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Small Business Economics

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26.08.2022

Productivity and firm exit during the COVID-19 crisis: cross-country evidence

verfasst von: Silvia Muzi, Filip Jolevski, Kohei Ueda, Domenico Viganola

Erschienen in: Small Business Economics | Ausgabe 4/2023

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Abstract

This paper examines whether the economic crisis induced by the COVID-19 pandemic exhibits a Schumpeterian “cleansing” of less productive firms. Using firm-level data collected for 34 economies up to 18 months into the crisis, the study finds that less productive firms have a higher probability of permanently closing during the crisis, suggesting that the process of cleansing out unproductive activities is occurring. The paper also uncovers strong and negative relationships of firm exit with digital presence and with innovation. These relationships are driven by small firms. The study further finds that a burdensome business environment increases the probability of firm exit, also driven by small firms, and that a negative relationship exists between firm exit and age. Finally, evidence shows that the cleansing process is disrupted in countries which have introduced policies imposing a moratorium on insolvency procedures.

Vorheriger Artikel Digital economic activity and resilience for metros and small businesses during Covid-19

Nächster Artikel Translational research: from basic research to regional biomedical entrepreneurship

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The paper uses the data collected and published up to September 2021.

More information on the ES methodology is available at https://www.enterprisesurveys.org/en/methodology.

The questionnaires used for the ES and COV-ES surveys are available on the Enterprise Surveys website https://www.enterprisesurveys.org/en/enterprisesurveys data are also publicly available for download. The website also presents indicators built from the establishment-level data.

In addition to this analysis, for which results are presented in the on-line appendix, we also compared the differences between survivors and exiters based on the assumed exit and survivors and exiters based on the confirmed exit. The differences between the two groups are the same in the two samples.

Additional details about the sample are presented in the Appendix.

The time between surveys is measured by the time that elapsed between the baseline and the follow-up survey measured in 6-month periods.

Note that outliers are removed through the following procedure: total annual sales and number of permanent full-time employees are first log-transformed, then trimmed at plus and minus three standard deviations from the mean.

Analyses using TFP typically rely upon economic census, often only for manufacturers and have mostly focused on developed economies, although a growing number of studies have examined the issue in developing countries.

Following the Oslo Manual’s indications (OECD/Eurostat, 2018), a product innovation is defined as a new or improved product or service. A firm is considered as innovator independently from whether the new product or service introduced is also new to the establishment’s market.

All measures are taken at the year of the baseline survey.

We present results for a sample that varies based on the number of observations in each specification; however, results hold when the regressions are run on the constant sample determined by the specification with fewer observations. We also conduct sensitivity analysis by removing one country at the time and all the main results hold. In addition, an extreme value test is conducted in which the base specification excludes (1) the top 1% of firms, (2) the bottom 1%, and (3) both top and bottom 1 percent of firms in terms of labor productivity per country. The results remain robust, indicating that the findings are not driven by tail-end observations.

We run a standard diagnostic test on multicollinearity based on the analysis of the variance inflation factor (VIF) among the variables in the richest specification reported in table 3 column 5. The mean VIF for the variables in the model is 1.26 (range between 1.08 and 1.73) suggesting that multicollinearity is not a concerning issue for the estimates.

To compute the differences in the probability of exit between percentiles, first we identified the 10^th and the 90^th percentile of labor productivity within each country. Then, we computed the average of these percentiles across countries. Finally, we computed the predicted marginal effects by plugging in the averages of the 10^th and 90^th percentiles and the means of all the other regressors. The last step was to identify the difference between the predicted exit rate associated with the average 10^th percentile of labor productivity and the predicted exit rate associated with the average 90^th percentile.

The results of the main specification are confirmed when the more conservative measure of exit—the one in which only businesses that confirmed to have ceased operations permanently are considered as exiting firms—is considered. Results are presented in Table 17 in the appendix.

It is worth noting that this measure is not available for firms in one of the more strenuously affected sectors—the hospitality industry.

The full list encompasses: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Jordan, Kazakhstan, Latvia, Lebanon, Lithuania, Moldova, Mongolia, Montenegro, Morocco, North Macedonia, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, and Zambia.

We acknowledge that results of the model where interactions are reported separately may be biased due to omitted variables concerns; however, the alternative model with interactions run simultaneously may suffer from severe multicollinearity issues due to too many regressors all being interacted at the same time with the dummy of interest.

While interacting the measure of sales per worker is one alternative econometric strategy, we rely on splitting the sample in order to allow the slope of the independent variables to vary. Since the sample size for each regression is smaller, any significance in the results is indicative of robustness due to the larger standard errors.

The analysis has been replicated for a different definition of small firms (5–19 employees) as well as for small and medium firms (5–99 employees) versus large firms (100 + employees) and the results hold. The choice of presenting the results for small firms (5–49 employees) versus medium and large firms is driven by the small sample of large firms that exited the market.

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Titel: Productivity and firm exit during the COVID-19 crisis: cross-country evidence
verfasst von: Silvia Muzi
Filip Jolevski
Kohei Ueda
Domenico Viganola
Publikationsdatum: 26.08.2022
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 4/2023
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-022-00675-w

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