1

Import Competition and Market Power: 

Canadian Evidence

by

Aileen J. Thompson*

Federal Trade Commission

Washington, DC

January, 2001

* This paper is a revised version of Carleton Economic Paper #99-14 and Statistics 

Canada Research Paper Series #139. It was largely written while I was an Associate 

Professor at Carleton University. I am grateful to the Micro-Economic Analysis 

Division of Statistics Canada for a Visiting Fellowship and support that allowed me to 

complete this project. I thank Bob Gibson and Joanne Johnson for providing insight 

into the data, John Baldwin for many thoughtful discussions, and Mike Vita, 

anonymous referees and workshop participants at Carleton University, Clark 

University, Statistics Canada, and the General Acounting Office for helpful 

comments. I also thank the University of Michigan for providing an interesting 

research environment during my sabbatical visit, the Social Science and Humanities 

Research Council of Canada (SSHRC) for financial support and Ying Kong for 

research assistance. The views expressed in this paper are those of the author and do 

not necessarily represent the views of Statistics Canada or the Federal Trade 

Commission or any individual Commissioner. 
2

Import Competition and Market Power: 

Canadian Evidence

Aileen J. Thompson

Federal Trade Commission

Abstract

This paper estimates price-marginal cost mark-ups for Canadian manufacturing 

industries during the 1970s in order to assess the impact of import competition on 

domestic market power. The results are mixed. Based on the analysis, there is no 

consistent evidence that imports had a beneficial impact on competition in the Canadian 

market during that period. One possible explanation for this finding is that trade may have 

differential impacts among firms within industries. Detailed firm-level analysis may 

therefore provide a more complete understanding of the impact of imports on competition. 
3

1. Introduction

A number of international trade models have now been developed that account for 

imperfect competition. Although some of these models provide insight into situations 

where trade protection may be welfare improving, most indicate that imperfect 

competition provides additional sources of gains from trade. Many of these gains result 

from the “pro-competitive” effect of trade: import competition increases the perceived 

elasticity of demand for domestic firms, leading them to reduce their mark-ups of price 

over marginal cost.1 

Applied general equilibrium models suggest that these effects may be important 

quantitatively. Harris (1984) and Cox and Harris (1985) perform a number of simulations 

of trade liberalization experiments calibrated for 1976 Canadian data. They find that the 

estimated welfare gains based on models incorporating imperfect competition are 

substantially greater than the estimated gains based on the corresponding perfectly 

competitive model. Similarly, in a study of the potential impact of the Canada-United 

States Free Trade Agreement, Brown and Stern’s (1989) model suggests that the procompetitive effects of Canadian tariff reductions may be quite strong in many Canadian 

industries.

Early econometric studies analyzing the impact of trade on market power employ 

the mark-up of price over average variable cost as a measure of non-competitive behavior. 

These studies generally find that import competition reduces average cost mark-ups, 

particularly in domestically concentrated industries.2 Economic theory, however, predicts 

 

1

See Markusen (1981).

2

 See, for example, Caves et al. (1980), Jacquemin et al. (1980), Pugel (1980), de Melo and Urata (1986), 

and Domowitz et al. (1986) and Katics and Petersen (1994).
4

that import competition reduces the mark-up of price over marginal cost, which is not 

directly observable. More recent studies draw on the work of Roberts (1984) and Hall 

(1988) to estimate price-marginal cost mark-ups from equations derived from profit 

maximizing conditions. Three studies apply this approach to plant-level data to analyze 

the impact of trade reform on competition in developing countries. Levinsohn (1993) 

finds that price-marginal cost mark-ups fell in Turkish industries where trade was 

liberalized, and increased in industries where trade protection was increased. Similarly, 

Harrison (1994) finds that mark-ups are negatively related to import competition in the 

Cote d’Ivoire, and Krishna and Mitra (1998) present evidence that mark-ups fell during 

the trade reform period in India. 

This paper estimates price-marginal cost ratios using detailed establishment-level 

data for manufacturing industries in Canada during the 1970s. This period of study was 

chosen due to the considerable variation in trade and trade protection across industries as 

well as the increase in trade that took place between the early 1970s and the late 1970s. 

In 1970, average tariff rates for individual 2-digit industries ranged from 5.2% to 50.9%.3 

As a result of the Kennedy Round of trade negotiations, begun in 1966, the average tariff 

level for Canadian manufacturing fell from 10.7% in 1970 to 7.8% in 1978. 4 The share of 

domestic consumption of manufactures that was accounted for by imports rose from 26% 

in 1971 to 32.6% in 1979.5 While this increase in import competition was not as 

substantial as that which occurred in the developing countries discussed above, it was 

 

3

 Baldwin and Gorecki (1986: 7). Table 1 provides the average import intensity levels for the major 

industry groups.

4

 Baldwin and Gorecki (1986: 7). 

5

 Baldwin and Gorecki (1986: 8). Imports in Canada increased even more dramatically during the 1990s, 

beginning in 1993. The data necessary to analyze this time period, however, were not available at the 

time the study was begun.
5

nonetheless significant and has been analyzed to evaluate the effect of international trade 

on various aspects of the Canadian economy.6 

The relationship between mark-ups and imports is estimated for two separate 

cross-sections: the early 1970s and the late 1970s. In addition, the data for the two 

periods are combined to analyze the impact of changes in import competition on mark-ups 

over time. The primary conclusion that can be drawn from the analysis is that there is no 

consistent evidence that import competition reduced the market power of firms operating 

in the Canadian market during this period.

2. Empirical Framework

2.1 Estimating Price-Marginal Cost Ratios

Profit-maximization with respect to output yields the following relationship 

between price and marginal cost:

P

MC

s it

it

it it

it

= it +











 ≡

−

1

1

q

h

b, (1)

where sit is the market share of the firm, hit is the market elasticity of demand in industry 

i, andqit is the conjectural variations parameter (= ¶Qt/ ¶qit ). As defined above, bit is 

the profit-maximizing ratio of price to marginal cost. The estimated value of this 

parameter can be used to calculate the Lerner index, the mark-up of price over marginal 

cost:

b

1

= 1−

−

P

P MC

.

 

6

 See, for example, Baldwin and Gorecki (1986) and Caves (1990). 
6

To estimate price-marginal cost ratios, I follow the approach employed by 

Levinsohn (1993). Consider the production function for a representative firm i: 

 qit = fit f Lit ( ) (2) 

where Lit is a vector of j factors of production, f is a function common to all firms in the 

industry, and fit is a firm- and period-specific productivity shock that is assumed to 

follow a random walk:

fit = fi t eit +

, − 1 ,

,

eit ≈N(0,s ). 2

Furthermore, it is assumed that eit is composed of a time-specific productivity shock that 

is common to all firms within a given industry and a productivity shock that is specific to 

the individual firm:

eit = lt mit +

.

.

To derive an estimating equation for bit , totally differentiate (2), 

dqit it ( f

it Ljit )dLjit f ( )

j

= it t it











f ∑ ¶ / ¶ + l + m . (3)

Profit-maximization with respect to input markets implies that the firm employs each input 

until its marginal revenue product is equal to its price. Thus, 

f

¶

¶

it it

it b

ijt

it ijt p

f

L

w

−

=

1

, (4)

Solving (4) for (¶f / ¶L )

it it and substituting it into (3) yields:

dq ( )

w

p

dL f

it it

ijt

j t

= ijt it t it b ∑ + l + m . (5)
7

Thus, bit can be estimated by estimating the relationship between changes in output and 

factor price-weighted changes in inputs. 

Price-marginal cost mark-ups are estimated for individual Canadian industries at 

the 3-digit Standard Industrial Classification (SIC) level for two years during the early 

1970s and two years during the late 1970s. Following Levinsohn, three econometric 

issues are addressed. First, the term lt is modeled as a time-period fixed effect.7 

Second, the output price, pt, is potentially endogenous since an individual firm (and shocks 

affecting that firm) may affect the industry price level. To address this concern, the 

wholesale price index is used as an instrument for the industry-level price index. The final 

concern is that the disturbance term of (5), f

it(lt

+ mit ), is heteroskedastic owing to the 

presence of the f

it term, which is a function of firm size. To address this issue, it is 

assumed in the estimation that the variance of the disturbance is proportional to the square 

of labor expenditures. 

2.2 Estimating the Relationship between Price-Cost Ratios and Import Competition

In the second stage of the analysis, the impact of imports on price-marginal cost 

ratios is estimated. A negative relationship between trade and price-cost margins is 

indicated by a number of models, including Chamberlinian trade models (e.g., Krugman 

(1979)), Cournot models with free entry and exit (e.g., Dixit and Norman (1980)), and 

Cournot models with segmented markets (e.g., Brander (1981)). The relationship 

between price-cost ratios, imports and other explanatory variables can be expressed as:

 

7

 It is likely that lt is correlated with the changes in inputs. In this case, the fixed effects specification 

will lead to unbiased and consistent, but inefficient estimates. An alternative approach is to employ 
8

h

k

h k hk b = ∑ gx + n , (6)

where bh is the ratio of price to marginal cost for industry h, x is a vector of industry 

characteristics, including a measure of import competition, and nh is the disturbance term, 

assumed to have a zero mean and constant variance of sn

2

. 

Studies specifically deriving the relationship between imports and price-cost 

margins have found that the appropriate empirical specification includes the interaction 

between imports and measures of the competitiveness of domestic suppliers.8 This is 

because import competition can be expected to have the greatest impact on industries 

where domestic market conditions are such that competition would otherwise be weak. It 

is well known that it is difficult to capture differences in market structure by a single 

measure (or a manageable set of measures).9 The four-firm concentration ratio is the most 

frequently employed indicator of domestic competition for studies of import competition 

and profitability and is therefore used here for comparability.10 

In addition, owing to the high correlation between imports and exports (see 

below), it is important to control for the potential impact of exports so that the impact of 

imports can be isolated. Theoretical models lead to ambiguous predictions about the 

impact of exports on price-cost mark-ups. Suppose, for example, that export markets are 

 

instrumental variables estimation although, as discussed by Levinsohn, appropriate instruments are not 

readily available. 

8

 See the discussion in Caves (1985).

9

 See Bresnahan (1989) for a discussion of this issue.

10 For previous versions of the paper, the model was also estimated using market share turnover between 

1970 and 1980 as a measure of domestic competition. Thisvariable is measured as the percentage shift of 

market share from declining establishments to growing establishments. The results were not substantially 

altered.
9

more competitive than domestic markets. In other words, domestic firms perceive higher 

elasticities of demand in export markets, resulting in lower mark-ups on exports than on 

domestic output. With segmented markets, it will be profitable for domestic firms to 

export as long as marginal revenue exceeds marginal cost. With constant marginal costs, 

the mark-up on domestic output is unchanged. With increasing marginal costs, however, 

the mark-up on domestic output actually increases. Since estimated mark-ups reflect a 

weighted average of the mark-ups on domestic output and exports, the net impact of 

exporting on mark-ups is ambiguous under this scenario. 

The fact that price-marginal cost ratios are estimated (with error) rather than 

observed raises the issue of heteroskedasticity. Replacing bh

 with b$

h

, we have the 

following equation,

h

k

h k hk b = ∑ gx + w

ˆ , (6’)

where w h nh xh = + , andxh is the estimation error of b$

h

. The disturbance term is likely 

to be heteroskedastic owing to the fact that the variance ofxh is not constant across 

industries. Equation 6’ is therefore estimated using feasible generalized least squares 

(GLS) following the procedure described in Appendix A. 

3. Data

Price-marginal cost ratios are estimated for individual Canadian industries at the 3-

digit Standard Industrial Classification (SIC) level over the periods 1971-2 and 1978-9 

(based on changes from 1970-1 and 1971-2 for the first period and 1977-1978 and 1978-9 

for the second period). Data for two years are combined for each estimate to reduce the 
10

sensitivity of the estimates to the particular year chosen as well as to increase the number 

of observations for each 3-digit industry. 

To estimate equation (5), price and quantity data are required for output and 

factors of production. Five factors of production were initially considered: production 

workers, non-production workers, materials, fuel, and capital. Establishment-level data 

were obtained from the annual Census of Manufactures survey for: value of manufacturing 

production, hours worked by production workers, number of non-production workers, 

value of materials used in manufacturing production, and expenditures on fuel and energy. 

This survey covers every establishment assigned to the manufacturing sector. However, 

only establishments for which manufacturing activity accounts for at least 90% of total 

activity and for which there were no missing data for at least two consecutive years are 

included in the estimation. In addition, the analysis for each time period is based on the 

industries that had at least 20 observations. The final sample for which all data are 

available (including the international trade variables and other industry characteristics) 

consists of 97 industries for 1971-2 and 99 industries for 1978-9.

Data for capital investment were obtained from the Statistics Canada Capital 

Expenditures Survey. When the capital expenditures file was merged with the Census of 

Manufactures file, however, the sample size was significantly reduced. Preliminary 

analysis indicated that including capital as a factor of production did not significantly alter 

the estimates of the mark-ups for the sample of plants for which capital data were 

available.11 Capital was therefore not included as a factor of production in the estimates 

below so that a larger sample could be used. 

 

11 The correlations between the mark-ups estimated with and without capital were 97.8% and 98.8% for 

1971 and 1979, respectively. 
11

Quantities for production, materials, and fuel were computed by dividing the 

values of these variables by industry-level price indices. These indices were obtained from 

the KLEMS database, made available through the Input-Output division of Statistics 

Canada. Industry-level wages and salaries were calculated for each 3-digit SIC code by 

dividing total wages earned by production workers by the number of hours worked and by 

dividing total salaries earned by non-production workers by the number of non-production 

workers.

Data on imports and exports at the 3-digit SIC level were obtained from the 

publication, Commodity Trade by Industrial Sector, Historical Summary, 1966-1983, 

published by the Department of Regional Industrial Expansion, Canada. Both the import 

and export data were corrected for re-exports. Import intensity is defined as the share of 

domestic consumption accounted for by imports, where domestic consumption is 

calculated as (domestic shipments - exports + imports). Export intensity is defined as the 

ratio of exports to shipments. The shipments data were obtained from the Statistics 

Canada publication Manufacturing Industries in Canada. 

Table 1 summarizes the trade data by major manufacturing groups for the 3-digit 

industries considered in this study.12 Both import and export intensities increased in 

almost every major industry group.13 As a result, the pattern of trade across industries is 

similar for both periods. This suggests, unfortunately, that it may be difficult to distinguish 

a differential impact of increased trade over the period on mark-ups. The industries with 

the greatest import intensities in both the 1971-2 and the 1978-9 periods are leather, 

textiles, knitting mills, primary metals, machinery, transportation equipment, electrical 

 

12 The data in this table represent only the 3-digit industries used in this study and therefore do not 

correspond directly to trade data calculated at the 2-digit level.
12

equipment, and chemicals. Three of these industries, primary metals, machinery, and 

transportation equipment, are also among the industries with the largest export intensities. 

The correlations between import and export intensities are 0.55 and 0.50 for the 1971-2 

and 1978-9 periods, respectively.

The data were provided by the Micro-Economic Analysis Division of Statistics 

Canada unless otherwise specified. 

4. Results

4.1 Estimates of Price-Marginal Cost Ratios

Before discussing the results with respect to import competition, it is useful to 

summarize the estimates of the price-marginal cost ratios. The number of observations 

employed in the estimations ranged from 20 to 1370 for the individual 3-digit industries. 

Table 2 reports the summary statistics for both the 1971-2 and 1978-9 periods. The mean 

estimated price-marginal cost ratio for 1971-2 is 1.15 while the mean estimate for 1978-9 

is 1.09, indicating a fall in the average mark-up from 12.7% to 8.6%. This is within the 

range of estimates reported by other studies based on plant-level data. Harrison (1994), 

for example, reports an average mark-up across sectors of 8%. 

Owing to the significant increase in trade during the 1970s, the general reduction 

in estimated price-marginal cost ratios during this period is consistent with the hypothesis 

that trade increases competition. In addition, the proportion of ratios that is statistically 

significantly greater than one fell from 70% to 44%. The increase in competition is not 

 

13 The exceptions are petroleum and coal, where import intensity fell and knitting mills where export 

intensities fell.
13

uniform, however. Panel C of Table 2 provides summary statistics for the change in pricemarginal cost ratios. Although 29% of the industries experienced a statistically significant 

decline in their mark-ups, 15% actually experienced a statistically significant increase.14 

Table 3 summarizes the 3-digit mark-ups according to 2-digit industry groups. 

Six industries had average ratios above the median for both the early 1970s and the late 

1970s: tobacco products, electrical products, non-metal mineral products, petroleum and 

coal, rubber and plastic, and miscellaneous; while six industries had average ratios below 

the median for both periods: paper and related products, wood products, clothing, 

printing and publishing, knitting mills, and food and beverages.

4.2 Cross-sectional Analysis: Benchmark Model

Table 4 presents the cross-sectional GLS results of equation (6’) estimated 

separately for 1971-2 and 1978-9. The question here is whether domestically 

concentrated industries with high levels of import competition have lower price-marginal 

cost margins than domestically concentrated industries with low import competition. The 

coefficient on the interaction between imports and concentration is negative as predicted 

for both time periods, although not statistically significant. As will be discussed more fully 

below, this relatively weak relationship between mark-ups and import competition may 

reflect, in part, the simultaneity problem that high mark-ups attract imports.

The relationship between mark-ups and export intensity is negative for all of the 

equations in Table 4, although it is not statistically significant. With respect to domestic 

concentration, the results indicate that concentration did not have a significant impact on 

price-marginal cost ratios for the 1971-2 period. It did, however, have a significant 

 

14 To estimate the correct standard errors, the data were combined for the two periods and a dummy 
14

positive impact for the 1978-9 period. It is interesting that the estimated effect is stronger 

for the 1978-9 period. Due to the increase in international trade during the 1970s, one 

could predict that the level of domestic concentration would have been less important in 

the late 1970s than in the early 1970s. This result may reflect the weakness of using a 

single measure to capture the complex variations in market structure. 

For purposes of comparison, Domowitz et al. (1988) find that concentration has a 

small, but significant, positive impact on estimated price-marginal cost mark-ups for U.S. 

manufacturing industries. When the analysis is performed for different types of industries, 

however, they find that the relationship between concentration and mark-ups is strong for 

consumer goods and durable goods industries, but insignificant for producer goods and 

non-durable goods industries. In addition, they find that the relationship varies over the 

business cycle.

4.3 Instrumental Variables Estimates

As mentioned above, there is a potential simultaneity between price-cost ratios and 

imports. If imports are determined endogenously, then the GLS estimates will be biased. 

Instrumental variables estimates are reported in columns (3) and (4) in Table 4. 

Appropriate instruments are correlated with imports, but uncorrelated with the error term 

of equation (6’). The set of instruments consists of the nominal tariff rate and dummy 

variables for natural resource and labor-intensive industries.15 Interactions between these 

 

variable was employed to capture the change in mark-ups with the corresponding standard error.

15 The tariff data were generously provided by Larry Schembri. These data were defined according to the 

input-output “PL” level classification and concorded to SIC codes. The labor and natural resource 

dummy variables are based on the OECD (1987) taxonomy adapted for the Canadian economy by Baldwin 

and Raffiquzzaman (1994).
15

instruments and concentration are also included as instruments for the estimation 

equations that include the interaction between concentration and imports. An alternative 

set of regressions was estimated using the effective rate of protection as an instrument in 

place of the nominal tariff rate. The results were very similar to those discussed below.16 

As discussed by Bound, Jaeger and Baker (1995), the finite sample bias of IV 

estimates may be quantitatively important when the correlation between the endogenous 

variables and the instruments is weak. In particular, a good approximation of the bias of 

IV estimates relative to OLS estimates is provided by (1/F), where F is the F statistic for 

the instruments in the first stage regression. The F-statistics for the instruments employed 

here range from to 2.1 to 4.4, indicating that the potential bias of the IV estimates is small 

relative to the OLS estimates. 

The IV results are reported in columns (3) and (4).17 The IV estimates differ in 

magnitude from the OLS estimates and are estimated with larger standard errors. The 

implications of the results, however, are similar. Based on the Hausman test, the 

hypothesis that the two sets of estimates are the same cannot be rejected at the 5% level of 

significance for any of the estimating equations. Therefore, the hypothesis that imports are 

exogenous cannot be rejected.

4.4 The Impact of Multinational Corporations

In this section, the role of multinational corporations in determining the 

relationship between import competition and mark-ups is examined. This is motivated by 

theoretical and applied general equilibrium work by Markusen et al. (1995) that suggests 

 

16 Effective rates of protection measures were generously provided by John Baldwin for 1970 and 1978.
16

that the pro-competitive effect of trade may be dampened by the presence of multinational 

corporations. This is because an increase in imports may actually increase the market 

share and mark-up of foreign-owned firms operating in the domestic market if imports 

originate from the parent company. In this case, trade liberalization will not necessarily 

have the overall pro-competitive effect that has been emphasized in much of the trade 

policy literature. 

A related issue is intra-firm trade. Increases in imports may be due, in part, to 

increases in intermediate goods imports within the same firm. In this case, an observed 

increase in imports will not necessarily represent an increase in competition. These are 

potentially important issues in Canada. It has been estimated that foreign-controlled 

importers accounted for approximately 70% of Canadian imports in 1978.18 Furthermore, 

approximately two-thirds of the total imports by foreign-controlled firms were intraaffiliate imports in 1986, the first year for which this type of information is available.19

 

To investigate the impact of multinational corporations on the potential procompetitive effects of trade, the estimation equation is augmented to include an interaction 

term between imports, domestic concentration, and a measure of foreign ownership. The 

prediction is that the coefficient on this variable will be positive, indicating that foreign 

ownership weakens the potential for imports to increase competition in domestically 

concentrated industries. The percentage of industry imports that were imported by foreign 

controlled firms (FIMP) is used as the measure of the importance of foreign ownership. 

This variable is only available for 1978.20 The 1978 value is used for both time periods 

 

17 The IV estimates are also based on GLS estimation to account for the fact that the mark-ups are 

estimated with error.

18 Statistics Canada (1978). 

19 Mersereau (1990: 403).

20 Statistics Canada (1978).
17

based on the assumption that it was relatively stable over time.21 Unfortunately, detailed 

data on intra-affiliate imports are not available. They are likely, however, to be correlated 

with FIMP. 

The results of the augmented regression are reported in columns (2) and (6) in 

Table 5. The coefficient on the interaction between imports, concentration, and foreign 

ownership is positive as predicted, although not statistically significant. Comparing these 

results to the benchmark model (repeated in columns (1) and (5)), the coefficient on the 

interaction between imports and concentration becomes larger in absolute value and is 

marginally significant at the 10% level for the 1971-2 period. These results provide 

some, albeit weak, evidence that the potential disciplining effect of imports is diminished in 

industries with a relatively high degree of foreign ownership. This is consistent with the 

prediction of Markusen et al. (1995), and has not previously been tested.

4.5 Additional Control Variables

In this section, the estimation equation is augmented further to include additional 

control variables. The first is demand growth, which is defined to be the industry-level 

percentage change in demand during the relevant periods. For the 1971-2 period, for 

example, it is the percentage change between 1972 and 1970.22 

The other additional control variable is a measure of entry into the industry. It is 

defined as the mean over the period 1970 to 1982 of the proportion of new entrants in a 

 

21 Alternatively, the equations were estimated using a dummy variable indicating whether the value of this 

variable was greater than the mean. The results were not significantly affected.

22 Domestic demand is defined as (domestic shipments-exports+imports) and is calculated at the 2-digit 

level to minimize potential endogeneity problems. The data were taken from the Statistics Canada 

publication, Manufacturing Trade and Measures, 1966-1984. Quantities were deflated by industry-level 

price indices calculated from the KLEMS database.
18

year.23 24 It is hypothesized that mark-ups will be lower when new entry is relatively easy. 

The entry variable is also interacted with concentration and the interaction between 

imports and concentration since the effect of concentration on mark-ups should be 

reduced when there is potential entry in the industry. 

The results are presented in columns (3), (4), (7), and (8) in Table 5. The 

coefficients for the growth variables are not statistically significant. Nor are the entry 

variables for the 1971-2 period. For the 1978-9 period, the interaction between entry and 

concentration is negative and significant, suggesting that entry reduces mark-ups in 

concentrated industries. The coefficient on entry alone is positive and significant. The 

overall impact of entry on mark-ups, however, is insignificant.25 The important point to 

note from these results is that the conclusions with respect to the international trade 

variables are not affected by the addition of these variables.

4.6 Analysis Based on Changes Between 1971-2 and 1978-9

A well-known problem with cross-sectional analysis of industry performance is 

that there are likely to be important industry characteristics that are either unobservable or 

difficult to measure.26 If unobserved characteristics are correlated with the explanatory 

variables, then the cross-sectional estimates will be biased. If unobserved industry effects 

are relatively time invariant, estimation based on changes over time can control for these 

fixed effects. 

 

23 These data were generously provided by John Baldwin.

24 Alternative specifications were estimated using the capital/revenue ratio and a product differentiation 

dummy variable as measures of barriers to entry. The results with respect to the international trade 

variables were not affected.

25 The coefficients on entry are small and insignificant when the interaction terms are not entered into the 

regressions.

26 See Bresnahan (1989) and Schmalensee (1989) for discussions.
19

Table 6 presents the results based on changes between the 1978-9 and the 1971-2 

periods. Unless otherwise specified, all variables are expressed in terms of differences 

between these two periods. Following Katics and Petersen (1994), it is assumed that all 

industry characteristics other than the international trade variables are constant over time. 

This includes the domestic concentration ratio.27

The interaction between the concentration ratio and changes in imports is included 

to determine whether import competition had a differential impact between concentrated 

and unconcentrated industries.28 The coefficient on this interaction term is positive and 

marginally significant at the 10% level. Therefore, in contrast to the cross-sectional 

results, the results here indicate that imports may have actually increased mark-ups in 

domestically concentrated industries. The implications of these results are not changed 

when the estimation controls for the impact of multinational imports (column (2)) or 

differences in growth rates between the two periods (column (3)). 

To pursue these results further, the equations were estimated for the subsample of 

industries for which the 1971-2 price-marginal cost ratio was statistically significantly 

greater than one. These are the industries where one would expect increased import 

competition to have the greatest effect. The results were generally the same as those 

reported in Table 6: there is no evidence that increased imports led to a reduction in markups.

These findings are inconsistent with the hypothesis that imports increase 

competition in the domestic market and are in contrast to the results of similar studies that 

are based on developing countries (e.g., Levinsohn (1993) and Harrison (1994)). One 

 

27 The domestic concentration ratio is relatively constant. The sample mean of this variable falls from 

43.7 to 43.2 between 1970 and 1980, and the correlation between the two periods is 0.94.

28 The average of the 1970 and 1980 concentration ratios is employed to calculate this variable.
20

potential reason for the differences in these results is that the trade liberalizations in 

Turkey and the Cote d’Ivoire were much more dramatic than the Canadian experience of 

the 1970s. The weighted average tariff for the sectors included in Harrison’s study of the 

Cote d’Ivoire, for example, fell from 102% to 72% during the sample period. In contrast, 

the average tariff level for Canadian manufacturing fell from 10.7 in 1970 to 7.8 in 1978.29 

Although small relative to trade liberalizations in developing countries, the increase 

in import competition in Canada during the 1970s was nonetheless significant. Thus, one 

would expect that changes in imports would have had an impact on price-cost margins. 

As discussed above, however, trade increased in almost all industries. Therefore, the 

results may reflect a difficulty in distinguishing a differential impact across industries of 

increased trade over the period. 

Another possible explanation is that the level of aggregation is too broad to 

capture the impact of import competition. Products within 3-digit industries may face 

different levels of protection. In addition, even within more narrowly defined industries, 

individual firms may respond differently to trade. Thus, detailed firm-level analysis may 

provide a more complete understanding of the potential pro-competitive effects of 

imports. 

An interesting result from Table 6 is that the coefficient for changes in exports is 

negative and statistically significant at the 5% level for all four estimations. The negative 

relationship between mark-ups and export orientation reinforces the cross-sectional 

analysis and suggests that participation in export markets places competitive pressure on 

domestic exporting firms. As discussed above (section 2.2), however, this does not 

 

29 Baldwin and Gorecki (1986: 7). 
21

necessarily imply that mark-ups on domestic output fall since the mark-ups reflect a 

weighted average of the mark-ups on domestic output and exports. 

5. Conclusion

This paper estimates price-marginal cost mark-ups for Canadian manufacturing 

industries in order to assess the impact of import competition on domestic market power. 

The results are mixed. There is some weak cross-sectional evidence to suggest that 

imports reduced market power in domestically concentrated industries. Changes in 

imports between the two periods, however, had a (weak) positive impact on mark-ups in 

concentrated industries. One possible explanation for this finding is that trade may have 

differential impacts among firms within industries. Detailed firm-level analysis may 

therefore provide a more complete understanding of the impact of imports on competition. 
22

6. Appendix A

The disturbance term of equation 6’ is w h = nh xh+

,

. It is assumed that 

E(nh,xh ) = 0. The variance of w h sw,

2

, is therefore equal to sn sx

2 2

+

h

. Let wh

represent the residuals from an OLS regression of equation 6’. The variance of w h can be 

estimated as follows.

( )

p

w

n k n k

h

h

lim

var 2 $

∑ 2 ∑

−

















= +

−

s

b

n

Δ

 (A1)

Thus,

( ) $

v$ar $

s

b

v

h w h

n k n k

=

−

















−

−

∑ ∑

2 Δ

 (A2)

The first term on the right hand side of equation (A2) can be calculated from the residuals 

of equation 6’ and the second term can be derived from the variance estimates of equation 

(5) for the individual industries. Equation 6’ is then estimated using feasible generalized 

least squares where the observations are divided by $ va$r( )

$ sn

+ Δbh.
23

Table 1: Mean Import and Export Intensities by Major Industry Group

Import intensity Export intensity

Industry 1971-2 1978-9 1971-2 1978-9

Food and beverage industries 0.090 0.130 0.142 0.172

Tobacco products industries 0.016 0.022 0.008 0.008

Rubber and plastic products 0.179 0.227 0.048 0.106

Leather industries 0.264 0.351 0.076 0.095

Textile industries 0.232 0.280 0.041 0.059

Knitting mills 0.184 0.235 0.018 0.014

Clothing industries 0.090 0.130 0.098 0.099

Wood industries 0.082 0.127 0.184 0.245

Furniture and fixture industries 0.060 0.101 0.045 0.094

Paper and allied industries 0.072 0.087 0.259 0.266

Printing and publishing 0.145 0.162 0.022 0.034

Primary metal industries 0.233 0.301 0.440 0.458

Metal fabricating industries 0.135 0.150 0.054 0.083

Machinery industries 0.630 0.668 0.344 0.383

Transportation equipment 0.359 0.362 0.372 0.385

Electrical product industries 0.256 0.348 0.111 0.155

Non-Metallic mineral products 0.182 0.193 0.133 0.163

Petroleum and coal products 0.079 0.033 0.043 0.085

Chemical products 0.214 0.251 0.086 0.127

Miscellaneous manufacturing 0.370 0.408 0.155 0.165

Note: These data summarize the import and export intensities of the 3-digit industries included 

in this study and therefore do not correspond directly to trade data calculated at the 2-digit level.
24

Table 2: Summary Statistics

Price-Marginal Cost Ratios

# Obs Mean Standard Implied

Deviation Mark-up

Panel A: 1971-2 Period

Full Sample 97 1.146 0.182 0.127

Significantly greater than one 68 1.225 0.134 0.184

Not significantly different from one 24 1.012 0.072 0.012

Significantly less than one 5 0.724 0.174 -0.381

Panel B: 1978-9 Period

Full Sample 99 1.094 0.219 0.086

Significantly greater than one 44 1.262 0.149 0.208

Not significantly different from one 42 1.023 0.129 0.022

Significantly less than one 13 0.756 0.103 -0.323

Panel C: 

Change between two periods

Full Sample 97 -0.057 0.279 -

Significant increases 15 0.329 0.196 -

No significant change 54 -0.020 0.178 -

Significant decrease 28 -0.335 0.171 -

Note: Significance refers to statistical significance at the 5% level.
25

Table 3: Mean Mark-ups for Major Industry Groups

1971-2 1978-9

#Obs. Price/cost Implied Price/cost Implied

(a) ratio mark-up ratio mark-up

Food and beverage industries 9 1.063 0.059 1.074 0.069

Tobacco products industries 1 1.374 0.272 1.641 0.391

Rubber and plastic products 2 1.130 0.115 1.272 0.214

Leather industries 3 1.099 0.090 1.215 0.177

Textile industries 7 1.113 0.101 1.076 0.070

Knitting mills 2 1.108 0.098 0.999 -0.001

Clothing industries 6 1.089 0.082 0.959 -0.043

Wood industries 6 1.091 0.083 1.100 0.091

Furniture and fixture industries 4 1.067 0.063 1.193 0.162

Paper and allied industries 3 (4) 1.092 0.084 0.851 -0.175

Printing and publishing 3 1.027 0.027 1.012 0.012

Primary metal industries 7 1.065 0.061 1.127 0.113

Metal fabricating industries 8 1.179 0.152 1.032 0.031

Machinery industries 3 1.285 0.222 1.098 0.089

Transportation equipment 5 1.112 0.101 1.137 0.121

Electrical product industries 6 1.247 0.198 1.185 0.156

Non-metallic mineral products 8 (9) 1.250 0.200 1.105 0.095

Petroleum and coal products 2 1.158 0.136 1.238 0.192

Chemical products 7 1.282 0.220 1.028 0.027

Miscellaneous manufacturing 5 1.183 0.155 1.172 0.146

(a) The number of 3-digit industries included in each major industry group. The numbers in 

parentheses indicate the number of industries for 1978-9 when different from the 1971-2 period.
26

Table 4: Results

GLS Estimates IV Estimates

1971-2 1978-9 1971-2 1978-9

Imports 0.444 0.423 1.045 -0.076

(0.228) (0.252) (0.710) (0.770)

Imports*C4 -0.436 -0.687 -1.255 -0.468

(0.432) (0.494) (0.993) (1.107)

C4 0.129 0.453* 0.292 0.410

(0.117) (0.157) (0.211) (0.285)

Exports -0.084 -0.182 -0.134 -0.009

(0.111) (0.128) (0.195) (0.219)

Constant 1.060* 0.905* 0.955* 0.985*

(0.055) (0.073) (0.124) (0.163)

# Observations 97 99 97 99

Hausman - - 0.870 1.017

Statistic

Results are based on feasible generalized least squares estimation following the procedure 

described in Appendix A.

Standard errors are in parentheses. 

* Statistically significant at the 5% level.
27

Table 5: Extended Analysis

1971-2 1978-9

(1) (2) (3) (4) (5) (6) (7) (8)

Imports 0.444 0.525* 0.523* 0.497* 0.423* 0.434* 0.440* 0.278

(0.228) (0.230) (0.230) (0.248) (0.252) (0.255) (0.257) (0.271)

Imports*C4 -0.436 -1.064 -0.985 -0.435 -0.687 -0.815 -0.816 -0.680

(0.432) (0.573) (0.575) (0.471) (0.494) (0.653) (0.656) (0.509)

Imports*C4* - 0.718 0.676 - - 0.248 0.242 -

FIMP (0.481) (0.480) (0.543) (0.546)

Imports*C4* - - - -1.693 - - - 8.163

entry (6.173) (7.446)

C4 0.129 0.174 0.179 0.073 0.453* 0.424* 0.429* 0.806*

(0.117) (0.119) (0.119) (0.189) (0.157) (0.159) (0.162) (0.223)

C4*entry - - - 1.419 - - - -9.894*

(3.785) (4.591)

Entry - - - -0.841 - - - 3.769*

(1.360) (1.604)

Exports -0.084 -0.092 -0.083 -0.089 -0.182 -0.177 -0.171 -0.192

(0.111) (0.110) (0.110) (0.115) (0.128) (0.128) (0.131) (0.127)

Growth - - -0.347 - - - -0.043 -

(0.270) (0.191)

Constant 1.060* 1.046* 1.089* 0.905* 0.915* 0.909* 0.734*

(0.055) (0.055) (0.065) (0.073) (0.074) (0.074) (0.123)

# Observations 97 95 95 95 99 97 97 97

Note: Results are based on feasible generalized least squares estimation following the 

procedure described in Appendix A. 

Standard errors are in parentheses.

* Statistically significant at the 5% level.
28

Table 6: Results based on Changes between 1971-2 and 1978-9

(1) (2) (3)

Import intensity -1.159 -1.023 -1.166

(1.107) (1.112) (1.134)

Imports*C4 3.730 3.281 3.888

(2.151) (3.599) (3.712)

Imports*C4*FIMP - 0.499 0.262

(4.218) (4.244)

Export intensity -1.111* -1.093* -1.128*

(0.486) (0.484) (0.488)

Demand growth - - -0.166

(0.239)

Constant -0.046 -0.054 -0.055

(0.034) (0.034) (0.034)

# Observations 97 95 95

Note: Results are based on feasible generalized least squares estimation following the 

procedure described in Appendix A.

Standard errors are in parentheses.

* Statistically significant at the 5% level.
29

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