across four types of country GVC participation: commodities, limited manufacturing, advanced manufacturing and services, and innovative activities.

# **3.2 Methodology**

# _Estimation Methodology_

To use the BPS data for assessing the impact of COVID-19 on firm performance in the initial phase and the recovery phase, we estimate the following equation: where _Yict_ is one of a list of outcomes for firm _i_ in country _c_ at the time of the survey _t_ : change in total sales (in the domestic and foreign markets), an indicator of financial fragility (probability of falling into arrears), indicators for changes in employment at the intensive or extensive margin, indicators for increasing the use of digital technology and for engaging in product innovation, and an indicator for accessing public support.<sup>5</sup> The vector of controls _Xi_ includes pre-pandemic firm size categories, sector fixed effects, and an indicator for the firm being covered in the BPS panel, _πc_ designates country fixed effects, _Shockct_ captures the severity of the lockdown in the country at the time of the survey, _LR_ is an indicator for the latest survey round , and _εict_ is an independent and identically distributed error term. By including the interaction between each variable or vector of variables and _LR_ , our specification allows for changes in the coefficients between the pandemic’s initial phase (first survey round) and the recovery phase (latest survey round). Our main regressor of interest is _Engagementi_ which is one of the firm global engagement measures defined below.

To assess whether globally engaged firms experience a differential adjustment in a given outcome we estimate the margins (or marginal effects) for the measure of global engagement, keeping the distribution of firm size, sector, and country constant across the two survey rounds to ensure that our results are not driven by differences in sample composition.<sup>6</sup> For visual clarity, we show the estimated marginal effects from each regression in a figure, along with the corresponding confidence intervals at the 95% confidence level. Since sampling weights are not consistently available across countries, we control for variations in sample size by estimating Equation (1) by weighted least squares, using as weights the inverse of the number of observations in each country. The results presented in Section 5 rely on pooled cross-section analysis including the full sample of firms covered in each of the survey rounds, unless specified. In unreported results we replicate all analyses using only firms in the BPS panel, that is, those that are observed in both survey rounds. Restricting the sample to panel firms allows us to evaluate if attrition and replacement firms might affect the results obtained. Overall, results are consistent across the pooled cross-section and the panel samples. The panel results are available upon request.<sup>7</sup>

> 5Change in total sales is the reported percentage change in the 30 days prior to the survey relative to the same period of 2019. Increasing the use of digital technology is an indicator for firms that started using or increased the use of internet, online social media, specialized apps, or digital platforms in response to the COVID-19 outbreak.

> 6For information about predictive margins see Williams (2012).

> 7Cirera et al. (2021a) deal extensively with firm attrition in the panel, a methodological challenge in the use of BPS

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