Perceptions of pandemic resume gaps: Survey experimental evidence from the United States

Regina Bateson

doi:10.1371/journal.pone.0281449

. 2023 Mar 16;18(3):e0281449. doi: 10.1371/journal.pone.0281449

Perceptions of pandemic resume gaps: Survey experimental evidence from the United States

Regina Bateson ^1,^*

Editor: Simona Lorena Comi²

PMCID: PMC10019729 PMID: 36928893

Abstract

As a result of the COVID-19 pandemic, millions of people found themselves out of work in 2020 and 2021. Going forward, will their pandemic resume gaps be stigmatized or forgiven? In a recent survey experiment in the United States, I find that US adults have negative perceptions of individuals who were unemployed during the novel coronavirus pandemic. When asked to select among fictional applicants for a job opening in the hospitality industry, respondents prefer those who were employed continuously throughout the pandemic. Respondents are about 20% less likely to choose applicants with pandemic resume gaps, regardless of whether they were laid off, stopped working to supervise virtual school, or yo-yoed in and out of employment. Respondents also describe applicants with pandemic resume gaps in more negative terms, perceiving them as less hardworking, less dedicated, less professional, and less qualified than otherwise identical applicants who remained employed. Public opinion toward individuals with breaks in employment during the pandemic matters because it may affect public policy, and because stigma harms job seekers in multiple ways. Furthermore, the results of the experiment are consistent among survey respondents with hiring and managerial experience. While we should always be cautious about generalizing from survey experiments, these findings suggest that people who were out of work during the COVID-19 pandemic may face disadvantages when they return to the labor market.

Introduction

Since the COVID-19 pandemic hit the United States in March 2020, an unprecedented number of Americans have found themselves out of work. Some were laid off, while others stopped working due to illness, concerns about health and safety, or new caregiving responsibilities. The unemployment rate spiked to 14.8% in April 2020 [1], a development so sudden and widespread that it had "no modern equivalent" [2]. By July 2021, 3.4 million Americans had been unemployed for 6 months or more [3]. At the same time, millions more Americans were not working, but they did not meet the criteria to be categorized as "unemployed" because they were not actively seeking new jobs [4]. These trends were mirrored around the world, as the employment rate collapsed in countries ranging from Britain [5] to Japan [6] to Mexico [7].

The effects of these pandemic-era job losses may linger for years, well after the acute phase of the coronavirus pandemic has ended. Long-term unemployment typically causes “unemployment scarring” [8], in addition to negative impacts on health and psychological wellbeing [6–17]. Even when job losses result from an exogenous shock like the Great Recession, some research finds that lengthy periods out of work can harm future employment prospects [18–21]–which may bode poorly for those who were out of work during the COVID-19 pandemic [22, 23].

Yet as one displaced worker explained, "it feels like you have permission to be unemployed [due to the pandemic]. Obviously, being let go wasn’t based on my performance or ability to help the bottom line. It truly was a condition of nature, right?" [24] Media commentators have reinforced this impression, reassuring job seekers that pandemic resume gaps will not be held against them [25, 26]. And indeed, some studies find that spells of unemployment do not necessarily harm workers’ prospects for re-employment [27–29].

So going forward, how will individuals who were out of work during the pandemic be perceived? Will their pandemic resume gaps be stigmatized or forgiven? In a recent survey experiment in the United States, I find preliminary evidence of stigma toward those who were unemployed during the COVID-19 pandemic. These attitudes exist among the general public, and among survey respondents with hiring or managerial experience.

Among the general public, individuals with pandemic resume gaps are viewed more negatively than those who remained employed in 2020 and 2021. When presented with a vignette experiment, a nationally representative sample of US adults prefers fictional job applicants who were employed continuously throughout the coronavirus pandemic, compared to those who were out of work during the first waves of the pandemic. The survey respondents also describe people with pandemic resume gaps less favorably. Consistent with signaling theory [30–33], fictional job applicants with long periods out of work or who yo-yoed in and out of employment are significantly less likely to be perceived as hardworking, dedicated, professional, or qualified.

These results are not entirely surprising; public opinion is often critical of the unemployed, as has been demonstrated across multiple countries [34–37]. However, public opinion toward people whose employment was interrupted by the COVID-19 pandemic is particularly important for several reasons. First, public opinion matters for public policy [38]. If public opinion is critical of those who were out of work during the COVID-19 pandemic, this may affect whether or not they are seen as deserving of government assistance during the economic recovery. In addition, social stigma has negative consequences for individuals who are out of work. Social networks, word-of-mouth, and personal connections—even with "weak-tie" acquaintances [39]—all matter in a job search [40–42]. If people with pandemic resume gaps are viewed adversely by their peers, they may miss out on informal interactions and recommendations that could lead to new job opportunities. Moreover, simply being aware of others’ disdain ("stigma consciousness") can be harmful for the unemployed [43, 44].

Like the general public, survey respondents who have experience in hiring or supervisory roles are more likely to select fictional job applicants who were continuously employed throughout the pandemic. They also describe fictional applicants with pandemic resume gaps less positively than those who remained employed. While these results suggest the experiment’s findings could plausibly apply to real-world hiring scenarios, it is important to note that this is not an audit study or a field experiment, so it does not offer direct evidence of hiring discrimination in the labor market. However, this study suggests that policymakers should be attuned to the potential for discrimination against those who were out of work during the COVID-19 pandemic. If real-world hiring managers and recruiters share the assessments of the survey respondents, individuals with gaps in employment during the pandemic may face unique challenges when they seek to rejoin the workforce.

Methods and data

Recruitment and respondents

From July 7–10, 2021, I conducted a pre-registered survey experiment with a nationally representative sample of 974 US adults. The survey data and pre-registration are available on the Open Science Framework (OSF), https://osf.io/pqkbu. Though not randomly drawn, the sample was constructed to match the US census on key demographics. The survey experiment was fielded using Lucid Theorem, a reputable source of online survey respondents [45].

Eligibility was restricted to individuals who consented and passed two attention check questions—a particularly important step given burgeoning concerns about inattentiveness among online survey-takers [46, 47]. 1,000 respondents began the survey, and 974 finished it. Partial data from 26 subjects who attritted was discarded. This study was approved by the University of Ottawa Research Ethics Board, file number S-05-21-6805. All human subjects provided written informed consent.

Survey instrument and experimental setup

The survey began with questions about the respondents’ demographic characteristics, professional backgrounds and work histories, political and social views, and experiences during the COVID-19 pandemic. Next, in the experimental module, respondents read two vignettes about different hiring scenarios (SI Table 1 in S1 File). The first vignette asked respondents to select a new server for a restaurant in their town or city. The second vignette asked them to select a new front desk clerk for a hotel.

Table 1. Characteristics of fictional applicants, first hiring scenario.

	Applicant A	Applicant B	Applicant C
Age	33 years	28 years	26 years
Parental Status	3 children	2 children	2 children
Education	1 year of college	High school graduate	2 years of college
Work Experience	12 years	8 years	4 years
Most Recent Position	Restaurant server	Bartender	Restaurant server
Most Recent Wage	$9 per hour	$11 per hour	$10 per hour
Race	[Black / white]	[Black / white]	[Black / white]
Gender	[male / female]	[male / female]	[male / female]
Pandemic Employment History	[continuously employed / continuously unemployed / yo-yo unemployment / supervised virtual school]	[continuously employed / continuously unemployed / yo-yo unemployment / supervised virtual school]	[continuously employed / continuously unemployed / yo-yo unemployment / supervised virtual school]

Open in a new tab

Characteristics in italics were randomly assigned. This 2 x 2 x 4 randomization resulted in 16 different versions of each applicant profile. See SI Table 2 in S1 File for the full text of the applicant profiles in narrative form, as presented to the survey respondents.

Following each vignette, the respondents saw profiles of three fictional job applicants. The fictional profiles are summarized in Tables 1, 2 and presented in full in SI Tables 2, 3 in S1 File. Within each profile, several attributes were held constant, including age, parental status, work experience, education, and most recent wage. At the same time, in a research design reminiscent of a Goldberg paradigm experiment [48] or an audit study [49], race, gender, and pandemic-era work histories were randomly assigned. SI Tables 4–6 in S1 File summarize the distribution of the randomized characteristics.

Table 2. Characteristics of fictional applicants, second hiring scenario.

	Applicant D	Applicant E	Applicant F
Age	32 years	35 years	29 years
Parental Status	2 children	3 children	2 children
Education	High school graduate	1 year of college	Bachelor’s degree
Work Experience	8 years	11 years	5 years
Most Recent Position	Hotel concierge	Hotel office assistant	Hotel clerk
Most Recent Wage	$17 per hour	$22 per hour	$20 per hour
Race	[Black / white]	[Black / white]	[Black / white]
Gender	[male / female]	[male / female]	[male / female]
Pandemic Employment History	[continuously employed /continuously unemployed /yo-yo unemployment /supervised virtual school]	[continuously employed /continuously unemployed /yo-yo unemployment /supervised virtual school]	[continuously employed /continuously unemployed /yo-yo unemployment /supervised virtual school]

Open in a new tab

Characteristics in italics were randomly assigned. This 2 x 2 x 4 randomization resulted in 16 different versions of each applicant profile. See SI Table 3 in S1 File for the full text of the applicant profiles in narrative form, as presented to the survey respondents.

The experiment included four possible pandemic employment trajectories: 1) continuously employed; 2) continuously unemployed; 3) yo-yo unemployment; and 4) supervised virtual school. These conditions reflect the unusual range of workers’ experiences during the COVID-19 pandemic. Due to high levels of COVID-19, lockdowns, or the changed business climate, some workers were laid off for long periods of time ("continuously unemployed"). Others cycled through a pattern of yo-yo unemployment: as restrictions and cases waxed and waned, they were repeatedly laid off, re-hired, and laid off again ("yo-yo unemployment"). In addition, the closure of schools for in-person classes prompted some parents and caregivers to reduce their work hours or stop working to supervise online learning for their children ("supervised virtual school").

After viewing the fictional applicants for each hiring scenario, respondents were asked to choose one applicant. The probability of being selected is the main dependent variable for this study. Furthermore, for three of the applicant profiles, respondents were asked to describe the applicants. Respondents were provided with a list of 11 adjectives, including both positive and negative attributes (SI Table 7 in S1 File). While viewing each applicant’s profile, they were instructed to check all the terms that described the applicant, with multiple selections allowed. They also used a slider bar to indicate how much they would offer to pay the applicant, if they selected him or her. The attributes selected and hourly wages offered are used as additional dependent variables in the analysis.

Results

The results of survey experiment are analyzed for three sets of respondents: the full sample, respondents with hiring or managerial experience, and additional subgroups of respondents. The full sample reflects US public opinion toward those who were out of work during the pandemic. Meanwhile, the restricted sample of respondents with hiring or managerial experience offers insights into external validity, and the additional sub-group analyses explore heterogenous treatment effects among respondents with different life experiences, political party affiliations, ideological beliefs, and attitudes toward COVID-19.

Because each of the fictional applicant profiles had slightly different characteristics—such as age, education, and years of experience—all models include fixed effects by applicant profile. This controls for differences across the applicant profiles, as well as any inadvertent ordering or labelling effects (the profiles were labelled A, B, C, D, E, F). In addition, all models use robust standard errors clustered by respondent.

Public perceptions of pandemic resume gaps

Among the full sample of US adult respondents, fictional job applicants’ pandemic work histories significantly affect their chances of being selected in the survey experiment. Individuals who worked throughout the pandemic are chosen most frequently (Fig 1). Fictional job applicants randomly assigned to the "continuously employed" condition are selected 38.5% of the time (95% confidence interval: 36, 41). By contrast, continuously unemployed applicants are selected 31.2% of the time (95% confidence interval: 28.8, 33.5), those who experienced yo-yo unemployment are selected 32% of the time (95% confidence interval: 29.6, 34.4), and those who stopped working to supervise virtual school are selected 31.5% of the time (95% confidence interval: 29.2, 33.9).

Fig 1 — Coefficients and 95% confidence intervals from an OLS regression with fixed effects by applicant profile and robust standard errors clustered by respondent. The reference category is a continuously employed applicant. The unit of analysis is the applicant profile. N = 5,844. Full results and robustness checks in SI Table 8 in S1 File.

Put differently, having been out of work for a long period during the pandemic decreases an applicant’s probability of being selected by about 20%. This result is similar regardless of the reason why the hypothetical job applicant was out of work. Applicants with a history of yo-yo unemployment do not seem to get extra credit for having briefly returned to work—perhaps because an erratic work history can send negative signals about job applicants’ attitudes and "soft skills" [33]. And although workers who “opt out” to care for children usually face particular penalties [50], applicants who stopped working to supervise virtual school do not fare any worse than those who were laid off.

Contrary to expectations, the impact of a pandemic resume gap does not vary according to applicants’ racial-gender identities, and this experiment does not find evidence of discrimination against Black and/or female job applicants. As noted in SI Table 8 in S1 File, respondents seem to prefer Black and female applicants. And in a null result, neither the fictional applicants’ race, gender, nor employment history significantly affects the wages they are offered, perhaps due to strong anchoring effects. In the experimental vignettes, respondents were told each applicant’s most recent wage, and respondents tended to propose wages that hewed closely to those numbers.

However, as reported in Fig 2, pandemic-era employment history significantly affects the public’s perceptions of job seekers. Survey respondents describe fictional job applicants who were continuously unemployed, experienced yo-yo unemployment, or supervised virtual school less positively than those who continued working; they are markedly less likely to be labelled hardworking, dedicated, professional, or qualified. These effects are large and statistically significant. For example, a continuously employed fictional job applicant is about 50% more likely to be called “hardworking,” compared to someone who has been out of work since April 2020 (p<0.001). This is consistent with the idea that long periods of unemployment can signal low productivity [31, 32]. The results for the yo-yo unemployment condition are also consistent with prior research showing that frequent job-changing can be interpreted as a sign that an individual has a poor attitude toward work [33].

Respondents describe fictional job applicants who were out of work for different reasons in somewhat different terms (Fig 2). Consistent with prior research [51], continuous unemployment seems to signal a lack of motivation. Meanwhile, those who supervised virtual school are more likely to be seen as caring and family-oriented. These results are, in effect, a manipulation check; they show that respondents read, understood, and thought about the applicant profiles presented to them.

Results for respondents with hiring or supervisory experience

In addition to providing insights into US public opinion, this study also offers preliminary data on how individuals with management and hiring experience perceive job applicants with pandemic resume gaps. Before they completed the experimental module, respondents were asked whether they had ever participated in hiring an employee, and whether they had ever supervised anyone at work. 46% of respondents said they had hiring experience, and 62% said they had supervisory experience. The main findings of the experiment are largely consistent among these respondents (Fig 3), though the N is smaller, so the standard errors are larger than in the analysis with the full sample of respondents.

Among respondents with hiring experience, a history of yo-yo unemployment or supervising virtual school has a negative impact on the probability that a fictional job applicant will be selected to fill an open position (Fig 3, Panel A). These results are statistically significant at the p<0.01 and p<0.05 levels, respectively. Meanwhile, the impact of having been unemployed continuously is negative but only marginally significant, with p<0.1.

The results are similar among respondents with supervisory experience (Fig 3, Panel B), who are less likely to select fictional applicants with a history of continuous unemployment, yo-yo unemployment, or supervising virtual school during the pandemic, compared to those who remained employed. These results are all statistically significant at the p<0.01 level.

Like the general public, respondents with hiring and/or supervisory experience view applicants with pandemic resume gaps less positively than otherwise identical applicants who remained continuously employed throughout the pandemic (Fig 4). The direction, magnitude, and significance of these results is similar to those reported in Fig 2.

Fig 4 — This figure reports coefficients from 11 different OLS regressions. The dependent variables are binary variables indicating whether each profile was described with a given adjective. All models include fixed effects by applicant profile and robust standard errors clustered by respondent. Black brackets are 95% confidence intervals. The unit of analysis is the applicant profile. For each regression, N = 1,932. Full results in SI Tables 12 and 13 in S1 File.

Overall, respondents with hiring and supervisory experience react to the experiment in much the same way as the general public: they display preferences for continuously employed job applicants, and they ascribe more negative characteristics to applicants with breaks in employment during the pandemic. While this suggests that pandemic resume gaps could plausibly affect hiring decisions in the labor market, we should be cautious about generalizing from this study. Survey experiments have strengths, in that they allow us to study emerging phenomena about which we may not yet have other sources of observational or field experimental data. Yet they also have limitations, particularly with regard to external validity. One study finds that paired vignette studies return results similar to real-world behavior [52], but the fact remains: survey experiments are necessarily simplified and decontextualized, so it is difficult to know whether people would react similarly in more complex, noisy situations.

Indeed, the results of this study are only partially congruent with audit studies examining how employment history affects callbacks for job interviews. While the yo-yo unemployment findings line up nicely with recent research on the negative signaling effects of frequent job changes [33], the relationship with audit studies on long-term unemployment is less clear—in part because the literature is so muddled, with inconsistent results. Some researchers find that recent long-term unemployment results in a lower rate of callbacks [21, 32, 53, 54], while others find no effect [27] or effects only under certain circumstances [28, 29, 55].

These differences may be due to contextual factors or design choices, which vary considerably across studies. Some studies use only male job applicants [21, 53], others use only female applicants [28, 29, 32]; some studies use young adult job applicants [21, 27, 32, 53–55], others use older adult applicants [28, 29]. Additionally, audit studies typically send fictitious applications to job openings for administrative assistants [28, 29, 32] and other white-collar positions in finance, banking, insurance, sales, and similar fields [27, 54]. This makes it difficult to draw direct comparisons with this survey experiment, which focuses on the hospitality industry. So while the present survey experiment provides initial insights into perceptions of pandemic resume gaps, further research will be needed to establish whether and how pandemic-era lapses in employment affect job seekers in the real world.

Heterogenous treatment effects

To evaluate heterogenous treatment effects, the survey included questions about demographics, partisanship, ideology, and a range of experiences during the COVID-19 pandemic. Following best practices [56], these variables were measured pre-treatment, before respondents encountered the experimental hiring modules.

Surprisingly, having experienced the struggles described in the vignettes is not associated with greater tolerance for pandemic resume gaps. If anything, respondents who lost jobs and/or income due to COVID-19 may have slightly stronger preferences against job applicants who have been out of work during the pandemic (Fig 5).

Fig 5 — Coefficients and 95% confidence intervals from two OLS regressions with fixed effects by applicant profile and robust standard errors clustered by respondent. The reference category is a continuously employed applicant. The unit of analysis is the applicant profile. Panel A includes data from respondents who lost jobs or income due to COVID-19; N = 2,088. Panel B includes data from respondents who did not lose jobs or income; N = 3,756. Full results in SI Table 14 in S1 File.

The experimental results are also fairly consistent across respondents who see COVID-19 as a serious threat to public health and those who do not (Fig 6). However, there are notable differences by political party (Fig 7). In the United States, views on work and the welfare state shape political party identification, and vice versa [57, 58]. In addition, partisanship is strongly associated with attitudes and behaviors related to COVID-19 [59, 60]. And indeed, those relationships show up here. Compared to Democrats, respondents who identify as Republicans are more likely to penalize job applicants who were out of work during the COVID-19 pandemic (Fig 7). Ideology matters too, with moderate and conservative respondents driving the overall results of the experiment (Fig 8).

Fig 6 — Coefficients and 95% confidence intervals from two OLS regressions with fixed effects by applicant profile and robust standard errors clustered by respondent. The reference category is a continuously employed applicant. The unit of analysis is the applicant profile. Panel A is based on data from respondents who say that COVID-19 is a serious threat; N = 4,062. Panel B is based on data from respondents who say COVID-19 is not a serious threat; N = 1,782. Full results in SI Table 15 in S1 File.

Fig 7 — Coefficients and 95% confidence intervals from two OLS regressions with fixed effects by applicant profile and robust standard errors clustered by respondent. The reference category is a continuously employed applicant. The unit of analysis is the applicant profile. Panel A is based on data from Democratic respondents; N = 2,556. Panel B is based on data from Republican respondents; N = 2,010. Full results in SI Table 16 in S1 File.

Fig 8 — Coefficients and 95% confidence intervals from two OLS regressions with fixed effects by applicant profile and robust standard errors clustered by respondent. The reference category is a continuously employed applicant. The unit of analysis is the applicant profile. Panel A is based on data from liberal respondents; N = 1,782. Panel B is based on data from moderate respondents; N = 1,656. Panel C is based on data from conservative respondents; N = 2,406. Full results in SI Table 17 in S1 File.

Discussion

A recent survey experiment finds that in the United States, gaps in employment during the COVID-19 pandemic are perceived negatively by the general public and by individuals with hiring and managerial experience. These results are broadly consistent with Vishwanath’s model of unemployment stigma in the labor market [31], but the relationship with audit studies on unemployment duration and callbacks from employers is less certain. Future research will be needed to thoroughly understand the real-world impact of pandemic resume gaps.

In this survey experiment, however, the results are clear: compared to fictitious job applicants who worked continuously throughout the novel coronavirus pandemic, individuals with breaks in employment during the pandemic are seen as less desirable hires. Survey respondents also perceive them as having less positive characteristics. All else being equal, a pandemic resume gap increases a fictional job applicant’s risk of being seen as lacking in professionalism, qualifications, motivation, and dedication.

But do these results matter in the context of a sizzling job market? The impact of unemployment stigma is typically blunted in a tight labor market [28], and as vaccines have become available and public health restrictions have eased, US employers are scrambling to re-staff their operations. This is especially true in the hospitality industry, where many businesses have had difficulty recruiting enough workers to meet demand. Yet even so, the highest-quality jobs continue to receive large numbers of applications [61]. People who were out of work during the pandemic may be excluded from these plum opportunities, losing out on better wages, benefits, and working conditions.

Furthermore, the pace of hiring will presumably slow again at some point–and when that happens, job seekers with pandemic resume gaps could find themselves at a disadvantage. This prospect is particularly troubling because COVID-related unemployment has disproportionately affected people of color and women [1, 62–64]. So although this experiment does not find evidence of racial or gender discrimination per se, the stigma of a gap in employment during the COVID-19 pandemic could still result in setbacks for racial and gender equity in the United States.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(50.8KB, docx)}

Acknowledgments

I am grateful for logistical assistance from the Telfer School of Management at the University of Ottawa, support from the Women, Gender, and Politics Research Section of the American Political Science Association, and feedback from Patrick Leblond and Kate Weisshaar.

Data Availability

All data and replication files are publicly available from the Open Science Framework (https://osf.io/pqkbu/).

Funding Statement

This study was funded by a small grant from (no grant number) from the Women, Gender, and Politics Research Section of the American Political Science Association, https://connect.apsanet.org/s16/. RB received the grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Congressional Research Service. Unemployment rates during the COVID-19 pandemic. CRS Report R46554. 2021 Aug 20.
2.Bick A, Blandin A. Real-time labour market estimates during the 2020 coronavirus outbreak. VoxEU. 2020 May 6. https://voxeu.org/article/real-time-labourmarket-estimates-during-2020-coronavirus-outbreak
3.Bureau of Labor Statistics. Economic situation summary. USDL-21-1434. 2021 Aug 6. https://www.bls.gov/news.release/empsit.nr0.htm
4.Coibion O, Gorodnichenko Y, Weber M. Labor markets during the COVID-19 crisis: A preliminary view. NBER Working Paper No. 27017, National Bureau of Economic Research. 2020 April. http://www.nber.org/papers/w27017
5.Powell A, Francis-Devine B, Clark H. Coronavirus: Impact on the labour market. House of Commons Library. 2022 Aug 9. https://researchbriefings.files.parliament.uk/documents/CBP-8898/CBP-8898.pdf
6.Kotera S, Schmittmann JM. 2022. The Japanese Labor Market During the COVID-19 Pandemic. International Monetary Fund Working Paper Series WP/22/89. 2022. https://www.imf.org/-/media/Files/Publications/WP/2022/English/wpiea2022089-print-pdf.ashx
7.Hoehn-Velasco L, Silverio-Murillo A, Balmori de la Miyar JR, Penglase J. The impact of the COVID-19 recession on Mexican households: evidence from employment and time use for men, women, and children. Review of Economics of the Household. 2022;20:763–797. doi: 10.1007/s11150-022-09600-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Arulampalam W, Gregg P, Gregory M. Unemployment scarring. Econ. J. 2001;111:F577–F584. [Google Scholar]
9.Strandh M, Winefield A, Nilsson K, Hammarström A. Unemployment and mental health scarring during the life course. European Journal of Public Health 2014;24(3):440–445. doi: 10.1093/eurpub/cku005 [DOI] [PubMed] [Google Scholar]
10.Paul KI, Moser K. Unemployment impairs mental health: meta analyses. Journal of Vocational Behavior. 2009. June;74(3):264–282 10.1016/j.jvb.2009.01.001 [DOI] [Google Scholar]
11.Milner A, Page A, LaMontagne AD. Long-term unemployment and suicide: A systematic review and meta-Analysis. PLOS One. 2013. doi: 10.1371/journal.pone.0051333 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Maier R, Egger A, Barth A, Winker R, Osterode W, Kundi M, et al. Effects of short- and long-term unemployment on physical work capacity and on serum cortisol. International Archives of Occupational and Environmental Health. 2006;79:193–198. doi: 10.1007/s00420-005-0052-9 [DOI] [PubMed] [Google Scholar]
13.Dieckhoff M. The effect of unemployment on subsequent job quality in Europe: A comparative study of four countries. Acta Sociologica. 2011;54(3): 233–249. [Google Scholar]
14.Wanberg CR. 2012. The individual experience of unemployment. Annual Review of Psychology. 2012;63:369–96. [DOI] [PubMed] [Google Scholar]
15.Voss M, Nylén L, Floderus B, Diderichsen F, Terry PD. Unemployment and early cause-specific mortality: A study based on the Swedish twin registry. American Journal of Public Health. 2003;94(12):2155–2161. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Daly M, Delaney L. The scarring effect of unemployment throughout adulthood on psychological distress at age 50: Estimates controlling for early adulthood distress and childhood psychological factors. Social Science and Medicine. 2013;80:19–23. doi: 10.1016/j.socscimed.2012.12.008 [DOI] [PubMed] [Google Scholar]
17.Roelfs DJ, Shor E, Davidson K, Schwartz JE. Lose life and livelihood: A systematic review and meta-analysis of unemployment and all-cause mortality. Social Science and Medicine. 2011;72:840–854. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Bell DNF, Blanchflower DG. Young people and the Great Recession. Oxford Review of Economic Policy. 2011;27(2): 241–267. [Google Scholar]
19.Bell DNF, Blanchflower DG. UK unemployment in the Great Recession. National Institute Economic Review. 2010. October;214:R3–R25. 10.1177/0027950110389755 [DOI] [Google Scholar]
20.Tumino A. The scarring effect of unemployment from the early ’90s to the Great Recession. ISER Working Paper No. 2015–05. Institute for Social and Economic Research. 2015 March. http://hdl.handle.net/10419/126476
21.Ghayad R. The jobless trap. Northeastern University. 2013. [Google Scholar]
22.Chen VT, Sharone O. The second phase of unemployment will be harsher. The Atlantic. 2020 Apr 19. https://www.theatlantic.com/ideas/archive/2020/04/americas-compassionfor-the-unemployed-wont-last/610243/
23.Sharone O. A crisis of long-term unemployment is looming in the U.S. Harvard Business Review. 2021 Mar 18. https://hbr.org/2021/03/a-crisis-of-long-termunemployment-is-looming-in-the-u-s
24.Hartman M. With long-term unemployment comes long-term challenges. Marketplace. 2021 Jun 3. https://www.marketplace.org/2021/06/03/with-long-termunemployment-comes-long-term-challenges/
25.Elkind E. Don’t worry about that COVID resume gap, career expert says. Here’s why. CBS This Morning. 2021 May 20. https://www.cbsnews.com/news/covidresume-gap-career-expert/
26.Feintzeig R. Don’t sweat your pandemic résumé gap. Wall Street Journal. 2021 Jun 20. https://www.wsj.com/articles/dont-sweat-your-pandemic-resume-gap11624233600
27.Nunley JM, Pugh A, Romero N, Seals RA. The effects of unemployment and underemployment on employment opportunities: Results from a correspondence audit of the labor market for college graduates. ILR Review. 2017;70(3):642–669. [Google Scholar]
28.Farber HS, Silverman D, Von Wachter TM. Determinants of callbacks to job applications: An audit study. American Economic Review. 2016;106(5):314–18. [Google Scholar]
29.Farber HS, Silverman D, Von Wachter TM. Factors determining callbacks to job applications by the unemployed: An audit study. RSF: The Russell Sage Foundation Journal of the Social Sciences. 2017;3(3):168–201. [Google Scholar]
30.Spence M. Job market signaling. The Quarterly Journal of Economics 1973;87(3):355–374. [Google Scholar]
31.Vishwanath T. Job search, stigma effect, and escape rate from unemployment. Journal of Labor Economics. 1989;7(4):487–502. [Google Scholar]
32.Oberholzer-Gee F. Nonemployment stigma as rational herding: A field experiment. Journal of Economic Behavior & Organization. 2008;65(1):30–40. [Google Scholar]
33.Cohn A, Maréchal MA, Schneider F, Weber RA. Frequent job changes can signal poor work attitude and reduce employability. Journal of the European Economic Association. 2021;19(1):475–508. [Google Scholar]
34.Furåker B, Blomsterberg M. Attitudes towards the unemployed: An analysis of Swedish survey data. International Journal of Social Welfare. 2003;12:193–203. [Google Scholar]
35.Maassen G, de Goede M. Changes in public opinion on the unemployed: The case of the Netherlands. International Journal of Public Opinion Research. 1991;3(2):182–194. [Google Scholar]
36.Buffel V, Van de Velde S. Comparing negative attitudes toward the unemployed across European countries in 2008 and 2016: The role of the unemployment rate and job insecurity. International Journal of Public Opinion Research. 2019;31(3):419–440. [Google Scholar]
37.Eardley T, Matheson G. Australian attitudes to unemployment and unemployed people. Australian Journal of Social Issues. 2000;35(3): 181–202. [Google Scholar]
38.Burstein P. 2003. The impact of public opinion on public policy: A review and an agenda. Political Research Quarterly. 2003;56(1): 29–40. [Google Scholar]
39.Granovetter M. The strength of weak ties. The American Journal of Sociology. 1973;78(6):1360–1380. [Google Scholar]
40.Calvó-Armengol A, Jackson MO. The effects of social networks on employment and inequality. American Economic Review. 2004;94(3): 426–454. [Google Scholar]
41.Korpi T. Good friends in bad times? Social networks and job search among the unemployed in Sweden. Acta Sociologica. 2001;44: 157–170. [Google Scholar]
42.Granovetter M. Getting a Job: A Study of Contacts and Careers. 1st ed. Chicago: The University of Chicago Press; 1974. [Google Scholar]
43.Gurr T, Jungbauer-Gans M. Stigma consciousness among the unemployed and prejudices against them: Development of two scales for the 7th wave of the panel study, "Labour Market and Social Security (PASS)." Journal for Labour Market Research. 2013;46:335–351. [Google Scholar]
44.Krug G, Drasch K, Jungbauer-Gans M. The social stigma of unemployment: Consequences of stigma consciousness on job search attitudes, behaviour and success. Journal for Labour Market Research. 2019;53(11):1–27. [Google Scholar]
45.Coppock A, McClellan O. Validating the demographic, political, psychological, and experimental results obtained from a new source of online survey respondents. Research & Politics. 2019;6(1):2053168018822174. [Google Scholar]
46.Ternovski J, Orr L. A note on increases in inattentive online survey takers since 2020. Journal of Quantitative Description: Digital Media. 2022;2:1–35. 10.51685/jqd.2022.002 [DOI] [Google Scholar]
47.Peyton K, Huber G, Coppock A. The generalizability of online experiments conducted during the COVID-19 pandemic. Journal of Experimental Political Science. 2022;9(3):379–394. [Google Scholar]
48.Goldberg P. Are women prejudiced against women? Transaction. 1968;5:28–30. [Google Scholar]
49.Bertrand M, Mullainathan S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review. 2004. September;94(4):991–1013. [Google Scholar]
50.Weisshaar K. From opt out to blocked out: The challenges for labor market re-entry after family-related employment lapses. American Sociological Review. 2018;83(1):34–60. [Google Scholar]
51.Van Belle E, Caers R, De Couck M, Di Stasio V, Baert S. Why is unemployment duration a sorting criterion in hiring? IZA Discussion Paper No. 10876. Institute for Labor Economics. 2017 July. http://ftp.iza.org/dp10876.pdf
52.Hainmueller J, Hangartner D, Yamamoto T. Validating vignette and conjoint survey experiments against real-world behavior." PNAS. 2014;112(8):2395–2400. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Nüß P. Duration dependence as an unemployment stigma: Evidence from a field experiment in Germany. Economics Working Paper No. 2018–06. Kiel University. 2018. http://hdl.handle.net/10419/179941
54.Kroft K, Lange F, Notowidigdo MJ. Duration dependence and labor market conditions: Evidence from a field experiment. The Quarterly Journal of Economics. 2013;128(3):1123–1167. [Google Scholar]
55.Eriksson S, Rooth DO. Do employers use unemployment as a sorting criterion when hiring? Evidence from a field experiment. American Economic Review. 2014;104(3):1014–39. [Google Scholar]
56.Montgomery JM, Nyhan B, Torres M. How conditioning on posttreatment variables can ruin your experiment and what to do about it. American Journal of Political Science. 2018. July;62(3):760–775. [Google Scholar]
57.Bartels LM, Cramer KJ. Work, welfare, and partisan change. Annual Meeting of the American Political Science Association. 2020 Sept. https://my.vanderbilt.edu/larrybartels/files/2011/12/BC-Work-Welfare-and-Partisan-Change.pdf
58.Jeong J, Lee H. Public attitudes toward the minimum wage debate: Effects of partisanship, ideology, and beliefs. The Social Science Journal. 2021;58(2):164–175. [Google Scholar]
59.Gadarian SK, Goodman SW, Pepinsky TB. Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic. PLOS One. 2021. doi: 10.1371/journal.pone.0249596 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Grossman G, Kim S, Rexer JM, Thirumurthy J. Political partisanship influences behavioral responses to governors’ recommendations for COVID-19 prevention in the United States. PNAS. 2020;117(39):24144–24153. doi: 10.1073/pnas.2007835117 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Tayeb Z. This pizza chain owner who pays $16 an hour says there’s no labor shortage, just a shortage of businesses willing to pay a decent wage. Business Insider. 2021 Jul 11. https://www.businessinsider.com/pizza-chain-ceo-saystheres-no-labor-shortage-if-you-pay-well-2021-7?utm_source=copylink&utm_medium=referral&utm_content=topbar
62.Kurtzlaben D. Job losses higher among people of color during the coronavirus pandemic. Weekend Edition Sunday, National Public Radio (NPR). 2020 Apr 22. https://www.npr.org/2020/04/22/840276956/minorities-often-work-thesejobs-they-were-among-first-to-go-in-coronavirus-layo
63.Alon T, Doepke M, Olmstead-Rumsey J, Tertilt M. This time it’s different: The role of women’s employment in a pandemic recession. NBER Working Paper No. 27660. National Bureau of Economic Research. 2020 Aug. http://nber.org/papers/w27660
64.Montenovo L, Jiang X, Lozano Rojas F, Schmutte IM, Simon KI, Weinberg BA, et al. Determinants of disparities in Covid-19 job losses. NBER Working Paper 27132. National Bureau of Economic Research. 2020 May; revised 2021 June. 10.3386/w27132 [DOI]

PLoS One. doi: 10.1371/journal.pone.0281449.r001

Decision Letter 0

Simona Lorena Comi

11 Sep 2022

PONE-D-22-16366Unemployment scarring and the COVID-19 pandemic: How pandemic resume gaps affect perceptions of job-seekersPLOS ONE

Dear Dr. Bateson,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

One of the reviewers is negative and recommends rejection, mainly because of the not convincing external validity of the results.

The other reviewer is more positive and he/she acknowledges that the paper has some potential, but it still requires substantial work before it can be considered for publication.

After my careful reading of the paper, I agree with the second reviewer that your analysis is potentially interesting and the results may be policy relevant, but the current version of the paper suffers from a number of limitations and, as stated by both reviewers, it is not effective in explaining your theoretical arguments and the empirical results.

Although I believe it is a risky revision, I decided to give you the opportunity to thoroughly revise the paper following all the detailed comments of the second Reviewer.

I agree with both reviewers that the existing literature should be thoroughly reviewed, and more studies on other countries different from the US should be included, in order to be able to better interpret your results. Furthermore, I believe addressing the first main concern of reviewer 2 and, eventually use for the baseline estimates those with some experience in hiring, will further improve the paper.

Please let me remark that, given the two referee reports, the revision process will actually require to partly revise your empirical strategy and to rewrite substantially your manuscript.

Please submit your revised manuscript by Oct 26 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Simona Lorena Comi

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The article deals with an important topic from the perspective of both the social sciences and society. It is well written and, although somewhat underdeveloped in its theoretical background as in the discussion on possible stratification consequences, still it provides clear arguments and comprehensible results, with interesting findings especially in the section dealing with heterogeneity at the group level. In the author's place, I would prefer to view the results as indicative of positive discrimination of those who are continuously employed, since no clear differences emerge between individuals with other labour market trajectories. As a further suggestion, I would try to use variables related to the age and health status of applicants, if available. That said, despite the robustness check provided by the authors, the potential external validity of the analytical design is not entirely convincing. Overall, regrettably I do not consider the manuscript, in its current form, suitable for publication in this journal.

Reviewer #2: Referee Report „Unemployment scarring and the COVID-19 pandemic: How pandemic resume gaps affect perceptions of job-seekers”

The paper provides a survey experiment on the signalling effect of resume gaps during the COVID-19 pandemic. The paper provides evidence for the negative effect of different resume gaps, indicating the potential negative consequences of the pandemic for future labor market outcomes. This original research tries to fill an important current gap in the literature on the negative consequences of unemployment during the pandemic.

I have two main concerns and one minor point for the current version of the manuscript. While they imply a major revision of the manuscript, given the great design and analysis, I have no doubt that the revision is feasible and would make a great future publication in PLOS ONE. This is why I recommend a revise and resubmit.

Main Concern 1:

The paper conducts a survey experiment on a national representative sample of the United States. However, the general population is not suitable to make implications about negative consequences during the hiring process. The paper provides mainly evidence for perceptions of job-seekers in the general population, not what is observable during the hiring process. While the perceptions of the general population are of importance for political interventions, a survey experiment on negative consequences in the labor market should focus on decision makers in the hiring process. I therefore welcome the robustness check which focuses on individuals with experience in the hiring process. Yet, the results focusing on this subgroup (Figure 7 Panel a)) provides no statistical significant evidence for negative consequences for continuous unemployment.

Suggestion:

I therefore recommend deciding to focus on individuals with hiring experience (which is common practice in such survey experiments) or to reframe the paper in perceptions of the general population. This is of importance, given the fact that the results are not in line with previous audit studies on the impact of the unemployment duration during the hiring process for the United States.

Main Concern 2:

My second main concern is related to the literature work of the manuscript, which does not cover a substantial literature relevant for the research question. In the following I give an overview of this literature and show why which papers are of relevance.

Motivated by the Great Recession, there is a growing literature of audit studies that look at the effect of the unemployment duration for the job finding rates. The advantage of these papers compared to survey experiments is that they allow to avoid demand effects. 3 out of 5 such audit studies do not find discrimination against long term unemployed (Farber et al., 2016; Farber et al., 2017; Nunley et al., 2017). The remaining two papers find evidence for negative effects during the hiring process, under specific circumstances (Ghayad, 2013; Kroft et al., 2013). Given that the findings stand in contrast with most of this literature, I would at least want to see a discussion that tries to explain these differences.

There are further paper for the European context, which might be of relevance for different reasons (Cohn et al., 2021; Eriksson and Rooth, 2014; Nüß, 2018; Oberholzer-Gee, 2018).

The paper needs a theoretical foundation why unemployment during the pandemic should effect labor market outcomes at al. Given the findings, the perceived lack of motivation indicates that the signalling theory is a good starting point, also supported by previous experiments (Oberholzer-Gee, 2008; Kroft et al., 2013).

The paper by Cohn et al. (2021) provides a laboratory experiment, a survey experiment as well as audit study and shows that yo-yo unemployment is interpreted as a signal for low reliability which leads to worsen labor market outcomes. This paper is closest to the manuscript and should be therefore considered. This might also help to bring the manuscript more in context of the literature.

Lastly, two papers consider business cycle effects of unemployment spells (Korft et al., 2013; Nüß, 2018). These experiments show that unemployment hast only negative effects on job findings in strong labor markets (i.e. low unemployment). In line with the signalling theory, unemployment spells are interpreted as a negative signall when unemployment is low and individuals should easily find a job. If people still do not find a job, employers get sceptical about the productivity and motivation of the applicants. In contrast, when unemployment is high and it is more difficult to find a job, employers interpret less into the unemployment. Given the fact that the results of the survey experiment find negative effects during the pandemic, independent of the reason for the unemployment spell, the manuscript should at least discuss its findings in context of this literature.

Minor Concern:

A last minor point is related to the title, which covers the term “Unemployment scar”. An unemployment scar, is the long run effect of long time past unemployment on current labor market outcomes. In contrast, negative consequences of recent/current unemployment is named an unemployment stigma (Vishwanath, 1989).

Suggestion:

So to analyse an unemployment scar in a survey experiment would mean to provide participants information about past unemployment. It can be ruled out that findings of survey experiments and audit studies are due to an unemployment scar because Eriksson and Rooth (2014) tested current and past unemployment experience in an audit study, providing evidence for discrimination based on current unemployment spells, while past unemployment spells had no effect. So technically calling it a scar is the wrong term and it would be great to be more precise with it.

References:

Cohn, A., Maréchal, M. A., Schneider, F., & Weber, R. A. (2021). Frequent job changes can signal poor work attitude and reduce employability. Journal of the European Economic Association, 19(1), 475-508.

Eriksson, S., & Rooth, D. O. (2014). Do employers use unemployment as a sorting criterion when hiring? Evidence from a field experiment. American economic review, 104(3), 1014-39.

Farber, H. S., Silverman, D., & Von Wachter, T. (2016). Determinants of callbacks to job applications: An audit study. American Economic Review, 106(5), 314-18.

Farber, H. S., Silverman, D., & Von Wachter, T. M. (2017). Factors determining callbacks to job applications by the unemployed: An audit study. RSF: The Russell Sage Foundation Journal of the Social Sciences, 3(3), 168-201.

Ghayad, R. (2013). The jobless trap. Northeastern University.

Kroft, K., Lange, F., & Notowidigdo, M. J. (2013). Duration dependence and labor market conditions: Evidence from a field experiment. The Quarterly Journal of Economics, 128(3), 1123-1167.

Nunley, J. M., Pugh, A., Romero, N., & Seals, R. A. (2017). The effects of unemployment and underemployment on employment opportunities: Results from a correspondence audit of the labor market for college graduates. Ilr review, 70(3), 642-669.

Nüß, P. (2018). Duration dependence as an unemployment stigma: Evidence from a field experiment in Germany (No. 2018-06). Economics Working Paper.

Oberholzer-Gee, F. (2008). Nonemployment stigma as rational herding: A field experiment. Journal of Economic Behavior & Organization, 65(1), 30-40.

Vishwanath, T. (1989). Job search, stigma effect, and escape rate from unemployment. Journal of Labor Economics, 7(4), 487-502.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: Referee Report Corona Pandemic Effects on Unemployment.pdf

Click here for additional data file.^{(216.8KB, pdf)}

PLoS One. 2023 Mar 16;18(3):e0281449. doi: 10.1371/journal.pone.0281449.r002

Author response to Decision Letter 0

10 Nov 2022

Thank you for this opportunity to revise my manuscript, and thank you for the valuable feedback. I have made multiple significant changes to the manuscript, and I hope the new draft meets with your approval.

While I appreciate the advice of both reviewers and the editor, I would particularly like to thank R2 for their exceptionally thorough and helpful review. R2's analysis and advice are spot-on, and I have implemented all the changes they recommended. I believe these efforts have significantly improved the manuscript, resulting in a more appropriate framing, better engagement with the literature, a stronger theoretical foundation, and more attention to studies from other countries.

In line with the recommendations of the reviewers and the editor, I have made multiple major changes to the manuscript, including:

-reframing and rewriting the paper (including a new title!)

-incorporating new literature, doubling the number of references in the manuscript, and adding studies and data from multiple other countries

-expanding, clarifying, and placing greater emphasis on the discussion of external validity

Below, I describe these major changes, then I respond to additional points raised by each reviewer.

Major Changes

1. Reframing and reorienting the manuscript

I agree with R2's observation that because the survey experiment was conducted with a nationally representative sample of US adults, it is primarily a study of public opinion toward those who were out of work during the COVID-19 pandemic. As R2 correctly points out, "the paper provides mainly evidence for perceptions of job-seekers in the general population." R2 therefore suggests that I either reframe the paper to focus exclusively on respondents with hiring experience, or to reframe the paper to be about "perceptions of the general population."

In this revision, I have made major changes to emphasize that this is primarily a study of public attitudes toward the unemployed. I changed the title, rewrote the abstract, and edited the language throughout the paper to place greater emphasis on perceptions, rather than actual hiring of job applicants. For example, I have greatly reduced the use of the term "job-seeker." I also reduced the use of the terms "hiring" and "hiring decision" when discussing the survey experiment. Instead, in this draft, I describe how respondents selected, chose, or preferred fictional applicants with different employment histories. I believe this is more accurate and transparent, and this language is more consistent with what is actually happening in the survey experiment.

In the new introduction, I situate the paper in relation to other studies of perceptions of the unemployed, and I articulate why public opinion toward the unemployed matters (see lines 93-105). I explain that public opinion shapes public policy, and stigma has multiple negative impacts on unemployed individuals. For instance, through "stigma consciousness" (Gurr and Jungbauer-Gans 2013), merely being aware of others' judgments can affect job search behavior and outcomes (Krug, Drasch, and Jungbauer-Gans 2019). While multiple prior studies have examined public perceptions of the unemployed in other contexts (Maassen and de Goede 1991, Eardley and Matheson 2000, Furåker and Blomsterberg 2003, Buffel and Van de Velde 2019), this is (to the best of my knowledge), to the best of my knowledge this manuscript is the first research to offer insights into perceptions of individuals who were out of work at the height of the COVID-19 pandemic.

2. Expanded engagement with the literature & more theory

I would like to thank R2 for the extensive list of recommended references. I have cited all of R2's recommended references into this draft of the manuscript. In addition, I have added more than 20 other references to give the paper a better theoretical grounding, to make its contributions and limitations clearer, and to better situate it in an international context.

Equipped with this additional research and reading, I have also revised the manuscript to clarify the theoretical interpretation of my results. I agree with R2 that this is really a study about unemployment stigma, not unemployment scarring (thank you, R2, for pointing that out!). I now discuss stigma at multiple points in the paper. I also argue that signaling is the most likely mechanism for the results of the experiment. The paper now includes citations to the literature on the negative signaling effects of periods of unemployment (Spence 1973, Vishwanath 1989) and frequent job changes (Cohn et al 2021).

In addition, as recommended by R2, the paper now includes a substantial discussion of differences with prior audit studies on unemployment duration and callback rates for job interviews. This is primarily found on lines 291-308, though I also edited the introduction to clarify that the results in this literature mixed (see lines 70 and 77-78).

Finally, addressing the editor's request for more discussion of studies from other countries, the revised manuscript includes new references to data and research from Japan, Britain, Germany, Switzerland, Sweden, Australia, the Netherlands, Mexico, and a multi-country study conducted across Europe.

3. More emphasis on external validity

R1, R2, and the editor all raised concerns about external validity. I take that feedback seriously, and I have tried to reframe the manuscript to present the results more appropriately.

In this revision, I have not used the results from respondents with hiring and managerial experience as the baseline models (although the editor recommended that I do so). I did not do this because doing so would contravene my pre-registered pre-analysis plan, and because it would be very unusual to present the results for a sub-group of respondents without first presenting the results for the full sample of respondents.

However, I have taken taken multiple steps to expand and place greater emphasis on the results for the respondents with hiring and managerial experience. First, I moved this section up in the manuscript, so it appears more prominently at an earlier point in the manuscript. Second, I added a new figure (Figure 4) that shows how respondents with hiring and managerial experience describe the fictional job applicants. Third, the revised manuscript includes additional discussion of external validity on lines 280-290 and 305-308.

I have made best efforts to address R1's concerns about external validity, and I agree that we should always be careful about generalizing from survey experiments—as I note in the abstract (line 48) and multiple times in manuscript on (lines 284 and 372-74). However, despite their limitations, PLOS One regularly publishes survey experiments on a wide range of topics, including perceptions of COVID-19 policies (Zhang et al 2020), trust in government (Martin et al 2020), discrimination in the housing market (Ghekiere et al. 2022), choices among different COVID-19 vaccines (Kreps and Kriner 2022), pandemic-induced racial and ethnic prejudice (Kaushal, Lu, and Huang 2022), discrimination toward literary authors (Weinberg and Kapelner 2022), and evaluations of restaurants (Maezawa and Kawahara 2021). With the improvements in this draft, I hope this manuscript can eventually be published as well.

Additional Responses to Reviewers

I would like to thank R1 for their attention to the manuscript.

In this revision, I have adopted several of the changes recommended by R1. First, I have improved the theoretical background for the paper. Second, I have described the results as positive discrimination at several points (including lines 38-39 in the abstract and lines 86-87 in the body of the manuscript). Thank you for this helpful suggestion.

I also appreciate R1's interest in analyzing the age and health status of the fictional job applicants, but I was not able to act on this request because age was held constant in the experimental vignettes and the profiles did not include any information about the fictional applicants' health status (as clarified in SI Tables 12-13).

I am deeply grateful for R2's comprehensive, constructive review, which was instrumental in informing my revisions. I also appreciate R2's praise for the manuscript's "great design and analysis."

In response to R2's first main concern, I re-oriented most of the paper to focus more on public opinion toward individuals who were unemployed during the pandemic. However, I also partially adopted R2's second recommended strategy, which was to focus more on the results from respondents with hiring experience. That expanded discussion now appears more prominently in the manuscript, starting on line 233.

In the expanded section on the results for respondents with hiring and managerial experience, I also address R2's concern about statistical significance of the coefficient on continuous unemployment. As noted in the manuscript, the standard errors are larger for that analysis, because the N is smaller (Lines 240-242). The p-value for that coefficient is 0.08, which is above the conventional 0.05 threshold but under 0.1. I acknowledge this in the manuscript and describe that result as only marginally statistically significant (Lines 257-258). If I were to combine the respondents with hiring experience together with the respondents with managerial experience (to create a larger N), this result would be statistically significant with p<0.05. However, I did not make this change in the manuscript because it would amount to p-hacking, which is not a good research practice. I hope R2 understands this rationale.

In addition, I revised the title to remove the term "unemployment scarring," and I removed most discussions of "scarring" from the paper. I would like to thank R2 for pointing out this problem, and for advising me on the correct term ("unemployment stigma").

R2's literature references, the suggestion to consider signaling theory, and the recommendation to engage more with audit studies were all extremely helpful. Thank you so much!

R2 went above and beyond in writing their review, contributing substantially to my revisions. Thank you again, I hope you enjoy reading this new version of the manuscript!

WORKS CITED

Buffel V, Van de Velde S. Comparing negative attitudes toward the unemployed across European countries in 2008 and 2016: The role of the unemployment rate and job insecurity. International Journal of Public Opinion Research. 2019;31(3):419-440.

Cohn A, Maréchal MA, Schneider F, Weber RA. Frequent job changes can signal poor work attitude and reduce employability. Journal of the European Economic Association. 2021;19(1):475-508.

Eardley T, Matheson G. Australian attitudes to unemployment and unemployed people. Australian Journal of Social Issues. 2000;35(3): 181-202.

Furåker B, Blomsterberg M. Attitudes towards the unemployed: An analysis of Swedish survey data. International Journal of Social Welfare. 2003;12:193-203.

Ghekiere A, Verhaeghe P, Baert S, Derous E, Schelfhout S. Introducing a vignette experiment to study mechanisms of ethnic discrimination on the housing market. PLoS One 2022;17(10): e0276698. https://doi.org/10.1371/journal.pone.0276698

Gurr T, Jungbauer-Gans M. Stigma consciousness among the unemployed and prejudices against them: Development of two scales for the 7th wave of the panel study, "Labour Market and Social Security (PASS)." Journal for Labour Market Research. 2013;46:335-351.

Kaushal N, Lu Y, Huang X. Pandemic and prejudice: Results from a national survey experiment." PLOS One. 2022;17(4): e0265437. https://doi.org/10.1371/journal.pone.0265437

Kreps S, Kriner DL. Communication about vaccine efficacy and COVID-19 vaccine choice: Evidence from a survey experiment in the United States. PLoS One. 2022; 17(3): e0265011. https://doi.org/10.1371/journal.pone.0265011

Krug G, Drasch K, Jungbauer-Gans M. The social stigma of unemployment: Consequences of stigma consciousness on job search attitudes, behaviour and success. Journal for Labour Market Research. 2019;53(11):1-27.

Maassen G, de Goede M. Changes in public opinion on the unemployed: The case of the Netherlands. International Journal of Public Opinion Research. 1991;3(2):182-194.

Maezawa T, Kawahara JI. A label indicating an old year of establishment improves evaluations of restaurants and shops serving traditional foods. PLoS One. 2021;16(11): e0259063. https://doi.org/10.1371/journal.pone.0259063

Martin A, Orr R, Peyton K, Faulkner N. Political Probity Increases Trust in Government: Evidence from Randomized Survey Experiments. PLoS One. 2020;15(2): e0225818. https://doi.org/10.1371/journal.pone.0225818

Spence M. Job market signaling. The Quarterly Journal of Economics 1973;87(3):355-374.

Vishwanath T. Job search, stigma effect, and escape rate from unemployment. Journal of Labor Economics. 1989;7(4):487-502.

Weinberg, DB, Kapelner A. 2022Do book consumers discriminate against Black, female, or young authors?" PLoS One. 2022;17(6): e0267537. https://doi.org/10.1371/journal.pone.0267537

Zhang B, Kreps S, McMurray N, McCain RM. Americans' perceptions of privacy and surveillance in the COVID-19 pandemic. PLOS One. 2020;15(12): e0242652. https://doi.org/10.1371/journal.pone.0242652

Attachment

Submitted filename: Response Memo.docx

Click here for additional data file.^{(22.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0281449.r003

Decision Letter 1

Simona Lorena Comi

8 Dec 2022

PONE-D-22-16366R1Perceptions of pandemic resume gaps: Survey experimental evidence from the United StatesPLOS ONE

Dear Dr. Bateson,

Specifically, I agree with Reviewer 2 that your paper is much improved and almost ready to be accepted for publication. Indeed, Reviewer 2 suggested to accept the paper as it is. However, I would like to raise a couple of last very minor issues that, if taken into account, would support your empirical strategy even more.

1) Your paper is missing a discussion (and a table) about the distribution of the characteristics of the applicants that you randomized (gender, race, age, number of children, level of education, previous wage, and so on…) across the applicants’ profiles you are using in the analysis (continuous unemployment, yo-yo unemployment, supervising school or continuous employment). Are they balanced?

Related to this, it would also be helpful to understand your analysis better if you could explain what you mean by “fixed effects by applicant profile” in the notes below each table and explain what variables you are controlling for. Are you already controlling for the applicant characteristics, the distribution of which I am asking you to document (gender, race, age, number of children, level of education, previous wage, and so on…)? If so, I encourage you to add a table with the full estimates in which you report the coefficients of these variables, even in SI.

2) It would be best if you numbered your Table in order of appearance. The first Table mentioned in the paper is SI Table 11 (page 5, line 134). I suggest dividing the Tables provided in the SI into appendixes; in this way, Table 11 SI, together with all the tables about the survey (Table 12 and 13), could enter into Appendix A, and the table number could become Table A1 in Appendix A (Table A2 and A3). (very minor point)

Please submit your revised manuscript by Jan 22 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Simona Lorena Comi

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Thank you for this great resubmission. While I liked the design and analysis already in the first submission, The adjustments regarding the motivation and the theoretical bachground greatly imporved the paper.

Also thank you for not adjusting the analysis regarding the statistical sginfificance for participants with HR experience. In fact, signficance on the 10% level are more than acceptable. Making the significance level transparent was indeed the scietifically better solution.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

PLoS One. 2023 Mar 16;18(3):e0281449. doi: 10.1371/journal.pone.0281449.r004

Author response to Decision Letter 1

23 Dec 2022

To the editor and reviewers:

Thank you for the invitation to revise and resubmit my manuscript. Once again, I would like to thank R2 for their detailed comments and constructive advice. Additionally, I appreciate the queries and suggestions from the editor.

In this revision, I have made multiple changes to address the editor's comments and questions.

There are 5 improvements in this revision:

1. Better explanation of the randomization in the survey experiment

The editor raised a number of questions about the randomization in the survey experiment. I agree that the randomization process was not thoroughly explained in the prior drafts; thank you for drawing my attention to this issue.

For this revision, I created two new tables (Table 1 and Table 2 on pp. 6-7 of the manuscript). These tables summarize the characteristics that were held constant and and the characteristics that were randomized within each applicant profile.

I hope Tables 1 and 2 clarify that there were only 3 characteristics randomized within the applicant profiles: pandemic employment history, race, and gender. I also added new text describing the randomization process (see lines 139-143 on p. 5).

Additionally, in the captions for Tables 2 and 3, I explain that this is a 2 x 2 x 4 randomization, resulting in 16 different versions of each applicant profile.

I hope this additional information better explains what was held constant, and what was randomized.

2. More information about the distribution of randomized characteristics

The editor also requested additional information about the distribution of the randomized characteristics across the applicant profiles.

In the SI, I have added 3 new tables summarizing the counts and percentages of the employment histories and racial and gender identities assigned to each fictional applicant profile. These are SI Tables 4-6, which are mentioned in lines 142-143 on p. 5 of the manuscript.

The randomization was programmed using the "evenly present elements" function in Qualtrics, and overall it was quite well-balanced. However, as noted in SI Tables 4-6, within some applicant profiles, there are a few slight imbalances. I believe this may have resulted from the attrition of a small number of survey respondents, or from the variation inherent in the randomization process.

However, even if we control for the race and gender assigned to each applicant profile, the impact of a pandemic resume gap remains negative and statistically significant (see SI Table 3, model 2).

3. Additional robustness checks

As noted above, I have revised SI Table 8, which reports the main results from Fig 1 in the manuscript.

Now SI Table 8 includes two new robustness checks (see models 2 and 3). Model 2 adds controls for the race and gender randomly assigned to each applicant profile, and Model 3 drops the fixed effects by applicant profile and the controls for race and gender. Across all these models, the coefficients on the pandemic employment histories are negative, statistically significant, and similar in magnitude.

4. Better explanation of the fixed effects by applicant profile

To better explain the fixed effects by applicant profile, I have added new text on lines 188-191 of the manuscript (pp. 8-9).

In short, Applicants A, B, C, D, E, and F each have slightly different biographies, with multiple characteristics that are fixed (not randomized) throughout the experiment, such as their age, number of children, level of education, years of work experience, most recent job title, and most recent wage. In addition, the applicants are labelled A, B, C, D, E, and F. These differences could make certain applicants seem more appealing, regardless of their employment histories. That is why I use fixed effects by applicant profile in my main models. The fixed effects by applicant profile control for the differences across the applicant profiles, allowing us to isolate the effect of the randomly assigned employment histories.

As recommended by the editor, I am now reporting the coefficients for the fixed effects by applicant in SI Table 8. I did not add the coefficients for the fixed effects to all the subsequent tables, because it would make all the tables much longer, and the coefficients for each applicant profile don't really convey any substantively interesting information. However, I am open to reporting the coefficients for the fixed effects in all tables if the editor thinks it is essential.

5. Re-organizing and re-numbering the SI tables

Finally, I have acted on the editor's request to re-number and re-organize the SI tables. There are now 17 tables in the SI, and they appear in the order that they are mentioned in the manuscript.

I hope these changes meet with your satisfaction, and I look forward to hearing back from you soon. Thank you again for considering my work, and thank you for your detailed feedback and guidance.

Attachment

Submitted filename: Bateson 2nd Revision memo.docx

Click here for additional data file.^{(15.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0281449.r005

Decision Letter 2

Simona Lorena Comi

24 Jan 2023

Perceptions of pandemic resume gaps: Survey experimental evidence from the United States

PONE-D-22-16366R2

Dear Dr. Bateson,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Simona Lorena Comi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0281449.r006

Acceptance letter

Simona Lorena Comi

6 Mar 2023

PONE-D-22-16366R2

Perceptions of pandemic resume gaps: Survey experimental evidence from the United States

Dear Dr. Bateson:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Simona Lorena Comi

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File

(DOCX)

Click here for additional data file.^{(50.8KB, docx)}

Attachment

Submitted filename: Referee Report Corona Pandemic Effects on Unemployment.pdf

Click here for additional data file.^{(216.8KB, pdf)}

Attachment

Submitted filename: Response Memo.docx

Click here for additional data file.^{(22.7KB, docx)}

Attachment

Submitted filename: Bateson 2nd Revision memo.docx

Click here for additional data file.^{(15.2KB, docx)}

Data Availability Statement

All data and replication files are publicly available from the Open Science Framework (https://osf.io/pqkbu/).

[pone.0281449.ref001] 1.Congressional Research Service. Unemployment rates during the COVID-19 pandemic. CRS Report R46554. 2021 Aug 20.

[pone.0281449.ref002] 2.Bick A, Blandin A. Real-time labour market estimates during the 2020 coronavirus outbreak. VoxEU. 2020 May 6. https://voxeu.org/article/real-time-labourmarket-estimates-during-2020-coronavirus-outbreak

[pone.0281449.ref003] 3.Bureau of Labor Statistics. Economic situation summary. USDL-21-1434. 2021 Aug 6. https://www.bls.gov/news.release/empsit.nr0.htm

[pone.0281449.ref004] 4.Coibion O, Gorodnichenko Y, Weber M. Labor markets during the COVID-19 crisis: A preliminary view. NBER Working Paper No. 27017, National Bureau of Economic Research. 2020 April. http://www.nber.org/papers/w27017

[pone.0281449.ref005] 5.Powell A, Francis-Devine B, Clark H. Coronavirus: Impact on the labour market. House of Commons Library. 2022 Aug 9. https://researchbriefings.files.parliament.uk/documents/CBP-8898/CBP-8898.pdf

[pone.0281449.ref006] 6.Kotera S, Schmittmann JM. 2022. The Japanese Labor Market During the COVID-19 Pandemic. International Monetary Fund Working Paper Series WP/22/89. 2022. https://www.imf.org/-/media/Files/Publications/WP/2022/English/wpiea2022089-print-pdf.ashx

[pone.0281449.ref007] 7.Hoehn-Velasco L, Silverio-Murillo A, Balmori de la Miyar JR, Penglase J. The impact of the COVID-19 recession on Mexican households: evidence from employment and time use for men, women, and children. Review of Economics of the Household. 2022;20:763–797. doi: 10.1007/s11150-022-09600-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref008] 8.Arulampalam W, Gregg P, Gregory M. Unemployment scarring. Econ. J. 2001;111:F577–F584. [Google Scholar]

[pone.0281449.ref009] 9.Strandh M, Winefield A, Nilsson K, Hammarström A. Unemployment and mental health scarring during the life course. European Journal of Public Health 2014;24(3):440–445. doi: 10.1093/eurpub/cku005 [DOI] [PubMed] [Google Scholar]

[pone.0281449.ref010] 10.Paul KI, Moser K. Unemployment impairs mental health: meta analyses. Journal of Vocational Behavior. 2009. June;74(3):264–282 10.1016/j.jvb.2009.01.001 [DOI] [Google Scholar]

[pone.0281449.ref011] 11.Milner A, Page A, LaMontagne AD. Long-term unemployment and suicide: A systematic review and meta-Analysis. PLOS One. 2013. doi: 10.1371/journal.pone.0051333 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref012] 12.Maier R, Egger A, Barth A, Winker R, Osterode W, Kundi M, et al. Effects of short- and long-term unemployment on physical work capacity and on serum cortisol. International Archives of Occupational and Environmental Health. 2006;79:193–198. doi: 10.1007/s00420-005-0052-9 [DOI] [PubMed] [Google Scholar]

[pone.0281449.ref013] 13.Dieckhoff M. The effect of unemployment on subsequent job quality in Europe: A comparative study of four countries. Acta Sociologica. 2011;54(3): 233–249. [Google Scholar]

[pone.0281449.ref014] 14.Wanberg CR. 2012. The individual experience of unemployment. Annual Review of Psychology. 2012;63:369–96. [DOI] [PubMed] [Google Scholar]

[pone.0281449.ref015] 15.Voss M, Nylén L, Floderus B, Diderichsen F, Terry PD. Unemployment and early cause-specific mortality: A study based on the Swedish twin registry. American Journal of Public Health. 2003;94(12):2155–2161. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref016] 16.Daly M, Delaney L. The scarring effect of unemployment throughout adulthood on psychological distress at age 50: Estimates controlling for early adulthood distress and childhood psychological factors. Social Science and Medicine. 2013;80:19–23. doi: 10.1016/j.socscimed.2012.12.008 [DOI] [PubMed] [Google Scholar]

[pone.0281449.ref017] 17.Roelfs DJ, Shor E, Davidson K, Schwartz JE. Lose life and livelihood: A systematic review and meta-analysis of unemployment and all-cause mortality. Social Science and Medicine. 2011;72:840–854. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref018] 18.Bell DNF, Blanchflower DG. Young people and the Great Recession. Oxford Review of Economic Policy. 2011;27(2): 241–267. [Google Scholar]

[pone.0281449.ref019] 19.Bell DNF, Blanchflower DG. UK unemployment in the Great Recession. National Institute Economic Review. 2010. October;214:R3–R25. 10.1177/0027950110389755 [DOI] [Google Scholar]

[pone.0281449.ref020] 20.Tumino A. The scarring effect of unemployment from the early ’90s to the Great Recession. ISER Working Paper No. 2015–05. Institute for Social and Economic Research. 2015 March. http://hdl.handle.net/10419/126476

[pone.0281449.ref021] 21.Ghayad R. The jobless trap. Northeastern University. 2013. [Google Scholar]

[pone.0281449.ref022] 22.Chen VT, Sharone O. The second phase of unemployment will be harsher. The Atlantic. 2020 Apr 19. https://www.theatlantic.com/ideas/archive/2020/04/americas-compassionfor-the-unemployed-wont-last/610243/

[pone.0281449.ref023] 23.Sharone O. A crisis of long-term unemployment is looming in the U.S. Harvard Business Review. 2021 Mar 18. https://hbr.org/2021/03/a-crisis-of-long-termunemployment-is-looming-in-the-u-s

[pone.0281449.ref024] 24.Hartman M. With long-term unemployment comes long-term challenges. Marketplace. 2021 Jun 3. https://www.marketplace.org/2021/06/03/with-long-termunemployment-comes-long-term-challenges/

[pone.0281449.ref025] 25.Elkind E. Don’t worry about that COVID resume gap, career expert says. Here’s why. CBS This Morning. 2021 May 20. https://www.cbsnews.com/news/covidresume-gap-career-expert/

[pone.0281449.ref026] 26.Feintzeig R. Don’t sweat your pandemic résumé gap. Wall Street Journal. 2021 Jun 20. https://www.wsj.com/articles/dont-sweat-your-pandemic-resume-gap11624233600

[pone.0281449.ref027] 27.Nunley JM, Pugh A, Romero N, Seals RA. The effects of unemployment and underemployment on employment opportunities: Results from a correspondence audit of the labor market for college graduates. ILR Review. 2017;70(3):642–669. [Google Scholar]

[pone.0281449.ref028] 28.Farber HS, Silverman D, Von Wachter TM. Determinants of callbacks to job applications: An audit study. American Economic Review. 2016;106(5):314–18. [Google Scholar]

[pone.0281449.ref029] 29.Farber HS, Silverman D, Von Wachter TM. Factors determining callbacks to job applications by the unemployed: An audit study. RSF: The Russell Sage Foundation Journal of the Social Sciences. 2017;3(3):168–201. [Google Scholar]

[pone.0281449.ref030] 30.Spence M. Job market signaling. The Quarterly Journal of Economics 1973;87(3):355–374. [Google Scholar]

[pone.0281449.ref031] 31.Vishwanath T. Job search, stigma effect, and escape rate from unemployment. Journal of Labor Economics. 1989;7(4):487–502. [Google Scholar]

[pone.0281449.ref032] 32.Oberholzer-Gee F. Nonemployment stigma as rational herding: A field experiment. Journal of Economic Behavior & Organization. 2008;65(1):30–40. [Google Scholar]

[pone.0281449.ref033] 33.Cohn A, Maréchal MA, Schneider F, Weber RA. Frequent job changes can signal poor work attitude and reduce employability. Journal of the European Economic Association. 2021;19(1):475–508. [Google Scholar]

[pone.0281449.ref034] 34.Furåker B, Blomsterberg M. Attitudes towards the unemployed: An analysis of Swedish survey data. International Journal of Social Welfare. 2003;12:193–203. [Google Scholar]

[pone.0281449.ref035] 35.Maassen G, de Goede M. Changes in public opinion on the unemployed: The case of the Netherlands. International Journal of Public Opinion Research. 1991;3(2):182–194. [Google Scholar]

[pone.0281449.ref036] 36.Buffel V, Van de Velde S. Comparing negative attitudes toward the unemployed across European countries in 2008 and 2016: The role of the unemployment rate and job insecurity. International Journal of Public Opinion Research. 2019;31(3):419–440. [Google Scholar]

[pone.0281449.ref037] 37.Eardley T, Matheson G. Australian attitudes to unemployment and unemployed people. Australian Journal of Social Issues. 2000;35(3): 181–202. [Google Scholar]

[pone.0281449.ref038] 38.Burstein P. 2003. The impact of public opinion on public policy: A review and an agenda. Political Research Quarterly. 2003;56(1): 29–40. [Google Scholar]

[pone.0281449.ref039] 39.Granovetter M. The strength of weak ties. The American Journal of Sociology. 1973;78(6):1360–1380. [Google Scholar]

[pone.0281449.ref040] 40.Calvó-Armengol A, Jackson MO. The effects of social networks on employment and inequality. American Economic Review. 2004;94(3): 426–454. [Google Scholar]

[pone.0281449.ref041] 41.Korpi T. Good friends in bad times? Social networks and job search among the unemployed in Sweden. Acta Sociologica. 2001;44: 157–170. [Google Scholar]

[pone.0281449.ref042] 42.Granovetter M. Getting a Job: A Study of Contacts and Careers. 1st ed. Chicago: The University of Chicago Press; 1974. [Google Scholar]

[pone.0281449.ref043] 43.Gurr T, Jungbauer-Gans M. Stigma consciousness among the unemployed and prejudices against them: Development of two scales for the 7th wave of the panel study, "Labour Market and Social Security (PASS)." Journal for Labour Market Research. 2013;46:335–351. [Google Scholar]

[pone.0281449.ref044] 44.Krug G, Drasch K, Jungbauer-Gans M. The social stigma of unemployment: Consequences of stigma consciousness on job search attitudes, behaviour and success. Journal for Labour Market Research. 2019;53(11):1–27. [Google Scholar]

[pone.0281449.ref045] 45.Coppock A, McClellan O. Validating the demographic, political, psychological, and experimental results obtained from a new source of online survey respondents. Research & Politics. 2019;6(1):2053168018822174. [Google Scholar]

[pone.0281449.ref046] 46.Ternovski J, Orr L. A note on increases in inattentive online survey takers since 2020. Journal of Quantitative Description: Digital Media. 2022;2:1–35. 10.51685/jqd.2022.002 [DOI] [Google Scholar]

[pone.0281449.ref047] 47.Peyton K, Huber G, Coppock A. The generalizability of online experiments conducted during the COVID-19 pandemic. Journal of Experimental Political Science. 2022;9(3):379–394. [Google Scholar]

[pone.0281449.ref048] 48.Goldberg P. Are women prejudiced against women? Transaction. 1968;5:28–30. [Google Scholar]

[pone.0281449.ref049] 49.Bertrand M, Mullainathan S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review. 2004. September;94(4):991–1013. [Google Scholar]

[pone.0281449.ref050] 50.Weisshaar K. From opt out to blocked out: The challenges for labor market re-entry after family-related employment lapses. American Sociological Review. 2018;83(1):34–60. [Google Scholar]

[pone.0281449.ref051] 51.Van Belle E, Caers R, De Couck M, Di Stasio V, Baert S. Why is unemployment duration a sorting criterion in hiring? IZA Discussion Paper No. 10876. Institute for Labor Economics. 2017 July. http://ftp.iza.org/dp10876.pdf

[pone.0281449.ref052] 52.Hainmueller J, Hangartner D, Yamamoto T. Validating vignette and conjoint survey experiments against real-world behavior." PNAS. 2014;112(8):2395–2400. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref053] 53.Nüß P. Duration dependence as an unemployment stigma: Evidence from a field experiment in Germany. Economics Working Paper No. 2018–06. Kiel University. 2018. http://hdl.handle.net/10419/179941

[pone.0281449.ref054] 54.Kroft K, Lange F, Notowidigdo MJ. Duration dependence and labor market conditions: Evidence from a field experiment. The Quarterly Journal of Economics. 2013;128(3):1123–1167. [Google Scholar]

[pone.0281449.ref055] 55.Eriksson S, Rooth DO. Do employers use unemployment as a sorting criterion when hiring? Evidence from a field experiment. American Economic Review. 2014;104(3):1014–39. [Google Scholar]

[pone.0281449.ref056] 56.Montgomery JM, Nyhan B, Torres M. How conditioning on posttreatment variables can ruin your experiment and what to do about it. American Journal of Political Science. 2018. July;62(3):760–775. [Google Scholar]

[pone.0281449.ref057] 57.Bartels LM, Cramer KJ. Work, welfare, and partisan change. Annual Meeting of the American Political Science Association. 2020 Sept. https://my.vanderbilt.edu/larrybartels/files/2011/12/BC-Work-Welfare-and-Partisan-Change.pdf

[pone.0281449.ref058] 58.Jeong J, Lee H. Public attitudes toward the minimum wage debate: Effects of partisanship, ideology, and beliefs. The Social Science Journal. 2021;58(2):164–175. [Google Scholar]

[pone.0281449.ref059] 59.Gadarian SK, Goodman SW, Pepinsky TB. Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic. PLOS One. 2021. doi: 10.1371/journal.pone.0249596 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref060] 60.Grossman G, Kim S, Rexer JM, Thirumurthy J. Political partisanship influences behavioral responses to governors’ recommendations for COVID-19 prevention in the United States. PNAS. 2020;117(39):24144–24153. doi: 10.1073/pnas.2007835117 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281449.ref061] 61.Tayeb Z. This pizza chain owner who pays $16 an hour says there’s no labor shortage, just a shortage of businesses willing to pay a decent wage. Business Insider. 2021 Jul 11. https://www.businessinsider.com/pizza-chain-ceo-saystheres-no-labor-shortage-if-you-pay-well-2021-7?utm_source=copylink&utm_medium=referral&utm_content=topbar

[pone.0281449.ref062] 62.Kurtzlaben D. Job losses higher among people of color during the coronavirus pandemic. Weekend Edition Sunday, National Public Radio (NPR). 2020 Apr 22. https://www.npr.org/2020/04/22/840276956/minorities-often-work-thesejobs-they-were-among-first-to-go-in-coronavirus-layo

[pone.0281449.ref063] 63.Alon T, Doepke M, Olmstead-Rumsey J, Tertilt M. This time it’s different: The role of women’s employment in a pandemic recession. NBER Working Paper No. 27660. National Bureau of Economic Research. 2020 Aug. http://nber.org/papers/w27660

[pone.0281449.ref064] 64.Montenovo L, Jiang X, Lozano Rojas F, Schmutte IM, Simon KI, Weinberg BA, et al. Determinants of disparities in Covid-19 job losses. NBER Working Paper 27132. National Bureau of Economic Research. 2020 May; revised 2021 June. 10.3386/w27132 [DOI]

PERMALINK

Perceptions of pandemic resume gaps: Survey experimental evidence from the United States

Regina Bateson

Roles

Abstract

Introduction

Methods and data

Recruitment and respondents

Survey instrument and experimental setup

Table 1. Characteristics of fictional applicants, first hiring scenario.

Table 2. Characteristics of fictional applicants, second hiring scenario.

Results

Public perceptions of pandemic resume gaps

Fig 1. How pandemic employment history affects the public’s preferences.

Fig 2. Public perceptions of fictional job applicants, by pandemic employment history.

Results for respondents with hiring or supervisory experience

Fig 3. Treatment effects among respondents with hiring and supervisory experience.

Fig 4. Perceptions of fictional job applicants among respondents with hiring or supervisory experience, by pandemic employment history.

Heterogenous treatment effects

Fig 5. Treatment effects by respondents’ experiences during the COVID-19 pandemic.

Fig 6. Treatment effects by respondents’ beliefs about the severity of COVID-19.

Fig 7. Treatment effects by respondents’ political party identifications.

Fig 8. Treatment effects by respondents’ ideologies.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Simona Lorena Comi

Roles

Author response to Decision Letter 0

Decision Letter 1

Simona Lorena Comi

Roles

Author response to Decision Letter 1

Decision Letter 2

Simona Lorena Comi

Roles

Acceptance letter

Simona Lorena Comi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases