Competition and Productivity:
Evidence from Canadian Manufacturing Firms
by
Jianmin Tang and Weimin Wang*
Micro Economic Policy Analysis Branch
Industry Canada
January 2002
Abstract
In this paper we estimate the impacts of product market competition, competition for skilled
workers, and market transparency on productivity performance of Canadian manufacturing
firms. The regression analyses are based on firm-level data from Statistics Canada’s Survey of
Innovation 1999, which was linked to the production data from the Annual Survey of
Manufacturers. We find that product market competition has a positive and significant impact
on productivity performance while competition for skilled workers has a negative and significant
impact. However, we find no evidence that market transparency has a significant impact on
productivity performance, which may be explained by its two opposing influences on
productivity.
JEL Codes: L0, O0
*
We thank Frances Anderson, Brian Nemes, Guy Sabourin, and Susan Schaan for excellent support and facilitation
of our access to the Linked 1999 Survey of Innovation database. We are also grateful to Someshwar Rao for helpful
comments and suggestions. Views expressed in this paper do not necessarily reflect those of Industry Canada. All
errors are our own.
1. Introduction
Many policy makers and researchers believe that product market competition improves
production efficiency because product market competition not only increases the pressure for
firms to develop and adopt new technologies, but also induces managerial effort.1 However, the
theoretical relationship between product market competition and productivity performance is
ambiguous, as indicated in Nickell (1996). Supporters for a positive relationship argue that
product market competition reduces managerial slack introduced by monopoly power, and that it
generates incentives to improve production efficiency through product, process, or organizational
innovation. The argument is often based on two observations. First, competition means more
than one firm in the same market. Performance of firms in the same market can be compared
with one another. The cross-comparison increases the pressure for firms to perform better than
their counterparts, which leads to more efficacy-improving effort. Meyer and Vickers (1997)
shown that in a two-period model, comparative performance information enhances efforts to cut
cost. Second, an increase in product market competition may raise the demand elasticity and
reduce demand for each individual firm. The higher demand elasticity implies that cost-reducing
improvements in productivity will generate larger increases in profit. The increased potential
profit induces higher managerial effort (see Willig (1987)), creating incentives for efficiencyenhancing activities such as innovation.2 On the other hand, the higher demand elasticity can
quickly reduce the demand for the products of under-performers, which increases the probability
of bankruptcy. To avoid such fate, firms have to be highly innovative and to improve their
efficiency (see Schmidt (1994)).
However, there are also some theoretical arguments in the literature against the positive
relationship between product market competition and productivity performance. Hermalin
(1992) and Horn et al (1994) claim that increased competition reduces managers’ expected
income and hence tends to reduce their managerial effort. It has also been argued along the line
1
For instance, Porter (1990), Nickell (1996), Van de Lundert and Smulders (1997), Boone and Van Dijk (1998), and
Tang (2001) show that more competition leads to more innovation. Tang and Wang (2001) examine the impact of
organizational innovation on productivity performance of Canadian manufacturing firms. They find organizational
innovation also generates a significant impact on productivity performance.
2
of Schumpeter (1942) that monopoly power enables firms to spend more on R&D because they
face less market uncertainty and have more stable cash flow.
Despite the theoretical debate, a few empirical studies suggest that product market competition
improve technical efficiency and thus productivity. Based on cross-section data, Caves and
Barton (1990), Green and Mayes (1991), and Caves et al. (1992) link technical efficiency to
market structure. They find that an increase in market concentration above a certain threshold
tends to reduce technical efficiency. Using survey data for 58 countries, Porter (2000) finds that
the intensity of local competition is the most influential single variable on GDP per capita
growth. Nickell (1996) confirms competition effects on productivity with panel data on 670
U.K. companies. Two relevant findings emerge from this study. First, competition, measured
either by increased numbers of competitors or by lower levels of rents, is associated with higher
rates of total factor productivity growth. Second, market power, as captured by market share,
leads to a lower level of productivity. These findings are consistent with Lever and
Nieuwenhuijsen (1999), using a panel data on nearly 2000 Dutch manufacturing firms. Gort and
Sung (1999) compare the experience of AT&T long lines, operating in an increasingly
competitive market, with that of eight local telephone monopolies. They show that AT&T
improves its efficiency at a markedly faster rate than the local monopolies, in either total factor
productivity growth or shifts in cost functions.
Is Canadian experience consistent with those empirical findings? In this paper, we examine the
impacts of product market competition, together with competition for skilled workers and market
transparency, on productivity performance, using Statistics Canada’s Survey of innovation 1999.
Competition for skilled workers is relevant since skilled workers have long been recognized as a
crucial input of production. When it is difficult for firms to hire and retain qualified staff and
workers, their economic performance will be adversely affected. After analyzing a survey data
on over 700 U.K companies, together with a number of in-depth cases, Bosworth and Wilson
(1993) show that there is a strong relationship among the deployment of highly qualified
personnel, their role in strategic management of the company, and dynamic economic
2
Jagannathan and Srinivasan (1999) find that product market competition reduces managerial slack, using a panel
data on 2970 U.S. firms over the period 1973 to 1990.
3
performance. Findings in Foley et al. (1993) suggest that skill shortages associated with craft
workers can act as a barrier to the use of new technologies and lead to a reduction in
productivity.
Competitive environment has other aspects that are not included in product market competition
and competition for skilled workers. In this paper, we also consider market transparency, an
index representing the easiness for a firm to predict its clients’ demands and its competitors’
actions, and the flexibility for a firm to replace its suppliers. And to some extent, the inverse of
this index reflects business uncertainty and riskiness. In general, market transparency exerts two
opposite influences on productivity. Market transparency reduces uncertainty, which moderates
firms’ wait-and-see attitude in investment in R&D and technology adoption. It improves firms’
efficiency by reducing unnecessary costs related to uncertainty. On the other hand, market
transparency may induce managerial slack and reduce motivation to improve economic
performance.
Thus, beyond output competition, our analysis also takes into account the impacts of labour input
competition and market transparency on productivity performance. A prominent feature of this
paper is that we model product market competition, competition for skilled workers, and market
transparency as unobservable variables. Each latent variable underlies multiple indicators. For
example, we use four indicators to measure product market competition: how easy clients of a
firm can substitute the firm’s products for the products of its competitors, whether the arrival of
new competitors is a constant threat, whether the arrival of competing products is a constant
threat, and how quick a firm’s products become obsolete. This approach is similar to the method
used in Gu and Tang (2001) to measure innovation, but different from the competition measures
usually employed in the literature.3 Our approach has three main advantages. First, it can
provide us with a more comprehensive measure of each competition-related factor than a single
indicator. Often, different indicators of a factor measure the factor from different perspectives.
Second, it can avoid multi-collinearity problems associated with directly using multiple
3
Multiple indicators are usually added to an econometrical model directly as a measure of competition. For
example, see Nickell (1996) and Lever and Nieuwenhuijsen (1999). The associated problem with this method is
multi-collinearity because these indicators are usually highly correlated.
4
indicators in an econometrical model since some indicators are highly correlated. Finally, it
reduces the number of variables in our analysis and helps us to summarize the data.
The paper is organized as follows: Section 2 describes the data, Section 3 contains measurement
issues, Section 4 discusses the sample profile and empirically links competition measures to
productivity performance, and Section 5 concludes. The method of estimating unobservable
latent variable is included in Appendix.
2. Data
The data used for this study is Statistics Canada 1999 Survey of Innovation (SI), which has been
linked to the production data from the 1997 Annual Survey of Manufacturers (ASM). The SI
was conducted in 1999 for the Canadian manufacturing and selected natural resources industries
for which the sample unit was the provincial enterprise. A provincial enterprise (firm thereafter)
includes all its establishments in the same province and industry at the 4-digit NAICS (North
American Industry Classification System) level. To reduce response burden, the SI surveyed
firms with at least $250,000 gross business income and more than 19 employees.4 All
information in the SI concerns firms’ innovation environment and related activities during the
1997-1999 period.5 After linkage to the 1997 ASM, the linked SI contains additional
information on firms’ operational activities such as value added and employment in 1997. We
use the linked SI database for this study.
The linked SI database contains data on 5,455 in-sample manufacturing firms. Each firm carries
a weight. The weight given to each in-sample firm allows that firm to represent other firms in
the population having similar characteristics. Thus, if the weight given to firm X is 5, firm X
represents five firms in the sample. To represent these five firms, the data obtained from the
sampled firm is multiplied by the weight, 5 in this case. The total population has 8921
manufacturing firms, which is equal to the sum of population weights of the in-sample firms.
4
However, after linked to the 1997 ASM, some firms with less than 20 employees in 1997 were also in the linked
database.
5
For the purpose of this study, however, we exclude 125 in-sample firms that have no complete
information6 or are considered to be outliers.7 Thus, the final sample for this study contains data
on 5,320 in-sample manufacturing firms.
The first question of the Survey is regarding the competitive environment facing surveyed firms.
The question is: “ For your firm, how strongly do you agree or disagree with each of the
following statements?” There are 11 statements as follows:
(1)
My client’s demand are easy to predict;
(2)
My clients can easily substitute my products (goods or services) for the products of my
competitors;
(3)
My competitors’ actions are easy to predict;
(4)
The arrival of new competitors is a constant threat;
(5)
The arrival of competing products (goods or services) is a constant threat;
(6)
My firm can easily replace its current suppliers;
(7)
It is difficult to hire qualified staff and workers;
(8)
It is difficult to retail qualified staff and workers;
(9)
My products (goods or services) quickly become obsolete;
(10) Production technologies change rapidly;
(11) Office technologies change rapidly.
Firms are asked to indicate their opinions by using a scale from 0 to 5, where 0 for not relevant, 1
for strongly disagree and 5 for strongly agree. We use the first 9 statements to form our
competition measures.8
3. Measurement Issues
5
6
7
8
For methodological issues and the overall description of the survey, please see Schaan and Anderson (2001).
For instance, several in-sample firms have no employment information.
For instance, we exclude firms with negative value added.
We exclude the last two statements since they are not firm specific.
6
In this section, we deal with measurement issues associated with product market competition,
competition for skilled workers, and market transparency. We first discuss product market
competition.
3.1. Product Market Competition
Product market competition can be characterized by a large number of firms and free entry &
exit.9 A large number of firms in an industry implies that no single firm can have a significant
influence on product price and that each firm takes the price as given when making decisions. In
addition, with free entry & exit, firms’ economic profits are kept down near to zero. Thus, to
measure product market competition for a firm, it is natural to use the number of competitors for
the firm or the firm’s profit margin. Unfortunately, these data are hardly available. In this paper,
we use the data from the SI.
As described in Section 2, the linked SI contains data related to competitive environment facing
firms. Let x i denote a firm’s response on statement i in question 1 of the Survey, where
i = 1, 2, L , 11 . We choose easy product substitution ( x 2 ), arrival of new competitors ( x 4 ),
arrival of competing products ( x5 ), and quick obsolescence of products ( x 9 ) as indicators of
product market competition. The four indicators all measure product market competition, but
from different perspectives.
Table 1 reports the correlation coefficients between indicators of product market competition.
As expected, arrival of new competitors ( x 4 ) and arrival of competing products ( x5 ) are highly
correlated. Easy product substitution ( x 2 ) is positively correlated with both arrival of new
competitors ( x 4 ) and arrival of competing products ( x5 ). When a firm’s products can be easily
substituted, the firm will begin to worry about the arrivals of both new competitors and
competing products. Quick obsolescence of products ( x 9 ) is positively but weakly correlated
9
Note, however, that the degree of competition may not necessarily relate to the number of rivals against which a
firm compete but rather to the ever-present possibility that its rivals may innovate and gain a decisive cost or
product-quality advantage (Metcalfe and Boden, 1993).
7
with both arrival of new competitors ( x 4 ) and arrival of competing products ( x5 ), and not
correlated with easy product substitution ( x 2 ).
With some of those indicators being highly correlated, the direct inclusion of those variables in a
competition and productivity regression analysis will cause a serious multi-collinearity problem.
To avoid such problem, in this paper we model product market competition as a latent variable (a
latent variable model is included in Appendix). The latent variable underlies the four indicators.
Let ξ p denote our measure of product market competition. The estimation of ξ p , ξ̂ p , can be
written as a weighted sum of the four qualitative indicators:
(1)
ξˆ p = wˆ 2 x2 + wˆ 4 x 4 + wˆ 5 x5 + wˆ 9 x9 ,
where ŵi is the weight for indicator x i with i = 2, 4, 5 and 9, and is estimated based on the
model in Appendix. The estimate weights are reported in Table 1. Based on these weights, we
can compute the estimated measure of product market competition by using Equation (1). As
shown in the table, the indicator of arrival of competing products ( x5 ) is weighted heavily,
contributing the most to the measure of product market competition. The higher the value of this
index, the higher is the perception of the degree of product market competition.
3.2. Competition for Skilled Workers
In the Survey, there are three indicators that are associated with competition for workers: arrival
of new competitors ( x 4 ), hardness to hire ( x 7 ) and hardness to retain ( x8 ) as indicators. Arrival
of new competitors is an indicator of competition for skilled workers since the arrival of new
competitors necessarily increases the demand for skilled workers. As a result, it leads to
competition for skilled workers for a given supply of skilled workers. Hardness to hire and
hardness to retain qualified staff and workers also indicate high degree of competition for skilled
workers. When the competition for skilled workers is intensive, firms would feel difficult to hire
and retain qualified staff and workers.
8
Table 2 reports correlation coefficients between indicators of competition for skilled workers.
As expected, the two indicators of hardness to hire and hardness to retain are highly correlated.
They are both positively correlated with the indicator of arrival of new competitors.
Like product market competition, competition for skilled workers is also modeled as a latent
variable. Let ξw denote our measure of competition for skilled workers. The estimation of ξw ,
ξ̂w , can be written as a weighted sum of the three indicators,
(2)
ξˆw = wˆ 4 x 4 + wˆ 7 x7 + wˆ 8 x8 ,
where ŵi is the weight for indicator x i with i = 4, 7, and 8, and is estimated based on the model
developed in Appendix. Table 2 also presents the estimated weights of these three indicators.
As shown in the table, the two indicators of hardness to hire and hardness to retain are weighted
heavily, significantly contributing to the measure of firms’ competition for workers. The higher
the value of this index, the higher is the perception of the degree of competition for skilled
workers.
3.3. Market Transparency
In the Survey, there are three indicators that are associated with market transparency: how easy a
firm can predict its clients’ demands (easy to predict clients’ demands) ( x1 ), how easy a firm can
predict its competitors’ actions (easy to predict competitors’ actions) ( x3 ), and how easy a firm
can replace its current suppliers (flexible suppliers) ( x 6 ). As shown in Table 3, the three
indicators are positively correlated.
Like product market competition and competition for skilled workers, market transparency is
also model as a latent variable. Let ξe denote the measure of market transparency. The
estimation of ξe , ξ̂e , can be written as a weighted sum of the three indicators,
9
(3)
ξˆe = wˆ 1 x1 + wˆ 3 x3 + wˆ 6 x6 ,
where ŵi is the weight for indicator x i with i = 1, 3, and 6. The weights are estimated using the
model developed in Appendix, and the estimated weights are reported in Table 3. As shown in
the table, the indicator of easy to predict competitors’ actions is weighted heavily, significantly
contributing to the measure of firms’ market transparency. The higher the value of the index, the
more transparency is a firm’s market.
4. The Empirical Linkage between Competition and Productivity
In this section we use the Survey data on Canadian manufacturing firms to empirically study the
relationship between competition-related factors and labor productivity. We first set up the
regression model.
4.1. Regression Model
Our framework utilizes a Cobb-Douglas production function with constant return to scale. The
regression model is as follows:
(4)
20
LPj = β0 + β1 LF j + β2 ξpj + β3 ξwj + β4 ξej + β5 SM j + β6 SL j + ∑ αn I nj + ε j ,
n =1
where
LPj is labor productivity (in logarithm), defined as value-added output per worker for
firm j ;
LF j is fuel and power consumption per worker (in logarithm) for firm j ;
ξ pj is the measure of product market competition for firm j ;
ξwj is the measure of competition for skilled workers for firm j ;
ξej is the measure of market transparency for firm j ;
10
SM j is a size dummy for medium-sized firms, 1 for firm j being medium-sized and 0
otherwise;
SL j is a size dummy for large firms, 1 for firm j being large-sized and 0 otherwise;
I nj is a binary industry dummy, 1 for firm j belonging to industry n and 0 otherwise;
ε j is the error term for firm j .
The competition variables are constructed in the previous section. Ideally, this regression model
should include capital intensity as an independent variable. However, there is no such data in the
linked SI. Because of the data limitation, we use fuel and power consumption per worker as a
proxy for capital intensity. This is based on our observation that working capital stock is highly
correlated with fuel and power consumption. Indeed, it has been used as a proxy for the same
purpose in Globerman, Ries and Vertinsky (1994).
Firm size and industry dummies are introduced to capture size-related and industry-related
residuals that are not captured by other variables. To capture the size effect, we divide firms into
three groups according to their size. The reference group in the regression model is a small-sized
group in which a firm’s number of employees is not greater than 100. In the medium-sized
group, the number of employees of each firm is greater than 100, but not larger than 500. In the
large-sized group, the number of employees of each firm is larger than 500. Industries are
grouped at the 3-digit NAICS level. The reference industry in the regression model is
Miscellaneous Manufacturing (NAICS 339).
4.2.
Sample Profile
The sample for our analysis contains data on 5320 in-sample manufacturing firms, of which
there are 3110 small-sized firms, 1860 medium-sized firms and 350 large-sized firms.
Table 4 presents the percentage of firms that highly agree10 with a statement regarding
competitive environment by size11 and by industry. Among the nine statements that we use as
10
11
A firm highly agrees with a statement if the firm scores 4 and 5 for the statement.
Due to confidentiality, medium and large size groups are combined.
11
indicators of competition in this study, there are three statements that are highly agreed by more
than 50% of all firms. The leading statement is “it is difficult to hire qualified staff and
workers”. For all manufacturing industries, 61.8% of surveyed firms highly agree with the
statement. This is followed by the two statements: “my clients can easily substitute my products
for the products of my competitors” and “the arrival of competing products is a constant threat”.
The two statements are highly agreed by 59.4% and 52.9% of firms, respectively. The statement
with least support is “my products quickly become obsolete”, followed by “my competitors’
actions are easy to predict” and “my firm can easily replace its current suppliers”.
Small-sized firms tend to have a different profile than medium- and large-sized firms. In terms
of the percentage of firms that highly agree with a statement regarding their competitive
environment, small-sized firms are less threatened than medium- and large-sized firms by the
arrival of competing products and new competitors. They tend to be easier than medium- and
large-sized firms to predict their competitors’ actions and to replace its current suppliers.
However, it is more difficult for small-sized firms to hire and retain qualified workers.
The profile at industry level is also significantly different. Paper, Beverage & Tobacco, and
Petroleum & Coal are leading in “easy to predict clients’ demand”. Beverage & Tobacco and
Paper are leading in “my clients can easily substitute my products for the products of my
competitors”. Printing and Paper are leading in “my competitors’ actions are easy to predict”.
Leather, Apparel, and Food are leading in “the arrival of new competitors is a constant threat”.
Leather, Beverage & Tobacco, and Apparel are leading in “the arrival of competing products is a
constant threat”. Petroleum & Coal and Printing are leading in “my firm can easily replace its
current suppliers”. Machinery, Furniture, and Printing are leading in “it is difficult to hire
qualified staff and workers”. Furniture and Computer & Electronics are leading in “it is difficult
to retain qualified staff and workers”. Finally, Apparel, Computer & Electronics, and Printing
are leading in “my products quickly become obsolete”.
4.3.
Empirical Results
12
Table 5 reports the estimation results of Equation (4), based on ordinary least squares. As
expected, fuel and power consumption per worker, as a proxy for capital intensity, is positive and
significant. This is consistent with the fact that the higher the capital intensity, the higher the
labour productivity.
The most interesting results for this paper, however, are associated with competition-related
variables. Product market competition has a positive and statistically significant impact on labor
productivity. This is an important empirical result since it supports the view that higher product
market competition leads to higher productivity. Product market competition not only increases
the pressure for firms to develop and adopt new technology, but also to induce managerial effort,
which in turn improves productivity. This result is also consistent with the findings by Baily and
Gersbach (1995), Nickell (1996), Pilat (1996), and Rao and Ahmad (1996) that productivity is
strongly correlated with the exposure to competition with best-practice firms.
The estimation result also indicates that competition for skilled workers has a negative and
statistically significant impact on productivity. Surly, firms with perception of high degree of
competition for skilled workers tend to have a difficulty to fill their skilled positions. The skill
shortage weakens productivity performance. Martin and Porter (2001) state that productivity is
determined by the interplay of three broad influences: a nation’s political, legal and macroeconomic context, the quality of the micro-economic business environment, and the
sophistication of company operations and strategy. Firms require skilled and experienced
workers to develop systems that are associated with sophisticated products or production
processes. And, they rely those workers to operate the sophisticated systems more efficiently. In
addition, insufficient skills in a workforce will reduce the effectiveness of firms’ technology
adoption, which further reduce those firms’ productivity performance.
Market transparency is found to be negative, but insignificant. The insignificance may be
explained by the fact that market transparency can exert two opposing influences on
productivity. It improves efficiency by encouraging investment in R&D and technology
adoption and by reducing unnecessary costs related to uncertainty. On the other hand, it induces
13
managerial slack, reducing innovation effort that is necessary for improving production
efficiency.
In addition to competition-related variables, our estimation results also indicate that larger firms
are more productive than smaller firms. This result is consistent with the findings by Lee and
Tang (2001). At the industry dimension, productivity is substantially different. After controlling
for other variables, the most productive manufacturing industry is Beverage & Tobacco
Products, followed by Chemicals and Computer & Electronic Products. The least productive
manufacturing industry is Leather & Allied Products, followed by Textile Mills, Wood and
Food, which are all resource-based industries.
5. Concluding Remarks
Many policy makers and researchers believe that product market competition increases the
pressure for firms to undertake product, process, or organizational innovation, which in turn
improve productivity. The empirical evidence from this paper strongly supports this view. It
shows that firms with a perception of higher degree of product market competition are
significantly more productive than others. This finding strongly supports a competition policy,
which is geared to encourage innovation and to improve productivity performance. This finding
also indicates that Canada needs to rethink its regulations in areas such as foreign ownership
restrictions that prevent entry and reduces the benefits of competition.
In this paper, we also find that firms with a perception of higher degree of competition for skilled
workers are significantly less productive than other firms. The result implies that skills and
productivity go hand in hand, and that skill shortage weakens productivity performance. A good
supply of skilled labour will be certainly helpful to boost the productivity performance of firms
in Canada and to narrow the Canada-U.S. productivity gap.12 In this respect, lower taxes and
12
Canada is relatively scarce in highly skilled labour when compared to the United States. For instance, the labour
force in Canada has a lower percentage of people with university or higher degrees than in the United States,
although it maintains a higher percentage of people who have post-secondary or higher degrees than in the United
States.
14
other means to retain and attract top skilled labour would be highly desirable. In addition,
education and continuous learning should form the basis of the government’s policy agenda.
15
References
Baily, M. and H. Gersbach, 1995, “Efficiency in Manufacturing and the Need for Global
Competition”, Brookings Papers on Economic Activity: Microeconomics, pp. 307-58.
Baldwin, J., 1995, The Dynamics of Industrial Competition – A North American Perspective,
Cambridge University Press.
Boone, J. and Van Dijk, T., 1998, “Competition and Innovation,” De Economist 146, No. 3.
Bosworth, D. and Wilson, R., 1993, “Qualified Scientists and Engineers and Economic
Performance”, in New Technologies and the Firm, page 153 – 74, edited by Peter Swann,
Routledge: London and New York.
Caves, R. E. and Barton, D. R., 1990, Efficiency in U.S. Manufacturing industries, Cambridge,
Mass.: MIT press.
Caves, R. E., et al., 1992, Industrial Efficiency in Six Nations, Cambridge, Mass.: MIT press.
Foley, P. D., Watts, H. D. and Wilson B., 1993, “New Technologies, Skills Shortages and
Training Strategies”, in New Technologies and the Firm, page 131 – 51, edited by Peter
Swann, Routledge: London and New York.
Geroski, P.A., 1995, “Innovation and Competitive Advantage”, OECD working paper, vol. III,
no. 85.
Globerman, S., Ries, J. C., and Vertinsky I., 1994, “The Economic Performance of Foreign
Affiliates in Canada”, Canadian Journal of Economics 27, page 141-56.
Gort, M. and Sung, N., 1999, “Competition and Productivity Growth: The Case of the U.S.
Telephone Industry”, Economic Inquiry, vol. 37, issue 4, page 678 - 92.
Gu, W. and Tang, J., 2001, “A New Perspective on the Canadian Manufacturing InnovationProductivity Linkage”, Manuscripts, Industry Canada.
Green, A. and Mayes, D. G., 1991, “Technical Inefficiency in Manufacturing Industries”,
Economic Journal, vol. 101, page 523 - 38.
Hermalin, B. E., 1992, “The effects of Competition on Executive Behavior”, Rand Journal of
Economics, vol. 23, page 350 - 65.
Horn, h., Lang, H. and Lundgren, S., 1994, “Competition, Long Run Contracts and Internal
Inefficiencies in Firms”, European Economic Review, vol. 38, page 213 - 33.
Jagannathan, R. and Srinivasan, S. B., 1999, “Does Product Market Competition Reduce Agency
Costs?” The North American Journal of Economics and Finance, vol. 10, page 387 - 99.
16
Lee, F. and Tang, J., 2001, “Multifactor Productivity Disparity between Canadian and U.S.
Manufacturing Firms”, Journal of Productivity Analysis 15, pp.115-28.
Lever, M. and Nieuwenhuijsen, H., 1999, “The Impact of Competition on Productivity in Dutch
Manufacturing”, in Innovation, Industry Evolution, and Employment, page 111 - 28,
edited by David B. Audretsch and Roy Thurik, Cambridge University Press.
Martin, R. L., and Porter, M. E., 2001, “Canadian Competitiveness: A Decade after the
Crossroads”, C. D. Howe Institute working paper 2001-1.
Metcalfe, J. S. and M. Boden, 1993, “Paradigms, strategies and the evolutionary basis of
technological competition”, in New Technologies and the Firm: Innovation and
Competition, edited by Peter Swann, New York: Roatledge.
Meyer, M. A., and Vickers, John, 1997, “Performance Comparisons and Dynamic Incentives”,
Journal of Political Economy, vol. 105, page 547 - 81,
Nickell, S.J. 1996, “Competition and Corporate Performance”, Journal of Political Economy,
vol. 104, page 725 - 46.
Perrakis, S., 1989, Canadian Industrial Organization, Prentice Hall.
Pilat, D., 1996, “Competition, Productivity, Efficency”, OECD Economic Studies 27, pp. 107-46.
Porter, M., 1990, The Competitive Advantage of Nations, New York: Macmillan.
Porter, M. 2000, “The Current Competitiveness Index: Measuring the Microeconomic
Foundations of Prosperity,” in The Global Competitiveness Report 2000, World Economic
Forum.
Raith, M., 2001, “Competition, Risk and Managerial Incentives”, manuscript, University of
Chicago.
Rao, S. and A. Ahmad, 1996, “Canadian Small and Medium-sized Enterprises: Opportuniteis
and Challenges in the Asir Pacific Region”, in The Asia Pacific Region in the Glabal
Economy: A Canadian Perspective, Edited by R. Harris, Galgary: University of Calgary
Press.
Schaan, Susan and Frances Anderson, 2001, Innovation in Canadian Manufacturing: National
Estimates, Cat. No. 88F0006XIE01010, Statistics Canada.
Schmidt, Klaus M., 1994, “Managerial Incentives and Product Market Competition”, discussion
paper # A-430, Bonn University.
Schumpeter, J. A., 1942, Capitalism, Socialism, and Democracy, New York: Harper.
17
Soreskog, Karl and Dag Sorbom, 1996, LISREL 8: User’s Reference Guide, Scientific Software
International, Inc.
Tang, J., 2001, “Competition and Innovation Activities: Micro Evidence”, manuscript, MicroEconomic Policy Analysis, Industry Canada.
Tang, J. and Wang, W., 2001, “Managerial Practices and Productivity: Evidence from Canadian
Manufacturing Firms”, manuscript, Micro-Economic Policy Analysis, Industry Canada.
Van de Klundert, T. and Smulders, S., 1997, “Growth, Competition and Welfare,” Scandinavian
Journal of Economics, pp.99-118.
Willig, R. D., 1987, “Corporate Governance and Market Structure”, In Economic Policy in
Theory and Practice, Edited by Assaf Razin and Efraim Sadka, London: Macmillan.
18
Table 1: Weights and Correlation Coefficients between
Indicators of Product Market Competition
Indicators
x 2 : easy product substitution
x 4 : arrival of new competitors
x5 : arrival of competing products
x 9 : quick obsolescence of products
Estimated Weights
x2
x4
x5
x9
1.000
0.224 1.000
0.265 0.604 1.000
0.044 0.140 0.202 1.000
0.007 0.030 0.243 0.006
Source: The Linked SI
Table 2: Weights and Correlation Coefficients between
indicators of Competition for workers
Indicators
x4
x7
x8
x 4 : arrival of new competitors
1.000
x 7 : hardness to hire
0.159
1.000
x8 : hardness to retain
0.180
0.478
Estimated Weights
0.057
0.247
1.000
0.350
Source: The Linked SI
Table 3: Weights and Correlation Coefficients between Indicators of
Market transparency
Indicators
x1
x3
x1 : easy to predict clients’ demands
1.000
x3 : easy to predict competitors’ actions
0.388
1.000
x 6 : flexible suppliers
0.115
0.161
Estimated Weights
0.145
0.325
x6
1.000
0.046
Source: The linked SI
19
Table 4: Percentage of manufacturing firms that highly agree*** with a statement by industry
NAICS
Industry
x3
x1
x2
easy to predict clients’ demand
easy product substitution
easy to predict competitors’
actions
Small*
M&L**
Total
x4
x5
arrival of new competitors
arrival of competing products
Small*
M&L**
Total
Small*
M&L**
Total
Small*
M&L**
Total
Small*
M&L**
Total
311
Food
31.6
29.8
30.8
66.5
60.6
63.9
21.1
21.7
21.4
48.9
59.6
53.6
56.7
63.9
59.9
312
Bever. & tobacco
36.8
41.9
39.6
66.1
83.3
75.5
14.7
17.1
16.0
48.0
45.6
46.7
67.0
64.7
65.8
313
Textile mills
7.8
19.2
12.9
65.2
64.8
65.0
18.0
16.3
17.2
43.9
62.9
52.5
55.4
71.5
62.6
314
Textile product
22.9
43.8
29.3
55.9
67.0
59.3
16.2
14.6
15.7
46.2
45.1
45.9
55.5
66.2
58.8
315
Apparel
25.7
24.7
25.3
48.6
59.4
52.6
21.1
15.9
19.2
52.6
56.2
53.9
62.8
66.2
64.1
316
Leather
27.8
28.5
28.1
57.3
49.5
53.5
22.5
16.4
19.5
54.2
74.8
64.2
72.2
62.6
67.6
321
Wood
34.1
39.7
36.1
62.5
67.7
64.3
22.9
24.4
23.4
51.5
51.7
51.6
54.9
54.3
54.6
322
Paper
38.7
44.0
42.0
63.1
80.0
73.5
37.6
25.5
29.9
30.2
52.1
43.6
40.7
55.6
49.9
323
Printing
32.4
36.2
33.3
65.9
59.4
64.3
31.4
39.6
33.5
50.1
40.9
47.8
52.3
48.7
51.4
324
Petroleum & coal
31.4
48.0
39.6
56.9
80.0
68.3
35.3
12.0
23.8
33.3
48.0
40.6
45.1
52.0
48.5
325
Chemical
39.0
34.5
37.2
62.7
57.5
60.6
22.0
21.3
21.7
43.3
52.4
46.9
49.7
58.2
53.1
326
Plastics & rubber
31.8
28.6
30.5
58.5
51.8
55.8
24.6
22.2
23.6
49.6
42.3
46.6
57.0
55.9
56.5
327
Nonmetal. mineral
36.4
35.9
36.3
63.4
67.4
64.3
26.8
25.9
26.6
44.8
43.3
44.4
47.6
53.0
48.8
331
Primary metal
27.0
34.1
31.0
66.1
59.7
62.4
22.6
12.7
17.0
51.5
41.9
46.0
56.3
50.8
53.1
332
Fabricated metal
32.5
31.8
32.4
59.2
47.2
56.8
28.4
16.0
25.9
48.5
55.0
49.8
41.7
49.0
43.2
333
Machinery
24.3
24.5
24.4
54.4
66.6
58.1
21.3
17.5
20.1
42.9
47.8
44.4
47.8
50.2
48.5
334
Comp. & electro.
26.3
22.9
24.7
46.2
46.6
46.4
20.4
10.7
15.9
42.4
46.8
44.4
55.7
64.5
59.8
335
Electrical equip.
21.8
25.0
23.4
54.6
68.9
61.6
27.4
20.8
24.2
49.4
46.1
47.7
55.5
54.2
54.9
336
Transpor. Equip.
34.0
36.0
35.1
49.7
51.3
50.6
18.7
18.8
18.8
40.9
44.3
42.8
46.5
44.9
45.6
337
Furniture
34.0
21.3
30.4
55.4
55.5
55.4
25.6
21.6
24.5
44.8
51.5
46.7
48.5
53.0
49.8
339
Misce. Manuf
33.5
29.2
32.5
57.9
53.9
57.0
13.6
26.0
16.5
47.7
61.0
50.7
56.5
58.8
57.0
31.1
31.5
31.3
59.0
60.0
59.4
23.9
20.5
22.7
47.0
50.7
48.3
51.1
55.9
52.8
Total Manufacturing
20
Table 4: Continued
NAICS
x6
Industry
Small*
x7
flexible suppliers
M&L**
Total
x8
Small*
hardness to hire
M&L**
Total
Small*
x9
hardness to retain
M&L**
Total
quick obsolescence of products
Small*
M&L**
Total
311
Food
27.9
22.4
25.5
54.2
47.8
51.4
34.1
36.1
35.0
16.8
15.5
16.3
312
Bever. & tobacco
22.4
25.6
24.1
26.4
54.4
41.6
31.4
25.7
28.3
12.4
10.3
11.3
313
Textile mills
22.4
16.3
19.6
62.4
60.7
61.6
38.1
37.3
37.8
13.4
16.6
14.8
314
Textile product
20.9
16.3
19.5
67.9
70.6
68.7
31.2
20.6
28.0
11.0
18.9
13.4
315
Apparel
21.5
20.1
21.0
60.3
69.3
63.6
38.9
30.7
35.8
22.3
33.3
26.4
316
Leather
10.1
5.6
7.9
70.5
53.3
62.1
54.6
24.3
39.9
17.6
16.8
17.2
321
Wood
27.1
24.5
26.2
59.7
52.0
57.0
32.5
34.2
33.1
9.5
7.8
8.9
322
Paper
18.1
22.8
21.0
46.8
53.6
51.0
20.3
27.1
24.5
9.4
7.8
8.4
323
Printing
33.2
29.5
32.3
72.3
60.8
69.4
33.2
29.4
32.3
18.2
22.6
19.3
324
Petroleum & coal
31.4
36.0
33.7
60.8
48.0
54.5
13.7
20.0
16.8
23.5
12.0
17.8
325
Chemical
24.0
14.1
20.1
46.5
49.5
47.7
27.2
28.6
27.8
11.1
5.8
9.0
326
Plastics & rubber
22.6
19.2
21.2
62.8
65.9
64.1
37.3
36.7
37.1
12.5
6.8
10.2
327
Nonmetal. Mineral
28.8
21.2
27.0
53.1
52.3
52.9
27.0
28.9
27.5
7.2
6.0
6.9
331
Primary metal
25.0
21.0
22.7
63.1
46.2
53.5
28.6
29.1
28.9
7.7
2.6
4.8
332
Fabricated metal
26.2
19.7
24.9
70.1
60.5
68.3
31.2
21.2
29.3
4.4
9.5
5.4
333
Machinery
31.7
23.9
29.3
73.7
68.0
71.9
34.7
34.2
34.5
6.6
9.5
7.5
334
Comp. & electro.
28.2
20.9
24.8
59.4
68.3
63.5
42.4
43.6
43.0
20.2
20.1
20.2
335
Electrical equip.
15.7
17.0
16.3
54.4
59.8
57.1
34.4
29.9
32.2
9.8
9.4
9.6
336
Transpor. Equip.
18.7
19.6
19.2
60.6
60.0
60.2
37.8
33.6
35.5
9.7
7.4
8.4
337
Furniture
23.1
17.2
21.4
70.1
72.0
70.7
46.2
40.9
44.7
12.5
7.3
11.0
339
Misce. Manuf
20.9
12.5
19.0
71.7
57.0
68.4
34.6
35.6
34.8
8.3
12.4
9.2
All industries
25.7
20.7
23.9
63.6
58.6
61.8
34.0
32.2
33.4
11.0
11.8
11.3
* Small-sized firms. ** Medium- and large-sized firms. *** A statement is highly agreed by a firm if the firm scores 4 or 5.
Source: The linked SI.
21
Table 5: Estimated Effects of Competition on Productivity
Variables
Intercept
Fuel & power consumption
Product market competition
Competition for workers
Market transparency
Medium-sized firms
Large-sized firms
Food
Bever. & tobacco
Textile mills
Textile product
Apparel
Leather
Wood
Paper
Printing
Petroleum & coal
Chemical
Plastics & rubber
Nonmetal. mineral
Primary metal
Fabricated metal
Machinery
Comp. & electro.
Electrical equip.
Transpor. Equip.
Furniture
R-Square
Parameter
Estimates
4.335
0.237
0.061
-0.087
-0.015
0.080
0.210
-0.242
0.397
-0.359
-0.329
-0.245
-0.360
-0.285
-0.151
-0.056
0.251
0.371
-0.147
-0.199
-0.082
-0.058
0.129
0.278
0.048
-0.005
-0.185
t Values
67.80
25.44
2.45
-5.91
-0.75
4.35
5.84
-4.42
4.77
-4.93
-4.02
-4.19
-3.75
-5.20
-2.38
-0.92
2.47
6.36
-2.54
-3.19
-1.21
-1.08
2.38
4.47
0.70
-0.09
-3.07
.2570
P values*
0.000
0.000
0.014
0.000
0.454
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.018
0.359
0.013
0.000
0.011
0.001
0.227
0.282
0.017
0.000
0.482
0.927
0.002
* The P value (or marginal significance level) is the probability of observing a test statistic no less extreme
than the one actually observed.
22
Appendix: Estimation of an Unobservable Latent Variable
Let ξ denote an unobservable latent variable that is to be estimated from its indicators. The
empirical relationship between these indicators and the latent variable can be written as:
(A1)
x = ëξ + ä .
Where x = ( x1 M x 2 M...M xn ) is the vector of indicators, ë = ( λ1 M λ2 M...M λn ) is the vector of
coefficients of x on ξ , and ä = (δ1 Mδ2 M...Mδn ) is the vector of error terms.
Assume that the error terms ä = (δ1 Mδ2 M...Mδn ) are orthogonal to the latent variable ξ , the
covariance matrix of x can be written as:
(A2)
xx T ≡ ∑ = ë ξξ T ë T + ää T .
Normalize the variance of ξ to 1, i.e., var (ξ) ≡ ξξ T = 1 . As ∑ is known, we can derive the
estimate of ë , denoting ë̂ , by minimizing the determinant of
(A3)
È ≡ ää T = ∑ −ëë T .
Thus, the parameters of the model are estimated by minimizing the difference between the
sample co-variances of all indicators and the co-variances predicted by the model. The
estimation is done by generally weighted least squares method since all observed variables are
ordinal. The weight matrix is the inverse of the estimated asymptotic covariance matrix of the
polychoric correlations of the observed variables in this paper. Here it is important to use the
generally weighted least squares method. When other methods such as Maximum Likelihood or
Generalized Least Squares are used, parameter estimates may be distorted and the chi-square
goodness-of-fit measure and standard errors may not be reliable (Soreskog and Sorbom, 1996).
Replacing ë by ë̂ in equation (A1) gives x = ëˆ ξ + residual. The estimation of ξ is
ˆ = ëˆ ëˆ T , we have wˆ ∑
ˆ = ( ëˆ T ëˆ ) -1 ëˆ T ëˆ ëˆ T = ëˆ T
ξˆ = ( ëˆ T ëˆ ) −1 ëˆ T x ≡ w
ˆ x , with wˆ = ( ëˆ T ëˆ ) −1 ëˆ T . As ∑
which implies
(A4)
ˆ −1 .
ξˆ = ŵx , with wˆ = ëˆ T ∑
23
It is clearly for equation (A4) that an indicator is not only weighted by its correlation with the
latent variable, reflected by ë̂ , but also by the variance of the correction and its co-variances
with other indicators. The smaller the variance of the correlation, the higher is its weight.
24