The impact of gender on routes for registered unemployment exit in Poland
DOI:
https://doi.org/10.24136/eq.v12i4.38Keywords:
competing risk, cumulative incidence function, the Lunn-McNeil model, unemployment, genderAbstract
Research background: Numerous studies show that men?s and women?s situation on the labour market differs. Women?s disadvantageous position on the labour market in Poland has been confirmed by statistical data (Kobiety?, 2016). The most common reasons for de-registration from the employment office are finding a job and deleting from the records due to an unjustified refusal to accept an offer by an unemployed person. Additionally, registered unemployed people can for example: retire, apply for invalidity pension, receive early retirement benefits or start full time studies. The above causes are regarded as competing risks of various kinds.
Purpose of the article: The purpose of this article is to assess the effect of the unemployed individual?s gender on the probability and intensity of de-registering from the labour office lists due to finding a job, de-registering or other causes.
Methods: In the study the survival analysis methods were used. The assessment of the probability of de-registration due to a specific cause was made by means of the cumulative incidence function. The intensity of de-registration was tested with the Lunn-McNeil model. Differences in the effect of gender on the de-registration possibility were tested with the use of Gray?s test. The study was based on individual data of people registered by the Labour Office in Szczecin.
Findings & Value added: Among women, job-finding was the most common cause of de-registration, followed by the removal from the register. In the case of men the order was reversed, the most probable de-registration cause was the removal, followed by job-finding. The remaining causes were of marginal significance, both for men and women. Women took up a job more intensively than men and were less intensively removed from the register. The differences between males and females in the intensities of de-registering due to the remaining causes were not statistically relevant.
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References
Bieszk-Stolorz, B. (2015). Geometric method of determining hazard for the con-tinuos survival function. Folia Oeconomica Stetinensia, 15(1). doi: 10.1515/foli -2015-0031.
Bieszk-Stolorz, B., & Markowicz, I. (2015). Influence of unemployment benefit on duration of registered unemployment spells. Equilibrium. Quarterly Journal of Economics and Economic Policy, 10(3). doi: 10.12775/EQUIL.2015.031.
Bryant, J., & Dignam, J. J. (2004). Semiparametric models for cumulative inci-dence functions. Biometrics, 60(1). doi: 10.1111/j.0006-341X.2004.00149.x.
Crowder, M. (1994). Identifiability crises in competing risks. International Statis-tical Review, 62(3). doi: 10.2307/1403768.
Crowder, M. (1996). On assessing independence of competing risks when failure times are discrete. Lifetime Data Analysis, 2(2). doi: 10.1007/BF00128575.
Crowder, M. (1997). A test for independence of competing risks with discrete failure Times. Lifetime Data Analysis, 3(3). doi: 10.1023/A:1009696830515.
Gooley, T. A., Leisenring, W., Crowley, J., & Storer, B. E. (1999). Estimation of failure probabilities in the presence of competing risks: new representations of old estimator. Statistics in Medicine, 18(6). doi: 10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O.
Gray, R. J. (1988). A class of K-sample tests for comparing the cumulative inci-dence of a competing risk. Annals of Statistics, 16(3). doi: 10.1214/aos/1176350951.
Hadaś-Dyduch, M., Balcerzak, A. P., Pietrzak, M. B. (2016). Wavelet analysis of unemployment rate in Visegrad countries. In T. Kliestik (Ed.). 16th Interna-tional Scientific Conference Globalization and Its Socio-Economic Conse-quences. University of Zilina, The Faculty of Operation and Economics of Transport and Communication, Department of Economics, 5th ? 6th October 2016. (Part II.). Zilina: University of Zilina.
Kalbfleisch, J. D, & Prentice, R. L. (2002). The statistical analysis of failure time data. Hoboken: John Wiley & Sons, Inc.
Kaplan, E. L., & Meier, P. (1958). Non-parametric estimation from incomplete observations. Journal of American Statistical Association, 53(282). doi: 10.2307/2281868.
Klein, J. P., & Bajorunaite, R. (2004). Inference for competing risks. In N. Bala-krishnan & C. R. Rao (Eds.). Handbook of statistics: advances in survival analysis. Vol. 23. New York: Elsevier.
Klein, J. P., & Moeschberger, M. L. (1984). Asymptotic bias of the product limit estimator under dependent competing risks. Indian Journal of Productivity, Reliability and Quality Control, 9.
Klein, J. P., & Moeschberger, M. L. (2003). Survival analysis: techniques for cen-sored and truncated data. New York: Springer-Verlag.
Kleinbaum, D., & Klein, M. (2005). Survival analysis. A self-learning text. New York: Springer.
Kobiety i mężczyźni na rynku pracy (2016). Warszawa: Główny Urząd Statystycz-ny. Retrieved from http://stat.gov.pl/obszary-tematyczne/rynek-pracy/opracowania/kobiety-i-mezczyzni-na-rynku-pracy-2016,1,6.html (20.11.2016).
Kotowska, I. E. (2007). Remarks on family policy in Poland in the context of in-creases in fertility and women?s employment. Special Issue of the Polish Monthly Journal on Social Policy: Family Policy in Poland.
Landmesser, J. (2009). The survey of economic activity of people in rural areas ? the analysis using the econometric hazard models. Acta Universitatis Lodziensis, Folia Oeconomica, 228.
Markowicz, I. (2013). Hazard function as a tool to diagnose business liquidation. Folia Oeconomica Stetinensia, 13(2). doi: 10.2478/foli-2013-0019.
Marubini, E., & Valsecchi, M. (1995). Analysing survival data from clinical trials and observational studies. New York: JohnWiley & Sons.
Pepe, M. S. (1991). Inference for events with dependent risks in multiple endpoint studies. Journal of the American Statistical Association, 86(415). doi: 10.1080/01621459.1991.10475108.
Sączewska-Piotrowska, A. (2015). Poverty duration of hauseholds of the self-employed. Econometrics, 1(47). doi: 10.15611/ekt.2015.1.03.
Wycinka, E. (2015). Time to default analysis in personal credit scoring. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 381. doi: 10.15611/pn.2015.381.38.
