metadata: authors: 0: author: Egger,Peter H.
1: author: Masllorens.Gerard
2: author: Rocha Gaffurri,Nadia Patrizia
3: author: Ruta,Michele
count: World
docna: 0: docna: Scarcity Nationalism during COVID-19 :
            Identifying the Impact on Trade Costs
docty: Policy Research Working Paper
abstracts: cdata!: During the COVID-19 pandemic, many
            countries used export and import policy as a tool to expand
            the availability of scarce critical medical products in the
            domestic market (scarcity nationalism). This paper assesses
            the direct and indirect (via trade in intermediates)
            increases in trade costs of critical medical goods resulting
            from these uncooperative policies. The results show that
            scarcity nationalism led to substantial increases in trade
            costs between February 2020 and December 2021 for most
            COVID-19 critical medical products, particularly garments
            (for example, face masks) and ventilators. The exception is
            vaccines, which saw a reduction in trade costs, which,
            however, was driven by the reduction in indirect trade costs
            for high-income countries, consistent with the view of a
            COVID-19 vaccine production club.
display_title: Scarcity Nationalism during COVID-19
            : Identifying the Impact on Trade Costs
listing_relative_url: /research/2022/11/33942885/scarcity-nationalism-during-covid-19-identifying-impact-trade-costs
topicv3: Trade m1326924 698,Trade Policy m1327640 776,Trade in Goods m1504224 2608,Health m1504661 3480
publishtoextweb_dt: 2022-11-22T00:00:00Z
dois: 10.1596/1813-9450-10237
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 995092
3: author_key: 1245806
model_extractions: Policy Research Working Paper 10237 Scarcity Nationalism during COVID-19 Identifying the Impact on Trade Costs _Peter H. Egger Gerard Masllorens Nadia Rocha Michele Ruta_ Macroeconomics, Trade and Investment Global Practice November 2022
Policy Research Working Paper 10237

# **Abstract**

During the COVID-19 pandemic, many countries used export and import policy as a tool to expand the availability of scarce critical medical products in the domestic market (scarcity nationalism). This paper assesses the direct and indirect (via trade in intermediates) increases in trade costs of critical medical goods resulting from these uncooperative policies. The results show that scarcity nationalism led to substantial increases in trade costs between February 2020 and December 2021 for most COVID-19 critical medical products, particularly garments (for example, face masks) and ventilators. The exception is vaccines, which saw a reduction in trade costs, which, however, was driven by the reduction in indirect trade costs for high-income countries, consistent with the view of a COVID-19 vaccine production club.

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 nrocha@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
# Scarcity Nationalism during COVID-19: Identifying the Impact on Trade Costs

Peter H. Egger,<sup>¶</sup> Gerard Masllorens<sup>‖</sup> , Nadia Rocha<sup>∗∗</sup> , Michele Ruta<sup>††</sup>

**Keywords:** Covid-19; protectionism; trade; Global Trade Alert **JEL codes:** F13; F14

## **1 Introduction**

During the COVID-19 pandemic, we witnessed a reemergence of trade protectionism. However, while traditionally protectionism is something that is pursued on the part of importers to shield domestic producers from what they deem to be excessive competition from foreign suppliers, the protectionism applied during the current pandemic aimed at maximizing the availability of sensitive, foremost medical, products in the domestic market through a mix of trade facilitation and export controls (Evenett et al., 2022). We refer to this mix of export and import policy as ”scarcity nationalism” to distinguish it from traditional trade protectionist measures. This study empirically investigates the impact of these trade policies during COVID-19 on trade costs of critical medical goods.

The analysis relies on the Global Trade Alert (GTA) data on policy measures that have been adopted by countries at the HS6-traded-product level for medical products that have been critical

> ¶ETH Zurich, CEPR, CESifo, WIFO; Email: pegger@ethz.ch; Address: Leonhardstrasse 21, 8092 Zurich, Switzerland.

> ‖ETH Zurich. Email: gmasllorens@ethz.ch; Address: Leonhardstrasse 21, 8092 Zurich, Switzerland.

> ∗∗World Bank; Email: nrocha@worldbank.org; Address: World Bank, 1818 H Street, Washington DC, USA.

> ††International Monetary Fund; Email: mruta@imf.org ; Address: International Monetary Fund, 700 19th st NW, Washington, DC 20431, USA
to deal with the COVID-19 pandemic, such as face masks and ventilators, and for vaccines and vaccine inputs. After merging these data with bilateral product-level trade data, we measure the impact of trade policies on trade margins. We disentangle the effect of trade measures (restricting and facilitating) on medical products using monthly data for a span of time from January 2018 (prior to the pandemic, when no such measures were used) up until December 2021. We also look into product, time and geographic heterogeneity and provide a general picture of the impact on trade-cost of trade policies motivated by scarcity nationalism during COVID-19. In doing so, we separate between the direct effects of trade policy on trade costs and the indirect effects, as these measures affect the cost of trade in intermediate goods.

Interest in the effects of trade protectionism has increased in recent years as tensions in the world trade system have emerged. Our work relates to the recent literature on the economic effects of trade policy (Goldberg and Pavcnik, 2016; Handley et al., 2020; Amiti et al., 2019; Fajgelbaum et al., 2020). Differently from these studies, the focus in this paper is not on the consequences of the re-emergence of tariffs but on the broader set of trade policy measures that have targeted medical products during the COVID-19 pandemic. Moreover, we study both the direct effects of trade policy on trade costs and its indirect effects through the intermediate trade channel.

# **2 Data**

We use data from three sources. First, we construct binary indicators reflecting whether a country utilizes import or export policy measures or not. With import and export measures that can be restricting or facilitating, there are four indicators. The source of these data is the Global Trade Alert (GTA), and the data vary by country _j_ , product _k_ , and month _t_ . We use them at monthly frequency between January 2018 and December 2021, and we employ export measures in a way so that they are on the exports of a product to country _j_ . Note that the policy measures do not vary much across partner countries so that we make them importer-country-product-time-specific for both import and export (towards the importer) policies upfront. We call them _d_<sup>_m_</sup> _jk,t_<sup>forimport</sup> policies, a scalar for the _m_ th (of two: restraining and facilitating) import measures. Similarly, we refer to export measures importer _j_ is faced with in _k_ by _d_<sup>_x_</sup> _jk,t_<sup>,ascalarforthe</sup><sup>_m_th(oftwo:</sup> restraining and facilitating) export measures. Stacking these measures for all importers _J_ and products _K_ in year _t_ obtains the _JK ×_ 4 matrix ∆ _t_ in month _t_ . A typical row of ∆ _t_ may be referred to by ∆ _jk,t_ .

Second, we use bilateral export data from country _i_ to _j_ and product _k_ , _xijk,t_ for 180 products indexed by _k_ on a monthly basis between January 2018 and December 2021. As the monthly trade data are available for the European Union, the United States, and China, we treat those three as partner countries (exporters or importers) of _J_ = 247 countries and consider all possible bilateral relations between the mentioned three and all other countries in the exporting and importing directions. These data will be used to estimate parameters revealing the ad-valorem-equivalent trade costs associated with the policy variables in ∆ _t_ .

Third, we use cross-sectional bilateral export data for 180 + 1 products and _J_ ( _J −_ 1) country pairs from UNCTAD. These cross-sectional data are obtained from averaging annual export data for the period 2017-2019. Then, we construct a ( _J × J_ )( _K_ + 1) matrix of bilateral trade flows,<sup>1</sup> We combine these data with the _S_ (sector-by-sector) input-output matrix for the United States in

> 1Note that we consider _K_ + 1 products here, in order to account for the bulk of products (a residual category) beyond the 180 sensitive ones in focus here.

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the year 2012, which is available from the BEA. From the latter we impute a _K × K_ matrix by using product-sector concordance tables from UNCTAD. The obtained matrix will serve to compute input-output-repercussion indirect effects of trade policies on sensitive products.

# **3 Effects of pandemic-related trade policy measures on trade costs and imports**

We are interested in distinguishing between direct and indirect effects (i.e. through imported inputs) of the trade policy measures of interest during the time period January 2018-December 2021. For this, let us use _gjk,t_ to denote the import-value equivalent for importing country _j_ , product _k_ , and month _t_ . We will establish later how to estimate it. We postulate that _gjk,t_ is linearly composed of the direct effects associated with the four import- and (partner-)export-related policy measures which are collected in ∆ _jk,t_ , and with as many corresponding indirect effects.

Suppose a _JK × JK_ input-output matrix _H_ exists. This matrix is obtained from _H_ = _O_<sup>_⋆_</sup> _◦ M_<sup>_⋆_</sup> , where _M_<sup>_⋆_</sup> is a _JK × JK_ (block-diagonal) expenditure-shares matrix consisting of _J × J_ blocks of expenditure shares for each product _k_ (Eaton and Kortum, 2002; Caliendo and Parro, 2014). For this, start with _M_ , a _JK × J_ imports matrix. The diagonal elements (expenditure on domestic produce) can be predicted from exporter and importer fixed effects in a gravity equation per product, assuming domestic sales frictions are zero. The matrix _M_ is then row-normalized (dividing by aggregate expenditure by product) to obtain _M_<sup>_⋆_</sup> . _O_<sup>_⋆_</sup> , a _JK × JK_ input-output matrix is obtained after row-normalizing _O_<sup>ˆ</sup> = _COC_<sup>_′_</sup> , where _O_ in our case is the _S × S_ input-output matrix of the United States with sector number _S < K_ , and _C_ is a _K × S_ conversion matrix which associates products with sectors.<sup>2</sup> Assuming that an estimate of the (global country-product-pair) Leontieftype (at least pseudo-)inverse ( _IJK − H_ )<sup>_−_1</sup> exists, where _IJK_ is a ( _JK × JK_ ) identity matrix, we can define The latter is nothing else than ∆ _t_ + (<sup>�</sup><sup>_∞_</sup> _q_ =1<sup>_Hq_)∆</sup><sup>_t_,wherethefirsttermcapturestheindirect</sup> relevance of the application of policy measures in ∆ _t_ for each importer _j_ and product _k_ from other units (the same or other importers and the same or other products) in month _t_ . As ∆ _t_ is _JK ×_ 4, so is Λ _t_ , and the latter has typical row Λ _jk,t_ .

Using _ηk_ and _ξk_ for the 4 _×_ 1 parameter vectors on the columns of ∆ _jk,t_ and Λ _jk,t_ , respectively, we can define Conditional on the direct effects ∆ _jk,tηk_ , Λ _jk,tξk_ measures the indirect effects of the policy measures in ∆ _jk,tηk_ on _gjk,t_ .

We can estimate _gjk,t_ from bilateral product-level data on exports or imports _xijk,t_ , assuming a customary level-multiplicative or log-additive gravity model of product-level sales of country _i_ to _j_ in month _t_ :

> 2Conversion tables for traded products and various sector classifications are available from the United Nations Conference on Trade and Development.

3
where _αik,t_ , _βijk,t_ , and _γjk,t_ are parameters capturing sales/supply-potential factors of _k_ in _i_ , the trade freedom of _k_ between _i_ and _j_ , and the demand/consumption potential of _k_ in _j_ , all in logs and at time/month _t_ . When only trade but no domestic sales data are available, the unilateral frictions on _k_ enter _γjk,t_ only.

We proceed in two steps to identify the parameters _ηk_ and _ξk_ of interest: (i) estimate _γjk,t_ as importer-product-month fixed effects from monthly bilateral product-level export data _xijk,t_ on 180 sensitive products; (ii) estimate the impact of unilateral trade frictions on product _k_ from where _gjk,t_ is defined as in (2), and _γ_ ̃ _jk_ , _λ_<sup>_γ_</sup> _t_<sup>and</sup><sup>_uγ_</sup> _ik,t_<sup>areimporter-product-fixed,month-fixed,and</sup> importer-product-month residual effects, respectively.

As all ∆ _jk,t_ are binary indicators, all parameters _η_ measure (direct) semi-elasticities of imports. Note that under the assumption of a constant-elasticity-of-substitution model with (direct) trade � elasticity (1 _− σk_ ),<sup>3</sup> one can scale _γjk,t_ by (1 _− σk_ ) to obtain the iceberg-trade-cost increment associated with pandemic-related policies alone by product, importer, and month. E.g., _τjk,t_ = 0 _._ 02 then means that trade costs are (directly plus indirectly) raised by pandemic-related policies for product _k_ and country _j_ in month _t_ by about two percentage points. Apart from reporting on _τjk,t_ , we (trade-weighted) aggregate these trade costs effects for each of the 247 countries and each month _t_ to obtain _τj,t_ . Finally, we aggregate the same trade-cost effects across countries _j_ to obtain _τt_ for the average country and sensitive product.

# **4 Results**

In the following, we first display the evolution of _τk,t_ for selected sensitive products – namely, garments, ventilators, vaccines and tests. The results are summarized in Figure 1.

> 3The values of _σk_ are obtained from Fontagn ́e et al. (2022).

4
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negative contribution of the direct trade costs.

Upon aggregating trade-cost increases for the average month during the period after February 2020 until the end of 2021 for the (trade-weighted) average product, we obtain the following insights. First, import frictions were increasing at high levels in the average month in particular for the United States, T ̈urkiye, India, Japan, China and the Russian Federation followed by other countries, mostly in the northern hemisphere and in South America. Second, when considering changes between the period from February 2020 until the end of our sample coverage relative to the period from the beginning of 2018 to January 2018, we see that the change in the uptake of restraining policies was quite significant and widespread with important peaks in Russia, Indonesia, China and Argentina for the average sensitive product.

Finally, in Table 1, we aggregate the information on changing trade frictions into country groups by income bracket and report the change in percentage points between the period after February 2020 and the period before up until January 2020 in the average month.<sup>4</sup> The table is organized in three parts vertically: combined direct and indirect components of trade-cost levels and their policy-induced changes are at the top; the two panels underneath pertain to the direct (own-cost) and indirect components only. The numbers in the same cell of the two panels at the bottom sum up to the corresponding one at the top. The numbers suggest that the biggest trade cost increases happened for upper-middle-income countries (through partner export or own import policies) for the imports of ventilators and for high-income countries for the ones of garments. The biggest trade cost reductions occurred for vaccines in high-income countries (consistent with the findings in Figure 1). The indirect effects tend to soften the direct ones for tests, ventilators, and garments, whereas they reinforce the (negative) direct effects for vaccines. The indirect effects are particularly large and dominant for upper-middle- and high-income countries in the case of vaccines, whereas the direct effects tend to dominate in other cells. Interestingly, the indirect effects are of relatively large importance in comparison to the direct effects throughout the table pointing to the fact that the indirect costs of trade policy are key.

> 4We list the associations of countries with country groups in the table footnote.

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Table 1: Trade-cost change before and after February 2020 by country income group in percentage points

|||T|otal costs||
|---|---|---|---|---|
||Tests|Ventilat|ors<br>Vaccines|Garments|
|Low income|_−_0_._06|_−_|0_._43|_−_|
|Lower-middle income|0_._85|1_._75|_−_39_._27|1_._15|
|Upper-middle income|0_._38|2_._77|_−_98_._71|3_._68|
|High income|_−_0_._10|2_._39|_−_1111_._40|13_._38|
|||Di<br>|rect costs<br><br>||
||Tests|Ventilat|ors<br>Vaccines|Garments|
|Low income|_−_|0_._06|_−_0_._09|_−_|
|Lower-middle income|_−_0_._83|2_._24|_−_20_._59|1_._75|
|Upper-middle income|_−_0_._71|3_._67|_−_8_._78|6_._47|
|High income|_−_3_._62|2_._88|_−_36_._76|11_._23|
|||Ind|irect costs||
||Tests|Ventilat|ors<br>Vaccines|Garments|
|Low income|_−_|0_._04|0_._52|_−_|
|Lower-middle income|1_._68|_−_0_._49|_−_18_._68|_−_0_._61|
|Upper-middle income|1_._10|_−_0_._91|_−_89_._92|_−_2_._79|
|High income|3_._52|_−_0_._48|_−_1074_._64|2_._15|

Low income includes: AFG, BFA, BDI, CAF, TCD, COD, ERI, ETH, GMB, GIN, GNB, PRK, LBR, MDG, MWI, MLI, MOZ, NER, RWA, SLE, SOM, SSD, SDN, SYR, TGO, UGA, YEM. Lower-Middle income includes: AGO, DZA, BGD, BLZ, BEN, BTN, BOL, CPV, KHM, CMR, COM, COG, CIV, DJI, EGY, SLV, SWZ, GHA, HTI, HND, IND, IDN, IRN, KEN, KIR, KGZ, LAO, LSO, MRT, FSM, MNG, MAR, MMR, NPL, NIC, NGA, PAK, PNG, PHL, WSM, STP, SEN, SLB, LKA, TZA, TJK, TLS, TUN, UKR, UZB, VUT, VNM, PSE, ZMB, ZWE.

Upper-Middle income includes: ALB, ASM, ARG, ARM, AZE, BLR, BIH, BWA, BRA, BGR, CHN, COL, CRI, CUB, DMA, DOM, GNQ, ECU, FJI, GAB, GEO, GRD, GTM, GUY, IRQ, JAM, JOR, KAZ, UNK, LBN, LBY, MYS, MDV, MHL, MUS, MEX, MDA, MNE, NAM, MKD, PAN, PRY, PER, ROU, RUS, SRB, ZAF, LCA, VCT, SUR, THA, TON, TUR, TKM, TUV.

High income includes: AND, ATG, ABW, AUS, AUT, BHS, BHR, BRB, BEL, BMU, VGB, BRN, CAN, CYM, CHL, HRV, CUW, CYP, CZE, DNK, EST, FRO, FIN, FRA, PYF, DEU, GIB, GRC, GRL, GUM, HKG, HUN, ISL, IRL, IMN, ISR, ITA, JPN, KOR, KWT, LVA, LIE, LTU, LUX, MAC, MLT, MCO, NRU, NLD, NCL, NZL, MNP, NOR, OMN, PLW, POL, PRT, PRI, QAT, SMR, SAU, SYC, SGP, SXM, SVK, SVN, ESP, KNA, MAF, SWE, CHE, TWN, TTO, TCA, ARE, GBR, USA, URY, VIR.

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