the outset that inspection of the short-run results for each industry revealed that the coefficients obtained for the first differenced exchange rate did not follow any specific pattern such as the J-Curve. This is consistent with previous research pertaining to the relation between value of the dollar and the U.S. bilateral trade balance (Rose and Yellen 1989).

Second, using estimates of b, c, and d in Equation 3 and e, f, and g in Equation 4 we calculate the linear combination of the lagged variables in each model and denote it by [EC.sub.t-i]. After replacing the linear combination of the lagged level variables by [EC.sub.t-1] and after imposing the optimum number of lags selected by the AIC criterion, we reestimate Equations 3 and 4. A negative and significant coefficient obtained for [ECsub.t-1] in both models is another indication of cointegration among the variables. It is clear from Tables 1 and 2 that in almost all cases the lagged error-correction term (E[C.sub.t-1]) carries a significantly negative coefficient, supporting cointegration among the variables. (6)

Third, using the estimates of b, c, and d in Equation 3 we normalize c and d on b and report the results in Table 1. Similarly, from estimates of Equation 4, we report normalized long-run estimates of f and g in Table 2. Recall that in the export demand function for each industry, if real depreciation of the dollar is to stimulate export earnings of an industry, an estimate of d should be negative. From Table 1 it is evident that the real exchange rate carries a significantly negative coefficient in half of the industries included in this study. In the absence of any other study using U.S. export data at the micro level, it is difficult to compare our findings to those of the literature. However, our findings contradict those of Rose and Yellen (1989), who considered the bilateral trade balance between the United States and her six major trading partners and showed that exchange value of the dollar plays no role in the trade. Our results are indicative of the fact that the insignificant relationship between U.S. bilateral trade and the value of the dollar could be a result of lack of a significant relation between the value of the dollar and some industries but not all industries. The world income carries its expected positive and significant sign in most cases, indicating that as the rest of the world grows, most U.S. industries enjoy more export earnings.

Finally, we turn to Table 2 and long-run coefficient estimates of import demand for each industry. Recall that if real depreciation of the dollar is to decrease import value or outpayments of an industry, the exchange rate variable (RE) must carry a positive and significant coefficient. From Table 2 it is clear that in only 13 industries does the real exchange rate carry a significantly positive coefficient. In most cases, it carries an insignificant coefficient. Lack of significant relation between the value of the dollar and the value of imports in most industries is consistent with inelastic import demand found for 27 industries by Kreinin (1973), though Kreinin's results do suffer from two shortcomings: First, in the absence of import price for each industry, Kreinin had to construct them using the Paashe procedure, rendering the price data nonstochastic. Second, given the existing literature on unit roots, Kreinin used nonstationary data, which may yield spurious coefficients. A similar insignificant relation between the yen/dollar rate and the U.S. imports of 11 commodities from Japan was also found by Parsley and Wei (1993). Furthermore, the finding that more import industries are insensitive to the real value of the dollar than export industries (using aggregate or disaggregate data) could be because in order to maintain their share of the U.S. market, foreign exporters have squeezed their profit margin to offset the increase in their export prices from depreciation of the dollar. (7) The U.S. income carries a significantly positive coefficient in most cases, indicating that the relative strength of the U.S. economy is a major reason for the persistence of the U.S. trade deficit.

A few main features of the results deserve mention. First, in order to shed empirical light on our earlier argument that if aggregate data are employed significant exchange rate coefficients in some sectors could be more than offset by insignificant coefficients in other sectors, we aggregate the industry-level data and apply the same methodology to aggregate data. The long-run coefficient estimates are reported in Table 3.

Table 3 reveals that the real exchange rate carries an insignificant coefficient in the import (outpayment) model but not in the export (inpayment) model, providing partial support for the aggregation bias argument. Furthermore, the significant coefficient of -0.79 attached to the exchange rate in the export demand model coupled with an insignificant coefficient in the import demand model indicates that a 10% real depreciation of the dollar will improve the trade balance by 7.9%. We arrive at the same conclusion by concentrating on the significant coefficients obtained for the exchange rate in the import and export models at the industry level reported in Tables 1 and 2. (8)

Second, from Tables 1 and 2 we calculate the average income elasticity in the export equation to be 2.19 and in the import demand equation to be 1.65. This contradicts the income elasticity disparity commonly found in the literature that income elasticity of import demand exceeds that of the export demand, hence contributing to the persistence of the U.S. trade deficit. (9) This finding is consistent with Bahmani-Oskooee and Nirommand (1998), who employed a cointegration technique and estimated aggregate import and export demand functions for 26 countries. One contributing factor for the finding that in more recent studies income elasticity is higher in the export demand model than in the import demand function is the use of stationary data by recent studies compared to nonstationary data used by earlier studies. This new finding supports the importance of economic growth in the rest of the world as a major factor that is expected to improve the U.S. trade deficit.

Third, let us shed some empirical light on the proposition that durable goods are relatively more sensitive to exchange rate changes than nondurable commodities, as suggested by Burda and Gerlach (1992). Following the classification by the U.S. Commerce Department, Burda and Gerlach (1992) considered durables as one group and nondurables as another group and showed that durable imports are more price elastic than nondurable imports. Following their procedure, we aggregate the trade data for 27 durable goods and 39 nondurable goods and estimate four error-correction models where, again, the lag lengths are selected by AIC. The long-run results for each group and for each model are presented in Table 4.

From the coefficient assigned to the exchange rate, it appears that durables in both import and export models carry a higher coefficient than nondurables, supporting Burda and Gerlach (1992), though the estimated coefficient is only significant in the export model for durables. Even though the coefficient estimates obtained for the exchange rate in durables versus nondurables look different in size, they may not be significantly different because of different standard errors. Indeed, when we calculated a t-ratio for the difference using standard error of each coefficient, the t-statistic was insignificant, implying that the two coefficients are not significantly different from each other. (10) The same was true of the durables and nondurables in the export value model.

However, Burda and Gerlach's (1992) results as well as our results in Table 4, again, do suffer from the aggregation bias problem. The results reported in Tables 1 and 2 show that when durables and nondurables are disaggregated by commodities, there are sectors in each group that are significantly sensitive to changes in real exchange rate, and there are sectors that are not. For example, in Table 1 cigarettes as nondurable goods are highly sensitive to real exchange rate, whereas airplanes as durables are not. Generally, as indicated, there are 27 durables and 39 nondurables in our sample. In the export results reported in Table 1, 44% of durables (12 of the 27) carry an expectedly negative and significant exchange rate coefficient. The comparable figure for nondurables is 46% (18 out of 39). Even the average exchange rate coefficients are close for both commodity attributes, that is, -1.44 for durables versus -1.55 for nondurables. When we consider the import results in Table 2, 18.5% of durables versus 20% of nondurables carry an expectedly positive and highly significant exchange rate coefficient, and the average in durables is 1.04 versus 1.09 in nondurables. This detailed analysis at individual commodity level reveals that when only significant cases are considered, the average exchange-rate coefficient is almost the same across different classifications, which is consistent with the results reported in Table 4 at the aggregate level (based on formal tests reported earlier). In sum, nondurables are as important as durables in contributing to the U.S. trade imbalances. Our findings, however, support Breuer and Clements (2003), who investigated exchange rate elasticity of exports and imports of 58 industries between the United States and Japan only. Similar to our findings, Breuer and Clements (2003) found that more export commodities are responsive to the exchange rate than are import commodities and that there is no systematic difference between exchange rate sensitivity of commodities in durable and nondurable industries.

Another commodity attribute that we consider is whether industries with large market shares react differently to real exchange rate changes from those with small market shares. Inspection of the data in the last study period revealed that about 70% of the total exports by 66 industries are by 11 industries (i.e., ADP equipment 5%, airplane parts 3%, airplanes 4%, chemicals 14%, electrical machinery 13%, general industrial machineries 5%, metal manufacturers 2%, power-generating machines 6%, scientific instruments 5%, specialized industrial machines 4%, and vehicles 10%). Given the evidence in Breuer and Clements (2003, p. 326, footnote 10) that most of these 11 industries are also industries with high fixed costs, we reformulate our hypothesis and test Dixit's (1989a, 1989b) conjecture that high-cost industries are less sensitive to exchange rate changes than low-cost industries. To this end, we aggregate the trade data for 11 large industries and the remaining small industries and estimate four more error-correction models, where, again, the lag lengths are selected by the AIC. The long-run results for each group are presented in Table 5.

The results in Table 5 reject Dixit. In the import value model, it appears that neither large nor small industries' imports are sensitive to the exchange rate. However, in the export value model, both groups seem to be sensitive to the exchange rate. Furthermore, testing for equality of exchange rate coefficients across two groups revealed that they are not significantly different from each other in either model. This conclusion is again biased by aggregation. Inspection of the results reported in Table 1 indeed supports Dixit. Among 11 high-cost industries mentioned above, only two (ADP equipment and specialized industrial machines) carry a significant coefficient with correct negative sign, whereas in the remaining low-cost industries there are 24 industries with a significant negative exchange rate coefficient. Thus, more small industries are sensitive to the exchange rate than large industries. Furthermore, the average coefficient obtained for the exchange rate in the first group is -1.29 compared to an average of -1.66 in the second group. Thus, it appears that small industries with low fixed costs are more sensitive to the real value of the dollar than large industries with high fixed costs. This finding is also true of the results in Table 2 for imports. From the high-cost industries, only one, airplanes, carries an expected positive and highly significant exchange rate coefficient, whereas among the low-cost industries there are nine industries with highly significant and positive coefficients. These findings were masked when we lumped together the small industries as one group and large industries as another.

Finally, so far we used the estimated coefficients to assess the direct impact of real depreciation on inpayments and outpayments in each industry. These coefficients obtained for the real exchange rate could also be used to infer whether export and import demands are elastic. As mentioned before, if there is a perfectly elastic export supply, a significantly negative coefficient obtained for the real exchange rate in the export value model (inpayments) indicates that real depreciation increases export earnings. This implies that the elasticity of the rest of the world demand for exports of a particular industry should be significantly different from zero. This is because inpayments in each industry are measured in terms of domestic currency (dollar). On the imports side, a significantly positive coefficient obtained for the real exchange rate in the outpayments schedule (import value model, Table 2) is indicative of the fact that real depreciation lowers outpayments. This implies that U.S. import demand in each industry should have an elasticity greater than unity, and again, this is because outpayments are measured in terms of domestic currency (dollar). (11) To infer the size of trade elasticities, we thought of estimating export and import demand models in which the dependent variables are the volume of trade in each industry. Although export and import price indices (e.g., [P.sub.x] and [P.sub.m]) are not available at the commodity level to deflate export and import values, following the previous work we use aggregate export and import price indices. These indices available from the International Financial Statistics of the International Monetary Fund are considered a second-best alternative for the purpose of deflation.

As a first exercise we lumped together the real durable imports versus real nondurable imports. Similarly, we aggregated data across real durable exports versus nondurable exports. We then estimated four error-correction models. The long-run results are reported in Table 6. From Table 6 we gather that real nondurable imports carry an expected positive and highly significant coefficient. However, because the estimated elasticity is less than one, real depreciation of the dollar will not lower the U.S. outpayments. This is consistent with the estimates reported for value models in Table 4, where an insignificant coefficient was obtained for the real exchange rate in import value models. In the real export models the real exchange rate carries its expected negative sign but is significant only at a 10% level. This is also consistent with our earlier argument that if real depreciation of the dollar is to increase U.S. inpayments, the U.S. export demand must have an elasticity that is different from zero. Although it appears that real durables in each group have different exchange rate elasticities than real nondurables, when we formally tested for the significance of the difference, the t-ratio revealed that they are not significantly different. Furthermore, the results in these volume models do not support Burda and Gerlach (1992) that durables should carry a higher coefficient than nondurables. Again, our inference about the size of the coefficients is biased by aggregation. As shown in the value models, there could be some durables and some nondurables in each model that could respond favorably to real depreciation. To identify these industries, we report in Tables 7 and 8 the long-run results for each industry.

In the export value model, there were 24 industries in Table 1 that carried a significantly negative exchange rate coefficient. From Table 7, which reports the results for the export volume model, we gather that there are now 19 industries that carry significantly negative exchange rate elasticities. The five industries in which we have lost the significance of the exchange rate coefficient are crude fertilizers, furniture and bedding, meat and preparations, paper, and photographic equipment. This difference could result from using an aggregate price index rather than an industry-specific price index. In these 19 industries, because exchange rate elasticity is significantly different from zero, real devaluation of the dollar will increase their export earnings. Furthermore, included among these 19 industries are durables as well as nondurables. This finding was masked by the aggregate results reported in Table 6. Turning to the import value model, there were 12 industries in Table 2 in which the real exchange rate carried a significantly positive coefficient. The results of the import volume model reported in Table 8 indicate that in nine of these industries the exchange rate elasticity is greater than one, indicating that real depreciation of the dollar will lower volume of imports by a higher percentage than the increase in import prices (in dollars), with the net outcome of reducing outpayments in these industries. Furthermore, among the nine industries, there are five durable and four nondurable goods, indicating the importance of both groups. The four industries that happen to have an inelastic import demand are fish and preparations, power-generating machines, textiles, and vehicles. Again, these conflicting results could result from use of an aggregate import price index rather than an industry-specific price index.

Finally, from Table 7 we gather that out of 11 large industries, only two (i.e., ADP equipment and specialized industrial machinery) carry expected negative and significant exchange rate elasticity, whereas out of 55 small industries, 18 industries possess that property, supporting Dixit (1989a, b). In the import volume model (Table 8), only for one large industry (i.e., airplanes) is a positive and significant exchange rate elasticity obtained, whereas there are eight small industries with that property, again supporting Dixit and the fact that more small industries are exchange-rate sensitive than large industries. To determine whether this finding would have been masked by aggregating data across large versus small industries, we report in Table 9 the long-run estimates for the two groups after aggregating their trade data.

Clearly, results in Table 9 support the conclusion reached from disaggregated data that although small industries are sensitive to the real exchange rate, large ones are not. However, because the exchange rate elasticity of small industries in the import volume model is less than one, real depreciation of the dollar will not lower their outpayments. This is contradicted by the results at industry level from Table 8. There are seven small industries with a significant exchange rate elasticity that exceeds one, implying that real depreciation of the dollar will lower these industries' outpayments. In the export volume model, the insignificant exchange rate elasticity obtained for the large industries in the export volume model (Table 9) is inconsistent with the comparable figure in the export value model (Table 5), and this could be, again, a result of using an aggregate export price index to deflate the nominal values. (12)

4. Summary and Conclusion

Previous research that investigated the response of the U.S. trade balance to a change in the value of the dollar employed aggregate data and provided mixed results. The trend in recent years is to employ disaggregated data at the bilateral level to determine whether one could uncover new results that could shed some light on the results obtained from using the aggregate data. The attempt seems to be futile in that the results from bilateral data are no different from those of the aggregate data.

In this paper, however, we disaggregate the trade data further by industry. Using monthly import and export data from 66 industries in the United States (SITC Commodity Groupings) over the January 1991-August 2002 period as well as cointegration analysis, we estimated import and export value as well as volume models to answer two questions. First, how do changes in the value of the dollar affect inpayments and outpayments of specific industry? Second, how elastic or inelastic is a specific industry to a change in real effective exchange rate? Although the value models provide an answer to the first question, the volume models provide an answer to our second question. In the value models we found that in the long run real depreciation of the dollar stimulates export earnings of many U.S. industries, whereas it has no significant impact on most importing industries. Using significant coefficient estimates obtained for the real exchange rate in import and export value models, we showed that 10% real depreciation of the dollar improves the U.S. trade balance by 7.9%. Furthermore, world income and U.S. income were found to be major determinants of U.S. exports and imports at the industry level. Finally, our results do not support the notion that durables are more price elastic than nondurables. However, they support the hypothesis that small industries with low fixed costs play a more important role than large industries with high fixed costs in U.S. trade. (13)

Appendix: Data Definition and Sources

Monthly data over January 1991-August 2002 are used to carry out the empirical work. The data come from the following sources:

a. Bureau of Census, Foreign Trade Division (U.S. Federal Government).

b. International Financial Statistics of the IMF (CD-ROM).

Variables

V[X.sub.i] Value of exports by industry i. The data in millions of dollars come from source a.

V[M.sub.i] Value of imports by industry i. The data in millions of dollars come from source a.

[Y.sub.us] Measure of the United States income. It is proxied by the index of industrial production in the United States. The data come from source b.

[Y.sub.w] Index of industrial production in the world proxied by the index of industrial production in industrial countries, source b.

[P.sub.x] Aggregate export price index, source b.

[P.sub.m] Aggregate import price index, source b.

RE Real effective exchange rate defined in a way that a decrease reflects a real depreciation of the U.S. dollar against major currencies, source b.

We would like to thank Richard Perlman and tour anonymous referees for their very constructive comments. However, any error is ours.

Received January 2004; accepted May 2005.

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Marquez, Jaime, 1990. Bilateral trade elasticities The Review of Economics and Statistics 72:70-7.

Nadenichek, Jon. 2000. The Japan-US trade imbalance: A real business cycle perspective. Japan and the Worm Economy 12:255-71.

Parsley, David C., and Shang-Jin Wei. 1993. Insignificant and inconsequential hysteresis: The case of U.S. bilateral trade. The Review of Economics and Statistics 75:606-13.

Pesaran, Hashem M., Yongcheol Shin, and Richard J. Smith. 2001. Bounds testing approach to the analysis of level relationships. Journal of Applied Econometrics 16:289-326.

Rose, Andrew K., and Janet L. Yellen. 1989. Is there a J-curve? Journal of Monetary Economics 24:53-58.

Summary, Rebecca M. 1989. A political-economic model of U.S. bilateral trade. The Review of Economics and Statistics 71:179-82.

Warner, Dennis, and Mordechai E. Kreinin. 1983. Determinants of international trade flows. Review of Economics and Statistics 65:96-104.

Wilson, John F., and Wendy E. Takacs. 1979. Differential responses to price and exchange rate influences in the foreign trade of selected industrial countries. Review of Economics and Statistics 61:267-79.

(1) Husted and Kollintzas (1984) estimated an import demand function with rational expectations for four commodities (bauxite, cocoa, coffee and petroleum).

(2) Note that Haynes, Hutchison, and Mikesell (1986) and Cushman (1987, 1990) also used export and import values in their formulation of trade flow equations.

(3) Dornbusch (1980, p. 58) also employed import and export demand functions that included real exchange rate as a determinant rather than relative prices.

(4) Note that in these models the data for dependent variables vary across industry and time, but none of the independent variables differ by industry. Thus, the only variation in the right-hand side variables is time series variation. For this reason one cannot apply panel estimation technique or SURE technique.

(5) Full-information estimates of each model for each industry are available from the authors on request.

(6) Note that Kremers, Ericson, and Dolado (1992) argue that the lagged error-correction term is a relatively more efficient way of establishing cointegration.

(7) Similar arguments are also advanced by Haynes, Hutchison, and Mikesell (1986) in their analysis of U.S.--Japan bilateral trade.

(8) From Table 1, there are 30 industries that carry significant exchange rate coefficients, and the average is--1.04, implying that a 10% depreciation increases inpayments by 10.4%. On the other hand, from Table 2 there are 28 industries for which the real exchange rate carries a significant coefficient, and the average coefficient is +0.29, implying that a 10% depreciation worsens the outpayments by 2.9%.

(9) A similar conclusion is reached when we compare income elasticities from aggregate results reported in Table 3.

(10) Note that we assume estimated coefficients obtained for the exchange rate in durable and nondurable models are independently distributed as normal. This implies that the difference between the two coefficients has also a normal distribution. It also implies that the covariance between the two coefficients is zero. Thus, the variance of the difference is calculated as sum of the variances of the exchange rate coefficients from durable and nondurable models. From this variance, we calculate the standard error of the difference that is used in forming the t-ratio for the difference.

(11) For more on the relationships among inpayments, outpayments, currency depreciation, and the size of elasticities, see Kreinin (2002, pp. 316-24).

(12) We also tested for equality of the exchange rate elasticity across both groups in the export and import volume models. They were not statistically different from each other.

(13) It should be noted that even though we have disaggregated the trade data at commodity level, the results could suffer from aggregation bias, mostly because we have used commodity trade between the United States and the rest of the world. Future research should concentrate on commodity trade between the United States and a major trading partner.

Mohsen Bahmani-Oskooee, The Center for Research on International Economics and the Department of Economics, The University of WisconsinMilwaukee, Milwaukee, WI 53201, USA; E-mail [e-mail]; corresponding author.

Zohre Ardalani, The Center for Research on International Economics and the Department of Economics, The University of Wisconsin Milwaukee, Milwaukee, WI 53201, USA.

Table 1. Long-Run Coefficient Estimates of the Export (Inpayment)
Function

Commodities (a)                      Constant      Ln [Y.sub.W]

 1. ADP equip.; office machines     1.26 (1.01)     2.99 (11.69)
 2. Airplane parts                 -5.74 (3.98)     2.83 (7.42)
 3. Airplanes                      -4.45 (0.60)     1.65 (0.81)
 4. Alcoholic bev., distilled      -3.64 (2.31)     1.20 (2.90)
 5. Aluminum                       -0.85 (1.02)     2.76 (11.86)
 6. Animal feeds                    3.80 (1.39)     1.37 (1.75)
 7. Artwork/antiques               -2.58 (1.07)     1.01 (1.59)
 8. Basketware, etc.              -13.77 (5.64)     3.41 (5.32)
 9. Cereal flour                   -2.65 (1.72)     0.96 (2.30)
10. Chemicals                      -4.78 (1.69)     2.53 (3.00)
11. Cigarettes                     24.85 (4.31)    -0.10 (0.08)
12. Clothing                        7.07 (0.93)     2.77 (2.45)
13. Coal                           20.36 (4.93)    -0.41 (0.35)
14. Copper                          7.63 (3.49)     1.11 (1.74)
15. Cork, wood, lumber             17.38 (11.10)   -1.30 (3.08)
16. Corn                            8.66 (2.48)     1.29 (1.42)
17. Crude fertilizers/minerals      0.38 (0.66)     1.23 (8.08)
18. Electrical machinery           -7.84 (2.81)     3.89 (5.64)
19. Fish and preparations           7.70 (5.55)    -0.74 (2.02)
20. Footwear                        6.96 (2.92)     0.82 (1.93)
21. Furniture and bedding           0.46 (0.19)     2.75 (4.16)
22. Gem diamonds                  -120.9 (1.37)    22.60 (0.97)
23. General industrial mach.       -3.37 (2.29)     2.97 (8.82)
24. Glass                          -9.53 (4.98)     3.08 (5.84)
25. Glassware                      -7.88 (4.72)     3.07 (6.68)
26. Gold, nonmonetary              22.71 (3.87)    -1.82 (1.33)
27. Hides and skins                -1.89 (0.24)     1.88 (0.87)
28. Iron and steel mill prod       -4.61 (3.34)     3.36 (7.92)
29. Lighting, plumbing              1.66 (0.53)     1.27 (1.70)
30. Liquified propane/butane       -9.58 (1.97)     3.16 (2.50)
31. Live animals                   -1.60 (0.93)     0.64 (1.53)
32. Meat and preparations          -0.77 (0.25)     2.92 (4.11)
33. Metal manufactures, n.e.s.     -6.62 (1.09)     3.62 (3.58)
34. Metal ores; scrap             -24.45 (0.62)    -0.72 (0.15)
35. Metalworking machinery         -2.64 (1.99)     3.72 (12.12)
36. Mineral fuels, other            0.46 (0.19)     2.75 (4.16)
37. Natural gas                   -19.42 (1.82)    -2.61 (0.94)
38. Nickel                        -14.75 (5.78)     3.29 (3.14)
39. Oils/fats, vegetable            2.05 (0.32)     2.26 (1.32)
40. Optical goods                 -26.42 (12.52)    6.80 (10.20)
41. Paper and paperboard           -0.40 (0.14)     2.63 (4.04)
42. Petroleum preparations         -3.05 (0.98)     0.43 (0.53)
43. Photographic equipment          0.50 (0.54)     1.72 (6.97)
44. Plastic articles, n.e.s.      -14.72 (8.10)     4.17 (9.01)
45. Platinum                      -21.43 (5.46)     2.91 (2.95)
46. Pottery                         2.64 (1.49)     0.19 (0.44)
47. Power generating mach.         -6.31 (4.92)     2.79 (8.10)
48. Printed materials               1.61 (2.57)     1.17 (8.00)
49. Pulp and waste paper            2.95 (0.94)     1.38 (1.75)
50. Records/magnetic media         16.07 (2.78)    -0.41 (0.33)
51. Rice                            5.45 (2.46)     1.38 (3.34)
52. Rubber articles, n.e.s.       -16.92 (13.25)    4.68 (13.34)
53. Rubber tires and tubes         -1.69 (0.36)     3.40 (4.03)
54. Scientific instruments         -9.06 (11.42)    3.51 (15.21)
55. Ships, boats                    2.32 (0.76)     0.47 (0.58)
56. Silver and bullion              2.49 (0.35)     3.61 (1.97)
57. Specialized ind. mach.         -0.70 (0.60)     2.88 (10.11)
58. Textile yarn, fabric           -7.31 (6.76)     2.79 (8.99)
59. Tobacco, unmanufactured         9.26 (6.76)    -0.43 (1.22)
60. Toys/games/sporting goods       4.18 (1.55)     1.85 (3.32)
61. Travel goods                   -4.88 (1.78)     3.21 (6.51)
62. Vegetables and fruits           2.14 (1.47)     1.17 (3.10)
63. Vehicles                       -2.02 (0.83)     2.41 (3.79)
64. Watches/clock/parts            -2.65 (1.17)     1.72 (2.66)
65. Wheat                          14.39 (4.82)    -0.67 (0.85)
66. Wood manufactures               1.58 (1.22)     1.48 (4.47)

Commodities (a)                       Ln RE           Ecm(-1)

 1. ADP equip.; office machines   -1.51 (5.13)     -0.44 (4.64)
 2. Airplane parts                -0.10 (0.31)     -0.27 (3.36)
 3. Airplanes                      0.93 (0.60)     -0.24 (2.21)
 4. Alcoholic bev., distilled      0.33 (1.04)     -0.67 (7.91)
 5. Aluminum                      -1.34 (7.05)     -0.47 (4.90)
 6. Animal feeds                  -0.93 (1.33)     -0.21 (2.28)
 7. Artwork/antiques               0.51 (1.06)     -0.74 (8.58)
 8. Basketware, etc.               0.71 (1.45)     -0.18 (2.33)
 9. Cereal flour                   0.59 (1.84)     -0.34 (4.46)
10. Chemicals                      0.36 (0.51)     -0.10 (1.42)
11. Cigarettes                    -3.97 (3.82)     -0.23 (2.37)
12. Clothing                      -2.87 (2.63)     -0.12 (2.22)
13. Coal                          -2.76 (3.04)     -0.18 (2.67)
14. Copper                        -1.72 (3.27)     -0.30 (3.75)
15. Cork, wood, lumber            -1.14 (3.72)     -0.32 (3.65)
16. Corn                          -1.82 (2.63)     -0.27 (4.50)
17. Crude fertilizers/minerals    -0.26 (2.28)     -0.70 (8.49)
18. Electrical machinery          -0.37 (0.73)     -0.13 (2.32)
19. Fish and preparations          0.24 (0.88)     -1.12 (5.26)
20. Footwear                      -1.43 (3.76)     -0.34 (2.82)
21. Furniture and bedding         -1.65 (2.77)     -0.26 (3.58)
22. Gem diamonds                   4.22 (0.52)     -0.07 (1.06)
23. General industrial mach.      -0.56 (1.72)     -0.23 (2.92)
24. Glass                          0.06 (0.14)     -0.25 (2.25)
25. Glassware                     -0.49 (1.28)     -0.34 (3.32)
26. Gold, nonmonetary             -1.78 (1.64)     -0.49 (6.72)
27. Hides and skins               -0.43 (0.29)     -0.07 (1.60)
28. Iron and steel mill prod      -1.05 (2.71)     -0.33 (3.72)
29. Lighting, plumbing            -0.60 (1.18)     -0.22 (2.13)
30. Liquified propane/butane      -0.41 (0.43)     -0.39 (5.52)
31. Live animals                   0.53 (1.53)     -1.29 (4.39)
32. Meat and preparations         -1.38 (2.21)     -0.16 (2.53)
33. Metal manufactures, n.e.s.    -0.74 (0.77)     -0.11 (1.06)
34. Metal ores; scrap              7.11 (0.62)     -0.05 (0.77)
35. Metalworking machinery        -1.84 (7.22)     -0.46 (5.37)
36. Mineral fuels, other          -1.65 (2.77)     -0.26 (3.58)
37. Natural gas                    7.32 (2.21)     -0.23 (1.91)
38. Nickel                         0.58 (0.64)     -0.23 (2.24)
39. Oils/fats, vegetable          -1.71 (1.32)     -0.24 (3.33)
40. Optical goods                 -0.06 (0.09)     -0.25 (2.91)
41. Paper and paperboard          -1.09 (2.03)     -0.13 (2.10)
42. Petroleum preparations         1.44 (2.33)     -0.39 (5.39)
43. Photographic equipment        -0.60 (3.11)     -0.60 (5.49)
44. Plastic articles, n.e.s.       0.30 (0.76)     -0.25 (3.00)
45. Platinum                       2.42 (3.20)     -0.54 (4.62)
46. Pottery                       -0.30 (0.82)     -0.46 (2.91)
47. Power generating mach.         0.22 (0.72)     -0.33 (2.68)
48. Printed materials             -0.24 (1.83)     -0.65 (4.01)
49. Pulp and waste paper          -0.75 (1.21)     -0.17 (3.64)
50. Records/magnetic media        -1.70 (2.68)     -0.19 (2.06)
51. Rice                          -1.60 (5.14)     -0.85 (5.82)
52. Rubber articles, n.e.s.       -0.06 (0.19)     -0.37 (3.54)
53. Rubber tires and tubes        -1.87 (1.67)     -0.14 (1.68)
54. Scientific instruments         0.06 (0.28)     -0.43 (4.22)
55. Ships, boats                   0.01 (0.02)     -0.87 (6.44)
56. Silver and bullion            -3.40 (2.45)     -0.43 (5.95)
57. Specialized ind. mach.        -1.07 (4.73)     -0.38 (4.62)
58. Textile yarn, fabric           0.18 (0.71)     -0.30 (2.17)
59. Tobacco, unmanufactured       -0.54 (1.92)     -1.24 (4.250
60. Toys/games/sporting goods     -1.52 (3.31)     -0.23 (2.99)
61. Travel goods                  -1.45 (2.73)     -0.34 (2.45)
62. Vegetables and fruits         -0.25 (0.94)     -0.33 (2.51)
63. Vehicles                      -0.18 (0.40)     -0.36 (2.39)
64. Watches/clock/parts           -0.47 (0.92)     -0.35 (2.71)
65. Wheat                         -1.15 (1.94)     -0.32 (4.85)
66. Wood manufactures             -0.74 (2.98)     -0.38 (3.77)

Commodities (a)                   Adj [R.sup.2]

 1. ADP equip.; office machines        0.75
 2. Airplane parts                     0.44
 3. Airplanes                          0.41
 4. Alcoholic bev., distilled          0.32
 5. Aluminum                           0.45
 6. Animal feeds                       0.29
 7. Artwork/antiques                   0.35
 8. Basketware, etc.                   0.28
 9. Cereal flour                       0.27
10. Chemicals                          0.45
11. Cigarettes                         0.31
12. Clothing                           0.41
13. Coal                               0.34
14. Copper                             0.29
15. Cork, wood, lumber                 0.35
16. Corn                               0.12
17. Crude fertilizers/minerals         0.35
18. Electrical machinery               0.53
19. Fish and preparations              0.58
20. Footwear                           0.44
21. Furniture and bedding              0.34
22. Gem diamonds                       0.35
23. General industrial mach.           0.45
24. Glass                              0.55
25. Glassware                          0.32
26. Gold, nonmonetary                  0.31
27. Hides and skins                    0.21
28. Iron and steel mill prod           0.40
29. Lighting, plumbing                 0.28
30. Liquified propane/butane           0.19
31. Live animals                       0.46
32. Meat and preparations              0.18
33. Metal manufactures, n.e.s.         0.45
34. Metal ores; scrap                  0.45
35. Metalworking machinery             0.38
36. Mineral fuels, other               0.34
37. Natural gas                        0.44
38. Nickel                             0.45
39. Oils/fats, vegetable               0.21
40. Optical goods                      0.44
41. Paper and paperboard               0.39
42. Petroleum preparations             0.17
43. Photographic equipment             0.41
44. Plastic articles, n.e.s.           0.45
45. Platinum                           0.44
46. Pottery                            0.50
47. Power generating mach.             0.55
48. Printed materials                  0.42
49. Pulp and waste paper               0.35
50. Records/magnetic media             0.40
51. Rice                               0.35
52. Rubber articles, n.e.s.            0.43
53. Rubber tires and tubes             0.47
54. Scientific instruments             0.54
55. Ships, boats                       0.47
56. Silver and bullion                 0.20
57. Specialized ind. mach.             0.38
58. Textile yarn, fabric               0.61
59. Tobacco, unmanufactured            0.42
60. Toys/games/sporting goods          0.38
61. Travel goods                       0.42
62. Vegetables and fruits              0.55
63. Vehicles                           0.44
64. Watches/clock/parts                0.39
65. Wheat                              0.14
66. Wood manufactures                  0.29

Numbers inside the parentheses are absolute value of the t-ratios.

(a) ADP, automated data processing; n.e.s., not elsewhere specified.

Table 2. Long-Run Coefficient Estimates of Import (Outpayment)
Function

Commodities (a)                    Constant       Ln [Y.sub.US]

1. ADP equip.; office machines     2.65 (1.90)    2.54 (13.85)
2. Airplane parts                 -3.76 (0.83)    2.91 (2.74)
3. Airplanes                     -16.15 (7.75)    2.51(7.48)
4. Alcoholic bev., distilled      -4.50 (6.78)    1.18 (8.93)
5. Aluminum                       -1.40 (0.41)    1.95 (2.53)
6. Animal feeds                    1.72 (0.58)    1.62 (3.07)
7. Artwork/antiques               -8.15 (6.85)    2.19 (9.32)
8. Basketware, etc.              -11.04 (4.11)    2.18 (6.38)
9. Cereal flour                   -6.43 (2.02)    2.26 (4.08)
10. Chemicals                     -5.88 (3.75)    2.53 (8.25)
11. Cigarettes                   -16.63 (1.35)   -1.05 (0.38)
12. Clothing                      -1.22 (3.05)    1.87 (31.66)
13. Coal                          -5.45 (2.03)    1.11 (2.03)
14. Copper                        -2.96 (1.66)    2.68 (7.69)
15. Cork, wood, lumber            -1.19 (0.70)    1.62 (5.04)
16. Corn                          -4.51 (1.99)    1.29 (3.30)
17. Crude fertilizers/minerals     0.59 (0.99)    1.34 (11.41)
18. Electrical machinery           2.52 (1.73)    2.65 (11.23)
19. Fish and preparations         -1.70 (2.96)    1.29 (10.80)
20. Footwear                       2.28 (3.61)    1.02 (8.20)
21. Furniture and bedding          0.01 (0.00)    0.10 (0.08)
22. Gem diamonds                  -9.12 (6.92)    2.35 (11.15)
23. General industrial mach.      -0.27 (0.14)    2.06 (6.81)
24. Glass                         -4.54 (1.47)    2.20 (3.75)
25. Glassware                     -2.43 (8.19)    1.86 (39.54)
26. Gold, nonmonetary              5.26 (2.04)    1.72 (3.54)
27. Hides and skins                7.41 (5.58)   -0.17 (0.74)
28. Iron and steel mill prod       4.72 (2.90)    1.76 (5.46)
29. Lighting, plumbing           -11.18 (7.45)    3.02 (14.20)
30. Liquified propane/butane      -0.92 (0.21)    1.73 (1.95)
31. Live animals                   2.41 (1.09)    0.63 (1.80)
32. Meat and preparations         -5.08 (2.95)    0.29 (1.01)
33. Metal manufactures, n.e.s.     -4.2 (2.62)    2.25 (8.52)
34. Metal ores; scrap              8.38 (8.51)    1.03 (5.31)
35. Metalworking machinery         5.05 (2.97)    2.71 (9.78)
36. Mineral fuels, other           0.01 (0.00)    0.10 (0.08)
37. Natural gas                  -16.33 (4.53)    2.58 (3.58)
38. Nickel                         2.97 (1.26)    1.71 (3.64)
39. Oils/fats, vegetable           3.76 (3.17)    1.18 (5.71)
40. Optical goods                 -5.06 (4.80)    2.05 (10.92)
41. Paper and paperboard           0.37 (0.28)    1.88 (7.43)
42. Petroleum preparations        -7.87 (1.26)    1.82 (1.60)
43. Photographic equipment         2.60 (3.68)    1.48 (11.21)
44. Plastic articles, n.e.s.      -3.34 (1.53)    1.90 (5.24)
45. Platinum                     -10.46 (2.41)    3.69 (4.28)
46. Pottery                        3.33 (6.27)    0.72 (7.34)
47. Power generating mach.        -3.61 (5.69)    1.95 (16.64)
48. Printed materials             -2.55 (2.18)    1.77 (8.81)
49. Pulp and waste paper           7.89 (4.24)    1.36 (4.22)
50. Records/magnetic media         0.23 (0.29)    1.24 (8.17)
51. Rice                          -3.67 (1.40)    1.76 3.85)
52. Rubber articles, n.e.s.       -7.07 (2.17)    0.93 (1.06)
53. Rubber tires and tubes        -5.56 (4.06)    1.42 (7.01)
54. Scientific instruments        -5.39 (4.37)    2.71 (12.76)
55. Ships, boats                  -7.89 (2.70)    2.93 (5.12)
56. Silver and bullion             0.11 (0.07)    1.33 (4.69)
57. Specialized ind. mach.         1.98 (0.37)    0.35 (0.15)
58. Textile yarn, fabric          -3.17 (3.58)    1.51 (10.68)
59. Tobacco, unmanufactured        6.95 (2.06)   -0.68 (1.03)
60. Toys/games/sporting goods     -2.39 (3.78)    1.73 (14.78)
61. Travel goods                  -0.07 (0.13)    1.61 (18.09)
62. Vegetables and fruits         -0.59 (0.65)    1.39 (9.37)
63. Vehicles                      -3.33 (2.98)    2.05 (10.97)
64. Watches/clock/parts            2.13 (5.57)    1.03 (14.77)
65. Wheat                         -2.15 (0.55)    0.67 (0.89)
66. Wood manufactures             -8.58 (4.55)    2.56 (6.80)

Commodities (a)                      Ln RE          Ecm( - 1)

1. ADP equip.; office machines    -1.25 (4.23)    -0.31 (3.59)
2. Airplane parts                 -0.85 (0.54)    -0.11 (1.95)
3. Airplanes                       2.26 (3.96)    -0.77 (6.53)
4. Alcoholic bev., distilled       0.88 (4.91)    -1.03 (6.62)
5. Aluminum                       -0.34 (0.35)    -0.14 (2.09)
6. Animal feeds                   -1.15 (1.62)    -0.14 (2.02)
7. Artwork/antiques                0.75 (2.31)    -0.95 (11.01)
8. Basketware, etc.                1.42 (2.26     -0.16 (2.13)
9. Cereal flour                    0.14 (0.18)    -0.13 (1.24)
10. Chemicals                      0.51 (1.20)    -0.18 (2.50)
11. Cigarettes                     5.08 (1.45)    -0.12 (1.76)
12. Clothing                       0.16 (1.43)    -1.64 (8.46)
13. Coal                           0.91 (1.22)    -0.28 (3.35)
14. Copper                        -0.86 (1.78)    -0.27 (3.93)
15. Cork, wood, lumber            -0.02 (0.03)    -0.36 (5.30)
16. Corn                          -0.00 (0.00)    -1.52 (6.97)
17. Crude fertilizers/minerals    -0.48 (2.98)    -0.77 (6.74)
18. Electrical machinery          -1.32 (3.56)    -0.23 (3.89)
19. Fish and preparations          0.45 (2.74)    -0.51 (3.63)
20. Footwear                      -0.02 (0.11)    -0.64 (7.45)
21. Furniture and bedding          0.91 (0.40)    -0.11 (2.22)
22. Gem diamonds                   0.95 (2.82)    -0.63 (2.98)
23. General industrial mach.      -0.35 (0.68)    -0.20 (1.93)
24. Glass                         -0.17 (0.21)    -0.12 (1.60)
25. Glassware                     -0.32 (3.91)    -1.50 (7.94)
26. Gold, nonmonetary             -1.69 (2.50)    -0.43 (3.03)
27. Hides and skins               -0.92 (2.63)    -0.41 (3.63)
28. Iron and steel mill prod      -1.27 (2.93)    -0.27 (3.63)
29. Lighting, plumbing             0.56 (1.47)    -0.26 (3.12)
30. Liquified propane/butane      -0.58 (0.47)    -0.21 (3.86)
31. Live animals                  -0.08 (0.14)    -0.75 (8.09)
32. Meat and preparations          1.96 (4.22)    -0.99 (10.54)
33. Metal manufactures, n.e.s.     0.12 (0.29)    -0.16 (1.40)
34. Metal ores; scrap             -1.58 (5.91)    -0.43 (4.65)
35. Metalworking machinery        -2.44 (5.87)    -0.35 (4.10)
36. Mineral fuels, other           0.91 (0.40)    -0.11 (2.22)
37. Natural gas                    2.20 (2.20)    -0.20 (3.57)
38. Nickel                        -1.39 (2.16)    -0.43 (4.95)
39. Oils/fats, vegetable          -1.02 (3.14)    -0.62 (4.72)
40. Optical goods                  0.16 (0.53)    -0.40 (4.08)
41. Paper and paperboard          -0.48 (1.29)    -0.19 (3.16)
42. Petroleum preparations         1.35 (0.82)    -0.12 (2.05)
43. Photographic equipment        -0.73 (3.95)    -0.71 (7.17)
44. Plastic articles, n.e.s.       0.18 (0.33)    -0.11 (1.89)
45. Platinum                      -0.33 (0.26)    -0.29 (3.19)
46. Pottery                       -0.38 (2.78)    -0.66 (3.80)
47. Power generating mach.         0.45 (2.62)    -0.45 (4.09)
48. Printed materials             -0.05 (0.16)    -0.30 (2.51)
49. Pulp and waste paper          -1.86 (3.70)    -0.27 (4.62)
50. Records/magnetic media        -0.04 (0.18)    -0.44 (3.07)
51. Rice                          -0.45 (0.67)    -0.35 (2.70)
52. Rubber articles, n.e.s.        1.62 (1.22)    -0.11 (1.71)
53. Rubber tires and tubes         0.96 (2.59)    -0.29 (3.67)
54. Scientific instruments        -0.04 (0.12)    -0.30 (3.33)
55. Ships, boats                  -0.34 (0.43)    -0.74 (8.74)
56. Silver and bullion            -0.53 (1.36)    -0.61 (5.66)
57. Specialized ind. mach.         0.74 (0.26)    -0.10 (0.87)
58. Textile yarn, fabric           0.63 (2.40)    -0.31 (2.31)
59. Tobacco, unmanufactured        0.09 (0.09)    -0.40 (4.69)
60. Toys/games/sporting goods      0.29 (1.80)    -0.69 (3.70)
61. Travel goods                  -0.38 (2.73)    -0.80 (4.56)
62. Vegetables and fruits          0.12 (0.50)    -0.45 (2.33)
63. Vehicles                       0.61 (2.28)    -0.36 (2.96)
64. Watches/clock/parts           -0.32 (3.07)    -1.42 (8.30)
65. Wheat                          0.42 (0.40)    -0.33 (4.26)
66. Wood manufactures              0.56 (1.15)    -0.19 (2.03)

Commodities (a)                  Adj [R.sup.2]

1. ADP equip.; office machines            0.54
2. Airplane parts                         0.22
3. Airplanes                              0.46
4. Alcoholic bev., distilled              0.33
5. Aluminum                               0.23
6. Animal feeds                           0.32
7. Artwork/antiques                       0.49
8. Basketware, etc.                       0.39
9. Cereal flour                           0.48
10. Chemicals                             0.40
11. Cigarettes                            0.18
12. Clothing                              0.45
13. Coal                                  0.36
14. Copper                                0.23
15. Cork, wood, lumber                    0.17
16. Corn                                  0.39
17. Crude fertilizers/minerals            0.44
18. Electrical machinery                  0.19
19. Fish and preparations                 0.51
20. Footwear                              0.29
21. Furniture and bedding                 0.10
22. Gem diamonds                          0.71
23. General industrial mach.              0.33
24. Glass                                 0.29
25. Glassware                             0.41
26. Gold, nonmonetary                     0.36
27. Hides and skins                       0.31
28. Iron and steel mill prod              0.24
29. Lighting, plumbing                    0.57
30. Liquified propane/butane              0.10
31. Live animals                          0.39
32. Meat and preparations                 0.51
33. Metal manufactures, n.e.s.            0.49
34. Metal ores; scrap                     0.39
35. Metalworking machinery                0.38
36. Mineral fuels, other                  0.10
37. Natural gas                           0.08
38. Nickel                                0.30
39. Oils/fats, vegetable                  0.58
40. Optical goods                         0.32
41. Paper and paperboard                  0.35
42. Petroleum preparations                0.10
43. Photographic equipment                0.33
44. Plastic articles, n.e.s.              0.42
45. Platinum                              0.32
46. Pottery                               0.54
47. Power generating mach.                0.48
48. Printed materials                     0.33
49. Pulp and waste paper                  0.24
50. Records/magnetic media                0.29
51. Rice                                  0.31
52. Rubber articles, n.e.s.               0.44
53. Rubber tires and tubes                0.33
54. Scientific instruments                0.44
55. Ships, boats                          0.36
56. Silver and bullion                    0.37
57. Specialized ind. mach.                0.40
58. Textile yarn, fabric                  0.73
59. Tobacco, unmanufactured               0.22
60. Toys/games/sporting goods             0.71
61. Travel goods                          0.58
62. Vegetables and fruits                 0.64
63. Vehicles                              0.50
64. Watches/clock/parts                   0.47
65. Wheat                                 0.26
66. Wood manufactures                     0.32

Number inside the parentheses are absolute value of the t-ratios.

(a) ADP, automated data processing; n.e.s., not elsewhere specified.

Table 3. Long-Run Coefficient Estimates for Import and Export
Models Using Aggregate Data

Regressor        Import Model     Export Model

Constant         2.1907 (1.55)    2.9524 (2.72)
Ln [Y.sub.US]    1.8294 (6.59)         --
Ln [Y.sub.W]          --          2.4720 (13.1)
Ln RE            0.0549 (0.13)   -0.7928 (4.04)
Adj. [R.sup.2]       0.62             0.81

Note: Numbers inside the parentheses are absolute value of
the t-ratios.

Table 4. Long-Run Coefficient Estimates for Durables
versus Nondurables

                       Import Value Model

Regressor           Durables       Nondurables

Constant         -2.7295 (0.56)   0.8384 (1.42)
Ln [Y.sub.US]     1.7390 (2.92)   1.8335 (19.6)
Ln [Y.sub.W]           --               --
Ln RE             1.0922 (0.74)   0.1114 (0.69)
Adj. [R.sup.2]        0.77             0.49

                       Export Value Model

Regressor           Durables       Nondurables

Constant          2.6733 (1.71)    3.5119 (1.72)
Ln [Y.sub.US]          --               --
Ln [Y.sub.W]      2.6740 (13.1)    1.6105 (2.81)
Ln RE            -1.019 (3.81)    -0.297 (0.66)
Adj. [R.sup.2]        0.79             0.66

Numbers inside the parentheses are absolute value of the t-ratios.
Absolute value of the t-statistic to test equality of the exchange
rate coefficients in durables versus nondurables was calculated to
be 0.66 in the import model and 1.25 in the export model.

Table 5. Long-Run Coefficient Estimates
for Large versus Small Industries

                             Import Value Model

Regressor               Large               Small

Constant            -3.8983 (1.01)      0.8310 (0.85)
Ln [Y.sub.US]       2.0031 (5.85)       1.6195 (10.2)
Ln [Y.sub.W]              --                 --
Ln RE               1.0656 (1.04)       0.3437 (1.16)
Adj. [R.sub.2]           0.80               0.56

                               Export Value Model

Regressor               Large               Small

Constant            1.5535 (1.00)       4.8609 (4.71)
Ln [Y.sub.US]             --                 --
Ln [Y.sub.W]        2.7659 (12.6)       1.7439 (7.26)
Ln RE               -0.859 (3.50)       -0.736 (3.16)
Adj. [R.sub.2]           0.81               0.58

Numbers inside the parentheses are absolute value of
the t-ratios. Absolute value of the t-statistic to test
equality of the exchange rate coefficients in small
versus large industries was calculated to be 0.68 in
the import model and 0.36 in the export model.

Table 6. Long-Run Coefficient Estimates for
Durables versus Nondurables

                         Import Volume Model

Regressor           Durables       Nondurables

Constant         -3.3278 (2.77)   -4.9535 (15.1)
Ln [Y.sub.US]    1.8626 (8.03)    1.6828 (29.1)
Ln [Y.sub.W]           --               --
Ln RE            0.1283 (0.42)    0.5158 (5.81)
Adj. [R.sub.2]        0.74             0.59

                         Export Volume Model

Regressor           Durables       Nondurables

Constant         -0.1839 (0.05)   -7.8241 (0.63)
Ln [Y.sub.US]          --               --
Ln [Y.sub.W]     2.3876 (7.29)     0.239 (0.06)
Ln RE            -1.064 (1.65)    2.5182 (0.41)
Adj. [R.sub.2]        0.77            0.7266

Numbers inside the parentheses are absolute value of the
t-ratios. Absolute value of the t-statistic to test
equality of the exchange rate coefficients in durables
versus nondurables was calculated to be 1.18 in the
import model and 0.58 in the export model.

Table 7. Long-Run Coefficient Estimates of the Export Volume Model

Commodities (a)                      Constant          LYW

 1. ADP equip.; office machines     1.13 (0.90)     2.71 (10.66)
 2. Airplane parts                 -6.43 (4.44)     2.66 (6.34)
 3. Airplanes                      -5.08 (0.62)     1.50 (0.67)
 4. Alcoholic bev., distilled      -3.82 (3.51)     0.89 (3.22)
 5. Aluminum                       -0.71 (1.01)     2.47 (12.22)
 6. Animal feeds                    3.43 (1.52)     0.97 (1.47)
 7. Artwork/antiques               -2.38 (0.98)     0.71 (1.11)
 8. Basketware, etc.              -15.21 (7.57)     3.69 (6.12)
 9. Cereal flour                   -2.58 (1.81)     0.70 (1.82)
10. Chemicals                      -5.61 (3.14)     2.53 (5.24)
11. Cigarettes                     27.19 (3.56)    -0.42 (0.30)
12. Clothing                        6.70 (0.81)     2.45 (1.97)
13. Coal                           20.04 (4.73)    -0.65 (0.54)
14. Copper                          7.57 (4.05)     0.86 (1.57)
15. Cork, wood, lumber             17.36 (11.36)   -1.58 (3.79)
16. Corn                            8.70 (2.67)     1.01 (1.19)
17. Crude fertilizers/minerals      0.63 (1.46)     0.91 (7.82)
18. Electrical machinery           -8.39 (3.47)     3.85 (6.84)
19. Fish and preparations           8.06 (6.01)    -1.04 (2.95)
20. Footwear                        4.93 (2.63)     0.72 (1.85)
21. Furniture and bedding          -2.59 (0.66)     2.14 (2.91)
22. Gem diamonds                  -123.7 (1.35)    22.74 (0.95)
23. General industrial mach.       -4.09 (4.02)     2.65 (10.34)
24. Glass                         -10.01 (7.99)     2.85 (7.97)
25. Glassware                      -8.07 (8.00)     2.62 (9.79)
26. Gold, nonmonetary              22.67 (3.84)    -2.10 (1.53)
27. Hides and skins                -2.48 (0.33)     1.60 (0.78)
28. Iron and steel mill prod       -4.34 (3.91)     3.07 (8.91)
29. Lighting, plumbing              0.64 (0.28)     1.12 (1.93)
30. Liquified propane/butane       -9.47 (1.94)     2.88 (2.26)
31. Live animals                   -1.55 (0.80)     0.41 (0.86)
32. Meat and preparations          -1.54 (0.62)     2.64 (4.53)
33. Metal manufactures, n.e.s.     -9.55 (3.66)     3.85 (7.17)
34. Metal ores; scrap             -15.39 (0.76)     0.14 (0.05)
35. Metalworking machinery         -2.79 (2.28)     3.44 (12.05)
36. Mineral fuels, other            0.33 (0.15)     2.43 (3.94)
37. Natural gas                   -15.61 (2.45)    -1.74 (1.02)
38. Nickel                        -14.96 (8.07)     3.41 (4.80)
39. Oils/fats, vegetable            2.06 (0.32)     1.98 (1.16)
40. Optical goods                 -26.45 (12.33)    6.55 (9.19)
41. Paper and paperboard           -1.08 (0.49)     2.51 (5.13)
42. Petroleum preparations         -2.89 (0.95)     0.13 (0.17)
43. Photographic equipment          0.50 (0.59)     1.22 (5.01)
44. Plastic articles, n.e.s.      -15.23 (8.97)     3.88 (8.48)
45. Platinum                      -21.39 (5.29)     2.63 (2.59)
46. Pottery                         2.90 (1.33)    -0.08 (0.15)
47. Power generating mach.         -6.49 (4.87)     2.51 (6.73)
48. Printed materials               1.26 (2.72)     0.90 (7.21)
49. Pulp and waste paper            3.07 (1.02)     1.14 (1.52)
50. Records/magnetic media         14.77 (2.76)    -0.06 (0.06)
51. Rice                            5.71 (3.68)     1.08 (2.66)
52. Rubber articles, n.e.s.       -16.96 (14.46)    4.37 (13.14)
53. Rubber tires and tubes         -1.66 (0.30)     3.22 (3.15)
54. Scientific instruments         -9.07 (14.93)    3.17 (17.15)
55. Ships, boats                    2.55 (0.80)     0.16 (0.20)
56. Silver and bullion              2.60 (0.38)     3.32 (1.86)
57. Specialized ind. mach.         -1.49 (1.79)     2.65 (13.41)
58. Textile yarn, fabric           -7.30 (11.09)    2.51 (13.40)
59. Tobacco, unmanufactured         9.58 (6.55)    -0.74 (1.94)
60. Toys/games/sporting goods       3.44 (1.54)     1.63 (3.42)
61. Travel goods                   -5.48 (2.26)     2.93 (6.53)
62. Vegetables and fruits           1.75 (2.05)     0.98 (4.20)
63. Vehicles                       -2.76 (1.72)     2.28 (5.27)
64. Watches/clock/parts            -2.54 (1.02)     1.39 (1.95)
65. Wheat                          14.53 (5.22)    -0.95 (1.31)
66. Wood manufactures               1.62 (1.31)     1.20 (3.75)

Commodities (a)                        LRE           Ecm (-1)

 1. ADP equip.; office machines    -1.20 (3.94)    -0.46 (4.09)
 2. Airplane parts                  0.23 (0.75)    -0.26 (3.40)
 3. Airplanes                       1.22 (0.73)    -0.22 (2.13)
 4. Alcoholic bev., distilled       0.68 (3.20)    -0.98 (6.85)
 5. Aluminum                       -1.08 (6.61)    -0.53 (5.18)
 6. Animal feeds                   -0.45 (0.81)    -0.25 (2.90)
 7. Artwork/antiques                0.76 (1.57)    -0.73 (8.51)
 8. Basketware, etc.                0.75 (1.53)    -0.22 (2.61)
 9. Cereal flour                    0.84 (2.83)    -0.37 (4.71)
10. Chemicals                       0.52 (1.23)    -0.16 (1.76)
11. Cigarettes                     -4.14 (3.05)    -0.19 (1.92)
12. Clothing                       -2.47 (2.15)    -0.12 (1.96)
13. Coal                           -2.45 (2.67)    -0.18 (2.58)
14. Copper                         -1.45 (3.20)    -0.34 (3.97)
15. Cork, wood, lumber             -0.86 (2.78)    -0.32 (3.53)
16. Corn                           -1.54 (2.40)    -0.29 (4.66)
17. Crude fertilizers/minerals      0.00 (0.04)    -0.90 (6.89)
18. Electrical machinery           -0.21 (0.46)    -0.15 (2.26)
19. Fish and preparations           0.47 (1.78)    -1.15 (5.34)
20. Footwear                       -0.89 (2.78)    -0.36 (2.70)
21. Furniture and bedding          -0.33 (0.47)    -0.15 (1.80)
22. Gem diamonds                    4.69 (0.58)    -0.07 (1.05)
23. General industrial mach.       -0.10 (0.43)    -0.30 (3.03)
24. Glass                           0.40 (1.34)    -0.36 (2.71)
25. Glassware                      -0.00 (0.01)    -0.55 (5.93)
26. Gold, nonmonetary              -1.48 (1.36)    -0.48 (6.70)
27. Hides and skins                -0.03 (0.02)    -0.08 (1.58)
28. Iron and steel mill prod       -0.82 (2.63)    -0.40 (4.22)
29. Lighting, plumbing             -0.24 (0.59)    -0.28 (2.28)
30. Liquified propane/butane       -0.14 (0.15)    -0.39 (5.51)
31. Live animals                    0.76 (1.93)    -1.16 (4.05)
32. Meat and preparations          -0.94 (1.89)    -0.19 (2.65)
33. Metal manufactures, n.e.s.     -0.34 (0.59)    -0.18 (1.43)
34. Metal ores; scrap               4.36 (0.80)    -0.08 (1.07)
35. Metalworking machinery         -1.52 (6.46)    -0.49 (5.34)
36. Mineral fuels, other           -1.30 (2.35)    -0.28 (3.48)
37. Natural gas                     5.68 (3.44)    -0.34 (3.06)
38. Nickel                          0.51 (0.83)    -0.32 (2.60)
39. Oils/fats, vegetable           -1.43 (1.10)    -0.24 (3.34)
40. Optical goods                   0.21 (0.31)    -0.24 (2.69)
41. Paper and paperboard           -0.82 (1.93)    -0.16 (2.09)
42. Petroleum preparations          1.71 (2.82)    -0.39 (5.45)
43. Photographic equipment         -0.11 (0.53)    -0.66 (5.83)
44. Plastic articles, n.e.s.        0.70 (1.82)    -0.25 (2.93)
45. Platinum                        2.69 (3.46)    -0.52 (4.54)
46. Pottery                        -0.07 (0.17)    -0.37 (2.54)
47. Power generating mach.          0.55 (1.82)    -0.31 (2.62)
48. Printed materials               0.10 (1.03)    -0.77 (3.80)
49. Pulp and waste paper           -0.53 (0.91)    -0.18 (3.67)
50. Records/magnetic media         -1.76 (3.01)    -0.20 (1.90)
51. Rice                           -1.35 (4.42)    -0.86 (5.87)
52. Rubber articles, n.e.s.         0.26 (0.91)    -0.39 (3.48)
53. Rubber tires and tubes         -1.69 (1.21)    -0.12 (1.38)
54. Scientific instruments          0.40 (2.54)    -0.54 (4.26)
55. Ships, boats                    0.27 (0.43)    -0.85 (6.32)
56. Silver and bullion             -3.13 (2.32)    -0.44 (6.04)
57. Specialized ind. mach.         -0.68 (3.70)    -0.51 (5.63)
58. Textile yarn, fabric            0.47 (3.04)    -0.48 (2.74)
59. Tobacco, unmanufactured        -0.30 (0.98)    -1.16 (4.02)
60. Toys/games/sporting goods      -1.13 (2.97)    -0.27 (2.96)
61. Travel goods                   -1.04 (2.20)    -0.37 (2.26)
62. Vegetables and fruits           0.02 (0.14)    -0.52 (3.11)
63. Vehicles                        0.10 (0.31)    -0.51 (3.08)
64. Watches/clock/parts            -0.17 (0.29)    -0.32 (2.60)
65. Wheat                          -0.89 (1.61)    -0.34 (5.04)
66. Wood manufactures              -0.47 (1.94)    -0.39 (3.77)

Commodities (a)                   Adj [R.sub.2]

 1. ADP equip.; office machines       0.74
 2. Airplane parts                    0.41
 3. Airplanes                         0.40
 4. Alcoholic bev., distilled         0.36
 5. Aluminum                          0.47
 6. Animal feeds                      0.30
 7. Artwork/antiques                  0.35
 8. Basketware, etc.                  0.35
 9. Cereal flour                      0.28
10. Chemicals                         0.48
11. Cigarettes                        0.30
12. Clothing                          0.40
13. Coal                              0.35
14. Copper                            0.30
15. Cork, wood, lumber                0.35
16. Corn                              0.13
17. Crude fertilizers/minerals        0.38
18. Electrical machinery              0.52
19. Fish and preparations             0.59
20. Footwear                          0.42
21. Furniture and bedding             0.59
22. Gem diamonds                      0.35
23. General industrial mach.          0.47
24. Glass                             0.56
25. Glassware                         0.33
26. Gold, nonmonetary                 0.31
27. Hides and skins                   0.22
28. Iron and steel mill prod          0.42
29. Lighting, plumbing                0.29
30. Liquified propane/butane          0.19
31. Live animals                      0.44
32. Meat and preparations             0.20
33. Metal manufactures, n.e.s.        0.45
34. Metal ores; scrap                 0.46
35. Metalworking machinery            0.39
36. Mineral fuels, other              0.35
37. Natural gas                       0.43
38. Nickel                            0.47
39. Oils/fats, vegetable              0.21
40. Optical goods                     0.43
41. Paper and paperboard              0.42
42. Petroleum preparations            0.17
43. Photographic equipment            0.42
44. Plastic articles, n.e.s.          0.44
45. Platinum                          0.43
46. Pottery                           0.49
47. Power generating mach.            0.54
48. Printed materials                 0.41
49. Pulp and waste paper              0.35
50. Records/magnetic media            0.37
51. Rice                              0.35
52. Rubber articles, n.e.s.           0.44
53. Rubber tires and tubes            0.46
54. Scientific instruments            0.56
55. Ships, boats                      0.46
56. Silver and bullion                0.21
57. Specialized ind. mach.            0.43
58. Textile yarn, fabric              0.63
59. Tobacco, unmanufactured           0.41
60. Toys/games/sporting goods         0.38
61. Travel goods                      0.42
62. Vegetables and fruits             0.56
63. Vehicles                          0.45
64. Watches/clock/parts               0.38
65. Wheat                             0.15
66. Wood manufactures                 0.29

Numbers inside the parentheses are absolute value
of the t-ratio.

(a) ADP, automated data processing; n.e.s.,
not elsewhere specified.

Table 8. Long-Run Coefficient Estimates of Import Volume Model

Commodities (a)                      Constant           LYUS

 1. ADP equip.; office machines     1.33 (1.00)      2.38 (11.91)
 2. Airplane parts                 -5.06 (1.12)      2.64 (2.56)
 3. Airplanes                     -20.40 (11.13)     2.13 (5.88)
 4. Alcoholic bev., distilled      -4.75 (6.98)      1.13 (12.11)
 5. Aluminum                       -2.55 (0.75)      1.79 (2.25)
 6. Animal feeds                    0.38 (0.13)      1.43 (2.57)
 7. Artwork/antiques               -8.67 (7.25)      2.07 (8.75)
 8. Basketware, etc.               -8.38 (8.47)      2.00 (14.20)
 9. Cereal flour                   -8.65 (2.76)      2.00 (2.96)
10. Chemicals                      -7.07 (5.95)      2.35 (10.04)
11. Cigarettes                    -17.73 (1.44)     -1.23 (0.45)
12. Clothing                       -3.22 (6.34)      1.76 (29.32)
13. Coal                           -6.26 (2.42)      0.98 (1.87)
14. Copper                         -3.87 (2.84)      2.52 (9.42)
15. Cork, wood, lumber             -1.91 (1.09)      1.48 (4.47)
16. Corn                           -5.01 (2.21)      1.17 (2.99)
17. Crude fertilizers/minerals      0.01 (0.03)      1.21 (13.82)
18. Electrical machinery            1.65 (1.48)      2.52 (13.62)
19. Fish and preparations          -2.79 (5.65)      1.17 (12.39)
20. Footwear                        1.60 (4.37)      0.90 (12.76)
21. Furniture and bedding         -13.80 (7.83)      2.90 (15.46)
22. Gem diamonds                   -9.76 (8.20)      2.21 (11.90)
23. General industrial mach.       -1.73 (1.10)      1.93 (7.53)
24. Glass                          -6.10 (2.33)      2.02 (3.51)
25. Glassware                      -3.19 (11.46)     1.74 (40.02)
26. Gold, nonmonetary               4.82 (1.54)      1.54 (2.58)
27. Hides and skins                 7.13 (7.08)     -0.28 (1.54)
28. Iron and steel mill prod        4.13 (2.30)      1.60 (4.43)
29. Lighting, plumbing            -12.83 (11.54)     2.85 (18.07)
30. Liquified propane/butane       -1.94 (0.50)      1.50 (1.89)
31. Live animals                    1.72 (0.82)      0.50 (1.49)
32. Meat and preparations          -5.78 (3.30)      0.15 (0.53)
33. Metal manufactures, n.e.s.     -6.02 (3.12)      2.04 (7.41)
34. Metal ores; scrap               7.70 (7.60)      0.90 (4.50)

35. Metalworking machinery          4.27 (2.42)      2.57 (8.88)
36. Mineral fuels, other            0.15 (0.02)      0.02 (0.01)
37. Natural gas                   -17.25 (5.47)      2.38 (3.78)
38. Nickel                          2.25 (1.13)      1.53 (3.86)
39. Oils/fats, vegetable            2.96 (2.41)      1.04 (4.84)
40. Optical goods                  -5.56 (6.57)      1.94 (12.43)
41. Paper and paperboard           -0.42 (0.41)      1.70 (8.47)
42. Petroleum preparations         -8.46 (1.56)      1.59 (1.63)
43. Photographic equipment          1.68 (2.79)      1.38 (11.59)
44. Plastic articles, n.e.s.       -7.65 (2.74)      1.49 (2.97)
45. Platinum                      -11.26 (2.64)      3.55 (4.17)
46. Pottery                         2.82 (5.24)      0.58 (5.45)
47. Power generating mach.         -3.58 (6.09)      1.90 (22.78)
48. Printed materials              -3.66 (4.17)      1.61 (9.48)
49. Pulp and waste paper            7.27 (4.45)      1.24 (4.38)
50. Records/magnetic media         -0.47 (0.67)      1.10 (7.93)
51. Rice                           -0.75 (0.21)      1.74 (2.93)
52. Rubber articles, n.e.s.        -5.99 (2.40)      0.83 (0.91)
53. Rubber tires and tubes         -7.05 (3.83)      1.21 (4.43)
54. Scientific instruments         -6.43 (7.88)      2.53 (16.87)
55. Ships, boats                   -8.46 (2.93)      2.81 (4.95)
56. Silver and bullion             -0.54 (0.33)      1.20 (3.86)
57. Specialized ind. mach.         -2.73 (0.18)     -1.51 (0.19)
58. Textile yarn, fabric           -4.76 (8.37)      1.49 (19.78)
59. Tobacco, unmanufactured         6.30 (1.87)     -0.82 (1.23)
60. Toys/games/sporting goods      -2.73 (3.00)      1.58 (8.78)
61. Travel goods                   -1.12 (3.73)      1.49 (26.29)
62. Vegetables and fruits          -1.87 (1.46)      1.22 (4.80)
63. Vehicles                       -3.98 (5.38)      1.88 (15.91)
64. Watches/clock/parts             1.80 (3.38)      0.94 (9.56)
65. Wheat                          -2.95 (0.74)      0.53 (0.69)
66. Wood manufactures             -10.41 (4.80)      2.38 (5.20)

Commodities (a)                         LRE            Ecm(-1)

 1. ADP equip.; office machines    -0.80 (2.94)     -0.31 (2.82)
 2. Airplane parts                 -0.30 (0.21)     -0.10 (2.03)
 3. Airplanes                       3.52 (7.10)     -0.69 (6.08)
 4. Alcoholic bev., distilled       1.00 (5.60)     -1.47 (7.58)
 5. Aluminum                        0.07 (0.07)     -0.14 (2.03)
 6. Animal feeds                   -0.68 (0.95)     -0.14 (1.83)
 7. Artwork/antiques                0.99 (3.05)     -0.94 (10.93)
 8. Basketware, etc.                1.02 (3.88)     -0.39 (5.28)
 9. Cereal flour                    0.89 (0.96)     -0.11 (1.05)
10. Chemicals                       0.94 (2.92)     -0.23 (2.90)
11. Cigarettes                      5.50 (1.60)     -0.12 (1.79)
12. Clothing                        0.69 (5.42)     -1.62 (8.41)
13. Coal                            1.22 (1.68)     -0.30 (3.43)
14. Copper                         -0.51 (1.37)     -0.35 (4.43)
15. Cork, wood, lumber              0.29 (0.60)     -0.35 (5.26)
16. Corn                            0.23 (0.37)     -1.52 (6.99)
17. Crude fertilizers/minerals     -0.22 (1.82)     -0.84 (10.14)
18. Electrical machinery           -1.00 (3.53)     -0.28 (3.88)
19. Fish and preparations           0.80 (6.24)     -0.62 (4.33)
20. Footwear                        0.25 (2.56)     -1.10 (5.74)
21. Furniture and bedding           1.51 (3.45)     -0.23 (2.92)
22. Gem diamonds                    1.24 (4.04)     -0.71 (3.44)
23. General industrial mach.        0.10 (0.22)     -0.24 (1.99)
24. Glass                           0.34 (0.45)     -0.13 (1.51)
25. Glassware                      -0.03 (0.38)     -1.62 (8.01)
26. Gold, nonmonetary              -1.41 (1.72)     -0.35 (2.66)
27. Hides and skins                -0.75 (2.81)     -0.53 (3.89)
28. Iron and steel mill prod       -0.98 (2.03)     -0.25 (3.34)
29. Lighting, plumbing              1.08 (3.84)     -0.34 (3.73)
30. Liquified propane/butane       -0.13 (0.12)     -0.24 (4.07)
31. Live animals                    0.21 (0.36)     -0.78 (8.41)
32. Meat and preparations           2.25 (4.76)     -0.96 (10.22)
33. Metal manufactures, n.e.s.      0.72 (1.36)     -0.16 (1.43)
34. Metal ores; scrap              -1.30 (4.74)     -0.42 (4.52)
35. Metalworking machinery         -2.13 (4.97)     -0.34 (3.76)
36. Mineral fuels, other            0.98 (0.49)     -0.13 (2.36)
37. Natural gas                     2.60 (2.98)     -0.22 (3.82)
38. Nickel                         -1.06 (1.94)     -0.50 (5.37)
39. Oils/fats, vegetable           -0.70 (2.07)     -0.60 (4.43)
40. Optical goods                   0.39 (1.59)     -0.47 (4.11)
41. Paper and paperboard           -0.13 (0.46)     -0.23 (3.34)
42. Petroleum preparations          1.70 (1.21)     -0.13 (2.17)
43. Photographic equipment         -0.43 (2.60)     -0.78 (7.54)
44. Plastic articles, n.e.s.        1.51 (1.72)     -0.09 (1.45)
45. Platinum                       -0.01 (0.01)     -0.29 (3.16)
46. Pottery                        -0.13 (0.93)     -0.62 (3.34)
47. Power generating mach.          0.50 (3.16)     -0.67 (5.44)
48. Printed materials               0.35 (1.48)     -0.35 (2.48)
49. Pulp and waste paper           -1.61 (3.65)     -0.30 (4.63)
50. Records/magnetic media          0.25 (1.31)     -0.48 (3.04)
51. Rice                           -1.02 (1.31)     -0.26 (2.05)
52. Rubber articles, n.e.s.         1.51 (1.34)     -0.12 (1.61)
53. Rubber tires and tubes          1.49 (2.94)     -0.23 (3.26)
54. Scientific instruments          0.36 (1.57)     -0.40 (3.73)
55. Ships, boats                   -0.09 (0.11)     -0.74 (8.80)
56. Silver and bullion             -0.26 (0.60)     -0.56 (5.36)
57. Specialized ind. mach.          3.54 (0.35)     -0.05 (0.47)
58. Textile yarn, fabric            0.99 (6.45)     -0.56 (3.53)
59. Tobacco, unmanufactured         0.36 (0.39)     -0.40 (4.71)
60. Toys/games/sporting goods       0.54 (2.21)     -0.48 (2.96)
61. Travel goods                   -0.03 (0.43)     -1.20 (5.38)
62. Vegetables and fruits           0.57 (1.62)     -0.26 (1.76)
63. Vehicles                        0.92 (5.18)     -0.54 (3.93)
64. Watches/clock/parts            -0.14 (0.99)     -1.06 (8.13)
65. Wheat                           0.74 (0.69)     -0.32 (4.23)
66. Wood manufactures               1.13 (1.75)     -0.16 (1.95)

Commodities (a)                     Adj [R.sup.2]

 1. ADP equip.; office machines         0.55
 2. Airplane parts                      0.22
 3. Airplanes                           0.44
 4. Alcoholic bev., distilled           0.41
 5. Aluminum                            0.23
 6. Animal feeds                        0.31
 7. Artwork/antiques                    0.46
 8. Basketware, etc.                    0.37
 9. Cereal flour                        0.46
10. Chemicals                           0.43
11. Cigarettes                          0.18
12. Clothing                            0.45
13. Coal                                0.37
14. Copper                              0.26
15. Cork, wood, lumber                  0.17
16. Corn                                0.39
17. Crude fertilizers/minerals          0.42
18. Electrical machinery                0.21
19. Fish and preparations               0.51
20. Footwear                            0.33
21. Furniture and bedding               0.60
22. Gem diamonds                        0.72
23. General industrial mach.            0.35
24. Glass                               0.30
25. Glassware                           0.41
26. Gold, nonmonetary                   0.34
27. Hides and skins                     0.33
28. Iron and steel mill prod            0.23
29. Lighting, plumbing                  0.58
30. Liquified propane/butane            0.11
31. Live animals                        0.40
32. Meat and preparations               0.49
33. Metal manufactures, n.e.s.          0.48
34. Metal ores; scrap                   0.38
35. Metalworking machinery              0.37
36. Mineral fuels, other                0.11
37. Natural gas                         0.09
38. Nickel                              0.32
39. Oils/fats, vegetable                0.58
40. Optical goods                       0.34
41. Paper and paperboard                0.37
42. Petroleum preparations              0.11
43. Photographic equipment              0.33
44. Plastic articles, n.e.s.            0.46
45. Platinum                            0.32
46. Pottery                             0.53
47. Power generating mach.              0.49
48. Printed materials                   0.34
49. Pulp and waste paper                0.24
50. Records/magnetic media              0.29
51. Rice                                0.28
52. Rubber articles, n.e.s.             0.44
53. Rubber tires and tubes              0.31
54. Scientific instruments              0.49
55. Ships, boats                        0.37
56. Silver and bullion                  0.36
57. Specialized ind. mach.              0.40
58. Textile yarn, fabric                0.74
59. Tobacco, unmanufactured             0.22
60. Toys/games/sporting goods           0.69
61. Travel goods                        0.61
62. Vegetables and fruits               0.62
63. Vehicles                            0.52
64. Watches/clock/parts                 0.40
65. Wheat                               0.26
66. Wood manufactures                   0.28

Numbers inside the parentheses are absolute value of the t-ratios.

(a) ADP, automated data processing; n.e.s., not elsewhere specified.