Policy Research Working Paper 9498
Trade Policy Responses to the COVID-19
Pandemic Crisis
Evidence from a New Data Set
Simon Evenett
Matteo Fiorini
Johannes Fritz
Bernard Hoekman
Piotr Lukaszuk
Nadia Rocha
Michele Ruta
Filippo Santi
Anirudh Shingal
Macroeconomics, Trade and Investment Global Practice
December 2020
Policy Research Working Paper 9498
Abstract
This paper presents new high-frequency data on trade policy in their use of trade policy and the types of measures used.
changes targeting medical and food products since the Some countries acted to restrict exports and facilitate
beginning of the COVID-19 pandemic, documenting how imports, others targeted only one of these margins, and
countries used trade policy instruments in response to the many did not use trade policy at all. The observed hetero-
health crisis on a week-by-week basis. The data set reveals geneity suggests numerous research questions on the drivers
a rapid increase in trade policy activism in February and of trade policy responses to COVID-19, the effects of these
March 2020 in tandem with the rise in COVID-19 cases, measures on trade and prices of critical products, and the
but also uncovers extensive heterogeneity across countries role of trade agreements in influencing trade activism.
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 mruta@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
Trade Policy Responses to the COVID-19 Pandemic Crisis:
Evidence from a New Data Set*
Simon Evenett,# Matteo Fiorini,+ Johannes Fritz,# Bernard Hoekman,+
Piotr Lukaszuk,# Nadia Rocha,± Michele Ruta,± Filippo Santi+ & Anirudh Shingal++
Keywords: COVID-19, trade policy, export restrictions, import liberalization
JEL classification: F13, F52, I18
* This paper describes an ongoing EUI-Global Trade Alert-World Bank joint project to collect and
report information on trade policy measures put in place during the COVID-19 pandemic. The
findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.
They do not necessarily represent the views of the World Bank and its affiliated organizations, or
those of the Executive Directors of the World Bank or the governments they represent.
#
Global Trade Alert, University of St. Gallen
+
Robert Schuman Centre for Advanced Studies, EUI
±
World Bank Group
++
ICRIER (New Delhi)
1. Introduction
One of the instruments many governments resorted to in responding to the COVID-19 pandemic was
trade policy. Barriers to the importation of medical products and supplies and agricultural and food
products were lowered, and restrictions imposed on exports of such goods. The mix of import facilitation
and export controls was driven by the objective of maximizing the availability of critical supplies in the
domestic market. Real-time tracking of the use of trade policy measures is difficult as governments only
report changes in trade policy to the WTO with a lag and may not notify the WTO at all. Timely
information on trade policy measures matters for businesses seeking to expand production and needing
to source inputs to do so, to public authorities responsible for procuring critical supplies, and for policy
analysts interested in assessing the effects and effectiveness of policy responses to the pandemic.
This paper introduces a new data set on trade policy interventions – both to control exports and
facilitate imports of food, medical supplies, and personal protective equipment (PPE). The data set is
the product of an ongoing open data initiative conducted by the Global Trade Alert (GTA) in partnership
with the European University Institute and the World Bank to collect information on changes in trade
policy towards exports and imports of medical and food products starting in January 2020. 1 Until mid-
October 2020 the data were collected and reported on a weekly basis, providing the ability to track the
imposition and removal of measures across a wide range of countries. The high frequency of the data is
a unique feature of the exercise. The project is ongoing. The data used in this paper span the period from
January 2, 2020 to October 9, 2020.
Our aim in this paper is to provide a descriptive assessment of the trade policy measures implemented
in response to the COVID-19 pandemic. This paper and the accompanying database complement a
growing body of policy oriented economic research studying international trade during the COVID-19
pandemic. Early in the pandemic many analysts and organizations stressed the importance of keeping
borders open to allow supply chains to work efficiently in ramping up supply of critical products (e.g.,
Baldwin and Evenett, 2020; Bown, 2020a,b,c; Evenett, 2020; Espitia et al., 2020; Mattoo and Ruta,
2020; OECD, 2020; WTO, 2020). In practice, some countries did so; others did not. Understanding the
drivers of the differences in observed trade policy responses over time and across countries and sectors
is important for drawing inferences (and lessons) about the implications of the COVID-19 pandemic for
international cooperation and potential rule making looking forward. The data set allows empirical
analysis of a range of research questions. Some of these are discussed briefly on the penultimate section
of this paper, but we do not undertake such analyses here. Instead, our main aim is to raise awareness of
the data set in the hope others will be encouraged to use it.
The remainder of the paper is organized as follows. Section 2 presents the data set and provides basic
summary statistics. Section 3 highlights some stylized facts that emerge from the trade policy responses
to the COVID-19 pandemic. Section 4 discusses several hypotheses suggested by these stylized facts
and possible research questions that could be the subject of further analysis. Section 5 concludes.
2. Data set
The data collection initiative presented in this paper is confined to trade policy changes in two sectors:
medical goods and medicines, and agricultural and food products. The products falling under each of
1
Information on trade policy changes is processed and collated by the Global Trade Alert team. The data are posted at
https://www.globaltradealert.org/; https://globalgovernanceprogramme.eui.eu/covid-19-trade-policy-database-food-and-
medical-products/ and https://www.worldbank.org/en/topic/trade/brief/coronavirus-covid-19-trade-policy-database-food-
and-medical-products.
these sectoral headings are listed in the methodology note that accompanies the database. 2 Policy
interventions by national and sub-national governments, as well as actions taken at the supra-national
level by a customs union are included in the data collection exercise.
Trade policy measures are identified by the GTA team using the following methodology. First,
automated techniques are used to gather information in multiple languages on relevant trade policy
changes from many different sources including websites of relevant government agencies (which can
include finance ministries, trade ministries, customs agencies, health ministries or associated agencies,
and offices of the head of government or state); websites of relevant international organizations (IMF,
ITC, UNCTAD, WCO, WTO); online media sources, including social media; and non-state
organizations collecting information on the matters falling within the scope of this initiative, including
law firms, consulting companies, research institutes and other academic initiatives.
These sources are searched for key words in major languages relating to the policies and products
falling into the two sectors of interest. Such searches create “leads” which are processed to identify those
that legitimately characterize a trade policy change falling within the scope of the initiative. Every lead
recorded in the GTA’s systems is enriched in a multi-step analytical process. In the first step, an analyst
provides an initial assessment of whether the lead is of topical relevance (within scope as defined in the
last section), the implementing jurisdiction, the policy instrument category described, the product
categories affected, and the direction of the trade policy change. This initial assessment is subsequently
validated by a second analyst who confirms, amends, or rejects the first’s choices. Once past this initial
assessment, relevant leads then move into the GTA reporting pipeline. Where missing in the first step,
an official source is sought and, if found, saved for each lead. Once the official source has been located,
the responsible GTA trade policy analyst processes the lead into a GTA database entry that combines a
brief description of the state act along with identification of the most accurate policy instrument among
a rich taxonomy plus the appropriate United Nations product and sectoral codes. 3
The final database on trade policy changes includes information on the sector affected (medical
products and/or food); the type of information used to document the trade policy change (official source,
report on the Global Trade Alert website, media sources, or consultancy or law firm reports); the
jurisdiction implementing the trade policy change; an initial assessment whether the trade policy change
is liberalizing or restrictive; where relevant/available the date the trade policy change was
announced/was implemented and lapsed; and whether the trade policy change affects imports and/or
exports.
The version of the data set used in this paper spans the period up to October 9, 2020. It includes 701
policy measures covering 135 customs territories (the European Union, the South African Custom
Union, and the Eurasian Custom Union are considered as a single territory, independent from of their
member states). 4 For the ensuing descriptive analysis of trade policy measures across countries and over
time we consider the date of announcement as the starting date of each measure. We clean the data
accordingly to remove inconsistencies in the reported measures. We also exclude measures with a 2019
announcement date, i.e., before the COVID-19 outbreak. This leaves 645 measures, of which 284 have
no removal date indicated and 52 are to be removed in 2021 or later.
2
See https://globalgovernanceprogramme.eui.eu/wp-content/uploads/2020/05/Methodologynote050420.pdf.
3
For products this is the Harmonized Commodity Description and Coding System (HS); for sectors the UN use is made of
the International Standard Industrial Classification (ISIC).
4
In the case of the European Union, some member states also took individual actions to restrict or encourage trade in medical
supplies. These countries included Belgium, France, Germany, Greece, Hungary, Italy, Latvia, Poland and Romania. In the
analysis that follows the scores attributed to individual members of a custom union are added to whatever measures are
implemented by the union. That is, the EU and its member states are identified as distinct implementing jurisdictions. This
permits identification of both the EU as an entity and individual EU countries in the scatterplots that follow, with data for
member states comprising those measures implemented on top of the EU-level ones (which are identified by the label EU).
2
The discussion that follows focuses on two types of measures: export restrictions and import
liberalization-cum-facilitation. These measures cover most of the measures in the data set. Details on
the set of policy instruments included in these two categories are reported in Appendix Table A1. 5 The
data set encompasses significantly more measures than reported by the two international organizations
that have engaged in monitoring exercises. The first of these is the International Trade Centre (ITC),
which launched a COVID-19 dashboard in early 2020 that provides information on trade measures taken
by countries. The focus of this effort is not limited to food and medical products. As of October 9, 2020,
the ITC repository comprised 338 measures implemented by 145 countries, custom unions and
autonomous territories, substantially fewer than what is reported by the GTA. 6
The second is the WTO, which reports on COVID-19 related measures notified by its members and
maintains a separate data set on export and import-related measures compiled by the WTO Secretariat
that is drawn from official sources. The latter includes only measures that are verified by the WTO
members concerned. Transparency is a fundamental dimension of WTO membership. This also applies
to emergency measures. WTO members must notify quantitative restrictions that are motivated by
domestic emergencies, including public health. The relevant 2012 Decision on Notification Procedures
for Quantitative Restrictions (WTO G/L/59/Rev.1) stipulates that notifications must occur at two yearly
intervals, and more salient to the present context, report changes as soon as possible, and no later than
six months from their entry into force (Hoekman, 2020). 7 WTO members may engage in so-called
reverse notifications as well, reporting on measures imposed by other countries and customs territories.
In principle, transparency through notification and reverse notification supports discussion in the
WTO of measures taken. In practice many WTO members have not lived up to their transparency
obligations, notwithstanding commitments by G20 Trade Ministers to notify the WTO of trade-related
measures taken. As of October 9, 2020, 55 WTO members had submitted 245 notifications related to
COVID-19. These span export restrictions and import liberalization/trade facilitation measures as well
as changes in product regulation (standards) and economic support programs. Brazil is the leader in
having notified 28 measures, followed by Kuwait (16), the United States (14), the European Union (12),
the Philippines (11), Thailand (11) and the Republic of Korea (9) (Figure 1). Three-quarters of these
COVID-19 related notifications pertain to product standards for medical supplies and PPE. 8 Through
October 9, 2020 only 58 COVID-19 related notifications did not pertain to sanitary and phytosanitary
(SPS) or technical barriers to trade (TBT). This compares to 645 measures – both export restrictions and
import liberalization/facilitation – targeting food and medical products in the GTA data set. 9
5
Note that the data do not include changes in health and safety standards applicable to the products concerned, i.e., sanitary
and phytosanitary measures and technical barriers to trade (product standards).
6
The ITC considers overseas and autonomous territories separately from the home country. This is not done in the GTA or
WTO monitoring exercises. See https://www.macmap.org/covid19.
7
The 2013 Agreement on Trade Facilitation similarly has transparency requirements, requiring WTO members to publish
promptly information on import, export or transit restrictions or prohibitions.
8
This is consistent with the May 14, 2020 G20 Trade Ministerial commitment to: “Reduce sanitary and technical barriers
by encouraging greater use of relevant existing international standards and ensuring access of information on relevant
standards is not a barrier to enabling production of PPE and medical supplies.”
9
As noted, the GTA COVID-19 monitoring exercise does not encompass SPS and TBT measures.
3
Figure 1: GTA vs. WTO Monitoring
Panel A: Notifications Panel B: WTO Secretariat monitoring
Source: Hoekman (2020).
Notes: To increase legibility, both panels only report the countries with at. least 3 measures recorded in the GTA
data. This leaves out half of the countries for which the GTA collected information (54 out of 109), which account
for less than 13% of the total measure included in the GTA data.
Matters are even worse than suggested by the figure because some countries’ notifications concern
updates for the same measure and some pertain to support programs, neither of which are included in
the GTA COVID-19 data set. 10 The differences in terms of coverage are illustrated in Figure 1. Panel
(a) plots the number of non-SPS, non-TBT related notifications to the WTO (the red diamonds) against
the measures covered by the GTA. Panel (b) instead compares the data on export and import-related
measures compiled by the WTO Secretariat from official sources and that members have verified. This
shows more overlap with the GTA data, but still reveals a significant discrepancy: through October, 9
2020, the WTO secretariat reported 243 verified measures taken by 85 member/observer states,
substantially fewer than the 645 found by the GTA. 11
3. COVID-19 trade policy responses across countries and time
In this section we discuss some of the stylized facts emerging from the data on use of export restrictions
and import liberalization-cum-facilitation measures during the first nine months of the COVID-19
pandemic.
3.1. Food vs. Medical Products; Restrictions vs. Trade Facilitation
A first stylized fact is that measures targeting medical products and PPE dominate, accounting for two-
thirds of all trade measures taken. Food is less in focus. Medical sector measures are divided roughly
equally between export restrictions and import facilitation. For the food sector, import liberalization-
cum-facilitation represents 55% of the relevant policy changes (Figure 2).
10
For example, Australia has more notifications to the WTO (6) than policies captured by the GTA (1). The latter aims to
facilitate imports of PPE. Australia’s notifications pertain to updates for this one measure.
11
WTO information on notification and measures reported by the Secretariat were accessed on 9. October 2020 from
https://www.wto.org/english/tratop_e/covid19_e/notifications_e.htm#:~:text=COVID%2D19-,WTO%20members'%20no
tifications%20on%20COVID%2D19,notifications%20related%20to%20COVID%2D19 and
https://www.wto.org/english/tratop_e/covid19_e/trade_related_goods_measure_e.htm, respectively.
4
Figure 2: Export restrictions and import facilitation in food and medical products
Food:
Restriction
Medical: 16%
Facilitation
32%
Food:
Facilitation
19%
Medical:
Restriction
33%
Note: Some 7% of the policy measures recorded by GTA involve both Food and Medical products. These measures have been
counted in both sectors.
Table 1 reports the evolution of active measures over time. Apart from medical goods in March 2020,
import facilitating measures outnumber export restrictions. Overall, import facilitation slightly
dominates the trade policy responses in every month since February 2020, for both food and medical
products. Export restricting measures peak in April/May 2020, but the overall number of measures does
not vary significantly during the period.
Table 1: Active measures over time
Food Medical
Import Facilitation Export Restrictions Import Facilitation Export Restrictions
February 14 6 13 6
March 17 8 24 49
April 47 33 141 120
May 59 32 170 126
June 68 30 177 112
July 66 26 172 109
August 70 28 180 112
September 80 34 183 114
October 75 33 149 107
Notes: The number of active trade policies is computed at the 15th of each month. Roughly 7% of total policies
recorded by GTA involve both Food and Medical products. These measures have been counted in both sectors.
Similar patterns of relative importance across sectors and categories emerge when looking at the value
of trade covered by recorded measures. To provide a rough estimate of such values we have used UN
Comtrade data for 2019 (or latest year available) to estimate the amounts of trade which may be
implicated by the interventions in our data set. For the inclusion into the GTA database, the products
affected by each intervention are classified according to the Harmonized System (HS). For our analysis,
we use HS codes at the 6-digit-level, the most granular product breakdown for which there is comparable
international trade data available through UN Comtrade. To estimate the amount of trade that may be
implicated by each intervention, we sum over the trade flow values in the affected products in the year
prior to the intervention. The estimates account for geographic restrictions on the potentially affected
trading partners such as FTA exemptions or targeted interventions. Furthermore, to remove de minimis
trade flows, we apply a minimum threshold of USD 1 million per HS code – trading partner combination.
5
In terms of the value of trade covered, trade policy changes in medical goods outweigh those in food
products by a factor of 3 or higher (Table 2, panel a). At the same time, trade reforms in both product
groups outweigh the newly erected barriers in our sample. We estimate that the export curbs in medical
goods and medicines covered international trade worth $135 billion. Whereas the many import reforms
in the same sector covered $165 billion of 2019 trade. In the case of food and agri-food products, the
comparable totals are $39 billion and $42 billion, respectively. The instability of trade patterns is an
important caveat for the estimates presented above. Basing our estimates on trade values in medical
supplies and equipment that pre-date the COVID-pandemic may misrepresent the scale of the problem.
Global demand for medical supplies and equipment spiked when the pandemic encompassed ever more
countries. The estimates presented for these products below may thus understate the magnitudes of
implicated trade.
Table 2 (panel b) disaggregates the information by policy measure, while Table 3 looks at the share
of 2019 trade affected by the trade interventions over time. 12 The data show the importance of export
bans as trade restrictive measures in medical goods and tariff changes among the trade liberalizing
measures. In terms of the evolution of the share of trade covered by policy interventions over time, we
observe a surge in February-March-April 2020 for both trade liberalizing and restrictive measures on
medical and food products. Thereafter, the trade coverage of trade restrictive measures slightly declines,
while the coverage of liberalizing measures stabilizes.
Table 2: trade coverage of measures tracked by GTA database (US$ bn)
Panel a: Annualized totals of 2019 world trade
Export restraints Import reforms
Medical 134.6 165.2
Food 39.4 42.2
Panel b: Medical goods only
Trade restrictive measures Worldwide export coverage
All included instruments 134.6
Of which:
Export ban 101.8
Export licensing requirement 66.2
Export quota 1.4
Trade liberalizing measures
All included instruments 165.2
Of which:
Import ban 0
Import licensing requirement 6.3
Import quota 0
Import-related non-tariff measure, nes 7.5
Import tariff 145.7
Import tariff quota 12.5
Internal taxation of imports 88.6
12
Trade data are for 2019 given 2020 trade data are not yet available. The intention is simply to give a sense of the coverage
of the trade measures used. The figures in Table 2 estimate the total value of 2019 trade in essential goods that were affected
at least once according to the GTA data. The percentages reported in Table 3 are based on al interventions in force at the
end of the given month, using as denominator the total value of 2019 world trade in the food or medical essential goods,
respectively.
6
Table 3: Affected portion of trade over time
Food Medical
Import reforms Export restraints Import reforms Export restraints
February 4.8% 3.3% 5.6% 1.4%
March 5.1% 4.6% 8.4% 6.4%
April 6.1% 5.7% 10.7% 7.4%
May 6.2% 5.6% 11.3% 4.6%
June 7.0% 5.5% 12.2% 4.6%
July 6.7% 3.8% 11.3% 4.5%
August 6.9% 3.8% 11.1% 4.4%
September 6.8% 3.6% 11.4% 4.3%
Notes: All data in Tables 2 and 3 are based on interventions in the GTA database which were implemented since 1
January 2020 and affecting at least one product from the list of essential goods (available at
https://globalgovernanceprogramme.eui.eu/wp-content/uploads/2020/05/Methodologynote050420.pdf). Trade data
from UN Comtrade for 2019 or latest year available.
3.2. Who did what for how long?
There are some clear patterns in changes to trade policies observed during the first nine months of the
COVID-19 pandemic. First, there is a big jump in trade policy activism starting in February and
accelerating in March (top panel Figure 3), with the initial increase occurring in tandem with the rise in
the number of COVID-19 cases (the bottom part of Figure 3 plots the weekly change in global COVID-
19 cases).
Figure 3: Global COVID-19 Cases and Trade Policy Measures
7
Notes: For each regional sub-figure the upper part plots the number of active trade policy measures and the absolute
number of COVID-19 cases over time while the lower part plots the weekly variation of COVID-19 cases.
Source: COVID-19 data from John Hopkins University CSSE database.
As noted previously, there is a more marked increase in the number of measures pertaining to medical
products/PPE than actions targeting food products, and, for both sectors we observe a greater number
of liberalization-cum-facilitation measures than trade restrictions. The latter pattern is stronger after May
2020, reflected in a gradual decline in the overall number of export control measures for medical
products. Starting in August-September we also see a decline in the number of import-facilitating
measures, reflecting a partial rollback of temporary reductions in import barriers (tariffs, taxes) for
medical goods. The number of measures affecting food trade rise steadily throughout the period, with
trade facilitating measures only showing a slight decline in late September/early October. The
correlation between trade policies and the weekly increase in global COVID-19 cases is high, as
expected, and appears to be stronger for food-related measures than for trade policies towards medical
products (Table 4). Correlations that also consider cross-country variation reveal greater similarity
between food and medical product-related trade policy activism.
Table 4: Correlation between trade measures and number of global COVID cases
Weekly Variation Total Cases
Food Medical Food Medical
Restrictions .867* .666* .743* .515*
Liberalization .926* .806* .827* .657*
Source: COVID-19 data from John Hopkins University CSSE database.
Regional patterns (see Appendix Figure A1) confirm to some extent the timing of the aggregate (global)
response, but also reveal significant heterogeneity. Countries responded to the COVID-19 pandemic
with heterogeneous timings and different combinations of export controls and import liberalization-
cum-trade facilitating measures. Some countries acted both on the restrictive and liberalizing side
creating long term changes in their pre-COVID trade policy structures for the food and medical sectors;
other countries acted only on one side, either restricting trade or liberalizing it. However, short term
changes in trade policy were very frequent. Finally, many countries did not employ any trade policy
tool.
Figure 4 offers a visual representation at the country level of these different strategies by identifying
countries that did not implement any change in their trade policy structures, and, for active jurisdictions,
by distinguishing between temporary versus open ended changes in policy. Sectoral heterogeneity
emerges (again) in shaping trade policy responses across countries. The food sector (Panel (a) Figure 4)
is characterized by a higher number of inactive countries (colored white), and a higher tendency to
implement temporary rather than open-ended trade policy changes. Some major net exporters of food
tended to adopt a mix of import facilitation and export restrictions. Argentina and Indonesia did so on
an open-ended basis, whereas the Russian Federation and Kazakhstan did so on a temporary basis. Other
major food producers only took action to liberalize-facilitate imports of food products (e.g., Brazil and
the United States). Many net importers in Africa did the same, but on an explicitly temporary basis.
Turning to trade policies towards medical products, there are again countries that take only measures
to facilitate trade. Australia, Chile, and New Zealand are among those that did so on a temporary basis,
while Canada, Colombia and Thailand are examples of nations that did so on an open-ended basis. In
contrast, many European countries stand out by imposing measures to temporarily control exports, while
other countries – Mexico and North African countries – do so on an open-ended basis. Many countries
act on both the export and import margin to enhance domestic access to (availability of) medical supplies
and PPE. This is the case for several large countries, e.g., Brazil, China, India, South Africa, and the
United States.
8
Figure 4: Trade policy responses and COVID-19 cases across countries
Notes: Blank (white) fill implies no measure of any type was implemented. Bubbles are representative of the
absolute number of COVID-19 cases in each country for which data were available. Data on COVID-19 cases are
sourced from The John Hopkins University CSSE COVID-19 Data. All information was extracted on 9 October
2020.
Figure 5 explores the variation of trade policy responses over time and across geographic regions. As
far as the food sector is concerned, South Asia appears as the most active region in the implementation
of both restrictive and liberalizing trade policy changes. When it comes to policies affecting medical
goods, the Latin America and the Caribbean region has the highest number of active import
9
liberalizations, followed by East Asia and Pacific. The Western Europe and Balkans sub-region in the
Europe and Central Asia region shows instead the highest number of active export restrictions.
Figure 5: Trade policy responses over time and across geographic regions
Note: Number of active policies refers to the declared termination date.
A richer visualization of the different trade policy strategies in given in Figure 6. In both panels (A for
food and B for medical goods) countries are displayed in terms of the duration (in days) of the longest
period in which there is at least one active import facilitation action (measured on the horizontal axis)
and the duration of the longest period with at least one active export restriction (measured on the vertical
axis). The value "Open" at the end of each axis identifies open-ended measures (with no end date
specified in the relevant documents) and measures with an end date that is in 2021 or later. The size of
each country code displayed in the chart is proportional to the total number of trade measures
implemented (both facilitating and export restricting), while its color identifies the associated
geographic region defined in the legend at the bottom of each panel.
Figure 6 drills down further into the timing of policy changes by country/jurisdiction and highlights
the variation across countries in the duration of temporary interventions. Compare for instance the very
short duration of German and French restrictions on medical exports (displayed on the lower part of the
vertical axis in Figure 6, Panel B) with the longer duration export restriction strategy chosen by Belgium
or Italy (at the upper end of the vertical axis).
10
Figure 6: Trade policy responses. Who did what for how long?
Panel A: Food
Panel B: Medical
11
Notes: Colored lines point to the names of countries located in the same space of a graph. Countries are
displayed in terms of the duration (in days) of the longest period with at least one active import liberalization
measure (horizontal axis) and at least one active export restriction (vertical axis). The value "Open" at the end
of each axis identifies measures without an end date or that extend beyond December 2020. The font size of
each country code is proportional to the total number of measures implemented; colors identify the associated
world region.
Keeping the focus on European countries, Figure 6 also captures the heterogeneous responses of EU
Member States with respect to each other as well as to the EU common commercial policy. The figure
illustrates that the EU chose an open-ended import liberalization strategy for the food sector (see the EU
label in the bottom-right corner of Panel A). Apart from Romania (which imposed a temporary export
ban), other EU member states did not adopt any idiosyncratic measure to complement the EU-level
policy response. Once again, the picture looks different for medical goods. In that sector the strategy of
the EU – temporary export restrictions (lasting less than 100 days) combined with a 6 month period of
sustained import facilitation – has been accompanied by numerous idiosyncratic measures affecting
trade at the EU Member State level, with a common feature being that national actions complement
those imposed for the EU as a whole by involving export controls and restrictions.
More broadly, Figure 6 shows how countries differ in the duration and mix of trade policy measures
used for each sector. It identifies which countries primarily engaged in import facilitation (located at the
far right of the horizontal axis); others restricted exports without facilitating imports (those located at
the top of the vertical axis) and many did both, with some imposing many more measures than others
(reflected in the size of the font of each country label).
The data also allow identification of the specific type of measure beyond the broad categories of
export restrictions and import liberalization-cum-facilitation. Appendix Figure A2 uses all information
in the database to plot the structure of trade policy responses in the medical products sector for three
populous countries with the highest absolute number of COVID-19 cases: Brazil, India and the United
States. Comparing the three panels in Figure A2, we see substantial cross-country heterogeneity in the
use of specific instruments.
Figure 7 reports the region-specific and global distribution of temporary versus de jure open-ended
measures (Panel A). Within the category of temporary measures, it also distinguishes between those that
were removed during the nine-month period under consideration and those that remained in place as of
October 9, 2020 (Panel B).
At the global level, there is an approximate balance between temporary and open-ended measures,
but there is significant heterogeneity at the reginal level. The Western Europe and Balkans region has
the highest percentage share of temporary measures (Panel A) but also the highest share of measures
that were still active (Panel B). The East Asia and Pacific and the Europe and Central Asia regions also
reveal a prevalence of temporary changes in their trade policy structures, with most of the measures
removed as of Octobers 2020. All other regions trade policy responses tend to use more intensively
open-ended measures.
Finally, Figure 8 presents yet another dimension of heterogeneity in trade policy responses: the
degree to which jurisdictions employ price-based (tax) as opposed to quantitative restrictions. The
import facilitation side of the global trade policy response mostly revolves around price-based measures
(reductions in taxes and import tariffs). The opposite is true for export controls, especially for medical
products where systematically across regions more than two-thirds of trade policy changes targeted
quantities rather than prices.
12
Figure 7 – Medical Sector: Temporary VS Long-term (open-ended)
Panel A:Temporary vs Open Ended Panel B: Removed vs Active Temporary measures
Notes: Panel (a) reports medical related import liberalization/facilitation and/or export restrictions by declared
duration. Open-ended measures include all policies for which no removal date is specified or with a duration
that extends beyond 2020. Panel (b) focuses on measures that were reported as explicitly temporary with end
dates in 2020, distinguishing between those that had been removed as of October 9, 2020 and those that
remained to be withdrawn before the end of 2020.
Figure 8 – Price versus quantitative measures
Panel A: Food Panel B: Medical
Notes: Price targeting measures include export taxation, internal import taxation, import tariffs and tariff
quotas. Quantitative measures include import/export quotas, generic non-tariff measures, import/export bans,
licensing requirements. The residual “Unknown” category groups all measures with no clear description. Food-
and Medical- related measures are reported in Panel A and Panel B respectively.
13
4. Avenues for future research based on these data
As mentioned, the primary purpose of this paper is to raise awareness of the COVID-19 trade policy
data set and encourage its use. In this section we briefly sketch out possible research questions that are
suggested by the patterns we observe in the data.
4.1 Policy choice
A first set of research questions centers around understanding the motives underlying choices in the
design and implementation of trade policy responses to the pandemic, reflected in the substantial cross-
country heterogeneity observed in the data. These include the timing, the duration, and the use of
different instruments. Potential explanatory variables include mechanisms pertaining to the medical, the
economic and the political spheres, e.g., public health parameters of the COVID-19 pandemic; features
of the pre-crisis trade and trade policy structures, such as trade dependency, GVC participation and
positioning; degree of existing protection; integration in multilateral or plurilateral trade policy
initiatives; the political forces determining the quality of government action etc. An important corollary
exercise would be to test whether and how such empirical relationships vary between the food and the
medical sectors and potentially between more disaggregated product-level categories.
Herding effects could be considered as well, calling into question whether individual observed policy
choice is independent of the choices of others. Another source of interdependence across policy choices
arises from membership of regional trade agreements. In this regard, the role that the European
Commission played in replacing and then reining back the export controls imposed initially by Member
States of the European Union is worthy of further study.
Econometric analyses could be complemented by some well-chosen country case studies. For
instance, the Indonesian government started by exhorting local producers not to sell masks abroad. Then,
Jakarta imposed an export limit or share. When that did not ‘work’ they banned exports of masks. This
sequence of policy steps is different than jumping in with an export ban in the first place, reflecting,
potentially, different political economy factors. Another example of potentially interesting country-
specific case study relates to Vietnamese export controls on rice. The government initially banned
exports, but then the Prime Minister progressively watered down and abandoned the export controls.
News reports suggest that opposition from rice farmers was important, but did other factors play a part
in this policy reversal?
An example of research in this vein concerns the relationship between recourse to export controls
and actions to facilitate imports. Do governments imposing export restrictions also act on the import
side? Insofar as governments are expected to use both types of policy measures to maximize local access,
we can examine whether there is more liberalization on the import side conditional upon imposing
export restrictions estimating an equation as follows:
������������������������������������ ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������ = exp(������������ + ������������������������������������������������ ������������������������������������������������������������������������������������������������������������������������������������������������������������ + ������������′������������������������ ) + ������������������������ (1)
where ������������������������������������ ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������ and ������������������������������������ ������������������������������������������������������������������������������������������������������������������������������������������������������������ are the logarithm of 1 plus the number of import
liberalizations and export restrictions, respectively, implemented by jurisdiction ������������; ������������������������ is a vector of
control variables; ������������ is a constant and ������������������������ the error term. The results from estimating equation (1) using
the Poisson Pseudo Maximum Likelihood Estimator, reported in Table 5, suggest that conditional upon
having imposed export restrictions on medical products, there is more liberalization on the import side
(the coefficient on export restrictions is statistically significant at 1%). Ceteris paribus and on average,
a 10% rise in the number of export restrictions is found to be associated with a 5% increase in the number
of import liberalization measures in these results. This is consistent with a prior that countries using
trade policy instruments in response to the crisis will do so by targeting both the import and export
margin of trade.
14
The role of foreign policy variables, e.g., national security/defense relationships or the extent to
which countries seek to give preferential treatment to (some) developing countries or former colonies is
another potential research question. A feature of the implementation of export restrictions in the EU was
that actors could request exemptions based on such considerations. But presumably there was also
lobbying by countries, firms and interest groups (NGOs) for selective removal of measures. An
assessment of the impact of trade measures on developing countries using the data set could be an input
into such more granular political economy research questions.
Table 5: Import liberalization conditional on export controls (medical products)
Dependent variable: ������������������������������������ ������������������������������������������������������������������������������������������������������������������������������������������������������������������������
������������������������������������ ������������������������������������������������������������������������������������������������������������������������������������������������ 0.54***
(0.18)
������������������������������������(1 + TAR������������ ) 0.59**
(0.21)
Constant -1.69**
(0.83)
Observations 108
R-squared 0.59
Note. Regression includes controls for government effectiveness; revealed comparative advantage;
population; GDP weighted geographic distance to global markets; number of COVID-19 cases and import
dependence. Robust standard errors, clustered by country, in parentheses. Levels of significance: *10%,
**5%, ***1%.
4.2 Policy impact
A second set of potential questions revolves around the quantification of the effects of different trade
policy strategies. Natural outcome variables varying at the country, sector and time level include trade
flows, extent of/reliance on GVC participation and positioning, and (changes in) offshoring and
reshoring. Observed trade policy strategies may also be associated with firm-specific performance, the
timing and scope of fiscal policies or features of applied lock-down measures.
We take a first look at some of these effects by assessing the potential impact that decreases in the
global supply of food and medical products could have on international prices because of an increase in
export restrictions imposed on these products. 13 To calculate the impact of export restrictions imposed
on product k on its international price, we use a standard partial equilibrium approach and divide the
changes in global supply of such product by its import elasticity:
∆������������������������������������������������������������������������ ������������������������������������������������������������������������������������
������������ℎ������������������������������������������������ ������������������������ ������������������������������������������������������������������������ = ������������������������������������������������������������������������������������������������������������������������� (2)
������������
Changes in global supply are captured by the share of world exports that are covered by exports bans
that have been imposed by exporters between January and October 2020. 14 Product elasticities are
estimated for a panel covering 152 importing countries by Fontagné, Guimbar and Orefice (2019) and
13
For other applications see Espitia, Rocha and Ruta (2020a,b).
14
The trade flows used to compute export shares are based on UN Comtrade statistics for 2019 or most recent year available.
These calculations do not account for the total duration of the export bans imposed by countries.
15
are assumed to be constant across countries. Our estimates focus on those restrictions that prohibit
exports (export bans) and therefore could be considered as a lower bound estimation of the potential
impact of restrictive export policy on prices.
Results by product category are presented in Table 6. 15 Due to export bans, food prices rise by 0.7
percent on average, with increases in prices for different food categories ranging between 0.1 for
chemical products such as pesticides to 1.2 for fresh fruits and vegetables. The impact of export bans on
medical products is more pronounced: prices increase by 3.3 percent on average, with increases of 12.9
percent on prices of key critical items identified by the World Health Organization Covid-19 Disease
Community Package (DCP) to deal with the current crisis. Within the critical medical products, personal
protection equipment such as face masks and protective clothing have experienced increases in prices
of 22.2 percent and 20 percent, respectively.
Table 6: EXPORT SUPPLY AND PRICE EFFECTS OF EXPORT BANS
Change in prices
Product Affected Trade Share
(Trade weighted)
Medical Products 8.1% 3.3%
Critical Products 7.4% 12.9%
Case Management 1.8% 0.7%
Diagnostics 7.1% 1.4%
Hygiene 1.0% 0.1%
Personal Protection Equipment 40.0% 17.7%
Non-Critical Products 8.3% 1.4%
Anti-epidemic goods 0.9% 0.7%
Food preparations 0.5% 0.3%
Manufacturing of Masks 0.8% 1.2%
Medical Equipment 0.5% 0.7%
Medical Supplies 7.4% 2.4%
Medicines 14.4% 1.1%
Food Products 0.8% 0.7%
Cereals 0.9% 0.2%
Chemical products 0.6% 0.1%
Fertilizers 0.0% 0.0%
Fresh Fruits – Vegetables 1.5% 1.2%
Inputs for animal feed 0.0% 0.0%
Miscellaneous edible preparations 0.3% 0.0%
Oil-bearing 0.9% 0.0%
Seeds 0.0% 0.0%
Stimulant crops 0.0% 0.0%
Sugar 3.6% 0.9%
Vegetable and animal oils 1.0% 0.6%
4.3 Compatibility with WTO rules and trade agreement commitments
The trade policy monitoring exercise does not take a view on the consistency of the changes in trade
policy that are registered with a jurisdiction’s commitments under the WTO or extant regional trade
agreements. However, the extent and determinants of compliance with obligations in trade agreements
are both important research questions. Trade agreements permit governments to restrict trade in response
to a public health emergency such as the COVID-19 pandemic, but whether and how this is done may
reflect a desire to exempt close partners and may or may not result in designing interventions to conform
15
To aggregate the estimations in equation 1 by product category we use a weighted average across products where the
weights are represented by the share of world exports of each product.
16
with rules relating to transparency and temporariness of measures. One reason why we have stressed
that the data set distinguished between open-ended and time-bound measures is that such information is
needed to assess performance considering international disciplines. The data set permits analysis of
compliance with – and the effects of – applicable trade agreements. An example of such research is
Hoekman et al. (2020), who investigate the relationship between the use of trade policy observed in the
first 6 months of the pandemic and characteristics of national public procurement regimes, including
membership of the WTO Agreement on Government Procurement and deep trade agreements that
include provisions on public procurement. They find statistically strong associations between
procurement regimes and use of trade policy.
A related research question concerns the potential impact of cooperation aimed at enhancing
transparency. Several observers have pointed to the example of the Agricultural Monitoring and
Information System (AMIS) as a possible explanation for the limited recourse to export restrictions on
food – and a potential model for cooperation to generate information on production capacity and trends
in markets for critical medical supplies (Hoekman, Fiorini and Yildirim, 2020). Does the way AMIS
was designed and executed—maybe in terms of crops covered and information collected and made
available—account for the smaller number of export controls observed in the food and agri-food sector?
Here it would be important to understand where this transparency mechanism has teeth and where it
shows up in the data.
5. Concluding remarks
This paper presents new, high-frequency data on trade policy changes in two sectors that are critical
during the COVID-19 pandemic: medical goods and medicines, and agricultural and food products. The
data were collected on a weekly basis, and span the period from January 2 to mid-October 2020. 16 The
data record the jurisdiction implementing the policy change, the direction of the measure (i.e. trade
liberalizing or restrictive), the type of measure (e.g. export ban, tariff reduction), the timeline of the
measure and the sectors covered by the policy.
The data reveal several stylized facts on trade policy changes over the first nine months of the
COVID-19 pandemic:
• There was a big jump in trade policy activism starting in February 2020. This accelerated in March,
with the initial increase occurring in tandem with the rise in the number of COVID-19 cases. At
the global level, there is an approximate balance between temporary and open-ended measures,
but there is significant heterogeneity at the regional/country level.
• Measures targeting medical products and PPE dominate, accounting for two-thirds of all trade
measures taken. Food is less in focus. In terms of the value of 2019 trade covered, trade policy
changes in medical goods outweigh those in food products by a factor of 3 or more.
• Export curbs in medical goods covered international trade worth $135 billion (of 2019 trade).
Whereas import reforms in the same sector covered $165 billion. In the case of food and agri-food
products, the comparable totals are $39 billion and $42 billion, respectively. Because these
estimates are based on 2019 (i.e. pre-pandemic) trade data, they may understate the magnitudes of
implicated trade, especially for medical products.
• Countries responded to the COVID-19 pandemic with different combinations of export controls
and import liberalization measures. Some countries acted both on the restrictive and liberalizing
side creating long term changes in their pre-COVID trade policy structures for the food and
16 Weekly data are available through October 15, 2020. Looking forward, the data will continue to be compiled monthly.
17
medical sectors; other countries acted only on one side, either restricting trade or liberalizing it.
Although many countries resorted to trade policy instruments, it is noteworthy that numerous
countries did not resort to trade policy tools at all.
• There is also substantial heterogeneity across countries in the types of trade instruments used and
the extent and speed with which crisis-motivated trade measures were removed.
The patterns of observed trade policy activism in response to the COVID-19 pandemic raise many
questions regarding the determinants and underlying political economy of trade policy. We have
suggested several areas for research using the data set and hope the data collection exercise will stimulate
such research. Understanding the motives underlying choices in the design and implementation of trade
policy responses to the pandemic, reflected in the substantial cross-country heterogeneity observed in
the data, is one area. Another concerns quantification of the effects of different trade policy strategies,
both for the world as a whole and for the countries concerned. The preliminary analysis reported above
suggests that export bans increased international prices of critical medical goods, especially for PPE. A
third policy research question the data can help address revolves around the broader question of the
value of trade agreements. To what extent did countries comply with basic provisions of trade
agreements calling for transparency, and for trade restrictions in response to emergencies to be
temporary and necessary? These are just some of the research questions suggested by the stylized facts
revealed by the data set. No doubt there are many others, including questions that are not directly related
to trade.
References
Baldwin, R. and S. Evenett (eds). 2020. COVID-19 and Trade Policy: Why Turning Inward Won't Work,
VoxEU.org eBook, CEPR Press. At: https://voxeu.org/content/covid-19-and-trade-policy-why-
turning-inward-won-t-work
Bown, C. 2020a. “Trump's trade policy is hampering the US fight against COVID-19,” March 13.
https://www.piie.com/blogs/trade-and-investment-policy-watch/trumps-trade-policy-hampering-us-fight-
against-covid-19
Bown, C. 2020b. “EU limits on medical gear exports put poor countries and Europeans at risk,” March
24. https://www.piie.com/blogs/trade-and-investment-policy-watch/eu-limits-medical-gear-exports-put-poor-
countries-and
Bown, C. 2020c. “COVID-19: Trump’s curbs on exports of medical gear put Americans and others at
risk,” April 9. https://www.piie.com/blogs/trade-and-investment-policy-watch/covid-19-trumps-curbs-
exports-medical-gear-put-americans-and
Dong E, H. Du and L. Gardner. 2020. An interactive web-based dashboard to track COVID-19 in real
time. Lancet Inf Dis. 20(5): 533-34.
Espitia, A., N. Rocha and M. Ruta. 2020a. “Trade and the COVID-19 crisis in developing countries,” at
https://voxeu.org/article/trade-and-covid-19-crisis-developing-countries
Espitia, A., N. Rocha and M. Ruta. 2020b. “COVID-19 and Food Protectionism: The Impact of the
Pandemic and Export Restrictions on World Food Markets.” Policy Research Working Paper; No.
9253. World Bank, Washington, DC.
Evenett, S. 2020. “Tackling COVID-19 Together: The Trade Policy Dimension,” Global Trade Alert,
March 23. At: https://www.globaltradealert.org/reports/51
Fontagné, L., H. Guimbard and G. Orefice. 2019. “Product-Level Trade Elasticities.” CEPII working
paper 2019-17.
Hoekman, B. 2020. “COVID-19 Trade Policy Measures, G20 Declarations and WTO Reform,”
“COVID-19 Trade Policy Measures, G20 Declarations and WTO Reform,” in S. Evenett and R.
18
Baldwin (eds.), Revitalising Multilateralism: Pragmatic ideas for the new WTO Director-General.
London: CEPR Press.
Hoekman, B. M. Fiorini and A. Yildirim. 2020. “COVID-19: Export controls and international
cooperation,” in R. Baldwin and S. Evenett (eds.), COVID-19 and Trade Policy: Why Turning
Inward Won’t Work. CEPR Press.
Hoekman, B. A. Shingal, V. Eknath and V. Ereshchenko. 2020. “COVID-19. public procurement
regimes and trade policy,” COVID Economics: Vetted and Real Time Papers, 55, 81-99.
Leibovici, F. and A. Santacreu. 2020. International trade of essential goods during a pandemic. Covid
Economics 21. https://cepr.org/sites/default/files/news/CovidEconomics21.pdf
Mattoo, A. and M. Ruta. 2020. “Don’t close borders against coronavirus,” Financial Times, 13 March.
OECD. 2020. “COVID-19 and International Trade: Issues and Action,” https://read.oecd-
ilibrary.org/view/?ref=128_128542-3ijg8kfswh&title=COVID-19-and-international-trade-issues-and-actions
WTO. 2020. “Trade in Medical Goods in the Context of Tackling COVID-19.” April.
https://www.wto.org/english/news_e/news20_e/rese_03apr20_e.pdf
19
APPENDIX
Table A1: Specific measures included in the two broad categories of export restrictions and
import facilitation
Export Control Import Facilitation
Export ban 123 Import Internal taxation 54
Export licensing requirement 73 Import licensing requirement 13
Export quota 13 Import quota 8
Export tax 5 Import tariff 161
Export-related non-tariff measure, nes 2 Import tariff quota 15
Export-related, Unknown measure 21 Import-related non-tariff measure, nes 5
Import-related, Unknown measure 46
Total 237 Total 302
Notes: GTA Intervention Classes as included in the classification adopted in this paper. 106 measures are excluded
from the current analysis (81 and 25 targeting imports and exports respectively)
20
Figure A1: World and regional aggregate trade policy responses and COVID-19 cases
21
Notes: For each regional sub-figure the upper part plots the number of active trade policy measures and the
absolute number of COVID-19 cases over time while the lower part plots the weekly variation of COVID-19 cases.
22
Figure A2: Trade policy response in the medical sector, selected large countries
Panel A: US Panel B: Brazil Panel C: India
Notes: Measures reported by announcement date and expected duration. A black contour indicates that no
removal date is reported; a red contouring indicates that the measure is expected to be lifted after Dec 31st,
2020.
23