In all tables and analysis, we use sampling weights provided in the ES COVID-19 Follow-up Surveys to correct for unequal probability of selection as well as ineligibility for all reported statistics. We also rescale the sampling weights to give equal weight to all countries.

# **2.1 How many firms received government support and when?**

Our sample includes 3,647 firms across 15 countries that reported whether they received government support in Rounds 1, 2, and 3 and reported changes in performance in Round 3. Table 1 reports the share of firms that received government support by survey round. About 4 in 10 firms reported receiving support in Round 1; in Round 2 and Round 3 the share of firms reported doing so drops to about a quarter. Cumulatively, by Round 3 just over half of firms (55 percent) reported having received support. The support measure reaching by far the largest share of firms was wage subsidies, with 44 percent of firms – or 8 out of 10 firms that received any type of support – receiving such subsides. The second most wide-reaching measure was cash transfers (22 percent), followed by fiscal relief and payment deferrals (12 percent each). New credit was the least commonly reported support measure (8 percent). To minimize reverse causality in examining the relationship between firm performance in Round 3 and government support, we will focus on support in Rounds 1 and 2 in our analysis below. The relative ordering of the most common support measures is similar.

Table 2 also reports the share of firms that received government support but does so by the share of firms that received government support in each combination of rounds. Considering all three rounds (Panel A), firms could receive up to 3 rounds of support in 8 possible combinations: from receiving support in each of the three rounds to none. Most firms which received any government support did so only in Round 1: 20 percent of firms or about one-third of firms receiving support. This was followed by firms receiving support in all three rounds (9 percent), and Rounds 1 and 2 and Rounds 1 and 3 (7 percent each). The combinations of receiving support in Round 2 only, Round 3 only, or Rounds 2 and 3 was reported by fewer than 5 percent of firms each. About 45 percent of firms received no support at all. The ordering of combinations is proportional when considering only Rounds 1 and 2 (Panel B).

# **2.2 What are the firm characteristics and changes in performance during COVID-19?**

Table 3 Panel A presents the summary statistics of firm background characteristics from the pre-COVID19 ES. To measure productivity, we use labor productivity, defined as (log of) sales over total number of permanent full-time employees.<sup>7</sup> Labor productivity is preferred to total factor productivity due to limitations in measuring non-labor inputs in the ES (World Bank 2021a). The average firm in our sample had a log of labor productivity of 10.59. At the same time, the average firm had 28 employees (2.62 in log units) and was 20 years old (2.88 in log units). Only 20 percent of the firms had a female top manager. The sample includes few firms with at least 10 percent state or foreign ownership (less than 1 and 7 percent, respectively). Less than half the firms reported that they had a loan (42 percent) or innovated a product or process during 2017 to 2019 (43 percent), but 65 percent owned a website. Most firms reported that their main market was national (48 percent) or local (41 percent) versus

> 7 In calculating labor productivity, the outliers are eliminated by first log-transforming total annual sales and the number of permanent full-time employees, and then trimming at plus and minus three standard deviations from the mean, as described in World Bank (2021a).

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