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.

Malick Souare
Industry Canada

Weimin Wang
Industry Canada^{Footnote *}

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

• References
• Figures
• Tables

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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 serviced^{Footnote 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 companies^{Footnote 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 items^{Footnote 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 activities^{Footnote 4}. Mani (2002) provides similar evidence for Philippines, which is also one of the leading exporters of high-technology products from the developing world^{Footnote 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 chain^{Footnote 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 innovations^{Footnote 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 4th June 2007), Jeremy Peat – Director of the David Hume Institute^{Footnote 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 above^{Footnote 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 $\xi$ 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:

where $x=(x_1,x_2,\dots ,x_n)$ is the vector of indicators, $\lambda =({\lambda }_1,{\lambda }_2,\dots ,{\lambda }_n)$ is the vector of coefficients of x on $\xi$, and $\delta =({\delta }_1,{\delta }_2,....,{\delta }_n)$ is the vector of error terms.

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

Normalize the variance of $\xi$ to 1, i.e., $\mathrm{var}\left(\xi \right)\Xi \xi {\xi }^T=1$. As $\Sigma$ is known, we can derive the estimate of $\lambda$, denoted $\stackrel{\mathrm{\&hat;}}{\lambda }$, by minimizing the determinant of

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, $\xi$, is

where $\stackrel{\mathrm{\&hat;}}{w}={\stackrel{\mathrm{\&hat;}}{\lambda }}^T{\Sigma }^{-1}$.^{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 $\stackrel{}{\lambda }$, 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 ${\xi }_{CI}$ denote our composite indicator. The estimate of ${\xi }_{CI}$, ${\stackrel{\mathrm{\&hat;}}{\xi }}_{CI}$ can be written as the weighted sum of the three indicators:

where $RD$ and $ME$ represent respectively the R&D and M&E investment-to-GDP ratio, $HK$ denotes human capital, and ${\stackrel{}{w}}_i$ ( 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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5. Trends in Composite Indicator, Explanatory Factors, and Link with Productivity

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:

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:

where ${\epsilon }_{it}$ 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 ${\epsilon }_{it}$ are serially uncorrelated, the (first-differenced) innovations $\Delta {\epsilon }_{it}$ should have significant (negative) first-order serial correlation but no significant second-order serial correlation. As for the orthogonal deviation procedure, if the innovations ${\epsilon }_{it}$ 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 productivity^{Footnote 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 cycle^{Footnote 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

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Resource-based Manufacturing

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Labour-Intensive Manufacturing

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High-Tech Manufacturing

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Services

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

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Tables