Introduction
While I was browsing for jobs with high pay in bay area, I stumbled upon this website. It has some really great visualization enough to keep one hooked on to. Information is indeed beautifully captured and keeps the user engaged! This should take all you folks back to first week of the coursework, where professor mentioned what should be done to make your visualization appealing. I picked up the Gender Pay Gap which is the difference between women’s and men’s average annual pay. This is just a topic which pulled my attention and is not meant to offend anyone in any manner. So, let’s dig deeper and explore the visualization.
The line chart compares the yearly salary of both the genders across different categories of job and between two countries (US and UK). It displays the job types across Y axis and yearly salary (in $000) on above X axis. Colors are used to differentiate gender, Green for men and Purple for women. The chart is made interactive in 3 areas – by country (US, UK), Plot by (Salary, Gap), Sort by(Job category, Widest Gap, Narrowest Gap, Highest Paid Men Job, Highest Paid Women Job, Ascending, Descending).
Audience: Organizations working towards equal pay.
Claim: Race and ethnicity hampers gender wages in both men and women.
What makes it beautiful?
It’s easy to compare the earnings because of the easily locatable filters. The job categories are grouped and segregated by horizontal dotted line when sorted by job category. It includes exhaustive list of jobs for comparison. Type of Currency is clearly mentioned in both the countries. With line graph, the visualization gives good amount of information in a simple and effective way.
Areas of Improvement
All occupations: When sorted by Job Category, the visualization includes ‘All occupations’ at the bottom. This adds an element of confusion as it doesn’t align with definition of Job category.
Color code: I have been seeing the use of blue color is associated with men and pink with women. It’s good to have a color standard (or I just like to see it that way).
Gender Issues: Why are women paid lesser in almost every sector. One of the factor which is most talked about is LGBT community. Gender discrimination is a major issue when it comes to LGBT group of people.
https://www.pri.org/stories/2015-04-18/why-we-cant-forget-transgender-people-when-talking-about-pay-gap
https://www.americanprogress.org/issues/lgbt/news/2012/04/16/11494/the-gay-and-transgender-wage-gap/
Race: The visualization does not target any specific race and ethnicity to compare the salaries. Hispanic and Black earn lesser than white counterparts due to job market discrimination. If racism is one of the reason, what percentage and which race is bringing down the salary aspect as per gender. Filtering the salary based on race and ethnicity adds more importance to the existing visualization supporting the claim.
http://fortune.com/2017/04/03/equal-pay-day-2017-gender-gap-states/
http://www.huffingtonpost.com/entry/racial-wage-gap_us_57e05f86e4b0071a6e091153
http://www.pewresearch.org/fact-tank/2016/07/01/racial-gender-wage-gaps-persist-in-u-s-despite-some-progress/
Data validity: It’s shown that there is no job in US where women earns higher than men as per the data displayed. However, when I researched on this aspect, I found that there are actually few jobs where women are paid more than men. Social Worker is top one among them. This makes the visualization not so trustworthy.
http://money.cnn.com/2016/03/23/pf/gender-pay-gap/
http://www.cnbc.com/2016/11/25/10-jobs-where-women-earn-more-than-men.html
Open Interpretations: When plotted by Gap and sorted by Job Category, the X axis is displayed in percentage. But fails to say percentage of what? Is it comparing with the particular Industry standards? The data is left for the viewer to interpret. Also, when the job category is plotted across Salary, it’s better to have population information which was used to calculate the yearly salary. Another important point, is the hourly rate which appears at the bottom X axis which is confusing as what it relates to. I’m assuming it is for ‘All occupations’.
Experience Level: The most important characteristic of pay is Experience level. What is the experience level of workers. It would have been better if there was one more filter which gives out the Salary Gap based on individual’s experience level. This would attract larger masses from an Intern to highly experienced person.
Conclusion
One can concentrate on why there is the gap between the the gender pay. Is gender discrimination one of the reason behind it? If so, hiring and equality laws against LGBT workers should be strengthened. The author should validate the details before constructing visualization else viewer would doubt the truthfulness of the content. The graph doesn’t call for any change(Enlighten) and just provides information to the viewer. Overall, it’s a simple and informative visualization and could be made better if focused on improvement areas.
References: http://www.informationisbeautiful.net/visualizations/gender-pay-gap/