Application of the Mincer Earning Function in Analyzing Gender Pay Gap in Serbia Cover Image

Application of the Mincer Earning Function in Analyzing Gender Pay Gap in Serbia
Application of the Mincer Earning Function in Analyzing Gender Pay Gap in Serbia

Author(s): Stojanka Dakic, Mirko Savić
Subject(s): Gender Studies, National Economy
Published by: Универзитет у Нишу
Keywords: gender pay gap; labour market; regression model; EU-SILC

Summary/Abstract: Better economic status of women in the labour market and reduction of gender pay gap is an important determinant of economic and social progress of the country. Gender pay gap is one of the key indicators of women's access to economic opportunities and undoubtedly one of the most constant features of the labour market. Failure to comply with the principle of equality and equal opportunities for women and men is considered a violation of basic human rights. As a result there are significant losses in the economy of countries such as loss of business and economic benefits, and insufficient use of available human resources. If there is no economic independence, all other measures taken to improve the position of women in society in general have much less success and influence. The aim of this paper is to determine whether there is a difference between men and women regarding wages. Mincer earnings function according to which individuals' earnings are function of the achieved level of education and work experience, served as the basis for analysis of the factors that determine the formation of wages. For the analysis we have used data collected by the survey EU-SILC in 2014 in Serbia. Regression model was built and confirmed the presence of the gender gap in earnings and the impact of gender on the formation of wages in the context that females earn less than males. Due to the inadequacy of the available data, the height of the gender gap in earnings has not been determined, nor its decomposition done.

  • Issue Year: 14/2017
  • Issue No: 2
  • Page Range: 155-162
  • Page Count: 8
  • Language: English