Abstract
We study the impact of COVID-19 on firm performance. Using financial accounts of a large number of German firms, we document that industry affiliation is an important economic dimension of the crisis. Motivated by this fact, we analyze an important industry-specific regulation, system relevance, which allows businesses to remain open in times of lockdown and other restrictions. A difference-in-differences estimation strategy shows that relative revenues of system-relevant firms increase by 6–9 percent and profits by 17–25 percent due to COVID. Controlling for channels that are arguably not driven by the system-relevance regulation, the impact on revenues decreases but remains significant. Overall, results indicate that regulations affecting the ability to operate as well as industry-level shocks play important roles for firm performance during a pandemic-induced crisis.
Funding source: Bundesministerium für Arbeit und Soziales
Award Identifier / Grant number: DKI.00.00016.20
Appendix A: Descriptives and Validation of Bureau van Dijk Data
Difference in revenues before and after the pandemic – SBS versus BvD. Notes:The graph compares revenue growth 2019–2020 between SBS and BvD data for the sectors that SBS provides data on. Differently from BvD, SBS lacks data on, agriculture, administrative support services, education, health and social work, financial activities and insurance activities, art and entertainment and other services. For the BvD data the balanced panel for 2020 is used.
Summary statistics.
| A: full sample | Mean | Std. dev. | P 5 | P 50 | P 95 | Obs. |
|---|---|---|---|---|---|---|
| Revenues | 22.43 | 146.92 | 0.77 | 2.90 | 88.66 | 150,620 |
| Profits | 2.73 | 13.15 | −0.00 | 0.50 | 10.62 | 24,128 |
| Total assets | 13.67 | 95.69 | 0.33 | 1.40 | 52.39 | 150,620 |
| Tangible assets | 2.82 | 16.70 | 0.01 | 0.18 | 11.12 | 150,620 |
| Technical assets | 8.71 | 28.66 | 0.00 | 1.20 | 37.15 | 17,185 |
| Employment | 76.14 | 292.95 | 5.00 | 20.00 | 315.00 | 149,193 |
| B: means by year | 2017 | 2018 | 2019 | 2020 | 2020–2019 | Obs. |
| Revenues | 21.46 | 22.63 | 23.11 | 22.52 | −0.59 | 150,620 |
| Profits | 2.77 | 2.75 | 2.63 | 2.76 | 0.13 | 24,128 |
| Total assets | 12.88 | 13.43 | 13.94 | 14.43 | 0.50 | 150,620 |
| Tangible assets | 2.66 | 2.76 | 2.89 | 2.97 | 0.08 | 150,620 |
| Technical assets | 8.19 | 8.59 | 8.83 | 9.22 | 0.39 | 17,185 |
| Employment | 75.16 | 75.81 | 76.74 | 76.85 | 0.12 | 149,193 |
| C: standard deviation by year | 2017 | 2018 | 2019 | 2020 | 2020–2019 | Obs. |
| Revenues | 143.28 | 156.83 | 152.35 | 134.22 | −18.13 | 150,620 |
| Profits | 13.31 | 12.94 | 13.68 | 12.64 | −1.03 | 24,128 |
| Total assets | 92.04 | 93.86 | 95.76 | 100.88 | 5.12 | 150,620 |
| Tangible assets | 15.92 | 16.26 | 16.95 | 17.61 | 0.66 | 150,620 |
| Technical assets | 27.16 | 28.08 | 28.75 | 30.52 | 1.77 | 17,185 |
| Employment | 358.43 | 265.55 | 266.92 | 271.27 | 4.34 | 149,193 |
-
Notes: The financial variables are measured annually and expressed in million of euros. Employment is in total headcounts. The sample is the balanced panel of firms during 2017–2020. Panel A present summary statistics of the full sample. Panel B reports the means for each of the years and the difference between 2020 and 2019. Panel C reports the standard deviations for each of the years and the difference between 2020 and 2019.
Share of economic activity by firm size – comparison of BvD with SBS data.
| [0, 10) | [10, 20) | [20, 50) | [50, 250) |
|
|
|---|---|---|---|---|---|
| BvD data | |||||
| Share employment | 0.03 | 0.08 | 0.16 | 0.25 | 0.49 |
| Share firms | 0.25 | 0.29 | 0.27 | 0.13 | 0.04 |
| Share revenues | 0.05 | 0.06 | 0.13 | 0.30 | 0.45 |
| SBS data | |||||
| Share employment | 0.19 | 0.11 | 0.12 | 0.18 | 0.40 |
| Share firms | 0.83 | 0.10 | 0.05 | 0.02 | 0.00 |
| Share revenues | 0.10 | 0.06 | 0.08 | 0.17 | 0.58 |
-
Notes: BvD and SBS data are compared with respect to revenues, number of firms and employment for the five firm size categories (SBS definition). Comparison is performed using the balanced sample between 2017 and 2019.
Share of economic activity by industry – comparison of BvD with SBS data.
| BvD data | SBS data | |
|---|---|---|
| Firm share | ||
| Mining | 0.003 | 0.001 |
| Manufacture | 0.203 | 0.079 |
| Electricity/gas/steam | 0.009 | 0.018 |
| Water/waste management | 0.011 | 0.003 |
| Construction | 0.175 | 0.139 |
| Wholesale/retail | 0.234 | 0.227 |
| Transportation/storage | 0.050 | 0.042 |
| Accommodation/food | 0.024 | 0.092 |
| Information/communication | 0.045 | 0.051 |
| Real estate | 0.018 | 0.063 |
| Professionals | 0.087 | 0.195 |
| Employment share | ||
| Mining | 0.001 | 0.002 |
| Manufacture | 0.254 | 0.257 |
| Electricity/gas/steam | 0.011 | 0.011 |
| Water/waste management | 0.008 | 0.009 |
| Construction | 0.072 | 0.080 |
| Wholesale/retail | 0.169 | 0.211 |
| Transportation/storage | 0.058 | 0.077 |
| Accommodation/food | 0.025 | 0.080 |
| Information/communication | 0.045 | 0.047 |
| Real estate | 0.007 | 0.018 |
| Professionals | 0.065 | 0.090 |
| Value added share | ||
| Mining | 0.005 | 0.003 |
| Manufacture | 0.285 | 0.345 |
| Electricity/gas/steam | 0.079 | 0.025 |
| Water/waste management | 0.007 | 0.013 |
| Construction | 0.045 | 0.066 |
| Wholesale/retail | 0.343 | 0.174 |
| Transportation/storage | 0.037 | 0.062 |
| Accommodation/food | 0.006 | 0.027 |
| Information/communication | 0.041 | 0.071 |
| Real estate | 0.007 | 0.041 |
| Professionals | 0.055 | 0.094 |
-
Notes: BvD and SBS data are compared across industries with respect to firm, employment, and value added share. This is done using the balanced sample during 2017–2019 and across the 11 broad sectors for which we have data in both sources (see also Figure A1). Since we are not printing BvD sectors for which we do not have SBS data, and vice versa, the shares printed in the table do not sum to one.
ANOVA decomposition of variation – 5-digit industries and LMRs for 2018 and 2019.
| Source | 2-Digit industries | 5-Digit industries | ||||
|---|---|---|---|---|---|---|
| Partial SS | Share (%) | F | Partial SS | Share (%) | F | |
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Panel A: 2018 | ||||||
| Model | 30.09 | 1.21 | 2.08 | 110.26 | 4.44 | 1.51 |
| Industry | 16.11 | 0.65 | 3.03 | 96.28 | 3.87 | 1.51 |
| LMR | 13.08 | 0.53 | 1.42 | 12.67 | 0.51 | 1.39 |
| Residual | 2455.24 | 98.79 | 2375.07 | 95.56 | ||
| Total | 2485.33 | 2485.33 | ||||
| Panel B: 2019 | ||||||
| Model | 26.77 | 1.34 | 2.30 | 73.71 | 3.69 | 1.25 |
| Industry | 15.03 | 0.75 | 3.52 | 61.97 | 3.10 | 1.20 |
| LMR | 10.69 | 0.53 | 1.45 | 10.36 | 0.52 | 1.40 |
| Residual | 1972.79 | 98.66 | 1925.85 | 96.31 | ||
| Total | 1999.56 | 1999.56 | ||||
| Panel C: 2020 | ||||||
| Model | 224.07 | 7.39 | 13.52 | 382.34 | 12.62 | 4.71 |
| Industry | 203.59 | 6.72 | 33.52 | 361.86 | 11.94 | 5.09 |
| LMR | 15.08 | 0.50 | 1.44 | 13.12 | 0.43 | 1.29 |
| Residual | 2806.54 | 92.61 | 2648.26 | 87.38 | ||
| Total | 3030.61 | 3030.61 | ||||
| Observations per year | 37,655 | 37,655 | ||||
-
Notes: ANOVA is run on the balanced panel. Panel A presents the ANOVA results of the revenue growth in 2018, whereas Panel B presents the ANOVA for 2019 and Panel C the ANOVA for 2020. A cross-sectional version of the data set for 2018, 2019 and 2020 respectively, is used for the three panels. The first three columns show the results of an ANOVA run on two-digit industries (82 industries) and labor market regions (141 LMRs), whereas the three following columns report results of the ANOVA run on five-digit industries (976 industries) and labor market regions. The outcome variable is the revenue growth. Column (2) and (5) report the share of explained variation for the ANOVA on two-digit and five-digit industries, respectively. The critical F-statistic F(140, 36,692) for LMR is 1.30 for the 1 % significance level. The critical F-statistic F(81, 36,751) for two-digit industries and 0.1 significance level is 1.64, whereas the critical F-statistic F(975, 35,857) for five-digit industries and 0.1 significance level is 1.22.
One implication of industries playing such an important role in the COVID shock is that this should be true across countries. Table A5 indeed shows that the additional variation in industry-level performance has high commonality between Germany and Sweden plus the Netherlands, Austria, and France. The table reports coefficients from regressing German revenue growth (individual BvD firms in columns 1 and 2; two-digit SBS industry data in columns 3 and 4) onto two-digit industry revenue growth separately for the four other countries. While these coefficients in the preceding years are always small and only partially statistically significant, they are substantial (at least 0.6) and an order of magnitude larger in 2020. They are also statistically significant at the one percent level and R-squared rises at least sixfold. This clearly shows that the COVID shock has hit different industries across countries in the same way.
Commonality in industry-level revenue growth between Germany and four other European countries during 2017–2020.
| Revenue growth DE (BvD) | Revenue growth DE (SBS) | |||
|---|---|---|---|---|
| 2017–2019 | 2019–2020 | 2017–2019 | 2019–2020 | |
| (1) | (2) | (3) | (4) | |
| Revenue growth SE | 0.257*** | 0.712*** | 0.266** | 0.845*** |
| R-squared | 0.002 | 0.042 | 0.079 | 0.649 |
| Revenue growth NL | 0.094*** | 0.611*** | 0.072* | 0.586*** |
| R-squared | 0.000 | 0.028 | 0.008 | 0.466 |
| Revenue growth AU | −0.007 | 0.613*** | 0.213*** | 0.939*** |
| R-squared | 0.000 | 0.035 | 0.109 | 0.656 |
| Revenue growth FR | 0.043*** | 0.686*** | −0.080 | 1.025*** |
| R-squared | 0.000 | 0.037 | 0.014 | 0.717 |
-
Notes: Each entry in the table represents the point estimate from a regression of German revenue growth on a specific country revenue growth. Columns (1) and (2) present these results for the firm level data (BvD), whereas columns (3) and (4) report results for aggregated data (SBS). We use SCB data for Sweden. For the other countries we use two-digit industry data from SBS. The point estimate is presented for those industries for which there is data in SBS in each of the respective countries. Robust standard errors. * p < 0.1, ** p < 0.05, *** p < 0.01.
Appendix B: Example of a Balance Sheet and Income Statement
We present an annual financial statement of a firm in our sample in order to illustrate the type of information used in the analysis. Searching by the name of the firm and year provided in BvD, one can download the particular financials from the Federal Gazette (“Bundesanzeiger”).[45]
Not all firms have to submit an annual financial statement to the Federal Gazzette. Reporting depends on the legal form, type of operations, size, performance and whether they are a subsidiary or not. In particular, legal forms such as corporates (AGs or GmbHs), cooperatives, commercial partnerships (GmbH & Co. KGs, GmbH & Co. OHG with only corporations as partners) have to submit their accounts. Credit institutions, external capital management companies, pension funds and insurance companies have to submit the financial statement regardless of the legal form, performance, or size. Furthermore, all firms, regardless of their legal form, have to submit if they fulfill two out three of the following conditions: (1) total assets over € 65 million, (2) revenues exceeding € 130 million and (3) an average of at least 5000 employees. BvD is more inclusive than the Federal Gazzette as it draws the data from Crefo credit ratings agency, which in turns collect information from the Federal Gazzette but also financial statements that are directly sent by the firms. Last, subsidiaries can be exempted from the obligation to disclose.
Here we take the example of a large company in the manufacturing sector (chemicals industry) because such firms report on a broader set of entries. The level of detail in the financial statement depends on the size of the firm and the type of operations. For instance, not all firms own intangible assets and there is thus no need to report this entry in the annual accounts. We present the two most important statements of the financial accounts, the balance sheet statement and the income statement.
The balance sheet statement contains the Assets Statement (Aktiva in German) and the Equity and Liabilities Statement (Passiva). These balance each other out, hence the name. Figure B1 displays the Assets Statement of our example firm, which contains information on fixed assets (Anlagevermögen), tangible assets (Sachanlagen), financial assets (Finanzanlagen), or current assets (Umlaufvermögen). This is one side of the balance sheet. The other side is the Equity and Liabilities Statement, which is shown in Figure B2. This contains information on equity (Eigenkapital), liabilities (Verbindlichkeiten), as well as other relevant entries (e.g. accruals – Rückstellungen) to the company. In our analysis, we take the information on assets from the balance sheet statement.[46]
Balance sheet statement – assets. Note:The figure shows the assets side of the balance sheet statement (aktiva in German).
Balance sheet statement – equity and liabilities. Note:The figure shows the equity and liabilities side of the balance sheet statement (passiva in German).
The other key statement in the annual financial accounts is the income statement (Gewinn- und Verlustrechnung or GuV in German). The income statement of our example firm is presented in Figure B3. It contains information on revenues from sales (Umsatzerlöse), other operating revenues (sonstige betriebliche Erträge), net profits (Jahresüberschuss), and other relevant entries (e.g. taxes – Steuern) to the company. Revenues from sales are the “revenues” in our analysis and net profits is what we use for “profits”. The last important variable we use from the BvD data is employment, which is often reported (but not always) in the notes to the financial statement.
Income statement. Note:The figure shows the income statement (Gewinn- und Verlustrechnung or GuV in German).
Appendix C: Effect of COVID-19 Local Incidence Rate
This section studies the relationship between firms’ revenue growth and an indicator of the COVID-19 shock that varies at the regional level, the local incidence rate. It is constructed using the 7-day average incidence rate, where the daily incidence rate is calculated as the number of infected individuals per one hundred thousands inhabitants. The 7-day incidence rate is then aggregated (i.e. averaged) at the yearly level to get a measure for 2020. This measure is a proxy of how labor market regions have been hit by the COVID-19 pandemic. In our regressions we use the log of the incidence rate to measure the elasticity of firm revenue growth with respect to it.[47]
Both of these data are retrieved from the official COVID-19 reporting database at the district level, which is maintained by the Robert-Koch-Institute (RKI). Following Kosfeld and Werner (2012) the districts are aggregated into 141 labor market regions covering both West and East Germany. Each district uniquely pertains to one and only one labor market region. We use variation across labor market regions and not districts to account for commuting across districts.
We start by studying the correlation between revenue growth in 2020 and incidence rate in the labor market region where the firm operates. Figure C1 plots the correlation between weighted regional values (by the number of workers in the initial year 2017) of revenue growth in 2020 and average 7-day incidence rate. Circles represent the 141 labor market regions and their size indicates the number of workers in the initial year in each labor market region. Locations which had a higher incidence rate are associated with a slightly larger increase in revenues. This first piece of evidence hence does not indicate that regions with a higher incidence rate were hindered in performing their economic activity (e.g. because they had to go through more severe closures).
Relationship between revenue growth and incidence rate. Notes:Growth rate of revenues is the difference in log revenues between 2020 and 2019. Number of firms used to produce the graph is 37,655. Each circle represents one of the 141 labor market region, with circle sizes reflecting the number of workers in the initial year (2017) in that region (and the regression line weighed by this).
We still want to study performance by incidence rate over a longer period, including any potential pre-trends. We follow the same empirical strategy as for the system relevance presented in Section 3.2. First, we look at whether revenues of high-incidence and low-incidence regions (splitting at the mean incidence rate) have exhibited parallel trends prior to the COVID-19 shock. Figure C2 indicates that revenues do not develop similarly during the pre-period, but instead they increased in high-incidence regions from 2017 to 2019 compared to low-incidence regions. Compared to this, the differences of relative log revenues in 2019 versus 2020 are tiny (they are generally small at about 0.2 percent). To account for the pretends, we include specifications with time trends across high and low incidence rate regions in our regressions that follow.
Relative firm revenues in high COVID-19 incidence regions over time. Notes:This graph shows an event study impact of the pandemic (year 2020) on relative log revenues of firms in high incidence rate regions. The event study regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the labor market region level and 95 % confidence bounds drawn around the point estimates.
Table C1 presents the resulting estimates of the DiD model where the treatment is the high incidence rate dummy in specifications (1)–(3) and log incidence rate in specifications (4)–(6). High-incidence regions have statistically significantly higher revenues in 2020 – coefficient on post × high incidence is positive at more than two percentage points – in columns (1) and (2). However, when we account for the pre-trends – via a linear time trend specific to high- and low-incidence regions – in column (3), this all goes away and the point estimate even turns negative though insignificant. Results using continuous log incidence rates (columns 4–6) are similar and never significant, neither statistically nor economically in either direction.
Firm log revenues and local COVID-19 incidence rates.
| Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | |
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Post | 0.0025 | 0.0025 | −0.0606*** | −0.0518 | −0.0518 | −0.0075 |
| (0.0072) | (0.0072) | (0.0066) | (0.0675) | (0.0675) | (0.0618) | |
| High incidence | 0.1465 | 0.1152 | −35.0907** | |||
| (0.1583) | (0.1344) | (15.3632) | ||||
| Post × high incidence | 0.0222** | 0.0222** | −0.0127 | |||
| (0.0110) | (0.0110) | (0.0111) | ||||
| Log incidence | 0.3172 | 0.2765 | −32.9321 | |||
| (0.2090) | (0.1902) | (24.1246) | ||||
| Post × log incidence | 0.0173 | 0.0173 | −0.0156 | |||
| (0.0179) | (0.0179) | (0.0166) | ||||
| Linear time trend × incidence | No | No | Yes | No | No | Yes |
| Firm FE | No | Yes | Yes | No | Yes | Yes |
| Observations | 150,616 | 150,616 | 150,616 | 150,616 | 150,616 | 150,616 |
| R-squared | 0.001 | 0.002 | 0.002 | 0.003 | 0.003 | 0.003 |
-
Notes: Outcome variable is log revenues. High incidence rate is 1 for values above the mean of the average 7-day incidence rate in the firm’s labor market region and 0 otherwise. Log incidence is the logarithm of the average 7-day incidence rate. The regressions are weighed by the number of workers in the initial year (2017). Standard errors are clustered at the labor market region. * p < 0.1, ** p < 0.05, *** p < 0.01.
Overall, these results suggests that there is no detectable effect of local incidence rates on revenue growth. This is consistent with regional variation mattering little for the variance of revenue growth during the pandemic and contrary to the strong role of industry affiliation and system relevance that we found in the main text.
Appendix D: Unbalanced Panel
DiD estimation on retention of firms in the sample.
| Retention | Retention | Retention | |
|---|---|---|---|
| (1) | (2) | (3) | |
| System relevance | 0.083*** | 0.082*** | – |
| (0.029) | (0.029) | ||
| System relevance × post | 0.035* | 0.025 | 0.015 |
| (0.019) | (0.021) | (0.021) | |
| Year dummy | Yes | Yes | Yes |
| Controls | No | Yes | Yes |
| Firm FE | No | No | Yes |
| Observations | 589,764 | 589,764 | 589,760 |
| R-squared | 0.031 | 0.036 | 0.416 |
-
Notes: The outcome variable is a dummy, which equals 1 when the firm is present in the data set in any year between 2017 and 2020 and 0 otherwise. “Controls” include firm size category (of the initial year) and tangible assets (of the initial year) interacted with year dummies. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the two-digit industry level. * p < 0.1, ** p < 0.05, *** p < 0.01.
DiD estimates on system relevance for the unbalanced panel.
| Log(rev) | Rev. growth | Rev. growth | Rev. growth | |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| System relevance | – | −0.000 | 0.006 | – |
| (0.005) | (0.004) | |||
| System relevance × post | 0.074*** | 0.057*** | 0.056*** | 0.060*** |
| (0.019) | (0.018) | (0.015) | (0.015) | |
| Year dummy | Yes | Yes | Yes | Yes |
| Controls | Yes | No | Yes | Yes |
| Firm FE | Yes | No | No | Yes |
| Observations | 329,273 | 220,832 | 220,832 | 187,196 |
| R-squared | 0.994 | 0.011 | 0.034 | 0.415 |
-
Notes: The first column is an estimation of Equation (1), where the outcome variable is log revenues. The three following columns are from estimations of Equation (3), where the outcome variable is the change of log revenues (“revenue growth”). “Controls” include firm size category following the SBS definition (of the initial year), labor market region (141 unique values) and tangible assets (of the initial year) interacted with year dummies. The unbalanced panel is used for these estimations. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the two-digit industry level. * p < 0.1, ** p < 0.05, *** p < 0.01.
Appendix E: Additional Tables on System Relevance
Impact of COVID-19 on system-relevant versus other firms’ log profits. Notes:This graph plots the event study impact of the pandemic (year 2020) on system-relevant firms’ relative log profits, for those firms that report positive profits. The event study regressions are weighted by number of workers in the initial year (2017). Standard errors clustered at the two-digit industry level and 95 % confidence bounds drawn around the point estimates.
System relevance and share of essential workers for the 2-digit industries.
| Industries | Share Sys.Rel | Share essent-I | Share essent-II | Ind. cont. index |
|---|---|---|---|---|
| Crop and animal production | 1.00 | 0.038 | 0.073 | 0.0 |
| Forestry and logging | 0.00 | 0.065 | 0.065 | 0.0 |
| Extraction of crude petroleum and natural gas | 0.00 | 0.081 | 0.081 | 0.0 |
| Other mining and quarring | 0.00 | 0.182 | 0.182 | 0.0 |
| Mining support service activities | 0.00 | 0.084 | 0.094 | 0.0 |
| Manufacture of food products | 1.00 | 0.273 | 0.274 | 0.0 |
| Manufacture of beverages | 1.00 | 0.184 | 0.187 | 0.0 |
| Manufacture of tobacco products | 0.00 | 0.124 | 0.131 | 0.0 |
| Manufacture of textiles | 0.00 | 0.113 | 0.114 | 0.0 |
| Manufacture of wearing apparel | 0.00 | 0.139 | 0.141 | 0.0 |
| Manufacture of leather and related products | 0.00 | 0.107 | 0.107 | 0.0 |
| Manufacture of wood and of products of wood and cork | 0.00 | 0.072 | 0.074 | 0.0 |
| Manufacture of paper and paper products | 0.00 | 0.107 | 0.107 | 0.0 |
| Printing and reproduction of recorded media | 0.00 | 0.072 | 0.096 | 0.0 |
| Manufacture of coke and refined petroleum products | 1.00 | 0.104 | 0.106 | 0.0 |
| Manufacture of chemicals and chemical products | 0.00 | 0.107 | 0.110 | 0.0 |
| Manufacture of basic pharmaceutical products | 1.00 | 0.185 | 0.203 | 0.0 |
| Manufacture of rubber and plastic products | 0.00 | 0.083 | 0.085 | 0.0 |
| Manufacture of other non-metallic mineral products | 0.00 | 0.099 | 0.100 | 0.0 |
| Manufacture of basic metals | 0.00 | 0.071 | 0.073 | 0.0 |
| Manufacture of fabricated metal products | 0.00 | 0.061 | 0.063 | 0.0 |
| Manufacture of computer, electronic and optical products | 0.04 | 0.053 | 0.064 | 0.0 |
| Manufacture of electrical equipment | 0.00 | 0.063 | 0.068 | 0.0 |
| Manufacture of machinery and equipment | 0.00 | 0.062 | 0.067 | 0.0 |
| Manufacture of motor vehicles, trailers & semi-trailers | 0.00 | 0.075 | 0.079 | 0.0 |
| Manufacture of other transport equipment | 0.00 | 0.058 | 0.067 | 0.0 |
| Manufacture of furniture | 0.00 | 0.075 | 0.075 | 0.0 |
| Other manufacturing | 0.63 | 0.101 | 0.285 | 0.0 |
| Repair and installation of machinery and equipment | 0.00 | 0.068 | 0.075 | 0.0 |
| Electricity, gas, steam and air conditioning supply | 1.00 | 0.112 | 0.126 | 0.0 |
| Water collection, treatment and supply | 1.00 | 0.362 | 0.365 | 0.0 |
| Sewerage | 1.00 | 0.513 | 0.515 | 0.0 |
| Waste collection, treatment and disposal activ. | 0.36 | 0.572 | 0.574 | 0.0 |
| Remediation activities and other waste management services | 0.00 | 0.254 | 0.254 | 0.0 |
| Construction of buildings | 0.00 | 0.022 | 0.023 | 0.0 |
| Civil engineering | 0.00 | 0.081 | 0.082 | 0.0 |
| Specialised construction activ. | 0.00 | 0.034 | 0.035 | 0.0 |
| Wholesale and retail trade and repair of motor vehicles | 0.00 | 0.107 | 0.109 | 2.3 |
| Wholesale trade, except of motor vehicles and motorcycles | 0.20 | 0.251 | 0.260 | 5.2 |
| Retail trade, except of motor vehicles and motorcycles | 0.22 | 0.235 | 0.275 | 5.3 |
| Land transport and transport via pipelines | 0.94 | 0.749 | 0.751 | 0.0 |
| Water transport | 1.00 | 0.251 | 0.257 | 0.0 |
| Air transport | 1.00 | 0.088 | 0.089 | 0.0 |
| Warehousing and support activities for transport. | 0.94 | 0.575 | 0.577 | 0.0 |
| Postal and courier activities | 1.00 | 0.840 | 0.842 | 0.0 |
| Accommodation | 0.00 | 0.042 | 0.043 | 7.1 |
| Food and beverage service activ. | 0.00 | 0.063 | 0.064 | 6.0 |
| Publishing activities | 0.00 | 0.042 | 0.342 | 0.0 |
| Motion picture, video and tv production, music | 0.00 | 0.030 | 0.181 | 0.0 |
| Programming and broadcasting activities | 1.00 | 0.034 | 0.341 | 0.0 |
| Telecommunications | 1.00 | 0.045 | 0.056 | 0.0 |
| Computer programming, consultancy | 0.08 | 0.075 | 0.098 | 0.0 |
| Information service activ. | 0.91 | 0.086 | 0.207 | 0.0 |
| Financial service activities, wøinsurance & pension fund | 1.00 | 0.025 | 0.779 | 0.0 |
| Insurance, reinsurance and pension funding, wøsoc. sec. | 1.00 | 0.031 | 0.692 | 0.0 |
| Activities auxiliary to financial services and insurance activ. | 1.00 | 0.025 | 0.553 | 0.0 |
| Real estate activities | 0.00 | 0.050 | 0.077 | 0.0 |
| Legal and accounting activities | 0.00 | 0.011 | 0.675 | 0.0 |
| Activities of head offices; management consultancy activ. | 0.00 | 0.109 | 0.155 | 0.0 |
| Architectural & engineering activ. | 0.00 | 0.059 | 0.066 | 0.0 |
| Scientific research and development | 0.00 | 0.093 | 0.129 | 0.0 |
| Advertising and market research | 0.00 | 0.031 | 0.105 | 0.0 |
| Other professional, scientific and technical activ. | 0.00 | 0.056 | 0.079 | 2.6 |
| Veterinary activities | 0.00 | 0.721 | 0.957 | 0.0 |
| Rental and leasing activities | 0.00 | 0.194 | 0.214 | 0.0 |
| Employment activities | 0.00 | 0.327 | 0.329 | 0.0 |
| Travel agency, tour operator | 0.00 | 0.081 | 0.090 | 7.1 |
| Security and investigation activities | 0.00 | 0.882 | 0.883 | 0.0 |
| Services to buildings and landscape activities | 0.00 | 0.364 | 0.366 | 0.0 |
| Office administrative, office support | 0.00 | 0.158 | 0.183 | 0.6 |
| Education | 0.00 | 0.372 | 0.537 | 0.0 |
| Human health activities | 0.94 | 0.794 | 0.813 | 0.0 |
| Residential care activities | 1.00 | 0.815 | 0.823 | 0.0 |
| Social work activities without accommodation | 0.56 | 0.771 | 0.781 | 0.0 |
| Creative, arts and entertainment activities | 0.00 | 0.045 | 0.057 | 6.2 |
| Libraries, archives, museums and other cultural activ. | 0.09 | 0.133 | 0.427 | 6.6 |
| Gambling and betting activities | 0.00 | 0.501 | 0.505 | 7.4 |
| Sports activities and amusement and recreation activ. | 0.00 | 0.133 | 0.142 | 3.2 |
| Activities of membership organisations | 0.00 | 0.261 | 0.333 | 0.0 |
| Repair of computers and personal and household goods | 0.00 | 0.074 | 0.081 | 0.0 |
| Other personal service activities | 0.07 | 0.242 | 0.288 | 6.3 |
-
Notes: The table provides the share of system relevant firms and share of essential workers of the “first hour” (column 3) and “second hour” (column 4) for each one of the 82 two-digit industries.
List of system-relevant 5-digit industries within non-fully system-relevant 2-digit industries.
| 2-Digit industries which are not fully system-relevant | 5-Digit system-relevant industries |
|---|---|
| Manufacture of computer, electronic and optical products | Manufacture of irradiation, electromedical and electrotherapeutic equipment |
| Other manufacturing | Manufacture of medico-technical instruments and supplies |
| Manufacture of orthopaedic appliances | |
| Dental laboratories | |
| Waste collection, treatment and disposal activities; materials recovery | Collection of non-hazardous waste |
| Collection of hazardous waste | |
| Wholesale trade, except of motor vehicles and motorcycles | Agents involved in the sale of agricultural raw materials, live animals, textile raw materials and semi-finished goods |
| Agents involved in the sale of sugar, chocolate and sugar confectionery | |
| Agents involved in the sale of other food, beverages and tobacco | |
| Agents involved in the sale of pharmaceutical, medical and orthopaedic goods, laboratory equipment, physicians’ and dental material and equipment, dentists’ instruments, material and equipment for hospitals and for nursing care provided to old people | |
| Wholesale of live animals | |
| Wholesale of fruit and vegetables | |
| Wholesale of meat and meat products | |
| Wholesale of dairy products, eggs and edible oils and fats | |
| Wholesale of beverages | |
| Wholesale of sugar and chocolate and sugar confectionery | |
| Wholesale of coffee, tea, cocoa and spices | |
| Wholesale of fish, crustaceans and molluscs | |
| Wholesale of flour and cereals products | |
| Wholesale of food n.e.c. | |
| Non-specialised wholesale of frozen food | |
| Non-specialised wholesale of other food, beverages and tobacco | |
| Wholesale of pharmaceutical goods | |
| Wholesale of medical and orthopaedic goods, dental and laboratory material and equipment | |
| Wholesale of solid fuels | |
| Wholesale of liquid and gaseous fuels and related products | |
| Retail trade, except of motor vehicles and motorcycles | Retail sale of food, beverages or tobacco in non-specialised stores |
| Other retail sale in non-specialised stores with food, beverages or tobacco predominating | |
| Retail sale of fruit and vegetables in specialised stores | |
| Retail sale of meat and meat products in specialised stores | |
| Retail sale of fish, crustaceans and molluscs in specialised stores | |
| Retail sale of bread, cakes, flour confectionery and sugar confectionery in specialised stores | |
| Retail sale of beverages in specialised stores | |
| Other retail sale of food in specialised stores | |
| Retail sale on behalf of others of automotive fuel in specialised stores (filling stations acting as agencies) | |
| Retail sale of private-brand automotive fuel in specialised stores (independent filling stations) | |
| Dispensing chemist in specialised stores | |
| Retail sale of medical and orthopaedic goods in specialised stores | |
| Retail sale via stalls and markets of food, beverages and tobacco products | |
| Retail sale of fuels from stock | |
| Land transport and transport via pipelines | Passenger rail transport, interurban |
| Freight rail transport | |
| Urban and suburban passenger land transport | |
| Scheduled long-distance passenger transport by motor bus | |
| Non-scheduled passenger transport by motor bus | |
| Land passenger transport n.e.c. | |
| Freight transport by road | |
| Warehousing and support activities for transportation | Operation of car parks and garages |
| Operation of road infrastructure | |
| Operation of railroad infrastructure | |
| Operation of terminal facilities for passenger transport, including bus stations | |
| Operation of stations for the handling of goods carried by rail or road (except cargo handling) | |
| Service activities incidental to land transportation n.e.c. | |
| Operation of waterway infrastructure | |
| Operation of ports, harbours and piers | |
| Navigation, pilotage and berthing activities | |
| Service activities incidental to water transportation n.e.c. | |
| Operation of airports and airfields | |
| Service activities incidental to air transportation n.e.c. | |
| Cargo handling | |
| Freight forwarding | |
| Organisation of group consignments by sea | |
| Other transportation support activities n.e.c. | |
| Computer programming, consultancy and related activities | Computer facilities management activities |
| Information service activities | Data processing, hosting and related activities |
| News agency activities | |
| Other information service activities n.e.c. | |
| Human health activities | Hospital activities (excluding university hospitals, preventive care and rehabilitation centres) |
| Activities of university hospitals | |
| Activities of preventive care and rehabilitation centres | |
| Other own-account activities pertaining to human health | |
| Social work activities without accommodation | Domestic social service activities |
| Other social work activities without accommodation for the elderly and disabled | |
| Libraries, archives, museums and other cultural activities | Operation of historical sites and buildings and similar visitor attractions |
| Other personal service activities | Activities of morticians |
| Operation of cemeteries and crematoriums |
-
Notes: This table lists all the 5-digit industries, which are system relevant within the 2-digit industries which are partially system-relevant.
Difference-in-differences estimates including share of essential workers and industry-level containment measures.
| Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | |
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Share essential workers-I | 0.731 | −0.980*** | ||||
| (0.544) | (0.173) | |||||
| Share essential workers-I × post | 0.114*** | 0.128*** | ||||
| (0.038) | (0.034) | |||||
| Share essential workers-II | 0.714 | −0.916*** | ||||
| (0.551) | (0.186) | |||||
| Share essential workers-II × post | 0.123*** | 0.139*** | ||||
| (0.037) | (0.034) | |||||
| Industry containment index | 0.029 | 0.075** | ||||
| (0.058) | (0.033) | |||||
| Industry containment index × post | −0.007 | −0.007 | ||||
| (0.007) | (0.007) | |||||
| Year dummy | Yes | Yes | Yes | Yes | Yes | Yes |
| Controls | No | Yes | No | Yes | No | Yes |
| Observations | 150,620 | 150,620 | 150,620 | 150,620 | 150,620 | 150,620 |
| R-squared | 0.011 | 0.780 | 0.011 | 0.778 | 0.001 | 0.771 |
-
Notes: Column (1) and (2) estimate the relative effect of COVID-19 shock for the share of the essential workers of the “first hour”. Column (3) and (4) estimate the relative effect of COVID-19 shock for the share of the essential workers of the “second hour”. Column (5) and (6) estimate the effect of industry containment index. “Controls” include firm size category following the SBS definition (of the initial year), labor market region (141 unique values) and tangible assets (of the initial year) interacted with year dummies. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the two-digit industry level. * p < 0.1, ** p < 0.05, *** p < 0.01.
DiD estimates on system relevance excluding mining and energy sectors (columns (1A) and (1B)), media and culture broad sector (columns (2A) and (2B)), health industries (columns (3A) and (3B)) and industries that suffered the most from closures and consumers’ behavioral change (columns (4A) and (4B)).
| Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | |
|---|---|---|---|---|---|---|---|---|
| (1A) | (1B) | (2A) | (2B) | (3A) | (3B) | (4A) | (4B) | |
| System relevance | 0.908*** | −0.226 | 0.947*** | −0.195 | 0.946*** | 0.116 | 0.919*** | −0.210 |
| (0.244) | (0.166) | (0.237) | (0.165) | (0.258) | (0.107) | (0.242) | (0.165) | |
| System relevance | 0.077*** | 0.087*** | 0.076*** | 0.087*** | 0.061*** | 0.075*** | 0.065*** | 0.078*** |
| ×post | (0.022) | (0.019) | (0.021) | (0.018) | (0.019) | (0.016) | (0.021) | (0.019) |
| Year dummy | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Controls | No | Yes | No | Yes | No | Yes | No | Yes |
| Observations | 148,142 | 148,142 | 150,431 | 150,431 | 147,862 | 147,862 | 147,770 | 147,770 |
| R-squared | 0.046 | 0.771 | 0.050 | 0.768 | 0.038 | 0.782 | 0.048 | 0.771 |
-
Notes: Columns (1A) and (1B) estimate the Equation (1) excluding the mining and energy sectors. Columns (2A) and (2B) excluding the broad sector of media and culture. Columns (3A) and (3B) exclude health industries, namely human health activities and residential care activities. Columns (4A) and (4B) exclude the industries that suffered the most from closures and consumers’ behavioral change, namely accommodation, food and beverages services, creative, arts and entertainment activities, travel agency and tour operator, gambling and betting activities and sports activities, amusement and recreation activities. “Controls” include firm size category following the SBS definition (of the initial year), labor market region (141 unique values) and tangible assets (of the initial year) interacted with year dummies. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the two-digit industry level. * p < 0.1, ** p < 0.05, *** p < 0.01.
DiD estimates excluding firms with up to ten workers in 2019.
| Log(rev) | Log(rev) | Rev. grth | Rev. grth | |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| System relevance | 0.923*** | −0.197 | 0.001 | 0.010** |
| (0.235) | (0.165) | (0.006) | (0.004) | |
| System relevance × post | 0.096*** | 0.091*** | 0.072*** | 0.063*** |
| (0.022) | (0.018) | (0.018) | (0.014) | |
| Year dummy | Yes | Yes | Yes | Yes |
| Controls | No | Yes | No | Yes |
| Observations | 142,613 | 142,613 | 104,961 | 104,961 |
| R-squared | 0.050 | 0.766 | 0.032 | 0.079 |
-
Notes: The first two columns are from estimations (1), where the outcome variable is log revenues. The last three columns are from estimations (3), where the outcome variable is the change of log revenues (“revenue growth”). “Controls” include firm size category following the SBS definition (of the initial year), labor market region (141 unique values) and tangible assets (of the initial year) interacted with year dummies. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the two-digit industry level. * p < 0.1, ** p < 0.05, *** p < 0.01.
Correlation between log revenues and key assets post COVID-19 shock.
| Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | Log(rev) | |
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Log(lagged total assets) | 0.776*** | 0.789*** | ||||
| (0.009) | (0.011) | |||||
| Log(lagged total assets) × post | 0.010*** | 0.006 | ||||
| (0.003) | (0.004) | |||||
| Log(lagged fixed assets) | 0.466*** | 0.457*** | ||||
| (0.011) | (0.013) | |||||
| Log(lagged fixed assets) × post | 0.011*** | 0.005 | ||||
| (0.003) | (0.004) | |||||
| Log(lagged tangible assets) | 0.439*** | 0.440*** | ||||
| (0.012) | (0.014) | |||||
| Log(lagged tangible assets) × post | 0.008** | 0.003 | ||||
| (0.003) | (0.004) | |||||
| Year dummy | Yes | Yes | Yes | Yes | Yes | Yes |
| Controls | Yes | Yes | Yes | Yes | Yes | Yes |
| Industry#Year | No | No | No | Yes | Yes | Yes |
| Observations | 112,962 | 112,962 | 112,962 | 112,959 | 112,959 | 112,959 |
| R-squared | 0.909 | 0.790 | 0.768 | 0.925 | 0.830 | 0.817 |
-
Notes: Outcome variable is log revenues. “Controls” include firm size category following the SBS definition (of the initial year) and labor market region (141 unique values) interacted with year dummies. Columns (4)–(6) control in addition for the interaction term between two-digit industries and year dummies. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the firm level. * p < 0.1, ** p < 0.05, *** p < 0.01.
Difference-in-differences estimates: effects on tangible assets.
| Log(tangible assets) | Log(tangible assets) | Log(tangible assets) | |
|---|---|---|---|
| (1) | (2) | (3) | |
| System relevance | 1.759*** | 1.015*** | – |
| (0.486) | (0.332) | ||
| System relevance × post | −0.010 | 0.006 | 0.009 |
| (0.027) | (0.018) | (0.018) | |
| Year dummy | Yes | Yes | Yes |
| Controls | No | Yes | Yes |
| Firm FE | No | No | Yes |
| Observations | 150,618 | 150,618 | 150,618 |
| R-squared | 0.096 | 0.479 | 0.990 |
-
Notes: “Controls” include firm size category (of the initial year) and labor market region (141 unique values) interacted with year dummies. The regressions are weighted by the number of workers in the initial year (2017). Standard errors are clustered at the 2-digit industry level. * p < 0.1, ** p < 0.05, *** p < 0.01.
Difference-in-differences effects on profits including zeros and negatives.
| 1[profit > 0] | Arc(profit) | |||||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| System relevance | 0.077* | 0.086** | – | −0.010 | −0.145 | – |
| (0.041) | (0.040) | (0.134) | (0.137) | |||
| System relevance × post | 0.079** | 0.053* | 0.054* | 0.353*** | 0.296*** | 0.301*** |
| (0.040) | (0.029) | (0.030) | (0.122) | (0.094) | (0.094) | |
| Year dummy | Yes | Yes | Yes | Yes | Yes | Yes |
| Controls | No | Yes | Yes | No | Yes | Yes |
| Firm FE | No | No | Yes | No | No | Yes |
| Observations | 24,126 | 24,122 | 24,118 | 24,126 | 24,122 | 24,118 |
| R-squared | 0.012 | 0.115 | 0.831 | 0.004 | 0.145 | 0.804 |
-
Notes: Columns (1) to (3) report the difference-in-difference estimates where the outcome is a dummy variable, which equals 1 for positive profits and 0 otherwise. Columns (4) to (6) report the results for the outcome Arc(profit). In this case profits are transformed using the inverse hyperbolic sine, i.e.
References
Aghion, Philippe, Nicholas Bloom, Brian Lucking, Raffaella Sadun, and John Van Reenen. 2021. “Turbulence, Firm Decentralization, and Growth in Bad Times.” American Economic Journal: Applied Economics 13 (1): 133–69. https://doi.org/10.1257/app.20180752.Search in Google Scholar
Alfaro, Laura, and Maggie Xiaoyang Chen. 2012. “Surviving the Global Financial Crisis: Foreign Ownership and Establishment Performance.” American Economic Journal: Economic Policy 4 (3): 30–55. https://doi.org/10.1257/pol.4.3.30.Search in Google Scholar
Andersen, T. M., S. Holden, and S. Honkapohja. 2022. “Economic Consequences of the Pandemic–The Nordic Countries.” Swedish Government Official Report. Statens Offentliga Utredningar. https://coronakommissionen.com/wp-content/uploads/2022/02/underlagsrapport-economic-consequences-of-thepandemic_the-nordic-countries.pdf (accessed November 5, 2022).Search in Google Scholar
Bachmann, Rüdiger, Benjamin Born, Olga Goldfayn-Frank, Georgi Kocharkov, Ralph Luetticke, and Michael Weber. 2021. “A Temporary VAT Cut as Unconventional Fiscal Policy.” NBER Working Paper 29442. National Bureau of Economic Research.10.3386/w29442Search in Google Scholar
Bai, John Jianqiu, Erik Brynjolfsson,Wang Jin, Sebastian Steffen, and Chi Wan. 2021. “Digital Resilience: How Work-From-Home Feasibility Affects Firm Performance.” NBER Working paper 28588.10.3386/w28588Search in Google Scholar
Balleer, Almut, Sebastian Link, Manuel Menkhoff, and Peter Zorn. forthcoming. “Demand or Supply? Price Adjustment Heterogeneity During the Covid-19 Pandemic.” International Journal of Central Banking.Search in Google Scholar
Barrero, Jose Maria, Nicholas Bloom, and Steven J. Davis. 2021. “Why Working from Home Will Stick.” NBER Working Paper 28731.10.3386/w28731Search in Google Scholar
Bartik, Alexander W., Marianne Bertrand, Zoe Cullen, Edward L. Glaeser, Michael Luca, and Christopher Stanton. 2020. “The Impact of COVID-19 on Small Business Outcomes and Expectations.” Proceedings of the National Academy of Sciences 117 (30): 17656–66. https://doi.org/10.1073/pnas.2006991117.Search in Google Scholar
Berge, Philipp vom, Corinna Frodermann, Tobias Graf, Stephan Grießemer, Steffen Kaimer, Markus Köhler, Claudia Lehnert, et al.. 2021. “Weakly Anonymous Version of the Sample of Integrated Labour Market Biographies (SIAB) – Version 7519 V1.” Technical report. Research Data Centre of the Federal Employment Agency (BA) at the Institute for Employment Research (IAB). The data access was provided via on-site use at the Research Data Centre (FDZ) of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB) and subsequently remote data access. Institute for Employment Research.Search in Google Scholar
Bloom, Nicholas, Steven J. Davis, and Yulia Zhestkova. 2021a. “COVID-19 Shifted Patent Applications Toward Technologies that Support Working from Home.” AEA Papers and Proceedings 111: 263–6. https://doi.org/10.1257/pandp.20211057.Search in Google Scholar
Bloom, Nicholas, Robert S. Fletcher, and Ethan Yeh. 2021b. “The Impact of COVID-19 on US Firms.” NBER Working Paper 28314.10.3386/w28314Search in Google Scholar
Boddin, Dominik, Francesco D’Acunto, and Michael Weber. 2020. “Did Targeting Financial Constraints During COVID-19 Make Sense?” Chicago Booth Research Paper.10.2139/ssrn.3724427Search in Google Scholar
Brusselaers, Nele, David Steadson, Kelly Bjorklund, Sofia Breland, Jens Stilhoff Sörensen, Andrew Ewing, Sigurd Bergmann, and Gunnar Steineck. 2022. “Evaluation of Science Advice During the COVID-19 Pandemic in Sweden.” Humanities and Social Sciences Communications 9 (1): 1–17. https://doi.org/10.1057/s41599-022-01097-5.Search in Google Scholar
Buchheim, Lukas, Jonas Dovern, Carla Krolage, and Sebastian Link. 2022a. “Sentiment and Firm Behavior During the COVID-19 Pandemic.” Journal of Economic Behavior & Organization 195: 186–98. https://doi.org/10.1016/j.jebo.2022.01.011.Search in Google Scholar
Buchheim, Lukas, Carla Krolage, and Sebastian Link. 2022b. “Sudden Stop: When Did Firms Anticipate the Potential Consequences of COVID-19?” German Economic Review 23 (1): 79–119. https://doi.org/10.1515/ger-2020-0139.Search in Google Scholar
Chaisemartin, Clément de, and Xavier D’Haultfoeuille. 2023. “Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey.” The Econometrics Journal 26: C1–30. https://doi.org/10.1093/ectj/utac017.Search in Google Scholar
Chancellery of Austria. 2022. Österreichisches Programm zum Schutz kritischer Infrastrukturen (APCIP). https://www.bundeskanzleramt.gv.at/themen/sicherheitspolitik/schutz-kritischer-infrastrukturen.html (accessed September 24, 2022).Search in Google Scholar
Comin, Diego A., Marcio Cruz, Xavier Cirera, Kyung Min Lee, and Jesica Torres. 2022. “Technology and Resilience.” NBER WP 29644.10.3386/w29644Search in Google Scholar
Core, Fabrizio, and Filippo De Marco. 2021. “Public Guarantees for Small Businesses in Italy During Covid-19.” CEPR Discussion Paper 15799.Search in Google Scholar
Coronakommissionen. 2022. “Sverige under Pandemin. Slutbetänkande Av Coronakommissionen.” Swedish Government Official Report. Statens Offentliga Utredningar. https://www.regeringen.se/rattsliga-dokument/statensoffentliga-utredningar/2022/02/sou-202210/ (accessed November 5, 2022).Search in Google Scholar
Cororaton, Anna, and Samuel Rosen. 2021. “Public Firm Borrowers of the US Paycheck Protection Program.” The Review of Corporate Finance Studies 10 (4): 641–93. https://doi.org/10.1093/rcfs/cfab019.Search in Google Scholar
European Commision. 2020. Proposal for a Directive on the Resilience of Critical Entities. https://home-affairs.ec.europa.eu/system/files/2020-12/15122020_proposal_directive_resilience_critical_entities_com-2020-829_en.pdf (accessed November 5, 2022).Search in Google Scholar
European Council. 2022. Proposal for a Council Regulation on an Emergency Intervention to Address High Energy Prices. https://data.consilium.europa.eu/doc/document/ST-12999-2022-INIT/en/pdf (accessed November 5, 2022).Search in Google Scholar
Federal Government of Germany. 2020. Die Mehrwertsteuer sinkt – wichtige Fragen und Antworten. https://www.bundesregierung.de/breg-de/themen/coronavirus/faq-mehrwertsteuersenkung-1764364 (accessed November 5, 2022).Search in Google Scholar
Federal Republic of Germany. 2016. BSI-Kritisverordnung vom 22. April 2016 (BGBl. I S. 958), die zuletzt durch Artikel 1 der Verordnung vom 6. September 2021 (BGBl. I S. 4163) geändert worden ist. https://www.gesetze-im-internet.de/bsi-kritisv/BJNR095800016.html.Search in Google Scholar
General Secretariat for Defence and National Security (SGDSN) of the Government of France. 2022. The Critical Infrastructure Protection in France. http://www.sgdsn.gouv.fr/communication/la-securite-des-activites-dimportance-vitale/ (accessed September 24, 2022).Search in Google Scholar
German Federal Employment Agency. 2022. Kurzarbeitergeld. https://www.arbeitsagentur.de/k/corona-kurzarbeit (accessed September 24, 2022).Search in Google Scholar
German Federal Office of Civil Protection and Disaster Assistance. 2022. Kritische Infrastrukturen. https://www.bbk.bund.de/DE/Themen/Kritische-Infrastrukturen/kritische-infrastrukturen_node.html (accessed September 24, 2022).Search in Google Scholar
German Federal Office of Civil Protection and Disaster Assistance. 2023. Sektoren und Branchen KRITIS. https://www.bbk.bund.de/DE/Themen/Kritische-Infrastrukturen/Sektoren-Branchen/sektorenbranchen_node.html (accessed October 5, 2023).Search in Google Scholar
German Ministry of Economic Affairs. 2022. Informationen zu Corona-Hilfen des Bundes. https://www.bmwk.de/Redaktion/DE/Coronavirus/informationen-zu-corona-hilfen-des-bundes.html (accessed November 5, 2022).Search in Google Scholar
German Ministry of Finance. 2020. Weg für Gewährung der Corona-Bundes-Soforthilfen ist frei – Umsetzung durch die Länder steht. https://www.bundesfinanzministerium.de/Content/DE/Pressemitteilungen/Finanzpolitik/2020/03/2020-03-29-PM-Verwaltungsvereinbarung-Soforthilfe.html (accessed November 5, 2022).Search in Google Scholar
Ghoroubi, Narges, Emilie Counil, and Myriam Khlat. 2022. “Socio-Demographic Composition and Potential Occupational Exposure to SARS-CoV2 under Routine Working Conditions Among Key Workers in France.” International Journal of Environmental Research and Public Health 19 (13): 7741. https://doi.org/10.3390/ijerph19137741.Search in Google Scholar
Giroud, Xavier, and Holger M. Mueller. 2017. “Firm Leverage, Consumer Demand, and Employment Losses During the Great Recession.” Quarterly Journal of Economics 132 (1): 271–316. https://doi.org/10.1093/qje/qjw035.Search in Google Scholar
Goldberg, Pinelopi K., and Tristan Reed. 2023. “Is the Global Economy Deglobalizing? And if so, why? And what is next?”BPEA Conference Draft, Spring. https://www.brookings.edu/articles/is-the-global-economy-deglobalizing-and-if-so-why-and-what-is-next/.10.3386/w31115Search in Google Scholar
Gopinath, Gita, Şebnem Kalemli-Özcan, Loukas Karabarbounis, and Carolina Villegas-Sanchez. 2017. “Capital Allocation and Productivity in South Europe.” Quarterly Journal of Economics 132 (4): 1915–67. https://doi.org/10.1093/qje/qjx024.Search in Google Scholar
Government of the Netherlands. 2022. COVID-19: Childcare for Children of People Working in Crucial Sectors. https://www.government.nl/topics/coronavirus-covid-19/documents/publications/2020/12/15/childcare-for-children-ofpeople-working-in-crucial-sectors (accessed September 24, 2022).Search in Google Scholar
Isphording, Ingo E., Marc Lipfert, and Nico Pestel. 2021. “Does Re-Opening Schools Contribute to the Spread of SARS-CoV-2? Evidence from Staggered Summer Breaks in Germany.” Journal of Public Economics 198: 104426. https://doi.org/10.1016/j.jpubeco.2021.104426.Search in Google Scholar
Koebe, Josefine, Claire Samtleben, Annekatrin Schrenker, and Aline Zucco. 2020. “Systemrelevant, aber dennoch kaum anerkannt: Entlohnung unverzichtbarer Berufe in der Corona-Krise unterdurchschnittlich.” Technical report. The German Institute for Economic Research.Search in Google Scholar
Konings, Jozef, Glenn Magerman, and Dieter Van Esbroeck. 2023. “The Impact of Firmlevel Covid Rescue Policies on Productivity Growth and Reallocation.” European Economic Review 157: 104508, https://doi.org/10.1016/j.euroecorev.2023.104508.Search in Google Scholar
Kosfeld, Reinhold, and Alexander Werner. 2012. “Deutsche Arbeitsmarktregionen – Neuabgrenzung nach den Kreisgebietsreformen 2007–2011.” Raumforschung und Raumordnung 70 (1): 49–64. https://doi.org/10.1007/s13147-011-0137-8.Search in Google Scholar
Ministry of Justice and Security, The Netherlands. 2022. Critical Infrastructure (Protection), National Coordinator for Counterterrorism and Security. https://english.nctv.nl/topics/critical-infrastructure-protection (accessed September 24, 2022).Search in Google Scholar
Republic of Austria. 2020. Bundesgesetz, mit dem ein Bundesgesetz über die Errichtung des COVID- 19-Krisenbewältigungsfonds (COVID-19-FondsG) und ein Bundesgesetz betreffend vorläufige Maßnahmen zur Verhinderung der Verbreitung von COVID-19 (COVID-19-Maßnahmengesetz) erlassen sowie das Gesetzliche Budgetprovisorium 2020, das Bundesfinanzrahmengesetz 2019 bis 2022, das Bundesgesetz über die Einrichtung einer Abbaubeteiligungsaktiengesellschaft des Bundes, das Arbeitsmarktpolitik-Finanzierungsgesetz, das Arbeitsmarktservicegesetz und das Arbeitsvertragsrechts- Anpassungsgesetz geändert werden (COVID-19 Gesetz). https://www.ris.bka.gv.at/Dokumente/BgblAuth/BGBLA_2020_I_12/BGBLA_2020_I_12.pdfsig.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
