Purpose This paper aims to address the immediate effects of the COVID-19 crisis in the Portuguese tourism and hospitality industry by examining whether some specific characteristics make people more vulnerable or more immune to unemployment. Design/methodology/approach Using an extensive micro-level data set of personal and job-related attributes containing all unemployed individuals in the Portuguese tourism and hospitality industry, a logit model with 56,142 observations is estimated to assess how each characteristic contributed to the unemployment odds during the COVID-19 crisis (until the end-July 2020), relatively to the pre-COVID period. Findings The most vulnerable workers to COVID-19 unemployment seem to be older, less educated, less qualified, women and residents in regions with a higher concentration of people and tourism activity. Moreover, the COVID-19 crisis is generating a new type of unemployment by also affecting those who were never unemployed before, with more stable jobs and more motivated at work, while reducing voluntary disruptions. Practical implications Public effort should be made not only to increase workforce education but especially to reinforce job-specific skills. The COVID-19 crisis has broken traditional protective measures against unemployment and separated workers from their desired occupations, which justifies new and exceptional job preservation measures. Policy recommendations are given aiming at strengthening worker resilience and industry competitiveness in the most affected sub-sectors and regions. Originality/value This study extends the current understanding of worker vulnerability to economic downturns. Herein, this paper used a three-level approach (combining socio-demographic, work-related and regional factors), capturing the immediate effects of the COVID-19 crisis and focussing on the tourism and hospitality industry (the hardest-hit sector worldwide).

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Purpose The purpose of this paper is to verify if adult education can contribute to social mobility by analysing how the socioeconomic and professional background of the students affects dropout and graduation hazards in higher education. Design/methodology/approach An event history analysis approach, with competing risks and discrete time, implemented under a multinomial logit model, is used to investigate how an extensive set of covariates affects the risk of graduation, dropout and persistence of 834 adult student workers from a higher education institution in Portugal. Findings Adult education may indeed be effective in promoting social mobility, as academic achievement is higher for student workers that have low educated parents and low income levels. Also, the probability of achieving graduation seems to be higher for those seeking for higher transformation. Practical implications Adult education should be encouraged as it generates both efficiency and equity benefits. Some policy recommendations are suggested for the higher education system to adapt better to the particular characteristics of adult workers and provide conditions to improve the job–study–family conciliation, namely, by adjusting the schedule and composition of classes, appreciating the curriculum and providing orientation to candidates, and introducing shorter/simplified versions of the degrees. Originality/value A separate treatment is given to adult student workers, whose characteristics are very particular, enriching the literature on academic achievement that has been focussed on traditional students. Additionally, the studied data set merges five sources and provides extensive and original information on personal, degree and employment variables of the students.

Purpose The COVID-19 pandemic caused job losses to rise dramatically. Herein, the purpose of the article is to identify which personal and job characteristics make individuals more vulnerable or more resilient to COVID-19 unemployment in Portugal and thus to help policymakers, organizations and individuals themselves, in creating mechanisms to avoid unemployment within this new context.Design/methodology/approach Using extensive personal and job-related data on the complete population of newly unemployed in Portugal over several months after the emergence of the COVID-19 pandemic, a logit model is estimated to identify the characteristics that make workers more resilient or more vulnerable to COVID-19 unemployment, in comparison with the pre-crisis period.Findings The COVID-19 crisis is shown to be disruptive by changing the unemployment structure, increasing socioeconomic inequalities and weakening traditional mechanisms of employment protection. Additionally, the authors identify a higher vulnerability of low-skilled individuals and of those in occupations with low working-from-home feasibility and/or from non-essential sectors (particularly tourism).Practical implications Policy indications are given aiming to protect the most vulnerable individuals, sectors and regions in Portugal, in this new and unprecedented context.Originality/value A seven-month period following the emergence of the pandemic is considered, which allows investigating both the immediate and the medium-term effects of the COVID-19 crisis on job losses. Additionally, by matching data from three different sources, an extensive set of multilevel variables is considered, some of them new in the literature.

Digital transformation can become a complex process when workers have insufficient skills, which makes training in the digital field essential. Herein, we intend to relate the digital literacy perceived by workers with their training needs for the Portuguese public sector context. Additionally, based on the Human capital theory, we also investigate which professional/demographic characteristics increase training propensity in digital fields. Through an online questionnaire, a dataset with information on 573 workers was obtained. The data analysis was made by using a probabilistic regression model and additional statistical techniques. The results revealed that workers with higher levels of education and higher professional skills have higher probability of participating in training in the digital field. On average, workers reveal low levels of digital knowledge (2.7 in a 1–5 scale) and low participation in training in the digital fields (72% of the sample had no training over the last two years), but the majority present a willingness to participate in future training sessions, especially in the fields of Dataset management, Cybersecurity and Communication systems. This study provides information on training in the digital field of public workers, which is essential for public organizations to better prepare for digital transformation. Additionally, it contributes to a very recent literature on digital learning, and it can be extended to other contexts.