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.

2