![Sample our Economics, Finance,Business & Industry journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5931/TOC-Subject-Sample-2021-Economics-Finance-Business-Industry.jpg"})
ABSTRACT
This paper uses firm-level data across about 30 nations to study the effect of the coronavirus on firms’ closures, with attention to firms with female managers and female owners. We also consider the influences of firms’ characteristics, the role of the government, economy-wide attributes, and industry type. The estimation uses a logit strategy, with country-year level clustered standard errors reported and industry fixed-effects included. Results show that, with somewhat limited statistical support, firms with female managers were more likely to exit, while those with female owners were less likely to. Further, fertility and gender inequality made firms’ exit more likely. Larger and older firms were less likely to exit. Finally, firms located in urbanized nations and those located in nations with larger governments were more likely to close, while the reverse was true in nations with better governance. We find weak support for the notion that heightened and aggressive government efforts to control the pandemic contributed to business closures. Finally, we find that various firm-level and macro factors impact firms’ exit during the COVID-19 pandemic.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Note that the stringency measures across nations and within nations have been changing over time (https://covidtracker.bsg.ox.ac.uk/). Thus, the choice of the time frame of the index might impact the degree of stringency captured. To somewhat address this issue, we took the average of the stringency index in the month preceding when the survey was conducted in a nation ().
4 The following excerpt from the Enterprise Surveys provides details about the sampling methodology: “The sampling methodology for Enterprise Surveys is stratified random sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group. This method allows computing estimates for each of the strata with a specified level of precision while population estimates can also be estimated by properly weighting individual observations. The sampling weights take care of the varying probabilities of selection across different strata. Under certain conditions, estimates’ precision under stratified random sampling will be higher than under simple random sampling (lower standard errors may result from the estimation procedure).
The strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Firm size levels are 5–19 (small), 20–99 (medium), and 100+ employees (large-sized firms). Since in most economies, the majority of firms are small and medium-sized, Enterprise Surveys oversample large firms since larger firms tend to be engines of job creation”, https://www.enterprisesurveys.org/en/methodology.
Furthermore, the interactions between the formal sector and informal firms might be important, and, in some cases, the informal sector firms can be instrumental in the closure decisions of formal sector firms (Goel and Saunoris Citation2022). Unfortunately, the Enterprise Surveys consider only firms organized in the formal sector firms in the context of the impact of COVID-19.
5 Firms included in the baseline Enterprise Surveys that did not respond to the follow-up COVID-19 survey were designated as missing values in response to this question and hence were dropped from the sample. This issue is addressed further with the “apparent closure” measure considered below. Finally, ES dropped the modifier “since the COVID-19 outbreak” in asking the survey question above. Hence it is possible that some firms may have permanently closed sometime between when the baseline survey was conducted and the start of the pandemic. This is mitigated, however, since only countries with baseline surveys conducted in 2019 and later are included.
6 Included in the latter category were firms that (1) did not reply to the phone survey after having called on different days of the week and in different business hours, (2) had the phone line out of order or no dial tone, (3) phone number no longer exists, (3) firms that did not respond or could not be contacted for a self-administered online survey.
Specifically, Apparent_closed includes all confirmed plus categories 91–96 (classified as missing with confirmed) and some of 621–626 (most of these also classified as confirmed, but in some cases there are also missing values in these categories in the confirmed data), where, for example, 91 = No reply after having called in different days of the week and in different business hours (1235 observations); 92 = Line out of order (209 observations); 93 = No tone (107 observations); 94 = Phone number does not exist (242 observations); 616 = The firm discontinued businesses - (Establishment went bankrupt), (511 observations); 618 = The firm discontinued businesses - (Original establishment disappeared and is now a different firm), (45 observations); 619 = The firm discontinued businesses - (Establishment was bought out by another firm), (80 observations); and so on (complete details are at http://www.enterprisesurveys.org.)
7 For further details, see https://hdr.undp.org/en/content/gender-inequality-index-gii.
8 Results for the specific individual industry categories considered in this analysis are not reported to conserve space but are available from the authors upon request.
9 As a comparison, there is some evidence of COVID-19-induced exit rates from Japan. Miyakawa, Oikawa, and Ueda (Citation2021) found that the pandemic potentially increased firm exits by around 20% compared to the previous year (under the assumption that the recent reduction in firm sales is temporary). No gender aspects were considered in this study.
10 https://ourworldindata.org/covid-stringency-index. Also, as we calculate the Stringency Index variable using data on the average value of the Stringency Index over the month preceding to most recent ES COVID-19 Follow-up Survey, it is negatively correlated (−0.35) with the Survey_gap control variable included in each regression setup, thus introducing potential multicollinearity issues between these two variables. (Government policies tended to become less aggressive over time.) The Stringency Index variable is consistently statistically significant if the Survey_gap control variable is excluded from the model.
11 The World Bank’s Enterprise Survey data are somewhat like Census data, where there are significant nondisclosure requirements. Individual firms in the data set are identified by a number, not the firm name. As a result, there is no way of determining if a female manager and female owner of a firm are the same person (see http://www.enterprisesurveys.org for additional details).
The share of female managers in the manufacturing and retail sectors, respectively, was 19.5% and 16.4%, while the corresponding shares for female owners in the two sectors were 21.0% and 17.6%, respectively.
12 One should, however, bear in mind that firms in all industries do not necessarily benefit similarly from internet presence. For instance, wholesale firms and retail firms would be expected to benefit differently.
13 Firms in all other sectors were in the default group. Service industries make up the largest part of this omitted category.
14 Complete details of how the stringency index is calculated along with further description of the specific policy responses considered here to combat the pandemic can be found here: https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/codebook.md.
15 A detailed analysis of the different dimensions of stringency is beyond the scope of this paper, which is primality examining the link between executive gender and business closings.
16 As discussed in the data section, Enterprise Surveys for different nations were done at different times. Thus, a continuously comparable data across nations is not available. Given appropriate data, one could focus on the non-closed firms and conduct a survival analysis.