#7 Employment Transitions at the Beginning of the COVID19 Epidemic
15.05.2020
In this blog post, we are interested in employment transitions at the beginning of the COVID19 government measures. In particular, we look at group differences by gender, age, financial security, and social “safety” networks. Figure 1 shows the transition flows in the workforce between February and April, indicating an increasing precarisation. The figure shows that 37.5% of our respondents reported a transition to more precarious working conditions: 17.5% of previously full-time employed workers reported a transition into part-time, temporary or marginal employment, and 9.2% of those previously part-time employed switched to marginal- or temporary employment. Finally, 10.8% reported to have lost their jobs.
Employment transitions are characterised by pre-existing gender differences in employment conditions in the Austrian population. Close to 50% of women in Austria are in part-time employment, whereby this only applies for around 10% of the male population1. Not surprisingly, as shown in Figure 2, men were significantly more likely to transition from full-time to part-time employment conditions, whereas women were significantly more likely to transition from part-time- to temporary and minor employment. Although transitions into unemployment was more frequent for men, no statistically significant differences occurred.
Next, we look at age differences. We only included individuals who were 61 or younger, since the average retirement age in Austria revolves around 60.4 years2. To prevent biases in age effects respondents in education or retirement were excluded. As Figure 3 shows, individuals between 36 and 45 years were significantly less likely to transition into unemployment than the youngest (18-25 years) cohort and the second oldest (46-55years) cohort. Although not significantly higher compared to the other age groups, the 36-45 cohort also showed the most frequent descend from full-time to part-time employment. Further significant effects were found for the oldest cohort, which is less likely to transition to unemployment compared to the youngest cohort. However, transitions could be attributable to the pre-existing employment situations among the cohorts before the crisis, for unemployment is more common among younger and older population groups in Austira3. Thus, factors responsible for this distribution may further influence employment transitions. Nevertheless, this is something that should be watched closely, because after all, employers are encouraged to consider social indicators, such as family responsibilities and/or duration of employment (rewarding seniority), when terminating employment contracts. This is also formalised in the Austrian Labor Constitutional Act § 105 Abs. 3 ArbVG. For the remaining age groups and employment conditions no significant differences were identified.
To measure financial security, we asked our respondents if they had “financial reserves they could rely on after job loss”. Figure 4 displays the probability for employment transitions for those with and without such savings. Those who reported to have no savings were significantly more likely to transition to unemployment. However, financial reserves did not play a significant role for transitions within the employed. The branches most affected by the COVID19 measures are tourism (145,1% more unemployed compared to march 2019), construction (94,8%), transportation and logistics (+83,8%), art and entertainment (+49,1%), personnel agency (+34,0%) and system irrelevant parts of the retail sector (34,4%)4. Note that the concerned sectors also tend to have lower income structures5. Additionally, individuals with lower qualifications are most likely to be affected by job losses6. Combined with these numbers, our findings indicate that those who were already financially disadvantaged, e.g. had no possibilities to save money, are most severely affected by the pandemic.
Last, we examined the availability of social networks to cushion financial emergency situations. Respondents were asked if they could receive “financial support from relatives and / or friends in emergency situations?” Figure 4 suggests that especially those who have a weak social network were more likely to become unemployed or to descend from part-time to marginal/temporary employment conditions. We identified two explanations for this observation: First, the unavailability of a social network may lead to a higher likelihood of job loss. This is because social capital often protects from unemployment by lowering information and transition costs, easing worker placement and provide conflict management7. Second, the job loss itself may be experienced as a disruptive event, often related to feelings of shame and psychological distress, accompanied by the belief that this situation is temporary. Such feelings may discourage individuals to expect and seek financial support from others8,9,10.
Conclusion: A not negligible part of the Austrian population faced employment transitions most likely caused by the COVID19 government measures. Many either experienced a deterioration within the scope of their working hours, or even lost their employment. As shown, the transitions occurred along pre-existing gender and age differences in employment conditions and resulted in increasing precarious working conditions (e.g. part-time, temporary- or unemployment). Our analysis has shown that males were more likely to descend from employment into part-time, marginal, or temporary employment conditions and females from part-time to marginal or temporary occupations. Furthermore, the youngest respondents (18-25yrs.), as well as the 46-55 years old were most likely to face unemployment. Additionally, the already financially disadvantaged (reported to inherit no savings) were most likely to lose their employment. Finally, we also found that respondents, who reported to have no social network to rely on in financial emergencies, are more likely to transition into unemployment.
Notes:
1. http://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/arbeitsmarkt/arbeitszeit/teilzeitarbeit_teilzeitquote/062882.html
2. Gumprecht, D. (2019): Pensionierungstafeln Bundesländer: Ergebnisse für das Jahr 2018. Statistik Austria, Vienna.
3. https://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/arbeitsmarkt/arbeitslose_arbeitssuchende/index.html
4. https://www.wifo.ac.at/news/corona-schock_auf_dem_arbeitsmarkt
5. https://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/soziales/personen-einkommen/allgemeiner_einkommensbericht/index.html
6. https://viecer.univie.ac.at/corona-blog/corona-blog-beitraege/blog09/
7. Freitag, M., Kirchner, A. (2011). Social Capital and Unemployment: A Marco-Quantitative Analysis of the European Regions. Political Studies, 59(2), 389-410. DOI: https://doi.org/10.1111/j.1467-9248.2010.00876.x
8. Atkinson, T., Liem, R., Liem, J.H., (1986). The Social Costs of Unemployment: Implications for Social Support. Journal of Health and Social Behavior, 27(4), 317. DOI: 10.2307/2136947.
9. Wanberg, C.R. (2012). The individual experience of unemployment. Annual review of psychology, 63(1), 369-396. DOI: 10.1146/annurev-psych-120710-100500.
10. Brand, J.E. (2015). The Far-Reaching Impact of Job Loss and Unemployment. Annual Review Sociology, 41(1), S. 359-375. DOI: 10.1146/annurev-soc-071913-043237.