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    Disparities in Reemployment After the Pandemic Recession Based on Worker Characteristics

    alessia-leibert

    by Alessia Leibert
    September 2023

    Education, Job Quality, and Race are Factors Driving Length of Unemployment and risk of Permanent Job Losses

    What is the short-term influence of education in the reemployment market? Did formal education shelter people from long periods of unemployment after being laid off in the first months of the COVID-19 recession? And did it protect workers from the risk of new job losses after the recession? What other worker characteristics besides education facilitated a more successful reintegration into the workforce? This information can help identify the characteristics of workers with the highest need for targeted reemployment services.

    The study population consists of 245,091 Twin Cities Metro area residents who filed for Unemployment Insurance (UI) benefits in the first months of the COVID-19 pandemic. Since we are interested in people moving from unemployment to employment, we only examine individuals of prime working age (20 to 54) who were employed in Minnesota in 2021.

    Risks to Avoid After a Layoff

    An unemployed person typically seeks to avoid three kinds of risk: taking a long time to return to work; suffering future permanent job losses; and experiencing earning losses relative to pre-layoff earnings. These three goals are measured in this study as follows:

    1. Length of unemployment, measured as the number of weeks of UI benefits collected by each individual during the first four months of the Pandemic Recession (March through June 2020). This measure represents a person's difficulty in returning to work;
    2. Being permanently laid off during the post-recession recovery period, defined as the period beginning after Sept 2021 (when the extended benefits program ended1) until June 2022. This is a measure of a worker's protracted difficulty in securing stable employment even as the economy rebounded;
    3. Suffering a wage loss. This is a measure of economic damage from the pandemic-related job displacement.

    This study aims to identify the factors that helped workers get back to work quickly and permanently. A separate article focuses on wage outcomes.

    Which Categories of Workers Experienced the Longest Duration of Unemployment in 2020 and the Highest Risk of Permanent Layoff in 2022?

    The Pandemic Recession was the shortest in U.S. history, lasting only two months. Most Minnesotans were able to rejoin the workforce a few weeks after being laid off or furloughed. The average claimant in our dataset filed an average of 13.7 weeks of continuing claims from March to September 2020, and only a small minority, 10.8%, reported being on permanent layoff from Sept 2021 to June 20222 (see Table 1). The first outcome is linked to the second because workers who spent a longer time out of work initially (first outcome) were also at higher risk of falling back to the most severe form of unemployment (second outcome) a year and a half after the initial layoff.

    Table 1 - Risk of experiencing unfavorable outcomes, by demographic characteristics

    Workers' characteristics

    Incidence of unfavorable outcomes
    1st OUTCOME: Avg weeks collecting UI benefits from March to Sept 2020* 2nd OUTCOME: Pct who permanently lost job from Sept 2021 to June 2022
    Total 13.7 10.8%
    Gender
    Male 13.2 10.2%
    Female 14.2 11.3%
    Race
    White 12.9 8.0%
    Black 17.1 22.4%
    Hispanic/Latino 14.3 11.9%
    Asian 12.9 8.4%
    American Indian 15.4 14.8%
    More than one race 16.0 16.6%
    Disability status
    Without disability 13.6 10.5%
    With a disability 16.2 16.2%
    Did not respond 15.1 14.3%
    Place of residence
    Metro, excluding City of Minneapolis and St. Paul 13.0 9.4%
    City of St. Paul 14.8 13.7%
    City of Minneapolis 15.8 13.6%
    Education (highest attained)
    High school or less 14.9 14.7%
    1 or 2 years of college/vocational school 14.1 12.0%
    Associate degree or three years of college 13.5 9.8%
    Bachelor's degree 12.8 6.6%
    Master's degree or above 10.6 4.7%
    *Defined as the number of continuing claims filed from March 15, 2020 to Oct 1, 2020

    Table 1 identifies the following risk factors:

    • Gender: females were unemployed, on average, one week longer than males (14.2 versus 13.2) and were at greater risk of experiencing a permanent layoff in future months. Women were hurt disproportionately in the Pandemic Recession, both because of the industry sectors that employ them and the school and day care closures during the first six months of the pandemic.
    • Race: this is the factor with the greatest influence on both outcomes. Black claimants had the longest duration of unemployment (17.1 weeks on average) and the highest share of claimants (22.4%) on permanent layoff after September 2021. At the opposite end of the spectrum, white and Asian claimants had the shortest unemployment spells (12.9 weeks) and the lowest risk of future permanent layoff (8% and 8.4% respectively). The risk of a permanent layoff was nearly three times as high for Black claimants as for white claimants. Other race groups experienced less favorable outcomes than white and Asian claimants, but more favorable than Blacks.
    • Disability: Claimants with a disability used UI benefits much longer (16.2 weeks) and faced a much higher risk of future permanent lay off (16.2%) than others. Similar outcomes were observed among individuals who chose not to respond to the disability question. Longer time spent out of work among these individuals is likely related to the fact that disability is often correlated with poor health and, therefore, higher vulnerability to COVID-19.
    • Place of residence: Residents in the cities of St. Paul and Minneapolis returned to work more slowly (14.8 and 15.8 weeks respectively) and faced a higher risk of future permanent layoff (13.7% and 13.6% respectively) compared to other Metro residents. This suggests that place of residence was a factor determining how quickly claimants were able to return to work as well as the stability of the jobs held upon reemployment. Higher risk of unfavorable outcomes for St. Paul and Minneapolis residents may also be linked to higher rates of businesses closures in downtown areas.
    • Education: Claimants with a high school degree or less used UI benefits much longer (14.9 weeks on average) and faced a much higher risk of permanent lay off (14.7%) relative to other claimants. As education level increases, the risk of experiencing either of the two unfavorable outcomes decreases.

    Besides being determined by demographic characteristics, some vulnerabilities stem from the types of jobs held. Table 2 lays out the most significant ones.

    Table 2- Risk of experiencing unfavorable outcomes, by Job characteristics pre-layoff

    Job Characteristics Pre-layoff

    Incidence of unfavorable outcomes
    1st OUTCOME: Avg weeks collecting UI benefits from March to Sept 2020 2nd OUTCOME: Pct who permanently lost job from Sept 2021 to June 2022
    Wages pre-layoff
    Less than $15.00 an hour 16.1 13.3%
    From $15 to $21.49 an hour 14.6 12.7%
    From $21.49 to $33.00 an hour 13.2 9.2%
    Above $33.00 an hour 10.7 5.3%
    Full-time status pre-layoff
    Part-time or seasonal 14.7 12.7%
    Part-time year-round 16.3 13.1%
    Fulltime year-round 11.8 8.7%
    Tenure pre-layoff
    Laid off from a job with <= 1 year of tenure 15.0 14.4%
    Laid off from a job with > 1 year of tenure 13.0 8.7%
    Industry sector (selected)
    Construction 10.5 6.5%
    Manufacturing 10.2 7.7%
    Retail Trade 13.3 11.8%
    Temp Help 16.8 18.4%
    Administrative Svc Except Temp hHelp 14.3 14.7%
    Clinics, Hospitals & Home Health Care Services 9.5 6.8%
    Nursing & Residential Care Facilities 14.8 20.0%
    Social Assistance 15.0 16.6%
    Accommodation & Food Services 18.8 14.0%
    Occupational category (selected)
    Management 12.7 8.4%
    Business & Financial Operations 12.1 7.9%
    Computer & Mathematical 11.5 7.7%
    Architecture & Engineering 9.6 3.4%
    Community & Social Service 12.9 10.0%
    Education Training & Library 14.0 10.4%
    Healthcare Practitioners & Technical 8.6 4.0%
    Healthcare Support 12.3 14.0%
    Food Preparation & Serving Related 18.8 14.3%
    Building & Grounds Cleaning & Maintenance 14.5 13.1%
    Personal Care & Services 12.3 12.4%
    Sales & Related 15.7 11.0%
    Office & Administrative Support 18.8 11.9%
    Construction & Extraction 10.3 6.2%
    Installation Maintenance & Repair 10.5 9.3%
    Production 11.4 11.7%
    Transportation & Material Moving 15.4 11.3%
    * Measured from Sept 2021 to June 2022, for a total of 10 months.
    • Wage: Being employed in a low-wage job (less than $15 an hour) led to longer periods of unemployment and higher risk of future permanent layoff. Workers laid off from jobs that paid less than $15 an hour claimed benefits for an average of 16.1 weeks, six weeks more than workers who were in jobs paying more than $33 an hour. They also faced a much higher risk of permanent layoff, 13.3% versus 5.3%, indicating that low-income claimants were less able, or willing, to exit the UI system quickly.
    • Job stability: Being employed in a part-time job, led to longer use of unemployment benefits and higher risk of future permanent layoff. In contrast, workers who were employed full-time year-round before the pandemic returned to work faster and had lower rates of future permanent job losses than others. This suggests that workers who held a stable job - which is also an aspect of job quality3 - were able to minimize work interruptions in the short-term and permanent displacement in the medium-term.
    • Industry: Claimants from high turnover or seasonal industries such as Temp Help, Nursing & Residential Care Facilities, and Accommodation & Food Services were slower to return to work and had higher rates of future permanent job losses. In contrast, workers from industries with greater employment stability and lower turnover, such as Clinics & Hospitals, experienced much shorter spells of unemployment (9.5 weeks) and lower rates of permanent job losses (6.8%) than others.
    • Occupation: Being employed in Food Preparation & Serving occupations led to longer use of unemployment benefits (18.8 weeks) and higher than average risk of future permanent lay off (14.3%). This is not surprising, given that these occupations were hit the hardest by COVID-19 temporary shutdowns. In contrast, those employed in higher skilled occupations with greater employment stability, such as Architecture & Engineering, were able to exit the UI system much more quickly (9.6 weeks) and experienced only a 3.4% incidence of future permanent job losses.

    When Disadvantages Pile Up

    Many of the characteristics displayed in Tables 1 and 2 are correlated with one another and can compound a worker's risk of experiencing negative outcomes. Part of the reason claimants' race is such an important driver of outcomes is that race is strongly associated with other risk factors of a socio-economic nature, such as wages pre-pandemic, educational attainment, and being a resident of St. Paul or Minneapolis, where claimants suffered from the most disruptions due to pandemic shutdowns4. Table 3 breaks down these factors by race to show how racial disparities in outcomes are often the result of multiple sources of disadvantage.

    Table 3 – Socio-economic factors and their correlation with race

    Race With no education beyond high school Resident in City of Minneapolis or St Paul Pre-layoff wages <$21.49 (1) Returned to pre-layoff employer upon reemployment
    American Indian 43% 41% 63% 53%
    Asian 45% 35% 62% 62%
    Black 53% 48% 80% 42%
    Latino 54% 40% 66% 54%
    More than one race 42% 43% 67% 47%
    White 23% 26% 34% 61%
    TOTAL with valid reported race 33% 32% 54% 57%
    1. This is a measure of earnings before the pandemic.

    The key takeaways from this evidence are:

    • Claimants of color had significantly lower educational attainment than white claimants. More than a half (53%) of Black claimants had no education beyond high school versus 23% of white claimants;
    • Claimants of color were significantly more likely to live in Minneapolis and St. Paul. In particular, Black claimants were nearly twice as likely as white claimants (48% versus 26%) to be resident in these cities. Residence is also a proxy for other characteristics not available in the dataset, such as family income and foreign-born status. According to census estimates, household income is lower in St. Paul and Minneapolis than in the Metro as a whole, and the two cities are home to more foreign-born residents than the Metro as a whole5;
    • Claimants of color had significantly lower earnings pre-pandemic than white claimants. Eighty percent of Black claimants had earnings lower than $21.49, versus 34% of white claimants;
    • Claimants of color were less likely to return to their pre-layoff employer than white claimants. Only 42% of Black claimants were able to return to their original employer versus 61% of white claimants6.

    Table 4 displays factors related to job quality and job segregation by industry and occupation. Although our data cannot accurately measure a multi-dimensional concept such as job quality, we can capture two of its most important dimensions in addition to wages (already reported in table 3): part-time status, which reflects lower access to healthcare/retirement benefits and opportunities for career advancement; and tenure, which reflects job stability.

    Table 4 – Job quality and job segregation factors and their correlation with race

    Race/ethnicity Working part-time pre-layoff Employed in very short-tenured jobs (1) Employed in the Temp Help industry Employed in Healthcare Support occupations (2)
    American Indian 54% 41% 3% 6%
    Asian 39% 31% 4% 7%
    Black 60% 51% 8% 14%
    Latino 51% 42% 4% 5%
    More than one race 61% 48% 4% 6%
    White 46% 32% 2% 5%
    TOTAL with valid reported race 48% 36% 3% 7%
    1. Short-tenure is defined as one year or less.
    2. This category Includes PCAs, Home Health Aides, and Nursing Assistants.

    The results show the following:

    • Claimants of color had higher shares of part-time work than white claimants;
    • Claimants of color had higher shares of short-tenured work than white claimants;
    • Claimants of color were significantly more likely than white claimants to work in high-turnover, short-tenured industry sectors such as Temp Help;
    • Claimants of color were significantly more likely than white claimants to hold jobs in Healthcare Support occupations, which typically suffer from high turnover due to low pay and difficult work conditions.

    This evidence suggests that the racial disparities in outcomes displayed in Table 1 are not caused by race per se but by a combination of socio-economic differences, job quality differences, and job segregation by race that pushes workers of color towards low-quality jobs. Workers who enter a low-quality job track (low wage, short tenure, high turnover, high shares of part-time work, in industries/occupations with few career ladders) face a higher risk of work terminations.

    Racial inequalities in the returns to education and the role of job quality and job segregation by industry in driving such differences is well documented in this dashboard and corresponding report, but more research is needed to examine the effects of these inequalities on the risk of unemployment. The present study hypothesizes that the underlying factors making workers of color more vulnerable to unemployment than their white counterparts are unequal access to skills training and faster skills depreciation over time due to job segregation and/or racial bias in hiring, access to training, and promotions. A companion report lends some support to these hypotheses by documenting large disparities in lifelong earnings by race among the same population of workers analyzed in this article, suggesting that workers of color are more subject to skills depreciation over the course of their working lives. Greater risk of skill depreciation over time may contribute to higher reliance on the UI system among workers of color as a bridge between bouts of precarious, low-paid employment.

    Distilling the Effects of Education from Other Factors

    The interconnections between some of the factors listed in Tables 1 and 2 makes it hard to isolate the effect of education, job characteristics, and race on the two outcomes of interest. In order to disentangle the effects of all these factors we conducted a linear regression analysis using our first outcome, length of claim measured in weeks8, as the dependent variable. Independent variables are those displayed in Table 1 and 2, plus some additional ones9.

    The factors most strongly associated with an increase in the duration of unemployment are:

    • Being permanently laid off, as opposed to temporarily laid off, led to 6 additional weeks of unemployment, on average;
    • Being employed in a firm that closed led to 2.7 additional weeks of unemployment;
    • Being of Black race led to an average of 2.6 additional weeks of unemployment relative to being of white race. Claimants of color faced significantly longer periods of unemployment than white claimants, while keeping all other characteristics constant;
    • Being in the oldest age range (46 to 54) led to 2 additional weeks of unemployment relative to being in the 20 to 24 age range. Older age was definitely a barrier to finding jobs out of unemployment;
    • Being resident in the City of Minneapolis and City of St. Paul led to 0.6 and 0.9 additional weeks of unemployment, respectively, relative to being resident in the rest of the Twin Cities Metro;
    • Having a disability led to 1.1 additional weeks of unemployment relative to not having a disability.

    The factors most strongly associated with a decrease in the duration of unemployment were:

    • Having had a job with more than 12 years of tenure led to a decrease of 1.8 weeks of unemployment relative to having had a job with a year or less of tenure. Put another way, claimants with more than 12 years of job tenure became reemployed 1.8 weeks faster than others;
    • Having been employed full time year-round before the pandemic led to a decrease of 1.1 weeks of unemployment;
    • Having a master's degree led to a decrease of 0.7 weeks of unemployment relative to having a high school diploma or less;
    • Being male led to a decrease of 0.3 weeks of unemployment relative to being female;
    • Having higher hourly wages pre-pandemic led to a decrease in the length of unemployment. In particular, workers with pre-layoff wages above $33.00 per hour returned to work 1 week faster than workers with wages lower than $21.48 per hour.

    The model, reported in Appendix 1, explains 27% of the variation10 in number of weeks claimed.

    To further examine the effect of education, we ran a second model11 that interacted education level with race, allowing education level to have a different effect on the duration of unemployment depending on race. This turned out to be significant, demonstrating that whites are more sheltered from prolonged unemployment than other racial groups even within the same education level.

    What about our second outcome variable, the risk of permanent lay off? We ran a logistic regression predicting the probability of a permanent layoff from September 2021 to June 2022 (see Appendix 2). The factors most strongly associated12 with an increase in the probability of a permanent layoff were the following:

    • Number of weeks on UI during the first year of the pandemic: each additional week of benefits claimed led to a 5% increase in the probability of a future permanent layoff. This result confirms the strong inter-connection between short-term and medium-term outcomes: individuals who took longer to return to work in the initial months of the pandemic were also at higher risk of experiencing permanent layoffs after September 2021;
    • Having been permanently laid off in the initial four months of the pandemic was associated with a 25% increase in the probability of being permanently laid off again. This means that permanently displaced workers were more likely to lose their subsequent jobs than temporarily displaced workers;
    • Being Black, American Indian, Latino or more than one race led to significant increases in the probability of a permanent layoff relative to being of white race. Asians were the only claimants with a lower probability of a permanent layoff than white claimants, as shown by the fact that the regression coefficient for Asians has a negative sign; however, the effect is very small, only 6%. In contrast, Black claimants faced a 75% higher probability of being permanently laid off than white claimants with equal characteristics. This result does not mean that race was the cause of the outcome; our model does not prove causation, but only association;
    • Having a disability led to a 46% higher probability of being permanently laid off than not having a disability;
    • Being employed pre-pandemic in occupations like Home Health & Personal Care Aides or Nursing Assistants led to a 49% increase in the probability of a permanent layoff;
    • Being employed pre-pandemic in the Temp Help industry led to a 31% greater probability of a permanent layoff. This is a consequence of the temporary nature of employment in the sector;
    • Being employed in a firm that subsequently closed is associated with a 20% greater probability of a permanent layoff;
    • Being resident in St. Paul or Minneapolis is associated with a 21% and 12% greater probability of a permanent layoff, respectively.

    The factors most strongly associated with a decrease in the probability of a permanent layoff were related to job quality (see Table 4), with the only exception of education:

    • Tenure: Having had a job with more than 12 years of tenure was associated with 49% less probability of future permanent layoffs;
    • Wages pre-pandemic: The higher the wages, the lower the risk of future permanent layoffs. In particular, workers with pre-layoff wages above $33.00 an hour had a 30% lower probability of a permanent layoff relative to workers with pre-layoff wages lower than $21.48 an hour. This result suggests that higher income workers are more sheltered from the risk of permanent job loss than lower income workers;
    • Employment status pre-layoff: Being employed full-time year-round, an indicator of job stability as well as access to healthcare and retirement benefits, lowers the risk of a permanent layoff by 4%;
    • Unionization: Having had a work history as a member of a union12 from 2019 to 2020 led to a 54% lower probability of suffering a permanent layoff;
    • Postsecondary education. The protective effect of education is significant at each education level and increases with education. Holding a master's degree or higher offers the greatest protection (-46%) relative to holding only a high school diploma or less). Claimants with 1 or 2 years of college had the smallest protection, with a decrease of only 5% in the probability of a layoff.

    This evidence shows that workers with less stable and lower quality employment histories were more likely to fall back into unemployment after being laid off during the COVID-19 recession, either by choice or because they could not secure stable employment. Also important, had we not controlled for job quality characteristics the effect of race/ethnicity would have been even more pronounced. If we remove these variables from the model, Black claimants are shown to have 88% greater probability of a permanent layoff than white claimants, instead of the 75% revealed in the full model. Therefore, job quality characteristics help explain, at least in part, why Black claimants faced higher risks of permanent job losses.

    It is also worth noting that, in both models, the occupations associated with unfavorable outcomes are predominantly low-skilled. Having a work history in high-skilled, licensed occupations such as Engineers and Registered Nurses & Other Healthcare Practitioners offered strong protection against the risk of permanent layoff, while a work history in low-skilled occupations such as Food Processing, Other Personal Care & Service (including childcare), and Home Health & Personal Care Aides appears to raise the risk of a permanent layoff in the post-pandemic recovery period.

    Despite some data limitations13, both models confirm that job quality, race, education level, and job sorting by occupation/industry are factors driving length of unemployment and risk of permanent job losses.

    Conclusions

    This study uses a unique dataset that encompasses UI claims data merged with Minnesota payroll records, allowing us to examine how length and severity of unemployment vary by race, education, and other characteristics. This analysis concludes that the key underlying factors that shape a worker's ability to withstand disruptive events such as a recession are education level, race, and a set of job characteristics such as choice of occupation/industry and job quality in general.

    Here is a summary of findings:

    • Higher levels of education lead to less time unemployed. However, the effects of race, occupation/industry, and other factors linked to job quality are larger than the effect of education. Even after taking into account a rich set of demographic and job characteristics, we find that claimants of color faced longer periods of unemployment in the early months of the COVID-19 pandemic;
    • Higher levels of education also lower the risk of a permanent layoff, which is the most severe form of displacement. However, race and other factors linked to job characteristics -and job quality in particular- seem to play a greater role than postsecondary education. Despite controlling for various demographic and job characteristics, we find that claimants of color, with the only exception of Asians, faced significantly higher risk of permanent layoff than white claimants;
    • Claimants who collected UI claims for longer periods during the initial months of the pandemic were also more likely than others to experience a permanent displacement from September 2021 to June 2022. That's because the same barriers that delayed reemployment also hindered holding on to these newly found jobs upon reemployment. This study identified the following barriers: being laid off from a firm that closed; being Black, Latino, American Indian or more than one race; having a disability; being resident in Minneapolis or St. Paul; having low educational attainment; and having a work history in low-wage, part-time, short-tenured jobs or in industry/occupations offering few opportunities for career advancement and skills development.

    In conclusion, this study highlights the importance of job quality in protecting workers from the adverse effects of job displacement. We find that the quality of jobs – including wages, job tenure, unionization, and fulltime status – matters more than simply having jobs. Hopping from one short-tenured, low-pay, dead-end job to another offers little protection from future displacements.

    This body of evidence also suggests that workers who experienced unfavorable outcomes might simply lack the skills or the networks needed to secure sustainable employment and may be trapped in jobs where it's harder to develop such skills or networks. The fact that many claimants of color, who were the most likely to experience unfavorable outcomes, were overrepresented in frontline jobs such as Home Health Aides and Nursing Assistants should raise concern. Their struggles have repercussions not only on employers but also on Minnesotans who rely on these services.

    Workforce boards and other workforce development program providers can use this information to deliver targeted training and reemployment services aimed at promoting more stable employment.

    APPENDIX 1 - Weeks of Benefits Claimed Regression Results (dependent variable: number of weeks)

    Observations: 209,610 - R-square: 0.272

    Variables

    Grey shade= characteristics related to job quality

    		 Coefficient Statistical significance
    ***coefficient is statistically different from zero at the <0.001 level
    ** <0.01 level
    * <0.05 level
    Gender-Male (reference: Female) -0.344 ***
    Race-Black (reference: white) 2.615 ***
    Race-Asian 0.303 ***
    Race-Latino 0.444 ***
    Race-American Indian 0.508 ***
    Race-More than one race 1.475 ***
    Disability-with a disability (reference: without a disability) 1.097 ***
    Disability-did not answer the disability question 0.740 ***
    Age 25-30 (reference: 20-24) 1.267 ***
    Age 31-36 1.778 ***
    Age 37-45 1.937 ***
    Age 46-54 1.998 ***
    Education-1 or 2 years of college (reference: high school or less) -0.124 **
    Education- Associate degree or 3 years of college -0.270 ***
    Education-Bachelor's -0.564 ***
    Education-Master's -0.677 ***
    Residence- St. Paul (reference: Metro except St. Paul and Minneapolis) 0.933 ***
    Residence- Minneapolis 0.615 ***
    Laid off from a firm that closed 2.713 ***
    Job tenure pre-layoff-from 1 to 3 years (reference: <=1 year) -0.096 *
    Job tenure-from 4 to 12 years -0.671 ***
    Job tenure-more than 12 years -1.841 ***
    Hourly wage pre-layoff from $15 to $21.49 (reference: <$15) -0.564 ***
    Hourly wage pre-layoff $21.49-$33 -1.206 ***
    Hourly wage pre-layoff >$33 -1.000 ***
    Work Status-Employed full-time year-round pre-layoff (reference: part-time or seasonal) -1.142 ***
    Permanently separated from employer anytime from Mar to Jun 2020 6.001 ***
    Occupation-Engineers (reference: Sales workers) -0.723 ***
    Occupation-Social Scientists & Related Workers -3.285 ***
    Occupation-Other Teachers & Instructors (Tutors, Substitute Teachers) 1.885 ***
    Occupation-Media & Communication Equipment Workers 2.632 ***
    Occupation-Home Health & Personal Care Assistants 0.933 ***
    Occupation-Food & Beverage Serving Workers 3.195 ***
    Controls for firm size Yes -
    Controls for 65 industry categories Yes -

    The number of observations, 209,610, is lower than the starting dataset due to the need to exclude claimants who did not report an occupation.

    Source: Author's calculations based on Minnesota UI claims data merged with MN UI wage records.

    APPENDIX 2 -Probability of Permanent Job Loss (separation) Regression Results (dependent variable: 1=permanently separated at any time Sept 2021 -June 2022, 0=not permanently separated)

    Observations: 168,924 - Nagelkerke R-square: 0.215

    Variables

    Grey shade= characteristics related to job quality

    		 Coefficient (in percentages) Statistical significance
    ***coefficient is statistically different from zero at the <0.001 level
    ** <0.01 level
    * <0.05 level
    Gender- Male (reference: female) -9% ***
    Race-Black (reference: white) 76% ***
    Race-Asian -6% *
    Race-Latino 17% ***
    Race-American Indian 42% ***
    Race-More than one race 36% ***
    Veteran 24% ***
    Disability-with a disability (reference: without a disability) 46% ***
    Disability-did not answer the disability question 41% ***
    Age 25-30 (reference: 20-24) 9% ***
    Age 31-36 11% **
    Age 37-45 7% **
    Age 46-54 -0.1% -
    Education-1 or 2 years of college (reference: high school or less) -5% *
    Education- Associate degree or 3 years of college -17% ***
    Education-Bachelor's -38% ***
    Education-Master's -46% ***
    Residence- St. Paul (reference: Metro except St. Paul and Minneapolis) 21% ***
    Residence- Minneapolis 12% ***
    Laid off from a firm that closed 20% ***
    Job tenure pre-layoff-from 1 to 3 years (reference: <=1 year) -20% ***
    Job tenure-from 4 to 12 years -37% ***
    Job tenure-more than 12 years -49% ***
    Hourly wage pre-layoff from $15 to $21.49 (reference: <$15) -5% *
    Hourly wage pre-layoff $21.49-$33 -13% ***
    Hourly wage pre-layoff >$33 -30% ***
    Work Status-Employed full-time year-round pre-layoff (reference: part-time or seasonal) -4% *
    Belonged to a Union before Sept 2020 -54% ***
    Permanently separated from employer from Mar to Jun 2020 25% ***
    Number of weeks claimed Mar 2020-Mar 2021 5% ***
    Occupation-Engineers (reference: Sales workers) -38% ***
    Occupation-Registered Nurses & Other Healthcare Practitioners -21% ***
    Occupation-Food processing workers 49% **
    Occupation-Other personal care workers (childcare, etc.) 44% ***
    Occupation-Home Health, PCAs, and Nursing Assistants 49% ***
    Controls for firm size Yes -
    Controls for 65 industry categories Yes -
    The number of observations, 168,924, is lower for this regression compared to the Weeks of Benefits regression due to the need to exclude claimants without an employment record in two out four quarters from q2 2020 to q1 2021. Under normal circumstances, UI eligibility requires working in Minnesota during the past 12 to 15 months and having earned at least a minimum amount of wages. Therefore, these exclusions are needed to discard individuals who temporarily qualified for UI during the initial months of the pandemic but likely lost eligibility in late 2021.

    1On September 6, 2021 the additional weeks of pandemic federal benefits ended in all states.

    2This time span was chosen because it begins exactly 20 months after the initial wave of COVID-related layoffs and covers a 10 months period of economic recovery.

    3Full-time year-round employment is an indicator of job quality because it is typically associated with access to health care and/or retirement benefits.

    4Higher frequency of business closures in these two Cities during the last three years likely stems from longer pandemic-related shutdowns as well as from downtown office buildings being emptied out as employees switched to remote work.

    5From 2017 to 2021, the estimated share of foreign-born persons is 19.1% in St Paul and 14.8% in Minneapolis. In 2021, median household income was $ 63,483 in St. Paul and $70,099 in Minneapolis. In contrast, the Twin Cities Metro area's share of foreign-born persons was 10.8% (13.8% in Hennepin county) and the median household income in 2021 was $87,433. Therefore, the socio-economic characteristics in the two cities are different and less favorable than those in the rest of the Metro. Source: Minnesota Compass Saint Paul data and Minnesota Compass Twin Cities Region, household by income and Data USA Minneapolis, MN.

    6Although some individuals might have chosen not to return to the same employer due to workforce safety concerns or other reasons, the most popular decision was to return whenever possible to avoid having to search for a new job at a time when hiring activity was frozen.

    7The dependent variable ranges from 1 to 28 weeks.

    8Including the following: age, veteran status, SOC code at the 3 digits, an indicator measuring whether a claimant reported being permanently separated, and industry interacted with permanent separation indicator.

    9The R square of this model is .272. All factors listed in the article are statistically significant.

    10The model with interactions is available upon request.

    11All factors listed in the article are statistically significant.

    12This variable is included in UI claims records. Claimants from the Mining, Utilities, and Construction industries are the most likely to be union members and get referred to a hiring hall for reemployment after being laid off. In particular, employers in the construction industry often choose to hire exclusively through referrals from union hiring halls.

    13The second model fits the data less well than the first model, with an explanatory power of 21.5% versus 27.2%. A limitation of the second model is that it relies on information collected in Spring of 2020, which might have become outdated in 2021. For example, a claimant who reported having a high school education and being employed as a Cashier in March 2020 might have subsequently earned an associate degree and switched to a higher skilled job over the course of 2021-2022, which is unmeasurable with our data. Another limitation of both models is that important characteristics could not be measured, such as family income, quality of education (at the k-12 and post-secondary level), access to occupational training, criminal record status and citizenship status.

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