metadata: authors: 0: author: Chepeliev,Maksym
1: author: Maliszewska,Maryla
2: author: Osorio-Rodarte,Israel
3: author: Seara E Pereira,Maria Filipa
4: author: Van Der Mensbrugghe,Dominique
count: World
docna: 0: docna: Pandemic, Climate Mitigation, and Reshoring
            : Impacts of a Changing Global Economy on Trade, Incomes,
            and Poverty
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Transport Services,Inequality,Climate Change Mitigation and Green House Gases
teratopic: International Economics and Trade,Transport,Poverty Reduction,Environment
abstracts: cdata!: The resilience of global value chains
            has been put to the test by the COVID-19 pandemic, extreme
            weather events, and trade tensions spurred by growing
            economic nationalism and protectionism. Shocks in production
            and trade can be transmitted from one country to another by
            global value chains, although they can also help to lessen
            the blow of a domestic shock, such as a lockdown, and drive
            economic recovery. What shocks to global value chains should
            be anticipated in the coming years? Is it possible to design
            policies that can enhance resilience to trade shocks in
            developing countries without endangering growth? This paper
            explores simulations from the ENVISAGE global computable
            general equilibrium model to enhance understanding of the
            potential longer-term impacts of COVID-19 and the policy
            responses it engenders in developing countries. The paper
            assesses the likely impacts of measures designed to reshore
            production and reduce reliance on imports. It also evaluates
            other key factors shaping the global economy, including
            stylized scenarios to capture the essential elements of
            policies to achieve carbon emission reductions that will
            have an impact on trade.
repnme: repnme: Pandemic, Climate Mitigation, and Reshoring
            : Impacts of a Changing Global Economy on Trade, Incomes,
            and Poverty
display_title: Pandemic, Climate Mitigation, and
            Reshoring : Impacts of a Changing Global Economy on Trade,
            Incomes, and Poverty
listing_relative_url: /research/2022/03/33749939/pandemic-climate-mitigation-reshoring-impacts-changing-global-economy-trade-incomes-poverty
topicv3: Transport m1326661 135,Poverty m1326687 695,Trade m1326924 698,Climate Change Mitigation m1327079 579,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-03-03T00:00:00Z
dois: 10.1596/1813-9450-9955
historic_topic: International Economics and Trade,Transport,Poverty Reduction,Environment
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 55
author_keys: 0: author_key: NA
1: author_key: 545393
2: author_key: 903799
3: author_key: 1444235
4: author_key: 428604
model_extractions: Policy Research Working Paper 9955 Pandemic, Climate Mitigation, and Reshoring Impacts of a Changing Global Economy on Trade, Incomes, and Poverty _Maksym Chepeliev Maryla Maliszewska Israel Osorio-Rodarte Maria Filipa Seara e Pereira Dominique van der Mensbrugghe_ Macroeconomics, Trade and Investment Global Practice March 2022
Policy Research Working Paper 9955

# **Abstract**

The resilience of global value chains has been put to the test by the COVID-19 pandemic, extreme weather events, and trade tensions spurred by growing economic nationalism and protectionism. Shocks in production and trade can be transmitted from one country to another by global value chains, although they can also help to lessen the blow of a domestic shock, such as a lockdown, and drive economic recovery. What shocks to global value chains should be anticipated in the coming years? Is it possible to design policies that can enhance resilience to trade shocks in developing countries without endangering growth? This paper explores simulations from the ENVISAGE global computable general equilibrium model to enhance understanding of the potential longer-term impacts of COVID-19 and the policy responses it engenders in developing countries. The paper assesses the likely impacts of measures designed to reshore production and reduce reliance on imports. It also evaluates other key factors shaping the global economy, including stylized scenarios to capture the essential elements of policies to achieve carbon emission reductions that will have an impact on trade.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mchepeli@purdue.edu and mmaliszewska@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Pandemic, Climate Mitigation, and Reshoring: Impacts of a Changing Global Economy on Trade, Incomes, and Poverty

Maksym Chepeliev, Maryla Maliszewska, Israel Osorio-Rodarte, Maria Filipa Seara e Pereira, and Dominique van der Mensbrugghe<sup>1,2</sup>

**Keywords** : economic growth of open economies; measurement and analysis of poverty; computable general equilibrium modeling; distributional impacts of trade; trade agreements; trade and environment.

**JEL Classification** : F16, F17, F18, I32, F63, O53, C68.

1 Maksym Chepeliev and Dominique van der Mensbrugghe are with the Purdue University. Maryla Maliszewska, Israel Osorio-Rodarte, Maria Filipa Seara e Pereira are with the World Bank. We are grateful to Paul Brenton, Michael Ferrantino and Antonio Nucifora for their guidance and to the participants of the 23rd Annual Conference on Global Economic Analysis (Virtual Meeting) for their useful comments.

2 This paper is a product of the staff of the Trade and Regional Integration Unit at the International Bank for Reconstruction and Development/the World Bank. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at <u>mchepeli@purdue.edu</u> and mmaliszewska@worldbank.org.

The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. This material should not be reproduced or distributed without the World Bank's prior consent.
# 1. Introduction: Shift from globalized to localized world would slow recovery

The resilience of global values chains (GVCs) has been put to the test by the COVID-19 pandemic, extreme weather events, and trade tensions spurred by growing economic nationalism and protectionism. The sudden drop in global trade in the first half of 2020 was massive by historical standards (WTO 2020). Econometric evidence shows that shocks in production and trade can be transmitted from one country to another by GVCs, though participation in GVCs may help to lessen the blow of a domestic shock such as a lockdown (Espitia et al. 2021). But GVCs can also drive recovery, spreading the benefits, for example, as countries come out of lockdowns at different times. In light of this experience, is it possible to design policies that can enhance resilience to trade shocks in developing countries without endangering growth?

The past three decades of globalization have lifted international trade to new heights while helping to drive extreme poverty to new lows (World Bank 2020a). GVCs have lengthened and strengthened to provide countries and consumers worldwide with vital goods and services, from vaccines to vacuum cleaners. The evidence from the scenarios in this chapter on the economic and social benefits of GVCs is emphatic: globalization will strengthen a recovery while localization will weaken it. Moreover, localization could drive more people into extreme poverty while globalization could lift millions above the poverty line by 2030. In short: developing and developed countries are better off in a globalized world.

In the public debate, the recent disruptions of the supply chains have cast doubt on whether the gains of GVCs outweigh the associated risks of transmission of shocks. Consequently, a new array of literature continues to appear with the goal of gaining a better understanding of the benefits of globalization and resilience of GVCs. Furthermore, one of these most recent disruptions, the Covid-19 pandemic, offers a rare opportunity to understand the role that GVCs play in making countries more resilient. Bonadio et al. (2020) and Espitia et al. (2021) both illustrate this role in their work. They show that in a pandemic-style labor supply shock, if countries try to artificially weaken the entry of foreign inputs by means of a renationalization, this will only result in a higher level of exposure to domestic shocks, more fragility and would not mitigate contractions in GDP.

A re-localization of supply chains as a possible solution to fight uncertainty seems also destined to fail. Not only would this increase costs for firms and consumers, but it also hardly protects countries from external shocks. The OECD (2020b), using its METRO computable general equilibrium (CGE) model, finds that in a localized system, with reduced GVCs, economies would have significantly lower levels of economic activity and lower incomes, making countries more – not less – vulnerable to shocks. When looking into a more specific example of the impact of a China lockdown on the US economy, Eppinger et al. (2021), using a multi-country, multi-sector trade model, reinforce the conclusion that repatriating supply chains does little to mitigate the welfare losses caused by a China shock, and the policy itself carries substantial welfare costs.

GVCs, on the other hand, not only play a role in offering to countries some protection against external and domestic shocks, but also, they have been shown to be resilient and thus offer some security to businesses and consumers. GVCs display resilience when confronted with shocks, such as natural disasters. The largest earthquake ever recorded, which occurred in Japan in 2011, had no long-term consequences on the reconfiguration of the GVCs and did not lead to reshoring nor import diversification (Freund et al. 2021).

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Few studies exist that analyze the impact of climate change mitigation measures, and more specifically the Carbon Border Adjustment Mechanism (CBAM), on GVCs. It has been shown that the implementation of carbon pricing measures consistent with reaching the Paris Agreement targets can have negative impacts on economic growth, a higher incidence of poverty, but result in progressive income distribution (Chepeliev et al., 2021). But little has been written on mitigation policies’ impacts on trade and GVCs. Our paper tries to fill this gap and lead the way in a better understanding of the impact of CBAM on GVCs, and how supply chains can play a role in mitigating the adverse effects of climate policies.

Policies that are “friendly” rather than “hostile” to GVCs could prove critical to the strength of the economic recovery from the pandemic. Developing countries stand to gain the most from strengthening GVCs. Three scenarios examining the potential impact of “GVC friendly” and “GVC hostile” policies in 2020-2030 show that shortening supply chains through reshoring or localization would short-change developing and developed countries. The first scenario, _Reshoring Leading Economies_ , examines a case in which high-income economies and China raise barriers to imports and increase subsidies to agriculture and manufacturing in an attempt to achieve reshoring. The second scenario, _Reshoring All_ , looks at the impact of even wider localization and diminished globalization when developing countries join the reshoring efforts. The third scenario, _GVC Friendly Liberalization + Trade Facilitation (TF)_ , examines a case in which developing countries seek to reduce trade costs and make it easier to use imports in domestic production. These scenarios are evaluated for the year 2030 against a baseline in which the economic recovery from COVID-19 is L-shaped; that is, pre-COVID growth rates are achieved but the loss of potential output suffered in 2020 is permanent. The main messages would, however, remain the same under different post-COVID-19 recovery pathways.

In a world where major countries try to reshore their production through subsidies and tariffs, global income declines by 1.5 percent and global exports decline by 17 percent relative to the baseline in 2030. If developing countries join in attempting to reshore, global income drops by 2.2 percent and global exports drop by 21.4 percent relative to the baseline. By contrast, when developing countries move to lower trade costs, global income increases by 0.4 percent relative to the baseline. A “hostile” environment with a shift toward global reshoring could drive an additional 51.8 million people into extreme poverty, while a more “friendly” one could lift 21.5 million additional people out of poverty by 2030 relative to the baseline.

The post-COVID-19 recovery in developing countries will be much stronger in a globalized world. Our simulations indicate that the overall increase in real income in developing countries between 2019 and 2030 could be 7 percentage points higher in a globalized world. While between 2019 and 2030 in a localized world real income in developing countries grows by 48 percent, in a globalize world the corresponding increase could reach 55 percent.

We examine three main trends with potential implications for GVCs, impact on economic growth and poverty reduction. Firstly, we delve into the impact of shocks, such as COVID-19, and how that plays out in terms of economic growth, poverty reduction, and resilience. Secondly, we look at the impact of protectionism, economic nationalism, and reshoring. Finally, we analyze the impact of changes in climate change mitigation policies, including carbon pricing.

First, the COVID-19 crisis is having significant impact on GVCs and government policies to support domestic industry and reshoring efforts, will have impacts on jobs, income, and poverty. Given the 3
complexity of the policy and economic environment in which firms and governments are responding to COVID-19, a CGE model can be a useful tool. It provides an assessment of the impacts of COVID-19 and various policy changes on economic growth, sectoral trade flows, output, and employment.

Second, trade tensions have thrown up new challenges and uncertainties. These include recent US-China trade barriers and challenges to the legitimacy of the World Trade Organization (WTO). The unprecedented step back by the United States from the multilateral approach to trade governed by the WTO has seen significant bilateral trade restrictions imposed on U.S.-China trade. This in turn has led to swift changes to bilateral trade flows.

Third, new regulations on greenhouse gas (GHG) emissions to combat climate change are throwing up new complexities and competitive challenges for some industries changing the comparative advantage of countries. As part of attempts to limit climate change and meet obligations under the Paris Agreement, several countries are developing domestic regulations to reduce GHG emissions, that can dramatically alter comparative advantages of countries, as well as taxation of imported goods that could have significant effects on their trading partners. For example, the European Commission has announced that a carbon border adjustment mechanism (CBAM) will be part of the ambitious European Green Deal (European Commission 2019). This scheme will be the first in which a major border tax adjustment is to be used in climate policy.

The CGE tool can inform an understanding of the medium to long-term implications of the recalibration of GVCs. The analysis designs forward-looking scenarios to anticipate medium run impacts of COVID-19 and potential policy changes on trade in goods and services, employment and wages by sector, skill, and gender, as well as on poverty and income inequality. The analysis builds on a global dynamic CGE model i.e. the multi-regional input-output (MRIO) version of the ENVISAGE model, and the global microsimulation framework Global Income Distribution Dynamics (GIDD). The ENVISAGE-GIDD top-down assessment approach allows for the analysis of global development and structural transformation, incorporating the complex interactions of productivity differences at the country, sector, or factor level, shifts in demand as income changes, demographic and skill dynamics in factor markets, and changes in comparative advantage. This application includes an extension of the standard modeling framework by incorporating MRIO tables that distinguish between imports of intermediate, final, and investment goods to better capture the nature of trade typical for GVCs. The CGE model relies on the GTAP 10 Data Base with a 2014 reference year and runs until 2030. The analysis covers 27 sectors and 21 countries/regions.

The CGE tool goes hand-in-hand with the GIDD microsimulation tool which translates the CGE results into implications for poverty and income distribution. This includes impacts on employment and wages of female and male workers. The GIDD simulations are based on a Global Micro Database, which covers over 80 percent of the global population and GDP. It includes harmonized household surveys for 130 countries. This study presents scenarios of globalized or segmented worlds and analyzes the implications of various shocks, such as future trade policy changes, disruptions in production, and climate mitigation policies. Their impacts on patterns of international trade, including GVCs, along with their implications for poverty and distribution, are the key questions addressed.

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# 2. Methodological framework and scenarios

To provide a quantitative assessment of the policy scenarios, we rely on a soft linkage between the global recursive dynamic CGE model ENVISAGE (van der Mensbrugghe 2019) and the GIDD microsimulation framework (Bussolo et al. 2010). First, the ENVISAGE model is run to provide an assessment of policy scenarios under consideration. Next, selected indicators from this assessment are transferred to the GIDD model. These are further implemented as an external shock relative to the baseline scenario within the GIDD modeling framework to estimate the distributional impacts of the considered set of policies. Such soft-linking approach does not incorporate any backward linkages from the GIDD to ENVISAGE and can be considered as a decomposition of the aggregate ENVISAGE results using the microsimulation framework.

Key data input to the ENVISAGE model for the reference year representation is the Global Trade Analysis Project (GTAP) 10 MRIO Data Base with 2014 as the reference year (Aguiar et al. 2019; Carrico et al. 2020). Relying on the MRIO data, which distinguishes agent-based demand for imports by region of origin, selected manufacturing sectors in the ENVISAGE model are represented using the MRIO specification, while all other sectors use the standard Armington assumption, treating imports as imperfect substitutes with domestically produced commodities—and sourcing is done at the national level. The model is solved as a sequence of comparative static equilibria where the factors of production accumulate over time. Nested constant-elasticity-of-substitution (CES) functions are used to represent production technologies. 141 regions and 65 sectors of the GTAP 10 Data Base have been aggregated to 21 countries/regions and 27 sectors for the current assessment. Annex A reports sectoral and regional mappings, as well as provides identification of the sectors of the model that are represented using the MRIO specification. Annex B provides technical details of the modeling framework.

The GIDD is a top-down macro-micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks obtained from the CGE model. Transferred shocks are distributed in a predefined set of linkage aggregate variables that communicate between the two models. The GIDD model relies on a collection of 130 nationally representative household surveys with harmonized variables on demographics, education, labor, welfare and consumption. The sample covers more than 10.5 million individuals from countries constituting 83.4% of global GDP and 86.3% of global population. The data is described with more detail in Ahmed et al. (2020). Additional details regarding the GIDD microsimulation framework can be found in Bussolo et al. (2010).

The ENVISAGE-GIDD model linking process is performed in a sequence of steps, where the two models are pre-calibrated to the same demographic and macroeconomic baseline assumptions. ENVISAGE and GIDD models are connected through changes in labor supply, skill formation, and real earnings. In terms of labor supply, the macro and micro models incorporate projections for skilled and unskilled labor over time. The GIDD framework, in addition, reallocates workers across sectors based on aggregate general equilibrium conditions. After reallocation, the GIDD simulates impacts on real earnings, income growth, and changes in relative prices for food and non-food expenditures. The shocks are transmitted through a set of linkage variables that connect the macro and micro models. Figure 1 provides an overview of the general approach. For additional details on the model linkage implementation an interested reader is referred to Chepeliev et al. (2021).

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<!-- Start of picture text -->
- macroeconomic assumptions;<br>- demographic assumptions; Distributional impacts of the<br>- technological changes; policies under consideration<br>- other baseline assumptions. Reconciliation of the input<br>macroeconomic and<br>demographic assumptions<br>( )<br>ENVISAGE GIDD<br>ENVISAGE-GIDD<br>linkage<br>- policy impacts on the energy sector - consumer price index (CPI) for<br>and environment; food and non-food commodities;<br>- economy-wide and sectoral impacts - change in wages, real GDP,<br>(income, prices, output, gdp, etc.). consumption.<br>Income/expenditure change<br>estimator<br><!-- End of picture text -->
The consumption patterns under home-based work and lockdowns have changed with consumers decreasing spending on services requiring human interaction.<sup>3</sup> We assume that the demand for the former category of services drops by 15 percent.

- _Fourth,_ we introduce a food supply shock, to simulate a post-harvest loss in food producing sectors. The measures put in place by many governments to contain the spread of COVID-19 can have a disruptive effect on the availability of agro-food products to consumers.<sup>4</sup> Therefore, to take into consideration this disruption of the logistics of food distribution, we assume that the cost of agricultural and food commodities production increases by 3 percent.<sup>5</sup>

- _Fifth_ is the shift toward communication technologies in the service sectors and away from transportation activities, particularly air transportation. To represent the shift from (air) transportation toward communication services used by service sectors we decrease by 20 percent a share of air transport activity in the cost structure of services production and increase the share of communication input to the services cost structure by 20 percent.

- _Finally,_ we introduce a reduction in global oil prices (in 2020 relative to 2019) by increasing the supply of oil. We further assume that in the post-2021 period oil prices would rebound to the 2019 levels in the next several years and exceed 2019 prices by 2030 – in line with International Energy Agency (IEA) price projections (IEA 2020). All these shocks are implemented for the 2020 simulated year together with the GDP target consistent with the GEP 2021 forecast (World Bank 2021).

Our baseline scenario also includes implementation of the trade frictions between the United States and its trading partners (mainly China) implemented in the model via increases in import tariffs based on the detailed tariff line data from the CARD database (Li 2018). We assume that these trade policy shocks stay in the model throughout the entire simulated timeframe, i.e. until 2030. We also include all existing and ongoing trade agreements with a pre-defined set of tariff changes based on the published tariff schedules reported by the International Trade Center (ITC 2020). We refer to this scenario as the globalized world, as it represents the continuation of past trends.

# Policy scenarios

## _Introducing two scenarios that are “hostile” to GVCs and one that is “friendly.”_

Our scenarios examine the impact of changes in trade and climate policies, including reshoring and carbon taxes, on the pace of economic recovery from the COVID-19 pandemic (Figure 2). We further explore how developed and developing countries are impacted by such polices and what actions developing countries can take to cushion the impact of policy shocks. In this section, we discuss the construction of the corresponding policy shocks. A summary of the scenarios is provided in Annex C.

> 3 U.S. retail sales data https://www.census.gov/retail/index.html

> 4 See OECD (2020a).

> 5 <mark>World Bank's Food Price Index increased by 3 percent in 2020 according the October 2020 edition of World Bank's Commodity Markets Outlook.</mark>

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<!-- Start of picture text --> Pre-COVID-19 GEP projections ti! ~~ NDS Aico<br>GEP projectionsbaseline till : 2022,post-SnoalcianaadNE)etal~ §01ng erties A implementpeerion-speci abeene NDC with<br>2021 (before i from pre-COVID carbon tax<br>pandemic) and i asdpee&<br>SSP2 post-2021 "EU Green Deal In addition to NDCs<br>i EU implements more<br>subsidize taget<br>Rashoving: by Leadingproductioneconomies and ambitious mitigation<br>i encourage use of EU implements<br>i domestic inputs stylized CBAM<br>, Reshoring by all<br>GVC friendly Eliminate tariffs on intermediate<br>policies and TF inputs, increase flexibility of<br>production, TF measures<br><!-- End of picture text -->
substitution elasticity by 50 percent. Finally, developing countries reduce costs to trade by implementing trade facilitation measures in line with the implementation of the WTO Trade Facilitation Agreement, which according to estimates by Moise and Sorescu (2013) and WTO (2015), reduces trade costs by between 14-16 percent.<sup>7</sup> As in the case of reshoring, these policies are phased in between 2022 and 2026.

Another set of policy options that we investigate deals with climate change mitigation, which has significant implications for the comparative advantage of countries. These polices may be viewed as slowacting shocks to GVCs. There is a lot of uncertainty over the exact shape these policies will take and how they are going to be implemented. In order to explore their potential consequences for GVCs, we include a stylized interpretation of the Paris Accord and the EU Green Deal including CBAM. First, we implement the so-called Nationally Determined Contributions (NDCs), which refer to emission reduction targets under the Paris Accord, following Chepeliev et al. (2021) and Böhringer et al. (2020). These targets are specified in the form of CO2 emission reductions in 2030 relative to the pre-COVID baseline scenario levels. We assume that countries implement carbon pricing policies to reach the NDC commitments. The model estimates the carbon price level (for each region) consistent with these emission reduction targets. Corresponding 2030 carbon prices range from less than US$5/tCO2 in Sub-Saharan Africa countries, India, and Malaysia to more than US$30/tCO2 in Brazil, Turkey, the EU, and some other high-income countries.

In addition to NDC targets, we consider more ambitious climate mitigation efforts by the EU in a stylized EU Green Deal scenario. This effort is consistent with the recently announced EU Green Deal mitigation plans to cut emissions by 55 percent in 2030 relative to 1990 levels (EC 2019). This more ambitious emission reduction policy in the EU is achieved by further increases in carbon prices in the model. We first explore the impact of the EU Green Deal without implementation of a Carbon Border Adjustment Mechanism (CBAM)<sup>8</sup> and then add the latter policy measure to our assessment. In the assessment, we implement CBAM as an excise tax imposed on region- and commodity-specific carbon content of imports to the EU. The carbon price level that is used to determine the CBAM rate is estimated as the difference between the carbon price in the EU and the carbon price in the country/region of the imported commodity origin. Only imported commodities that correspond to the EU’s Emissions Trading Scheme (ETS) sectors are assumed to be covered by the CBAM.

To provide an accounting of the CO2 emissions embodied in bilateral trade,<sup>9</sup> we follow an approach outlined in Peters (2008). Country-specific CO2 emissions per unit of output by sector are used to estimate emissions associated with bilateral trade flows. For every commodity, the total CO2 emissions associated with fossil-fuels combustion and embodied in trade flows from region _r_ to region _s_ ( _frs_ ) are estimated as _frs_ = _Fr(E - Ar)_<sup>_-1_</sup> _ers,_ where _Fr_ is a vector of region-specific CO2 emissions per unit of output by industries, _E_ is an identity matrix, _Ar_ is the technological matrix, which represents the industry requirements of domestically produced products in region _r,_ and _ers_ corresponds to the bilateral trade flow from region _r_

> 7 The decline amounts to 14 percent in East Asia, South Asia, Europe and Central Asia, and Middle East, and to 16 percent in SubSaharan Africa and Latin America and the Caribbean. In the model, these measures are implemented as a reduction in Iceberg parameter.

> 8 CBAM is one of the policy measures discussed in the European Green Deal (EC 2019; EC 2021). It is aimed to protect domestic industries, create incentives for other countries to adopt carbon taxes, avoid carbon leakage, and limit the reallocation of the EUbased industries to the countries with less stringent climate regulations.

> 9 Only CO2 emissions from fossil-fuel combustion are considered in this study. Under the implemented approach, we cover Scope 1, Scope 2 and Scope 3 emissions under. This implementation of CBAM is different from the recent proposal of the Euro Commission (EC 2021). The latter has more narrow emission scope and commodity coverage.

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to region _s_ . Essentially the role of the CBAM is to bring the level of emissions per unit of imported output to the average sectoral level in the domestic economy, in this case of the EU.

# 4.  The risks of GVC reshoring

The COVID-19 shock and recovery in a post-pandemic world The COVID-19 pandemic has been both a supply and a demand shock. The supply shock was sparked by lockdowns shutting down production. The demand shock was caused by a drop in the ability and willingness of consumers to consume, shifting expenditures away from services that required human interaction and toward goods necessary to work from home. Looking at the long run impacts of COVID19, it is the developing countries that are the most affected in the COVID L-shape recovery scenario, with South Asia showing the slowest recovery (a difference of 31 percentage points between real income growth between 2019 and 2030 in the no-COVID-19 baseline and an L-shape recovery from COVID-19). Developed countries also see their real income levels reduced in an L-shape recovery scenario, with China and the United States losing respectively 8 and 5 percentage points (Figure 3.a and 3.j), but still doing better than Western Europe (with a drop of 9 percentage points by 2030) (Figure 3.c).

Welfare in the Middle East and North Africa, China, Rest of East Asia and Pacific, and Europe and Central Asia is hit the hardest by reshoring of production.<sup>10</sup> Developing countries are the most negatively affected in a reshored world, with the Middle East and North Africa, Rest of East Asia and Pacific, and Europe and Central Asian regions being hit the most severely. In the case of the Middle East and North Africa, the real income growth between 2019 and 2030 would amount to 70 percent in an L-shape recovery, compared to a 56 percent increase in a reshored world. Developing countries also stage a slower recovery in a reshored world, with China’s real income growth rate reduced by 11 percentage points in a world where all countries attempt to reshore. In the rest of East Asia and Pacific (EAP), the decline in real income could be even more dramatic, dropping from an increase of 71 percent under an L-shape recovery to 62 percent under reshoring. This is due to much higher levels of openness and integration in GVCs in the EAP region.

In terms of exports, reshoring would see South Asia suffer the biggest export drop with China recording the biggest drop in imports (Figure 4). When all countries reshore their production, the region of South Asia sees the highest reduction of exports (a 45 percentage points drop from a global world with COVID to a reshored world), followed by China (37 percentage points reduction), and the rest of East Asia and Pacific (34 percentage points difference). These are all regions that are deeply integrated in the GVC network. The highest reductions of imports between a globalized and reshoring world would be seen in China (42 percentage points drop), Rest of East Asia and Pacific (35 percentage points reduction), and SubSaharan Africa (33 percentage points decrease). In a world where all countries attempt to reshore their production, both global exports and global imports go down by 27 percent, changing from 25 percent growth in the COVID-19 scenario without reshoring, to a 2 percent decrease in the scenario with reshoring.

> 10 All percentage changes in this and the following paragraph are stated relative to the 2019 baseline without COVID-19.

10
<!-- Start of picture text --> a - China<br>180<br>160 Pt<br>140 2 a“11<br>120<br>100<br>ep<br>40<br>20<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> b - Rest of East Asia and Pacific<br>100<br>89<br>90<br>70 -18 Lo<br>60 8<br>50<br>40<br>20<br>10<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> c- Western Europe<br>25<br>20<br>15 —<br>-9 -1<br>10<br>5<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> f - Middle East and North Africa<br>100<br>30 88<br>70<br>so<br>50 -14<br>40<br>a0<br>20<br>10<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> e- Latin America ‘and Caribbean<br>60<br>55<br>° i. —<br>30 -3<br>a<br>-<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> d- Europe and Central Asia<br>x<br>62<br>50<br>. 3 ZZ!8<br>30<br>20<br>10<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> i - Sub-Saharan Africa<br>120<br>100 97<br>hs<br>80 -18 ——-4<br>60<br>4020<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> g - Rest of High-income country<br>35<br>30 30<br>25 1<br>20 LL-7 ——<br>15<br>10<br>5<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> h - South Asia<br>90<br>80<br>70<br>60<br>50 34 a<br>40 5<br>.<br>10<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text --> j. USA<br>35<br>30<br>25 —<br>5 4.<br>.<br>15<br>am<br>5<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text --> <!-- Start of picture text -->
|. DEVELOPING COUNTRIES*<br>80<br>50<br>CU”<br>sy 0<br>40 ar<br>”<br>20<br>10<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text -->

<!-- Start of picture text --> k. - World<br>60<br>53<br>—e<br>* 100<br>30<br>20<br>20<br>0<br>Covid L-shape recovery<br>Baseline without Covid Reshoring all<br><!-- End of picture text -->
Figure 4. Real exports and real imports in 2030 w.r.t 2019 following reshoring(%) <!-- Start of picture text --> 140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20%<br>-40%<br>China Rest of Western Europe & Latin Middle Rest of South Asia Sub- USA World<br>East Asia Europe Central America East & High Saharan<br>& Pacific Asia and North Income Africa<br>Caribbean Africa Countries<br>Baseline without Covid Covid L-shape recovery Reshoring all<br>Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports Real Exports Real Imports<br><!-- End of picture text --> Source: ENVISAGE simulations.

# Rethinking GVC restructuring

The economic impact of COVID-19 has fueled an already heated debate about the benefits and costs of global supply chains, yet reshoring would have a negative impact in most regions. The pandemic has highlighted the role of GVCs as channels for the faster spread of shocks, resulting in higher risks and vulnerabilities to external disruptions. With this assumption in mind, countries might be tempted to restructure their GVCs. A possible scenario is a reshoring of production by the leading economies and China, which would have a noticeably negative impact in most regions, particularly in developing regions, leaving them more vulnerable. Global real income would decrease by 1.5 percent, compared with the COVID L-shape recovery. China sees its real income falling by 2.6 percent. The regions that are more negatively affected are the Middle East and North Africa, with a drop in real income of 5.1 percent, followed by East Asia and Pacific with 3.8 percent, and Europe and Central Asia with 3.5 percent.

The best policy response to reshoring by leading countries is to make developing economies more GVCfriendly rather than responding in kind. To counter this sharp negative impact on regions that are highly dependent and integrated in the GVC, countries may be tempted to respond in kind by pursuing their own reshoring policies. Or they can follow a more GVC-friendly liberalization approach. As our analysis indicates, having all economies reshore would compound the damage done by leading economies reshoring their production. In fact, global real income would drop by 2.2 percent, versus 1.5 percent if only leading economies reshore. China would suffer a decrease of 4.2 percent in real income, a further loss of 1.6 percentage points compared to the previous scenario. The most negatively affected regions 12
are once more the developing regions, with the Middle East and North Africa decreasing up to 8.3 percent in a global reshoring war. The rest of the high-income countries would see a slight increase in real income, due mainly to the terms of trade effect.

Closer integration in the global economy would pay dividends for developing countries. Eliminating input tariffs and implementation of trade facilitation measures would strengthen the integration of developing countries in regional and global value chains. An alternative response by developing countries to the reshoring of production by the leading economies would be to alleviate its negative impacts with more GVC-friendly policies and support for more integration. The gains would be evident in all developing regions. In global terms, real income increases by 0.4 percent compared to the COVID L-shape recovery. East Asia and Pacific and South Asia are the regions which stand to benefit the most, with real income increasing 4.7 percent and 4.3 percent, respectively. In other words, GVC-friendly policies could reverse the real income losses inflicted by the reshoring efforts of the leading economies (Figure 5).

Figure 5. Real income compared with COVID L-shape recovery – 2030 (%) <!-- Start of picture text --> 6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>Reshoring leading economies Reshoring all GVC friendly liberalization + TF imports<br><!-- End of picture text --> Source: ENVISAGE simulations.

Reshoring by the leading economies would drastically reduce exports and imports for most regions (Table 1). Global exports and global imports both see a decrease of 17 percent in this scenario in 2030. The deepest declines in trade would be for the countries engaging in the reshoring policies, with declines in export for China, the United States, and Western Europe, ranging from 20 to 25 percent. Developing regions would suffer collateral damage, with exports declining in the range of 1 to 5 percent. But in the scenario in which developing countries also attempt to reshore, losses in trade for the developing countries are much larger. Compared to a scenario in which only leading economies reshore, losses in exports in the case where developing countries also reshore increase from 4.3 percent to 26.0 percent for South Asia, from 2.9 percent to 22.7 percent in Latin America, and from 1.3 percent to 12.1 percent in MENA.

However, if developing regions choose to follow the GVC-friendly path, the global exports and global imports reduction is less pronounced. As compared with doing nothing or reshoring, both of which would lead to declining trade for developing countries, a policy of GVC-friendly liberalization would actually lead 13
to increases in trade in developing regions, ranging from an increase of about 12 percent in imports and exports for MENA, to an increase of 18 percent for exports and more than 25 percent for imports in South Asia. Similarly, developing regions enjoy increases in real incomes in the _GVC Friendly Liberalization and Trade Facilitation_ scenario. This illustrates dramatically that despite choices made by the high-income economies and China, developing countries can be in control of their own trade outcomes rather than passive victims of the decisions of others.

Table 1 **.** Real income, real exports and real imports compared with COVID L-shape recovery – 2030 (%)

||Rhi|Real Income|<br>GVC fidl|Rhi|Exports|GVC fidl|Rhi|Imports|GVC fidl|
|---|---|---|---|---|---|---|---|---|---|
|%|esorng<br>leading<br>economies|Reshoring<br>all|reny<br>liberalization<br>+TF imports|esorng<br>leading<br>economies|Reshoring<br>all|reny<br>liberalization<br>+TF imports|esorng<br>leading<br>economies|Reshoring<br>all|reny<br>liberalization<br>+TF imports|
|Middle East &<br>North Africa|-5.1|-8.3|1.1|-1.3|-12.1|11.6|-9.9|-22.2|12.3|
|Rest of East<br>Asia & Pacific|-3.8|-4.8|4.7|-4.7|-22.6|14.2|-9.4|-24.4|20.4|
|Europe &<br>Central Asia|-3.5|-5.5|2.1|-0.8|-16.1|12.0|-8.4|-22.2|14.2|
|Sub-Saharan<br>Africa|-2.7|-5.5|2.8|-3.0|-15.2|11.3|-9.0|-21.6|15.8|
|China|-2.6|-4.2|-1.9|-20.8|-22.1|-20.4|-22.8|-27.7|-20.5|
|Latin America<br>and<br>Caribbean|-2.1|-1.8|2.2|-2.9|-21.7|12.6|-8.2|-21.4|17.1|
|South Asia|-0.8|-3.0|4.3|-4.3|-26.0|18.3|-6.7|-21.3|25.5|
|Western<br>Europe|-0.7|-0.6|-0.4|-22.2|-21.1|-22.8|-19.7|-19.0|-19.7|
|United States|-0.4|-0.5|-0.2|-24.9|-24.7|-25.2|-20.1|-20.5|-19.4|
|Rest of High<br>Income<br>Countries|0.1|0.5|0.3|-24.2|-23.0|-25.0|-20.9|-19.5|-21.0|
|World|-1.5|-2.2|0.4|-17.0|-21.4|-12.6|-16.9|-21.5|-9.2|

Source: ENVISAGE simulations.

Reshoring by leading economies hits developing countries hard, but selected sectors could see their exports expand as trade is diverted from leading economies to the rest of the world (Table 2). When leading economies reshore their production, some sectors see expansion of their exports, even in regions that were badly hit, such as MENA, which sees its computer and electronics sector expanding by 43 percent compared to the COVID L-shape recovery (highest increase within the region). Some of the biggest increases are recorded in the livestock sector in South Asia (103 percent) and in China (51 percent), and in the computer and electronics sector in Sub-Saharan Africa (51 percent).

Sectoral exports are hit hard when developing countries reshore. When developing economies respond in kind to reshoring by leading economies, this aggravates the impact on exports. Developing countries, which before enjoyed expansion in certain sectors, now suffer contraction. In East Asia and Pacific, where 14
motor vehicles exports were increasing by 17 percent in the reshoring by leading economies are now contracting by 18 percent. In Europe and Central Asia where computer and electronics were expanding 45 percent before, they are now decreasing by 1 percent.

Table 2. Sectors with top three increases in exports for the “Reshoring leading economies” scenario. Exports compared with COVID L-shape scenario

|Country/region|Sector|Reshoring<br>leading<br>economies|Reshoring<br>all|GVC friendly<br>liberalization<br>+ TF imports|
|---|---|---|---|---|
||Livestock|51|101|51|
|China|Accommodation, food and service activities|2|4|-1|
||Meat products (inc. fisheries) and other food|2|20|-4|
|Rest of East Asia &|Motor vehicles, parts and transport equipment|17|-18|57|
|<br>Pifi|Refined oil|16|-19|36|
|acc|Computer, electronic and optical products|7|-27|41|
||Electricity|-3|-5|-6|
|Western Europe|Communications|-4|-6|-5|
||Natural resource products|-4|-2|-6|
|E & Ctl|Computer, electronic and optical products|45|-1|97|
|urope enra<br>Ai|Motor vehicles, parts and transport equipment|15|-14|35|
|sa|Other manufacturing|15|-14|39|
|Latin America and|Computer, electronic and optical products|21|-12|41|
|<br>Cibb|Air transport|18|3|22|
|arean|Wood and paper products|13|-12|36|
|Middl E & Nh|Computer, electronic and optical products|43|-7|123|
|e ast ort<br>|Metals|11|-13|17|
|Africa|Motor vehicles, parts and transport equipment|9|-17|29|
|Rest of Hih-Income|Natural resource products|7|5|5|
|g<br>Ci|Air transport|0|3|-4|
|ountres|Electricity|0|-3|-3|
||Livestock|103|129|103|
|South Asia|Motor vehicles, parts and transport equipment|26|-26|71|
||Computer, electronic and optical products|26|-31|124|
||Computer, electronic and optical products|51|5|115|
|Sub-Saharan Africa|Motor vehicles, parts and transport equipment|18|-12|70|
||Refined oil|14|-20|25|
||Natural resource products|3|2|0|
|United States|Electricity|-1|-4|-5|
||Non-metallic minerals|-4|-9|-5|
||Natural resource products|-2|-5|-4|
|World|Electricity|-3|-7|-6|
||Metals|-3|-9|-6|

Source: ENVISAGE simulations.

Eliminating intermediate input tariffs and implementation of trade facilitation measures under _GVCfriendly_ scenario leads to faster integration of developing countries into regional and global value chains. Exports of several sectors deeply integrated into GVCs expand such as motor vehicles and transport equipment in Sub-Saharan Africa, or computer, electronic, and optical products in South Asia. Table 3 displays percentage increases as compared to the COVID L-shape recovery scenario. Often these impressive export growth rates apply to a very small base. In value terms, the fastest increase of exports 15
is likely to occur in different sectors, e.g. metals and metal products, motor vehicles, textiles and apparel in Sub-Saharan Africa, or other manufacturing, chemical products, motor vehicles, crops, and textiles in South Asia.

**Table 3.** Top 3 import sectors for the Reshoring leading economies scenario. Exports compared with COVID L-shape scenario

|**Country/region**|**Sector**|**Reshoring**<br>**leading**<br>**economies**|**Reshoring**<br>**all**|**GVC friendly**<br>**liberalization**<br>**+ TF imports**|
|---|---|---|---|---|
||Electricity|5|-1|2|
|China|Natural resource products|1|-2|-1|
||Air transport|1|-5|-1|
|Rest East Asia &|Oil extraction|5|-25|-2|
|<br>Pacific|Metals|4|-12|-8|
||Computer, electronic and optical products|3|-25|8|
||Electricity|-4|-6|-6|
|Western Europe|Metals|-4|-3|-8|
||Non-metallic minerals|-6|-8|-8|
|E & Ctl|Metals|7|-6|-11|
|urope enra<br>Asia|Natural resource products|3|-10|-11|
||Non-metallic minerals|0|-11|-13|
|Ltin Amri nd|Oil extraction|5|-35|23|
|a eca a<br>Cibb|Metals|3|-10|-15|
|arean|Computer, electronic and optical products|3|-14|-9|
|Middle East &|Metals|3|-9|-14|
|<br>North Africa|Natural resource products|0|-6|-17|
||Water transport|-2|-11|-1|
|Rest of High-|Natural resource products|-3|-2|-7|
|<br>I Ci|Electricity|-4|-6|-7|
|ncome ountres|Livestock|-6|-9|-7|
||Gas extraction and distribution|3|-22|-7|
|South Asia|Oil extraction|1|-17|-15|
||Natural resource products|0|-7|-11|
||Metals|3|-10|-17|
|Sub-Saharan Africa|Gas extraction and distribution|3|-9|-15|
||Non-metallic minerals|1|-10|-18|
||Metals|1|-1|-2|
|United States|Electricity|-1|-4|-4|
||Non-metallic minerals|-3|-6|-5|
||Natural resource products|-1|-5|-6|
|World|Electricity|-3|-7|-6|
||Metals|-3|-9|-10|

Source: ENVISAGE simulations.

# Implications for GVC participation

With the reduction in the level of the economic activity due to the COVID-19 pandemic, overall trade participation rate by countries is also decreasing. While results suggest that due to the changes in trade 16
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@Reshoring all TM Reshoring leading economies TMGVC friendly A Total impact (under GVC friendly) GVC participation rate
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scenario (a reduction of 4.1 percentage points from the reshaping leading economies scenario). A small number of countries see their real income decrease less in this scenario. For example, Thailand, where real income was expected to decline by 4.4 percent in the _Reshoring leading economies_ scenario, now in the _Reshoring All_ scenario sees its real income decrease by 2.4 percent (a positive difference of 2 percentage points – the highest increase between _Reshoring leading economies_ and _Reshoring All_ from all countries and regions). This relative gain in income is driven by terms of trade effects as real GDP decline is still higher under _Reshoring All_ scenario.

Implementing GVC-friendly policies reverses the losses due to reshoring by leading economies in all regions. Instead of reshoring their production, developing countries have the option to counteract reshoring policies, by generating a more GVC-friendly environment, which leads to faster growth of trade and income and deeper integration into GVCs in line with their comparative advantages. Countries that were previously already deeply integrated are the ones expected to see the highest gains, with Thailand (10.7 percent), Malaysia (7.2 percent), Turkey (7 percent), and Vietnam (6.8 percent) benefiting the most from this approach (Figure 10). On the other hand, the only countries showing losses in terms of real income are the countries reshoring their production, i.e. China (-1.9 percent), followed by Europe (-0.4 percent), and the United States (-0.2 percent). However, it is noteworthy that each of these losses are smaller than in each of the previous scenarios. In fact, developed countries and China are better off in a scenario where developing countries implement GVC-friendly measures.

Figure 10. Real income compared with COVID L-shape recovery under reshoring by leading economies, by all countries and in the GVC-friendly scenario in 2030 (deviations from the L-shape COVID recovery, percent) <!-- Start of picture text --> 15.00<br>10.00<br>5.00<br>0.00<br>-5.00<br>-10.00<br>-15.00<br>Reshoring leading economies Reshoring all GVC friendly liberalization + TF imports<br>%<br><!-- End of picture text --> Source: Envisage simulations.

The reshoring of production by the leading economies will inevitably lead to a decrease in global trade by 17 percent. Highest losses are concentrated in developed countries. The countries suffering the highest reduction in exports are the United States (-24.9 percent), rest of high-income countries (-24.2 percent), Europe (-22.2 percent) and China (-20.8 percent) (Figure 11). Developing countries also suffer a reduction in exports, albeit to a lesser degree. The Philippines, the developing country that has the highest reduction 21
of exports, sees its exports going down by 8.6 percent relative to the COVID L-shape recovery scenario. In terms of imports, all the developed regions including China (-22.8 percent), rest of high-income countries (-20.9 percent), the United States (-20.1 percent) and Europe (-19.7 percent), register the highest reductions. The developing country with the highest reduction is Nigeria, where imports drop by 13.4 percent.

Reshoring production leads to a significant drop in global trade flows. Developing countries register the highest reductions, with Vietnam (-30.1 percent), the Philippines (-30.1 percent), and rest of South Asia (- 28.5 percent) (Figure 11) expected to see the highest reductions of exports. In terms of imports, Vietnam (-30.1 percent), China (-27.7 percent), and Brazil (-25.1 percent) register the biggest declines. The destructive consequences of this scenario are illustrated in the case of a country like Vietnam, where GVCs play an essential role in the economy, helping boost economic growth and reduce poverty. Such massive reductions in trade, a direct consequence of countries shifting their production home, have a devastating impact on welfare.

Where developing countries implemented GVC-friendly policies, all developing countries see their exports and imports increase relative to the COVID L-shape recovery scenario. The only reductions observed are in developed countries, with the United States experiencing the biggest decrease in exports (-25.2 percent), and the rest of high-income countries in imports (-21 percent). The countries with the highest increases of exports are Thailand (19.8 percent), Vietnam (19.8 percent), and Brazil (19.3 percent), while the highest increases of imports are expected in Thailand (28.6 percent), India (27.2 percent), and South Africa (26.9 percent). In terms of trade, the implementation of policies that follow a more GVC-friendly approach not only counteracts the negative impact that a reshoring by leading economies may bring, but also generates gains that go beyond the COVID L-shape recovery scenario, helping to mitigate the damaging effect that the pandemic has had in developing countries.

22
Figure 11. Real exports and real imports compared with COVID L-shape recovery – 2030 (%) <!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>Real Exports Real Imports<br>Reshoring leading economies Reshoring all GVC friendly liberalization + TF imports<br>USA Rest of High Income Countries Europe China Philippines Vietnam Rest of South Asia Rest of East Asia and Pacific Brazil India Rest of Sub-Saharan Africa Malaysia South Africa Rwanda Turkey Mexico Rest of Latin America and Caribbean Middle East & North Africa Nigeria Thailand Europe & Central Asia World China Rest of High Income Countries USA Europe Nigeria Rest of East Asia and Pacific Vietnam Philippines Brazil Middle East & North Africa Europe & Central Asia Rest of Sub-Saharan Africa Mexico Malaysia Rest of South Asia Rest of Latin America and Caribbean India South Africa Rwanda Turkey Thailand World<br><!-- End of picture text --> Source: Envisage simulations.

# Resilience to supply shocks and reshoring efforts

Becoming more GVC-friendly makes developing countries more resilient to supply shocks. In the _Reshoring All_ scenario and the _GVC-friendly_ scenario countries have different abilities to adapt to external shocks. In the _Reshoring All_ world, countries face higher trade barriers and have less ability to substitute between domestic and imported commodities, as well as choose between different import sources. At the same time, in the _GVC-friendly_ world both developed and developing regions are more resilient in the face of disruptions in production or supply chains.

A _GVC-friendly_ world provides better shock absorbers than the localized world. In this section we showcase such difference by looking into a stylized experiment where we estimate impacts of a hypothetical supply shock in Thailand and its disruption to the supply chain of electronics in a similar way in which the floods impacted the output of electronics in Thailand in 2010 (e.g. Haraguchi and Lall 2015). We assume a 10 percent negative shock to the total factor productivity of Thailand’s computer, electronic and optical products industry in 2027 and explore how this shock impacts both domestic and global supply chains under the _Reshoring All_ and _GVC-friendly_ world assumptions. In terms of the impacts on Thailand’s domestic economy, in the _GVC-friendly_ world, the country’s real income falls by 26 percent less compared to the case when the same shock is implemented in the _Reshoring All_ scenario (-2 percent vs -2.7 percent respectively) (Figure 12). Due to the lower trade barriers and higher trade substitution possibilities, output of Thailand’s electronics is impacted more negatively in the _GVC-friendly_ world. At the same time, output of all other sectors increases substantially (compared to the _Reshoring All_ scenario), as there is a partial reallocation of resources away from electronics production towards other sectors of the economy. As a 23
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<!-- Start of picture text --> Trade in Thailand's electronics Use of Thailand's electronics<br>25,000 3.5% 100% 10%<br>© 3.1% XS 90%<br>8 30% — 32% 35%<br>B 20,000 8 80% 42%<br>< 2.5% “s<br>E © 2.3% « 70% 27% ;<br>8 15,000 : 2.0% & 60% .<br>x @ 1.8% & 10% 17%<br>8 15% 2 50% 19%<br>S 10,000 3%x 6a 40% 20% 18%<br>® .0% 1.0% £ 30%<br>3 5,000 5 55%<br>> 0.5% 2 20% 40%<br>2 29% 31%<br>0 0.0% ” 10%<br>China USA Rest of high Western ROW 0%<br>income Europe China USA Rest of highincome Western Europe<br>m Exports from Thailand, mn USD @ Share of imports from Thailand in total imports, % mElectronics production minvestements mOther manufacturing mTM Other uses<br><!-- End of picture text --> <!-- Start of picture text --> Change in the output of electronics,% Change in imports of electronics used as an input in the electronics production<br>14 0 1.0<br>. 12 a2 3Rx. 3 08 x<br>310 & -10 ' g<br>g 0.9 E= iS 2 4 06 §<br>2 , E<br>= 08 0.6 07 0.7 =£ 59 : 0! “4 on 2<br>€ 06 0.4 5 -25 02 2<br>%£04 bas 0.4: Es£ -30 0.x €©<br>6 é 00 &<br>0.2 % -35 0, ¢<br>5 @ 02 420<br>0.0 liad :<br>China USA Rest of high Western Europe -45 -0.4<br>income China USA Rest of high income Western Europe<br>m@Reshoring TMGVC-friendly @ Reshoring (imp from THA) TM@ GVC-friendly (imp from THA) @ Reshoring (total imp) @GVC-friendly (total imp)<br><!-- End of picture text -->
# 5. Distributional impacts of reshoring

Before COVID-19, additional efforts were needed to put the world on track to eradicate poverty by 2030. According to our estimates, in the baseline scenario, without the effect of COVID-19, the number of people living in extreme poverty would have fallen to 375 million by 2030 – down from 663 million in 2018. This is equivalent to halving the poverty headcount ratio from 9.0 percent to 4.5 percent in 12 years. Despite the sustained trend of poverty reduction in the baseline scenario (without the effect of COVID19), it still falls above the global target of 3 percent in the extreme poverty headcount ratio. In fact, an additional 125 million people must be lifted from extreme poverty to reach that target – as stated in the World Bank goals of ending poverty and promoting shared prosperity.

The effect of COVID-19 sent 122 million into extreme poverty, and a quarter of them will not be lifted out of extreme poverty by 2030. Our estimates under the COVID L-shape recovery indicate that extreme poverty increases to 738 million in 2020 and, as economic activity resumes, it declines to 405 million by 2030 – equivalent to a global poverty headcount ratio of 4.9 percent. Further, 122 million people fell into extreme poverty as a result of the economic and health consequences of the pandemic, and one in four of them will remain living in extreme poverty by 2030<sup>14</sup> (Figure 15.a). Low- and middle-income countries in South Asia will constitute the majority of the new COVID-19 induced poor, with 71 million additional people living in extreme poverty in 2020, followed by Sub-Saharan Africa with 27.7 million (Figure 15.b). By 2030, 14.4 million in South Asia and 13 million in Sub-Saharan Africa would still be living in extreme poverty as a result of the pandemic.

Reshoring production could impair recovery by increasing the number of people living in extreme poverty by 51.8 million (Figure 15.c). In the _Reshoring Leading Economies_ scenario, poverty reduction resumes at a lower rate than in the L-shape recovery. By 2030, reshoring efforts by leading economies would lead to an increase in the number of people living in extreme poverty by 19.4 million. Under the _Reshoring All_ scenario by 2030, 51.8 million additional people would fall into extreme poverty, the equivalent to an increase in 0.6 percent in the global extreme poverty headcount ratio. Figure 15.d shows that in both cases, under reshoring by leading or all economies, Sub-Saharan Africa is the most affected region, contributing to around 80 percent of the new poor caused by reshoring, followed by South Asia and the Middle East and North Africa region.

But a GVC-friendly liberalization could lift 21.5 million people from extreme poverty. The _GVC-friendly_ scenario accelerates poverty reduction and brings the world close to full recovery from the effects of COVID-19. Extreme poverty would be reduced by 21.5 million people by 2030 with respect to the COVID L-shape recovery, and the global poverty headcount ratio would be 4.6 percent or 10.5 million above the baseline without the effect of COVID-19. A _GVC-friendly_ scenario would help to reduce extreme poverty, particularly in South Asia, which was the region very heavily affected by the effects of COVID-19 (Figure 16). Under this scenario, 56.2 million would graduate into global middle-class status, measured as individuals with a per capita consumption of more than PPP$10.00 a day. Table 4 shows that more than half of the new entrants in the global middle-class, or 30.9 million, would be in South Asia and between 6.3 and 7.2 million would come from each of the regions of East Asia and Pacific, Latin America and Caribbean, and Sub-Saharan Africa. It is important to highlight that high-income countries would see a

> 14 This is in line with the COVID-19 induced new poor presented in Lakner et al. (2020). For a useful discussion see: <u>https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty-looking-back-2020-and-outlook2021</u>

26
<!-- Start of picture text --> 10)<br>150<br>76734700e<br>/ 100<br>663 / NN<br>612 SS<br>N<br>N<br>> > 457<br>~<br>~ > 406 50<br>386<br>375<br>3% Targetnn<br>2010 2014 20182020 2025 2030 0<br>— Baseline without COVID19 —- COVID19 L-shape recovery '90 '21 '22 '23 '24 '25 '26 '27 '28 '29 '30<br>— Reshoring leading economies — Reshoring all<br>_ GVC ; ; a ; TM@ East Asia & Pacific M@ Europe & Central Asia<br> friendly liberalization + TF imports lH Latin America & Caribbean «iM. Middle East & North Africa<br>® South Asia TM@ Sub-Saharan Africa<br>§ Reshoring all Reshoring leading economies<br>So<br>So<br>a7<br>§<br>=]<br>*8<br>o<br>T<br>8<br>'23 '24 '25 '26 '27 '28 '29 '30 '23 '24 '25 '26 '27 '28 '29 '30<br>o<br>9 | East Asia & Pacific | Europe & Central Asia<br>Reshoring Reshoring GVC triendly WB Latin America & Caribbean Middle East & North Africa<br>all leading liberalization WO South Asia HB Sub-Saharan Africa<br>economies 4+ TF imports<br><!-- End of picture text -->
<!-- Start of picture text --> GVC friendly liberalization + TF imports<br>ro)<br>1<br>3<br>3<br>3<br>‘23 ‘24 "25 '26 ‘27 '28 "29 ‘30<br>WE east Asia & Pacific WE Europe & Central Asia<br>WE Latin America & Caribbean WH Middle East & North Africa<br>OO) South Asia Wl sub-Saharan Africa<br><!-- End of picture text -->
<!-- Start of picture text --> East Asia & Pacific Europe & Central Asia Latin America & Caribbean<br>Reshoring all ~o = a eo|<br>Reshoring leading economies a| so 2.<br>GVC friendly liberalization + TF _.~ ma ~» TM «<br>Middle East & North Africa South Asia Sub-Saharan Africa<br>Reshoring all , # i | . =<br>Reshoring leading economies a | 2, 3,<br>GVC friendly liberalization + TF os =,” mm:<br>Low- and middle-income countries High-income countries World<br>Reshoring leading economies a , | hs | nets , |<br>GVC friendly liberalization + TF my x me + a .<br>-10 -5 0 5 -10 5 0 5 -10 -5 0 5<br>® Bottom 40% @ Top 60%<br>East Asia & Pacific Europe & Central Asia Latin America & Caribbean<br>Reshoring all<br>Reshoring leading economies<br>GVC friendly liberalization + TF<br>Middle East & North Africa South Asia Sub-Saharan Africa<br>Reshoring all<br>Reshoring leading economies<br>GVC friendly liberalization + TF<br>Low- and middle-income countries High-income countries World<br>Reshoring all<br>Reshoring leading economies<br>GVC friendly liberalization + TF<br>-1 -.5 0 5 1 -1 -5 0 5 1-1 -5 0 5 1<br><!-- End of picture text -->
<!-- Start of picture text --> Extreme Poverty Global Middle Class<br>PPPS1.90/day PPP$10.00/day<br>(scenarios) (scenarios)<br>Region8 GVC GVC<br>. Reshoing . . Reshoing .<br>Reshoring . friendly §Reshoring . friendly<br>all leading . +TF all leading . +TF<br>economies .imports economies .imports<br>East Asia& Pacific -0.06 0.19 0.41 -29.48 -22.65 6.34<br>Europe& Central Asia -0.26 0.22 0.02 -7.21 -5.22 2.66<br>Latin America & Caribbean -0.54 0.38 0.59 -6.99 -6.99 6.96<br>Middle East& North Africa -3.47 -2.06 0.27 -16.90 -10.59 2.11<br>South Asia -4.95 -1.04 6.10 -20.54 -5.91 30.90<br>Sub-Saharan Africa -42.54 -15.44 14.10 -12.66 -6.38 7.19<br>Low and middle income countries -51.82 -19.33 21.49 -93.78 -57.73 56.15<br>High-income countries -0.02 -0.02 -0.01 -0.76 -0.96 -0.53<br>World -51.84 -19.35 21.48 -94.54 -58.69 55.62<br><!-- End of picture text -->

||Ext<br>P|reme Povert<br>PPS1.90/day|y|Glob<br>PP|al Middle Cl<br>P$10.00/da|ass<br>y|
|---|---|---|---|---|---|---|
|||(scenarios)|||(scenarios)||
|Region<br>8|.<br>Reshoring<br>all|Reshoing<br>.<br>leading<br>.<br>economies|GVC<br>.<br>friendly<br>+TF<br>.<br>imports|.<br>§Reshoring<br>all|Reshoing<br>.<br>leading<br>.<br>economies|GVC<br>.<br>friendly<br>+TF<br>.<br>imports|
|EastAsia& Pacific|-0.06|0.19|0.41|-29.48|-22.65|6.34|
|Europe& Central Asia|-0.26|0.22|0.02|-7.21|-5.22|2.66|
|Latin America & Caribbean|-0.54|0.38|0.59|-6.99|-6.99|6.96|
|MiddleEast& North Africa|-3.47|-2.06|0.27|-16.90|-10.59|2.11|
|South Asia|-4.95|-1.04|6.10|-20.54|-5.91|30.90|
|Sub-Saharan Africa|-42.54|-15.44|14.10|-12.66|-6.38|7.19|
|Lowand middleincomecountries|-51.82|-19.33|21.49|-93.78|-57.73|56.15|
|High-incomecountries|-0.02|-0.02|-0.01|-0.76|-0.96|-0.53|
|World|-51.84|-19.35|21.48|-94.54|-58.69|55.62|
<!-- Start of picture text --> it 0<br>Eg 25 -13 -2.5 -<br>oS 5 -4.6 38 o—5.0 2.9 “38 5<br>om . -45 -<br>3 57 61 “52<br>c ® -8.6 -73 ez<br>= -10 “8.1 @ 102 e-88 -10 8<br>e =]<br>F3 @ -13.9 c=<br>2-15 -15 9<br>2 ® -160 =<br>= a<br>wi oo<br>© -20 @ -20.1 “20 4<br>2 4<br>uu :<br>ES35 ® . -240 25 0=<br>&Za=<br>Lt)<br>Ba -30 -30<br>UG<br>-35 -35<br>East Asia South Asia Sub-Seharan Middle East Europe and Latin China USA Western RestofHigh World<br>and Pacific Africa and North Central Ase Amercaand Europe Income<br>Africa ‘Caribbean Countries<br>mi Change in CO2 emésions due to Covid @NDC target<br><!-- End of picture text -->
Table 5. Changes in output by sector and region following NDC implementation (top three sectors with the largest output reduction in each region are reported), % change w.r.t. post-COVID baseline in 2030

|Region\most|To<br>|p1<br>|Top2<br>|<br>|Top3<br>||
|---|---|---|---|---|---|---|
|impacted sector|Sector|Change, %|Sector|Change, %|Sector|Change, %|
|China|Coal<br>extraction|-6.0|Gas extraction<br>and distribution|-3.0|Wearing apparel<br>and leather<br>products|-0.2|
|Rest of East Asia<br>& Pacific|Air<br>transport|-10.6|Coal extraction|-8.6|Other transport|-5.8|
|Europe|Coal<br>extraction|-33.2|Air transport|-2.5|Gas extraction and<br>distribution|-2.2|
|Europe &<br>Central Asia|Coal<br>extraction|-15.7|Gas extraction<br>and distribution|-1.1|Non-metallic<br>minerals|-0.6|
|Latin America<br>and Caribbean|Coal<br>extraction|-18.3|Gas extraction<br>and distribution|-3.3|Refined oil|-1.3|
|Middle East &<br>North Africa|Coal<br>extraction|-5.7|Gas extraction<br>and distribution|-0.3|Wearing apparel<br>and leather<br>products|-0.2|
|Rest of High<br>Income<br>Countries|Coal<br>extraction|-18.5|Gas extraction<br>and distribution|-3.3|Textiles|-1.2|
|South Asia|Coal<br>extraction|-6.1|Wearing apparel<br>and Leather<br>products|-0.4|Gas extraction and<br>distribution|-0.4|
|Sub-Saharan<br>Africa|Coal<br>extraction|-8.9|Gas extraction<br>and distribution|-0.2|Meat products<br>(inc. fisheries) and<br>other food|-0.2|
|United States|Coal<br>extraction|-25.0|Gas extraction<br>and distribution|-4.9|Other transport|-0.5|
|World|Coal<br>extraction|-10.9|Gas extraction<br>and distribution|-2.2|Air transport|-0.9|

Notes: Top three sectors with the largest reduction in output are reported for each region. Numbers in brackets indicate corresponding change in output. Source: ENVISAGE simulations Consistent with the observed changes in production patterns, exports and imports of fossil fuels are impacted the most (Figure 20). With lower global demand for fossil fuels, volumes of trade for these commodities are expected to decline, accompanied by a moderate reallocation of trade to places with less stringent environmental regulations. China, as the world’s largest net importer of fossil fuels with a relatively low carbon price consistent with its NDC target, increases imports of fossil fuel commodities. Both Sub-Saharan Africa and East Asia, being large net energy exporters, suffer reductions in exports of fossil fuels. In the case of Sub-Saharan Africa, reduction in exports of fossil fuels is compensated for by an increase in exports of other goods and services, including agricultural commodities.

32
Figure 20. Change in exports and imports due to the NDC policies implementation, % change in 2030 relative to the post-COVID baseline <!-- Start of picture text --> 0 2 4 6 8 10 12<br>3 Exports 100<br>2 80<br>1 60<br>0 40<br>-1 20<br>-2 0<br>-3 -20<br>-4 -40<br>-5 -60<br>-6 -80<br>-7 -100<br>-8 -120<br>Total Total Agriculture Total Natural Resources Total Manufacturing Total Services<br>0 2 4 6 8 10 12<br>2 Imports 100<br>80<br>1<br>60<br>0 40<br>-1 20<br>0<br>-2<br>-20<br>-3 -40<br>-4 -60<br>-80<br>-5<br>-100<br>-6 -120<br>Total Total Agriculture Total Natural Resources Total Manufacturing Total Services<br>Change in exports, %<br>Trade Specialization Coefficient, %<br>Change in imports, %<br>Trade Specialization Coefficient, %<br><!-- End of picture text --> Notes: Trade Specialization Coefficient (TSC), plotted with circles in the figure, represents the ratio of net exports to total trade (exports + imports); TSC>0 indicates that the region is a net exporter of the specific commodity, while TCF<0 indicates a net importing commodity-region pair. Fossil fuel commodities are aggregated within the natural resources category. Source: ENVISAGE simulations.

# Climate policies and Carbon Border Adjustment Mechanism

We split the impact of the stylized EU Green Deal into two parts: more ambitious emissions reductions by the EU and implementation of the Carbon Border Adjustment Mechanism (CBAM), a charge on imports into the EU of some raw materials from “less climate ambitious countries.” The impacts of the EU Green Deal are measured relative to the scenario with NDC-consistent climate mitigation. These impacts are 33
driven through two key channels. Firstly, due to the increasing carbon price in the EU (from US$39/tCO2 under the NDC target to US$213/tCO2 under the EU Green Deal target), there is lower demand for fossil fuels within the bloc. This has an adverse impact on regions like MENA and Central Asia, including the Russian Federation, as the EU is one of their primary destinations for fossil fuel exports. Secondly, due to the increasing price of carbon in the EU, production becomes more expensive, which negatively impacts key destinations of EU energy-intensive exports, such as neighboring European countries. Unlike the EU carbon tax, which mainly impacts demand and prices for fossil fuels, CBAM puts more pressure on energyintensive goods, such as metals, chemical products, non-metallic minerals (cement, lime, etc.), and electricity. As a result, main exporters of these commodities to the EU (ECA – chemicals, metals, electricity; China and MENA– chemicals) are among the regions that experience lower exports following CBAM implementation.

The impact of climate policies on other countries will depend on the degree of their carbon intensity and reliance on related exports (Figure 21). Separating macro impacts of the CBAM on EU trading partners from domestic EU mitigation policies (within the EU Green Deal), we can observe that the impacts correlate with the carbon intensity (in kg per US$1 of exports) and magnitude (share of exports to the EU in a country’s GDP) of exports to the EU. For estimating both carbon intensity and the share of exports in a country’s GDP we focus on commodities that correspond to the EU ETS sectors and thus face the CBAM in our simulations. The top three regions most impacted by the CBAM – Europe and Central Asia, Middle East and North Africa, and Sub-Saharan Africa – have high carbon intensity of exports per US$1 and at least 1 percent of the EU-designated export share in country GDP. At the same time, high-income countries, including the United States, having the lowest carbon intensity of exports to the EU, experience negligible impacts from the CBAM.

Figure 21. Impacts of the Carbon Border Adjustment Mechanism on total exports by EU trading partners and carbon intensity of exports to EU, % change in 2030 relative to the EU Green Deal implementation scenario <!-- Start of picture text --> 0.2 0.1 3.5<br>0.0<br>3.1<br>0.0 3.0<br>-0.2 -0.1 2.5<br>-0.1 -0.2 -0.2 -0.1<br>-0.4 2.0<br>-0.6 -0.4 1.5<br>1.4<br>-0.8-1.0 0.70.4 0.60.9 1.10.6 0.5 0.8 0.40.5 0.60.5 0.7 0.5 1.00.5<br>0.2 0.3<br>-1.2 -1.0 0.0<br>Change in total exports due to CBAM,% Carbon intensity of exports to EU, kg per USD<br>Share of exports to EU in country's GDP, %<br>Change in total exports, %<br><!-- End of picture text --> Source: ENVISAGE simulations.

34
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BEU Green Deal CBAM EU Green Deal + CBAM
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mw EU Green Deal CBAM &EUGreenDeal+CBAM «Imports-to-output ratio, %
**Table 6. Impacts of the EU Green Deal and CBAM on selected sectors in EU trading** **<u>partners</u>**

|**Sector**|**Change in**<br>**EU imports,**<br>**%**|**Key source region**<br>**(share in total**<br>**imports)**|**Change in exports**<br>**to EU for key**<br>**source region, %**|**Change in**<br>**total exports**<br>**for key source**<br>**region, %**|**Change in**<br>**output for key**<br>**source region,**<br>**%**|
|---|---|---|---|---|---|
|Coal|-69.1|United States (38.9%)|-70.8|-19.7|-5.4|
|Electricity|-28.2|Europe and Central<br>Asia (58.2%)|-34.3|-15.6|-1.2|
|Non-metallic<br>minerals|-28.0|China (48.7%)|-33.8|-4.2|-0.2|
|Wood and paper<br>products|-28.0|China (33.9%)|-37.5|-7.4|-0.6|
|Chemicals|-23.2|China (18.4%)|-38.4|-5.8|-0.7|
|Metals|-23.0|Europe and Central<br>Asia (26.6%)|-38.7|-9.2|-3.2|
|Natural gas|-22.8|Europe and Central<br>Asia (45.9%)|-20.9|-9.1|-1.5|
|Petroleum<br>products|-11.5|Europe and Central<br>Asia (47.1%)|-12.6|-5.4|-1.2|
|Crude oil|-9.3|Europe and Central<br>Asia(52.5%)|-5.6|-1.6|-0.4|

Notes: Share in total imports for key source region is estimated for total imports excluding intra-EU trade. For the case of chemical products (CHM), the second most important EU trading partner is reported (i.e. CHN), as for the first most important partner (United States), observed changes in flows were non-significant. Selected sectors with the largest reduction in EU imports are reported. Source: ENVISAGE simulations.

EU trade with the rest of Europe and Central Asia are likely to be hardest hit by the CBAM, with carbonintensive sectors most vulnerable. The most impacted sectors in terms of output changes are reported in Figure 24. The impact of the CBAM on changes in the output of the exporting country depends on two key drivers – carbon intensity of exports and the share of total output that is exported to the EU. ECA commodities that have both high carbon intensity and a large share of production that is exported to the EU (metals and chemicals) suffer the largest reductions in output. At the same time, output of highly carbon-intensive commodities, like electricity, is not impacted significantly due to the low share of exports to the EU in total output.

The potential negative impacts on output of selected sectors in developing countries can be mitigated by own policy responses. By pursuing more ambitious climate mitigation policies themselves, developing countries could transform potential income losses into income gains by supporting the use of more efficient and cleaner technologies, and facilitating the green transition. In addition, developing countries would benefit from the environmental and health co-benefits of more stringent climate action. Even in the case of energy exporters, a group of countries that is substantially hit by the EU Green Deal implementation due to the reduction in the global prices of fossil fuels, transition from a traditional diversification to proactive asset diversification (with increasing R&D investments) within ambitious climate mitigation scenarios, could be an important welfare-improving channel, as shown by Peszko et al. (2020).

37
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BHDC @EUGreenDeal BS CBAM #6VC participation
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GNDOC SEU GreenDeal SCBAM #GVC participation
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vulnerable. The impact on real income is going to be uneven across countries, with most of the losses falling in countries that are the most deeply integrated into GVCs.

In such situation, the best policy response to reshoring by leading countries is to make developing economies more GVC-friendly rather than responding in kind. As our analysis indicates, having all economies reshore would compound the damage done by leading economies reshoring their production. On the other hand, elimination of input tariffs and implementation of trade facilitation measures would strengthen the integration of developing countries in regional and global value chains. The gains would be evident in all developing regions through the increase in real incomes. We also find that becoming more GVC-friendly makes developing countries more resilient to supply shocks. We showcase this point by looking into a stylized experiment where we estimate impacts of a hypothetical supply shock in Thailand and its disruption to the supply chain of electronics.

Before COVID-19, additional efforts were needed to put the world on track to eradicate poverty by 2030. The COVID pandemic has further sent 122 million into extreme poverty, and a quarter of them will not be lifted out of extreme poverty by 2030. Low- and middle-income countries in South Asia will constitute the majority of the new COVID-19 induced poor, followed by Sub-Saharan Africa. Reshoring production by leading economies could only impair the recovery by increasing the number of people who leave in extreme poverty by 19.6 million. At the same time, the GVC-friendly scenario accelerates poverty reduction and brings the world close to full recovery from the effects of COVID-19. Furthermore, our estimates suggest that in line with the World Bank goal of shared prosperity, a GVC-friendly liberalization helps raise the income of the bottom-40 percent.

Reducing the level of economic activity and shifting the production patterns, the COVID-19 pandemic has lowered the global environmental pressure. While sharp reduction in the CO2 emissions observed during 2020 was a short-term trend, it would also have a limited longer-term implications. Our simulations suggest that by 2030 global CO2 emissions would fall by around 3.8 percent relative to the pre-COVID-19 baseline if the Paris commitments were fully implemented.<sup>17</sup> In the case of some aggregate regions – South Asia, Sub-Saharan Africa, and Middle East and North Africa, COVID-19-induced CO2 emissions reduction could even be enough to overreach their NDC targets.

We find that while in general climate mitigation policies lead to declining GVC participation, some countries benefit from changes in trade patterns and become more integrated into GVCs. This group includes India, South Africa, the Philippines, and several countries in Latin America. Among considered climate policy measures, CBAM has the most adverse impacts on GVC participation. With increasing taxes applied to carbon intensive EU imports, not only do Western European countries observe a decline in GVC participation (-1.5 percent), a more substantial reduction is experienced by some EU trading partners, with Europe and Central Asia seeing a decline of 2 percent. At the same time, new opportunities arise for countries that are relatively carbon efficient in key tasks along GVCs. While fossil fuels and energyintensive sectors experience a reduction in GVC participation rates, service sectors and light manufacturing activities experience a moderate increase. These include highly GVC-integrated sectors like computers and electronics, motor vehicles and parts, and other light manufacturing. Climate mitigation

> 17 The global average unconditional NDC emissions reduction target, according to our interpretation of NDCs, is around 10.8 percent relative to the baseline in 2030.

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policies stimulate both trade and GVC participation for these commodities, making them even more integrated in GVCs.

While we believe that our approach allows to develop a qualitatively robust policy message, a study with such a broad focus cannot avoid limitations and potential for further improvements. First of all, it should be noted that our scenarios are of a stylized and exploratory nature and can be further improved along multiple dimensions. In particular, this includes a more refined implementation of the climate mitigation policies with a better specification of the technological mitigation potentials and a more accurate design of the CBAM implementation. In addition, our understanding of the COVID-19 impacts evolves rapidly, as country-specific recovery paths are updated almost real-time. While in this paper we provide a longerterm look on the impacts of recovery policies and quantitatively support our conclusions, we first of all would like to stress the qualitative interpretation of our results.

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