An Exploratory Analysis of Female Employment in Industries Worldwide

As part of an initiative by the Women in AI(WAI) community to help its members gain hands-on experience on projects, practice and gain new skills, network and establish an online portfolio we were honored to be a part of the maiden Beginner Data Analytics group to work on a hands-on project.

Our background is a mixed bag which makes it unique. We are made up of university students, people trying to switch up careers into the AI field and also others trying to gain more hands-on experience in the field. All the same, we do have one thing in common. We came in with the mindset of a beginner, hungry and eager to learn and collaborate.

Our Experience

With the help of our mentor, we had the opportunity to work with the python language as well as libraries and tools such as Pandas, Jupyter notebooks, the Anaconda suite, Github among others.

Our team collaborated mostly through zoom calls and slack where we participated in discussions, sharing of ideas and helping each other get up to speed. Through this we gained experience in collaborating with each other remotely especially in these unprecedented times as a result of the covid-19 pandemic. Additionally, we also gained a better understanding on how to deliver a real-life data analytics project contrary to the traditional college work done.

Although there were many positives, we faced a couple of challenges along the way. One was trying to juggle individual work with the group project work. Second was also trying to get a time that suited everyone for meetings to discuss what had been done over the period and also brainstorming to improve upon the project. All the same, we found a way to make it work by leveraging our slack group, communicating what was discussed in the meetings for those who couldn’t attend a meeting for one reason or the other and also sharing ideas.

The Project

Considering the current global pandemic situation and its effects on the economy around the globe, we as a team wanted to do some analysis along those lines. Specifically, we wanted to look at how the covid situation had affected working women worldwide. Unfortunately, existing datasets were not found to help with the idea we had in mind. Hence we went with what was available. We resolved to use our principal dataset, Percentage of all female employment in the industry sector which was sourced from Gapminder. These datasets result from a combination of data from multiple sources into one unique whole across multiple time frames.

About the Datasets

The Female industry workers dataset from Gapminder contains the percentage of all female employment that works in the industry sector globally over a period between 1991 to 2022. It’s worthy to note that although the data contains entries for 2020 to 2022 as well, these are forecasted values and do not take into account the covid-19 pandemic.

The second dataset, the exports (% of GDP) data contains the percentage of GDP particularly exports from 1960 to 2019. Exports here represent the value of all goods and values provided to the rest of the world including merchandise, freight transport, travel, royalties and other services such as communication, construction,financial services etc Source : [https://www.gapminder.org/data/] ( https://www.gapminder.org/data/)

After finalizing the datasets, we worked on how to analyze the given data by reading through some journals and articles and sites such as Kaggle and with the help of our mentor, we came up with different questions for the analysis.

On further discussion, we narrowed our work to analyze: Top countries which have high female employment rates, performance of wealthier countries on gender balance, how the 2008 economic crisis impacted the female employment rate as well as how Ireland fared in female employment rate in industries.

Distribution of data across a section of the years 2015 to 2019

We wanted to explore how women fared in recent times of employment within the industries, so we chose the years 2015 to 2019 exempting 2020 to 2022 because these were forecasted data points which did not reflect the current covid-19 pandemic. From the boxplot it can be seen that the variability of data across the years 2015 to 2019 are approximately the same with majority of the data points between the 0 and 30 mark in addition to notable outliers which is visible across the years mostly clustered around the 30 to 40 mark as well as the 55 to 60 mark. Also, 50% of the data lies between the 8 to 15 mark.

Top Countries with the highest Percentage of Females in Industries

Surprisingly, none of the developed countries came in the top 10. Tonga recorded the highest percentage of females in industries globally and they can be found in the Oceania/Pacific region. Additionally, 70% of the top 10 are african countries of which include Algeria, Tunisia, Djibouti, Burkina Faso, Sudan, Libya, Lesotho and Cambodia while 20% come from Asia which consist of Turkmenistan and Cambodia.

Are Wealthier Countries Better at Gender balance?

We wanted to explore a bit further to understand why the majority of the developing countries were in the top 10 and if we could find out what happened to the developed countries this time adding GDP dataset to see if we could have some interesting insights. However it was seen that there wasn’t much of a correlation between wealthier countries and the number of females in industries. A greater portion of the data points were concentrated around the center of the graph ranging from around 7 dollars to 200 dollars in terms of GDP. With Tonga and Algeria recording the highest percentage of females in industries although recording values at the lowest part of the gdp range, approximately adobe $20.

Countries Significantly affected by the 2008 crisis

We compared the previous year 2007 to 2008 to find the change in values.

It was discovered that approximately 54.7% of countries worldwide during the 2008 crisis saw a significant drop in the percentage of females in industries.

Delving deeper, we also found the top 10 countries which were most affected by the crisis. It was found that Iraq, Serbia, Maldives and Malta recorded the highest differences.

Let’s see some stats from Ireland

The team is based in Ireland so we were particularly interested in what kind of analysis we could draw from their data points.

From the study, it shows that Ireland has seen an overall drop in the percentage of females in Industries over the years. Despite few peaks around 1997 and 2004, compared to the top 10 countries worldwide there hasn’t been much improvement over the years unfortunately.

Conclusion

From the data and our analysis, we see that African countries have significantly more females in industries than their male counterparts. This could be attributed to the relatively low standard of living across board and the fact that most of the women are single mothers and are doubling as bread winners of the family.

Secondly, wealthier countries are not found to be better at gender balance. None of the developed countries were found amongst the top 10 countries worldwide.

Thirdly, the percentage of females in Industries in Ireland has dwindled over the years.

As beginners, we were able to learn fundamental ways to approach a problem statement. We explored different tools and methodologies to perform exploratory analysis on the dataset we were interested. It was a great learning experience even if our insights may or may not reflect the real-time events.