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13-08-2021

Impacts of COVID-19 on the self-employed

Authors: Charlene Marie Kalenkoski, Sabrina Wulff Pabilonia

Published in: Small Business Economics | Issue 2/2022

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Abstract

This study estimates random effects and difference-in-difference-in-differences models to examine the initial impacts of COVID-19 on the employment and hours of unincorporated self-employed workers using monthly panel data from the Current Population Survey. For these workers, effects were visible in March as voluntary social distancing began, largest in April as complete shutdowns occurred, and slightly smaller in May as some restrictions were eased. We find differential effects by gender that favor men, by marital status and gender that favor married men over married women, and by gender, marital, and parental status that favor married fathers over married mothers. The evidence suggests that self-employed married mothers were forced out of the labor force to care for children presumably due to prescribed gender norms and the division and specialization of labor within households. Remote work and working in an essential industry mitigated some of the negative effects on employment and hours.

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For a comprehensive review of all the labor market effects of the pandemic, see Handwerker et al. (2020).

Authors’ calculations based on the Current Population Survey.

Employees include those classified in the CPS as government workers, private sector workers, and nonprofit workers. Together, employees and incorporated self-employed workers are considered wage and salary workers.

Parental status is defined as there being a child under age 18 in the household.

Full replication files including the data and STATA code are available here: https://doi.org/10.5281/zenodo.4757153 (Kalenkoski & Pabilonia, 2021).

The March CPS reference week was March 8 through 14. The April CPS reference week was April 12 through 18. The May CPS reference week was May 10 through 16.

These included Washington, California, Hawaii, Maryland, Indiana, Kentucky, Pennsylvania, Utah, and New York (Fullman et al., 2020). In a sensitivity analysis, we drop these states from our analyses. Our main results are similar, suggesting that early declarations did not affect our March results (see Appendix Table 6).

To observe whether our estimates are subject to composition bias due to nonresponse in some months, we compare employment rates for the unincorporated self-employed in each month for those who were in month-in-sample 1 and 5 in February 2020 by how many subsequent months they were observed in the sample (4 possible months). None of the differences is statistically significant. We repeated this analysis for those in month-in-sample 2 and 6 in February and compared March and April employment rates.

We drop a small number of workers who can be matched on CPS identifying variables (HHRID HHRID2 PULINENO) but do not match on age and sex.

May is not included in the DDD models, because there was a different treatment in May as the country began reopening.

Although our analyses examine whether unincorporated self-employed workers are doing any work in subsequent months compared to February, some of the unincorporated self-employed reported that they had transitioned into wage-and-salary jobs. Of those actively working in March, 5% of men and women switched to a wage-and-salary job. In April, 5% of men and 8% of women switched to a wage-and-salary job. By May, 11% of men and 18% of women switched to a wage-and-salary job. The last finding is statistically significantly different from zero at the 8% level.

Bick et al. (2021), using the Real-Time Population Survey (a CPS-like questionnaire), show that changes in the share of remote workers between February and May 2020 is strongly positively correlated with who could plausibly work at home as defined by Dingel and Neiman (2020).

CPS final weights are used in the descriptive analyses.

T-tests for all the employment and hours differences by gender, marital status, and parental status reported in this descriptive section are statistically significant at the 5% level unless otherwise stated.

However, approximately 17% of single individuals in our sample are living with an unmarried partner.

Logit or probit random-effects models would be appropriate due to the dichotomous nature of the dependent variable. However, the models would not converge.

When we examine all workers, these include dummies for class of worker (employee or incorporated self-employed).

We estimate linear DDD models for hours rather than tobit models because it is not straightforward to estimate nonlinear DDD models (e.g., Puhani, 2012).

When we examine all workers as a sensitivity analysis, h_i is a vector including dummies for class of worker (employee or incorporated self-employed).

Adding individual fixed effects would not change the coefficient estimates but would provide slightly smaller standard errors.

Predictions are provided in Appendix Table 8.

Predicted hours from the RE tobit model are for observed hours.

Results using DDD models are provided in the Appendix Table 9. The differences among the types of workers are qualitatively similar but slightly smaller in magnitude.

Appendix Tables 10 and 11 show the predicted probabilities and predicted hours, respectively, while Table 12 presents the summary statistics. Across months in 2020, demographics are similar, suggesting that the results should not suffer from nonresponse bias due to any differential reduction in nonresponse. We also estimated a specification where we used a binary indicator for unincorporated self-employed and at work as the outcome using the sample who were initially unincorporated self-employed and at work in February. Thus, in this specification, workers who transitioned to wage and salary employment are no longer counted as employed and at work. The differences presented in Appendix Table 13 show the differential impacts of leaving self-employment. The magnitude of the differences between groups in Table 2 increases by only about one percentage point.

This specification includes additional interactions between month, female, and age of child. We did a similar analysis for single individuals (see Appendix Table 14). We find no gender differences in the impacts of COVID on employment but do find that single mothers of school-aged children fared better than single fathers of school-aged children in April in terms of hours reductions.

Summary statistics for these DDD models are presented in Appendix Table 15.

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Title: Impacts of COVID-19 on the self-employed
Authors: Charlene Marie Kalenkoski
Sabrina Wulff Pabilonia
Publication date: 13-08-2021
Publisher: Springer US
Published in: Small Business Economics / Issue 2/2022
Print ISSN: 0921-898X
Electronic ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-021-00522-4

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