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Using quasi-experimental designs for causal effects

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Języki publikacji
EN
Abstrakty
EN
Purpose: This paper discusses the concept of identifying the causal effects using quasiexperimental methods and applies this method to investigate the impact of high license fees on the quality of mobile Internet in Poland. Design/methodology/approach: Quasi-experiment design, especially the difference-indifferences method and the interrupted time series design were used to examine the causal effects of spectrum fees in Poland. Data on the quality of mobile Internet in Poland and around the world published by Akamai and data provided by Ookla® under the agreement1 were used for analysis. Findings: The study did not confirm the impact of high spectrum fees on the quality of the Internet in Poland. Practical implications: The results obtained can help policymakers in Poland and other countries in making decisions on spectrum management. Originality/value: This is the first paper using the quasi-experimental method to examine the effects of the 4G auction in Poland.
Rocznik
Tom
Strony
217--232
Opis fizyczny
Bibliogr. 26 poz.
Bibliografia
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  • 3. Bondonio, D. (2021). Cross-Regional Sequential Difference in Difference (CR-SEQDD): An Empirical Approach for Evaluating EU Thematic-ObjectiveInterventions with Regional Data Aggregated at the National Level. Publications Office of the European Union. Retrieved from: https://ec.europa.eu/regional_policy/en/information/publications/evaluations-guidance-documents/2021/cross-regional-sequential-difference-in-difference-cr-seqdd, 15.11.2023.
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  • 6. Coleman, R. (2019). Designing experiments for the social sciences: how to plan, create, and execute research using experiments. Thousand Oaks, California: SAGE Publications, Inc.
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  • 8. European Commission (2010). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions A Digital Agenda for Europe (COM/2010/0245). Retrieved from: https://eur-lex.europa.eu/legal-content/en/ALL/?uri=celex%3A52010DC0245, 17.05.2023.
  • 9. Garrison, R.H., Noreen, W.E., Brewer, C.P. (2010). Managerial Accounting. New York: McGraw-Hill. Retrieved from: https://eduguidehome.files.wordpress.com/2019/02/managerial-accounting-garrisonnoreenbrewer-13e.pdf, 15.03.2023.
  • 10. Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, Vol. 225, Iss. 2, pp. 254-277. doi: https://doi.org/10.1016/j.jeconom. 2021.03.014.
  • 11. GSMA (2019). The impact of spectrum prices on consumer. Retrieved from: https://www.gsma.com/spectrum/wp-content/uploads/2019/09/Impact-of-spectrum-prices-on-consumers.pdf, 5.04.2023.
  • 12. Imai, K., King, G., Stuart, E.A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 171. Iss. 2, pp. 481-502. doi: 10.1111/j.1467-985X.2007.00527.x
  • 13. ITU (2019). ITU Contribution to the Implementation of the WSIS - Outcomes 2019. Retrieved from: https://www.itu.int/en/itu-wsis/Pages/Contribution.aspx. 23.03.2023.
  • 14. Kim, Y, Steiner, P. (2016). Quasi-Experimental Designs for Causal Inference. Educational Psychologist, Vol. 51, Iss. 3-4, pp. 395-405. doi: 10.1080/00461520.2016.1207177.
  • 15. Kuś, A. (2020). Polish experience from first-ever spectrum auction. Telecommunication Policy, Vol. 44, Iss. 7, pp. 1-11. doi: 10.1016/j.telpol.2020.101971.
  • 16. Kuś, A. (2023). Do the high spectrum prices harm consumers? Evidence from Poland. European Research Studies Journal, Vol. XXVI, Iss. 3, pp. 415-437. doi: 10.35808/ersj/3222.
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  • 18. Linden, A., Arbor, A. (2015). Conducting Interrupted Time-series Analysis for Single- and Multiple-group Comparisons. The Stata Journal, Vol. 15, Iss. 2, pp. 480-500. doi: 10.1177/1536867X1501500208
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  • 20. Sagan, A. (2011). Przyczynowość w badaniach marketingowych. Zeszyty Naukowe Uniwersytetu Szczecińskiego, Problemy zarządzania, finansów i marketingu, Vol. 38, pp. 273-283. doi: 10.18276/pzfm.2015.38-25.
  • 21. Sagan, A. (2015). Zależności przyczynowe w diagnozie efektów komunikacyjnych. Zeszyty Naukowe UE w Poznaniu, Vol. 208, pp. 73-84.
  • 22. Shadish, W.R., Cook, T.D., Campbell, D.T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin Company.
  • 23. Somers, M., Zhu, P., Jacob, R., Bloom, H. (2013). The validity and precision of the comparative interrupted time series design and the difference-in-difference design in educational evaluation. MDRC Working paper on research methodology. New York, NY: MDRC. Retrieved from: https://www.mdrc.org/sites/default/files/validity_precision_ comparative_interrupted_time_series_design.pdf, 27.11.2023.
  • 24. Turner, S.L., Karahalios, A., Forbes, A.B., Taljaard, M., Grimshaw, J.M., McKenzie, J.E. (2021). Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Medical Research Methodology, Vol. 21, Iss. 1. doi: 10.1186/s12874-021-01306-w.
  • 25. White, H., Sabarwal, S. (2014). Quasi-Experimental Design and Methods, Methodological Briefs: Impact Evaluation, 8. Florence UNICEF Office of Research. Retrieved from: https://www.unicef-irc.org/KM/IE/img/downloads/Quasi-Experimental_Design_and_ Methods_ENG.pdf, 22.09.2023.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-44a0930b-5d14-4049-b485-0c47c9658b74
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