Simulace.info - User contributions [en]

File:Wheel.jpg

2018-01-24T17:00:01Z

Amelievh:

File:PDCA.jpg

2018-01-24T16:59:28Z

Amelievh:

File:HouseofQualities.jpg

2018-01-24T14:22:57Z

Amelievh:

File:ProcessApproach.jpg

2018-01-24T14:09:45Z

Amelievh: Example of a process approach to earn the ISO 9001 : 2005

Example of a process approach to earn the ISO 9001 : 2005

Gender Pay Gap

2018-01-16T20:06:04Z

Amelievh:

Project Name: Gender Pay Gap

Class: 4IT496 – Simulation of Systems

Author: Amélie Van Hoecke

Model type: System Dynamics

Software used: Vensim

=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:

[[File:causaldiagram.jpg]]

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.

[[File: stockflow.jpg]]

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:

[[File:BASE.jpg]]

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.

[[File:TOTALPAYGAP.jpg]]

[[File:ZoomPG.jpg]]

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%

[[File:Education.jpg]]

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.

[[File:EducationZoom.jpg]]

=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=
[[Media:PayGapSimulation.zip]]

=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

http://igvm-iefh.belgium.be/sites/default/files/91_-_de_loonkloof_tussen_vrouwen_en_mannen_2016_nl.pdf

Gender Pay Gap

2018-01-16T20:05:02Z

2018-01-16T19:45:09Z

Amelievh: Amelievh uploaded a new version of File:Causaldiagram.jpg

2017-12-10T20:31:05Z

Amelievh:

Assignments WS 2017/2018

2017-12-10T20:29:45Z

Amelievh:

Assignments WS 2017/2018

2017-12-10T20:28:18Z

Amelievh:

== Simulation Proposal (xvatj00) ==

Software: Vensim

I am a contemporary gospel choir conductor (choir and full professional band). We organize more or less 3 concerts a year. It has been a long time since we released our last CD and now we would like to earn money to be able to start recording a new one. For this purpose, I would like to find out those factors (such as choir performance, band performance, tickets’ price, concert’s location, etc.) that influence potential audience when choosing a band to go see and how to improve them, and thus get more people to come to our concerts - and earn more money for the tickets and in general. I will use a survey to get data about people’s preferences.
[[User:Xvatj00|Xvatj00]] ([[User talk:Xvatj00|talk]]) 17:45, 27 November 2017 (CET)
::For this kind of simulation you would need ritch historical data so that you would be able to find premises you would then build the equations on (and to be able to verify the model when you compare its results with the historical data). Unfortunately the survey will not help you to quantify the parameters and the number of concerts is really low to be usable for such simulation. I probabbly would suggest a different topic. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 18:43, 30 November 2017 (CET)
:::I can get data up to 10 years back. The number of concerts included only those concert that are organized by us only, but we perform in many other concerts as well. Do you have any suggestions how to make the simulation possible? Thank you. [[User:Xvatj00|Xvatj00]] ([[User talk:Xvatj00|talk]]) 18:59, 30 November 2017 (CET)
::::: You would have to be able to define parameters that determine the demand for the individual concerts, and based on the data quantify them and quantify their impact on the demand for a concert. Based on that one could then discuss how the concert and its content should be se up so that you get maximum profit out of it. That is not a really easy ...[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 15:07, 6 December 2017 (CET)

=== Intersection Optimalization (NEW ASSIGNMENT) ===
Software: NetLogo

Almost every day, I walk by the intersection of Anglická and Bělehradská / Škrétova. During peak hours, there are traffic jams on only one street leading to the intersection, which I find interesting. Because of this fact, I would like to simulate the intersection in order to find out if the lights are really optimally set there, and potentially, find out the optimal setting of the intersection’s lights.

As I’ve already mentioned, there are lights directing the intersection. Also, there is a tram track on the Bělehradská / Škrétova street which goes straight, while most of the cars coming from Bělehradská street turn left. I will use real intervals of all of the lights from a chosen time during peak hours, and the number of cars and trams coming to the intersection (including their speed, direction, etc.). At first, I will set the lights to constant ticks according to the reality to simulate the real situation. After that, I will try to find out an optimal setting of the lights and evaluate, what the optimal setting is or if it meets with the reality. [[User:Xvatj00|Xvatj00]] ([[User talk:Xvatj00|talk]]) 18:48, 8 December 2017 (CET)

=== Bars in a concert hall serving a dance event (yaua00) ===

Software: NetLogo

None party exists without alcohol, so let’s imagine a dance event in a concert hall Lucerna with 2 bars and in the concert hall. People during a dance event are randomly distributed along 1300 m^2. Prices are regulated by the owner of the concert hall, so they are equal for each bar. This fact means, that people choose to which bar to go considering just the distance, this also means that both should have almost the same profit.

But one greedy owner of one of the bars wants to have greater profit then the other one by moving to another position and attracting customers in such a way.

One more question of this simulation would be – what would happen if more people open their bars there (luckily the size of the hall allows it) and they bring their own prices to the concert hall, so the people can now choose not only the distance to the bar, but also the price of their favorites drinks. [[User:Yaua00|Yaua00]] ([[User talk:Yaua00|talk]]) 22:46, 9 December 2017 (CET)

[['''Social media post (Amelievh''']]

Software: Netlogo

Nowadays social media is a hot topic, and a lot of recruiters and other business people use linked in to attract new employees or just to share their thoughts.

For my simulation, I was thinking about researching the reach of a social media post. Someone posts something on LinkedIn, and depending on the amount of connection and amount of sharing I want to check how many people you can reach with one post. I will try to find real-world numbers and make it a useful tool for the business world.