The effects of currency fluctuations and trade integration on industry trade between Canada and Mexico

Research in Economics 64 (2010) 212–223

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The effects of currency fluctuations and trade integration on industry trade between Canada and Mexico✩ Mohsen Bahmani-Oskooee a,∗ , Marzieh Bolhassani b , Scott W. Hegerty c a

Center for Research on International Economics, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, United States

b

Department of Business and Economics, University of Wisconsin-Sheboygan, Sheboygan, WI 53801, United States

c

Department of Economics and Finance, Canisius College, Buffalo, NY 14201, United States

article

info

Article history: Received 30 October 2009 Accepted 14 May 2010 Keywords: Canada Mexico Industry trade Bounds testing

abstract Since studies of North American trade flows tend to focus on the United States as the main trading partner, trade between Canada and Mexico has received relatively little attention. Here, we examine bilateral trade flows for 62 Canadian export industries to Mexico and 45 import industries from Mexico to assess the effects of currency fluctuations and trade integration on these individual trade flows. We find that Mexico’s largest export industries respond to depreciation more than Canada’s largest export industries do. Both countries’ trade flows are influenced even more by trade integration. Since there is evidence of strong intra-industry trade between these two countries, we can attribute this effect to the exploitation of economies of scale. © 2010 University of Venice. Published by Elsevier Ltd. All rights reserved.

1. Introduction The ‘‘competitive devaluation’’, through which a country can improve its trade balance by weakening its currency, has long been of particular interest to domestic policymakers. Even under floating-rate regimes, leaders may choose not to intervene against a depreciation, with the hopes that net exports will increase as a result. But empirical studies on the effects of devaluation have not always shown this to be the case. As described by Bahmani-Oskooee and Bolhasani (forthcoming), these empirical studies tend to fall into four groups. The first involves testing the price elasticities of imports and exports to address whether the ‘‘Marshall-Lerner’’ condition is met. A devaluation lowers the price of exports, but if quantities increase enough to offset this, the overall trade balance will improve. The same result might occur if import quantities decrease enough to offset price increases. For either case, the elasticities must be sufficiently large. The Marshall-Lerner condition is met if the sum of the absolute values of the import and export price elasticities is greater than one. This condition is often not met in the short run, however, since trade contracts and market imperfections may lead to slow adjustment in trade quantities. The second group of studies incorporates the short run, assessing the time path of the overall trade balance. At first, a devaluation might initially decrease the value of exports and increase the value of imports. The resulting drop in the trade balance is overcome in the long run, leading the trade balance to follow a ‘‘J’’-shaped pattern. This branch of the literature is explained in detail and reviewed by Bahmani-Oskooee and Ratha (2004). The third family of studies investigates the socalled ‘‘S-Curve’’, which earns its name from the shape of the cross-correlation function of deviations (from the filtered

✩ Valuable comments of an anonymous referee are greatly appreciated. Any remaining error, however, is ours.

∗

Corresponding author. E-mail address: [email protected] (M. Bahmani-Oskooee).

1090-9443/$ – see front matter © 2010 University of Venice. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.rie.2010.05.001

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trend) of the real exchange rate with past, present, and future deviations of the trade balance. This approach, introduced by Backus et al. (1994), measures these correlation coefficients over a certain number of periods in the past and future. The ‘‘lower’’, left-hand-side portion of the S represents a process similar to that of the J-curve, where the trade balance decreases after an increase in the number of domestic currency units per dollar. The ‘‘upper’’, positive part of the curve represents the trade surplus that eventually results. This branch of the literature has only recently begun to be explored in detail. Also less developed is the fourth group of studies, which examines the effects of currency depreciation on the values of imports and exports rather than trade volumes. First introduced by Haynes and Stone (1983) and extended by Cushman (1987), this method looks at the inpayments and outpayments of a specific country, either at the aggregate level or with selected trade partners. This approach is particularly useful in studies that have disaggregated data at the industry level. Since time-series data are often available for dollar values of commodity trade, but not for commodity prices, it is not always possible to calculate trade quantities necessary for an elasticity-based empirical analysis. The majority of the literature on Mexico and Canada has been part of studies of US bilateral trade flows, using the first two approaches listed above. For Mexico–US trade, studies include Fullerton et al. (1997), Fullerton and Sprinkle (2005), McDaniel and Agama (2003), and Pacheco-López (2005). These have generally found that trade liberalization has helped to increase trade flows, and that exchange-rate changes have some impact at the bilateral level. Canada has been included in many multicountry studies of US trade with its main trade partners. These include Rose and Yellen (1989), Rose (1991), Marwah and Klein (1996), Bahmani-Oskooee and Niroomand (1998), Caporale and Chui (1999), and Bahmani-Oskooee et al. (2008). Many of these fail to find evidence of improvement after a devaluation. More recently, the S-curve approach has been applied in studies of major US trade partners, finding more evidence of a response. Bahmani-Oskooee and Ratha (2004), for example, show that US trade with Canada does indeed follow an ‘‘S’’ pattern. The fourth approach has been used less frequently in the case of Mexico than it has been for Canada, which has been included not only as a trading partner of the United States (Cushman, 1987), but also the UK (Bahmani-Oskooee et al., 2005) and even Japan (Bahmani-Oskooee and Goswami, 2004). Bahmani-Oskooee et al. (2005) study Canada’s bilateral trade with 20 partners, but Mexico is not included among them. Many recent studies of this type have been conducted at the bilateral level and not disaggregated further. The method has only recently been used to address the dynamics of disaggregated trade flows. Bahmani-Oskooee and Ardalani (2006) examine US trade flows for 66 individual industries, and the approach has also been used in commoditylevel analyses of bilateral trade between the United States and Canada or Mexico. These include Bahmani-Oskooee and Hegerty (2009), who investigate bilateral industry trade flows for 102 industries between the United States and Mexico; and Bahmani-Oskooee and Bolhasani (forthcoming), who look at this type of trade for 152 commodities between the United States and Canada. These studies generally find mixed results. Bahmani-Oskooee and Bolhasani (forthcoming) show that depreciation of the US dollar has a long-run effect on only 60 percent of US export industries (which comprise only onefifth of the value of total bilateral exports), while 68 percent of import industries (roughly a third of the total import value) register a significant impact. Bahmani-Oskooee and Hegerty (2009) also find that the long-run effects of devaluation are insignificant in many industries; they attribute this to strong intra-industry trade links, through which companies are able to mitigate their exposure to currency fluctuations. This study addresses this type of commodity trade, for 62 import industries and 45 export industries, between Canada and Mexico from 1973 to 2006. As the two smaller members of the North American Free Trade Agreement, both countries have a large amount of trade with the United States. Nevertheless, bilateral trade between Mexico and Canada is considerable (it valued over $20 billion in 2007). Trade patterns are expected to mimic Mexico–US trade more than they follow Canada-US trade, mostly due to Mexico’s role as a manufacturer of goods using imported inputs and as a source of relatively inexpensive labor. Table 1 shows Canada’s largest import and export industries vis-à-vis Mexico. While lumber-rich Canada is a leading exporter of paper products, and Mexico is a known clothing manufacturer, many products are both imported and exported in large quantities. In particular, Road Motor Vehicles has the largest share of both imports and exports. Telecommunications Apparatus and Office Machines make both Top Ten lists as well. We thus surmise that intra-industry trade is strong between Canada and Mexico, so that trade integration will increase trade more than a weakening currency will. Using the bounds testing approach to cointegration introduced by Pesaran et al. (2001), the short- and long-run effects of devaluation on industry trade flows are assessed. These effects are shown to be weak for most industries, but strong for certain key manufactures. The rest of the paper is organized in the following manner: Section 2 provides the models and the methodology. Section 3 gives the empirical results. The conclusion is presented in Section 4, and data and their sources are given in an Appendix. 2. The models and the methodology Applying the standard trade models of Goldstein and Khan (1976) or, more recently, those of Bahmani-Oskooee and Bolhasani (forthcoming), these flows are modeled as functions of the Canadian dollar/peso real exchange rate and the purchasing country’s real income. The real exchange rate is given as the nominal rate (Canadian dollars per peso, so that an increase represents a depreciation of the Canadian dollar), multiplied by the ratio of the Mexican Producer Price Index (PPI) to the Canadian PPI. We expect that increases in Canadian income will result in an increase in its import flows, while increases in the real exchange rate should lead to a decrease in these flows as they become relatively more expensive. The opposite sign for the exchange-rate coefficient is expected for Canadian exports, while Mexican income growth will still

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Table 1 Top ten Canadian import and export industries by percentage of total. Imports

%

732-Road motor vehicles 724-Telecommunications apparatus 714-Office machines

22.56 732-Road motor vehicles 17.36 581-Plastic materials, regenerd. cellulose 5.91 673-Iron and steel bars, rods, angles, shapes and sections, including sheet piling 4.80 719-Machinery and appliances-non electric 4.12 724-Telecommunications apparatus 3.38 641-Paper and paperboard

722-Electric power machinery and switch 821-Furniture 711-Power generating machinary and parts thereof, nes 861-Scientific, medical, and optical goods 719-Machinery and appliances-non electric 729-Other electrical machinery and apparatus 841-Clothing except fur clothing

Exports

3.32 2.99 2.76 2.14

%

674-Universals, plates and sheets of iron 734-Aircraft 714-Office machines 251-Pulp & waste paper

23.26 4.36 4.28 3.70 3.56 2.42 1.93 1.88 1.78 1.59

Note: The trade shares are calculated using commodity trade flows from source (a) in the Appendix. nes = not elsewhere specified.

have a positive effect. In addition, dummy variables are included to account for Mexico’s entry into the General Agreement on Tariffs and Trade in 1986 and the implementation of the North American Free Trade Agreement in 1994. Each industry is analyzed separately through cointegration analysis. Because not all variables will be integrated of order one (or nonstationary), we apply the Autoregressive Distributed Lag (ARDL) method of Pesaran et al. (2001). Not only can it be used when variables have mixed orders of integration, this method works well with small samples, and also provides short- and long-run coefficient estimates, as well as a cointegration test, within a single equation. The ARDL specification for each Canadian export and import equation is as follows:

∆ ln VXt = α1 + α2 GATTt + α3 NAFTAt +

n1 −

βj ∆ ln VXt −j +

j =1

n2 −

γj ∆ ln YtMex −j +

j =0

n3 −

δj ∆ ln REXt −j

j =0

+ θ1 ln VXt −1 + θ2 ln YtMex −1 + θ3 ln REXt −1 + ε1t

(1)

and

∆ ln VMt = α4 + α5 GATTt + α6 NAFTAt +

n1 −

λj ∆ ln VMt −j +

j =1

+ θ4 ln VXt −1 + θ5 ln YtCan −1 + θ6 ln REXt −1 + ε1t .

n2 − j =0

πj ∆ ln YtCan −j +

n3 −

ϑj ∆ ln REXt −j

j=0

(2)

In (1) it is assumed that the export earning of a Canadian industry (VX ) depends positively on the Mexican real income

(Y Mex ) and also positively on the real exchange rate (REX ). Similarly, in (2) it is assumed that outpayments or import value of each Canadian industry (VM) is positively related to Canada’s own real income (Y Can ) and negatively to the real exchange rate. Each equation incorporates the short-run dynamics (first-differenced variables), as well as the long-run equilibrium relationship (as the lagged level variables). This specification resembles that of Engle and Granger (1987), except for the fact that in place of a grouped ‘‘Error Correction’’ term that consists of a linear combination of the lagged variables, these equations include the lagged level variables individually. This allows for a cointegration test: In equilibrium, all of the difference terms should drop to zero, leaving only the lagged level terms. These terms thus can form a ‘‘cointegrating vector’’ if they are indeed jointly significant. To test for this significance, an F-test is conducted on each regression in Eqs. (1) and (2). If each set of θ terms is jointly significant, we can say that the variables are cointegrated. Pesaran et al. (2001) provide a set of critical values for this non-standard version of the F-test. The first, or ‘‘lower bound’’, is an F-statistic below which the variables will be considered not to be cointegrated. The second, ‘‘upper bound’’ is the threshold above which the variables are considered to be cointegrated. If the F-statistic lies in-between, we perform an alternative test in which we group the fitted values of the ‘‘cointegrating vector’’ of lagged level variables into a single error-correction term and test the significance of the coefficient of the lagged error-correction term. If it is significantly negative, we can say that any shock is ‘‘canceled out’’ and the variables are forced back to a stable long-run equilibrium. Thus, Eqs. (1) and (2) are able to provide short-run estimates for changes in income and prices on the value of Canadian exports and imports, as well as a vector with the long-run effects (which can be normalized to provide long-run coefficients) and a test for cointegration among the variables. We proceed to test Eq. (1) for 45 Canadian export industries, and Eq. (2) for 62 Canadian import industries.1

1 For other applications of this approach see Halicioglu (2007), Narayan et al. (2007), Tang (2007), Mohammadi et al. (2008), Wong and Tang (2008), De Vita and Kyaw (2008), Payne (2008), and Bahmani-Oskooee and Gelan (2009).

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3. Results Annual data that cover the period from 1973 to 2006 are used to estimate the models. Fortunately, export and import values for each industry come in terms of reserve currency, i.e., the US dollar. This makes our interpretation easy since inpayments of a Canadian industry which is outpayments of the same industry by Mexico are measured in terms of a foreign currency. The US dollar values of trade flows are available for a total of 62 Canadian import and 45 Canadian export industries. These are the industries for which continuous data over the period 1973–2006 were available (see Appendix for details). Following the literature, the lag lengths n in models (1) and (2) are selected from a maximum of four by minimizing the Akaike Information Criterion. At optimum lags, our first step is to test for long-run relationships among the variables. As mentioned in the previous section, this amounts to applying the F test to determine the joint significance of lagged level variables in each model associated with each of 45 export industries and 62 import industries. The results are given in Table 2. As per Pesaran et al. (2001), the lower-bound critical value for the F-statistic is 3.17 at the 10 percent level. In the models associated with nine export industries and 14 import industries the calculated F statistics are below this bound, and thus variables are found not to be cointegrated. The upper bound is 4.14; variables in 32 export industries and 40 import industries are thus shown by this test to be cointegrated. The F statistic in four export industries and eight import industries lie in the ‘‘intermediate’’ range between the bounds. For these, the alternative ECM test suggests cointegration. We thus proceed to assess the short-run results for all industries and the long-run effects for the 36 export and 48 import industries where cointegration is established. We begin with an analysis of Canadian exports. The short-run results are provided only for changes in the real exchange rate; these and all long-run coefficients are given in Table 3. Only about half of the industries (23 of 45) given in Table 3 show evidence of a significant short-run response of trade flows to changes in the real exchange rate. The signs vary as well, and even alternate. Canada’s largest export industry, 732-Road Motor Vehicles (making up 23% of the total), follows an ‘‘inverted U’’ path with negative coefficients at zero and two lags, but a positive response at one lag. These effects do not carry into the long run for this industry; the long-run response to real-exchange-rate changes is insignificant. In fact, while Mexican income has its expected positive effect on three-quarters of Canada’s cointegrated export industries (27 of 36 cointegrated industries), a real exchange-rate depreciation has its expected positive coefficient on only 10 commodities. These industries are all very small, making up nine percent of total trade. A further ten industries, also small (8.5 percent of total trade), have significantly negative coefficients. Overall, this analysis shows that these trade flows are relatively insensitive to exchange-rate movements. Those industries with long-run cointegrating relationships among the variables make up only 55 percent of total exports, and this total is dominated by one large industry (Road Motor Vehicles). Since the North American auto industry is known for its high levels of industry integration, it is no surprise that this commodity shows little effect in the long run. More industries register significant shifts due to trade integration than to exchange-rate changes. The export flows of 14 Canadian commodities are influenced by Mexico’s 1986 accession to GATT, indicating that Canada was indeed affected by Mexico’s efforts to lower its trade barriers vis-à-vis the entire world. In addition, NAFTA had an impact on 14 industries (five of which were also influenced by GATT), highlighting the effects of regional integration. Most interesting is that Road Motor Vehicles saw its trade flows increase through both rounds of increased economic integration. As suggested by Bahmani-Oskooee and Hegerty (2009), trade flows in highly integrated industries and trading blocs might be less sensitive to exchange-rate fluctuations, but exploiting economies of scale through larger markets might be a more effective way to increase trade. This highly important, highly integrated industry appears to be an example of this effect. Diagnostic statistics are given in Tables 4 and 6. Three tests are performed: The Lagrange Multiplier test for autocorrelation in the presence of lagged variables, the Ramsey RESET test for specification, and the Cumulative Sum of residuals and squared residuals (CUSUM and CUSUMSQ) tests for model stability. On the whole, these models appear to be autocorrelation-free, correctly specified, and stable. In addition, the high adjusted R2 shows that most models has high explanatory power. Next, we examine the effects of income increases, currency fluctuations, and trade integration on Canadian imports from Mexico. Many in the United States have feared that their industries have suffered because of increasing competition from Mexico; while Canada does not share a border with the country, Mexico still might serve as a growing source of imported goods after the implementation of NAFTA. We examine a larger number of Canadian import industries than her export industries due to data availability, and those that we investigate sum to 70 percent of total trade. The results from our estimation of Eq. (2) are given in Table 4, and the Diagnostic Statistics are provided in Table 5. Our first finding is that the price effect is relatively stronger for Mexican exports to Canada than it was for Canadian exports to Mexico, and the other determinants are relatively weaker. Income growth has a positive effect on a smaller percentage of the industries (31 of 48, or 65 percent, compared to three-fourths of the cointegrated Canadian export industries). Exchange-rate fluctuations have an effect on more, and larger, Canadian import industries. In the short run, larger share of the import industries – 40 of 65 – have at least one significant ∆lnREX coefficient. Only about half of Canada’s export industries showed such a response. The relatively larger influence of exchange-rate fluctuations on Canadian imports compared to exports carries into the long run as well. We expect that an increase in the Canadian dollar/peso exchange rate will decrease Canada’s outpayments or her import values. This is equivalent to saying that a peso

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Table 2 Cointegration test results. Industry code and name

Canadian imports F

001-Live animals 022-Milk and cream 031-Fish, fresh & simply preserved 051-Fruit, fresh, and nuts-excluding oil 053-Fruit, preserved and fruit preparation 054-Vegetables, roots & tubers, fresh or dried 055-Vegetables, roots & tubers preserved or prepared 071-Coffee 075-Spices 099-Food preparations, nes 112-Alcoholic beverages 221-Oil-seeds, oil nuts and oil kernels 231-Crude rubber-including synthetic & reclaimed 243-Wood, shaped or simply worked 251-Pulp & waste paper 273-Stone, sand and gravel 276-Other crude minerals 291-Crude animal materials, nes 292-Crude vegetable materials, nes 512-Organic chemicals 513-Inorganic chemical elements, oxides and halogen salts 514-Other inorganic chemicals 541-Medicinal & pharmaceutical products 551-Essential oils, perfume and flavour 581-Plastic materials, regenerd. cellulos 599-Chemical materials and products, nes 612-Manufacturing of leather or composition leather, nes 621-Materials of rubber 629-Articles of rubber, nes 632-Wood manufactures, nes 641-Paper and paperboard 642-Articles of paper, pulp, paperboard 651-Textile yarn and thread 653-Text fabrics woven excluding narrow or special fabrics 655-Special textile fabrics and related products 656-Made-up articles, wholly or chiefly 657-Floor coverings, tapestries, etc. 661-Lime, cement, and fabricated construction materials, except glass and clay materials 662-Clay and refractory construction materials 663-Mineral manufactures, nes 664-Glass 665-Glassware 666-Pottery 667-Pearls, precious and semi-precious stones 673-Iron and steel bars, rods, angles, shapes and sections, including sheet piling 674-Universals, plates and sheets of iron 678-Tubes, pipes and fittings of iron ore 683-Nickel 691-Finished structural parts 694-Nails, screws, nuts, bolts, rivets and similar articles of iron, steel, copper or aluminum 695-Tools for use in the hand or in machines 697-Household equipment of base mtals

ECMt −1

Canadian exports Cointegrated?

22.86 7.09 7.33 2.78

−1.03 (6.49) −0.38 (4.04) −0.30 (2.54) −0.54 (2.82)

Yes Yes Yes No

5.07

−0.63 (3.54)

Yes

11.70 10.19 4.84 11.19 2.75

−0.91 (4.36) −1.09 (5.43) −0.63 (4.04) −1.24 (4.96) −0.11 (2.53)

Yes Yes Yes Yes No

7.96

−0.52 (2.71)

Yes

3.69 2.05

−0.38 (2.62) −0.38 (2.39)

Yes No

5.63 4.94 11.55

−0.41 (2.85) −0.64 (3.06) −1.09 (4.82)

Yes Yes Yes

3.94 3.4 3.12 9.01

−0.61 (2.44) −0.32 (1.83) −1.36 (4.39) −0.88 (4.32)

Yes Yes No Yes

9.24

−1.36 (5.46)

Yes

3.11

−0.41 (2.23)

No

0.79 10.9 2.67

−0.10 (3.65) −0.90 (5.88) −0.52 (3.02)

No Yes No

8.24

−0.49 (4.45)

Yes

10.14 7.69 7.24

−0.82 (3.82) −0.56 (3.37) −0.71 (4.28)

Yes Yes Yes

6.23

−0.36 (4.80)

Yes

15.37 1.69 3.82 4.46 13.44

−0.43 (6.06) −0.33 (2.10) −0.59 (2.91) −0.24 (2.88) −1.28 (6.74)

Yes No Yes Yes Yes

15.85

−1.16 (4.73)

Yes

1.99

−0.16 (2.86)

No

F

ECMt −1

Cointegrated?

2.99 4.60

−0.64 (3.25) −0.25 (1.04)

No Yes

5.73

−0.85 (4.45)

Yes

7.53

−1.46 (4.52)

Yes

7.93 2.35

−0.45 (2.17) −0.37 (2.65)

Yes No

5.22 11.54

−0.53 (3.35) −0.62 (2.65)

Yes Yes

6.8 3.55

−0.48 (4.33) −0.45 (2.78)

Yes Yes

4.08 2.46

−0.66 (3.28) −1.02 (6.34)

Yes No

5.53 5.05

−1.02 (2.43) −1.15 (4.09)

Yes Yes

3.51 5.11 1.93

−0.58 (3.88) −1.08 (4.69) −0.24 (3.60)

Yes Yes No

6.53

−0.41 (1.62)

Yes

9.88

−1.20 (5.54)

Yes

8.59

−2.58 (5.39)

Yes

2.98 8.04 4.93 5.3 3.01

−0.74 (3.69) −1.06 (5.30) −0.80 (3.85) −0.88 (4.41) −0.66 (4.19)

No Yes Yes Yes No

2.12

−0.21 (2.39)

No

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Table 2 (continued) Industry code and name

698-Manufactures of metal, nes 711-Power generating machinery, other than rotating electric plant of power generating machinery 714-Office machines 715-Metalworking machinery 717-Textile and leather machinery 718-Machines for special industries 719-Machinery and appliances-nonelectrical 722-Electric power machinery and switch 723-Equipment for distributing electricity, nes 724-Telecommunications apparatus 725-Domestic electrical equipment 729-Other electrical machinery and apparatus 732-Road motor vehicles 733-Road vehicles other than motor vehicle 734-Aircraft 812-Sanitary, plumbing, heating & lighting 821-Furniture 831-Travel goods, handbags and similar 841-Clothing except fur clothing 851-Footwear 861-Scientific, medical, and optical goods 891-Musical instruments, sound rcorders 892-Printed matter 893-Articles of artificial plastic material 894-Perambulators, toys, games, sporting goods 896-Works of art, collectors’ pieces and antiques 897-Jewellery and gold/silver-smiths wares 899-Manufactured articles, nes

Canadian imports

Canadian exports

F

ECMt −1

Cointegrated?

F

ECMt −1

Cointegrated?

5.55 11.1

−0.79 (4.04) −1.76 (3.88)

Yes Yes

4.49 5.21

−1.41 (3.60) −0.82 (2.94)

Yes Yes

5.6

−0.64 (6.37)

Yes

7.38

−0.77 (4.91)

Yes

3.05

−0.30 (2.21)

No

5.86 0.78 9.4 2.59 13.24

−1.58 (3.09) −0.30 (1.28) −1.07 (6.16) −0.41 (2.79) −2.04 (9.76)

Yes No Yes No Yes

4.42

−0.15 (3.85)

Yes

6.16 7.27

−0.44 (2.57) −0.90 (4.77)

Yes Yes

6.92 6.26 3.09

−0.83 (5.05) −1.07 (4.50) −0.26 (2.90)

Yes Yes No

4.33

−0.44 (3.75)

Yes

5.97

−1.12 (4.43)

Yes

5.74 12.3

−0.47 (2.05) −1.02 (5.76)

Yes Yes

21.81

−1.15 (9.38)

Yes

13.92 4.03 2.92 5.68 7.03 1.97 28.36 3.89 5.7 11.16 3.75

−2.03 (5.87) −1.45 (4.02) −0.18 (2.65) −1.17 (4.04) −0.52 (4.19) −0.55 (3.87) −0.90 (7.00) −0.28 (2.25) −1.13 (3.37) −0.71 (2.86) −0.36 (2.22)

Yes Yes No Yes Yes No Yes Yes Yes Yes Yes

7.39 5.53

−1.35 (7.29) −1.57 (4.57)

Yes Yes

3.89

−0.57 (3.74)

Yes

7.65

−1.10 (4.98)

Yes

6.91 5.29

−1.25 (4.18) −0.72 (4.03)

Yes Yes

2.7

−0.57 (2.97)

No

3.29

−0.64 (2.97)

Yes

9.69

−0.43 (2.23)

Yes

7.86

−1.40 (2.48)

Yes

Notes: The critical values of the F-test for cointegration are 3.17 (lower bound) and 4.14 (upper bound) at the 10% level of significance. Source: Pesaran et al. (2001, Table CI, p. 300). nes = not elsewhere specified. The numbers inside parentheses are the absolute values of the t-ratios.

depreciation should lead to an increase in Mexican export earnings. This is indeed the case for nine industries, including the very large industries of Road Motor Vehicles and Telecommunications Apparatus as well as Office Machines and Power Generating Machinery. These nine industries add up to 50 percent of total trade. Even though 17 Mexican export industries show a significantly positive response to a peso depreciation, the fact that they together make up less than eight percent of the country’s exports suggests that peso depreciation will result in an overall increase in Mexico’s exports to Canada. A peso devaluation would help Mexico’s trade balance more than a Canadian dollar devaluation would help Canada. Trade liberalization has a significant impact on even more industries than do currency movements: 20 industries show a shift due to GATT, and 24 do so because of NAFTA. Seven industries are influenced by both. Most importantly, Road Motor Vehicles shows an increase after both GATT and NAFTA, and Telecommunications Apparatus registers a positive response to NAFTA. Clearly, North American economic integration is expanding trade for these large, interconnected industries. Finally, diagnostics reported in Table 5 support autocorrelation free residuals, correctly specified models and stale short-run and long-run coefficient estimates in majority of the models.2 4. Conclusion Although the United States is studied more often, the trading relationship between Canada and Mexico is an important one. Few studies have looked specifically at these two countries’ trade flows, particularly using disaggregated, industry-level data. This study performs the first such analysis, examining Canada’s export and import flows with Mexico for 45 export

2 As an additional exercise we replaced real income in both models by real per capita income to determine whether population growth has any impact on the results. Since there were no significant changes, rather than reporting the new results, we make them available upon request.

0.4 (0.54) −0.16 (0.65) −0.61 (1.96) −0.33 (1.62) −0.04 (0.13) −0.71 (1.44) −2.02 (2.79) 1.42 (2.35) 0.11 (0.63) 0.89 (1.84) −0.39 (1.1) 0.43 (0.48) −0.62 (1.00) 0.29 (0.80) 1.28 (2.05) −0.67 (0.98) −1.22 (2.04) −1.38 (1.76) 0.12 (0.30) 0.42 (0.80) 1.23 (1.41) −0.23 (0.26) −1.99 (2.08) 0.33 (1.44) −0.49 (1.46) 0.73 (0.99) 0.57 (1.98) 0.02 (0.04) 0.15 (0.27) −0.19 (0.88) −0.25 (0.83) −0.08 (0.52) −0.08 (0.21) 0.4 (0.55) −0.16 (0.34) 0.61 (1.82) −0.34 (2.44) −0.47 (0.59) −0.26 (0.33) 0.67 (1.34) −0.32 (0.82) −0.72 (1.9) −0.36 (0.8) −0.26 (0.43)

−0.29 (0.56)

0.3 (1.04)

0.3 (0.96)

−1.88 (1.94) −0.32 (0.77)

−0.54 (0.95) 0.1 (0.47)

0.63 (1.22)

0.95 (2.01)

−0.24 (1.7)

−1.11 (1.96) −1.31 (3.06)

−0.32 (0.28)

0.37 (0.7)

0.74 (1.24)

−0.87 (2.58)

−0.62 (1.16)

0.25 (0.62) 0.34 (2.16)

−0.87 (1.69)

0.11 (0.23) 0.09 (0.09) 1.44 (1.77)

1.33 (2.77)

0.1 (0.12)

0.4 (0.39)

−2.48 (4.5)

−0.69 (2.94)

−1.09 (2.44)

−1.16 (1.62)

1.42 (1.66)

−3.42 (4.61)

0.17 (0.61)

−0.99 (1.01)

−0.39 (1.58)

−1.72 (2.25)

1.1 (1.35)

1.18 (1.69) 1.82 (2.14) 2.45 (3.50) 1.59 (3.33) −1.69 (2.85) 8.11 (3.33) 6.32 (2.63) −10.5 (2.27) 5.56 (1.48) 3.54 (2.98) 4.99 (2.11) 3.35 (1.20) 7.15 (5.47) 4.39 (3.68) 5.62 (2.88) 7.42 (4.19) −5.41 (0.67) −0.76 (0.61) −0.58 (0.29) 5.70 (11.94) 0.85 (0.31) 2.03 (0.83) −3.16 (0.95) 6.25 (2.64) 4.25 (2.70) 2.15 (0.70) 5.45 (6.30) 2.42 (3.64) 1.26 (2.27) 5.81 (1.35) 3.04 (5.24) 1.65 (0.66) 3.06 (7.94) 2.71 (2.71) 4.25 (2.08) 8.80 (2.35) 3.81 (4.26) 3.93 (10.39) 6.59 (2.45) 3.42 (2.73) 4.83 (2.39) 2.19 (2.22) 5.34 (7.61) 5.49 (3.67) 2.72 (6.09) 3.83 (2.76) 1.09 (1.36) −0.35 (0.58) −0.23 (0.36) 1.04 (2.34) −0.16 (0.50) 1.65 (3.11) 0.83 (1.98) 6.60 (1.35) −0.90 (2.11) −0.81 (1.64) −0.33 (2.64) −0.04 (0.06) 1.94 (3.24) 0.05 (0.07) 1.03 (1.24) 0.18 (0.34) 3.63 (1.52) 0.01 (0.05) 0.60 (1.84) −0.67 (2.22) −0.41 (0.30) 0.67 (2.07) 0.75 (0.99) 0.26 (2.11) 1.75 (2.91) 2.00 (3.66) −1.12 (1.01) 0.29 (0.95) −0.18 (1.55) 0.31 (0.46) 0.07 (0.24) 0.89 (1.44) 0.90 (1.07) −0.43 (2.07) 0.67 (1.01) −1.34 (5.10)

−1.62 (1.81)

1.89 (1.37)

0.69 (0.69)

−0.43 (0.62) −0.74 (3.97) −0.36 (2.93) −1.19 (5.03) −0.20 (0.28)

2.16 (0.63)

−2.94 (1.47)

lnREX

Long-run coefficient estimates

∆lnREXt −3 lnY Mex

∆lnREXt −2

∆lnREXt

∆lnREXt −1

Short-run coefficient estimates

Note: Absolute values of the t-ratios are in parentheses.

001 022 231 251 276 291 512 513 514 541 581 599 621 629 641 642 651 655 663 673 674 678 683 691 694 695 698 711 714 715 717 718 719 722 723 724 729 732 734 812 841 861 892 893 899

Industry

Table 3 Short-run and long-run coefficient estimates for Canadian export equations.

1.82 (0.14) 21.21 (2.68) −3.25 (1.16) 1.81 (0.97) 13.58 (6.21) −27.62 (2.79) −14.87 (1.82) 44.65 (2.62) −6.51 (0.44) −5.12 (1.19) −12.42 (1.31) −7.26 (0.67) −21.59 (4.23) −11.54 (2.36) −9.81 (1.28) −23.13 (3.29) 33.40 (0.90) 7.15 (1.46) 5.72 (0.72) −15.90 (8.50) 6.42 (0.60) 0.94 (0.10) 19.27 (1.47) −17.66 (1.88) −9.37 (1.52) 3.55 (0.25) −14.81 (4.19) 1.82 (0.70) 2.17 (0.96) −17.30 (1.00) −4.72 (1.96) 3.93 (0.38) −2.61 (1.65) −1.10 (0.28) −7.72 (0.95) −31.50 (2.03) −5.61 (1.65) −6.19 (4.10) −18.02 (1.69) −8.99 (1.83) −14.27 (1.80) 0.71 (0.16) −17.85 (6.55) −17.01 (2.68) −8.43 (4.69)

Constant 0.78 (0.89) −0.14 (0.47) −0.02 (0.10) 0.57 (3.22) −0.40 (0.48) −1.54 (1.20) 7.01 (2.79) 0.31 (0.19) −0.28 (0.43) 1.58 (1.41) 2.70 (2.07) −1.57 (2.95) 0.20 (0.42) −1.82 (2.23) −0.70 (0.92) −1.74 (0.58) 2.44 (4.76) 1.92 (2.27) −0.72 (3.72) −2.03 (1.78) 0.88 (0.86) 0.40 (0.30) −1.54 (1.51) 0.41 (0.55) 0.31 (0.25) −0.68 (1.88) −0.50 (1.72) 1.07 (4.54) −0.74 (0.39) 0.44 (1.70) 0.00 (0.00) 0.56 (4.01) −0.06 (0.13) −0.15 (0.17) −1.82 (1.14) −0.30 (0.78) 0.30 (1.94) −4.46 (3.94) −0.89 (1.68) 0.84 (0.97) 0.26 (0.58) 0.30 (1.05) 0.27 (0.46) 0.69 (3.64)

−3.79 (1.95)

NAFTA

0.33 (0.76) 0.36 (0.49) 0.72 (1.15) 15.46 (1.55) 0.16 (0.25) −0.43 (0.58) −0.36 (2.24) 0.22 (0.24) 0.73 (0.82) 0.92 (0.85) −0.14 (0.12) −2.09 (2.63) 3.44 (1.05) −0.48 (1.47) −0.41 (1.61) −0.56 (1.28) −2.69 (1.76) 0.24 (0.51) −0.31 (0.30) 0.37 (1.96) 2.29 (2.04) 0.92 (1.15) 1.44 (1.24) −0.60 (1.64) 0.36 (2.10) 0.93 (0.95) −0.87 (1.99) 1.52 (1.90) 0.45 (0.49) −0.11 (0.47) 1.63 (1.95) −0.68 (1.66)

−2.07 (2.08) −0.50 (0.55) −0.66 (1.42)

1.24 (1.17)

−1.47 (1.96) −2.59 (1.89)

1.27 (1.17)

−0.90 (2.24) −0.73 (0.87)

0.60 (3.52)

−0.22 (0.22) −0.56 (2.04)

0.12 (0.10)

GATT

0.05 0.12 0.29 1.59 0.12 0.65 0.46 0.02 0.02 0.39 4.36 1.58 0.02 0.12 2.42 0.92 0.13 0.20 0.03 4.28 1.93 0.16 0.02 0.20 0.16 0.26 0.45 0.66 1.78 0.09 0.19 0.62 3.70 0.85 0.28 3.56 0.67 23.26 1.88 0.01 0.21 0.88 0.21 0.64 0.06

Share (%)

218 M. Bahmani-Oskooee et al. / Research in Economics 64 (2010) 212–223

M. Bahmani-Oskooee et al. / Research in Economics 64 (2010) 212–223

219

Table 4 Diagnostic statistics for Canadian export equations. Industry

LM

RESET

CUSUM

CUSUMSQ

Adj.R2

001-Live animals 022-Milk and cream 231-Crude rubber-including synthetic & reclaimed 251-Pulp & waste paper 276-Other crude minerals 291-Crude animal materials, nes 512-Organic chemicals 513-Inorganic chemical elements, oxides and halogen salts 514-Other inorganic chemicals 541-Medicinal & pharmaceutical products 581-Plastic materials, regenerd. cellulos 599-Chemical materials and products, nes 621-Materials of rubber 629-Articles of rubber, nes 641-Paper and paperboard 642-Articles of paper, pulp, paperboard 651-Textile yarn and thread 655-Special textile fabrics and related 663-Mineral manufactures, nes 673-Iron and steel bars, rods, angles, shapes and sections, including sheet piling 674-Universals, plates and sheets of iron 678-Tubes, pipes and fittings of iron ore 683-Nickel 691-Finished structural parts 694-Nails, screws, nuts, bolts, rivets and similar articles of iron, steel, copper or aluminum 695-Tools for use in the hand or in machines 698-Manufactures of metal, nes 711-Power generating machinery, other than rotating electric plant of power generating machinery 714-Office machines 715-Metalworking machinery 717-Textile and leather machinery 718-Machines for special industries 719-Machinery and appliances-nonelectrical 722-Electric power machinery and switch 723-Equipment for distributing electricity, nes 724-Telecommunications apparatus 729-Other electrical machinery and apparatus 732-Road motor vehicles 734-Aircraft 812-Sanitary, plumbing, heating & lighting 841-Clothing except fur clothing 861-Scientific, medical, and optical goods 892-Printed matter 893-Articles of artificial plastic material 899-Manufactured articles, nes

1.98 1.77 1.19 2.91 0.10 0.00 0.35 2.59 0.10 5.69 0.13 2.80 0.02 1.89 0.27 0.80 0.00 0.32 0.03 0.19 0.12 5.22 0.07 0.74 0.30 0.80 0.13 0.11

0 2.81 7.99 0.87 2.74 2.03 0.98 16.50 4.55 0.15 0.02 4.32 1.09 0.00 1.56 4.02 0.49 5.54 0.04 0.08 5.16 1.27 7.64 0.22 2.22 0.93 3.78 5.13

S S S S S S S U S S S S S S S S S U S S S S S S S S S S

S U S S S S S S S S U S S S S S S S U U S S S S S S S S

0.67 0.04 0.37 0.36 0.58 0.44 0.43 0.48 0.34 0.48 0.39 0.66 0.46 0.35 0.47 0.43 0.75 0.11 0.47 0.57 0.25 0.49 0.31 0.43 0.54 0.57 0.28 0.40

6.91 0.09 1.71 1.55 2.27 0.17 0.79 0.82 0.72 4.73 0.06 0.12 1.94 7.75 6.96 0.67 5.62

S S S S S S S S S S S S S S S S S

S S S S S S S U S S S S S S S S S

0.41 0.43 0.69 0.16 0.82 0.74 0.45 0.34 0.44 0.82 0.67 0.53 0.36 0.57 0.58 0.39 0.41

3.97 0.27 1.42 0.01 0.61 4.73 1.31 1.77 0.21 3.07 0.22 1.71 0.33 0.50 1.52 1.62 2.00

Notes: LM = Lagrange multiplier test of residual serial correlation. It is distributed as χ 2 (1). RESET = Ramsey’s test for function form. It is distributed as χ 2 (1). S = ‘‘Stable’’, U = ‘‘Unstable’’. nes = not elsewhere specified.

industries and 62 import industries. We apply the ‘‘bounds testing’’ cointegration methodology of Pesaran et al. (2001) to assess the short- and long-run influences of income, the real exchange rate, and trade integration on each industry. We arrive at some interesting and relevant policy conclusions. We find that the effect of depreciation is relatively weak, perhaps because of a high level of industry integration that shields companies from currency fluctuations. This is particularly true for Canadian exports. Trade integration might thus be more effective in promoting trade, so that economies of scale could be exploited through larger markets. Overall, while roughly half of Canada’s export industries respond to currency fluctuations in the short run, only ten small industries register long-run increases in trade values (inpayments) due to depreciation. Mexican exports (Canadian outpayments) respond more strongly, but not in every case. A larger share of these industries shows a significant short-run response to depreciation,

031 051 053 054 055 071 075 099 112 221 243 273 276 292 512 513 514 541 551 581 612 632 642 651 653 655 656 657 661 662 663 664 665 666 667 695 697 698 711 714 717 719 722 724 725 729

Industry

0.15 (0.85) 0.19 (2.41) 0.02 (0.10) 0.06 (0.39) −0.58 (1.06) −0.36 (1.80) −0.22 (0.57) 1.15 (2.30) 0.06 (1.22) −0.09 (0.31) 1.10 (1.83) 0.20 (0.70) 0.30 (2.29) 0.14 (1.01) 0.01 (0.06) 0.37 (0.92) −1.32 (2.60) 0.03 (0.11) −0.74 (1.80) 0.62 (2.72) 0.54 (1.10) 0.11 (0.48) −0.05 (0.16) 0.36 (1.52) 0.16 (0.27) −0.01 (0.02) 0.71 (2.06) 0.27 (0.70) 0.48 (2.29) 0.22 (1.04) 0.05 (0.22) −0.34 (0.46) 0.62 (1.95) 0.04 (0.27) 0.39 (0.52) −0.50 (0.90) −0.73 (1.56) −0.16 (0.48) −0.69 (1.42) −0.15 (0.65) −0.15 (0.31) 0.36 (1.24) −0.06 (0.42) −0.22 (1.95) −0.88 (1.69) 0.08 (0.51) 0.66 (2.83)

0.96 (2.43)

−1.17 (5.35)

0.58 (1.52) 0.36 (1.48)

−0.25 (1.29)

0.36 (0.80)

−0.31 (2.26)

1.56 (2.34)

2.12 (1.99) 0.93 (3.46)

0.55 (1.7) 0 (0.01) 0.32 (2.81) 1.05 (2.21)

0.34 (1.97)

0.4 (2.53)

−0.51 (1.38)

0.59 (0.89)

−1.50 (2.58) −0.50 (1.78)

−0.1 (0.38) −0.79 (1.40) −0.84 (3.09)

0.61 (2.06)

−1.06 (1.72) −0.23 (0.77)

−1.62 (2.89)

0.41 (1.31)

−0.7 (1.26)

0.79 (2.75)

−0.33 (0.5)

−1.99 (4.14)

0.64 (0.75) 0.34 (1.14) 1.13 (2.84) 0.99 (3.3) −1.71 (2.72) 0.42 (1.71)

−0.15 (0.89)

−0.18 (1.09)

−0.45 (2.62) −0.47 (2.51) −0.67 (1.75)

−1.55 (2.5)

−0.24 (0.97)

0.37 (0.61)

0.18 (0.72)

−0.72 (1.19)

0.15 (2.87)

−1.26 (2.79)

−0.59 (3.19) −0.15 (1.74) −1.02 (4.1) 0.33 (0.67) 2.43 (4.85) −1.09 (1.24) 3.26 (4.22) 8.88 (3.64) −2.28 (3.80) 7.19 (6.29) 5.58 (1.69) 4.13 (31.00) −22.97 (0.49) −2.18 (1.62) 2.09 (0.93) 2.50 (3.05) 2.39 (4.06) 2.86 (3.50) 2.51 (2.68) 5.32 (4.18) 3.60 (2.61) 2.36 (1.94) 4.43 (7.24) 5.20 (4.54) 1.17 (0.91) −10.44 (0.20) 4.41 (6.37) 1.63 (0.64) 0.23 (0.15) 4.20 (5.11) −1.41 (0.92) 1.31 (1.23) 2.26 (0.84) 2.72 (1.93) 1.64 (0.25) 1.53 (1.26) −4.18 (1.43) −0.90 (0.45) 1.24 (1.23) 7.21 (0.76) 5.01 (4.84) 2.68 (5.56) 4.34 (2.93) 3.86 (1.97) 3.66 (1.50) 0.79 (0.22) 5.26 (10.13) 4.35 (4.16) 2.79 (1.58)

0.35 (2.76) 0.09 (0.60) 1.58 (3.02) 0.16 (0.99) 0.21 (0.38) −0.20 (1.69) −0.32 (1.32) 0.85 (1.04) 0.04 (0.85) −1.78 (0.39) 2.36 (3.69) 0.40 (0.55) −0.21 (0.94) 0.84 (1.92) 0.30 (1.29) 1.17 (4.64) 1.67 (2.99) −0.99 (1.39) −0.62 (1.93) −0.56 (2.95) 1.60 (3.14) 0.65 (2.11) −1.37 (0.17) −1.12 (3.57) 3.18 (2.71) 1.48 (2.18) 0.67 (3.57) −0.93 (1.99) 0.18 (0.59) 0.68 (0.77) 1.79 (2.18) −0.48 (0.28) 0.11 (0.35) −0.70 (0.80) 0.27 (0.69) −0.10 (0.44) −4.10 (0.51) 0.16 (0.75) −2.21 (7.40) −1.22 (1.84) −0.27 (0.62) −0.34 (0.45) 1.41 (1.27) −0.41 (3.00) −0.85 (1.54) −0.67 (1.41)

lnREX

Long-run coefficient estimates

∆lnREXt −3 lnY Can

∆lnREXt −2

∆lnREXt

∆lnREXt −1

Short-run coefficient estimates

Table 5 Short-run and long-run coefficient estimates for Canadian import equations.

7.37 (3.79) −0.42 (0.22) 15.29 (4.21) −2.17 (0.73) −27.16 (2.81) 17.90 (7.62) −25.23 (5.63) −16.33 (1.21) −7.20 (12.88) 91.7 (0.53) 15.68 (2.88) 0.44 (0.06) −1.68 (0.52) −1.35 (0.55) −2.89 (0.89) −1.64 (0.45) −13.70 (2.67) −7.56 (1.25) −5.49 (1.16) −11.43 (4.79) −11.61 (2.52) 3.50 (0.68) 37.99 (0.22) −11.65 (4.17) 2.57 (0.25) 9.58 (1.44) −9.27 (2.87) 9.55 (1.58) −0.23 (0.06) −1.74 (0.14) −3.28 (0.56) −4.74 (0.18) 0.92 (0.20) 20.73 (1.99) 7.31 (0.92) 0.57 (0.14) −31.70 (0.66) −11.49 (2.79) −4.85 (2.37) −10.69 (1.91) −10.72 (1.39) −6.72 (0.71) 8.78 (0.55) −10.34 (5.10) −11.84 (2.92) −3.04 (0.44)

Constant 0.06 (0.25) 0.46 (1.63) 0.04 (0.10) −0.05 (0.16) −2.51 (2.13) 0.57 (2.16) −0.36 (0.68) 2.44 (1.67) 0.14 (2.03) 7.56 (0.45) 1.25 (2.09) −2.00 (1.62) −0.77 (1.71) −0.27 (0.67) 0.80 (2.01) 1.53 (3.69) −1.60 (2.44) 0.48 (0.93) 1.11 (1.76) 0.98 (3.02) −0.03 (0.04) 1.49 (2.48) −3.74 (0.17) −0.78 (2.32) 0.14 (0.10) 1.68 (1.92) 1.58 (3.99) 1.01 (1.74) 0.71 (1.28) 0.78 (0.80) 0.58 (0.74) −1.26 (0.37) 1.12 (1.82) 3.15 (2.51) 1.43 (1.54) 1.57 (3.27) −1.92 (0.30) 0.13 (0.27) 0.32 (1.38) 0.40 (0.59) −0.66 (0.79) −0.67 (0.54) 1.17 (1.14) 0.60 (2.25) 1.91 (2.95) 0.78 (0.90)

NAFTA 0.51 (2.09) 2.16 (2.49) −0.17 (0.61) −2.55 (2.95) 0.54 (2.72) −1.18 (2.86) −0.29 (0.26) −0.14 (3.26) 3.97 (0.52) 4.19 (4.16) 0.24 (0.34) −1.07 (2.63) 0.99 (1.40) 0.51 (1.39) −0.33 (0.89) 1.26 (1.47) −1.09 (0.93) −0.18 (0.37) −0.05 (0.19) 0.69 (0.88) 0.65 (1.28) 20.37 (0.33) −0.19 (0.40) 6.36 (2.46) 0.10 (0.10) 0.20 (0.68) 3.71 (5.19) 1.50 (3.81) 0.93 (1.15) 3.02 (2.02) 7.98 (2.48) 1.04 (2.23) 1.23 (1.42) −0.63 (0.92) 1.23 (3.62) 2.09 (0.79) 1.54 (4.22) 0.60 (1.36) 1.72 (2.67) 0.06 (0.08) 3.05 (3.03) 3.30 (1.61) −0.31 (1.60) 0.81 (1.44) 1.37 (2.14)

−0.59 (3.26)

GATT

0.03 1.18 0.10 1.90 0.07 0.10 0.01 0.20 0.87 0.00 0.00 0.01 0.10 0.05 0.59 0.08 0.03 0.18 0.00 0.47 0.15 0.06 0.17 0.23 0.27 0.10 0.18 0.18 0.02 0.05 0.09 0.21 0.15 0.01 0.00 0.06 0.06 1.43 3.38 5.91 0.19 2.99 4.80 17.36 1.78 2.76

Share (%)

220 M. Bahmani-Oskooee et al. / Research in Economics 64 (2010) 212–223

−0.33 (1.70)

0.2 (1.23)

0 (0.02) 0.4 (2.51)

0.06 (0.35)

−0.3 (0.81)

0.22 (1.18)

−0.65 (4.27) −0.72 (1.58) −0.6 (2.13) −0.76 (4.79) −0.73 (1.49)

−1.28 (3.45)

−0.12 (0.78) −0.09 (0.19) −0.55 (1.77)

0.54 (1.37) −1.69 (2.66) 0.12 (1.13) 0.32 (0.72) 0.45 (1.41) 0.26 (1.28) 0.08 (0.46) 0.84 (3.51) 0.28 (0.61) 0.91 (2.64) 0.43 (2.15) 0.16 (1.00) 0.23 (0.48) 0.23 (0.83) 0.03 (0.16)

0.17 (0.36) 0.17 (0.70) 0.40 (4.42) 3.76 (1.64) 1.12 (4.69) 2.14 (10.0) 0.29 (0.87) 0.46 (2.75) 1.80 (1.83) 1.45 (6.12) 0.69 (5.72) 0.05 (0.19) 0.68 (1.24) 0.43 (1.44) −0.60 (3.83)

−1.29 (3.03)

lnREX

0.46 (1.08) −0.25 (0.63)

−0.25 (0.56) −0.16 (0.45)

−0.26 (0.72) 1.12 (1.22) 6.53 (6.26) 1.61 (1.63) 4.99 (17.89) −0.21 (0.05) 3.15 (3.90) 4.46 (7.31) 0.29 (0.18) 4.43 (5.36) −0.73 (0.19) 3.30 (4.10) 4.74 (12.53) 1.81 (1.66) 1.98 (0.73) 4.80 (2.92) 3.45 (6.85)

Long-run coefficient estimates lnY Can

∆lnREXt −3

∆lnREXt −1

∆lnREXt

∆lnREXt −2

Short-run coefficient estimates

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

732 733 734 812 821 831 841 851 861 891 892 893 894 896 897 899

Industry

Table 5 (continued)

4.36 (1.25) −21.72 (5.16) −1.10 (0.27) −10.8 (10.03) 13.20 (0.73) −4.35 (1.39) −6.31 (2.66) 6.81 (1.12) −9.13 (2.73) 11.87 (0.78) −4.36 (1.34) −11.01 (7.52) 1.69 (0.39) −1.60 (0.15) −12.95 (1.97) −6.10 (3.11)

Constant

2.44 (4.76) 0.64 (1.18) 1.00 (2.32) −0.18 (1.19) 1.46 (0.54) 0.18 (0.37) 0.52 (1.23) 0.86 (1.35) 1.69 (4.60) 1.27 (0.86) 1.76 (4.52) 0.66 (3.83) 0.96 (1.92) −0.33 (0.26) −0.84 (1.09) 0.73 (2.49)

NAFTA

1.31 (3.04) 0.05 (0.07) −0.45 (1.18) 0.05 (0.39) 10.09 (2.36) −0.26 (0.76) 2.30 (2.91) 0.74 (1.16) 1.77 (6.48) 4.64 (2.56) −0.17 (0.39) 0.79 (4.47) 0.34 (0.99) −0.04 (0.04) 1.09 (2.17) −1.06 (4.69)

GATT

22.56 0.18 0.02 0.88 4.12 0.03 2.14 0.05 3.32 0.22 0.07 0.43 0.40 0.01 0.16 0.28

Share (%)

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Table 6 Diagnostic statistics for Canadian import equations. Code-Industry

LM

RESET

CUSUM

CUSUMSQ

Adj.R2

031-Fish, fresh & simply preserved 051-Fruit, fresh, and nuts-excluding oil 053-Fruit, preserved and fruit preparation 054-Vegetables, roots & tubers, fresh or dried 055-Vegetables, roots & tubers preserved or prepared 071-Coffee 075-Spices 099-Food preparations, nes 112-Alcoholic beverages 221-Oil-seeds, oil nuts and oil kernels 243-Wood, shaped or simply worked 273-Stone, sand and gravel 276-Other crude minerals 292-Crude vegetable materials, nes 512-Organic chemicals 513-Inorganic chemical elements, oxides and halogen salts 514-Other inorganic chemicals 541-Medicinal & pharmaceutical products 551-Essential oils, perfume and flavour 581-Plastic materials, regenerd. cellulos 612-Manufacturing of leather or composition leather, nes 632-Wood manufactures, nes 642-Articles of paper, pulp, paperboard 651-Textile yarn and thread 653-Text fabrics woven excluding narrow or special fabrics 655-Special textile fabrics and related products 656-Made-up articles, wholly or chiefly 657-Floor coverings, tapestries, etc. 661-Lime, cement, and fabricated construction materials, except glass and clay materials 662-Clay and refractory construction materials 663-Mineral manufactures, nes 664-Glass 665-Glassware 666-Pottery 667-Pearls, precious and semi-precious stones 695-Tools for use in the hand or in machines 697-Household equipment of base mtls 698-Manufactures of metal, nes 711-Power generating machinery, other than rotating electric plant of power generating machinery 714-Office machines 717-Textile and leather machinery 719-Machinery and appliances-nonelectrical 722-Electric power machinery and switch 724-Telecommunications apparatus 725-Domestic electrical equipment 729-Other electrical machinery and apparatus 732-Road motor vehicles 733-Road vehicles other than motor vehicle 734-Aircraft 812-Sanitary, plumbing, heating & lighting 821-Furniture 831-Travel goods, handbags and similar 841-Clothing except fur clothing 851-Footwear 861-Scientific, medical, and optical goods 891-Musical instruments, sound recorders 892-Printed matter 893-Articles of artificial plastic material 894-Perambulators, toys, games 896-Works of art, collectors’ pieces and antiques 897-Jewellery and gold/silver-smiths wares 899-Manufactured articles, nes

0.53 0.31 0.95 0.10 3.45 3.13 6.29 2.5 6.04 2.64 0.79 0.00 0.08 2.10 0.04 0.86 2.04 0.08 0.03 0.00 1.13 3.64 0.02 0.20 0.18 1.49 0.45 1.65 17.08 0.19 9.24 9.83 0.12 1.93 1.61 4.53 1.69 2.04 14.19

0.88 0.01 0.13 4.61 0.82 1.13 1.43 5.42 4.57 11.38 0.93 3.31 0.18 0.16 0.35 0.03 1.47 4.51 0.02 0.15 7.47 2.61 0.48 9.27 0.07 0.1 0.19 3.05 0.05 0.02 15.39 18.31 0.11 2.48 0.23 0.16 1.92 8.86 6.9

S S S S S S S S S S S S S S S S S S S U S S S S S S U S S S S S S S S S S S S

S S S S S S S S S S S S S S S S S S S S S S S S U S U S S S S S S S S S S S S

0.60 0.49 0.47 0.19 0.34 0.37 0.51 0.53 0.73 0.07 0.31 0.25 0.56 0.29 0.30 0.39 0.68 0.41 0.74 0.66 0.54 0.15 0.68 0.73 0.49 0.65 0.29 0.41 0.70 0.64 0.80 0.37 0.53 0.43 0.60 0.43 0.27 0.33 0.56

0.68 0.00 8.29 9.27 2.16 1.67 2.41 8.44 2.99 11.29 0.74 10.62 0.64 3.34 2.41 1.19 5.10 11.47 0.16 0.84 0.08 0.34 0.77

3.08 0.88 12.54 0.04 0.29 4.23 1.58 0.65 3.38 2.73 5.21 19.91 7.72 1.83 1.03 0.75 8.03 1.75 10.75 0.32 0.75 1.53 0.33

S S S S S S S S S S S S S S S S S S S S S S S

S S S S S S S S S S S S S S S S S S S S S S S

0.69 0.43 0.46 0.37 0.47 0.59 0.34 0.25 0.73 0.58 0.60 0.45 0.39 0.92 0.29 0.84 0.21 0.52 0.46 0.53 0.34 0.31 0.18

Notes: LM = Lagrange multiplier test of residual serial correlation. It is distributed as χ 2 (1). RESET = Ramsey’s test for function form. It is distributed as χ 2 (1). S = ‘‘Stable’’, U = ‘‘Unstable’’. nes = not elsewhere specified.

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and certain key Mexican export industries are positively affected by peso devaluations. These include large manufacturing industries such as motor vehicles—but these also respond positively to trade liberalization. Thus, it appears that Mexican exports might improve after peso depreciation more than Canadian imports would increase after a dollar depreciation. As a result, a depreciation or devaluation of either country’s currency would have asymmetric effects: Mexico’s overall trade balance would improve more than Canada’s would. Trade liberalization would be a better strategy for expanding these flows, particularly for Canada, but also for Mexico’s large export industries. Appendix. Data definitions and sources All data are annual (1973–2006) and are collected from the following sources: (a) United Nation COMTRADE data base available from the World Bank WITS System. (b) The International Financial Statistics of the IMF. Variables: VX = For each industry, this is the value of Canada’s exports to Mexico (in US dollars). The data for 45 industries come from source a. VM = For each industry, this is the value of Canada’s imports from Mexico (in US dollars). The data for 62 industries come from source a. Y Can = Measure of Canadian income. It is proxied by real GDP. The data come from source b. Y Mex = Mexican real GDP, source b.   REX = Real bilateral exchange rate between the Canadian dollar and Mexican peso, defined as

PPIMex ×NEX PPICan

, where PPI is

the Producer Price Index. NEX is the nominal bilateral exchange rate defined as number of Canadian dollars per peso. Thus, an increase in REX reflects a real depreciation of the Canadian dollar. Data on all variables come from source b. GATT = A dummy variable that equals zero from 1973 to 1985, and one from 1986 to 2006. NAFTA= A dummy variable that equals zero from 1973 to 1993, and one from 1994 to 2006. References Backus, D.K., Kehoe, P.J., Kydland, F.E., 1994. Dynamics of the trade balance and the terms of trade: the J-curve? American Economic Review 84, 84–103. Bahmani-Oskooee, M., Economidou, C., Goswami, G., 2005. How sensitive are the UK’s inpayments and outpayments to the value of the British pound. Journal of Economic Studies 32, 455–467. Bahmani-Oskooee, Mohsen, Ardalani, Zohre, 2006. Exchange rate sensitivity of US trade flows: evidence from industry data. Southern Economic Journal 72, 542–559. Bahmani-Oskooee, M., Ratha, A., 2004. The J-curve: a literature review. Applied Economics 36 (13), 1377–1398. Bahmani-Oskooee, Mohsen, Bolhasani, Marzieh, 2009. How sensitive is US-Canadian trade to the exchange rate: evidence from industry data. Open Economies Review (forthcoming). Bahmani-Oskooee, Mohsen, Goswami, Gour G., 2004. Exchange rate sensitivity of Japan’s bilateral trade flows. Japan and the World Economy 16, 1–15. Bahmani-Oskooee, Mohsen, Hegerty, Scott W., 2009. Trade liberalisation, the peso, and Mexico’s commodity trade flows with the United States. Journal of Development Studies 45 (5), 693–725. Bahmani-Oskooee, M., Gelan, A., 2009. How stable is the demand for money in African countries. Journal of Economic Studies 36, 216–235. Bahmani-Oskooee, Mohsen, Niroomand, F., 1998. Long run price elasticities and the Marshall Lerner condition revisited. Economics Letters 61, 101–109. Bahmani-Oskooee, M., Goswami, G., Talukdar, B., 2008. The bilateral J-Curve: Canada versus her 20 major trading partners. International Review of Applied Economics 22, 93–104. Caporale, Guglielmo Maria, Chui, Michael K.F., 1999. Estimating income and price elasticities of trade in a cointegration framework. Review of International Economics 7 (2), 254–264. Cushman, D.O., 1987. US bilateral trade balances and the dollar. Economics Letters 24, 363–367. De Vita, G., Kyaw, K.S., 2008. Determinants of capital flows to developing countries: a structural VAR analysis. Journal of Economic Studies 35, 304–322. Engle, R.F., Granger, C.W.J., 1987. Cointegration and error-correction representation, estimation and testing. Econometrica 55, 251–276. Fullerton Jr., T.M., Sprinkle, R.L., 2005. An error correction analysis of US-Mexico trade flows. International Trade Journal 19 (2), 179–192. Fullerton Jr., T.M., Sawyer, W.C., Sprinkle, R.L., 1997. Functional form for United States–Mexico trade equations. Estudios Económicos 12 (1), 23–35. Goldstein, M., Khan, M., 1976. Large versus small price changes and the demand for imports. IMF Staff Papers 23, 200–225. Halicioglu, F., 2007. The J-curve dynamics of turkish bilateral trade: a cointegration approach. Journal of Economic Studies 34, 103–119. Haynes, Stephen E., Stone, Joe A., 1983. Specification of supply behavior in international trade. The Review of Economics and Statistics 65 (4), 626–632. Marwah, Kanta, Klein, Lawrence R., 1996. Estimation of J-curve: United States and Canada. Canadian Journal of Economics 29, 523–539. McDaniel, C.A., Agama, L.-A., 2003. The NAFTA preference and US–Mexico trade: aggregate-level analysis. World Economy 26 (7), 939–955. Mohammadi, H., Cak, M., Cak, D., 2008. Wagner’s hypothesis: new evidence from Turkey using the bounds testing approach. Journal of Economic Studies 35, 94–106. Narayan, P.K., Narayan, S., Prasad, B.C., Prasad, A., 2007. Export-led growth hypothesis: evidence from Papua New Guinea and Fiji. Journal of Economic Studies 34, 341–351. Pacheco-López, P., 2005. The effect of trade liberalization on exports, imports, the balance of trade, and growth: the case of Mexico. Journal of Post Keynesian Economics 27 (4), 595–619. Payne, J.E., 2008. Inflation and inflation uncertainty: evidence from the caribbean region. Journal of Economic Studies 35, 501–511. Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, 289–326. Rose, Andrew K., 1991. The rose of exchange rates in a popular model of international trade: does the ‘Marshall-Lerner’ condition hold? Journal of International Economics 30, 301–316. Rose, Andrew K., Yellen, Janet L., 1989. Is there a J-curve?. Journal of Monetary Economics 24, 53–68. Tang, T.C., 2007. Money demand function for southeast asian countries: an empirical view from expenditure components. Journal of Economic Studies 34, 476–496. Wong, K.N., Tang, T.C., 2008. The effects of exchange rate variablity on Malaysia’s disaggregated electrical exports. Journal of Economic Studies 35, 154–169.