There are many factors influencing wages that are either connected with the individual attributes of employees or describe the general situation at the labor market and characterize the particular place (– institution or enterprise) of employment. The former may be the subject of potential wage disparities. Inequalities at the labor market concern different aspects and social relations such as [Cain 1986, p. 693]: gender, sexual orientation, age, race, disabilities, religion, etc. Labor market discrimination by gender, race, and ethnicity is the word-wide problem and estimation of these types of discriminations has become routine [Neuman & Oaxaca 2003].

“Equal pay for equal work” is one of the fundamental principles of the European Union. However, the Structure of Earnings Survey (SES) reports that in 2006 in all 27 EU countries the gender wage gap (GPG) was 18.4% on average, while in Poland it was 7.4%. In fact among 27 European Union member states situation at the labor market essentially differs since the smallest GPG is observed in Italy – 4.4%, and the biggest in Estonia – 30.3% [Witkowska & Matuszewska-Janica 2012].

The aim of our research is to identify determinants that affect earnings in Poland in selected periods (i.e. years from 2001 to 2011), and to find out if wages obtained by men and women depend on the same factors. Investigation is provided applying ordered multinomial logit models, that are estimated by Maximum Likelihood method [Boes & Winkelmann 2009, Gruszczyński 2010] employing individual data from the Polish Labor Force Survey (PLFS).

In our research we construct models for all respondents and models estimated separately for men and women that simplifies gender wage gap analysis. Such approach was proposed by Juhn, Murphy and Pierce (1991), and is often used in gender disparities analysis [Grajek 2001, Blau & Kahn 2006, Newell & Reilly 2001].

Explanatory variables, that are selected arbitrarily for the model construction, are often used in the research concerning wages, for instance [Newell & Socha 2007]. These variables describe respondents’ characteristics and employees’ firm characteristics.

1. FULLTEXT: Determinants of Wages in Poland, Zip archive data, at least v2.0 to extract, 0.1MB

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/30026 must be provided.

1. Art Market in Poland. Evaluation of the individual hedonic art price indexes for the selected Polish painters
2. Gender parity in public companies in Poland vs. EU equality directive
3. Structure of employment and wages in ICT and tourism sectors in Baltic Sea Region: gender analysis
4. Polish pension market performance in comparison to selected benchmarks
5. A Comparison of Global Stock Market before and after the 2007-2009 Global Financial Crisis
6. Investigation of the Strong Form Efficient Market Hypothesis: the Example of Selected Investment Funds
7. Momentum and winner-loser strategies: Evidence for the Warsaw Stock Exchange
8. A Study on the Stability of Ensemble Trees: Example of the Polish Credit Scoring Application
9. Application of Multidimensional Analysis to Banking Development Evaluation
10. Badanie efektywności GPW na przykładzie wybranych indeksów: test autokorelacji
11. Analiza wrażliwości rozwiązań wyznaczonych dla różnej struktury próby kredytobiorców: analiza dyskryminacyjna i drzewa klasyfikacyjne.
12. Modelowanie i prognozowanie kursu euro/dolar: modele autoregresyjne z rozkładami opóźnień i sztuczne sieci neuronowe