Trade liberalization, comparative advantage, and scale economies stock market evidence from Canada

Journal of INTERNATIONAL ECONOMICS ELSEVlER

Journal

of International

Economics

37 (1994)

1-27

Trade liberalization, comparative advantage, scale economies Stock market evidence from Canada

and

Aileen J. Thompson* Department

of Economics, Ottawa,

Received

June

Carleton University, 1125 Colonel Ontario, KlS 5B6, Canada

1992, revised

version

received

By Drive,

July 1993

Abstract

The Canada-United States Free Trade Agreement (FTA) provides an excellent opportunity to analyze the impact of a comprehensive trade liberalization. In this paper, a stock market event study is employed to capture investors’ expectations about the consequences of this agreement for manufacturing firms in Canada. The primary question addressed is whether abnormal stock market returns corresponding to news about the FTA are consistent with hypotheses based on comparative advantage and economies of scale. The results indicate that both comparative advantage and scale economies played a role in determining investors’ perceptions of the impact of the FIA. Key words: Free trade; Stock market prices JEL classification: F13

* This paper is based on my Ph.D. dissertation at the University of Michigan and was written in part while I was at the University of Toronto. I thank Alan Deardorff, Brian Erard, Nancy Gallini, James Levinsohn, Joel Slemrod, Robert Stern, Dan Trefler, Valerie Suslow, and seminar participants at the Canadian Economics Association Meetings, Carleton University, the University of Toronto, Wilfrid Laurier University, York University, the 1991 Summer Conference at the University of British Columbia, and the 1991 University of Western Ontario Conference on International Trade for helpful comments on earlier drafts of this paper and Maura Binley for research assistance. I am grateful to two anonymous referees for excellent suggestions. 0022-1996/94/$07.00 @ 1994 Elsevier SSDZ 0022-1996(94)01326-N

Science

B.V. All rights

reserved

2

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1. Introduction The Canada-United States Free Trade Agreement (FTA) was implemented on 1 January 1989. This agreement requires the phased elimination of all tariffs and some non-tariff barriers on Canadian-U.S. trade by 1998. Equally as important, a binational panel has been established to arbitrate disputes over safeguard procedures and to review antidumping and countervailing duty cases with the intent to provide a more predictable trade environment.’ The resulting reduction of uncertainty is likely to be as consequential to Canadian producers as the removal of existing tariffs. The FTA provides an excellent opportunity for evaluating hypotheses about trade liberalization. It is too early to estimate the actual effects of the FTA, but market predictions about its impact can be investigated using stock market data. In this paper I employ a stock market event study to examine the roles of comparative advantage and economies of scale in determining investors’ perceptions of the consequences of the FTA for individual Canadian manufacturing firms. A central proposition of international trade theory is the HeckscherOhlin (H-O) hypothesis, which predicts that a country will have a comparative advantage in the production of those commodities that use relatively intensively its relatively abundant (or inexpensive) factors. Presumably, if trade between the United States and Canada is based on comparative advantage and if the H-O hypothesis, in turn, provides an appropriate description of the determinants of comparative advantage, then Canadian industries that use intensively Canada’s relatively inexpensive factors should benefit from the FTA. On the other hand, during the free trade debates in Canada, a great deal of emphasis was placed on the potential gains to be achieved through the realization of economies of scale. It has been argued that trade protection encourages suboptimal scales of production. For example, Eastman and Stykolt (1967) posit that trade protection in concentrated industries enables firms to collude and set prices above average cost. The profits attract new firms, resulting in a market structure characterized by all firms producing output levels below minimum efficient scale. Similarly, Horstmann and Markusen (1986) find that trade protection may lead to the ‘inefficient entry’ of firms. It has been argued that trade liberalization will lead to the rationalization of production facilities: some domestic firms will exit the industry, output per firm will rise and average production costs will fall.’

’ See Winham (1988) for a detailed description of the treaty. illustrate that rationalization in * Brown and Stern (1989) and Brown (1991), however, monopolistically competitive industries depends not only on tariffs, but also on the factorintensity rankings of industries.

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3

These rationalization effects are largely responsible for the large welfare gains estimated by Harris (1984) and Cox and Harris (1985) in their simulations of trade liberalization in Canada.3 Although some firms within an industry expand as a result of rationalization, it is quite possible that all firms in the industry will experience economic losses until some firms exit the industry. Predictions based on economies of scale a,.: not necessarily inconsistent with the predictions based on the theory of comparative advantage and it is likely that both factors will be important.4 Two primary questions are addressed in this paper. The first is whether differences in relative factor intensities across industries played a role in determining investors’ expectations of the consequences of the ETA. The intention is not to test a particular version of the H-O model,5 but rather to ask whether it provides insight into the perceived comparative advantage of Canadian firms. The second question is whether firms operating in industries where the average plant scale is small, relative to the corresponding U.S. industries, were expected to experience losses as a result of the PTA. This may be interpreted as a sign that the industry was expected to rationalize. In the next section I develop a model that incorporates both the comparative advantage and the economies of scale hypotheses. To focus on these two hypotheses, it is assumed that identical technologies are available to both countries. Differences in production costs between countries therefore arise solely from differences in factor prices and plant scales. Capital is assumed to be sector-specific in the short run. Thus, the real return to capital rises in sectors that are positively affected by free trade and falls in sectors that are negatively affected.6 This is in contrast to the Stolper-Samuelson (1941) theorem which, under the assumption of perfectly mobile capital, predicts that the real return to capital will either rise in all sectors or fall in all sectors depending on the country’s factor endowments. Within this framework, the impact of the FIA on the value of capital employed by an individual firm is predicted to be a function of relative factor prices, relative factor shares, and the plant_ scale of domestic firms relative to their foreign competitors. With efficient capital markets, the

3 See Wormacott and Wonnacott (1967) for an early study of the gains from trade resulting from the realization of economies of scale. 4 For example, in the two-sector model developed by Helpman and Krugman (1985), intraindustry specialization occurs within the increasing returns to scale sector enabling the realization of scale economies, but net imports and exports are determined by comparative advantage based on factor endowments. 5 See Anderson (1981), Learner and Bowen (1981) and Aw (1983) for discussions of the conceptual difficulties involved in testing the H-O model. 6 See Mussa (1974) and Neary (1978) for a discussion of the specific-factors model.

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impact of the PTA is reflected in stock market prices at the time new information is learned about the agreement. The implications of this model are evaluated using a stock market event study. Event studies measure the effects of a particular event or series of events on security prices by estimating the ‘abnormal’ returns that correspond to the event date or dates.7 Two previous studies have analyzed the relationship between the PTA and stock prices. Brander (1991) analyzes the reaction of the Toronto Stock Exchange (TSE) index and four TSE subindices to the public opinion polls published during the 1988 Canadian General Election campaign (during which the PTA was the primary issue). Thompson (1993) compares industry-level abnormal returns corresponding to PTA-related events (including the General Election) to predictions based on industry studies of the Royal Commission on the Economic Union and Development Prospects for Canada (1985) and the lobbying positions of industry associations. The central question of these previous papers is whether the PTA news had a significant impact on stock market prices. In contrast, this paper focuses on the relationship between abnormal stock market returns and international trade theories. Abnormal returns corresponding to six events are estimated for individual Canadian manufacturing firms and industry-level portfolios. The abnormal returns are then related to the factor shares of natural resources and labor relative to capital, and a variable capturing the industry-level scale advantage or disadvantage of Canadian firms relative to U.S. firms. The results indicate that both the comparative advantage and economies of scale hypotheses are useful in explaining investors’ perceptions of the impact of the PTA on Canadian firms.

2. Theoretical foundation This section develops a theoretical framework to analyze the comparative advantage and economies of scale hypotheses discussed above. The model links the effect of the FTA on the market value of a firm’s capital to the relative factor shares employed by the firm and the relative plant scale advantage (or disadvantage) of the industry in which the firm operates. To derive a relationship between the value of a firm’s capital and factor shares and relative plant scale, a simple three-factor model is employed. Production requires inputs of capital, labor, and natural resources which are sufficiently immobile between countries to yield differences in factor prices. ‘See Hartigan et al. (1986), Grossman and Levinsohn (1989), Brander (1991), Puffer (1992), and Ries (1993) for examples of applications of this method in the field of international trade.

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5

Capital is specific to the sector in which it is employed in the short run, while labor and natural resources are perfectly mobile between sectors within a country. An important assumption of the model is that firms may freely enter and exit all industries in the long run. This assumption is supported by the conclusion of Baldwin and Gorecki (1991) that entry and exit have been significant in Canadian manufacturing industries. As a consequence, long-run prices will be equal to unit production costs. Furthermore, it is assumed that both countries have access to identical increasing returns to scale technologies. This implies that differences in production costs between countries arise solely from differences in factor prices and scales of production. The value of a firm’s capital is defined to be the expected discounted value of the future net cash flows to the firm. Consider an individual firm operating in industry i, hereafter referred to as ‘firm i’. In this three-factor model, the value of firm i’s capital at time t is equal to

(1) where r is the risk-free rate of interest and Z, represents the information at time t. The term Ci, represents net cash flows and is defined as Ci, = PiTqiT- w7Li7 - w,,NRi, - ki, ,

set

(2)

where qir is the output level of firm i at time 7, Pi, is the price of output at time T, w, is the wage rate at time T, w,, is the price of natural resources at time r, Li, and NR,, are the amounts of labor and natural resources employed by firm i at time r, and k,, is investment at time T. Let T represent the time at which the FTA will go into effect if implemented. For simplicity, let one unit of time be the short-run adjustment period during which capital is sector-specific, so that ki, = 0.” Short run economic profits and losses exist among different industries until capital is reallocated in response to the new trading environment. This reallocation is completed at the beginning of period T + 1, at which time expected economic profits return to zero. At time t, the expected value of net cash flows of firm i at time T may be expressed as a weighted average of the expected net cash flows with and without the FTA:

‘In reality, it is likely that this adjustment process began when the agreement was however, that the agreement would be anticipated. It was not known with certainty, implemented until 6 weeks before the actual implementation. It is therefore reasonable to assume that capital had not adjusted (completely) to the agreement prior to the beginning of period T.

6

A. J. Thompson

E[C,, 1Z,] = p,Cf;

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+ (1 - ~$2;~~’

,

37 (1994) l-27

(3)

where pr represents the perceived probability that the agreement will be implemented, and Cf: and CGfta represent the expected value of net cash flows at time T with and without the PTA, respectively. With efficient capital markets, the effect of new information on the expected value of a firm’s cash flows is reflected in the market value of the firm’s capital as soon as the information is learned. Suppose new information is learned at time t that alters p. The change in the value of the capital of firm i at time t is T-t

dV,,=

(

&

>

(dp,)(C;;

- CGfta) ,

(4)

where dp, equals the change in p at time t. Proportionally differentiating the expression for net cash flows in (2) and equating the price of a given variable factor to its marginal revenue product, the proportional change in the market value of firm i at time t may be expressed as fit =?

= (dp,)(&-)

T-f[ (+-)(p;a - ei,Gf’”- ei,G;” + qa:‘“)] , (5)

where f3, denotes the distributive sAhffTeof factor j employed by firm i (for example, O,, equals (wLi)I(Pjqi)), Pi represents the expected proportional effect of the PTA on the price of output in industry i at time T, Gfta and GE” represent the expected proportional effects of the PTA on the wage and price of natural resources at time T, Giftarepresents the expected short-run change in output, and mi represents the mark-up of price over marginal cost, (Pi - mc,)lP,. To determine the effect of the agreement on the value of the firm, it is necessary first to determine its effect on commodity prices and variable factor prices. In the absence of free trade, the difference between the expected price of a good in Canada and the United States at time T is equal to the expected tariff level and/or the tariff equivalent of a non-tariff barrier. Thus, E(P$ - PLT 1Z,) = E(tariff,

1Z,) ,

(6)

where Pr and Pp” are the non-PTA prices in Canada and the United States, respectively. Canada is assumed to be small enough relative to the United States that the PTA will have only a minimal effect on prices in the United States, but will have a relatively large impact on prices in Canada. Thus, the expected proportional change in the Canadian price of industry i’s output as a result of the agreement can be approximated by the proportional difference between the non-PTA prices in the United States and Canada:

A.J.

Thompson

Pl’” = jyc

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Pp” - Pf PF

=

37 (1994) 1-27

7

.

I With free entry and exit of firms, the price of output in the non-FTA equilibrium must equal average cost. To introduce increasing returns to scale in a simple way, it is assumed that production in industry i is characterized by a Cobb-Douglas production function: ,

qi = L;lNR;gK;i

(8)

where ai + bi + ci > 1 if industry i is characterized scale. The zero-profit condition is therefore pi =

Awei/w~re,kq;e;

,

by increasing

returns

to

(9)

where A is a constant, 0; is defined as (vi, - l)/vis, where qis is the elasticity of scale for industry i. This elasticity is equal to a, + bi + ci. The expression Oij is the distributive share of factor j in industry i as defined above. In terms of the Cobb-Douglas parameters, 19,~ = a,/(~, + bi + ci), Oj, = b,l(a, + bi + ci), and O,, = cil(u, + bi + ci). The proportional change in the price of industry i’s output can be expressed as a function of the non-PTA factor prices and output levels:

(10) where the caret tional difference words,

along with the superscript, (us - c), indicates the proporbetween U.S. and Canadian non-PTA levels. In other

w”s 1

W

.

us--c

wc

WC WZ”-

us--c

w,

““S-c

r

_

_

=

=-

_

’

w;

w; r

US -

rc

’

rc ’

Positive values of FY-~, Go”-“, and Y-c indicate that these prices are higher in the United States than in Canada and may be interpreted as revealing Canadian factor price advantages. Positive values of kji indicate that Canadian firms’ production levels in industry i are lower than those of their U.S. counterparts. All other things equal, this implies that average

8

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costs in industry i are higher in Canada if this industry is characterized by increasing returns to scale. Thus, a positive value of ei reflects a Canadian plant scale disadvantage and the term 0: represents the implications of this plant scale disadvantage for production costs. In other words, 19~~~‘-~ represents the percentage difference between U.S. and Canadian prices due to differences in plant scalesirta Substituting Eq. (10) for Pi into Eq. (5), and noting that with average cost pricing the elasticity of scale is equal to the mark-up over marginal cost:9 et =

(&,J(&)

T-r(;,,-, +!k(,,+us-c _ Gfta) + 2($“” -

,$;“)

(11) Thus, the impact of the FTA on the value of a firm’s capital is a function of relative factor prices, relative factor shares, relative plant scales, and the short-run change in output level. It is not possible to obtain a good proxy for the anticipated short-run change in output. It will therefore be assumed in the empirical work below that Gf’” is zero. This is a reasonable assumption given that capacity levels are fixed in the short run. The change in the capital value of firm i is negatively related to the plant scale disadvantage of Canadian firms relative to U.S. firms in that industry. All other things equal, if firms in any industry are small relative to their U.S. counterparts, then prices will fall in that industry as a result of the FTA, resulting in losses until capital is reallocated and long-run equilibrium is reached. The relationships between the value of a firm’s capital and the factor shares of labor and natural resources are functions of the Canadian-U.S. factor price differentials and the anticipated impact of the FTA on the relevant factor prices. As illustrated in the Appendix, under constant returns to scale and perfect competition, the coefficient corresponding to the factor for which Canada has the greatest factor price advantage or disadvantage will have the same sign as the factor price differential. Suppose, for example, that Canada’s greatest comparative advantage is in natural resource intensive products and that free trade increases the demand for natural resources, leading to an increase in the price of natural resources. In this case, both w~s-c and wz” are positive. The appendix indicates that WY= ’ w?” and therefore that (wz”-’ - wf”) > 0. In other words, the benefits from comparative advantage dominate potential changes in factor

9 I thank

an anonymous

referee

for bringing

this to my attention.

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Thompson

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prices. A similar scenario holds for the factor for which Canada has the greatest comparative factor price disadvantage. The factor share coefficients therefore reveal the sources of Canada’s greatest comparative factor price advantage and/or disadvantage. The sign of the coefficient corresponding to the ‘middle’ factor is indeterminate. It is difficult to calculate reliable estimates of factor price differences between countries due to differences in reporting practices as well as conceptual difficulties involved in the measurement and definition of capital. Nonetheless, data on Canadian and U.S. labor compensation and real interest rates provide a general idea of labor and capital costs in the two countries. These data, presented in Table 1, suggest that labor costs in the manufacturing sector in Canada have been lower than in the United States, both absolutely and relative to the cost of capital, although the gap has been closing. Due to the abundance of natural resources in Canada, it is assumed that natural resource prices are lower in Canada than in the United States both absolutely and relative to labor and capital costs. Therefore, it is hypothesized that, relative to the United States, Canada has the greatest factor price advantage in natural resources and the greatest factor price disadvantage in physical capital. This hypothesis is consistent with the findings of Baldwin and Hilton (1984) who analyze interindustry trade patterns to infer relative factor costs. With perfect competition and constant returns to scale, therefore, the predicted sign of the natural resource coefficient is positive and may be interpreted as revealing a comparative advantage in natural resource intensive goods. When increasing returns to scale and imperfect competition are introduced, the impact of trade liberalization on factor demands and thus on factor prices becomes more difficult to determine. For example, the faced by reduction of domestic tariffs not only decreases the demand individual domestic import-competing firms, but may also lead firms to reduce their price-cost margins and to increase their output due to the Table Labor Year

1985 1986 1987 1988

1 compensation

and interest Labor

rates

in Canada

and the United

States Interest

compensation”

rateb

U.S.

Canada

U.S.

Canada

12.96 13.21 13.46 13.90

10.81 11.00 11.97 13.58

4.14 4.21 2.20 2.78

5.43 4.87 3.85 5.43

a Average hourly compensation for production workers in U.S. dollars. b Rate of return on three month Treasury Bills minus the rate of inflation. Sources: Bank of Canada (1988, 1989), International Monetary Fund (1990), Department of Labor, Bureau of Labor Statistics (1989).

United

States

10

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‘pro-competitive’ effect of trade liberalization.” Thus, output and the demand for the variable factors of production in comparative disadvantaged industries may rise or fall depending on whether this pro-competitive effect is strong enough to offset the decrease in demand. Similarly, increased access to the U.S. market may increase the market power of Canadian exporters and lead to a reduction in output and thus their demand for variable factors.” The coefficients on the factor share variables in Eq. (11) are therefore more difficult to interpret. A positive natural resource coefficient, for example, may reflect a comparative advantage in natural resource intensive goods, an expected decrease in the price of natural resources, or both. The comparative advantage interpretation, however, seems to be the most plausible. For the price of natural resources to fall, industries that use a large share of natural resources would have to contract their output either because they have a comparative disadvantage relative to the United States or because increased access to the U.S. market has increased the market power of Canadian firms. Given the relative abundance of natural resources in Canada and the relatively low U.S. tariff levels, neither of these scenarios seems likely.

3. Method of study In this section, the above theoretical model is incorporated into an empirical framework to analyze the impact of news about the FIA on equity values. The theoretical model focuses on the firm-specific effects of the FTA resulting from trade liberalization. The goal here is to empirically isolate these firm-specific effects from changes in expectations due to either (1) the expected marketwide effects of the FTA, or (2) other marketwide fluctuations due to non-FTA-related information learned at time t. Following the event study literature, abnormal stock market returns for each of the event periods (known as event ‘windows’) are estimated as the residuals of the market model, rir = ai +

pirmt+ E,,

(12)

)

where rir is the return to security i at time t, pi is the systematic risk of security i, rmr is the return to the market portfolio, and lit is a stochastic error term, assumed to have a zero mean and a constant variance r?. The market portfolio is defined to be the portfolio of all securities traded on the Canadian market. Consequently, the return to this portfolio reflects market” See Markusen IL See Horstmann

(1981). and Markusen

(1986)

and Brown

and Stern

(1989).

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11

wide fluctuations in the Canadian economy including those due to news about the agreement. The abnormal returns represent estimates of the changes in the market value of equity net of any anticipated marketwide effects. The abnormal returns and their relation to factor share ratios and relative plant scales across firms are estimated in one step:

between the The dummy variable, DOS, is equal to one for every observation event window s (or event set s when the events are clustered together) and the preceding event window. This allows the market model parameters to change between events. The dummy variable, D,, is equal to one during event window e if event e increased the perceived probability that the -1 during event window e if event e agreement would be implemented, decreased the perceived probability that the agreement would be implemented, and zero otherwise. The coefficient of D, for firm i represents the abnormal return for event window e and is specified to be a function of the firm’s characteristics (i.e. its relative factor shares and relative plant scale) and an event-specific constant, a,,,, that captures shocks during the event window that are common to firms in the sample. The coefficient of a given characteristic variable in this representation is the product of two parameters, a 6 parameter that reflects the hypotheses discussed above and is constrained to be the same across events, and the parameter ‘y, that represents the absolute value of dp,( l/(1 + r))r-l for event window e and is permitted to vary across events. The estimates of the y parameters provide information about the relative importance of the events. This approach represents a hybrid of two approaches employed in previous studies, in which (1) the coefficients of the firm-specific variables are restricted to be the same for all (or subsets) of the events; or (2) unique coefficients are estimated for each event.12 By restricting the 6’s to be the same across events while estimating a unique y for each event, the approach employed in this paper places more structure on the model than approach (2), but is more flexible than approach (1). Eq. (13) is estimated using three approaches.13 The first approach is to use a non-linear least squares estimation procedure (NLS). The second estimation approach employed involves estimating Eq. (13) as a system of non‘* See, for example, Rose (1985) who employs both of these approaches. I3 Estimation was performed using SAS programs generously provided by Marlene Puffer.

A.J.

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linear seemingly unrelated regressions (SUR). This approach potentially leads to more efficient parameter estimates than least squares estimation because it allows for the correlation of errors across firms. If errors are correlated due to omitted variables, however, the SUR estimators will be more sensitive to specification error and less efficient than the least squares estimators.14 Finally, the third approach involves aggregating firms into industry-level portfolios. Estimating Eq. (13) for industry portfolios reduces contemporaneous correlation relative to the firm-level analysis. All three of these approaches allow the variances of the error terms to differ across equations.15

4. Data 4.1.

Events

A crucial element of an event study is the determination of when new information was learned. Although the long negotiating process that led to the eventual implementation of the FTA will unfortunately obscure many of the events, there are six events that can be considered potential candidates for an event study. Three of these events decreased the perceived probability that the agreement would be implemented and three of them increased the perceived probability that the agreement would be implemented. The events are listed in Table 2. This paper focuses on the last three of these events. At the time of the fast-track debates (the first two events), there was still much uncertainty about whether an agreement would be reached. The breakdown in negotiations in September 1987 (the third event) occurred in the midst of a difficult negotiating process. It therefore was not entirely unexpected. Moreover, some viewed this as a negotiating tactic.16 The last three events (reaching the agreement after difficult negotiations and the two events relating to the Canadian General Election), on the other hand, all involved clear elements of uncertainty and occurred during the last stages of the negotiation process I4 See Rao (1974) for a discussion of specification bias of SUR estimators and Prager (1989) who notes this issue in the context of an event study. Is As discussed by Smith et al. (1986), the error terms may be heteroscedastic with respect to the event windows if the abnormal returns are not completely explained by the firm characteristics. The White (1980)-Chamberlain (1982) procedure may be employed to estimate heteroscedastic-consistent standard errors. It is not practical to implement this procedure for the firm-level analysis due to the large number of equations to be estimated. Heteroscedasticconsistent standard errors were estimated for the portfolio-level analysis (not reported) and did not alter the conclusions reached below. I6 September 24, The Globe and Mail, 1987.

A.J. Table 2 List of event

Thompson

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windows

Dates

Description

1414186

Event window 1 : Threat to deny fast-track authorization The U.S. Senate Finance Committee threatened to deny fast-track consideration of the FTA. The first trading day after the news was reported in the newspapers.

23/4/86 2414186

Event window 2 : Approval of fast-track procedure The Senate Finance Committee vote was tied; negotiations could begin. The first trading day after the news was reported in the newspapers.

2319187 2419187

Event window 3 : Negotiations were discontinued Negotiations were discontinued. The first trading day after the news was reported

1114186

Event window 4 : Agreement was reached It was announced at midnight on 30 September that the possibility negotiations would be discussed. Negotiations were resumed. A trade accord was reached on Saturday, 3 October.

l/10/87 2/10/87 5/10/87”

of resuming

Event window 5: Turner gains popularity during the General Election campaign and vows to abrogate the agreement if elected Globe and Mail: Turner performed well in the televised debate. Globe-Environomics Poll: Turner was the clear winner of the televised debates. Angus Reid Poll (released Saturday, 29/10/88): Liberal and Conservative parties were tied. Globe-Environomics Poll: the Liberal party had pulled ahead. Gallup Poll: the Liberal party had a ten percentage point lead.

26/10/88 28/10/88 31/10/88 l/11/88 7/11/88”

Event window 6: Mulroney regains popularity and wins the election Globe-Environomics Poll: Liberal and Conservative parties were tied. Gallup Poll: Confirmed that the parties were tied. Three national public opinion polls released on Saturday, 19 November, indicated a large increase in support for Mulroney and the Conservative Party. On Monday, 21 November, Mulroney won the election.

10/11/88 14/11/88 21/11/88”

a Days

in the newspapers.

included

in one-day

event

windows.

when implementation of the agreement was imminent. Therefore, these last three events are likely to be the most important. With the exception of the fourth event, the only relevant information learned during the event windows related to the likely implementation of the agreement rather than to the content of the agreement. During the fourth event window, investors learned both the final elements of the agreement and the fact that an agreement had been reached. If some of the abnormal returns reflect news about unexpected aspects of the agreement,

14

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then this will introduce unfortunate noise into the empirical analysis. Prior to the beginning of this event window, however, most of the elements of the agreement had been agreed upon, including the phased elimination of tariffs between the two countries. Final concessions were made in a few industries, primarily non-manufacturing industries. The primary unresolved issue was the dispute-resolution mechanism which was essential to a successful agreement. Therefore, very little new information was learned during this event window about the content of the agreement, particularly with respect to manufacturing industries. The impact on the empirical analysis of unexpected aspects of the agreement is thus likely to be minimall To define the event windows, information from sources such as public opinion polls and press interviews was used to determine if and when the events were anticipated. The precise dates included in the event windows are provided in Table 2. The first three windows each include one event day and the following trading day. The following day is included because it is possible that investors did not learn the event information until the news was reported in the newspapers on the day after the event occurred. The last three event windows each include a series of related events. The fourth event window includes days when news was learned that the free trade negotiations would be resumed and that an agreement had been reached. The fifth and sixth event windows include days when public opinion polls relating to the Canadian General Election were released. In addition to these ‘full event windows’, one-day event windows are estimated for the single day in each of the last three event windows when the most important information was learned: Monday, 5 October, 1987, the first trading day after the agreement was reached; 7 November, 1988, the release of the most dramatic public opinion poll results of (the Gallup Poll indicating that the Liberal Party had a 10 percent lead); and 21 November, 1988 (Election Day), the first trading day after the release of three public opinion polls indicating that the Conservative Party would win the election. 4.2.

Firm-level analysis

The stock market data are drawn from the Toronto Stock Exchange/ University of Western Ontario database. The sample of firms is restricted to manufacturing firms that were listed during the period encompassing the estimation and event periods for all six events.” In addition, the firms must

I7 One approach to control for this potential problem would be to cumulate the abnormal returns over all of the event windows to capture the overall impact of FTA news. I* The estimation periods begin approximately one year prior to the first event window considered and end on the last day of the last event window considered. The observations corresponding to one week before and two weeks after the stock market crash of 19 October, 1987 are deleted from the sample.

A.J.

Thompson

Table 3 Industry classification

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of firms in sample

Industry

Number

Food and Beverages Tobacco Products Textile Mill Products Lumber and Wood Products Furniture Paper and Allied Products Printing Chemicals and Allied Products Rubber Products Nonmetallic Mineral Products Primary Metal Fabricated Metal Products Industrial Machinery Electrical Equipment Transportation Equipment

12 2 3 4 2 8 4 5 1 3 9 5 3 11 4

of firms

be listed in Moody’s Investors Service (1989). This leaves a total of 76 firms. Table 3 provides an industry breakdown of the sample of firms. It is interesting to mention that 14 of the firms are subsidiaries of U.S. corporations.” Experiments were performed to allow for the possibility that the FIA was expected to have differential effects among firms owned by U.S. parents and subsidiaries of other foreign firms or domestically controlled firms. The results of these experiments indicate that controlling for U.S. ownership does not significantly alter the results presented below. The relative labor intensity of a firm is defined above as the ratio of labor income to capital income (i.e. wLlrK). Firm-level data on labor income are not available and estimation of the income earned by capital owners is complicated by the fact that the shareholders of some of the firms in the sample earned negative returns during the sample period. The relative labor intensity of a firm is therefore estimated by dividing the number of employees by an estimate of the value of the firm’s capital stock. This is equivalent to assuming that wages and the return to capital are constant among industries and is consistent with the theoretical model derived above. In addition, -an alternative measure of labor intensity was calculated by multiplying the number of workers by an estimate of the industry level wage and then dividing this measure of labor income by the firm’s capital value. The results based on this measure are very similar to those reported below. Data for this variable are from 1988, the year before the FTA was implemented. Data on the number of employees are found in Moody’s Investors Service (1989), and the industry-level wages used in the sensitivity I9 Information

about

subsidiaries

was obtained

from

Dun’s

Marketing

Services

(1988)

16

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analysis are calculated from Statistics Canada (1988). Previous researchers of comparative advantage have measured capital stock as the book value of fixed assets.*’ In the above theoretical model, however, the value of a firm’s capital is defined to be equal to the sum of its debt and equity. This represents the value of the firm’s total assets including intangible assets. This is estimated here by adding the stock market value of the firm’s equity to the book value of long-term debt.*l The stock market value of the firm is calculated from the outstanding share and price data reported by the Toronto Stock Exchange (1988). The book value of long-term debt is from Moody’s Investors Service (1989). The natural resource content of a firm’s output is not available at the firm-level. Instead, this variable is based on the 2-digit Standard Industrial Classification (SIC) code assigned to the firm by Dun and Bradstreet (1988).” It is calculated as the percentage of the industry’s inputs arising from the primary sectors of the economy reported by Statistics Canada (1986). The primary sectors are defined to include agriculture, timber, and mining. The natural resource content of the industry is then divided by an estimate of the share of capital in the industry, Oik, for 1985. This estimate is derived from calculations made by Denny et al. (1992). To create the relative plant scale variable, firms are classified according to the 4-digit SIC code assigned to them by Dun and Bradstreet (1988). The plant size for a given industry is estimated by dividing the value of shipments of the industry by the number of establishments operating in the industry. These data are found in the most recent Census of Manufactures [U.S. Department of Commerce (1987)] and Statistics Canada (1987). The term OS/O,, is calculated using the 2-digit industry-level elasticity of scale estimates reported by Magun et al. (1988) and the capital share estimates of Denny et al. (1992). 4.3.

Portfolio-level analysis

To create portfolios, firms are classified according to their 2-digit SIC code. The individual stocks are weighted according to their market value reported by the Toronto Stock Exchange (1988). The ratio of labor income to capital income for each industry is derived from calculations made by

2o See, for example, Stern and Maskus (1981) and Bowen et al. (1987). ” Ideally, one would want to use the market value of debt. Unfortunately, market values are that the book value will provide a good not readily available. It is hoped, however, approxrmation to the market value. **Firms that are not listed in this directory are classified according to their annual reports or the ‘nature of business’ assigned to them by the Toronto Stock Exchange.

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Denny et al. The ratio of the natural resource share to capital share, the relative plant scale, and the scale elasticity are calculated as described above for the firm-level analysis.

5. Results The summary statistics for the abnormal returns and independent variables are presented in Table 4. The mean abnormal returns are all less than 0.5 percent, suggesting that the response of manufacturing stock prices to the events was not substantially different from that of the overall market index.23 Brander analyzes the impact of the Canadian election polls on two TSE subindices that include manufacturing firms: (1) the industrials index, which responded more strongly to the polls than the TSE 300 index; and (2) the paper and forest products index which responded less strongly. The fairly large range of abnormal returns reported in Table 4 is consistent with the view that there will be winners and losers within the manufacturing

Table 4 Summary

statistics No. of obs.

Mean

St. dev.

High

Low

Firm-level data Abnormal return - Evt. 4 Abnormal return - Evt. 5 Abnormal return - Evt. 6 Labor/capital Relative plant scale

76 76 76 76 76

-0.0001 0.0031 -0.0003 1.4868 0.6086

0.0148 0.0117 0.0235 1.6321 0.7518

0.0494 0.0696 0.1495 12.6000 3.0774

-0.0679 -0.0261 -0.0931 0.1805 -0.6630

Industry-level data Abnormal return - Evt. 4 Abnormal return - Evt. 5 Abnormal return - Evt. 6 Labor/capital Natural resource/capital Relative plant scale Elasticity of scale

15 15 15 15 15 15 15

0.0000 0.0019 0.0044 2.0709 0.8667 0.2388 1.0393

0.0101 0.0050 0.0116 0.7672 1.4068 0.4696 0.0516

0.0177 0.0148 0.0373 4.0960 4.2890 1.0390 1.1900

-0.0133 -0.0075 -0.0074 0.8400 0.0050 -0.4890 1.0000

Note:

The abnormal

returns

summarized

here are for the full event

23 Thompson (1993) finds that the Canadian market window relative to the U.S. market while both Thompson the Canadian market rose in response to the Conservative polls included in the last two windows.

windows.

index fell during the fourth event (1993) and Brander (1991) find that popularity reflected in the opinion

18

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sector.24 Thompson (1993) provides a detailed discussion of the industrylevel abnormal returns.” The relationship between abnormal stock market returns and relative factor shares and relative plant scale is estimated for two sets of event windows: the last three events and all six events. In each case, the last three event windows are represented in two ways: the ‘full event windows’ and the one-day event windows. Table 5 presents results for the last three event Table 5 Results based

on last three

Variables

events

Full event windows Individual SUR

firm NLS

One day event windows Portfolio

Individual

NLS

SUR

firm

Portfolio

NLS

1.0000

1.0000

Gamma4

1.0000

1.0000

1.0000

Gamma5

0.4381 (0.6316)

0.7320 (0.4142)

0.1277 (0.1731)

0.1823 (0.3060)

1.6466** (0.6404)

Gamma6

0.9787 (0.9889)

-0.2421 (0.3986)

-0.3994 (0.2360)

0.3719 (0.3180)

0.7191 (0.4085)

-0.0516 (0.3005)

Labor/capital

0.0090 (0.0070)

0.0076 (0.0067)

-0.0017 (0.0026)

0.0271* (0.0133)

0.0061 (0.0071)

-0.0046 (0.0048)

Natural resources/ capital

0.0001 (0.0005)

0.0021* (0.0009)

0.0031* (0.0015)

0.0044** (0.0015)

Scale

-0.0015 (0.0013)

-0.0027 (0.0016)

-0.0236** (0.0062)

-0.0068* (0.0022)

-0.0053* (0.0022)

-0.0333** (0.0110)

Number of firms or nortfolios

76

76

15

76

76

15

0.0040** (0.0014)

1.0000

NLS

0.0589 (0.3048)

0.0050* (0.0024)

* Significant at the 5 percent level. ** Significant at the 1 percent level.

24 As seen in the table, there is an extremely large abnormal return of 15 percent during event window six. This reflects a 40 percent increase in the value of the stock of Indal Corporation on 14 November, 1988, coinciding with a takeover bid (see Globe and Mail, 15 September, 1988, p. B8). Sensitivity analysis indicates that the results presented below are robust to the deletion of this observation. 25 The abnormal returns reported in Thompson (1993) do not correspond exactly to those reported in Table 4 because Thompson (1993) estimates cumulative abnormal returns for each event window (i.e. abnormal returns are estimated for each day in the event window and then cumulated) while in this paper an ‘average’ abnormal return for each observation is estimated for each event window by estimating the coefficient of a dummy variable that is equal to one for all days in the window.

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windows and Table 6 presents those for all six event windows. Results based on both NLS and SUR estimation procedures are presented for the firmlevel analysis. For the portfolio-level analysis, the results based on the two estimation procedures are very similar. Only the NLS results are presented. The value of y, for the first event in each sample is normalized to one to permit identification of the remaining parameters. The relative magnitudes of the y estimates indicate the relative strength of the news learned during the event windows. As discussed above, the dummy variables corresponding to ‘negative events’ (those events that reduced the perceived probability that

Table 6 Results based

on all six events

Variables

Full event

windows

Individual

firm

SUR

NLS

Gamma1

1.0000

1.0000

Gamma2

-0.3006 (0.5978)

0.1982 (0.2810)

Gamma3

1.5489 (1.0598)

0.4471 (0.3011)

Gamma4

0.2257 (0.4862)

0.4873* (0.2414)

0.2020 (0.3836)

One day event Portfolio

Individual

NLS

SUR

1.0000

windows”

firm

Portfolio

NLS

NLS

1.0000

1.0000

1.0000

-0.1293 (0.3059)

0.1503 (0.5716)

0.3856 (0.3687)

-0.1948 (0.2950)

-0.2242 (0.3114)

0.7851 (0.7046)

0.2511 (0.3529)

-0.2755 (0.3011)

1.0595** (0.4067)

2.5236 (1.6325)

1.3770* (0.6754)

0.7235 (0.4602)

0.4730* (0.2181)

0.0740 (0.1951)

0.7242 (0.9038)

2.4180* (0.9627)

0.3619 (0.4288)

0.6424 (0.5972)

-0.2218 (0.2328)

-0.2771 (0.2625)

0.8335 (0.9251)

0.8704 (0.5681)

-0.1091 (0.4112)

0.0030 (0.0050)

0.0073 (0.0074)

0.0023 (0.0022)

0.0070 (0.0056)

0.0044 (0.0048)

0.0058 (0.0031)

Natural resources/ capital

-0.0003 (0.0005)

0.0021* (0.0009)

0.0030* (0.0013)

0.0007 (0.0006)

0.0027** (0.0010)

0.0024 (0.0015)

Scale

-0.0035 (0.0021)

-O.OOSO** (0.0023)

-0.0143* (0.0059)

-0.0035 (0.0020)

-0.0050** (0.0018)

-0.0061 (0.0067)

Number of firms or portfolios

76

76

15

76

76

15

events.

See Table

Labor/capital

a One-day event windows refer only to the last three * Significant at the 5 percent level. ** Significant at the 1 percent level.

2.

20

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I Journal of International

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37 (1994) 1-27

the agreement would be implemented) are multiplied by negative one to facilitate the interpretation of the results. Thus, the expected signs of all of the y parameters are positive. Some of the estimates of these parameters are negative. A possible interpretation of a negative y estimate is that the FIA news released during an event window was overshadowed by other new information learned during that window. Focusing on the three-event sample, the relative labor intensity coefficient is generally positive although it is statistically significant for only one of the estimations. These weak results are consistent with the hypothesis that Canada has neither a strong comparative advantage nor a strong disadvantage in the production of labor intensive goods. The natural resource intensity coefficient is always positive and is statistically significant at the 5 percent level for all but one of the estimations. This suggests that investors expected natural resource intensive firms to benefit from the FTA. This may be interpreted as an indication that Canada has a comparative advantage in the production of natural resource intensive goods. As noted above, however, an alternative interpretation is that the price of natural resources was expected to fall as a result of free trade. Stock market prices of natural resource producing firms provide some indication of the expected impact of the FTA on natural resource prices. The values of the two TSE subindices relating to natural resources both rose during the fourth event window (which, as discussed below, is likely to be the most important event): the metals and minerals index increased by 4.1 percent and the oil and gas index increased by 0.6 percent. As a benchmark, the TSE 300 index increased by 0.4 percent. In terms of actual prices, the raw material price index increased both absolutely and relative to the industrial price index during 1989 and 1990, the first two years of the FTA, although it fell in 1991.26 These data cast doubt on the hypothesis that natural resource prices were expected to fall as a result of the FIA. It is more likely that the positive relationship between abnormal returns and the natural resource intensity of firms is an indication that investors perceived Canada to have a comparative advantage in natural resource intensive goods. The coefficient of the plant scale variable is consistently negative and is statistically significant at the 5 percent level for many of the estimations. These results are consistent with the economies of scale hypothesis and suggest that firms operating in industries where the average plant scale is small relative to the United States were expected to experience losses during the adjustment to free trade. Taken together, the results presented in Table 5 indicate that both scale economies and comparative advantage were perceived by investors to play a role in the adjustment of Canadian 26 Price index data were obtained

from Statistics

Canada

(1992).

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I Journal of International Economics

37 (1994) 1-27

21

manufacturing firms to the FTA. In addition, these results imply that international trade news has differential effects on stock prices, thus reinforcing previous evidence that capital is not perfectly mobile among Magee (1980), Grossman and Levinsohn industries [see, for example, (1989) and Thompson (1993)]. A comparison of the estimates based on the full event windows to those based on the one-day windows indicates that the implications of the results with respect to the factor share and plant scale variables are not sensitive to the definition of the event window. In addition, the full event windows were extended by one day to allow for the possibility that information is reflected in security prices on the day following the event day for infrequently traded securities that did not trade on the event day.” The results are similar to those reported in Table 5. It is interesting that the relative magnitudes of the y parameters for the firm-level analysis do depend on the definition of the event window. For example, consider the two NLS estimations. The estimate of ys based on the full event windows is approximately 0.7 and is statistically significant at the 10 percent level. The estimate of y6 is negative although not statistically significant. These results suggest that the abnormal returns corresponding to only events four and five reflect information about the FTA and that event four, reaching the agreement in October 1987, had the greatest impact on expectations about the FTA. The estimates based on the one-day windows, however, suggest that all three events had an important impact on expectations about the ETA. Because -y4 has been set equal to one for these estimations, the results provide only indirect evidence about the importance of the fourth event. If, instead, -ys is set equal to one, the relative magnitudes of the y estimates are very similar to those reported in Table 5 and the estimates of y4 are statistically significant at the 10 percent and 1 percent levels of significance for the full and one-day windows, respectively.28 Thus, the relative importance of the two election events is greater for the one-day event windows than the full event windows. One interpretation of these results is that some of the additional days included in the full event windows for the election events introduce more noise than information about the agreement and therefore obscure the FIA information that is reflected in the one-day windows. *‘Infrequent trading, which has been noted to be a characteristic of the Toronto Stock Exchange, may lead to an errors-in-variables problem in the estimation of the market model parameters. To investigate this potential problem, the results were re-estimated using the technique suggested by &holes and Williams (1977) and were found to be similar to those reported in Table 5. 28 The estimates of x and y6 are 1.34 and -0.34 for the full event windows and 0.52 and 0.48 for the one-day event windows. The estimates of the factor share and scale coefficients are similar to those reported in Table 5.

22

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At the portfolio level, there is no evidence that the abnormal returns relating to the election events reflect information about the FI’A. This is consistent with the finding of Thompson (1993) that the fourth event is the only event for which the abnormal returns to industry portfolios are consistent with prior hypotheses. Similarly, Brander (1991) finds that the differential impact of the election polls on TSE subindices does not provide much evidence that stock market reactions to the polls reflect expectations about the FTA. As discussed in these two previous papers, industry-level analysis may obscure the full impact of the FTA if firms within industries are affected differently. The comparison of the firm- and industry-level results illustrates that it is more difficult to isolate the impact of the FTA news using industry-level data, particularly when other important information is released during the event window. More generally, the results of these studies indicate that analysis of industry aggregates may not fully capture the impact of trade liberalization. Turning now to the six-event sample, the natural resource and scale coefficients are statistically significant for only three of the estimations. It is interesting that these estimations are the only ones for which any of the estimates of the y parameters for the last three events are statistically significant. The estimate of -y4is statistically significant for all three. These results are consistent with the belief that the abnormal returns corresponding to the last three event windows, and event window four in particular, are more likely to reflect expectations about the impact of the FTA than those corresponding to the first three events.

6. Concluding

remarks

In this paper, a stock market event study is employed to analyze the market’s expectations about the consequences of the FIA for Canadian manufacturing firms. Two primary questions are addressed: (1) whether differences in relative factor shares across industries played a role in determining investors’ perceptions of the comparative advantage of Canadian firms; and (2) whether firms operating in industries where the average plant scale is small relative to the corresponding U.S. industries were expected to experience losses as a result of the FIA. Abnormal returns corresponding to six FTA-related events are estimated for individual Canadian manufacturing firms and industry-level portfolios. The relationship between abnormal returns and relative factor shares and relative plant scale is estimated for a subset of events as well as for the full sample of events. The results suggest that investors anticipated natural resource intensive firms to benefit from the FTA relative to capital intensive firms. With respect to plant scale, a negative relationship is found between

A.J.

Thompson

I Journal of International Economics

37 (1994) l-27

23

abnormal returns and plant scale disadvantage. This result indicates that firms operating in industries where the average plant scale is small relative to their U.S. competitors were anticipated to be more likely to experience losses as a result of the FTA than firms operating in industries where the relative average plant scale is large. This may be interpreted as an indication that these industries were expected to rationalize their production facilities in response to free trade. These results are consistent with the hypothesis that both scale economies and comparative advantage were perceived by investors to play a role in the adjustment of Canadian manufacturing firms to the FTA.

Appendix As seen in Eq. (ll), the factor share coefficients depend not only on relative factor price differences, but also on the anticipated impact of the FIA on the relevant factor price. It is therefore necessary to investigate the likely impact of the FTA on factor prices. The FTA will alter the marginal revenue products of all three factors employed in each sector. In period T capital is fixed and labor and natural resources are reallocated between sectors until their marginal revenue products are equal among sectors. Full employment in the variable factor markets is therefore maintained. With perfect competition and constant returns to scale, the marginal revenue product of a given factor is simply equal to the output price multiplied by its marginal product. Therefore, when the output price falls, the demands for the variable factors fall. The factor market equilibrium conditions are

(14) and

,$NRi(W>

W,,

Pi> ki)

where S represents the supplies of labor and (1974) to consider the differentiation of these

=z

7

(15)

number of sectors, and ,? and NR represent the fixed natural resources. Extending the analysis of Mussa case of S specific factors and two variable factors, two conditions yields:

(16) and

A.J.

24

Thompson

I Journal of International Economics

37 (1994) 1-27

(17) where pij is the elasticity of good i:

of the demand

for factor j with respect

to the price

and

The term Alj represents the fraction of variable factor j that is employed in sector i, lij is the elasticity of demand for factor j with respect to its own price in sector i, and lijm is the elasticity of demand for factor j with respect to the price of factor m. Notice that C FZ1 pil = CfZ1 pj,, = 1. The proportionate changes in the variable factor prices are therefore weighted averages of the proportionate changes in output prices. Substituting Eq. (16) for Gfta and Eq. (17) for Gr” into Eq. (11) yields”

ct = (dp,)(&)‘-‘(ius-’ +6,z +a,$),

(18)

where 6, = b,,(

),y-c

-

y-c)

+

b,,(

*“s-c

_

)y”)

)

-

,-)

.

and 6,

=

bnk(

,y

-

;-)

+ b,,(

,y

Under this scenario, the change in the capital value of firm i is a linear function of the factor shares of labor and natural resources relative to the share of capital. The coefficients of these relative factor shares are, in turn, functions of the factor price differentials. The relationship between factor price differentials and the predicted signs of 6, and 8, is summarized in Table A.l. These coefficients reveal the sources of Canada’s greatest comparative factor price advantage and/or disadvantage. For example, if natural resources represent Canada’s greatest factor price advantage and labor represents Canada’s greatest factor price disadvantage, then G~s-c > rAus-c > ~us-c and the predicted signs of 6, and 6, are negative and positive, respectively. When capital represents the greatest advantage or disadvanZ9With constant

returns

to scale,

0: in Eq. (11) equals

zero.

A.J.

Thompson

Table A.1 Relationship between perfect competition

factor

! Jownul

of International Economics

price differences

and predicted

Predicted of 6,

signs of parameter

sign

of the

coefficient

estimates

with

Predicted of&

sign

>o >o ?
? o >o
tage, however, the sign factor is indeterminate.

25

37 (1994) 1-27

corresponding

to the

‘middle’

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Eastman, H.C. and S. Stykolt, 1967, The tariff and competition in Canada (Macmillan, Toronto). Grossman, G.M. and J.A. Levinsohn, 1989, Import competition and the stock market return to capital, American Economic Review 79, 1065-1087. Harris, R., 1984, Applied general equilibrium analysis of small open economies with scale economies and imperfect competition, American Economic Review, 74, 1016-1032. Hartigan, J.C., P.R. Perry and S. Kamma, 1986, The value of administered protection: A capital market approach, Review of Economics and Statistics 68, 610-617. Helpman, E. and P.R. Krugman, 1985, Market structure and foreign trade (MIT Press, Cambridge, MA). Horstmann, I.J. and J.R. Markusen, 1986, Up the average cost curve: Inefficient entry and the new protectionism, Journal of International Economics 20, 225-247. International Monetary Fund, 1990, International financial statistics (International Monetary Fund, Washington, DC). Learner, E.E. and H.P. Bowen, 1981, Cross-section tests of the Heckscher-Ohlin theorem: Comment, American Economic Review 71, 1040-1043. Magee, S.P., 1980, Three simple tests of the Stolper-Samuelson theorem, in: P. Oppenheimer, ed., Issues in international economics (Oriel Press, Stockfield). Magun, S., S. Rao, B. Lodh, L. Lavallee and J. Peirce, 1988, Open borders: An assessment of the Canada-U.S. Free Trade Agreement, Economic Council of Canada Discussion Paper No. 344. Markusen, J., 1981, Trade and the gains from trade with imperfect competition, Journal of International Economics 11, 531-551. Moody’s Investors Service, 1989, International manual (Moody’s Investors Service, New York). Mussa, M., 1974, Tariffs and the distribution of income: The importance of factor specificity, substitutability and intensity in the short run and long run, Journal of Political Economy 82, 1191-1203. Neary, J. P., 1978, Short-run capital specificity and the pure theory of international trade, Economic Journal 88, 488-510. Prager, R.A., 1989, Using stock price data to measure the effects of regulation: The Interstate Commerce Act and the railroad industry, Rand Journal of Economics 20, 280-290. Puffer, M., 1992, The international reaction to U.S. trade deficit announcements, Mimeo, University of Toronto. Rao, P., 1974, Specification bias in seemingly unrelated regressions, in: W. Sellekaerts, ed., Econometrics and economic theory: Essays in honour of Jan Tinbergen (MacMillan, London). Ries, J., 1993, Windfall profits and vertical relationships: Who gained in the Japanese auto industry from VERs?, Journal of Industrial Economics, 41, 259-276. Rose, N., 1985, The incidence of regulatory rents in the motor carrier industry, Rand Journal of Economics 16, 299-318. Royal Commission on Economic Union and Development Prospects for Canada, 1985, Report, vol. 1 (Supply and Services Canada, Ottawa). Scholes, M. and J. Williams, 1977, Estimating betas from nonsynchronous data, Journal of Financial Economics 5, 309-327. Smith, R.T., M. Bradley and G. Jarrell, 1986, Studying firm-specific effects of regulation with stock market data: An application to oil price regulation, Rand Journal of Economics 17, 467-489. Statistics Canada, 1986, The input-output structure of the Canadian economy, 15-201 (Supply and Services Canada, Ottawa). Statistics Canada, 1987, Manufacturing industries of Canada: National and provincial areas, 31-203 (Supply and Services Canada, Ottawa).

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27

Statistics Canada, 1988, Manufacturing industries of Canada: National and provincial areas, 31-203 (Supply and Services Canada, Ottawa). Statistics Canada, 1992, Canadian economic observer: Historical statistical supplement, 11-210 (Supply and Services Canada, Ottawa). Stern, R.M. and K.E. Maskus, 1981, Determinants of U.S. foreign trade, 1958-76, Journal of International Economics 11, 207-224. Stopler, W. and P.A. Samuelson, 1941, Protection and real wages, Review of Economic Studies 9, 58-73. Thompson, A.J., 1993, The anticipated sectoral adjustment to the Canada-United States Free Trade Agreement: An event study analysis, Canadian Journal of Economics 26, 253-271. Toronto Stock Exchange, 1988, The Toronto Stock Exchange review (Toronto Stock Exchange, Toronto). United States Department of Commerce, Bureau of the Census, 1987, Census of manufactures (Government Printing Press, Washington, DC). United States Department of Labor, Bureau of Labor Statistics, 1989, Bulletin (Government Printing Press, Washington, DC). White, H., 1980, A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica 48, 817-838. Winham, G., 1988, Trading with Canada: The Canada-U.S. Free Trade Agreement (Priority Press Publications, New York, NY). Wonnacott, R.J. and P. Wonnacott, 1967, Free trade between the United States and Canada (Harvard University Press, Cambridge, MA).