Modelling the Opinions of Poles about Key Aspects of Professional Work Using a Nested Logit Model
DOI:
https://doi.org/10.15678/AOC.2021.2405Keywords:
preference modelling, nested logit models, Bayesian approach, professional workAbstract
Objective: The objective of this paper is to examine the opinions of Poles about what they think is important in their professional work.
Research Design & Methods: The paper analyzes the preferences of Poles regarding occupational hygiene factors and motivating factors using Generations and Gender Survey data for Poland. Due to the frequent connections between the possible alternatives of choice, the use of the nested logit model to model the preferences of respondents was proposed in this study.
Findings: This study presents the factors that are important for Poles in their professional work depending on their socio-economic and demographic characteristics. For women, compared to men, options related to occupational hygiene and stable employment were less important than other motivating factors. However, for younger people, compared to people from the last age group, options related to occupational hygiene were also important.
Implications / Recommendations: In the research on the opinions and preferences of respondents, a common approach is to perform a comparative analysis using descriptive statistics or standard logistic regression models. The use of standard multinomial logit models may lead to erroneous conclusions, because in discrete choice problems the available options are rarely unrelated. In such cases, the suggested solution is to use nested logit models.
Contribution: The paper reveals the features of groups of respondents for whom good pay is not necessarily the most important factor in professional work, and so-called higher needs are also important.
References
Allison, P. D. (2009) Logistic Regression Using the SAS®. Theory and Application. Eighth edition. Cary, NC: SAS Institute Inc.
Anderson, S. P., De Palma, A. and Thisse, J. F. (1992) Discrete Choice Theory of Product Differentiation. Massachusetts: The MIT Press.
Baranowski, P., Gądek, A., Stelmasiak, D. and Wójcik, S. (2016) “Wyjechać czy zostać? Determinanty zamiarów emigracji zarobkowej z Polski”. Gospodarka Narodowa 284(4): 69–89, https://doi.org/10.33119/GN/100774.
Borowska-Pietrzak, A. (2014) “Dezagregacja czynników w modelu poczucia satysfakcji zawodowej”. Nauki o Zarządzaniu 18(1): 9–22, https://doi.org/10.15611/noz.2014.1.01.
Congdon, P. (2006) Bayesian Statistical Modelling. New York: Wiley & Sons.
Cramer, J. S. (2003) Logit Models from Economics and Other Fields. Cambridge: Cambridge University Press.
Eurostat (2019) Total Unemployment Rate. Retrieved from https://ec.europa.eu/eurostat/databrowser/view/tps00203/default/table (accessed: 27.04.2019).
Gamerman, D. (1997) “Sampling from the Posterior Distribution in Generalized Linear Mixed Models”. Statistics and Computing 7(1): 57–68.
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin D. B. (2000) Bayesian Data Analysis. London: Chapman & Hall/CRC.
GGP (2019) Generations and Gender Programme, http://www.ggp-i.org/ (accessed: 23.05.2019).
Grzenda, W. and Buczyński, M. K. (2015) “Estimation of Employee Turnover with Competing Risks Models”. Folia Oeconomica Stetinensia 15(2): 53–65, https://doi.org/10.1515/foli-2015-0035.
Herzberg, F., Mausner, B. and Snyderman, B. (1959) The Motivation to Work. New York: John Wiley.
Kubiak, P. (2017) “Jak Polacy poszukują pracy? – analiza logitowa”. Studia Prawno-Ekonomiczne 103: 193–201, https://doi.org/10.26485/SPE/2017/103/11.
Lahiri, K. and Gao, J. (2002) “Bayesian Analysis of Nested Logit Model by Markov Chain Monte Carlo”. Journal of Econometrics 11: 103–133, https://dx.doi.org/10.2139/ssrn.317779.
Maddala, G. S. (1983) Limited-dependent and Qualitative Variables in Econometrics. No 3. Cambridge: Cambridge University Press.
Marschak, J. (1960) “Binary-choice Constraints on Random Utility Indicators” in: K. Arrow (ed.) Stanford Symposium on Mathematical Methods in the Social Sciences. Stanford: Stanford University Press.
Marzec, J. (2008) Bayesowskie modele zmiennych jakościowych i ograniczonych w badaniach niespłacalności kredytów. Kraków: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie.
McFadden, D. (1974) “Conditional Logit Analysis of Qualitative Choice Behavior” in: P. Zarembka (ed.) Frontiers in Econometrics. New York: Academic Press, pp. 105–142.
McFadden, D. (1978) “Modelling the Choice of Residential Location” in: A. Karlqvist, L. Lundqvist, F. Snickars, J. Weibull (eds) Spatial Interaction Theory and Planning Models. Amsterdam: North-Holland, pp. 75–96.
Mikroekonometria: modele i metody analizy danych indywidualnych (2012) M. Gruszczyński (ed.). Warszawa: Oficyna a Wolters Kluwer business.
Rossi, P. E., Allenby, G. M. and McCulloch, R. (2005) Bayesian Statistics and Marketing. Chichester, UK: John Wiley & Sons.
Socha, M. and Sztanderska, U. (2002) Strukturalne podstawy bezrobocia w Polsce. Warszawa: Wydawnictwo Naukowe PWN.
Śliwicki, D. (2013) “Ekonometryczna analiza czynników bezrobocia długookresowego w Polsce”. Oeconomia Copernicana 2: 39–56.
Train, K. E. (2009) Discrete Choice Methods with Simulation. Second edition. Cambridge: Cambridge University Press.
Zalewska, A. (2003) Dwa światy. Emocjonalne i poznawcze oceny jakości życia i ich uwarunkowania u osób o wysokiej i niskiej reaktywności. Warszawa: Academica Wydawnictwo SWPS.
