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Effectiveness of the Anti-Crisis Policy in the Period of COVID-19 Pandemic in the Road Transport Industry

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  • Magdalena Osinska
  • Wojciech Zalewski

Abstract

Purpose: The purpose of the paper is to examine the effectiveness of the tools introduced as the Anti-Crisis Shield Acts, which have been announced by the government of Poland during the pandemic in the road transport industry. Design/Methodology/Approach: The contribution is based on a review of literature, preparing of an original questionnaire-based survey directed for road transport companies and estimating ordered multi-nominal logit, and binary logit model. Findings: Transport industry is particularly sensitive to the business cycle phases due to the fact that limitations in trade are very often the first sign of incoming recession. We found out that the government introduced the Anti-Crisis Shield Act aimed to preserve jobs was widely utilized by the transport industry. Practical Implications: The Polish government similar to other European authorities has introduced a programme to reduce losses in the economy caused by the pandemic. The program was intended to reduce negative short- term consequences of the crisis caused by the COVID-19. The transport enterprises widely used the offered aid although it was directed rather ex post (to reduce) than ex ante (to prevent). Originality/Value: The study is based on primary data obtained from road transport companies and related to the consequences of the COVID-19 pandemic for the road transport industry. The research is fairly up-to-date, since the survey was conducted in June-July, 2020. A logit model is applied to evaluate determinants of the wide use of the government aid by the enterprises useful for practitioners and analytics of the transport industry to forecast probability of economic performance of enterprises.

Suggested Citation

  • Magdalena Osinska & Wojciech Zalewski, 2020. "Effectiveness of the Anti-Crisis Policy in the Period of COVID-19 Pandemic in the Road Transport Industry," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 40-57.
  • Handle: RePEc:ers:journl:v:xxiii:y:2020:i:special2:p:40-57
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    References listed on IDEAS

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    1. Zhang, Yahua & Zhang, Anming & Wang, Jiaoe, 2020. "Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China," Transport Policy, Elsevier, vol. 94(C), pages 34-42.
    2. Rory Wolfe & William Gould, 1998. "An approximate likelihood-ratio test for ordinal response models," Stata Technical Bulletin, StataCorp LP, vol. 7(42).
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    Cited by:

    1. Magdalena Osińska & Wojciech Zalewski, 2023. "Vulnerability and resilience of the road transport industry in Poland to the COVID-19 pandemic crisis," Transportation, Springer, vol. 50(1), pages 331-354, February.
    2. Zhou, Huiyu & Wang, Yacan & Huscroft, Joseph R. & Bai, Kailing, 2021. "Impacts of COVID-19 and anti-pandemic policies on urban transport—an empirical study in China," Transport Policy, Elsevier, vol. 110(C), pages 135-149.
    3. Kamila Urbańska & Agnieszka Parkitna, 2021. "Success of SME enterprises in the era of pandemics," WORking papers in Management Science (WORMS) WORMS/21/03, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    4. Kamila Urbanska & Agnieszka Parkitna & Joanna Kubicka, 2021. "Success of SMEs in the Era of Pandemics," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 407-423.

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    More about this item

    Keywords

    COVID-19 pandemic; anti-crisis shield; survey data; logit models.;
    All these keywords.

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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