Women earn less money than men.

The UK government took a great step towards understanding the gender wage gap with the requirement that all companies with > 250 employees report the wage cap in their companies. We wanted to find the most equitable, and least equitable companies by region and see what set them apart.

As we did the research, we realized that we needed more data, such as each companies' benefits policy, board gender make-up, and number of female / male employees, that were not readily available. It was also hard to compare between regions and companies visually, since most of the data were in csv format. We realized that there's a limit to what we could get done in a weekend, and decided to focus on creating a tool that could help further research in this area.

What it does

We created an interactive map that shows the gender wage gap by region. Users can click on the map to see more details, such as the actual gender wage gap, and the top 3 and bottom 3 companies in terms of the gender wage gap. We researched the top 3 and bottom 3 companies' officers' gender make up to see if we could find a correlation.

There's also a search function for people to look up a company by name to see the company's gender wage gap. We included a form to crowdsource an organization's benefits information. It was one of our theories that benefits, such as maternity and paternity leaves would help close the gender wage gap by creating an environment where women don't have to choose between their careers and family.

How we built it

Our data are from:

  1. UK gender wage gap reports
  2. UK gender wage gap trends by year
  3. UK companies house officers information
  4. Namsor API Since we had no gender data, we relied on Namsor API to guess the gender based on names as a short term fix

Challenges we ran into

Our main issues fell into roughly three categories.

  1. Data We spent most of the first day trying to find data on company benefits policies because we wanted to see if we could find a correlation between different benefits, and the gender-wage gap. We were only able to find piecemeal information on job postings, and soon realized that we should make the database ourselves.

  2. Map visualization It was a challenge setting up clear borders for each region to make them clickable in order for our interactive elements to work. We found a web app that converted region names into a list of coordinates -- called geoJSON -- we could use on google maps to clearly carve out a region. The next challenge was getting Gatsby, the front-end javascript library we were using, to recognize geoJSON data.

  3. Network issues We had some issues downloading and uploading data because of slow internet.

Accomplishments that we're proud of

  1. Setting up an interactive map page
  2. Building a rails API with a custom route
  3. Learning to use new APIs and libraries
  4. Flexibility in changing course when one solution didn't work
  5. Getting the gender breakdown of the board members by learning python requests

What we learned

We learned how google maps API, gatsby, rails configuration worked and found several cool python APIs that automated API pulls for our data.

What's next for Hacking the gender gap

We would like to find more data in other regions. It would also be great to be able to crowdsource enough benefits data to research whether there is correlation between company benefits and a company's gender wage gap.

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