4. The cross-border impact of SDG-related activities

In the last two decades, the structure of the world economy has changed significantly with the emergence of more complex trade relations and global supply chains. Technological change, reductions in costs, greater access to resources and digitalisation have enabled new collaborative business models. Today, about 70% of the global production of goods and services ripple across long and complex international supply chain networks (OECD, 2020[1]). Conventional trade statistics, therefore, cannot fully capture the interconnectedness of the global economy. The growing international fragmentation of production networks and the complexity of national economic systems require new measures to better understand the economic relations between countries and regions.

The contribution of firms to the SDGs is no exception. Firms’ global impact on social goods, such as the environment, gender equality or energy transition has to be assessed at the global level, taking into account the network of suppliers and customers. Raising awareness – among firms, policy makers and other stakeholders alike – about firms’ global impact is crucial for achieving and accelerating the achievement of the SDGs.

This chapter builds on OECD’s ICIO tables (section “ICIO tables at a glance”), which underpin the development of Trade in Value Added (TiVA) indicators as well as other metrics related to global value chains (GVCs). Previous analyses have shown that accounting for GVCs is particularly important to fully understand the impact (or global footprint) of nations, and sectors within them that are associated with exporting and importing activities. For example, most of the child labour (93%) embodied in food products imported to Europe is associated with sectors whose goods and services are not directly exported, but are rather upstream industries, notably Agriculture (ILO, OECD, IOM and UNICEF, 2019[2]). Other analyses of this type include Trade in eMployment (TiM) (Horvát, Webb and Yamano, 2020[3]) and Trade in embodied CO2 (Wiebe and Yamano, 2016[4]).

The objective of this chapter is to understand how the domestic economy contributes to the SDGs in partner countries, and how other countries contribute to the SDGs in the domestic economy. For instance, essential goods and services may be exported to a partner country where they are used in the production of SDG-related goods and services, representing forward GVC participation. Alternatively, a country’s final demand for SDG-related goods and services may rely on imports, representing backward GVC participation.

This analysis can be used by companies to assess the SDG contribution, risks and opportunities associated with their involvement in GVCs. It can also be used by investors, to identify where value is added in the complex production of goods and services, and where they can invest for more sustainable outcomes, and by customers to explore how their purchases contribute to sustainable development. Lastly, it can be used by local, national and global governments to gather reliable evidence for the development of policies to achieve the SDGs.

This chapter focuses on four SDG indicators, three of which come directly from the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs): indicator 9.2.1 (manufacturing value added); indicator 9.B.1 (medium and high-tech industry value added); and indicator 9.5.1 (R&D expenditures). The fourth indicator is related to Target 2.4 (agriculture and food processing industry value added).

Three out of four indicators concern SDG 9 since it is one of the SDGs most related to firms’ core business activities (Figure 3.8). The four indicators were also chosen because of their presumed ability to capture strong cross-border impacts. Manufacturing, medium- and high-tech, and agricultural value added are all heavily traded, and therefore indicators around these topics are important in an era of global interconnectedness. Availability of data on the selected indicators relative to others as well as the transversality of certain indicators such as the one on R&D1 also influenced the choice of indicators.

The chapter showcases how combining data on OECD-ICIOs and the IAEG-SDGs can be used to draw insights on cross-border impacts of SDG-related business activities. Further work could be undertaken to expand this approach to other indicators. A few examples for which the data would probably allow such an extension are listed in Table 4.1.

The OECD ICIO tables are matrices that describe the annual monetary flows of intermediate and final goods and services worldwide. Figure 4.1 illustrates a simplified example of an ICIO table with three countries and two industries. The diagonal blocks of the ICIO table represent the domestic flows of intermediate and final goods and services values, while the off-diagonal blocks refer to the exports and imports between countries and industries. Hence, the ICIO can provide information about the interconnection between sectors within and across countries.

This chapter seeks to analyse the role of GVCs for SDG-related activities. To this end, OECD ICIO tables are used extensively to estimate the cross-border impacts of domestic production and the role of international trade for the SDGs. Tracing the origins of a final product, or even its components, requires capturing statistics not only in the market where the product is consumed or produced, but also along its supply chain. This task is beyond the scope of traditional survey and national accounting methods.

For example, ICIO tables (OECD, 2018[5]) shed light on the mix of inputs required from home and abroad, to generate one unit of output in a given industry and country. In, turn, it allows tracing the origin of the value added (direct and indirect) in a production process. Direct contribution captures the value added of a given industry in a specific country related to the production of goods or services for exports or final demand. Indirect contribution represents the value added of other upstream industries whose output enters into the production of the aforementioned goods or services for exports or final demand.

Target 9.2 of the SDGs is for countries to:

[p]romote inclusive and sustainable industrialisation and, by 2030, significantly raise industry’s share of employment and gross domestic product, in line with national circumstances, and double its share in least developed countries.

Related to this target, the United Nations proposes two indicators that highlight the role of the manufacturing industry in the economy, namely the share of manufacturing value added to gross domestic product (GDP) per capita; and the share of employment in the manufacturing industry in total employment. Using the former indicator, this section seeks to shed light on the role of manufacturing in the achievement of the SDGs in the context of an increasingly interlinked global economy.

The manufacturing sector contributed 15% to total world GDP in 2019 (World Bank, 2021[6]) and is often cited as a key driver of economic growth and well-being for both developing and developed countries. Additionally, manufacturing is an essential source of employment that has accounted for approximately half a billion jobs worldwide in 2019, or an equivalent of about 14% of world employment (ILO, 2020[7]).

Manufacturing value added and employment are considered as having positive spillovers on society. The secular decline in output and productivity growth and the accompanying increase in productivity dispersion and wage inequality (Berlingieri, Blanchenay and Criscuolo, 2017[8]) put a special emphasis on the inclusiveness of economic development, and on the fate of some categories of the population, like low-skilled low-wage workers, people living in disadvantaged areas, or the middle class (OECD, 2019[9]). In some instances, the manufacturing sector can provide economic opportunities to these groups. Rodrik and Sabel (2019[10]), among others, argue that access to “good jobs”, notably provided by the manufacturing sector, can reduce social costs and negative externalities (Autor, Dorn and Hanson, 2019[11]), and limit political polarisation (Autor et al., 2020[12]). Beyond direct jobs and value added, the manufacturing sector is often considered to trigger significant opportunities in upstream industries, including services sectors. In developing countries, industrialisation can also be an opportunity to stimulate the creation of formal employment and its related benefits (OECD, 2015[13]).

However, the impact of manufacturing activities on other SDGs (notably environment-related SDGs 7, 12, 13, 14 and 15) needs to be closely monitored and policies must accompany the transition to cleaner industrial production.

Manufacturing has largely evolved with increased globalisation, resulting in the splitting up of production processes across the GVCs. Notably, in 2015, more than 50% of trade in manufacturing involved intermediate products designed for additional processing in other countries (UNIDO, 2015[14]), providing cause for an in-depth analysis of the cross-border impact of manufacturing in achieving SDGs.

The 2005-15 period saw a relocation of manufacturing value added from developed to developing countries. Figure 4.2 illustrates that most countries faced a contraction of domestic manufacturing value added as a share of global manufacturing value added in the last two decades; the exception is the People’s Republic of China (hereafter “China”), which experienced a significant expansion.

As stated by the IAEG-SDGs, the share of manufacturing value added to GDP is an important indicator for measuring progress for inclusive industrialisation. However, this indicator does not give information on the interconnectedness through GVCs and the role of upstream industries.

Understanding this interconnectedness provides granular insight into the linkages across industries and countries. It also helps build more resilience within supply chains and therefore contributes to the attainment of SDG 9. For example, Arriola et al. (2020[16]) find that in many countries, imports in general tend to have complex supply chains and be more diversified than exports, which in turn are highly concentrated in a few supply chains. Hence, a disruption in the manufacturing industry in one region could possibly ripple through to other regions across the world, through the global manufacturing value chain.

Figure 4.3 below shows that, although the largest contribution to exports in the manufacturing industry comes from domestic value added of goods within the industry, a significant share of foreign inputs is also embodied within manufacturing exports. Japan relies the most on its domestic value added of goods for its manufacturing exports (61%), followed by the United States (60%) and Israel (59%).

Figure 4.3 also shows that manufacturing exports embody a significant share of services value added. Australia, New Zealand and Norway rely the most on domestic value added of services. Ireland, Luxembourg and the Netherlands on the other hand have the highest foreign value added of services as a share of manufacturing exports. Similarly, Greece, Hungary and Slovakia rely the most on foreign value added of goods.

Manufacturing value added embodied in exports does not originate only from exporting sectors, but also from upstream manufacturing industries. Figure 4.4 shows the domestic manufacturing value added embodied in exports, as a percentage of GDP, distinguishing between the direct and indirect contributions. The results indicate that, among the top ten countries in terms of domestic manufacturing value added, between 38% and 66% of manufacturing domestic value added was through indirect contributions. In the case of China, for instance, approximately two thirds of domestic manufacturing value added embodied in exports originated from indirect contributions, in the upstream industries.

Figure 4.3 and Figure 4.4 show that indirect contributions are almost as important as direct contributions for manufacturing exports. Knowing this and adapting policies accordingly can play a crucial role in helping countries build a competitive and resilient manufacturing sector, thereby fostering industrial development for the SDGs.

Figure 4.5 shows that, on average across all OECD countries, foreign value added corresponds to approximately 58% of the manufacturing value added embodied in final demand in 2015. However, this share is not homogenous across countries, which can be explained, for instance, by the diverse resource endowments of countries, differences in socio-economic settings, and varying levels of industrialisation. Highly industrialised countries including Japan, Korea and the United States tend to rely more on their domestic value added to fulfil the domestic demand, while small and less industrialised countries including Slovakia, Estonia and Luxembourg are heavily reliant on foreign value added to satisfy their domestic demand.

As foreign manufacturing value added plays a significant role in serving final demand for some countries, it is important to understand the composition of this foreign component by origin, uncovering the regional and global dynamics in GVCs.

Figure 4.6 shows, for each OECD country, the origin of foreign manufacturing value added by partner region. For example, approximately 50% of the foreign manufacturing value added embodied in the US final demand originates from the East and Southeast Asia, highlighting a global dimension to trade. Similarly, East and Southeast Asia contributes to more than 50% of foreign manufacturing value added in Japan’s, Korea’s and even Australia’s final demand. Countries such as Latvia, Lithuania, Portugal and Hungary are highly dependent on imports from Europe – with a foreign manufacturing value added in final demand of more than 75% from that region. This corroborates the point that trade also has an important regional dimension.

Taking the opposite perspective, Figure 4.7 illustrates the destination of domestic manufacturing value added embodied in exports by partner region. For instance, approximately 70% to 80% of Canadian and Mexican domestic value added of manufacturing industries is absorbed by the North American final demand (covering Canada, Mexico and the United States). However, in the case of the United States, the North American region represents only 20% of its domestic manufacturing value added embodied in exports.

Target 9.B reads as follows:

Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities.

The proposed indicator for this target by IAEG-SDGs is the “[p]roportion of medium and high-tech industry2 value added in total value added”.

The spillovers from medium- and high-tech industries (MHTIs) are usually higher than those of less technologically intensive industries. This is because MHTIs usually provide more opportunities for the upskilling of labour and for innovation in production processes (UNIDO, 2019[17]). In this way, MHTIs contribute to increased competitiveness and productivity, within and across industries both at domestic and global levels. This is especially important for developing countries which will require an upscaling of their MHTIs to achieve industrial development in a sustainable and inclusive manner (UNIDO, 2017[18]), but also remains valid for advanced economies. Moreover, beyond production, consumption of MHTI goods is also likely to result in spillovers. In this respect, exports of MHTI value added from advanced economies to developing economies contribute to SDG 9-Industry, Innovation and Infrastructure.

In the context of this indicator, it is also important to take a GVC perspective, given how processes in MHTIs have come to be spread all around the globe. In other words, the length of the value chain, as characterised by the average number of processes across the value chain that a good has to undergo (Muradov, 2016[19]), tends to be longer for MHTIs. A classic example to illustrate this is Apple Inc. Although its headquarters are well established in Silicon Valley, Apple’s processes are disaggregated and outsourced across the value chain; with R&D activities held upstream in the value chain and marketing activities and assembly performed downstream in the value chain (Mudambi, 2008[20]).

In absolute terms, Germany, Japan and the United States are the three main contributors to exports of MHTI value added.

In relative terms, Figure 4.9 shows that the value added from MHTIs embodied in exports ranges from 32% of total value added in Ireland to 1% in Australia. For some countries, domestic MHTI value added embodied in exports is higher than MHTI value added a share of total value added (indicator suggested by IAEG), suggesting that a significant proportion of MHTI value added is embodied in exports of downstream goods, whose production process uses MHTIs as an input.

Mexico and the European countries listed in Figure 4.9 mainly export to OECD countries, whereas Japan’s exports of MHTI value added are evenly split between OECD countries and other partner economies. Korea on the other hand predominantly exports to other partner economies.

There is a significant degree of heterogeneity regarding countries’ reliance on foreign MHTI value added. Figure 4.10 illustrates the domestic and foreign value added of MHTIs, as a percentage of domestic final demand. The United States and Japan are the two countries that rely the least on foreign content, with their domestic value added exceeding 80%. They are followed by Australia, Israel and New Zealand. In contrast, Luxembourg and Slovakia are the most reliant on foreign MHTI value added for their final demand.

Target 9.5 reads as follows:

Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending.

The IAEG-SDGs indicators relevant to this target are: “Research and development expenditure as a proportion of GDP” and “Researchers (in full-time equivalent) per million inhabitants”. OECD ICIO tables, TiVA indicators, OECD’s Gross Domestic spending on R&D, and number of researchers have been used here to obtain estimates on the amount of domestic R&D in exports and final demand.

Research plays an important role in the achievement of SDGs (Fayomi, Okokpujie and Udo, 2018[21]), since it fosters innovation and the upgrading of processes across GVCs (OECD, 2017[22]). Indeed, R&D is explicitly mentioned in the targets of several SDGs (2, 3, 7, 9, 14).

R&D contributes to improving sustainability of firms, as discussed in Chapters 2 and 5 of this analysis. It is generally commonplace to assume a strong positive relationship between spending on R&D and productivity growth. However, other than through productivity, R&D also allows firms to not only improve their production process, but also their product design. This sort of innovation adds to firms’ knowledge-based capital and helps them remain competitive domestically and along the GVC (Marcolin and Squicciarini, 2017[23]).

Although the indicator used in this chapter captures cross-country linkages and trade in R&D, it is not without limitations. While the R&D embodied in traded products captures important linkages, this measure does not take into account the spillovers of such trade, nor the R&D spillovers occurring through other channels (e.g. publications, conferences, transfer of personnel) (Hall, Mairesse and Mohnen, 2010[24]; Bloom, Schankerman and Van Reenen, 2013[25]).

The OECD indicator on Gross Domestic spending on R&D is defined as:

the total expenditure (current and capital) on R&D carried out by all resident companies, research institutes, universities and government laboratories, among many options in a country. It includes R&D funded from abroad, but excludes domestic funds for R&D performed outside the domestic economy. This indicator is measured in USD constant prices using 2010 base year and Purchasing Power Parities (PPPs) and as a percentage of GDP.

This indicator reveals a significant disparity in spending across OECD countries. In 2018, the lowest share of R&D spending as a percentage of GDP was for Colombia (approximately less than 0.3%), while Israel (approximately 5%) was the most R&D intensive. The shares of top ten countries in terms of spending in 2018 reveals that, along with Israel, Korea and Sweden form the top three countries with the highest gross domestic spending on R&D as a share of GDP (Figure 4.11).

The number of researchers per 1 000 people employed represents the number of “professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems, as well as in the management of the projects concerned”. The trends are significantly correlated with the R&D spending, which in turn reflect that an increase in R&D spending would lead to an increase in the number of researchers. As illustrated in Figure 4.12, Denmark has the highest number of researchers per 1 000 people employed, followed by Korea and Sweden.

Figure 4.13 investigates the reliance of countries on domestic and foreign R&D value added3 for their domestic final demand. Chile, Colombia, Latvia, Spain and the United States are the top five countries with highest domestic share of value added within domestic final demand, with over 90% domestic contribution therein. Luxembourg on the other hand tends to rely relatively more on imports of R&D – although the share of domestic value added is still around 75%. This share remains high compared to the corresponding results found for the previous indicators (manufacturing and MHTI value added).

The United States alone contributes to more than 40% of the worldwide R&D value added embodied in foreign final demand, with main destination regions being Europe and East and Southeast Asia.

Moreover, R&D is still traded regionally. For example, Europe seems to be one of the main destinations for R&D exports across many European countries, including, for example Switzerland and France, among others. In contrast, countries like Japan and Korea have a more diversified array of partner regions. For instance, among all OECD countries, Japan exported one third of its R&D to OECD partner economies. However, it is still noticeable that all of the OECD countries featured in Figure 4.14 seem to export mostly to other countries within the OECD area itself, implying some sort of collaboration and reliance between countries within the OECD.

Figure 4.15 illustrates the domestic R&D value added embodied in foreign final demand, by region of consumption. Between 2005 and 2015, most countries expanded their exports to regions that were not necessarily their main destination region in 2005. For example, many countries, including Germany, Sweden, the United Kingdom and the United States, expanded their exports of R&D to South and Central America between 2005 and 2015. Exports of R&D to North America also saw a rise, with Israel, Japan and Korea increasing their share of R&D embodied in exports to that region in 2015, relative to 2005. Interestingly, France stands out, with an increase in its share of R&D embodied in exports to Europe between 2005 and 2015.

Target 2.4 of the SDGs reads as follows:

By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality.

The official IAEG-SDGs suggested indicator is the “Proportion of agricultural area under productive and sustainable agriculture”. While this indicator sheds light on domestic food production systems, agricultural practices and their impact on the environment, it also has some shortcomings, because it does not capture the impact of domestic and cross-border trade on agricultural production. In the context of GVCs, it may thus be useful to analyse the global footprint of the agricultural industry,4 due to the role it plays in the achievement of SDGs.

Over and above its role in achieving food security, and as a key source of employment across GVCs (Greenville, Kawasaki and Jouanjean, 2019[28]), the agricultural industry also has spillover effects on SDG 3-Good health and Well-being, as well as important implications for SDG 13-Climate Action, SDG 14-Life Below Water and SDG 15-Life on Land, respectively for the impact it has on carbon emissions, land quality, deforestation and biodiversity.

The expansion of this industry, and the increased need for agricultural trade in GVCs, can be attributed to an increasing world population, rising incomes and growth of developing economies. For example, the agricultural industry is a key driver of economic activity and trade in the Association of Southeast Asian Nations (ASEAN) region, with agro-food exports from the region having quadrupled from USD 31 billion in 2000 to USD 129 billion in 2015 (Greenville and Kawasaki, 2018[29]). Likewise, in West Africa, agriculture contributed more than 20% to regional GDP in 2017 (African Development Bank Group, 2018[30]) and employed 66% of the regional labour force in 2018 (Allen, Heinrigs and Heo, 2018[31]).

Trade is key for resilience in this industry and for food security. While there is a case for self-sufficiency to achieve food security in the face of price volatility (Clapp, 2017[32]), this has been debated (Margulis, 2017[33]; HLPE, 2011[34]), and trade in the agricultural industry remains crucial because not all countries are equipped with the resource endowments to engage in sufficient and diversified agricultural production.

Given the unequal geographical distribution of natural resources and arable land across the world, some countries are better equipped than others to engage in primary agricultural production. This leads to heterogeneity in domestic production of agricultural output and reliance of countries on trade of agricultural products. To explore this point further, this section uses the ICIO tables, which allow for cross-country comparison and for estimation of import intensities for agricultural and food products.

Figure 4.16 indeed shows that dependency on foreign agricultural industry is largely heterogeneous across countries.5 Brazil, Cambodia, China, India, Indonesia and the Philippines all contribute domestically to more than 90% of their final demand for agricultural products. Belgium, Denmark and Estonia on the other hand are the countries that import the most agricultural industry value added to satisfy their domestic final demand. Although arable land and agricultural capacity plays a role, the countries that are the most reliant on foreign agricultural industry value added to meet their domestic final demand are OECD countries, compared to partner economies, including developing countries, which tend to rely more on their domestic production.

To illustrate the trend in trade of agricultural products, Figure 4.17 shows the growth in foreign agricultural industry value added embodied in final domestic demand for selected OECD countries.6 Overall, all the countries listed have seen a rise in their foreign value added, relative to the baseline. Countries saw this figure fall in 2009, as a result of the agricultural market crisis, when prices saw a significant upsurge (Gurría, 2011[35]). However, to dampen the effect of the food price crisis, more than 80 governments around the world implemented measures to counter the negative impact of the crisis on food security, income and employment (Maetz et al., 2011[36]). This led to the rise in trade and hence imports of agricultural products again, post crisis.

By 2015, foreign agricultural industry value added was above its pre-crisis level in some countries, such as Canada, Korea, New Zealand and the United States. In the case of the United States, this sustained increased in imports has been attributed to a strong dollar, and an economy which at the time experienced a steady recovery after the 2008 financial crisis (Gardiner, 2016[37]). In Korea, this has been due to declining shares of cultivated land over the years, coupled with increased consumption of agricultural products such as livestock, for which the country is less autonomous in production of (Cho, 2018[38]).

Following the analysis from Figure 4.16 and Figure 4.17, it is evident that domestic production is not always be sufficient to guarantee sustainable food production systems and attain food security. Food imports may thus be required for meeting domestic demand and diversifying supply.

Figure 4.18 shows that in absolute terms, the United States had the highest domestic value added in exports of agricultural output in 2015, followed by Brazil and China. Across these three countries, the main destination region for the agricultural output was East and Southeast Asia. Furthermore, a large share of exports from the United States and Canada are targeted to North America, suggesting a regional dimension to trade. Similarly, as can be seen in Figure 4.18, France, Germany, Ireland, Italy, the Netherlands and Spain, export most of their domestic value added of agricultural output to Europe. In contrast, India and Indonesia export most of their domestically produced agricultural output to “Other Regions”.

Moreover, the largest importer region in 2015 was Europe (approximately 32% of total agricultural industry value added embodied in foreign final demand), followed by East and Southeast Asia (26%).

In addition to the analysis provided above, it may be useful to look into the different components of agricultural industry, distinguishing between agriculture and food processing. The specialisation between these two components differs across countries depending on the natural and technological resource endowments of countries. As seen in Figure 4.19, Belgium and Germany contribute mainly to food processing. In contrast, Cambodia, Latvia, Romania and Russia, which have more arable land, contribute more to agricultural products in their exports of agricultural industry value added. This highlights that even among OECD countries, some countries specialise more in agricultural products while others undertake more food processing.


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← 1. In the UN framework, R&D, research or innovation are often cited as a lever to attain the SDGs. They are explicitly mentioned in the targets of SDGs 2, 3, 7, 9 and 14.

← 2. Medium and high-tech industries (MHTIs) include both manufacturing and services sectors. It consists in the following economic activities: manufacture of chemicals and chemical products; manufacture of basic pharmaceutical products and pharmaceutical preparations; manufacture of computer, electronic and optical products; manufacture of electrical equipment; manufacture of machinery and equipment n.e.c.; manufacture of motor vehicles, trailers and semi-trailers; manufacture of other transport equipment; publishing; computer and related activities; and Scientific Research and Development. All are according to the International Standard Industrial Classification of All Economic Activities Revision 4 (ISIC Rev.4). This classification has been used widely in the OECD, e.g. in its STructural ANalysis Database (STAN).

← 3. The rest of this section uses national accounts to measure R&D value added. Ideally, privately performed R&D would have been used to measure the private sector’s contribution to the SDGs through R&D activities, but this indicator is unfortunately not available for a large number of countries.

← 4. Agricultural industry refers to the agriculture and food-processing sectors.

← 5. Some partner economies are represented on Figure 4.16 and Figure 4.18. They were chosen based on their size, their production and consumption of products from the agricultural industry.

← 6. These are the countries included in Chapters 3 and 5.

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