With the data, we compute firm-level export and import values, volumes, prices, number of transactions, export destinations, import origin countries, and number of products. The average price calculation at the firm-level accounts for the fact that different firms produce different products. So, for every disaggregated product (8-digit HS code), we compute the average price per unit across all firms and all destinations in that month. For every firm, we compute a ratio most disaggregated produce-wise: the numerator is the price that the firm charges for that HS-8 product and the denominator is the average price of that product. Then, we compute a weighted firm-month average price per unit of these products in the firm’s export basket.

We define GVC firms as firms that imported and exported at least once in the Covid-19 preperiod (February 2019-November 2019). Non-GVC firms are either pure importers or pure exporters. There are 8,290 GVC firms (23.11%), 22,065 pure importers (61.52%), and 5,511 pure exporters (15.37%) in our data.

The data shows that 80.4 percent of export value for Indonesia was generated by GVC firms before Covid (i.e., before March 2020) and 82.1 percent after Covid. Overall, about 81.5 percent of Indonesia’s exports were generated by GVC firms in the sample period. These figures are slightly higher than what was reported in other studies where about two-thirds of export values were generated by importing-exporters (Cal`ı et al., 2022).

The data on non-tariff measures (NTMs) is sourced from the World Bank NTM database for Indonesia.<sup>1</sup> This dataset improves on existing data from UNCTAD-ERIA through more frequent updates, a wider variety of sources, and intensive forward and backward tracing that enables the creation of panel data. The data is hand collected through extensive regulatory checks, careful coding processes using the UNCTAD 3-digit MAST classification, and updated annually, making it one of the most up-to-date data on NTMs that exist, and ensuring only active regulations remain and inactive regulations are timely identified. As trade policy changes take place within the year, with over 13 government institutions issuing regulations and over 600 regulations in the data, this database allows for a timely assessment of policies, and changes to those policies, and more relevant in this paper, is sufficiently up to date for the analysis. While the data spans from January 2008 to December 2021, we use the 2019-2021 time span for the analysis to match with the trade data.

Figure 2 shows the monthly variation of incidences of NTMs in Indonesia both for products (a) and trade value (b) for all NTMs. While this gives some indication that there is some variation on a monthly basis and that the incidence has increased in 2021, it is too broad to draw meaningful interpretations. Most NTMs are designed to achieve public policy objectives, however, some NTMs can potentially affect trade. Even when this is not their main objective, such NTMs can hurt domestic producers by making imports more expensive, increasing business costs, and reducing productivity,

> 1Available at https://datacatalog.worldbank.org/search/dataset/0063543/indonesia_ nontariff_measures

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