Possibilities for Upgrading High-tech GVCs Toward Stronger SDG Performance
International Business and Sustainable Development Goals
ISBN: 978-1-83753-505-7, eISBN: 978-1-83753-504-0
ISSN: 1745-8862
Publication date: 31 July 2023
Abstract
International trade and production have been increasingly organized around the configuration and evolution of global value chains (GVCs), even as the sustainable development goals (SDGs) have been established in an era of deepening globalization. The connection between these two processes raises some concerns around how economies, firms, and individuals can benefit from participation in the global economy. This chapter looks at the relationship between the relative positions of countries in high-tech GVCs and the impact on potential fulfillment of the SDGs. In this chapter, the authors make a first approach (descriptive analysis) to the relationship between the relative positions of countries in high-tech GVCs, possibilities for upgrading, and corresponding levels of inequality. The authors focus particularly on a set of indicators corresponding to Goal 1 (no poverty), Goal 5 (gender equality), Goal 9 (industry, innovation, and infrastructure), and Goal 10 (reduced inequalities). The findings reveal that the relationship between the position of a country in terms of forward and backward participations and the relative distance to fulfillment of the SDGs differs among distinct GVCs. While some patterns of income inequality reduction are observed in high-tech-related industries, gender inequality is not similarly affected. This confirms the relevance of building a two-dimensional framework that looks simultaneously at GVCs (i.e., the type of GVC and type of participation) as well as the distance from SDG achievement.
Keywords
Citation
Biurrun, A. and Álvarez, I. (2023), "Possibilities for Upgrading High-tech GVCs Toward Stronger SDG Performance", van Tulder, R., Giuliani, E. and Álvarez, I. (Ed.) International Business and Sustainable Development Goals (Progress in International Business Research, Vol. 17), Emerald Publishing Limited, Leeds, pp. 285-308. https://doi.org/10.1108/S1745-886220230000017015
Publisher
:Emerald Publishing Limited
Copyright © 2023 Antonio Biurrun and Isabel Álvarez
1. Introduction
International trade and production have been increasingly organized around the conformation and evolution of GVCs. Meanwhile, the SDGs, defined by the United Nations in 2015, were established during an era of deepening globalization and adopted by governments under the Agenda 2030 umbrella. The connection between these two processes raises some concerns on how economies, firms, and individuals could benefit from participation in the global economy. The issue is that only a partial view is achieved when looking at the effects of GVCs merely in terms of economic growth and gains in competitiveness; a more complete framework can be built by taking a broader perspective that includes the challenges defined in the SDGs that refer to a more general development agenda embracing aspects of employment, equity, nutrition, and longevity (Kaplinsky, 2016).
Although a strong connection between approaches to the SDGs and to GVCs can be easily established, certain gaps persist in the literature focused on the relationship between GVCs and sustainability. As Negri, Cagno, Colicchia, and Sarkis (2021) point out, further study is needed to analyze the impacts of implementation and the development of performance measurement systems to assess supply chain sustainability. Beyond environmental issues, it is thought that economic gains resulting from intensified insertion into GVCs do not necessarily lead to shortening the distance to meeting the relevant SDGs. This is because participation in GVCs can raise several problems with effects on development outcomes, including the deepening or reproduction of social concerns such as income or gender inequalities.
This argument can be applied to all countries, but it is particularly important for developing countries, whose living standards and social conditions are often insufficient – not only in humanitarian terms, but as pre-conditions for further economic development. In fact, participation in the process of globalization by means of possibilities derived from the international fragmentation of production in GVCs has been viewed as a potential opportunity for upgrading skills and human capital under the “development in transition” approach (OECD, 2019); deeper study is needed in this respect. A successful catching-up process related to the integration of countries into GVCs should be reflected in the possibilities opened through the joint consideration of economic and social upgrading, across several dimensions (Biurrun, Castilho, Marín, & Quirós, 2021).
This chapter examines the relationship between the relative positions of countries in high-tech GVCs and the impact on meeting the SDGs. Our aim is to show that, even when countries succeed at GVC upgrading (i.e., by accessing better or more efficient technologies), the sign and size of the potential impacts on SDG performance may differ; and this is particularly true in the realm of inequality. Our expectation is that the relative positions of countries in GVCs may display highly differentiated patterns that could in turn be conditioned by the technological sophistication of the dominant industry. We make a first approach (descriptive analysis) to the relationship between the relative positions of countries in high-tech GVCs and corresponding inequality levels, which are addressed advances made toward meeting Goal 1 (no poverty), Goal 5 (gender equality), Goal 9 (industry, innovation, and infrastructure), and Goal 10 (reduced inequalities).
Section 2 briefly reviews the most relevant background literature. Section 3 is focused on the data sources and methodology. The results of the descriptive and econometric analyses are presented in Section 4. Finally, Section 5 offers some concluding remarks and an agenda for further research.
2. Literature Review
A high degree of international fragmentation of production has been a key feature in recent stages of the globalization era. Earlier phases of this process clearly favored high-income economies, mainly due to the concentration of industry and innovation and because, notably, globalization has been determined by the geographical localization of activities of value generation, with scale economies and firm size certainly acting as key determinant factors for success. In that model, developing economies took only partial advantage of the increasing internationalization of firms, markets, and economic activities (Baldwin, 2016; Krugman, 2015).
A lack of capabilities – mostly in terms of technology and human capital, low levels of productivity, and more conventional factors such as the ability to create sizeable domestic markets, or to serve larger international markets – have been behind the differentiated effects seen in developing countries and the poor results obtained in terms of catching-up. As Fagerberg and Srholec (2021) stated, capabilities are important not only at the firm level but at the country level; for this reason, the 1980s and 1990s were lost decades as regards the reduction of disparities in the income gap between leading (wealthy) economies and following (poorer) economies.
Although successful catching-up stories (including that of South Korea) have been noted, reverse examples also exist, such as Malaysia’s failure in the carmaking industry (Baldwin, 2016). In general, none of the applied strategies that were based on import substitution, industrialization, and the Washington Consensus proved able to generalize the expected positive effects of globalization in terms of development that could then spur an automatic catching-up process between leaders and followers. Meanwhile, a potential virtuous circle driven by externalities can be conceived in the current stage of globalization, based on information and communication technologies (ICTs) and the trend of higher international fragmentation of production described by Krugman (2015). As a matter of fact, new opportunities have been derived from both the dynamics of foreign direct investment (FDI) and the latest boom in ICTs that has clearly favored the establishment of a new model based on tasks, occupations, phases, and products. ICTs permit a more intense circulation of ideas across countries, and this model has become less dependent on geographical distance, while the indivisibility of production has lost relevance. However, the international dispersion of production phases is far from homogeneous and remains dependent on the types of organization involved and how countries may become a part of internationally fragmented production.
Cantwell (1989) was among the first scholars to develop a perspective of industrial dynamics and technological accumulation, moving from industrial structure to industrial evolution, in which FDI may favor the generation of technological advantages both at home and abroad. His work proved pioneering through its recognition of the need to combine the contract-based perspective under transaction costs theory with capability development theory. The dynamic capabilities approach enhances the orchestrating role played by multinational enterprises (MNEs) and the creation of new products and services, which serve as driving factors that explain actions by large MNEs that can enhance the vitality of business ecosystems. This framework emphasizes the importance of processes both within the MNE and outside (through external agents), enabling small and medium firms to likewise integrate into GVCs (Teece, 1977, 2014). As a consequence, possibilities for partial specialization along the value chain also open new opportunities for countries to catch up. In this respect, policies that favor competitiveness in a fragmented world are also affected by the joint consideration of industrial policies that focus on manufacturing activities together with companies offering industry-related services (Baldwin, 2016).
Gereffi (1994) helped economists to look beyond FDI through global commodity chains, and to understand the formation of producer-driven networks, and these were adopted in Hummels, Ishii, and Yi (2001) and tagged as GVCs. In this case, the manufacturer organizes external production and coordinates with marketing, sales, after-sales services, etc., thus being more likely to exchange and transfer knowledge. On the other hand, in buyer-driven networks, and in this case a large producer such as Tommy Hilfiger studies the US market and uses intermediaries for production in Malaysia. The issue is that different types of GVCs, according to the typology developed by Gereffi, Humphrey, and Sturgeon (2005), are based on the structure of power relations among the participant actors. This structure becomes critical for the analysis of the main effects associated with the possibility of country upgrading or “step-climbing” within a GVC, in terms of strategies for economic and social improvement. Types of step-climbing have been widely discussed in the literature; Pietrobelli and Rabellotti (2006) use a broad definition – the ability of firms (and countries) to “make better products, or to be able to manufacture products more efficiently, or to move towards higher-skills activities.” The result is that coordination in the supply chain is less dependent on the traditional internalization advantages of MNEs and highly dependent on governance schemes as well as cluster dynamics (Giuliani, Pietrobelli, & Rabellotti, 2005; Teece, 2014).
On the other hand, Mercer-Blackman, Xiang, and Khan (2021) found that variations in country participation in GVCs affect the sign and the size of FDI spillovers and give rise to seemingly contradictory results in different countries. In fact, involvement in specific high-tech GVCs reveals different patterns that are due to both intensity of participation and relative position. As an example, in previous works we demonstrated how the difference between forward and backward participations permits characterization of the position of an economy in value chains related to products associated with COVID-19 (Álvarez, Biurrun, & Martín, 2021).
Leading firms can define strategies wherever innovation centers are decentralized, because they need to innovate in developing countries as well, to address their specific needs and eventually bring home results, thereby helping to boost those developing countries’ exports (Govindarajan & Trimble, 2012). A positive side of these potential spillovers is that such strategies require the host (developing) country to build innovation capacities based on education and skills (Mudambi, 2008); the outcomes (higher exports and education levels) may in some ways reduce the distance to SDG targets.
Therefore, one key dimension to be considered is the potential of a country for GVC participation, and this is determined by multiple aspects including: its capacity for scale production; the availability of services necessary to support production and market integration; education and skills in the workforce that match the needs of global producers and buyers; and a capacity for innovation across various dimensions, including environment sustainability (Cattaneo, Gereffi, Miroudot, & Taglioni, 2013). It is plausible to consider that the type of participation (backward or forward) can also affect levels of externalities and the involvement of both MNEs and local host country firms in the achievement of SDGs.
The point, therefore, is to seek to disentangle the extent to which the participation of GVCs may affect inequality, which is directly related to several SDGs. This invites us to explore those aspects related to the reduction of inequality that can be easily observed and quantitatively analyzed, and to observe whether differences exist across the various types of GVCs or main industries involved.
This chapter addresses two arguments developed in the literature. On the one hand, a crucial issue for developing countries is not merely whether to belong to GVCs, but how to insert themselves, as argued by Taglioni and Winkler (2014). A second question is related to possibilities for increasing and intensifying participation, and how in turn such participation derives into sustainable development. On the other hand, new efforts have been made in this direction in the International Business (IB) literature, although the connection between GVCs and SDGs is a tricky one (Van Tulder, Rodrigues, Mirza, & Sexsmith, 2021). The fact is that the SDGs were defined for the national level, and their adaptation to the micro-level implies an exercise in translation that can be performed along the value chain. In this direction, Montiel, Cuervo-Cazurra, Park, Antolín, and Husted (2021) propose grouping the 17 SDGs into 6 clusters and linking them to the value chain, looking at knowledge and wealth creation and their effects on health. As these authors state (Montiel et al., 2021), it is possible to rethink MNE activities as well as to measure the effects of firms depending on their actions or their impacts on the reduction of negative externalities (or the increase of positive externalities).
According to these two aspects, what matters is not only the total participation of countries but also whether countries are engaged in backward and/or forward participations. In general terms, in the methodology proposed by Hummels et al. (2001) and Koopman, Wang, and Wei (2014), it is possible to consider two types of participation in a GVC: a country can participate in global supply chains either by using imported intermediate goods to produce exports (backward participation) or by exporting intermediate goods that will be used by other countries to produce their own exports (forward participation).
The key argument developed in subsequent sections assumes that in a very interdependent international economy, any individual action in one country can affect others through GVC connections. This justifies the interest in exploring: (i) how a country’s participation in GVCs may affect the distance to targets included in the SDGs (such as inequality); (ii) whether differentiated impacts can be observed in backward and forward participations; and (iii) the effect of having participated in high-tech GVCs in other industry-related chains.
3. Data and Methodology
Assessment of the effect that participation by countries in high-tech GVCs can derive in terms of inequality provides a suitable framework by which to explore the potential relationship of international business with sustainable development. To this end, our point of departure was to determine which types of value chains would be analyzed; our choice has been to study GVCs in a selection of industries that entail explicitly high-tech manufacturing and services. In particular, we analyze the GVCs in agriculture, computers, telecommunications, and services.
A second step involved the selection of countries. The objective was to consider a diversity of countries according to their relative participation in GVCs as well as their commitment to the relevant SDGs. Therefore, our choice was to include a variety of industrialized and high-income economies, some from the G7 (clearly the “winners” in the first globalization era – in particular the USA, Japan, and Germany), along with two catching-up economies from the European Union, Spain (considered intermediate from the economic and innovation perspectives, and highly integrated into GVCs) and Romania (a developing Eastern European country with high participation in regional value chains). The EU-28 countries were introduced as benchmark. Also included was the middle-income North American economy of Mexico, with high insertion into GVCs but facing significant struggles in terms of the SDGs. Thailand was selected as a South-East Asian representative for its active participation in high-tech value chains in the region. Moreover, two countries from Africa were taken into account: Morocco, within the Maghreb region, strongly inserted into GVCs, and South Africa, a regional representative with dynamic international insertion. These countries’ relative participation in GVCs will clearly differ, as will the terms of SDG performance. This diversity of countries allows us to look for any common patterns in the relationships between GVCs and inequality, regardless of income levels.
Remaining aware of the complexity that quantification of both GVCs and SDGs will imply, two data sources were selected to facilitate analysis. In particular, we worked with the Trade in Value Added (TiVA) database from the OECD, which takes official statistical sources (national input–output tables) as a basis and provides sectoral information on the types of value added (VA) incorporated into exports, imports, and final demand, disaggregated by country of origin and/or geographical destination.1 Within the TiVA database, an economy’s backward share is calculated as the foreign VA content of gross exports, while the forward share is calculated as the domestic VA content of other economies’ exports as share of gross exports.
Secondly, we worked with the SDG Indicators Database from the United Nations Department of Economic and Social Affairs. In particular, we focused on four SDGs that refer directly or indirectly to the attainment of more equal societies. The selection of SDGs and corresponding indicators is shown in Table 15.1. For SDG 1, the indicator reflects the magnitude of a country’s poor population, with the poverty line defined as under $1.90 a day at international prices of 2011. Regarding Goal 5, the indicator adopted reflects the state of advance in terms of the “glass ceiling effect” and is measured as the proportion of women in managerial positions. For Goal 9, our choice was to analyze research and development (R&D) expenditures as a share of gross domestic product (GDP), as well as the degree of mobile internet connectivity. For Goal 10, defined to consider the capacity for reduction of inequalities, two distinct indicators were selected: workers’ income as a share of GDP, and the values of the Gini coefficient.
A period of 11 years was covered, from 2005 to 2015, selected in accordance with data availability from two main datasets.
Because the aim of this chapter is to explore the potential relationship between GVC participation and country performance in terms of SDG achievements, we first conducted an exploratory analysis combined with certain econometric regressions, addressed in the next section.
Selection of SDGs and Indicators Included in the Analysis.
| SDG | Indicator |
|---|---|
| Goal 1. No poverty | Proportion of population below international poverty line (percent) |
| Goal 5. Gender equality | Proportion of women as share of the total number of persons employed in managerial positions |
| Goal 9. Industry, innovation, and infrastructure | - R&D expenditure as a proportion of GDP |
| - Proportion of population covered by at least a 3G mobile network (percent) | |
| Goal 10. Reduced inequalities | - Labor share of GDP (total compensation of employees and the labor income of the self-employed, given as a percent of GDP) |
| - Gini index, personal income distribution |
4. Results
4.1. Descriptive Analysis
The relationship between the backward and forward participations in GVCs of the primary sector allows us to observe certain counterproductive effects on the inequality indicators of every SDG under study. Regardless of the type of participation, at the beginning of the chain (forward) or at the end (backward) in those GVCs related to the agriculture industry, most of the indicators reveal a negative relationship (see dispersion graphs in the Appendix). The only exception corresponds to the labor share in national income, which clearly shows a differing relationship when forward and backward participations are considered. This is positively related at the end of the chain (backward participation), revealing the effect of higher specialization in final products than in intermediate suppliers. By contrast, at the beginning of these GVCs, the labor share is negatively affected, although the effects in both cases are not especially relevant, as can be observed in Graph 15.1.
Results are different for the high-tech GVCs; considering the computer industry among them, a country’s possible participation reflects differentiation with respect to the case of the primary sector. As a general pattern, participating in this type of chain fosters success in the achievement of most SDGs when the country is located at the beginning of the chain (forward participation), while the opposite effect is observed for backward participation. This is clearly visible through the two technological variables taken into account for Goal 9, which show dissimilar behavior when comparing forward and backward participations. R&D investments and internet connections are positively correlated with the forward position of countries, and both are negatively associated with the backward participation of countries.
Moreover, in the case of Goal 10 and the reduction of inequalities, both the share of labor in national income and the indicator of personal income distribution show positive connections with the forward participation of countries, while this relationship turns negative on the backward side (see dispersion graphs in the Annex). Graph 15.2 illustrates the latter relationship. Even poverty (Goal 1) is affected by this pattern, although that effect is minimal. It should be noted that the only exception is Goal 5, on gender inequality, where the greater the forward participation by a country, the lower the percentage of women in managerial positions (Graph 15.3).
As with the computer industry, observed participation in other high-tech chains related to telecommunications allows us to confirm that the relative positions of countries in GVCs may foster better achievement of most SDGs when that country is located at the beginning of the chain (forward participation), while the opposite effect prevails for backward participation. Nonetheless, the effects of the relationship seem to be low, and exceptions are found in both Goal 5 (gender) and Goal 9 (innovation). On the one hand, as mentioned, the more forward participation of a country, the lower the percentage of women in managerial positions (see graphs in the Appendix). On the other hand, the R&D expenditures of countries as a share of GDP are seen to be positively correlated with their forward participation, while these relationships turn negative in the case of backward participation (Graph 15.4).
Finally, when considering the tertiary sector in general, the participation of countries in GVCs related to services displays similar behavior to that of GVCs related to telecommunications. In particular, forward participation shows a positive correlation with Goal 1, with a negative slope observed in the poverty indicator. Also, there is a positive association with the R&D indicator for Goal 9, as well as with the two indicators referring to Goal 10, reflected through a positive relationship with the share of labor in national income and negatively related to the unequal distribution of income or Gini values. All of these take a radical turn in the relationship with backward participation. Again, the exception relates to Goal 5, where neither forward nor backward participation seems to improve the presence of women in top management (again, all dispersion graphs can be found in the Appendix, organized into sections ordered by GVC type).
4.2. Regression Results
To complete the picture, a regression analysis has been performed to check whether the participation in GVCs may contribute to a country’s progress in terms of sustainable development and fostering the reduction of inequalities (or, by contrast, negatively affecting the commitment to SDGs).
The econometric model simply considers a set of six dependent variables selected from the United Nations’ set of SDG indicators. The regressors are the level of participation in four different GVCs, with the GDP per capita taken as a control variable.
To develop a rigorous econometric analysis, some of the best-known panel data tests have been performed: a Hausman specification test, to evaluate the presence of unobserved heterogeneity; a Breusch–Pagan LM test, to evaluate consistency (De Hoyos & Sarafidis, 2006); a Wooldridge test, to evaluate the presence of serial correlation (Drukker, 2003); and a Breusch–Pagan/Cook–Weisberg test, to evaluate the presence of heteroscedasticity.
Given the absence of correlation between the unique errors and the regressors, cross-sectional dependence, serial correlation, and heteroscedasticity, and cognizant that our objective is to perform a simple estimation to analyze the interaction between certain SDGs and the selected countries’ participation in GVCs, we employ ordinary least squares as the estimator. The proxy variables are used in natural logarithms, in order to estimate elasticities produced by the independent variables. The same model has been estimated for forward and backward participations and estimation results are given in Table 15.2 (forward participation) and Table 15.3 (backward participation). Standard errors are presented in brackets.
4.2.1. Forward Participation Effects
Although descriptive analysis found scarce effects as regards gender inequality, the results of the regressions show that forward participation in the primary sector (including agriculture, forestry, and fishing GVCs) has a positive effect for countries toward the fulfillment of Goal 5, since this favors the proportion of women as a share of the total number of persons employed in managerial positions (Table 15.2). However, participation in these types of GVCs shows no positive effect toward the reduction of inequalities in income distribution, as reflected in the significant positive coefficient of the Gini index. The lack of significance in the other variable (labor share) further prevents us from affirming that a general reduction of inequality (Goal 10) is favored. Regarding Goal 9, there is no clear pattern, given a positive effect on the proportion of the population covered by a 3G (or more advanced) mobile network, alongside a negative effect on the indicator of R&D expenditures as a proportion of GDP.
Forward participation in GVCs related to computers, electronics, and electrical equipment helps countries to achieve Goals 1 and 9 by being inversely related to the percentage of population below the international poverty line, and directly related to R&D expenditures as a proportion of GDP. However, this is inversely correlated with more balanced integration of women into managerial positions, and it negatively affects more equal income distribution. This pattern seems to be more related to economic growth drivers than to the reduction of inequality in societies.
Effects of Forward GVC Participation of Countries on SDGs.
| SDG 1 | SDG 5 | SDG 9 | SDG 10 | |||
|---|---|---|---|---|---|---|
| Poverty | Gender | R&D | Internet | Labor | Gini | |
| Agriculture | 0.745 | 1.127*** | −0.377** | 0.278*** | 0.001 | 0.306*** |
| (0.862) | (0.140) | (0.179) | (0.103) | (0.057) | (0.097) | |
| Computers | −0.583* | −0.231*** | 0.240*** | 0.054 | 0.014 | 0.067* |
| (0.334) | (0.061) | (0.080) | (0.049) | (0.025) | (0.039) | |
| Services | −2.679** | −0.717*** | 0.252 | 0.027 | 0.480*** | −0.542*** |
| (1.167) | (0.214) | (0.253) | (0.128) | (0.078) | (0.139) | |
| Telecommunications | 3.083** | −0.321 | 0.314 | −0.297** | −0.289*** | 0.150 |
| (1.179) | (0.197) | (0.246) | (0.112) | (0.071) | (0.138) | |
| GDP (percent) | −0.260 | 0.262*** | 0.701*** | 0.063** | 0.049*** | −0.012 |
| (0.212) | (0.036) | (0.050) | (0.025) | (0.015) | (0.025) | |
| Constant | 13.237*** | 4.437*** | −2.136*** | 3.877*** | 2.528*** | 4.954*** |
| (3.742) | (0.672) | (0.814) | (0.349) | (0.226) | (0.446) | |
| #Observations | 63 | 94 | 99 | 56 | 110 | 68 |
| R2 | 27% | 64% | 86% | 36% | 64% | 41% |
*p<0.1, **p<0.05, and ***p<0.01.
Regarding the forward participation of countries in GVCs for aggregated services, some contribution to the achievement of Goals 1 and 10 is observed. This is inversely related to the percentage of population below the international poverty line and to the Gini index, and the coefficient of the labor share of GDP is statistically significant and positive. Again, this participation is counterproductive in terms of achievement of Goal 5, on gender equality, with the estimation showing a negative sign in the indicator of women working as managers.
In telecommunications-related GVCs, forward participation does not provide an enhancing dynamic for countries in favor of innovation or reductions in poverty and inequality (Goals 1, 9, and 10). On the contrary, the regression results show that higher participation in this type of GVC is directly correlated with the percentage of population below the international poverty line, also displaying involution dynamics regarding Goal 1. Moreover, it is inversely related to internet network connectivity and to the labor share of GDP, generating harmful effects about the corresponding SDGs.
The control variable of GDP per capita turns out to be a clear predictor of success for all the SDGs studied. Overall, it fosters the indicators for gender (Goal 5), national R&D efforts (Goal 9), internet connections (Goal 9), and the share of labor in GDP (Goal 10). However, its effects on the percentage of population below the international poverty line as well as on the Gini index are not significant; this reaffirms the importance of noting the difference between growth and competitiveness and income distribution, or the tragic and persistent tension between growth and inequality.
4.2.2. Backward Participation Effects
Results given in Table 15.3 show that backward participation in agriculture, forestry, and fishing GVCs generates a positive impact on countries toward achieving Goals 9 and 10, this being directly related to investment efforts in technological knowledge as measured by R&D expenditures as a proportion of GDP, as well as in labor’s share in the GDP. On the other hand, it is also related to a higher percentage of population below the international poverty line and to a higher Gini index score.
Effects of Backward GVC Participation of Countries on SDGs.
| SDG 1 | SDG 5 | SDG 9 | SDG 10 | |||
|---|---|---|---|---|---|---|
| Poverty | Gender | R&D | Internet | Labor | Gini | |
| Agriculture | 4.118*** | −0.232 | 0.882*** | −0.057 | 0.113** | 0.189*** |
| (0.638) | (0.150) | (0.126) | (0.086) | (0.046) | (0.070) | |
| Computers | −0.879*** | −0.043 | −0.472*** | −0.020 | −0.089*** | −0.126*** |
| (0.327) | (0.126) | (0.089) | (0.058) | (0.033) | (0.041) | |
| Services | −3.163*** | −0.917*** | 0.112 | −0.156 | 0.225*** | −0.572*** |
| (0.594) | (0.210) | (0.153) | (0.102) | (0.056) | (0.078) | |
| Telecommunications | −0.050 | 1.298*** | −0.633*** | 0.273** | −0.085 | 0.243*** |
| (0.626) | (0.191) | (0.161) | (0.106) | (0.056) | (0.075) | |
| GDP (percent) | −1.929*** | 0.229*** | 0.454*** | 0.093** | 0.133*** | −0.263*** |
| (0.258) | (0.068) | (0.060) | (0.035) | (0.019) | (0.030) | |
| Constant | 5.566** | 2.441*** | −0.634 | 4.161*** | 3.278*** | 5.051*** |
| (2.203) | (0.561) | (0.480) | (0.289) | (0.168) | (0.219) | |
| #Observations | 63 | 94 | 99 | 56 | 110 | 68 |
| R2 | 59% | 36% | 90% | 26% | 67% | 63% |
* p<0.1, ** p<0.05, and *** p<0.01.
While backward participation in computers, electronics, and electrical equipment GVCs may help countries to achieve Goals 1 and 10, reducing both poverty and income distribution inequality, it is inversely correlated with R&D investment and with labor’s share in GDP. Moreover, there is no significant effect on gender (Goal 5) or on internet connections, so the pattern for this high-tech group of GVCs only partially supports Goal 10.
Backward participation in aggregated services GVCs is favorable for countries as regards Goals 1 and 10, being inversely related to the percentage of population below the international poverty line and to the Gini index, and directly correlated with the labor share of GDP. However, the negative significant coefficient of the gender indicator reveals that it does not contribute to increasing women’s presence in managerial positions, in compliance with Goal 5.
On the other hand, backward participation in telecommunications GVCs has a positive impact on the achievement of Goal 5. Nevertheless, it is directly correlated with the Gini index, displaying a harmful relationship as regards Goal 10. It is also directly related to the proportion of population covered by a 3G mobile network or better, but inversely related to R&D expenditures as a proportion of GDP, so that its effects on Goal 9 reveal no clear pattern that would indicate improvements in knowledge inequality.
The control variable of GDP per capita is again a good predictor of success for all of the SDGs under study, in that it lowers the percentage of population below the international poverty line (Goal 1) as well as the Gini index, and it favors labor’s share in GDP (Goal 10). It also fosters advances in gender balance (Goal 5), national R&D efforts, and internet coverage (Goal 9).
4.2.3. Discussion of Results
Our findings confirm the differentiation in SDG performance by countries according to the positioning of their GVC participation (in a range from forward to backward), and they highlight the relevance of the specific type of industry. A summary of this relationship is illustrated in Fig. 15.1.
The differing results of the regression analysis invite us to first discuss whether a specific pattern is linked to a specific industry, considering both forward and backward participations in GVCs. In particular, positive effects toward SDG fulfillment are observed more clearly in some industries than in others. For instance, the participation of services-related GVCs generates positive impacts on the reduction of poverty as well as income inequality (Goals 1 and 10), independent of the type of participation; this is observed by comparing the size of the effect or coefficient across the different chains. In the cases of poverty (Goal 1) and income inequality (Goal 10), services GVCs present the greater positive impacts. In high-tech GVCs, such as computers, there is a positive impact on the poverty goal, and this also positively affects R&D, whatever the country’s position along the chains.
The type of participation is also a relevant factor, as results differ with regard to the SDGs. Remarkably, Goal 5 is only favored by forward participation in agriculture-related GVCs and by backward participation in telecommunications GVCs. A mixed pattern is found in the case of Goal 9, as R&D is positively enhanced by forward participation in computer GVCs and by backward participation in agriculture GVCs. In the case of internet coverage (connectivity), a positive impact is generated in those countries that participate at the beginning of agriculture chains and in the final phases of telecommunications GVCs.
5. Concluding Remarks
This analysis of the effects of GVCs on the performance of countries toward fulfillment of the SDGs allows us to observe and explicitly argue that the combination of two distinct dimensions is an adequate framework that has implications for both academic research and policy makers. On the one hand, there are observable differences in the participation of countries along GVCs when sustainable development is adopted as a challenge, with the understanding that sustainability applies to a broader range of issues than environmental concerns, including the aspiration toward more equal societies. On the other hand, the type of GVC under study does matter, and the integration of developing countries into certain chains becomes more likely when considering the potential effects under the Agenda 2030 framework, as some SDGs will be more affected than others.
Our contribution highlights that the intersection between the relative position within a GVC and the type of industry concerned may imply distinct impacts on meeting the SDGs. In particular, our findings confirm that poverty reduction is overall more favored in those countries specialized in phases occurring at the beginning of GVCs related to computers and services. When this goal is considered together with inequality in income distribution, those countries situated at the end of the chain are more able to achieve improvements, except as regards services-related GVCs. Progress toward innovation goals can be enhanced at the early stages in both low- and high-tech GVCs, while gender equality stands out as the goal less clearly favored by the international fragmentation of production; the only positive impacts pertaining to this goal are found at the beginning of chains related to the primary sector and at the end of telecommunications-related chains.
Further research should undertake a deeper analysis of the relationship discussed in this chapter by broadening the sample of countries and the set of industries, also inviting a more qualitative form of analysis. A suitable line of research would include closer examination of the interactions between MNEs and SMEs and analysis of potential feedback relationships, as well as cross-border spillover effects along these GVCs that could enhance positive externalities (gender equality, knowledge, and welfare) or reduce negative ones (environmental damages), thereby contributing to fulfillment of the SDGs.
Appendix
A1. Agriculture
A2. Computers
A3. Telecommunications
A4. Services
Note
The 2018 version of TiVA offers information for a set of 64 countries (including all OECD member countries, the EU-28, the G20 countries, most East and South-East Asian countries, and a selection of South American countries), with a total of 34 branches of activity for the period 2005–2015.
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Acknowledgments
This research has received funding from the European Union’s Horizon 2020 Marie Curie Research and Innovation Staff Exchange under grant agreement number 778398.
This research was supported by the Spanish Ministry of Science and Innovation under grant number PID2020-116913RA-I00.
- Prelims
- Part I: General Challenges for IB Scholarship
- Chapter 1: Introduction: International Business Scholarship and the Sustainable Development Goals (SDGs): Seizing Opportunities, While Tackling Challenges
- Chapter 2: International Business and the SDGs: Current Issues and Future Directions
- Chapter 3: Measuring and Managing the Impact of Business on the SDGs
- Part II: Strategic Challenges for MNEs
- Chapter 4: Walking the Talk: Making the SDGs Core Business – An Integrated Framework
- Chapter 5: Catalyzing Progress Toward the UNs' SDGs: Building Systemic Partnerships Across Organizations Using the I-RES Methodology
- Chapter 6: Addressing the Complexities in Implementing SDGs in International Business
- Chapter 7: SDGs and Strategic Priorities of MNEs for Sustainability Transformation: Lessons from IKEA
- Part III: The Nexus Challenge
- Chapter 8: Balancing Purpose and Profit in Foreign Direct Investment: How Development Finance Institutions Promote the SDGs While Being Profitable
- Chapter 9: The Nexus Between Cultural and Creative Sectors and the Sustainable Development Goals: A Network Perspective
- Chapter 10: Trade-offs in FDI Effects on SDGs in Sub-Saharan Africa Countries
- Part IV: Contextualizing the SDGs
- Chapter 11: Tax Impact of Multinationals in Central and Eastern Europe on Sustainable Development Goals
- Chapter 12: Climate Change Disclosures of Companies in Selected Developed and Emerging Countries with Impression Management Perspective
- Chapter 13: Multinational Corporations in Sustainable Cities: The Case of a Sustainable Headquarters Building
- Chapter 14: Ports and the Sustainable Development Goals: An Ecosystems Approach
- Chapter 15: Possibilities for Upgrading High-tech GVCs Toward Stronger SDG Performance
- Chapter 16: Tensions on the Road Toward Just Transitions in the Latin American Coffee Value Chain
- Part V: SDG-washing Challenges
- Chapter 17: Corporate Misbehavior in the Banking Industry: What Role Does the State Play?
- Chapter 18: Saving the Planet is Not for Everybody: A Model of CEOs' Reactions to Human Rights Defenders
- Index