1. IntroductionIt may seem like the line between male and female isslowly getting blurry. The reality, though, shows that the inequality problem isnothing but solved. Not only is the progression of women’s economicparticipation slow, and the number of politically powered women not particularlylarge, the gender pay gap is also still of great significance (Elsesser, 2016).It is important to identify the factors, and the extent to which these drive theissue, as it is the only way to tackle it.
The main aim of this research isanalyse the difference between the private sector and public sector in terms ofhow gender affects earnings. Lucifora and Meurs (2006) concluded in theirresearch on the pay gap in the private and public sector, that women werebetter off in the public sector as the pay gap, using data of the UK, Franceand Italy, showed to be less, especially in the lower percentile. The target ofthis research is therefore not prove but to confirm these results. Toinvestigate the research topic, a sample of 3000 people will be used with datafrom the national longitudinal survey of youth (Cooksey & Light, 1979). The group will bedivided into two groups of public and private sector workers, to which aquantitative method comparing male and female earnings will be correlatedagainst each other, using the multiple regression method, to identify the sizeof the gender pay gap in both sectors. Afterwards, the correlation coefficientsand regression models of both samples can be compared against each other toidentify the difference between both sectors.
In order to make this researchviable, a number of controlled variables were used, which were regarded asother important factors contributing to earnings as well such as education. This report will, first off, cover the theoreticalframework providing the concepts and theories regarding this research. Secondly,having assembled the guidance through the report, the full research method willbe described in greater detail and executed, gathering results to the theoryoutlined in the theoretical framework. Following up, an interpretation of theresults, and relations found between the variables in the data profiles.
Concluding with the discussion on the implications of the research on thedifference in gender pay inequality between the private and public sector. 2. Theoretical frameworkIt is important toconsider various concepts as sources of information during a research. Therefore,it is essential to study the different underlying economic theories before doingthe regression analysis. Lots of research has been done on what the effect isof being a woman on your earnings, the gender wage gap.
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The fact that there isa significant difference in earnings between men and women is widely accepted.Therefore, the aim of this research is not essentially to prove the gender paygap, but to reconfirm the claim and identify which labour sector is morevulnerable to gender inequality. First in this paragraph, the human capitalmodel will be discussed, and afterwards theories that cover a connectionbetween the gender pay gap and the private and public sector and its causes.
The basis of anyresearch on wage determination, what effect different factors have on earnings,is found in the human capital theory. Adam Smith set up the basis for whichwould later become the dominant theory behind economic performance ofindividuals in his book ‘The Wealth of Nations’ (Smith, 1776).There are various views on and explanations of the human capital model.Acemoglu’s and Autor’s (2011) view explain that the way in which society valuesan individual as employee, and hereby prefers one over the other, all comesdown to their set of skills, their human capital, which in turn will make themmore productive.
Their view of human capital combines Becker, Gardener and Schultz/Nelson-Phelps.People can distinguish themselves by means of theirqualities and their abilities, which makes them of value. Less skilledindividuals will be less productive and effective, and therefore lessprofitable.
The difference in qualities and abilities between individuals isultimately due to the ‘investment’ into that individual to improve or extendtheir ability, years and quality of schooling, training and non-schooling.However, this is only the basis of any wage differential. (Acemoglu & Autor, 2011) After considering thehuman capital model, the research by Lucifora and Meurs (2006) is considered ingreater detail, who investigated the public sector pay gap looking at bothfemale and male workers in the UK, France and Italy.
They used surveys fromseveral different investigative boards in all countries and analysed the datausing quantile regression methods and OLS-estimators and controlling thestandard human capital variables. Their research outlined that the wage gap isparticularly large in the UK private sector, where the standard deviation ofthe log hourly wage showed a difference of 0.051 compared to 0.019 in thepublic sector. This was partially blamed on the decentralised labour laws ofthe UK creating smaller union power and “sticky floors” existing in the lowerpercentile and “glass ceilings” in the higher percentile of the private sector.(Lucifora & Meurs, 2006) Arulampalam, Boothand Bryan (2007) continued this investigation on the glass ceiling effect intheir research using a sample from European Community Household Panel Survey,which was conveyed annually between 1994 to 2001 across several countries inEurope. They started by showing raw inequality data from both sectors that inall sample countries males earned, on average, more than their female counterparts.
For example, in the Netherlands the mean difference between log(male)and log(female) hourly wage was shown to be 0.481 in the public sector and0.643 in the private sector. They continued this research with a quantileregression method and formed an OLS-estimator and confirmed the raw data as inall countries the private sector showed a greater wage inequality than theprivate sector. Lack of opportunity, Maternity leave (less experience), Lack ofchildcare and discrimination (glass ceilings) where all listed as contributorsto gender pay inequality. (Arulampalam, Booth, & Bryan, 2007) In both previousmentioned papers above, the focus was often nog exactly on the differencebetween the private and public sector wage inequality but used it as anexplanation tool. Barón and Cobb-Clark (2008) focused specifically on theprivate and public sector separately in their research of occupationalsegregation and the gender wage gap using the Household, Income, and LabourDynamics in Australia Survey. Their results showed a relatively consistentinequality in the public sector and a rising inequality in the private sectorwith women being worse off again in the 90th percentile of theprivate sector.
The difference between the sectors is partially blamed for thegreater presence of anti-discrimination and inclusion laws in the publicsector. (Barón & Cobb-Clark, 2008) The Human Capitalmodel will always remain one of the most important theories outlined ineconomic theory, which partially explains the difference in wage between menand women. For example, when women get pregnant they will eventually have to goon maternity leave for several months and in turn lose the work experience theycould have gained in this time and hurt their earning potential as explained bythe human capital model. Being female and earning less than male counterparts istherefore not necessarily a causal relationship but simply shows a correlationdue to the other moderators and mediators.
However, in all researches mentionedabove, the common concept was mentioned of “glass ceilings” occurring for womenin higher occupations. In the investigation, we will introduce severalcontrolled variables in order to investigate what factors most contribute tothe difference in pay in both sectors. However, the underlying theory explainsthat the private sector will show a greater inequality than the public sector. 3. MethodWith the use of asample, we will investigate the research question.
This section will firstoutline the data set used and the variables that are used in the research withgreater detail followed with an explanation of the multiple regression analysisused. In this research,data from the National Longitudinal Survey of Youth (Cooksey & Light, 1979) will be used, whichrecorded data from a sample of 3000 people between 1979 and 2002. Examples ofthe measured values include marital status, education, and earnings and wererecorded and updated on a yearly basis. Earnings was recorded in dollars earnedper hour of labour. The sample will first be divided into two groups:labourers in the private sector and labourers in the public sector. Following, avariety of controlled variables have been chosen in order to greater explainthe relation between being female and earnings. The following variables havebeen chosen: educational level, work experience, work experience squared, andmarital status. Workexperience is also used as a squared quantity as experience does notnecessarily show a linear relationship.
The motivation forthe use of these variables come from previous researches and the underlyingtheory. Furthermore, males will be used as a reference group. In this research, we will use five mathematicalmodels. The variable female will be used as a dummy variable, meaning that a 1will be given if female and 0 if male. Similarly, we will use dummy variablesfor sector and marriage as well. The mathematical models will be estimatedusing the Ordinary Least Squares (OLS)-method and with the use of a logarithmictransformation of the regression models the results can be indicated aspercentages. For every model, a variable will be added to the previous model,which will in turn create a clear distinction of the individual effect of avariable on earnings.
The models include: Model1: Model2: Model3: Model4: Model5: The same models will be used for the private sectorinstead of the public sector creating a set of results for both sectors. As themathematical models expands, we expect the coefficient of determination ( to rise as more of the uncertainty will beexplained. For every variable added, it is important to confirm there is no endogeneity,which means the controlled variables correlate with the error term. This methodwas chosen as other researches showed to find successful and reliable correlationsusing these methods.
(Bun & Van Ophem, 2017) References Acemoglu, D., & Autor, D. (2011). Lectures in Labor Economics. Arulampalam, W., Booth, A.
L., & Bryan, M. L. (2007, January). Is There a Glass Ceiling over Europe? Exploring the Gender Pay Gap across the Wage Distribution .
ILR Review, 163-186. Barón, Juan D.; Cobb-Clark, Deborah A. (2008): Occupational segregation and the gender wage gap in private- and public-sector employment: a distributional analysis, IZA Discussion Papers, No. 3562, Bun, M., & Van Ophem, H.
(2017). Reader Introduction Econometrics. Amsterdam: Faculty of Economics and Business.
Cooksey, E., & Light, A. (1979). The National Longitudinal Survey of Youth. Survey. Elsesser, K. (2016, October 27).
Important facts about the global gender gap. Retrieved from Forbes: https://www.forbes.
com/sites/kimelsesser/2016/10/27/7-important-facts-about-the-global-gender-gap/#7f8da6474c22 Lucifora, C., & Meurs, D. (2006). The public sector pay gap in France, Great Britian and Italy. The Review of Income and Wealth, 43-59.
Smith, A. (1776). The Wealth of Nations. New York: Modern Library. Stewart, M.
(2014). Why is the Gender Pay Gap Higher in the Private Sector? The Economist. (2017). Gary Becker’s concept of human capital.
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