Impact of the Covid-19 Pandemic on the Pharmacy Market in Poland
Aleksandra Alicja Olejarz
Faculty of Economic Sciences, University of Warmia and Mazury in Olsztynhttps://orcid.org/0000-0002-9178-3471
Résumé
The outbreak of the COVID-19 pandemic has increased demand for medicines and hygienic personal care products. The increase in demand for medicinal products should increase the turnover of pharmacies. It was therefore hypothesised that the outbreak of the COVID-19 pandemic caused shocks to the pharmacy market in Poland. The aim was to identify and determine the nature of the shocks to the pharmacy market in Poland and compare them to the period before the pandemic. The subject of the study was the value of sales in open pharmacies in Poland in the years 2010–2021. To identify shocks and verify the hypothesis, an automatic TRAMO-SEATS procedure was used. The results obtained unequivocally confirmed the hypothesis, with the changes being more visible when analysing the value of total sales in open pharmacies expressed in current prices rather than in constant prices. The shocks were the result of increased demand for medicines and hygienic personal care products resulting from panic in the face of an unprecedented threat such as SARS-CoV-2.
Mots-clés :
COVID-19, drug sales, pharmacies, market shockRéférences
Baka, K. (2011). Ustawa o refundacji leków, środków spożywczych specjalnego przeznaczenia żywieniowego oraz wyrobów medycznych – komentarz do wybranych przepisów. Aptekarz Polski, 59, 4-10. Google Scholar
Balke, N.S. (1993). Detecting Level Shifts in Time Series. Journal of Business & Economic Statistics, 11(1), 81-92.
Crossref
Google Scholar
Blinder, A.S., & Rudd, J.B. (2013). The Supply-Shock Explanation of the Great Stagflation Revisite. National Bureau of Economic Research, University of Chicago Press, p. 1-81. Retrieved from https://www.nber.org/books-and-chapters/great-inflation-rebirth-modern-central-banking. Google Scholar
Chang, C., Tiao, G.C., & Chen, C. (1988). Estimation of Time Series Parameters in the Presence of Outliers. Technometrics, 30(2), 193-204.
Crossref
Google Scholar
Chen, C., & Liu, L.M. (1993). Joint Estimation of Model Parameters and Outlier Effects in Time Series. Journal of the American Statistical Association, 88(421), 284-297.
Crossref
Google Scholar
Coibion, O., Gorodnichenko, Y., & Weber, M. (2020). The cost of the COVID-19 crisis: lockdowns, macroeconomic expectations, and consumer spending. NBER Working Paper 27141, 1-51. Cambridge: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w27141.
Crossref
Google Scholar
Fox, A.J. (1972). Outliers in Time Series. Journal of the Royal Statistical Society, B (Methodological), 34(3), 350-363. Retrieved from https://www.jstor.org/stable/2985071.
Crossref
Google Scholar
Gardocka-Jałowiec, A., Śleszyńska-Świderska, A., & Szalonka, K. (2020). Wpływ SARS-CoV-2 na konsumpcję leków OTC w Polsce. In W. Nowak & K. Szalonka (Eds.). Zdrowie i style życia. Determinanty długości życia. E-Monografie Uniwersytetu Wrocławskiego, 170, 175-191. Google Scholar
Handbook on Seasonal Adjustment (2018). Luxembourg: Publications Office of the European Union. Google Scholar
Hawkins, D. (1980). Identification of Outliers. Series: Monographs on statistics and Applied Probability. Dordrecht: Springer. https://doi.org/10.1007/978-94-015-3994-4.
Crossref
Google Scholar
Healy, M.J.R. (1968). Multivariate Normal Plotting. Applied Statistics, 17, 157-161.
Crossref
Google Scholar
Hussain, R., Dawoud, D.M., & Babar, Z. (2021). Drive-thru pharmacy services: A way forward to combat COVID-19 pandemic. Research in Social and Administrative Pharmacy, 17, 1920-1924.
Crossref
Google Scholar
Liu, S., Luo, P., Tang, M., Hu, Q., Polidoro, J.P., Sun, S., & Gong, Z. (2020). Providing pharmacy services during the coronavirus pandemic. International Journal of Clinical Pharmacy, 40, 299-304. https://doi.org/10.1007/s11096-020-01017-0.
Crossref
Google Scholar
Muirhead, C.D. (1986). Distinguishing Outlier Types in Time Series. Journal of Royal Statistical Society, B (Methodological), 48(1), 39-47.
Crossref
Google Scholar
Odlanicka-Poczobutt, M. (2012). Specyfikacja kanałów dystrybucji leków biofarmaceutycznych zagranicznej firmy produkcyjnej na rynku polskim. Zeszyty Naukowe Politechniki Śląskiej, Organizacja i Zarządzanie, 60, 249. Google Scholar
Olejarz-Wahba, A.A., & Rutkowska-Ziarko, A. (2015). Wykorzystanie metody TRAMO SEATS do modelowania tendencji rozwojowej sprzedaży refundowanej w aptekach ogólnodostępnych w Polsce. Roczniki Kolegium Analiz Ekonomicznych, 36, 471-479. Google Scholar
Shibayama, T., Sandholzer, F., Laa, B., & Brezina, T. (2021). Impact of COVID-19 lockdown on commuting: a multi-country perspective. European Journal of Transport and Infrastructure Research, 21(1), 70-93. https://doi.org/10.18757/ejtir.2021.21.1.5135. Google Scholar
Sławatyniec, Ł. (Ed.) (2013). Prawo farmaceutyczne i refundacja leków. Warszawa: Wolters Kluwer Polska. Google Scholar
Szarucki, M., Noga, G., & Kosch, O. (2021). Wpływ pandemii COVID-19 na modele biznesu przedsiębiorstw sektora MŚP w Polsce. Horyzonty Polityki, 12(40), 95-114.
Crossref
Google Scholar
Trzęsiok, M. (2016). Identyfikacja obserwacji oddalonych w szeregach czasowych. Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 265, 95-105. Google Scholar
Faculty of Economic Sciences, University of Warmia and Mazury in Olsztyn
https://orcid.org/0000-0002-9178-3471
Licence
Ce travail est disponible sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International.
An Author declares that his paper has not been published before (under the same or another title, or is a part of another publication) and does not infringe copyrights of other persons**. At the same time, the Author transfers to the Publisher the exclusive right to publish and to circulate this work in print in the form of a non-serial journal publication and in a form of an electronic publication.
The journal is available on Creative Common license CC-BY-NC-ND
