Archived — Are Canadian Industries Moving Up the Value Chain?

The views and opinions expressed in the report are those of the authors alone and do not represent, in any way, the views or opinions of the Department of Industry or of the Government of Canada.

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Archived


Industry Canada


Industry CanadaFootnote *

Abstract

Moving up the value chain has recently made its way to the forefront of public policy debate (in many developed countries, including Canada) as a crucial means to improve productivity performance, job creation and thereby to improve living standards. A shortcoming of this debate, however, is that there is no readily comparable indicator across industries that allows to ascertain appropriately whether they are moving up (or going down) the value chain. In this paper, we develop a composite indicator of a number of individual fundamental determinants of productivity (using the latent variable approach) for undertaking a more comprehensive assessment as to whether an industry is moving up the value chain. The trends in the new indicator suggest that most Canadian industries are moving up the value chain. We find that increasing offshoring and greater competition induce industries to move up the value chain. We also find, as expected, that productivity tends to move in tandem with the composite indicator.

Key words: value chain, productivity, latent variable

Table of Contents

  1. Introduction
  2. What Do We Know about the Concept of Moving Up the Value Chain?
  3. Indicators of Investment in Knowledge and Adoption and Diffusion of New Technologies
  4. Latent Variable Approach
  5. Trends in Composite Indicator, Explanatory Factors, and Link with Productivity
  6. Concluding Remarks

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1. Introduction

The value chain, also known as value chain analysis, is a concept from business management that was first described and popularized by Michael Porter in his 1985 best-seller, Competitive Advantage: Creating and Sustaining Superior Performance. The value chain in its original sense was defined as a sequence of value-enhancing activities. In its simplest form, raw materials are formed into components, which are assembled into final products, distributed, sold, and servicedFootnote 1. In other words, the chain consists of a series of activities that create and build value. In most industries, however, it is rather unusual that a single company performs all value-creating activities (from, e.g., product design, production of components, and final assembly to delivery to the final user) by itself. Most often, particularly in these days of greater (international) economic integration, some (less efficient) activities in which a given company has no competitive advantage are outsourced or offshored – i.e., components or operations, e.g., that would normally be done in-house are done by other companiesFootnote 2.

From this perspective, examining whether a firm (or an industry) is moving up or going back the value chain would ideally require evaluating both which value each particular activity adds to the firm's products and services, and more importantly the extent to which the firms focus on activities in which they have a comparative advantage. For instance, in developed economies (particularly), firms, by exploiting their comparative advantages, reallocate resources towards higher value-added activities and move out of lower value-added activities (or transfer them abroad), and thereby move economic activity further up the value chain. This process ultimately maximizes value creation while minimizing costs. In the same line, OECD (2007) stresses that, if developed countries are to remain competitive in the global economy, they will have to increase the level of knowledge and technology embodied in production and exports, which would make competition from lower-income (lower-cost and lower-productivity) countries less likely in the relevant markets. On the other hand, as many developing countries have a strong comparative advantage in low-technology industries (i.e. lower value-added activities), specializing in these activities to exploit their competitive advantage would not therefore move them up along the value chain. Thus from this angle, one can appreciate how 'obscure' this concept of moving up (or going back) the value chain might be! Further, even within the same country, the distinctive features of industries (e.g. capital- vs. labor-intensive industries or technology- vs. knowledge- intensive industries) make it difficult to find a single common and comparable indicator that allows ascertain whether they are moving up the value chain.

Nonetheless, the concept of moving up the value chain has recently made its way to the forefront of public policy debate (in many developed countries, including Canada) as a powerful means to sustain economy growth, job creation and thereby to improve living standards. A shortcoming of this debate, however, is that there is no readily available data that allow us to determine directly (and a comparable manner) whether an industry is moving up along the value chain. In this paper, we take a first step to fill this gap by attempting to construct comparable indicators (across industries) that may allow assessment of whether Canadian industries are moving up the value chain. Furthermore, we explore the factors that might help explain the changes (over time) in these newly constructed indicators. Lastly, we analyze the relationship between the new indicators (measuring the extent of climbing the value chain) and productivity improvement.

The remainder of the paper is organized as follows. The next section provides a brief summary of what we know about the concept of moving up the value chain. Section 3 analyzes indicators of investment in knowledge and adoption and diffusion of new technologies across industries. Section 4 presents the framework for constructing and estimating a composite indicator or index of moving up (or going back) the value chain, using the latent variable approach. Section 5 examines the trends in composite indicator and analyzes its explanatory factors and link with labor productivity. Finally, Section 6 concludes the paper.


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2. What Do We Know about the Concept of Moving Up the Value Chain?

Notwithstanding the recent interest in the concept of moving up the value chain, it is worth mentioning at the outset that there is very little scholarly work on this topic in the literature. However, there exist some indicators that have been used to determine whether a given country is moving up the value chain. An indicator often used, particularly for developing or less developed countries, is the export (commodity) composition; or more specifically, the extent to which the share of higher-technology manufactures in the country's total (manufacturing) exports increases. Using this indicator, Canada's Foreign Affairs and International Trade Department (DFAIT) reports that China is moving up the value chain as complex manufactured goods account for a growing share of China's merchandise exports. For example, in 1995 clothing and textiles made up 29.6% of exports, but by 2005 this proportion had dropped to 17.1%. At the same time, the share of exports made up of electronics and machinery increased from 18.6% to 42.2%. Moreover, 6 of China's top 10 exports were textile-related in 1995, but by 2005 7 of the top 10 exports were related to machinery and electrical machinery. Thus, DFAIT argues that this change in export composition suggests that China is moving up the value chain as it develops, producing- and exporting-higher value itemsFootnote 3.

However, an assessment based solely on a country's export composition (by technological intensity) may be misleading, particularly for developing countries, because of the high import content in exports of higher-technology products. There is a general feeling that most developing countries such as China import parts and components and other intermediates from abroad and then merely assemble them (using locally cheap labor) for re-exports. Obviously, this process does not involve significant domestic value added. For example, Branstetter and Lardy (2006) doubt that China is moving up the value chain; they point out that most information and communications technology (ICT) exports to the U.S. are high-volume commodity products sold primarily by mass merchandisers of electronic products (DVD players, notebook computers and mobile telephones). Moreover, high-value-added components of these products – such as central processors and memory chips – are generally imported. Thus, domestic value added would only account for 15% of the value of exported electronic and information technology products. According to Branstetter and Lardy (2006), China provides mainly low-wage assembly services in accordance with its comparative advantage in labor-intensive activitiesFootnote 4. Mani (2002) provides similar evidence for Philippines, which is also one of the leading exporters of high-technology products from the developing worldFootnote 5.

Thus, according to OECD (2007), when exports depend heavily on imports in the same industries, exports shares do not reveal countries' strengths and weaknesses properly; the contribution of different industries to the trade balance identifies more appropriately countries' comparative advantage. In other words, the net exports by technological intensity of goods is a more appropriate indicator (than the respective gross exports) when examining whether a country is moving up the value chain. Using indicators of revealed comparative advantage (which reflect the contribution of different industries to countries' trade balances), OECD (2007, Fig. 4.12) reports that the comparative advantage of China is still concentrated in low-technology industries, despite its high export figures and its trade surplus in high-technology industries. However, the same indicator shows that only a few OECD countries are specialized in high-technology manufacturing (see OECD, 2007, Fig. 4.10)Footnote 6.

Besides, OECD (2007) uses another indicator – namely the share of countries' total value added accounted for by (higher) technology- and/or knowledge-intensive industries – to ascertain whether its member countries are moving up the value chainFootnote 7. Although this indicator is applied to the OECD area as a whole, it seems that OECD countries are moving to higher-value-added activities, as illustrated by the growing share of higher-technology-intensive industries and knowledge-intensive market services in OECD-area value added.

So far, the indicators we have reviewed focus on the relative importance (either in trade or value added) of industries classified as high- and medium-high technology. A drawback of this particular focus on higher-technology industries is that it draws attention away from the changes that are taking place in less technology-intensive industries. According to OECD (2007), the shift towards more technology and knowledge activities and investments is also taking place within lower-technology industries, as illustrated by the increasing research and development (R&D) intensity of these industries which has grown much more strongly than in other industries.

Finally, to assess the moving up the value chain that takes place in OECD countries, OECD (2007) considers two other indicators: investment in knowledge – defined as investment (relative to gross domestic product (GDP)) in research and development, higher education, and software – and investment (as a percentage of GDP) in machinery and equipment (M&E). Considering the ratio of investment in knowledge to GDP, it appears that OECD countries are moving up the value chain, as this ratio has increased for nearly all reported countries over the period 1994-2002. As for the share of M&E investment in GDP, the conclusion is mixed since this ratio has declined for half of the reported countries over the same period. It is needless to say that the use of these two indicators together is appealing and worth doing as this involves both fundamental and applied innovationsFootnote 8, which have been identified by the new growth theory as the major drivers of productivity improvements. However, an assessment based on these two indicators (as reported separately by OECD (2007)) may be problematic for some countries in which the investment in knowledge increases and investment in M&E decreases. As a result, unless we use subjective weights on these reported indicators, one cannot ascertain the direction in which these countries are moving along the value chain. Thus, as we will see in section 4, a contribution of this paper is to adopt a method that allows computing a composite indicator by making use of these indicators and avoid subjectivity in determining the weights of each component indicator.

Nonetheless, the few existing studies (or analyses) that address this issue of moving up the value chain lend support to indicators measured by investment in knowledge and/or M&E investment. For example, Sim (2004) developed a two-country analytical framework to examine (theoretically) the factors required for a small-open economy to move up the value chain. He found, among other things, that the small-open economy can move up the value chain when the size of its skilled labor force increases. Further, in the U.K newspaper, The Herald (on Monday ), Jeremy Peat – Director of the David Hume InstituteFootnote 9 – argues that higher education is vital for climbing the value chain. However, he stresses that just the provision of intellectual capital (by higher education institutions) is not enough; successful moving up the value chain implies emphasis on enhanced skills, much more R&D, (applied) innovation inducing continuing change and dynamic management. In other words, each of these factors is necessary but not sufficient by itself for successfully moving up the value chain. Finally, IMF (2006) stresses that moving up the value chain requires building human capital, absorbing domestic and foreign technologies, and integrating into multinational production chains.

Thus, given the arguments made above, the need for constructing a composite indicator (for moving up the value chain) that encompasses all of these indicators (i.e. human capital or skills, R&D expenditure, and M&E investment) is now self-evident. But, let first examine separately (in the next section) the evolution of these indicators within and across Canadian industries (both manufacturing and services).


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3. Indicators of Investment in Knowledge and Adoption and Diffusion of New Technologies

Figure 1 presents the R&D, M&E and skills intensities by Canadian industries (see Table 1 for the list of industries). The R&D and M&E intensities are defined as the nominal ratio of R&D spending and M&E investment to GDP, respectively, and the skills intensity is measured as the share of hours worked by employees with a university degree and aboveFootnote 10. Although we have classified industries into five groups (primary and construction, resource-based manufacturing, labor- intensive manufacturing, high-tech manufacturing, and services)Footnote 11, Figures 1.1 to 1.29 present these variables for each industry as defined in Table 1.

Overall, a key feature that emerges from Figure 1 is that there exists a notable discrepancy in the trends of R&D and M&E intensities and skills for most of the industries and across industries. Consequently, none of them can then be used as a single appropriate comparable indicator to ascertain whether those industries are moving up the value chain. However, since we should consider all three indicators when assessing the moving up that is taking place, we find it appealing (in the next section) to calculate a single index that encompasses all these indicators.


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4. Latent Variable Approach

As mentioned earlier, investment in both knowledge (as measured, e.g., by R&D intensity and the level of skills (or higher education)) and adoption and diffusion of new technologies (as measured, e.g., by M&E investment to GDP ratio) are all required factors to move up the value chain successfully. Thus, as all industries generate and/or exploit new technology and knowledge to different extents – i.e. some are more technology- and/or knowledge-intensive than others – it would be more appealing to account for all of them together in examining the move of industries along the value chain. Consequently, in this section, we construct a composite indicator using a latent variable model.

A latent variable is not observable, but is estimated as a weighted sum of its multiple indicators.Footnote 12 The latent variable approach offers three main advantages. First, it can provide us with a more comprehensive measure of moving up (or going back) the value chain than a single indicator for reasons just mentioned above. Second, it reduces the number of variables in the analysis and helps us to summarize the data – interestingly, this is done by avoiding subjectivity in determining the weight of each component variable. Finally, it resolves multicollinearity problems caused by directly using multiple indicators in a regression. Next, we provide a brief sketch on the estimation of a latent variable.

Let ξ denote an unobservable latent variable that is to be estimated from its n indicators. The empirical relationship between the latent variable and these indicators can be written as:

x = λ ξ + δ (1)

where x = ( x 1 , x 2 ,...., x n ) is the vector of indicators, λ = ( λ 1 , λ 2 ,...., λ n ) is the vector of coefficients of x on ξ, and δ = ( δ 1 , δ 2 , . . . . , δ n ) is the vector of error terms.

Assume that the error terms are orthogonal to the latent variable ξ . The covariance matrix of x can be written as:

x x T Σ = λ ξ ξ T λ T + δ δ T (2)

Normalize the variance of ξ to 1, i.e., var ( ξ ) ξ ξ T = 1 . As is known, we can derive the estimate of λ, denoted λ ̂ , by minimizing the determinant of

Θ δ δ T = Σ λ λ T (3)

Thus, the parameters of the model are estimated by minimizing the difference between the sample covariance of all indicators and the covariance predicted by the model.Footnote 13

Following Lanjouw and Schankerman (1999) and Joreskog and Sorbom (1996), the estimate of the latent variable, ξ, is

ξ ̂ = w ̂ x (4)

where w ̂ = λ ̂ T Σ 1 .Footnote 14

It is clear from equation (4) that the weight of an indicator depends not only on its correlation with the latent variable, reflected by λ̂ , but also on its variance and its covariance with other indicators. The smaller the variance of the indicator, the higher is its weight.

Thus, we model our composite indicator of moving up (or going back) the value chain as a latent variable, where R&D and M&E investments to GDP ratio and the level of skills (or human capital, measured by the share of hours worked by workers with university degree and above) are used as component indicators. More formally, let ξ C I denote our composite indicator. The estimate of ξ C I , ξ ̂ C I can be written as the weighted sum of the three indicators:

ξ ̂ C I = w ̂ 1 R D + w ̂ 2 M E + w ̂ 3 H K (5)

where RD and ME represent respectively the R&D and M&E investment-to-GDP ratio, HK denotes human capital, and ŵi ( i = 1,2,3) is the weight for the corresponding indicator.Footnote 15 It is worth mentioning that this approach is similar to the method used by Lanjouw and Schankerman (1999) to measure innovation.

The estimated weights of each component indicator (i.e. R&D and M&E intensities and skills) for different groups of industries are reported in Table 2. As can be seen, the skills intensity is most heavily weighted (relative to R&D and M&E intensities) in both resource-based and high-tech manufacturing; its weights are 1.38 and 1.78, respectively. On the other hand, the three indicators are almost equally weighted for primary and construction, labour-intensive manufacturing, and services. Overall, these average estimated weights are very consistent with the patterns of the three component indicators across industry groups as can be seen in Figure 1.


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Figure 2 plots the composite indicator of moving up the value chain as computed from equation (5) using the weights reported in Table 2. The trends in the new indicator suggest that all Canadian industries (except mining and utilities) are moving up the value chain, although at a different pace. In the primary sector, agriculture has made a substantial progress, with the index measuring the extent of moving up equals 256 in 2003, significantly up from 100 in 1982 – i.e. an increase of about 156%. In the manufacturing sector as a whole, moving up the value chain has also been particularly strong in the electrical equipment industry (in high-tech manufacturing), where the index increased from 100 in 1982 to 395 in 2001 – i.e. a rise of about 295%. However, the index has slightly decreased to reach the value of 351 in 2003. In the service sector, wholesale trade, FIRE and management, and professional, scientific and technical are the industries that showed a greater tendency to move up the value chain. For further details, see Table 3 where industries are ranked (within each group industry) according to their average annual growth rate. In the same Table 3, we also present the intensity of skills (which seems to be a key factor for many industries to move up the value chain) for the latest year available, i.e. 2003. Finally, it is worth reminding to say that this newly constructed composite indicator provides a more comprehensive assessment tool (than single conflicting component indicators) as to whether an industry is moving up the value chain.

Next, in order to identify some explanatory factors that may induce industries to move up the value chain, we assume the following function:

I M U P i t = ƒ i ( O f f s h i t C o m p i t , R E R a t e i t , X i t ) (6)

where IMUP represents the indicator of moving up the value chain, Offsh denotes offshoring, Comp stands for competition, RERate is the real exchange rate, X is a vector including other control variables such as industry and time dummies, and the indices i and t denote industries and years, respectively.

The offshoring variable (Offsh) is defined as the imported share of intermediate inputs. As only the imported share in final demand for each commodity can be derived from input-output tables, we simply assume that for each commodity the imported share in intermediate use is the same as the imported share in final demand. Under the assumption, the imported share of intermediate inputs for each industry can be calculated as the weighted sum of imported shares of all commodities used as intermediate inputs. All Canadian input-output tables are obtained from Statistics Canada. The competition variable (Comp) is calculated as the inverse of the regulation impact indicators from the OECD that measure the extent of anti-competitive product market regulations.Footnote 16 The OECD data is ISIC Rev3 based and covers the period of 1984 to 2003. The real exchange rate is estimated by deflating the nominal exchange rate using industrial GDP deflators. The nominal exchange rate and GDP data are obtained from Statistics Canada.

The intuitions behind these explanatory variables run as follows. In these days of (international) economic integration, a firm (or an industry) may decide to source certain activities that require low skills or standard technologies to external providers that have cheaper or more efficient production capabilities. This possibility of offshoring/ outsourcing would likely allow the firm to move up the value chain by focusing on high-value-added activities – i.e. those that are higher technology- and/or knowledge-intensive. Besides, greater competition (involved, e.g., by deregulation and liberalization in some domestic markets) will typically increase the pressure for firms to innovate and adopt new technologies, and thereby to move up the value chain. Another factor that may induce firms to move up the value chain is the appreciation of the domestic currency. For example, a higher Canadian dollar may seriously hamper the price-competitiveness of Canada's manufactures, which may compel them to undertake some innovative activities in order to restore their competitiveness. Actually, successful innovating firms will either have lower costs, and so be able to sell at a lower price, or will have superior quality goods, and in either case will strengthen their competitiveness. Further, another potential implication of the higher Canadian dollar that is often overlooked (but relevant in the context of this study) is the reduction in the costs of imported M&E for Canadian industries, which are in general major importer of this capital. Thus, the resulting potential increase in M&E investment will then allow Canadian industries to further move up the value chain as the latest technologies are typically embodied in new M&E capital.

Using the log-linear (first-order) dynamic panel specification of equation (6), we estimate the following stochastic equation:

ln ( I M U P i t ) = β 0 + β 1 ln ( I M U P i t 1 ) + β 2 ln ( O f f s h i t ) + β 3 ln ( C o m p i t ) + β 4 ln ( R E R a t e i t ) + β 5 X i t + ε i t (7)

where εit are the classical error terms or innovations.

This dynamic specification is likely to alleviate (if not eliminate) the observed serial correlation in innovations. To estimate equation (7), we use both (generalized) Least-Squares Dummy Variable (LSDV) and Generalized Method of Moments (GMM) estimation techniques (see Table 4).Footnote 17 As mentioned by Kiviet (1995), when a model for panel data includes lagged dependent explanatory variables, the standard estimation techniques (such as variant of LSDV) lead to estimators which (although consistent and asymptotically efficient) are often seriously biased in small samples, and yield test procedures that (although asymptotically valid) are afflicted with an actual type I error probability which differs considerably from the nominal size. It is because of this potential shortcoming that we also used the GMM estimation technique. More specifically, we employed the Arellano-Bond (1991)'s 1-step estimation procedure, which is well known to be appropriate for estimating dynamic panel models with cross-section fixed effects.Footnote 18

We now turn to the estimation results. As can be seen in Table 4, the parameter estimates by both LSDV and GMM procedures indicate that both offshoring and competition have a positive and significant effect on the composite indicator of moving up the value chain – although, the values of the estimated coefficients are higher with a a GMM approach. In this latter case, the estimated coefficients imply that a one percent increase in the offshoring and competition would raise the moving up indicator respectively by about 0.09 and 0.19 percent (other things being equal), and vice versa. With the GMM estimation as well, the real exchange rate variable enters with the expected negative sign, but its coefficient is statistically insignificant. Finally, there is some evidence (from both estimation methods) that the first-order autoregressive process model is well specified for the data series as is shown by the significant coefficients of the lagged dependent variable. It is also worth noting that the pattern of serial correlation in the residuals (from GMM) accords with the fact that we have used Arellano and Bond first-differenced procedure (instead of the orthogonal deviation approach). This procedure has the property that if the innovations εit are serially uncorrelated, the (first-differenced) innovations Δεit should have significant (negative) first-order serial correlation but no significant second-order serial correlation. As for the orthogonal deviation procedure, if the innovations εit are serially uncorrelated, the transformed innovations should also be serially uncorrelated.Footnote 19

As a sensitivity analysis, we substitute China's share in total Canadian imports for competition. This share may account for both the contribution of Chinese producers to competition in Canadian markets and the relative importance of China in Canadian industries' offshoring activities. As indicated in Table 4', the introduction of this variable significantly reduces the impact of offshoring, which even becomes insignificant in GMM estimation.

Next, we analyze the relationship between labor productivityFootnote 20 level and the composite indicator of moving up the value chain, using a log-linear (first-order) dynamic panel model similar to equation (7). These results are reported in Table 5. After controlling for other variables such as business cycleFootnote 21 and competition, we find that the elasticity of the labor productivity level with respect to the moving up indicator is about 0.03 (in GMM estimation – with zero p-value), indicating that the newly constructed indicator is statistically significant in explaining labor productivity performance.


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6. Concluding remarks

Moving up the value chain has recently made its way to the forefront of public policy debate (in many developed countries, including Canada) as a crucial means to improve productivity performance, job creation and thereby to improve living standards. In fact, products and services that are currently regarded as among the most innovative and experimental in developed countries eventually end up as commodities that can be produced anywhere and by many producers. As a result, OECD (2007) stresses that, to sustain economic growth and remain competitive in the global economy, developed countries will have to increase the level of knowledge and technology embodied in production and exports, which would make competition from lower-income (lower-cost and lower-productivity) countries less likely in the relevant markets. In other words, developed economies or their industries need to move up the value chain by inventing or using new technology, by innovating products and processes and by upgrading human capital.

However, notwithstanding the recent interest in the concept of moving up the value chain, there exists no readily comparable indicator across industries that allows to ascertain appropriately whether they are moving up (or going back) the value chain. In this paper, we develop a composite indicator of a number of individual fundamental determinants of productivity (using the latent variable approach) for undertaking a more comprehensive assessment as to whether an industry is moving up the value chain. The trends in the new indicator suggest that most Canadian industries are moving up the value chain. We find that increasing offshoring and greater competition induce industries to move up the value chain. We also find, as expected, that productivity tends to move in tandem with the composite indicator.


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References

Arellano, M. and S. Bond (1991), "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations", Review of Economic Studies, 58, 277–297.

Branstetter, L. and N. Lardy (2006), "China's Embrace of Globalisation", NBER Working Paper, No. 12373.

Conway, P., De Rosa, D., Nicoletti, G. and F. Steiner (2006) "Regulation, Competition and Productivity Convergence", OECD Economics Department Working Paper No. 509.

Guillaume, G., Lemoine, F. and D. Unal-Kesenci (2005), "China's Integration in East-Asia: Production Sharing, FDI & High-Tech Trade", CEPII Working Paper, No. 2005-09.

IMF (2006), Republic of Latvia – 2006 Article IV Consultation Mission – Preliminary Conclusions. June 2006.

Joreskog, K. and D. Sorbom (1996), LISREL 8: User's Reference Guide. Scientific Software International, Inc.

Kiviet, J. F. (1995) "On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models", Journal of Econometrics, 68, 53–78.

Lanjouw, J. O. and M. Schankerman (1999) "The Quality of Ideas: Measuring Innovation with Multiple Indicators", NBER Working Paper, No. 7345.

Mani, S. (2002) "Moving Up or Going Back the Value Chain: An Examination of the Role of Government with Respect to Promoting Technological Development in the Philippines", Discussion Paper Series, No. 2002-10, Maastricht: The United Nations University/Institute for New Technologies.

OECD (2007), Staying Competitive in the Global Economy: Moving Up the Value Chain, OECD, Paris.

Sim, N. C. (2004) "International Production Sharing and Economic Development: Moving Up the Value Chain for a Small-Open Economy", Applied Economics Letters, 11, 885–889.

Tang, J. and W. Wang (2005) "Product Market Competition, Skill Shortages and Productivity: Evidence from Canadian Manufacturing Firms", Journal of Productivity Analysis, 23, 317–339.


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Figures

Figure 1: R&D and M&E intensities and Skills by Industry

Primary and Construction

Figure 1.1: Agriculture Industry
Graph of Agriculture Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.1
Agriculture Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100.00 109.04 135.65 159.56 211.15 205.63 210.86 206.25 228.53 347.43 326.82 333.24 427.27 397.72 404.20 447.97 358.04 457.65 506.39 596.55 687.93 543.98
M&E 100.00 91.41 91.87 74.08 67.55 65.23 62.80 57.02 49.39 46.69 52.10 63.66 57.62 53.79 48.73 61.34 59.58 47.45 47.03 44.09 50.92 48.77
Skills 100.00 100.73 105.56 109.51 114.24 115.93 119.78 124.45 109.73 118.76 129.42 149.53 166.30 160.93 168.56 171.36 177.63 189.94 194.18 176.58 210.42 212.81
Figure 1.2: Mining Industry
Graph of Mining Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.2
Mining Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100.00 60.72 66.22 66.62 78.68 72.55 76.23 76.16 88.85 105.34 119.02 131.59 136.36 137.01 112.64 109.27 110.13 76.27 58.63 71.29 93.17 73.52
M&E 100.00 63.61 53.97 48.41 34.19 34.75 43.26 33.65 28.80 25.17 18.91 22.29 26.23 35.42 46.91 56.19 48.94 50.30 65.44 80.40 84.94 84.65
Skills 100.00 105.75 109.74 117.56 121.25 122.72 126.15 131.29 131.17 135.13 141.90 146.62 144.21 142.49 141.59 145.45 143.23 169.39 130.82 161.40 190.89 162.49
Figure 1.3: Construction Industry
Graph of Construction Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.3
Construction Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100.00 42.17 45.34 70.93 42.54 31.63 48.86 50.55 89.51 74.18 72.95 92.43 203.77 188.44 174.31 250.87 165.04 220.73 264.23 274.25 237.15 204.36
M&E 100.00 99.73 100.73 93.16 93.74 97.08 110.73 115.74 112.38 106.26 135.65 144.32 146.83 148.53 131.18 150.51 149.83 168.39 171.44 160.80 168.91 174.19
Skills 100.00 97.50 98.38 100.15 105.81 110.16 114.26 118.85 121.88 123.42 129.63 142.95 145.64 144.95 147.22 151.78 165.38 149.73 142.09 160.01 167.64 162.53

Resource-based Manufacturing

Figure 1.4: Food Industry
Graph of Food Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.4
Food Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100.00 91.98 87.03 86.83 91.40 84.19 85.62 77.24 72.47 62.65 64.43 73.03 87.91 101.34 91.17 86.34 71.51 67.18 72.66 64.35 81.03 93.33
M&E 100.00 99.32 99.10 93.69 103.06 129.68 139.32 155.45 148.14 148.40 155.31 135.32 135.71 140.11 162.87 149.01 157.93 170.10 132.53 117.27 152.82 147.18
Skills 100.00 105.61 116.50 126.39 129.48 133.41 137.96 141.67 146.57 153.44 165.89 186.52 193.90 197.48 199.12 203.85 182.06 182.93 193.29 188.24 215.12 213.52
Figure 1.5: Beverage and Tobacco Industry
Graph of Beverage and Tobacco Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.5
Beverage and Tobacco Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 81.97 72.26 66.89 55.46 56.90 44.89 41.14 51.54 45.77 53.95 73.38 72.82 70.04 59.49 57.12 51.77 70.78 75.00 70.60 80.66 117.77
M&E 100 143.98 159.96 157.25 119.64 129.06 141.78 137.90 183.23 144.29 178.22 159.77 156.36 158.10 135.33 169.66 247.97 194.76 162.59 176.89 189.86 212.12
Skills 100 102.19 106.92 111.03 115.09 121.10 128.09 136.86 148.13 159.09 174.12 196.43 208.20 220.70 221.88 228.53 296.57 268.58 217.85 218.17 174.68 271.10
Figure 1.6: Wood Industry
Graph of Wood Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.6
Wood Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 70.38 76.89 79.60 75.95 72.01 83.36 70.99 191.52 99.30 89.01 70.42 69.51 90.35 74.57 77.85 87.33 80.30 70.67 86.91 94.85 101.83
M&E 100 76.59 77.27 69.28 84.68 116.44 162.19 126.05 117.88 72.20 72.76 96.89 159.47 219.58 140.68 133.50 94.80 78.34 127.17 63.72 53.15 75.39
Skills 100 107.24 118.15 125.33 121.34 117.12 120.36 122.14 126.76 131.92 135.38 148.53 148.31 147.64 151.12 157.28 108.37 142.62 108.72 132.36 120.63 136.65
Figure 1.7: Paper Industry
Graph of Paper Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.7
Paper Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 87.10 81.08 93.10 95.72 75.32 110.19 120.90 106.52 118.26 126.36 127.34 102.21 69.70 78.61 95.24 101.36 78.83 125.63 246.02 271.00 297.77
M&E 100 47.69 50.06 84.20 77.14 100.71 132.04 193.64 160.57 116.24 74.41 60.74 63.45 93.43 83.01 73.65 61.05 51.03 52.09 44.23 32.90 53.36
Skills 100 97.86 99.04 100.40 108.22 114.93 121.37 128.61 136.91 138.44 142.45 153.37 154.81 155.18 154.86 157.77 150.44 156.98 182.39 152.29 171.52 195.24
Figure 1.8: Petroleum and Coal Industry
Graph of Petroleum and Coal Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.8
Petroleum and Coal Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 43.16 44.49 42.44 22.68 25.17 22.39 75.22 45.37 18.80 26.41 27.62 17.98 17.14 25.21 19.26 14.19 9.16 4.96 4.06 9.00 10.49
M&E 100 69.39 34.99 27.96 38.20 55.68 63.20 71.03 67.40 68.75 34.89 23.81 24.07 32.02 30.10 40.70 49.30 57.08 61.39 52.87 127.99 174.95
Skills 100 100.10 104.68 110.39 105.02 100.33 96.89 93.99 90.61 100.99 113.35 132.54 145.58 157.88 156.87 161.23 142.99 127.52 73.63 99.52 143.63 129.78
Figure 1.9: Plastics and Rubber Industry
Graph of Plastics and Rubber Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.9
Plastics and Rubber Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 73.03 62.33 60.40 59.61 51.56 50.53 39.62 38.27 51.33 58.44 65.18 69.78 80.30 75.29 70.43 76.25 73.33 66.98 64.90 63.25 79.53
M&E 100 58.49 62.21 65.44 76.81 83.88 84.38 110.67 134.20 96.30 101.42 67.80 77.30 85.82 78.16 93.41 100.38 88.27 78.62 70.29 56.78 66.11
Skills 100 95.63 93.90 93.10 97.82 101.09 105.21 109.38 116.05 119.46 127.30 143.43 149.12 153.16 153.87 158.51 184.41 171.82 164.62 194.94 231.57 217.06
Figure 1.10: Nonmetallic Mineral Industry
Graph of Nonmetallic Mineral Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.10
Nonmetallic Mineral Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 100.15 145.94 148.64 106.82 87.04 109.83 108.26 98.97 106.52 102.40 93.58 112.28 90.79 75.56 63.74 68.89 82.19 82.54 116.99 181.56 166.47
M&E 100 85.23 87.89 99.25 140.23 124.38 150.43 187.72 214.75 151.64 85.50 86.94 170.82 176.56 153.08 160.99 198.81 213.54 172.57 135.51 133.13 149.54
Skills 100 100.28 105.44 112.27 113.62 116.06 118.07 121.78 124.47 127.98 135.69 151.30 156.88 167.15 164.68 175.49 156.79 189.68 218.90 242.42 211.99 203.88
Figure 1.11: Primary Metals Industry
Graph of Primary Metals Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.11
Primary Metals Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 79.41 75.64 71.67 69.28 72.09 67.21 73.22 104.67 117.90 120.74 103.90 81.00 63.74 69.33 63.94 60.17 58.46 53.27 82.91 79.36 96.70
M&E 100 54.89 54.50 70.36 90.52 78.90 77.23 100.10 112.02 126.13 61.03 39.26 27.34 43.53 56.34 82.17 64.90 77.49 77.51 41.10 23.52 36.21
Skills 100 98.13 97.77 99.49 104.65 109.09 113.58 120.75 128.76 126.92 129.80 138.27 135.38 132.76 133.04 137.21 169.71 148.48 188.82 168.49 212.63 201.35
Figure 1.12: Fabricated Metal Industry
Graph of Fabricated Metal Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.12
Fabricated Metal Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 98.78 79.90 86.91 95.34 86.28 90.30 88.17 84.18 105.75 139.05 181.24 252.64 188.01 149.09 114.97 131.26 142.73 128.72 157.35 208.69 229.79
M&E 100 92.36 152.38 186.97 220.68 139.44 139.85 126.76 103.05 131.41 125.15 131.76 156.52 177.93 226.85 182.41 214.71 182.69 127.51 116.82 110.31 126.45
Skills 100 99.34 98.58 99.55 103.67 107.80 110.40 113.98 119.73 121.86 128.51 139.60 142.58 142.74 143.97 147.88 134.16 125.14 163.36 187.45 147.46 163.42

Labour-Intensive Manufacturing

Figure 1.13: Textile, Apparel, and Leather Industry
Graph of Textile, Apparel, and Leather Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.13
Textile, Apparel, and Leather Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 84.73 93.33 105.91 104.17 110.75 113.27 114.95 113.11 156.82 156.03 174.03 165.42 183.45 164.84 175.66 224.18 254.03 263.97 293.53 306.17 336.94
M&E 100 98.85 104.95 103.40 116.67 115.40 132.34 144.47 169.39 169.12 149.52 154.23 203.26 138.23 203.80 184.72 177.63 183.54 95.35 95.17 88.82 113.48
Skills 100 108.17 118.67 131.07 139.03 147.11 155.81 164.24 173.72 182.18 195.81 226.45 238.24 249.57 257.58 273.03 285.61 295.77 421.37 389.12 403.29 349.97
Figure 1.14: Furniture Industry
Graph of Furniture Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.14
Furniture Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 63.25 54.50 142.78 176.10 105.49 137.02 71.85 61.63 103.47 150.12 147.74 249.42 143.99 132.43 101.87 86.44 127.51 93.89 135.91 202.66 229.52
M&E 100 88.53 124.58 138.29 195.55 236.30 212.95 186.43 160.35 211.45 218.83 250.72 250.96 242.27 296.96 367.32 322.10 356.57 308.97 249.04 215.39 270.48
Skills 100 105.56 117.95 130.47 141.07 146.59 157.71 170.09 180.97 187.81 197.56 215.67 222.07 222.90 229.73 234.92 304.30 248.64 273.83 247.79 221.60 245.77
Figure 1.15: Miscellaneous Manufacturing Industry
Graph of Miscellaneous Manufacturing Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.15
Figure 1.15: Miscellaneous Manufacturing Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 110.44 112.20 146.71 215.05 205.78 159.81 167.07 184.56 191.61 214.41 230.71 257.80 245.18 194.88 184.89 195.21 182.87 176.46 208.98 192.57 193.96
M&E 100 136.33 154.47 215.20 285.76 276.66 178.97 196.66 230.36 256.36 340.07 328.94 382.78 303.76 437.59 248.60 229.23 357.86 275.50 210.71 209.31 254.86
Skills 100 97.41 99.42 103.41 106.32 111.93 116.76 121.00 123.87 129.81 137.63 153.90 158.70 158.03 161.65 161.05 165.14 103.08 150.14 212.98 168.76 182.10

High-Tech Manufacturing

Figure 1.16: Printing Industry
Graph of Printing Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.16
Printing Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 33.61 48.45 39.26 44.29 25.84 35.44 58.19 50.76 41.70 48.79 71.65 115.52 112.27 60.58 59.43 74.76 60.76 71.72 91.41 129.59 135.79
M&E 100 115.39 165.41 153.20 189.24 227.71 177.12 182.53 199.53 223.33 193.02 258.71 234.89 178.24 299.75 251.61 296.05 221.31 233.63 187.48 176.24 224.14
Skills 100 89.79 87.30 85.98 92.16 97.88 104.28 109.63 110.90 115.51 120.97 136.11 146.42 143.15 147.32 149.45 188.15 173.31 204.58 170.92 141.57 175.27
Figure 1.17: Chemical Industry
Graph of Chemical Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.17
Chemical Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 83.65 78.62 92.88 88.30 86.37 80.51 83.96 97.31 109.28 116.00 119.60 114.51 109.23 109.65 106.42 119.07 156.50 169.45 189.22 207.31 200.61
M&E 100 64.63 33.44 29.59 37.63 32.52 33.66 43.87 48.91 53.21 45.62 41.00 30.08 24.39 27.95 29.98 46.63 45.81 43.35 28.86 21.22 25.35
Skills 100 96.39 100.86 105.73 109.27 113.73 115.87 119.20 123.54 130.92 139.43 150.18 155.65 160.73 162.69 165.65 165.66 158.54 170.47 191.70 164.82 185.27
Figure 1.18: Machinery Industry
Graph of Machinery Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.18
Machinery Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 98.93 74.71 74.46 81.63 74.63 66.92 69.66 67.89 80.26 81.42 95.14 116.36 101.14 90.78 83.76 93.43 101.81 102.29 110.90 117.46 118.94
M&E 100 100.40 97.41 117.08 106.11 122.81 119.38 138.74 169.35 175.42 133.43 140.12 189.55 169.00 200.35 195.07 187.37 167.94 138.93 136.74 122.87 148.32
Skills 100 103.67 107.96 113.39 115.13 115.27 116.71 117.30 123.31 122.23 128.25 141.64 150.03 146.38 147.32 150.77 166.39 192.26 183.08 169.65 171.73 192.67
Figure 1.19: Computer and Electronic Industry
Graph of Computer and Electronic Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.19
Computer and Electronic Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 118.51 109.61 123.20 133.11 121.81 115.13 114.40 123.08 132.00 128.63 136.60 147.66 151.38 162.26 155.59 174.18 136.55 173.02 315.99 257.67 190.36
M&E 100 133.34 128.01 115.33 127.95 132.24 111.42 96.85 94.58 89.35 75.49 84.95 80.05 58.66 77.38 81.50 72.90 89.39 91.97 141.30 91.36 56.08
Skills 100 102.49 108.99 116.91 120.09 125.27 129.22 134.39 135.89 144.62 159.60 179.25 190.06 200.04 198.99 201.43 222.26 234.61 288.31 270.96 300.59 288.93
Figure 1.20: Electrical Equipment Industry
Graph of Electrical Equipment Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.20
Electrical Equipment Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 105.50 89.88 84.78 77.62 71.52 65.81 57.47 53.25 58.10 74.14 68.77 83.28 98.71 86.40 114.09 131.86 167.42 145.68 216.70 169.59 147.36
M&E 100 133.34 128.01 115.33 127.95 132.24 111.42 96.85 94.58 89.35 75.49 84.95 80.05 58.66 77.38 81.50 72.90 89.39 91.97 141.30 91.36 56.08
Skills 100 102.49 108.99 116.91 120.09 125.27 129.22 134.39 135.89 144.62 159.60 179.25 190.06 200.04 198.99 201.43 222.26 234.61 288.31 270.96 300.59 288.93
Figure 1.21: Transportation Equipment Industry
Graph of Transportation Equipment Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.21
Transportation Equipment Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 79.11 60.14 64.66 72.58 89.75 77.58 66.33 84.64 92.67 93.30 87.02 72.89 71.02 73.54 99.32 94.64 72.87 67.46 77.37 70.01 77.31
M&E 100 121.11 67.64 109.24 293.63 249.93 245.10 170.77 126.83 210.09 187.24 199.01 231.81 214.97 185.97 234.41 230.26 150.49 119.52 137.55 150.65 195.96
Skills 100 100.45 103.87 113.56 126.95 135.44 143.46 150.63 165.06 169.97 178.47 184.55 189.30 192.83 195.28 198.89 228.68 252.97 232.88 245.50 280.83 272.11

Services

Figure 1.22: Utilities Industry
Graph of Utilities Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.22
Utilities Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 84.86 92.28 103.21 113.49 111.40 105.91 105.77 102.45 95.89 90.95 85.56 81.27 71.29 78.26 61.43 73.50 65.63 60.20 53.43 40.52 37.73
M&E 100 95.82 72.13 62.80 56.27 57.69 68.06 80.45 95.75 106.74 97.00 64.57 52.76 50.13 42.89 44.51 54.78 58.67 52.11 52.97 47.97 60.13
Skills 100 100.30 102.67 106.33 112.44 117.96 122.42 128.03 133.88 135.10 141.01 150.93 151.80 151.62 151.22 152.38 128.07 139.20 158.76 150.68 180.46 169.10
Figure 1.23: Wholesale Trade Industry
Graph of Wholesale Trade Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.23
Wholesale Trade Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 253.36 545.53 1164.82 1254.42 1588.17 1475.63 1681.05 1753.11 1949.40 2464.56 3059.30 4189.51 4530.00 4820.52 5077.83 5139.37 5266.96 5985.57 4803.59 4724.13 4484.45
M&E 100 134.44 155.82 147.80 144.61 152.95 156.25 174.41 178.99 207.16 274.96 286.31 264.43 290.06 331.61 421.06 343.73 348.96 325.70 338.70 341.85 360.26
Skills 100 105.55 114.80 126.09 134.95 142.13 149.16 156.62 163.22 170.64 184.59 204.31 215.13 219.48 221.87 227.25 249.34 244.60 244.55 284.58 269.59 292.38
Figure 1.24: Retail trade Industry
Graph of Retail trade Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.24
Retail Trade Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 145.94 68.64 78.21 92.45 89.44 192.14 193.81 215.56 294.52 287.15 283.25 250.00 406.20 192.94 244.80 276.00 151.84 146.95 230.91 219.84 147.02
M&E 100 140.26 159.01 148.49 146.50 158.09 178.65 193.79 204.96 235.14 332.51 347.14 337.58 293.08 261.47 349.76 314.93 300.84 324.09 319.87 306.23 370.14
Skills 100 105.20 113.75 122.03 127.77 131.81 136.15 141.88 140.97 148.19 161.91 180.96 192.40 193.79 198.54 206.09 215.38 206.75 213.56 220.78 219.13 221.66
Figure 1.25: Transportation & Warehousing Industry
Graph of Transportation & Warehousing Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.25
Transportation & Warehousing Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 115.42 114.93 150.37 155.10 116.09 108.45 99.15 99.02 104.81 103.52 95.83 95.91 80.34 44.52 58.89 68.40 70.68 91.49 84.06 111.50 84.19
M&E 100 111.19 94.35 83.22 86.92 64.22 126.09 124.85 111.67 148.21 156.01 92.35 100.06 90.75 108.48 156.09 164.25 182.48 148.46 168.43 163.33 149.84
Skills 100 99.98 103.98 110.23 125.38 135.04 146.11 156.13 166.49 161.25 164.75 177.01 176.44 167.40 168.58 172.13 199.66 161.99 191.78 203.28 208.59 208.73
Figure 1.26: Information & Cultural Industry
Graph of Information & Cultural Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.26
Information & Cultural Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 93.17 91.66 106.14 127.86 116.32 92.98 100.50 113.44 116.17 133.64 194.74 188.71 185.95 151.05 132.16 115.55 112.80 125.40 217.13 223.48 383.10
M&E 100 83.76 85.70 84.56 88.55 101.45 109.14 117.54 122.60 125.58 140.45 124.29 123.23 126.91 140.54 172.00 173.07 154.65 174.60 181.23 124.77 120.06
Skills 100 102.03 108.39 115.36 122.08 127.79 131.78 138.84 141.09 147.64 158.58 175.02 182.57 186.58 188.00 190.69 193.41 197.78 201.97 204.64 208.67 220.07
Figure 1.27: FIRE, Management Industry
Graph of FIRE, Management Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.27
FIRE, Management Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 553.10 995.96 1263.90 2850.72 3749.40 3123.54 2647.28 2401.71 2307.72 3742.80 4396.84 4984.86 3723.74 2516.58 2069.50 1660.43 1278.21 1557.26 1780.41 2138.46 2224.47
M&E 100 129.83 173.00 216.32 233.50 305.49 331.26 336.07 297.11 241.88 210.24 245.21 266.59 286.40 304.12 323.64 374.56 434.26 460.97 394.93 394.46 404.93
Skills 100 103.63 109.68 115.62 121.79 129.10 134.81 140.42 142.89 148.23 160.60 175.97 185.35 188.28 189.70 192.07 205.24 214.26 219.34 227.88 232.74 236.39
Figure 1.28: Professional, Scientific & Technical Industry
Graph of Professional, Scientific & Technical Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.28
Professional, Scientific & Technical Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 149.72 208.09 282.38 342.05 314.29 264.30 242.59 261.44 270.99 318.97 332.33 425.80 391.05 325.86 290.64 288.84 290.05 317.60 453.13 441.46 417.13
M&E 100 149.87 198.67 258.29 359.18 421.90 479.44 518.38 519.26 643.37 1053.99 990.38 1550.19 1412.48 1454.73 1893.71 2334.62 2455.71 3225.29 2575.66 2634.14 2758.45
Skills 100 102.35 102.58 102.72 104.56 104.99 105.57 106.99 105.72 108.31 111.94 115.37 117.08 115.60 115.70 115.16 113.51 114.21 114.43 117.88 117.58 117.26
Figure 1.29: Other Services Industry
Graph of  Other Services Industry R&D and M&E intensities and Skills (the long description is located below the image)
Description of Figure 1.29
Other Services Industry
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
R&D 100 143.16 144.34 199.83 218.24 212.90 233.42 216.82 214.70 270.78 285.08 306.04 398.60 376.47 445.52 410.42 488.64 469.85 394.04 423.04 430.44 400.75
M&E 100 125.27 146.68 159.89 187.14 212.83 223.75 230.86 224.83 272.84 284.98 296.23 309.65 298.35 324.95 362.97 354.45 434.42 436.56 439.71 496.78 630.51
Skills 100 102.20 111.87 112.45 110.96 111.40 116.88 114.95 112.63 116.89 122.84 135.29 135.06 132.37 139.32 146.77 148.77 147.55 152.17 153.04 154.07 163.85

Figure 2: Indicators of Moving Up the Value Chain by Industry

Primary and Construction Sector Figure

Graph of Primary and Construction Sector, R&D and M&E intensities and Skills (the long description is located below the image)
Description of Primary and Construction Sector Figure
Primary and Construction Sector
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Agriculture 100 99.95 109.88 112.19 127.22 125.29 127.33 125.45 124.55 162.94 162.19 175.08 207.33 195.12 197.89 216.69 190.74 221.00 237.17 257.68 299.48 255.59
Mining 100 77.02 76.63 77.42 77.30 76.13 81.44 79.75 81.86 86.94 91.19 97.83 99.87 102.75 99.09 102.67 99.60 98.63 85.54 105.10 123.39 107.66
Construction 100 81.40 83.02 88.75 82.21 81.54 93.04 96.90 108.62 102.29 114.47 128.02 163.81 159.50 149.78 181.58 159.75 178.03 189.81 195.17 189.31 179.45

Labour-Intensive Manufacturing Figure

Graph of Labour-Intensive Manufacturing, R&D and M&E intensities and Skills (the long description is located below the image)
Description of Labour-Intensive Manufacturing Figure
Labour-Intensive Manufacturing Sector
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Textile, Apparel, and Leather 100 96.27 104.76 113.20 118.88 123.68 132.32 139.17 148.65 168.45 166.83 185.03 199.53 191.48 205.63 209.30 230.34 247.01 265.53 266.82 274.47 276.68
Furniture 100 81.42 89.02 128.94 159.47 145.25 152.45 120.54 109.92 142.99 165.94 179.39 220.95 176.79 189.17 197.25 179.45 185.75 173.81 193.08 195.53 226.22
Misc. Manuf. 100 114.81 122.27 154.60 203.30 197.58 152.58 162.02 173.91 187.06 223.55 233.15 264.66 235.21 256.45 198.94 199.41 235.56 210.66 199.15 203.08 217.52

Resource-Based Manufacturing Sector Figure

Graph of Resource-Based Manufacturing Sector, R&D and M&E intensities and Skills (the long description is located below the image)
Description of Resource-Based Manufacturing Sector Figure
Resource-Based Manufacturing Sector
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Food 100 103.66 111.58 118.05 122.39 129.91 135.23 140.57 142.65 147.33 158.14 170.52 177.02 181.32 186.22 186.94 171.27 173.95 175.10 167.96 195.88 194.32
Beverage and Tobacco 100 108.83 114.83 117.13 112.54 118.93 125.91 131.60 149.17 149.88 168.05 182.60 190.84 200.46 196.51 207.80 273.58 243.56 199.42 202.08 172.16 251.38
Wood 100 99.43 108.18 112.28 111.92 114.42 126.05 119.96 128.79 118.93 121.07 134.46 145.91 157.78 144.82 148.32 104.64 127.09 109.99 116.98 106.59 123.25
Paper 100 87.90 88.89 96.96 101.72 110.02 122.72 140.29 139.59 133.15 128.85 134.62 134.79 138.81 137.13 138.53 130.98 132.78 154.88 137.48 151.35 174.63
Petroleum and Coal 100 91.13 88.27 91.16 87.88 87.73 86.37 88.64 83.71 90.30 93.78 106.30 115.67 126.41 125.75 130.68 118.20 107.66 67.44 85.40 133.05 131.41
Plastics and Rubber 100 87.42 86.20 86.09 91.73 95.06 98.21 105.65 115.01 111.27 118.56 124.88 131.22 136.46 135.29 141.36 162.59 150.64 143.03 164.31 189.42 181.10
Non
metallic Mineral
100 97.47 104.48 111.91 118.19 115.96 123.63 133.30 139.84 131.17 124.44 136.02 156.94 164.56 157.44 166.42 159.61 188.01 202.50 215.35 195.56 191.62
Primary Metals 100 89.01 88.44 92.48 100.00 101.35 104.17 114.20 124.27 126.26 116.47 117.86 112.15 112.20 115.12 122.78 143.94 130.13 160.36 139.87 169.80 164.61
Fabricated Metal 100 98.01 107.54 115.12 125.00 112.47 114.74 114.89 114.60 122.72 128.48 140.51 151.44 151.87 159.71 152.44 149.01 136.87 154.71 172.57 144.02 160.31

High-Tech Manufacturing Sector Figure

Graph of High-Tech Manufacturing Sector, R&D and M&E intensities and Skills (the long description is located below the image)
Description of High-Tech Manufacturing Sector Figure
High-Tech Manufacturing Sector
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Printing 100 84.58 90.60 86.90 96.33 102.29 102.64 110.51 112.32 117.14 118.75 140.79 152.06 142.69 152.19 148.09 184.06 162.49 188.66 161.29 143.80 175.14
Chemical 100 90.91 89.93 95.16 98.04 100.48 101.35 105.48 111.21 118.90 125.31 133.26 135.33 137.68 139.60 141.55 145.30 145.35 155.79 172.72 154.56 169.21
Machinery 100 102.61 101.94 108.17 109.24 110.24 109.81 112.85 120.55 122.23 122.06 134.73 149.67 142.41 145.19 146.13 158.22 176.37 166.33 157.38 158.28 176.92
Computer and Electronic 100 108.34 111.26 117.64 122.88 125.57 125.14 127.20 129.31 136.48 145.50 162.29 171.34 176.82 179.76 181.08 198.22 203.80 249.16 262.66 270.45 248.03
Electrical Equipment 100 111.48 116.62 125.02 135.71 144.53 150.01 163.58 173.35 180.54 184.84 201.10 210.91 218.16 212.97 223.15 317.78 365.90 364.72 395.11 364.06 350.73
Transportation Equipment 100 99.72 93.39 105.98 138.14 141.92 145.54 140.72 149.04 163.36 167.14 172.07 177.30 177.71 176.58 188.53 209.44 215.16 195.95 208.80 235.40 235.18

Services Sector Figure

Graph of Services Sector, R&D and M&E intensities and Skills (the long description is located below the image)
Description of Services Sector Figure
Services Sector
type 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Utilities 100 94.24 89.37 90.83 93.94 95.84 99.34 105.52 111.83 114.02 111.49 102.70 97.80 93.91 93.40 89.39 87.41 90.50 93.94 89.24 94.76 93.81
Wholesale Trade 100 158.89 255.79 440.24 468.92 572.98 543.43 612.96 638.22 708.39 888.45 1075.25 1405.83 1516.62 1617.24 1725.21 1725.66 1763.35 1967.85 1637.80 1609.84 1553.62
Retail Trade 100 128.97 115.58 117.94 123.60 128.09 166.92 174.60 184.43 220.53 256.03 266.82 258.03 289.79 217.95 265.63 266.62 222.03 230.89 256.95 248.50 249.32
Transp. & Wareh. 100 108.29 103.99 113.04 121.28 105.73 128.28 128.82 128.20 140.21 143.74 124.68 127.07 116.02 111.80 133.30 149.03 141.87 147.65 156.39 164.75 152.22
Inform. & Cultural Ind. 100 93.30 95.85 102.32 112.57 115.58 112.74 120.38 126.73 130.96 145.14 163.86 164.52 166.41 161.19 167.14 163.60 158.23 170.18 200.49 184.96 234.75
FIRE, Manag.Footnote a 100 245.19 392.73 488.58 965.43 1257.75 1083.58 946.56 861.88 817.59 1235.55 1446.17 1630.53 1265.66 915.47 790.79 691.85 602.25 695.52 742.49 849.91 880.18
Prof., Scient. & Tech. 100 132.26 165.93 207.89 259.98 272.93 277.64 284.79 290.18 335.51 488.51 472.43 688.11 631.19 626.11 762.48 908.96 950.12 1216.01 1039.82 1055.84 1090.15
Other Services 100 122.02 133.12 154.15 168.16 175.35 187.09 183.85 180.35 214.57 225.06 239.62 271.38 260.06 291.93 297.05 318.05 338.82 318.85 328.79 350.46 390.09

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Tables

Table 1: List of Industries
sector NAICS ID Sector NAICS ID
Resource-based manufacturing High-tech manufacturing
Food 311 4 Printing 323 16
Beverage and Tobacco 312 5 Chemical 325 17
Wood 321 6 Machinery 333 18
Paper 322 7 Computer and Electronic 334 19
Petroleum and Coal 324 8 Electrical Equipment 335 20
Plastics and Rubber 326 9 Transportation Equipment 336 21
Nonmetallic Mineral 327 10 Services
Primary Metals 331 11 Utilities 22 22
Fabricated Metal 332 12 Wholesale Trade 41 23
Labor-Intensive Manufacturing Retail Trade 44–45 24
Textile, Apparel and Leather 313–16 13 Transportation & Warehousing 48–49 25
Furniture 337 14 Information & Cultural Industries 51 26
Miscellaneous Manufacturing 339 15 FIRE, Management Footnote a 52–55 Footnote a 27
Primary and Construction Professional, Scientific & Technical 54 28
Agriculture 11 1 Other Services 51–56
71-81
29
Mining 21 2
Construction 23 3
Table 2: Estimated Variable Weights for Composite Indicator of Moving Up the Value Chain
Industry Group R&D M&E Skills
Primary and Construction 0.65542 0.76331 0.72834
Resource-Based Manufacturing 0.10379 0.33985 1.37984
Labor-Intensive Manufacturing 0.77421 0.56774 0.62770
High-Tech Manufacturing 0.34832 0.27495 1.78129
Services 0.72185 0.81804 0.90340
Note: Estimation by maximum likelihood using LISREL 8 software. All coefficients are statistically significant at 5% level.
Table 3: Average Annual Growth Rate of Moving-Up Composite Indicator (CI) by Industry
sector NAICS Skills (%)
2003
CI's Average Growth Rate (%)
1982–2003
Primary and Construction
Agriculture 11 6.16 5.07
Construction 23 6.16 3.40
Mining 21 14.22 0.81
Manufacturing
Electrical Equipment 335 16.53 6.54
Beverage and Tobacco 312 24.26 5.31
Furniture 337 7.13 5.26
Textile, Apparel, and Leather 313–316 8.19 5.07
Miscellaneous Manufacturing 339 17.26 4.71
Computer and Electronic 334 35.88 4.57
Transportation Equipment 336 13.96 4.45
Nonmetallic Mineral 327 7.78 3.38
Food 311 10.27 3.32
Printing 323 10.81 3.31
Plastics and Rubber 326 11.67 3.12
Paper 322 12.40 2.99
Machinery 333 15.43 2.89
Primary Metals 331 10.70 2.84
Chemical 325 31.34 2.67
Petroleum and Coal 324 19.84 2.62
Fabricated Metal 332 8.54 2.57
Wood 321 5.81 1.64
Services
FIRE, Management 52, 53, 55 29.84 16.63
Wholesale Trade 41 17.79 15.89
Professional, Scientific & Technical 54 49.79 13.22
Other Services 51–56
71–81
15.80 6.93
Retail Trade 44–45 10.75 5.31
Information & Cultural Industries 51 32.15 4.45
Transportation & Warehousing 48–49 9.38 2.43
Utilities 22 22.82 −0.21
All industries 4.87
Table 4: Estimation Results for Moving-Up Equation
Dependent Variable: Indicator of Moving up the Value Chain
Independent Variable LSDV GMM
Coefficient P-Values Coefficient P-Values
Intercept 0.8071 0.0000 --- ---
Indicator of Moving Up (lagged 1 year) 0.8425 0.0000 0.7744 0.0000
Offshoring (lagged 1 year) 0.0805 0.0000 0.0887 0.0024
Competition* (lagged 1 year) 0.0881 0.0004 0.1873 0.0066
Real Exchange Rate (lagged 1 year) 0.0016 0.9143 −0.0194 0.5259
Cross-Section Fixed Effects Yes Yes
Number of Observations 609 580
Adjusted R-Squared 0.9705 ---
D.W. Statistics 1.9928 ---
First-Order Serial Correlation --- Yes
Second-Order Serial Correlation --- No
Table 4': Estimation Results for Moving-Up Equation.
(Sensitivity Analysis with China's Import Penetration)
Dependent variable: Indicator of moving up the value chain
Independent Variable LSDV GMM
Coefficient P-Values Coefficient P-Values
Intercept 1.2824 0.0000 --- ---
Indicator of moving up (lagged 1 year) 0.7688 0.0000 0.6994 0.0000
Offshoring (lagged 1 year) 0.0404 0.0276 0.0173 0.6085
China's Share in Imports (lagged 1 year) 0.0640 0.0000 0.0940 0.0000
Real Exchange Rate (lagged 1 year) −0.0031 0.8274 −0.0709 0.1518
Cross-Section Fixed Effects Yes Yes
Number of Observations 609 580
Adjusted R-Squared 0.9710 ---
D.W. Statistics 1.9409 ---
First-Order Serial Correlation --- Yes
Second-Order Serial Correlation --- No
Table 5: Estimation Results for Labor Productivity Equation
Dependent Variable: Labor Productivity Level
Independent Variable LSDV GMM
Coefficient P-Values Coefficient P-Values
Intercept 0.2943 0.0000 --- ---
Labor Productivity Level (lagged 1 year) 0.8804 0.0000 0.8467 0.0000
Indicator of Moving Up (lagged 1 year) 0.0170 0.0001 0.0272 0.0369
Competition (lagged 1 year) 0.0307 0.0040 0.0744 0.0381
Business Cycle 0.1759 0.0000 0.2834 0.0000
Cross-section Fixed Effects Yes Yes
Number of Observations 609 580
Adjusted R-squared 0.9941 ---
D.W. Statistics 1.8049 ---
First-Order Serial Correlation --- Yes
Second-Order Serial Correlation --- No
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