The Fears and Hopes of Ukrainian Migrant Workers in Poland in the Pandemic Era

Author:

Shelest-Szumilas OlenaORCID,Wozniak MarcinORCID

Abstract

AbstractDue to the COVID-19 pandemic, many immigrants found themselves in extremely unstable situations. The recent contributions show that employment decline in the first several months of the lockdown was higher for migrant workers than for natives. At the same time, migrants were less likely to find new employment in the recovery months. Such circumstances may result in an increased level of anxiety about one’s economic situation. On the other hand, an unfavorable environment may induce resources that could help to overcome it. The paper aims to reveal migrants’ concerns together with ambitions connected with the economic activity during the pandemic. The study is based on 30 individual in-depth interviews with Ukrainian migrant workers from Poland. The research approach was based on Natural Language Processing techniques. We employed sentiment analysis algorithms, and on a basis of selected lexicons, we extracted fears and hopes that appear in migrants’ narrations. We also identified major topics and associated them with specific sentiments. Pandemic induced several matters connected with e.g., the stability of employment, discrimination, relationships, family, and financial situation. These affairs are usually connected on the basis of a cause-and-effect relationship. In addition, while several topics were common for both male and female participants, some of them were specific for each group.

Funder

Uniwersytet im. Adama Mickiewicza w Poznaniu

Publisher

Springer Science and Business Media LLC

Subject

Anthropology,Cultural Studies,Demography

Reference65 articles.

1. Alsharawy, A., Spoon, R., Smith, A., & Ball, S. (2021). Gender differences in fear and risk perception during the COVID-19 pandemic. Frontiers in Psychology, 12, 689467. https://doi.org/10.3389/fpsyg.2021.689467

2. Asmussen, C. B., & Møller, C. (2019). Smart literature review: A practical topic modelling approach to exploratory literature review. Journal of Big Data, 6, 93. https://doi.org/10.1186/s40537-019-0255-7

3. Benoit, K., Muhr, D., & Watanabe, K. (2021). Stopwords: Multilingual stopword lists. R package version 2.3. https://CRAN.R-project.org/package=stopwords

4. Bernstein, H., González, J., Gonzalez, D., & Jagannath, J. (2020). Immigrant-serving organizations’ perspectives on the COVID-19 crisis. Washington, DC: Urban Institute. https://www.urban.org/sites/default/files/publication/102775/immigrant-serving-organizations-on-the-covid-19-crisis_0_0.pdf

5. Bhandari, D., Kotera, Y., Ozaki, A., Abeysinghe, S., Kosaka, M., & Tanimoto, T. (2021). COVID-19: Challenges faced by Nepalese migrants living in Japan. BMC Public Health, 21, 752. https://doi.org/10.1186/s12889-021-10796-8

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