Capturing value in GVCs through intangible assets: The role of the trade–investment–intellectual property nexus


Intangible capital, a broad category of knowledge-based assets lacking physical embodiment, is a key determinant of value capture in global value chains (GVCs). In this paper, we study how policies that govern the trade–investment–intellectual property nexus help countries to increase returns to intangible assets. Using inter-country input–output tables combined with data on factor income, we estimate returns to measured intangible capital (i.e., intangible assets reported in national accounts) and ‘unmeasured’ intangible capital (captured as a residual) in GVCs for a large number of countries and industries over the period 2005–2015. We use these data in an econometric analysis to examine policy determinants, focusing on the package of disciplines on trade, investment, and intellectual property that are increasingly used by countries in their trade agreements. We find that trade and investment openness, intellectual property protection, and competition enforcement are positively associated with returns to intangible assets measured in national accounts. The relationship with unmeasured intangible capital is more complex regarding intellectual property protection and competition enforcement. Our discussion highlights the importance of striking a balance between the different elements of the nexus in order to maximize a country’s potential to benefit from the growing role of intangibles in GVCs.


The ownership of economically valuable knowledge fundamentally shapes the growth prospects of companies and economies. While the pivotal role of new ideas and combinations of existing knowledge has long been emphasized in the literature (e.g., Schumpeter, 1934; Arrow, 1962; Grossman & Helpman, 1991), the rise of global value chains (GVCs) has led to a renewed interest in knowledge-based drivers of economic performance (Pietrobelli and Rabellotti, 2011; Bolatto, Naghavi, Ottaviano, & Zajc Kejzar, 2019; Jona-Lasinio, Manzocchi, & Meliciani, 2019; Durand & Milberg, 2020; Buckley, Strange, Timmer, & de Vries, 2020). The role of intangible assets in value capture in GVCs has been emphasized in several case studies, starting with the seminal paper by Dedrick, Kraemer, and Linden (2010) on iPods and notebook PCs. At the aggregate level, two recent papers using data on factor income in GVCs highlight that returns to intangibles have increased in the 2000s and are higher than returns to tangible capital (Chen et al., 2017; Chen, Los, & Timmer, 2018).

In terms of policies, the GVC literature has primarily focused on how developing countries can improve their participation in value chains and upgrade towards higher value-added activities (Cattaneo, Gereffi, Miroudot, & Taglioni, 2013; Kowalski, López González, Ragoussis, & Ugarte, 2015; Taglioni & Winkler, 2016). Less attention has been given to how policies affect value capture in GVCs and to policy determinants in developed countries (Van Assche, 2020). In this paper, we are interested in exploring how some policies can increase the income generated by intangible capital in GVCs. We focus on the trade–investment–intellectual property (IP) nexus that characterizes 21st-century trade and has changed trade rules (Baldwin, 2011a)1.

In GVCs, trade is more complex than exporting a final product that was fully produced in one location. It involves co-ordination processes and the orchestration of flows of knowledge, components, final products, and capital between different locations and agents. GVC trade thus requires different disciplines than traditional trade. While trade, investment, and other related policies are often described as operating in ‘silos’ (Hoekman, 2014), there is a growing awareness that these policies are complementary, and that trade today should be seen “as a package” extending beyond core trade policy and covering the trade–investment–IP nexus. Trade agreements have become trade and investment agreements or economic partnerships covering all these dimensions of market access, as well as additional disciplines related to domestic regulations. They increasingly offer an ‘offshoring package’ allowing firms to become more competitive in GVCs (Antràs & Staiger, 2012).

Using aggregate data on returns to intangible capital in 64 countries and 24 GVCs in the manufacturing and business services sectors, we find evidence that trade and investment openness, trade policy connectivity, IPR protection and competition enforcement are positively associated with the generation of value-added through intangible assets. We distinguish in our analysis intangible capital that is measured in national accounts, which includes a narrow set of four assets (‘research and development’, ‘mineral exploration and evaluation’, ‘computer software and databases’ and ‘entertainment, literary or artistic originals’) from ‘unmeasured’ intangible capital that we estimate as a residual in value added once the contribution of other factors of production is accounted for. All policies that govern the trade–investment–IP nexus have a positive correlation with returns to intangible assets measured in national accounts. However, when it comes to IPR protection and competition enforcement, there is a more complex relationship with returns to unmeasured intangible capital, as this residual category overlaps with profit derived from market power, which may be explained or not by intangible assets.

The paper is organized as follows. We first define intangible capital and review the literature on its role in GVCs. We then describe the methodology used to estimate returns to intangible capital along the value chain in a large sample of countries for GVCs in the manufacturing and business services sectors. Our dataset is based on the OECD inter-country input–output tables and an additional collection of data on factor income and capital stocks by the OECD. Next, we present some key findings on patterns of returns to intangible capital across countries and illustrate how they may be related to trade, investment and IP policies. Finally, we analyze econometrically the relationship between returns to intangible capital and a set of policy variables covering the trade–investment–IP policy nexus. These results allow us to explore in a concluding section some options for GVC-oriented policies aimed at generating more value added through intangible capital.


Also referred to as intellectual or knowledge-based capital (Lev, 2000), intangible capital encompasses a wide range of knowledge-based assets that lack a physical embodiment. Intangibles are frequently grouped into three categories (Corrado, Hulten, & Sichel, 2005): computerized information (such as datasets on consumer preferences), innovative property (e.g., a new financial product), and economic competencies (e.g., a strategy to improve information sharing between a multinational enterprise’s subsidiaries and the headquarters). As previously mentioned, the System of National Accounts (2008 SNA) only covers four types of ‘intellectual property products’. Most intangible assets (and in particular those related to economic competencies) are thus ‘unmeasured’ in national statistics and require alternative metrics and proxies.

This can explain why it is only recently that a nascent stream of empirical work specifically looks at the accumulation of intangible capital in GVCs. However, the role of intangible assets in the organization of the value chain has been previously acknowledged in the literature in different ways. A central theme in the literature relates to the uneven distribution of value capture along the value chain. Building on case studies in the ICT value chain, such as Dedrick, Kraemer, and Linden (2010), and the observations made by Acer’s founder Stan Shih, the concept of a ‘smile curve’ in GVCs highlights that high value-added activities at the beginning and at the end of the value chain enable firms to capture value through activities such as R&D, marketing, or distribution that are intensive in the use of intangible capital. Conversely, core manufacturing in the middle – which is a low value-added activity – can be left to other companies (Mudambi, 2007; Baldwin, 2017). In line with this pattern, specialization in GVCs is in tasks rather than in products or industries (Grossman & Rossi-Hansberg, 2008). There is a pronounced geography of specialization for activities that are intensive in knowledge, with developed economies specializing in R&D activities while emerging economies specialize in fabrication activities (Timmer, Miroudot, & de Vries, 2019; Buckley, Strange, Timmer, & de Vries, 2020).

An influential stream of literature emphasizes that such specialization is not only the result of relative costs and abundance in factors of production, as hypothesized by trade theory, but also related to the governance of the value chain and the way lead firms organize their production to capture value and protect their intangible assets. The complexity of knowledge, its codification, and transmission between firms underpin the theoretical framework proposed by Gereffi, Humphrey, & Sturgeon (2005) to describe different models of governance in the value chain. Leading firms do not only capture value through intangible assets but also choose different types of organization of the value chain based on the characteristics of these assets and the way they can share knowledge with their suppliers. Strategies to internalize knowledge in GVCs can rely on vertical integration and FDI by multinational enterprises (MNEs) or outsourcing arrangements coordinated by leading firms (Buckley & Strange, 2015).

The specific role of intangible assets in this process is explored by Bolatto, Naghavi, Ottaviano, and Zajc Kejzar (2019) in a property rights model of sequential value chains. When firms transmit knowledge to their suppliers, there is a cost to protect their intangible assets and this cost depends on the knowledge intensity of inputs and on the quality of intellectual property rights (IPR) protection. Whether knowledge-intensive inputs are found upstream or downstream leads to different types of organization of the value chain.

Moreover, the different types of governance of GVCs affect how innovation is shared across countries participating in the value chain and the potential for knowledge spillovers and learning (Pietrobelli & Rabellotti, 2011). The design of innovation systems can help to reduce the complexity of transactions in the value chain and to move towards weaker hierarchical forms of GVC governance where learning is more mutual. Buckley, Strange, Timmer, and de Vries (2020) note that catch-up in the value chain also depends on the development of indigenous innovation capabilities and that it may be facilitated by specific policies.

While the above literature has explored the role of intangible assets in value creation and in the organization of the value chain, a different question is whether investment in intangible assets increases the participation of countries in GVCs. This question is empirically addressed by Jona-Lasinio, Manzocchi, and Meliciani (2019) who find that intangible capital is positively related to participation in GVCs and fosters both backward and forward participation (i.e. participation through the use of foreign inputs and through the supply of inputs to foreign countries). However, there are differences in this positive effect depending on the type of intangible assets.

Finally, as intangible assets allow value capture, a further question is to what extent they can also be a source of market power and monopoly (Durand & Milberg, 2020). There is some evidence that intangible assets are linked to the increased market concentration observed in developed countries due to their scalability (Bajgar, Criscuolo, & Timmis, 2020). This suggests that part of returns to intangible capital may be rents and creates specific policy questions with respect to the trade–investment–IP nexus.

Most of the papers reviewed in this section mention that policies and institutions influence the way intangible capital shapes the organization of GVCs. As many related contributions tend to prioritize a specific policy or focus on an individual company or sector, there is a scarcity of contributions seeking to analyze this policy dimension in a systematic macro framework aimed at identifying patterns across countries and sectors. In this paper, we explore the impact of policies on the generation of value added through intangible capital in GVCs, focusing on trade, investment, and IP policies.


To empirically investigate some of the policy determinants that shape the distribution of factor income in GVCs, we rely on new data developed at the OECD that cover 64 countries and 36 industries over the period 2005–2015 (Alsamawi et al., 2020)2. Our dataset first consists in a full decomposition of gross value added across industries into: (i) taxes minus subsidies on production, (ii) labor compensation, (iii) returns to tangible capital, (iv) returns to measured intangible capital, and (v) returns to unmeasured intangible capital (calculated as a residual). This decomposition builds on the methodology developed by Chen et al. (2017) and Chen, Los, and Timmer (2018) but we split returns to intangible capital into returns to intangible assets accounted for in national accounts (measured intangible capital) and a residual corresponding to ‘unmeasured’ intangible capital.

In Chen et al. (2017) and Chen, Los, and Timmer (2018), all returns to intangible capital are estimated as a residual, due in particular to the period covered with many countries not reporting intangible assets according to the new standards of the SNA 2008. Our dataset benefits from a more recent data collection and efforts at OECD to build a full database of stocks of (measured) intangible assets. Still, it includes some estimates, as data are not available for all countries and industries3. To disentangle labor income from capital returns, assumptions are also made to split the mixed income that includes the remuneration of the self-employed. A share of mixed income is allocated to labor compensation and the rest goes to capital (so that there is no mixed income in the final decomposition of gross value added)4.

The dataset is consistent with OECD harmonized national accounts (SNA 2008) and OECD Inter-Country Input–Output (ICIO) tables that we use for the GVC analysis. We start from the following variables for which information is collected at the country and industry level: gross value added (VA), taxes minus subsidies on production (TX), labor compensation (wL) – adjusted to account for self-employment, tangible capital stock (KT), depreciation rate of tangible capital (\(\delta^{kt}\)), measured intangible capital stock (KI), and depreciation rate of (measured) intangible capital (\(\delta^{ki}\)).

Returns to unmeasured intangible capital in country c and industry s are calculated as:

$$rKU_{c,s} = VA_{c,s} - TX_{c,s} - wL_{c,s} - rKT_{c,s} - rKI_{c,s}$$

Following Chen et al. (2017), returns to tangible capital rKT are derived by multiplying the tangible capital stock KT by a rental price \(r^{kt}\), calculated as: \(r^{kt} = \delta^{kt} + \rho^{kt}\). Information on the depreciation rate of tangible capital (\(\delta^{kt}\)) is collected and an ex-ante real rate of return (\(\rho^{kt}\)) is assumed. Chen et al. (2017) and Chen, Los, and Timmer (2018) set the rate of return to 4 %. They also show that using alternative rates has no significant impact on results. While this assertion can be confirmed by running sensitivity analysis, our dataset includes a broader range of countries and relies on three rates of returns estimated based on the cost of debt for different groups of countries.5 The rate of return is 3.6 % for the most developed economies, 4.5 % for middle-income economies, and 5.6 % for developing economies. A similar approach applies to measured intangible capital where \(r^{ki} = \delta^{ki} + \rho^{ki}\). In the case of measured intangible capital, the ex-ante real rate of return is set to 4 % for all countries.6

Removing from gross value added taxes minus subsidies on production, labor compensation, returns to tangible capital, and returns to measured intangible capital, we obtain an estimate of returns to unmeasured intangible capital. This residual captures returns to other types of intangible assets that are not covered in the SNA, such as brand value, superior distribution strategies, or organizational capital. These assets often play a more important role in value creation than the measured intangible capital, hence the importance of accounting for them. However, with a residual approach, we can also capture other determinants of profit that are less related to intangible capital. This complicates the empirical analysis, as it prevents us from disentangling Schumpeterian returns to knowledge-based assets not recorded in the SNA from other returns associated with market power that is not based on intangible capital.

Once factor income is known for each industry and country, the next step consists in using the information from the OECD ICIO tables to estimate returns to intangible capital (both measured and unmeasured) along the value chain. As in Chen et al. (2017), the production process is decomposed into three stages: the production of intermediate inputs (I), the final assembly (F), and the distribution stage (D). The gross output (Y) of each country and industry (in purchaser prices) can be understood as the sum of the value added in these three stages, plus taxes minus subsidies on products (Tp):

$$Y_{(c,s)} \equiv VA_{(c,s)}^{I} + VA_{(c,s)}^{F} + VA_{(c,s)}^{D} + T_{(c,s)}^{p}$$

From the previous value-added decomposition, we can obtain returns to intangible capital at the final production stage in each country and industry (we show here the equations for measured intangible capital but they are the same for unmeasured intangible capital):

$$rKI_{c,s}^{F} = \frac{{rKI_{c,s} }}{{VA_{c,s} }}.VA_{c,s}^{F}$$

where \(VA_{c,s}^{F}\) is the value added coming from the production of final products and is available in the ICIO by multiplying final demand by the value added to output ratio.

The Leontief model is then used to calculate returns to intangibles in the intermediate production stages (i.e., all stages upstream that provide inputs to the final production and to the production of inputs).7 From the inter-country input–output tables, we can build a matrix A of dimension CS x CS (with C the number of countries and S the number of industries) that indicates for each unit of output (for a given country and industry in one of its columns), the share of intermediate inputs needed from each industry and country in the world (in its rows). With I an identity matrix of the same dimension, the Leontief inverse is \((I - A)^{ - 1}\) and can be used to calculate the value added generated in each country and industry by one unit of final production in a given country and industry. Returns to intangible capital at the intermediate and final production stages correspond to a specific share of this value added. If we define ski as a column vector of dimension 1 × CS with all the shares of measured intangible income in output in all countries and industries and i as a row unit vector of dimension CS × 1, we have:

$$rKI_{c,s}^{I} + rKI_{c,s}^{F} = i.\hat{s^{ki}} \left( {I - A} \right)^{ - 1} .F_{c,s}$$

where Fc,s is a column vector of dimension 1 × CS with the final demand for industry s in country c on one row and zeroes elsewhere, while \(\hat{s^{ki}}\) is the diagonal matrix of vector ski. We then subtract \(rKI_{c,s}^{F}\) (returns to intangible capital in the final production stage) from the results to obtain \(rKI_{c,s}^{I}\). Returns generated in the final production stage go to factors of production in country c, while returns generated in the intermediate production stages go to intangible capital accumulated in different countries (including c for domestic inputs). By not using the summation vector i in Eq. (4), we can, however, obtain returns to intangible capital as a column vector indicating in which country (and industry) they were generated. This is how we focus in the empirical section on returns generated in a specific country. Note, however, that what we mean by ‘generated’ is that the factor income was created in this country (as part of its GDP) and possibly influenced by the policies of this country. At the end, intangible capital owners (who ultimately receive the income generated in country c) can be located anywhere.8

Finally, the distribution stage consists of income generated in the distribution sector (wholesale and retail) that corresponds to trade margins in the ICIO framework.9 These trade margins (tm) are available as part of the underlying data of the OECD ICIO and can be expressed as a share of gross output. As in Chen et al. (2017), we decompose these margins using the factor income allocation of the distribution industry in each country.10 Returns to intangible capital in the distribution stage are thus:

$$rKI_{c,s}^{D} = tm_{c,s} .rKI_{c, distribution} .Y_{c,s}$$

The full returns to intangible capital along the value chain can then be obtained by adding \(\varvec{rKI}_{{\varvec{c},\varvec{s}}}^{\varvec{I}}\), \(\varvec{rKI}_{{\varvec{c},\varvec{s}}}^{\varvec{F}}\) and \(\varvec{rKI}_{{\varvec{c},\varvec{s}}}^{\varvec{D}}\) These returns are ultimately going to capital owners that may not be in country c, but we are interested in the impact of policies in country c on the generation of value added through intangible capital. The only way we modify the total returns in the next section is by taking into account the share of \(\varvec{rKI}_{{\varvec{c},\varvec{s}}}^{\varvec{I}}\) going to country c, i.e., returns to intangible capital that originated in inputs manufactured in country c and embodied in final products of country c and industry s.

When applying the methodology outlined above to our sample of countries and industries in 2015, returns to intangible capital (measured and unmeasured) account for a higher share of value added in GVCs (20 %) than tangible capital (15 %). The share of value added explained by returns to unmeasured intangibles (15 %) is higher than the part attributed to returns to intangible assets measured in national accounts (5 %).


Trade policy can promote knowledge creation through three channels: learning via importing, enhanced opportunities to exploit the scalability of intangible capital through improved access to export markets, and incentives to invest in “defensive innovation” in response to increased competition (Grossman & Helpman, 1991; Wood, 1995; Eaton & Kortum, 2002; Bustos, 2011). We thus expect trade liberalization to induce firms to invest in intangible assets.

The vertical axis of Figure 1 displays a country’s per-capita returns to intangibles (measured and unmeasured, summed across all industries) in GVCs in 2015. This indicator can be interpreted as a broad measure of the intangible-intensity of an economy’s overall participation in GVCs. The horizontal axis refers to the country’s average unweighted tariff level. A group of advanced economies, including Japan, Switzerland, Canada, and the USA, as well as Singapore stands out: their GVC returns to intangibles per capita are relatively high and these countries display low tariff levels. Conversely, several countries with higher tariff levels, such as India or Brazil, display substantially lower GVC returns to intangibles per capita. While Figure 1 merely provides a first descriptive exploration of the data, this general pattern appears in harmony with the idea that a country’s economic openness constitutes a key determinant of its involvement in the creation of returns to intangibles in GVCs.

Figure 1

The link between GVC returns to intangibles and tariffs.

Policies that lower import costs for firms or reduce establishment costs for foreign providers support knowledge creation by providing firms with improved access to inputs – e.g., advanced services or knowledge-intensive intermediate goods. For example, Colantone and Crinò (2014) show that new imported inputs have a strong positive effect on product creation in Europe. Similarly, imports of foreign services can enable firms to move up the quality ladder (Nordås, 2010). Given the close linkages between services and manufacturing activities within GVCs (Miroudot, 2019; Ariu, Breinlich, Corcos, & Mion, 2019), restrictions on trade and investment in services are likely to impinge on the innovative performance of manufacturing firms.

While lower barriers to trade can create opportunities for learning, they can also incentivize firms to invest in intangible capital in order to defend their market position against foreign competitors. Thus, firms in advanced economies may adopt a “defensive innovation” strategy in response to rising imports from low-wage countries (Wood, 1995). Import competition from China has been shown to induce firms in Europe (Bloom, Draca, & Van Reenen, 2016) and Mexico (Utar & Ruiz, 2013) to invest in knowledge creation.

In addition to openness to trade in goods and services, a regulatory environment permitting the country to attract foreign investments is likely to shape a country’s involvement in activities generating returns to intangible capital. MNEs increasingly combine the advantages of place-specific knowledge pools by creating “location portfolios” (Mudambi, 2008) and innovative activities involving corporate R&D centers in multiple countries are growing (WIPO, 2019). Reflecting strategies which may involve the acquisition of foreign firms to gain ownership of specific knowledge (Howell, Lin, & Worack, 2020), these patterns can be interpreted as efforts to create synergies between different intangible assets. Policies affecting a country’s integration in these networks are therefore highly relevant to activities of high intangible capital intensity.

Among the countries included in this analysis, the largest reduction in statutory restrictions as measured by the OECD FDI Regulatory Restrictiveness Index during the period 2005–2015 was observed in Vietnam. The example of this Southeast Asian economy illustrates the potentially transformative effects of a country’s integration in GVCs. Especially since its WTO accession in 2007, Vietnam has established itself as a hub for FDI-driven export-oriented manufacturing activities and several major electronics MNEs (e.g., Intel) have opened large production facilities in Vietnam (Athukorala & Tien, 2012).

Particularly noteworthy is the case of Samsung: In 2017, the company employed approximately 160,000 workers in Vietnam and exported goods worth more than US $50 billion (UNCTAD, 2018). A brief look at the evolution of Vietnam’s exports of telephony-related products shows rapid growth from the late 2000s onwards (Fig. 2). This picture can be interpreted as support for the argument that MNEs can often jump-start manufacturing activities in developing countries. Acting as carriers of advanced technology and central nodes in cross-border production networks, they can rapidly expand production capacities, especially regarding assembly tasks, “in little more than the time it takes to build the factory” (Baldwin, 2011b: 26). Our dataset also reveals a positive trend for Vietnam’s generation of returns to measured intangibles in the computer, electronics, and communication equipment sector (see Figure 3). Vietnam’s GVC returns to measured intangibles per capita in this sector remain relatively low but have increased significantly since the late 2000s.

Figure 2

Evolution of Vietnamese exports of radio and telephone transmitters.

Figure 3

GVC returns to measured intangibles in Vietnam’s computer and electronics sector.

The expectation that domestic firms might benefit from knowledge spillovers often motivates policy-makers to attract MNEs. As it can be used by others, intangible capital can create more spillovers than tangible capital (Haskel & Westlake, 2018). Yet, FDI-driven exports cannot be regarded as a smooth “autopilot” journey to higher levels of technological capabilities of domestic firms (Ernst, 2004; Padilla-Pérez & Martínez-Piva, 2009). Indigenous efforts are required to move beyond “hollow” assembly activities with limited connection to local firms’ capabilities (Iammarino & McCann, 2013).

The potential for spillovers associated with intangible capital reflects the imperfect appropriability of returns to investments in intangible assets. A firm’s ability to reap returns to its investments in intangibles is determined by the prevailing appropriability conditions (Cockburn & Griliches, 1988; Ceccagnoli, 2009). These conditions partly depend on aspects specific to the firm and the sector, such as an innovator’s ownership of complementary assets, imperfectly imitable and socially complex firm resources, and tacitness of knowledge (Barney, 1991; Hurmelinna‐Laukkanen & Puumalainen, 2007). With high levels of appropriability being the exception rather than the rule (Teece, 1986), IPRs constitute an important element of the strategies firms employ to defend an innovation against imitation. A central determinant of appropriability conditions is therefore shaped by the third element of the trade–investment–IP nexus, IPR protection policies. Strong IPR policies can render a country more attractive as a location for activities of high intangible capital intensity.

Figure 4 relates an economy’s GVC returns to both types of intangibles (summed across all industries) per capita in 2015 to the quality of IPR protection as measured by a World Economic Forum survey (with higher values indicating stronger IPR protection). There is a visible tendency for economies with higher levels of IPR protection, such as Japan, Canada, or Switzerland, to display higher returns to measured intangibles per capita than countries with a weaker IPR framework, such as Argentina or Russia. This association is in line with the role of IPR policy as a key determinant influencing a country’s attractiveness for intangible intensive activities. The next section investigates the links between returns to intangibles and different policies more systematically in a regression framework.

Figure 4

The link between GVC returns to intangibles and IPR protection.


To analyze the association between returns to intangible capital in GVCs and the different dimensions of the trade–investment–IP nexus in more depth, we introduce in this section a set of regressions with control variables. The dependent variables are country-industry level returns – either to measured intangible capital (i.e., intellectual property products reported in national accounts) or to unmeasured intangible capital (for which returns are obtained as a residual).

In line with the GVC perspective, these returns are computed for each industry of final production in a given country by summing all estimated returns to the respective category of intangibles at the intermediate, final production, and distribution stage. For the intermediate stages, returns only take into account value added generated in the final production country to ensure that the dependent variables reflect value creation taking place in the country implementing the corresponding policies.

Aimed at exploiting the richness of the dataset to provide the basis for a systematic exploration of the trade–investment–IP nexus, the empirical strategy uses a combination of fixed effects and several controls. However, one has to bear in mind that policies are not assigned randomly to countries. As this approach cannot address all concerns (e.g., reverse causality), the results do not necessarily reflect causal relationships.

The regressions cover both manufacturing and business services GVCs and rely on variations of the following specification:

$${ \log }\_rKi_{c,s,t} = \alpha_{0} + \beta_{1} Determinant_{c,\left( s \right),t - 1} + \beta_{2} Z_{c, t - 1} + \delta_{cs} + \delta_{st} + \varepsilon_{c,s,t}$$

where the dependent variable is the logarithm of the returns to intangibles in country c and industry s in year t. The key coefficient of interest is β1, which refers to an explanatory variable corresponding to a specific policy area of the trade–investment–IP nexus. Z refers to a vector of control variables encompassing the country’s GDP per capita, corporate income tax rate, natural resource rents as a percentage of GDP, as well as the estimated size of the shadow economy as a percentage of GDP.11

Table 1 provides an overview of the policy variables included in this analysis in order to explain the distribution of returns to intangible capital across countries and industries.12 Depending on data availability, variables are measured at the country-industry level (e.g., tariffs) or the country-level (e.g., quality of antimonopoly policy). All independent variables are lagged by 1 year to mitigate endogeneity concerns, except for the OECD Services Trade Restrictiveness (STRI, not lagged) and the measures of the quality of IPR protection and antimonopoly policy (lagged by 2 years).13

Table 1 Descriptive statistics for policy variables included in the regression analysis

Country-by-industry fixed effects (δcs) control for time-invariant characteristics of a given country-industry pair. For example, δcs capture a country’s geographic location as well as the average product sophistication of industry s in country c during the period of analysis. In addition, industry-by-year fixed effects (δst) absorb all time-varying shocks to a specific industry in all countries in a given year, such as a drop in the price of textiles or a global crisis of the automotive sector.


We start by exploring whether lower barriers to trade in goods, trade in services and investment, as well as a greater global economic connectivity through trade agreements, are associated with higher returns to intangible capital (Table 2).

Table 2 The link between trade and investment openness, connectivity, and returns to intangible capital

The link between tariffs and the returns to intangibles covered by the SNA (measured intangible capital) is statistically significant and negative (column 1). This finding is in line with the role of trade as a channel for knowledge diffusion and a powerful force incentivizing firms to invest in knowledge creation. It resonates, for example, with the results of a firm-level study focused on the case of India: Goldberg, Khandelwal, Pavcnik, and Topalova (2010) find that a reduction of tariffs on intermediate inputs boosted the introduction of new products by domestic firms. While this channel could similarly apply to unmeasured intangible capital, it is important to bear in mind that trade barriers also protect abnormal profits associated with market power – a type of rents that our methodology would capture in the residual category. This might explain why the corresponding coefficient is not statistically significantly different from zero (column 5).

With respect to services, Table 2 relates a country’s openness to services trade as measured by the OECD Services Trade Restrictiveness Index (STRI) to estimates of returns to measured intangible capital (column 2) and unmeasured intangible capital (column 6).14 The coefficients in both columns are negative, indicating that greater openness to services trade is associated with higher returns to both types of intangible capital. As the coefficient in column 2 is larger than in column 6, access to foreign services, e.g., computer services, seems to be a particularly important determinant of firms’ ability to generate returns to intangible assets covered in the SNA, such as software and databases.

Yet, the relevance of openness to services trade is clearly visible for both types of intangible capital. Services such as logistics or financial services can be expected to promote innovations beyond the set of intangible assets recorded in the SNA, e.g., in terms of improvements of distribution strategies. A growing body of empirical contributions shows that services play a crucial role in global value chains as key inputs that are closely intertwined with manufacturing activities (e.g., Miroudot & Cadestin, 2017). The sourcing of foreign services may enable firms to overcome bottlenecks caused by the limited local availability of essential inputs and thereby facilitate innovation and product upgrading (Nordås, 2010).

Importantly, the STRI does not only cover regulations applying to cross-border trade but also to trade via foreign affiliates and the temporary movement of individuals providing services.15 The channels highlighted in the literature on knowledge diffusion via FDI and labor mobility (Keller, 2010; Santacreu-Vasut & Teshima, 2016; Cho, 2018; Bahar, Choudhury & Rapoport, 2020) are therefore likely to contribute to the link between services trade policies and returns to intangible capital observed in columns 2 and 6. In accordance with this perspective, Markusen, Rutherford, and Tarr (2005) stress the importance of sales via foreign affiliates in the case of services that are intensive in knowledge capital. Similarly, Oldenski (2012) finds that this mode of supply is particularly relevant to services relying on complex tasks.16

As a further policy variable referring to investment, Table 2 also reports regressions using the OECD FDI regulatory restrictiveness index. The corresponding coefficient is negative when employing estimated returns to measured intangible capital (column 3). This link between openness to FDI and returns to measured intangible assets, such as R&D, is in line with MNEs’ role as the primary investors in business-funded innovative activities. In 2017, the top 2500 companies investing the largest sums in R&D in the world accounted for roughly 90% of global business R&D, with Philips alone contributing approximately 40% of all patent filings in the Netherlands (Vezzani et al., 2018). Beyond MNEs’ direct role as investors in intangible assets, MNEs also have the capacity to profoundly influence a country’s role in GVCs. Simultaneously linked to their host regions and to intra-firm networks, the subsidiaries of MNEs channel economically valuable knowledge across large distances and national borders (Meyer, Mudambi, & Narula, 2011).17

Investment policies seem to shape the distribution of returns to intangible capital in GVCs beyond the set of intangibles recorded in the SNA: The negative, marginally significant coefficient in column 7 suggests that barriers to FDI may similarly reduce returns to unmeasured intangible capital, e.g., superior marketing strategies. Large MNEs such as Apple or Airbnb have been highlighted as major creators of returns to knowledge-based assets that are imperfectly captured in the SNA (WIPO, 2017). Thus, all of the top ten applicants seeking to register European Union trademarks during 2010-2019 were large multinational enterprises, with the number one position occupied by the Korean multinational LG Electronics (EUIPO, 2020).

The fourth policy determinant explored in the regressions presented in Table 2 refers to a country’s participation in bilateral and plurilateral trade agreements (columns 4 and 8). For a given country, this independent variable was calculated as the total GDP of all economies with which the country has signed a trade agreement, expressed as a share of global GDP minus the GDP of the country under observation. This variable captures the way a country’s trade policy shapes its economic connectivity.18 The strong positive association with returns to measured (column 4) as well as unmeasured (column 8) intangible capital is likely to reflect multiple channels between participation in trade agreements and knowledge creation. As tariffs are lower between partners of a trade agreement, the mechanisms outlined above in the discussion of columns 1 and 5, especially regarding learning via importing and innovative efforts in response to foreign competition, are similarly relevant here.

In addition, the improved market access achieved by trade agreements may increase the incentives for firms to invest in intangible assets. As highlighted by Haskel and Westlake (2018), it is particularly in the context of economic integration that the scalability of intangible capital and the opportunities to exploit synergies between different intangible assets have been amplified. The scalability of intangible assets enables firms with limited ownership of physical capital to quickly expand their activities abroad without the need for a prolonged period of learning on the domestic market. For firms to be able to implement such rapid international scale-up strategies based on intangible assets, trade and investment policies have to ensure that market access is offered in a large number of foreign markets, thus putting the emphasis on connectivity through trade agreements with multiple partners.

Our finding regarding the role of trade agreements is also in accordance with the empirical results of Coelli, Moxnes, and Ulltveit-Moe (2020), who draw on detailed firm-level patent data covering 60 countries and find that improved market access through trade liberalization incentivizes firms to invest in knowledge creation.19

Trade and FDI in GVCs are closely intertwined and by lowering trade costs associated with tariffs, non-tariff measures and regulatory uncertainty, trade agreements also facilitate a country’s integration in GVCs and the networks of MNEs. The latter act as the primary conduits for global knowledge flows (Iammarino & McCann, 2013) and technology-intensive companies increasingly source relevant knowledge inputs from a global portfolio of locations with relevant R&D activities. Our results with respect to the link between policies related to economic openness and returns to intangible capital (see Table 2) can therefore be read as support for the argument stressed by Cano-Kollmann, Cantwell, Hannigan, Mudambi, and Song (2016) that patterns of international knowledge connectivity shape the changing distribution of global value creation.20


The third main element of the ‘policy nexus’ concerns IP policies and competition. By implementing and enforcing IP legislation, governments play an important role in shaping the incentives for investments in knowledge creation (Fink & Raffo, 2020). With respect to the design of IP policies, the priorities of innovators, i.e., technological leaders owning valuable knowledge assets, fundamentally differ from those of technological followers. While the former intends to minimize the risk of IPR infringements and maximize the economic returns to their investment in knowledge creation, the latter aim to maximize knowledge diffusion to accelerate their catch-up.

Table 3 reports results of regressions exploring the link between the quality of IPR protection as measured by an expert survey conducted by the World Economic Forum and returns to measured intangible assets (column 1) and unmeasured intangibles (column 4).21 The coefficient is positive and statistically significant when using returns to measured intangibles as the dependent variable (column 1). This finding is consistent with the idea that strong protection of IPRs enhances firms’ ability to reap returns to investments in intangible capital. Through its effect on trade and investment flows, the strength of the IPR framework is also likely to shape a country’s knowledge connectivity. This link is illustrated by a study by Palangkaraya, Jensen, and Webster (2017) who find that exporters limit their exports to destinations where the patent office displays a bias against foreign applicants, i.e., where firms might expect difficulties in protecting their intellectual property.

Table 3 The link between IPR protection, competition policy, and returns to intangible capital

Column 4 shows that the quality of IPR protection is negatively correlated with returns to unmeasured intangible capital. When interpreting this finding, one has to take into account that the estimate referring to unmeasured intangible capital captures returns to assets such as organizational capital that in many cases cannot be protected by formal IPRs. The finding may also reflect a situation where firms operating in a context characterized by weak IPR protection prioritize this type of intangibles and dedicate less efforts to intangibles covered in the SNA, for which IPR protection seems particularly relevant.

In addition, the quality of IPR protection tends to be correlated with other dimensions of the quality of regulation. Since we adopt a residual approach to estimate returns to unmeasured intangible capital, they may partly capture rents associated with market power related to a poor regulatory framework. If countries improving IPR protection simultaneously implement reforms ensuring a “level playing field” for all firms, this would reduce abnormal profits and could explain the negative link between IPR protection and returns to unmeasured intangible assets.

Our finding for the link between the quality of IPR protection and returns to measured intangible capital (column 1) supports the view that stronger IPR protection improves the functioning of markets and promotes trade and FDI in technology intensive sectors (Park & Lippoldt, 2008; Delgado, Kyle, & McGahan, 2013). Yet, the effect of IPR policies on knowledge diffusion through trade and investment is multi-faceted. Strong IPR protection entails higher cost of imitation, which might slow down the technological dynamism of lagging companies and economies (Maskus, 2019). Moreover, firms owning knowledge assets protected by IPRs might act in a monopolistic way and may employ a set of tactics to prevent competitors from exploiting these assets even after the expiry of the IPR protection (Maskus, 2004; Price & Nicholson, 2017).

Given the possibility that leading firms may seek to entrench their strong market position by creating barriers to entry, competition policy has an important role to play in ensuring that markets remain contestable (Kowalski, Rabaioli, & Vallejo, 2017). In the absence of strong antitrust regulation, the firms with the greatest market power are likely to reap monopoly rents – even if they do not continuously invest in innovative efforts. At the same time, the insufficient contestability of the market is likely to discourage firms without significant market power from investing in intangible assets. Conversely, high levels of competition motivate leading firms to innovate in order to defend their position against followers, whereas the latter invest in knowledge creation in order to gain market share and reap Schumpeterian rents.

Column 2 of Table 3 displays the result of a regression linking returns to measured intangible capital to the quality of a country’s antimonopoly policy (as evaluated through an expert survey conducted by the World Economic Forum). The coefficient is strongly significant and positive, suggesting that heterogeneity in terms of competition policy constitutes an important determinant of variation in returns to measured intangible capital among the countries included in our database. In contrast, the link with unmeasured intangible capital (column 5) is negative. This suggests that the residual approach used to estimate returns to intangible assets not recorded in the SNA may partly capture abnormal profits associated with market power in a context of weak antitrust enforcement.

Columns 3 and 6 of Table 3 show the results of regressions relying on mark-up estimates produced by Calligaris, Criscuolo and Marcolin (2018). As mark-ups capture market concentration, one might expect a positive link between the returns to intangibles and the gap between firms with the highest mark-ups and the “laggards”. The regressions reported in columns 3 and 6 explore this idea by using the difference between the 90th percentile and the 10th percentile of the mark-up distribution. The coefficients are positive for both types of returns to intangible capital. This pattern resembles the findings of Crouzet and Eberly (2019) who identify a positive link between intangible intensity and mark-ups at the firm-level and industry-level for the United States. Regarding the design of policies, our results for the link between returns to intangibles and the mark-up gap hence confirm the importance of taking into account that the ownership of valuable intangible assets can be translated into market power. In the context of trade and investment, economic integration is likely to amplify this effect by expanding opportunities to exploit the scalability of those assets. Steps towards greater economic openness and stronger IPRs should therefore be accompanied by a strengthening of antitrust authorities’ capacity.


Drawing on a comprehensive dataset on factor income in GVCs, this paper has analyzed the relationship between returns to intangible capital and a set of policies aimed at offering firms access to markets, capital and inputs, as well as facilitating and protecting flows of knowledge. These policies can be seen as part of growth-oriented strategies where participation in GVCs is a source of productivity gains. For policy-makers, the overall objective is to increase growth and income and, increasingly, to ensure that growth is inclusive. As such, achieving higher returns to intangible capital is not a policy objective per se, but to the extent that investment in intangibles is a source of future growth and allows domestic firms to capture more value in GVCs, designing policies that allow firms to benefit more from intangible investment can be part of a growth-oriented policy agenda.

From the perspective of firms, trade, investment, and the use of intellectual property are alternative forms of market access. The “trade as a package” view is therefore the starting point of any GVC-oriented policy. The increasing use of intangible assets by firms to capture value in GVCs suggests additional elements should be added to GVC-oriented policies, particularly against the backdrop of the digital transformation.

First, trade policy (in the broad sense defined in this paper) should be less selective in terms of economic partners and not so much based on existing export interests but anticipate the capacity of innovative firms to scale-up and serve a large number of new markets in new sectors. This provides a rationale for multilateral trade and investment liberalization or the negotiation of ‘mega-regional’ agreements rather than bilateral deals. It also suggests not focusing on a set of industries identified as ‘offensive interests’ as these priorities may soon become obsolete in view of the rapid scale-up of firms in new sectors and the shift from specialization in industries to specialization in specific activities in GVCs.

Second, the important role of economic openness on the import side for the competitiveness of firms in GVCs is further highlighted by knowledge spillovers related to intangible assets. Whether it is through imports of intermediate goods and services or through the presence of affiliates of MNEs, domestic firms’ chances of benefiting from knowledge spillovers will be related to the overall connectivity of the country and its capacity to be part of international knowledge networks. Similar scope for spillovers exists on the export side through the participation in GVCs and interactions with buyers and customers. It can lead to a paradigm shift in the design of trade and investment policies from governments negotiating bilaterally their reciprocal market access to strategies aimed at maximizing the spillovers from interactions with multiple firms in multiple countries. The latter can be based on unilateral action (at least on the import side) rather than reciprocity.

However, it should be stressed that spillovers are not automatic. Notwithstanding this study’s focus on the trade–investment–IP nexus, it is important to highlight that there is a strong role for proactive policies to support local agents’ absorptive capacity, enhance linkages between domestic firms and MNEs, and develop a long-term strategy to encourage indigenous innovation (Pietrobelli & Rabellotti, 2006; Fu, Pietrobelli, & Soete, 2011).

Third, the integration of rules on trade and investment in the same regulatory frameworks should be accentuated. Investment disciplines are increasingly found in trade agreements but there are still many bilateral investment treaties and other investment arrangements that overlap with provisions in trade agreements (Crawford & Kotschwar, 2018). Some coherence is needed and market access should be considered jointly for trade and investment, as part of the ‘trade as a package’ approach. This is already the case for services in agreements that include trade in services through ‘commercial presence’ (Mode 3 in the terminology of the WTO General Agreement on Trade in Services). A similar approach could be followed for goods to facilitate the integration of countries in networks of MNEs.

Fourth, trade agreements have to address new challenges related to IPR protection and competition that reflect the imperfect appropriability as well as the scalability of intangible assets. The two are related since IP protection is often based on monopoly rights given to technology owners. As intangible assets allow for the rapid scale-up of firms in digital-intensive sectors, the challenge of striking the right balance between incentives to innovate, technology diffusion and competition goes beyond the traditional debate on the trade-offs associated with IPR policies.

While the WTO Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs) provides for a minimum floor, bilateral and regional trade agreements increasingly include higher standards for IPR (Valdès & McCann, 2014). Agreements going beyond TRIPs often provide a more detailed treatment of IPRs covered in TRIPs (e.g., regarding geographical indications), cover new areas of IPR, or restrict the flexibility available under TRIPS (Kowalski, Rabaioli, & Vallejo, 2017). The inclusion of such provisions is generally pushed by developed economies but has also triggered reforms of IP regimes in developing countries (Biadgleng & Maur, 2011). Yet, the optimal level of IPR protection remains subject to debate. It can be illustrated with the example of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (TPP) signed by 11 economies of the Asia–Pacific region. After the withdrawal of the United States, the signatories suspended a number of IP provisions found in the original Trans-Pacific Partnership. Particularly in technologically lagging economies, attitudes towards IPR protection are shaped by a perception that stronger IPRs may lead to monopolistic behavior of MNEs and leave few opportunities for technological catch-up of domestic firms. One way to address these concerns is precisely to look beyond the IP regime at a set of GVC-oriented policies that can increase domestic investment in intangible assets and facilitate knowledge transfer (Maskus, 2019).

Such a package of policies should also cover competition. Against the backdrop of concerns about the growing market power of leading firms exploiting the scalability of intangible assets (IMF, 2019; Durand & Milberg, 2020), our results provide support for a strengthening of antitrust authorities’ capacity to monitor market concentration trends (Pike, 2018). The different elements of the policy nexus need to be carefully balanced to create a comprehensive regulatory framework that combines the advantages of international connectivity with strong incentives to innovate and simultaneously prevents leading firms from stymying competition. The fine-tuning of this delicate balance is a formidable challenge. Tensions between IPR policy and competition policy may have to be reconciled and the preferences of domestic and foreign firms are likely to diverge, especially in emerging economies (Kowalski, Rabaioli, & Vallejo, 2017; Brandl, Darendeli, & Mudambi, 2019).

It is clear that short-sighted approaches failing to strike a balance between the different elements of the nexus are unlikely to maximize a country’s potential to benefit from the growing role of intangibles in GVCs. Whereas economic openness is vital for participation in GVCs and acts as a powerful driver of knowledge diffusion and learning, the absence of an adequate IPR framework must be expected to limit a country’s ability to expand its involvement in knowledge-intensive activities in GVCs. As steps towards greater economic openness and improved IPR policies enhance the technological leaders’ capacity to exploit the scalability of intangible assets, a well-designed competition policy is required to prevent leading firms from creating barriers to entry and to ensure that markets remain contestable.

There is no consensus yet on the type of regulatory regime that can reconcile innovation and competition in the context of modern business strategies. Further empirical and policy research should be devoted to this issue as GVCs become more intangible capital intensive. With the development of more detailed data on intangible capital, this research could also further look at the role of specific types of intangible assets, going beyond the simple dichotomy between ‘measured’ and ‘unmeasured’ intangible capital that we have introduced in our analysis. There are, for example, different types of intellectual property products with different regulations and provisions in trade agreements. The policy debate would also benefit from a more fine-grained analysis.


  1. 1

    Baldwin (2011a) first describes it as the ‘trade–services–investment’ nexus and later as the ‘trade–services–investment–IP nexus’ (Baldwin, 2014). Since services can be regarded as already part of trade and investment, we simplify it as the ‘trade–investment–IP nexus’. Nevertheless, we understand the need to stress services as trade and investment policymakers tend to focus on manufacturing and to overlook the important contribution of services to GVCs.

  2. 2

    See the online appendix for the list of countries and industries. We rely on 24 industries from the manufacturing and business services sectors in our analysis. In particular, we exclude industries where value added is generated through natural resources or non-market services, as our methodology is not adapted to such sectors.

  3. 3

    See the online appendix for a full list of sources and countries for which data are estimated.

  4. 4

    See the online appendix for more details on this adjustment.

  5. 5

    See the online appendix.

  6. 6

    While there are empirical studies on returns to specific types of intangible assets, it is difficult to find estimates of a rate of returns for all (measured) intangible assets in national accounts. However, since most of capital is now intangible, the rate of return cannot depart too much from the value of 4 %, which is the average observed for returns to all capital (tangible plus intangible), as estimated for example in the Penn World Tables.

  7. 7

    See Timmer et al. (2014) for a more complete exposition of how final demand for a given country and industry can be decomposed into the value-added contribution of all countries and industries that have participated in the GVC.

  8. 8

    See Bohn (2019) for an empirical analysis of income flows and the gross national income of countries.

  9. 9

    There are also transport margins, which are much smaller, and not included in our analysis.

  10. 10

    Trade margins are likely to differ when products are exported but there is no information that would allow calculating different margins across different export destinations. We have to assume that factor income is allocated the same way. The fact that income may ultimately benefit wholesalers or retailers in other countries is not an issue since we are interested in the impact of policies put in place by the country where the products are manufactured (and which will determine whether they are exported or not in the first place).

  11. 11

    The control for GDP per capita relies on World Bank data and is meant to take into account that countries at higher levels of development tend to display higher R&D intensity and more advanced levels of technological complexity. Based on OECD data, the inclusion of the corporate income tax rate variable is motivated by the links between corporate strategies, intangibles in GVCs, and tax policies. The control for natural resource rents relies on the World Development Indicators dataset of the World Bank. Its inclusion helps to control for special dynamics related to non-produced assets in resource-rich economies. Estimates of the size of the shadow economy were taken from a dataset created by Wu and Schneider (2019). This variable is intended to control for the inherent difficulty of estimating factor returns for countries with a large informal economy.

  12. 12

    Table A5 in the online appendix also provides descriptive statistics for the controls and the dependent variables.

  13. 13

    The OECD STRI is only available from 2014 onwards. As the coverage of the main dataset including estimates of returns to intangible capital stops in 2015, the STRI variable is not lagged in order to avoid losing the panel dimension of the dataset. For a given country, the average STRI score across the 22 services sectors included in the STRI was computed. To obtain a measure that varies across manufacturing industries, this average STRI score was then weighted by each manufacturing industry’s services input share in the corresponding country. The variables referring to antimonopoly policy and IPR protection are lagged by 2 years, as it appears plausible to assume that changes in the enforcement of antitrust and IPR legislation only slowly translate into changes regarding firms’ perceptions of incentives to invest in knowledge-based assets.

  14. 14

    The OECD STRI as well as the FDI restrictiveness index are composite indicators designed to have a minimum of zero and a maximum of one, with a higher value representing a more restrictive regulatory framework.

  15. 15

    The STRI score of a given country relies on 1943 measures capturing regulatory policies affecting services trade. The measures refer to different modes of services supply and cover five different policy areas (restrictions on foreign entry, barriers to competition, regulatory transparency, restrictions to movement of people, other discriminatory measures). In comparison, the FDI restrictiveness index is less comprehensive and prioritizes a clear focus on FDI. The STRI’s broader coverage of several policy areas should be taken into account when considering the larger coefficients for the STRI compared to the FDI restrictiveness index (Table 2).

  16. 16

    Regarding the relatively large size of the coefficients corresponding to services trade restrictiveness (columns 2 and 6), one has to take into account that this composite indicator is by design restricted to a range from zero to one. Moreover, barriers to services trade remain substantially higher than barriers to trade in goods (Miroudot et al., 2013; WTO, 2019; Benz and Jaax, 2020) and especially producer services have been highlighted as the key inputs permitting firms to specialize and reap the benefits from greater market access (Francois, 1990; Deardorff, 2001). It therefore seems plausible that reductions of restrictions concerning services have a large effect on returns to intangible capital.

  17. 17

    MNEs’ location choices can play a crucial role in the emergence of technologically advanced clusters in emerging countries, as in Bangalore in India (Lorenzen & Mudambi, 2013), Heredia in Costa Rica, or Quéretaro in Mexico (Turkina, Van Assche, & Kali, 2016). Although MNEs have strong incentives to prevent the diffusion of knowledge related to design and development (McCann & Mudambi, 2005), they simultaneously have to provide subsidiaries and suppliers with instructions regarding modern machinery and management practices, etc., to ensure that inputs seamlessly fit into the production network (Baldwin & Lopez-Gonzalez, 2015). Moreover, MNEs may also raise domestic firms’ awareness of the value of intangible assets. Durán (2014) argues that competition among Mexican automotive component producers seeking orders from MNE subsidiaries acts as a main incentive motivating domestic firms’ R&D efforts and patenting.

  18. 18

    It takes into account all agreements included in the Design of Trade Agreements (DESTA) database (Dür, Baccini, & Elsig, 2014). For the case of Switzerland, for example, this measure indicates that countries that are signatories to a trade treaty with Switzerland accounted for 56% of the rest of the world’s GDP (i.e., world GDP minus Switzerland’s GDP) in 2015.

  19. 19

    Similarly, Bustos (2011) finds that improved access to the Brazilian market following the establishment of MERCOSUR induced Argentinian exporters to invest in technology upgrading.

  20. 20

    For example, patent data reveal a broadening of the geographic scope of the inventive activities of the Brazilian aerospace company Embraer, with locations in the U.S. and South Korea becoming more relevant in the 2010s (WIPO, 2019).

  21. 21

    The data on the quality of IPR protection and the measure of the effectiveness of a given country’s anti-monopoly policies rely on the World Economic Forum’s annual Executive Opinion Survey, which covers a broad range of economic and institutional questions. In 2015, this survey collected the opinions of more than 14,000 business leaders in 144 economies. For example, the question about the protection of intellectual property rights was worded as follows: “In your country, to what extent is intellectual property protected?” Respondents were asked to express their perception on a scale ranging from 1 (not at all) to 7 (to a great extent). More information on the design of this survey can be found in World Economic Forum (2015). The data are available via the World Economic Forum (–2018/downloads/) and the World Bank (


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The authors are writing in a strictly personal capacity. The views expressed do not reflect those of the OECD Secretariat or the member countries of the OECD. The authors are grateful to the deputy editor, Ari Van Assche, and three anonymous referees for their helpful suggestions. The authors thank Charles Cadestin for his support and comments. This paper draws inspiration from the authors’ fruitful collaboration with Ali Alsamawi, Charles Cadestin, Joaquim Guilhoto, and Carmen Zürcher. It has also benefitted from suggestions provided by participants of conferences held in Leeds (EIBA) and Stavanger (Geography of Innovation).

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Accepted by Ari Van Assche, Deputy Editor, 27 October 2020. This article has been with the authors for three revisions.

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Jaax, A., Miroudot, S. Capturing value in GVCs through intangible assets: The role of the trade–investment–intellectual property nexus. J Int Bus Policy (2021).

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  • global value chains
  • intangible capital
  • factor income
  • trade policy
  • investment policy
  • intellectual property
  • competition policy