Gender Pay Gap
Project Name: Gender Pay Gap
Class: 4IT496 – Simulation of Systems
Author: Amélie Van Hoecke
Model type: System Dynamics
Software used: Vensim
Contents
Problem definition
The Pay Gap, unequal pay between men and women, is one of the taboos nowadays that the European Union wants to tackle. It’s a difficult problem to solve because it is influenced by many other factors as education, part-time working, families, ... I want to try to solve this problem by researching how the different factors are interrelated with each other and what the best solution is. A simulation in Vensim with real numbers will help me with that. For the numbers, I will focus on one country, Belgium, because I found a good source with some good and interesting numbers on this country and it's my home country.
Method
I chose to work with Vensim, because this problem can be seen as a group of interacting and interdependent problems forming a complex whole. The reasons of the pay gap are caused by other factors etc. This results in a complex whole.
Theoretical background and facts
Before I start with the model, I want to explain the background of the problem and discuss some different numbers of the pay gap. There are multiple researches on this topic. The European Commission publishes each year an Annual Report on Gender Equality.
The gender pay gap is the difference between men’s and women’s pay, based on average difference in gross hourly earnings of all employees. On average, women in the EU earn around 16% less per hour than men. But it varies from country to country. The gender gap is a complex issue caused by a number of interrelated factors.
I had a lot of difficulties with simulating all this information in one model, but finally I found a sollution by separating the different factors. This is possible because each of the findings I used are also split up, so there is no overlap.
Finding totally up-to-date information is not easy, so I took the data found by a report made in 2016 by ‘Instituut voor de Gelijkheid van mannen en vrouwen’, a Belgian institution who fights for equality between Belgian men and women.
First of all we look at the employment rate of that year. This was 57,2% for women and 66,4% for men in 2013. The employment rate gives us the amount of working people between 15 and 64 years old. The reasons for a low employment rate for women is partially caused by the pay gap.
The total pay gap in 2013 was 8,432 billion euros. This amount can be split up by different reasons:
Part-time working. Reasons of part-time working: - Childcare: 21%
- Family reasons: 29%
- Doesn’t find full-time: 8%
- In combination with studies, retirement...: 7%
- Economic reasons: 0,5%
- Health reasons: 5%
- Only part-time possibilities: 15%
- Others: 5%
- Doesn’t want to work full-time: 9%
- Working conditions: 0,5%
Age
Related with experience and seniority, and the differences in generations. Older women are lower educated than younger women. Wages are increasing with age, for men and women. But the amount of changing depends on the sex. An explanation for this is the evolution of the carreer for men and women.
Education the grade of certificate also influence the pay gap.
Family
Civil state
Model
To get an overview of the different variables, I first made a causal diagram:
At the end of 2013, Belgium counted 5 676 207 women. I will use this number because the percentages are also from 2013. All the data used is completely split up for the sole reason so there is no overlap. To see which part of the pay gap is the biggest, I will divide the Gap the different causes of the gap.
Women’s working population = 0.572 * 5 676 207
Pay Gap caused by part-time working
- Women’s Part-time Rate = 55%
- Working Part-time = ("Women's part-time Rate"*Women's working Population)*(Child Care+Doesn't find fulltime+"doesn't want to work fulltime"+economic reasons+Family Reasons+health reasons+in combination with studies+"only part-time possibilities"+others+working conditions)
- Part-Time working Women = INTEG(Working Part-Time,0)
- Pay Gap part-time = Gap between Women working part-time and men working part-time = €1
==> Pay Gap caused by Part-Time = INTEG("Part-Time working Women"*"pay gap part-time",0)
Pay Gap caused by Age
- Women population = total population of Women in Belgium in 2013 = 5 676 207
- Women’s Employment rate = 57,2%
- Participation rate: -25 years: 22% / 25-34 years: 74% / 35-44 years: 77% / 45-54 years: 72% / 55-64 years: 35%
- Pay Gap per age: -25 years: €1,03 / 25-34 years: €0,69 / 35-44 years: €1,61 / 45-54 years: €2,92 / 55-64 years: €4,85
==> Pay Gap Caused by Age = INTEG(("-25"*"Pay Gap -25")+("25-34"*"Pay gap 25-34")+("35-44"*"Pay gap 35-44")+("45-54"*"Pay gap 45-54")+("55-64"*"Pay gap 55-64"),0)
Pay Gap caused by Education
- Participation Rate women: High school: 30% / Bachelor: 59% / Master: 78%
- Working after high School = Participation Rate after high school * Women’s working population
- Working after Bachelor = Participation Rate after bachelor * Women’s working population
- Working after master = Participation Rate after master * Women’s working population
- High school = INTEG(working after high school,0)
- Bachelor = INTEG(working after bachelor,0)
- Master = INTEG(working after master, 0)
- Pay gap: High school: €1,80 / Bachelor: €2,78 / Master: €5,33
==> Pay gap caused by Education: INTEG((Bachelor*pay gap bachelor)+(high school*pay gap high school)+(Master*pay gap master),0)
Pay Gap caused by Civil State
- Percentage of people Single, Married, Divorced or Widow: Single: 42,66% / Married: 38,03% / Divorced: 9,8% / Widow: 9,5%
-Working population being single = Single rate*Women's working Population
- Working population being married = Married rate*Women's working Population
- Working population being divorced = Divorced rate*Women's working Population
- Working population being widow = Widow rate*Women's working Population
- Single = INTEG(working population being single,0)
- Married = INTEG(working population being married,0)
- Divorced = INTEG(working population being divorced,0)
- Widow = INTEG(working population being widow,0)
- Pay gap single: €-0,34 / Pay gap married: €1,99 / Pay gap divorced: €1,48 / Pay gap widow: €0,63
==> Pay gap caused by Civil State = INTEG((Single*pay gap single)+(Married*pay gap married)+(Divorced*pay gap divorced)+(Widow*pay gap widow),0)
Pay Gap caused by Family Composition
- Participation rate: Single, no children: 58% / Single with child(ren): 59% / Couple without children: 62% / Couple with children: 65%
- Single working women = "Single, no children participation rate"*Women's working Population
- Single working mom = "Single, with children participation rate"*Women's working Population
- Working women with husband = "couple, no children participation rate"*Women's working Population
- Working Women without husband = "couple, with children participation rate"*Women's working Population
- Single, no children = INTEG(single working women,0)
- Single, with children = INTEG(single working mom, 0)
- Couple without children = INTEG(working women with husband, 0)
- Couple with children = INTEG(working mom with husband, 0)
- Pay gap in euro’s: Single, no children: €-0,65 / Single with child(ren): €0,38 / Couple without children: €2,09 / Couple with children: €2,39
==> Total Pay Gap caused by Family Composition = INTEG((Single with children*pay gap single mom)+("Single, no children"*Pay gap single woman)+(Couple with children*pay gap coupled mom)+(Couple without children*pay gap coupled women),0)
Results
I first did a simulation of everything how it is set, and we call this ‘Base’. We will use base to compare with the other data:
Pay gap is increasing with time, but that is not what we are researching. We will look at the differences of the pay gap when changing certain parameters. With the function SyntheSim I can now change certain parameters to see the influence of this on the total pay gap.
1. Increase the gross hourly pay gap for age with 10%, for every age. For every hourly pay gap of age * 1.1 (-25 years, 25-34, 35-44, 45-54, 55-64)
2. Decrease the gross hourly pay gap for age with 10%. For every hourly pay gap variable of age * 0,9 in the equation.
3. Increase pay gap per hour for part-time with 10%
4. Decrease pay gap per hour for part-time with 10%
5. Increase pay gap per hour for education with 10%
6. Decrease pay gap per hour for education with 10%
7. Increase pay gap per hour for civil state with 10%
8. Decrease pay gap per hour for civil state with 10%
9. Increase pay gap per hour for family composition with 10%
10. Decrease pay gap per hour for family composition with 10%
All the graphs increase or decrease in the same way and order. But some of them slightly more than the others. The increase and decrease of the pay gap for education has the biggest influence on the total pay gap. If you increase the gross hourly pay gap of this cause, the pay gap increases a lot. Followed by the cause Age. For decreasing we find the same conclusions.
To check which of the factors within Education (high school, bachelor, master) has the biggest impact, you change each of the three factors separately:
1. Decrease high school pay gap with 10%
2. Decrease Bachelor pay gap with 10%
3. Decrease Master pay gap with 10%
It is to small to see on this graph, so we zoom in: You see that decreasing the master pay gap has the biggest influence on closing the pay gap. Decreasing high school pay gap has the smallest influence.
Conclusion
I can make a top of the best indicators to close the pay gap, to the smallest influencer:
1. Pay gap caused by Education: Master – Bachelor – High school
2. Pay gap caused by Age
3. Pay gap caused by Family Composition
4. Pay gap caused by Civil State
5. Pay gap caused by Part-time working.
The easiest way to start closing the pay gap for the government, is reducing the pay gap based on education. Especially for master certificates. This can be explained by the low amount of women having a top function. Changing this is a good start for closing the gap.
Code
Resource
http://ec.europa.eu/justice/gender-equality/files/gender_pay_gap/140227_gpg_brochure_web_en.pdf
https://bestat.statbel.fgov.be/bestat/crosstable.xhtml?view=5fee32f5-29b0-40df-9fb9-af43d1ac9032
