Female representation in positions of power has been a global topic of interest for many decades. I came across a dataset put together by the United Nations that shows the proportion of seats held by women in national parliaments around the world. Digging deeper into the topic I found that the data did not match my expectations. Which countries have the most female representation in their national parliaments? How has female representation in national parliaments changed over the recent years?
Being from Bangladesh, where the prime minister has been a woman for most of my life time, I have consistently seen a woman at the highest level of authority in our National Parliament. My idea of my country in comparison to the United Kingdom or the United States was always that it is “backward,” and I expected women to have progressed much farther in the latter countries by now. The fact that the United States had their first female Vice President elect only this year was difficult for me to grasp. Thus I chose to also look into what factors may affect the gender ratio in national parliaments of different countries. I started by looking at education and gender ratios within education.
In addition to my own research, I wanted to create a tool for others to be able to find related data on countries of their choice.
Process + Tools
This study was conducted using 3 datasets which were all sourced from UNdata, put together by the United Nations. The datasets are as follows:
- Proportion of seats held by women in national parliament
- Ratio of girls to boys in primary, secondary and tertiary education
- Enrollment in primary, secondary and tertiary education levels
CSV files of these datasets were downloaded from the aforementioned website. Using Google Sheets, these datasets were cleaned up by removing any unnecessary fields and by renaming some countries from their official UN names to ones that would be recognizable more widely (e.g. Republic of Korea to South Korea).
Tableau Public was used as a platform to both visualize the data and present the analysis. The desktop version of the software was used to create 10 visualizations of the datasets that included a choropleth, bar graphs and line graphs. 1 visualization did not end up being used. The visualizations and analysis were both published on an interactive dashboard on Tableau online.
To keep the results consistent only data from 2005, 2010, 2015-2019 was used. The most recent data from the education datasets were from 2018, so data from the same years were used up until 2018.
Before introducing my visualizations I chose to begin by asking viewers some questions about their expectations on the topic. This was so that they could go through the process along with me and find out whether their expectations were close or far off from the truth.
The color palette had shades of pink to represent data on female representation in parliament as it is recognized as a color used in branding for women’s empowerment. Green, a complementary color to pink, was used to represent education in order to easily be able to identify it as a separate subject matter.
The first visualization is a choropleth, a color-coded map, that helps get an overview of female representation in parliaments around the world. An interactive slider was added to allow viewers to see how the representation changed from 2005 to 2019. Figure 1 shows how female representation has increased overall, but has been consistently higher in western European countries, southern African countries and South American countries.
The second visualization, Figure 2, shows the same data in a bar graph format arranged in descending order so that the country with highest female representation is on top. This makes it easier to see the exact percentage of seats held by women in parliament for each country. The descending arrangement allows us to immediately find out which countries have the most female representation in parliament, answering my first research question. Using the aforementioned slider we can see that Rwanda has consistently been the country with the highest proportion of women in parliament for all the years included.
Next, I was curious whether the ratio of girls to boys enrolled in education in each country would have some correlation with the proportion of women in their national parliament.
The bar graph shows the ratio of girls to boys in 3 levels of education in different countries, arranged in descending order, with the highest ratio first. There is a separate slider for this graphic that allows viewers to filter the data by year. Using the slider we can see that Qatar has had the highest ratio of girls to boys, especially in tertiary education, i.e higher education, for a few years. In Figure 3, which shows 2018 data, we can see that when the cursor hovers on Qatar, it shows their ratio of girls to boys for tertiary education is 7.823. This means that for every boy enrolled, there were 7.823 girls, or for every 1000 boys there were 7823 girls enrolled in higher education. The fact that the order of countries in this bar graph is completely different from that in the previous shows that the ratio of girls to boys in education has no correlation to proportion of seats held by women in parliament.
To confirm this conclusion, I wanted to look closely at a smaller number of countries. I chose to narrow down to the top 10 countries with female representation in parliament for the year 2018 as that was the most recent year with data from all 3 datasets.
Figure 4 shows the two bar graphs side by side to make it easy to compare. Even though the education data is missing for half of the countries, just by looking at the data we have, we can see that the order of the countries do not match up. Cuba has a higher ratio of girls to boys in education, but Rwanda has a higher proportion of seats held by women in parliament. This confirms that the gender ratio in education has no correlation with the proportion of women in parliament.
Next, I wanted to look at education as a whole. First I created a bar graph of the overall number of people enrolled in education in each country, once again with a slider to filter the data by year. Visualizing this quickly made me realize that it would not be relevant to my research due to the different sizes of population of each country.
Looking at Figure 5, we can see that India and China have the highest total number of students enrolled in education. It is widely known that India and China have two of the highest populations in the world and that may be the reason behind this result. In order to make this data useful for my research I would have to find out the population of each country for each year, then use the existing enrollment numbers to find out what percentage of the population was enrolled in education. Due to time constraints, I did not move forward with that plan. Instead I decided to see whether a change in number of students enrolled in each country had an effect on female representation in parliament over the years.
In order to be able to make a clearer comparison, I chose to stick to the same group of countries, the top 10 countries with female representation in parliament. Figure 6 shows the change in proportion of seats held by women in parliament and change in number of students enrolled in education for these countries over the same time period. Looking at them as line graphs side by side makes it easier to compare the trajectory of each factor. Selecting a country on the legend allows the viewer to highlight the line graphs for that country and compare them more easily.
As mentioned in my analysis, if we select Mexico we see that their proportion of women in parliament has increased with an increase in students enrolled in education. The 2 factors are directly correlated here. However, with South Africa we see their female representation has decreased since 2010 even though their enrollment in education has increased. The two factors are inversely correlated here.
The line graph comparison made me conclude that enrollment in education does not have the same correlation with proportion of women in parliament for all countries. This inspired me to create a tool that would allow myself and others who are curious on this subject matter to be able to visualize this data for a country of their choice.
As we see in Figure 7, the aforementioned tool allows viewers to select a country and see how the proportion of women in parliament has changed in that country as well the number of students enrolled in education.
I conducted 2 moderated user tests to find out whether the presentation and analysis of my study were clearly understandable and easy to navigate through. I recruited 2 participants who were between the ages of 25-31, one with an average amount of tech-savviness and one with above average. Participants were asked to navigate through the dashboard, read the text and answer the questions proposed to them. Participants were asked whether the data they came across was close to their expectations or not. There were 6 main findings mentioned in the section below. Recommendations were made based on the findings in order to improve the usability of the product as well as determine its future direction.
User test findings and recommendations
1. “Wish there was a way to see a combination of it altogether.”
One user mentioned it would be nice to view the data for each country all at once as he found himself scrolling up and down between the bar graph for female representation in parliament and the one for gender ratio in education. A recommendation to help achieve this would be to link the “country” dimension from all datasets and present each country’s female representation, gender ratio in education and total enrollment in education all in one row as separate bar graphs over the same period of time.
2. Ratio data is confusing (the different levels, understanding ratios)
Both users were slightly confused by the section on ratio of girls to boys in education. One user was unsure if all 3 levels of education were relevant to the research and another user was having difficulty understanding what the value meant. A recommendation for this would be to present a total ratio of girls to boys in education rather than breaking it up into levels of education. However, a better way might be to keep the levels but explain what each level means (e.g. tertiary is college and higher). To help understand the value of the ratio, a statement could be added to the legend explaining the value (e.g. “1.524 means 1524 girls for every 1000 boys enrolled”) or the values could be changed from decimals to integers (e.g. 1.524 to 1524) and the title could be changed to “number of girls for every thousand boys enrolled in education.”
3. Other factors to look into : social factors, population, population gender ratio
Both users mentioned that female representation in parliament may be affected by other factors that differ for each country such as social factors, population and gender ratio of the population. One user mentioned as an example that Cuba may have more women in parliament because of a higher percentage of women than men in their population. Finding a dataset for the population of all the countries would also help as it could be combined with the education enrollment data to find what percentage of the population of each country is educated. This is a great potential future direction for this research.
4. Meaning of “parliament”
One user was confused about the meaning of parliament, especially in regards to the United States, as the word is not as commonly used here. “Do mayors of small towns count?” A recommendation to help clear this confusion would be to do research on how the term parliament differs across the world and what roles are included in the parliament of each country.
5. “Where is Seychelles?”
One user tried to find Seychelles on the first visualization, the choropleth, after coming across the name several times in the bar graphs below. This could happen with a number of countries and therefore one recommendation to help users find this information faster would be to link the countries across all visualizations. This would highlight the country across all visualizations, including the map, helping users find all the information at once on that country.
6. The information is unexpected
Users generally found the data interesting and unexpected. One of the reasons behind this research was the data being unexpected and one of the goals was to give users information they might not have expected. Therefore, this finding inspires me to continue doing research on this topic.
By visualizing the data I was able to answer my questions for this study and by conducting user tests I got a clearer direction for the future of this study. As mentioned in the User Test findings, this research has the potential to be extended much farther by looking into other factors that may affect the proportion of seats held by women in parliaments of different countries. Research could not only include more data on the population and social factors of different countries but also data on female representation in other sectors like the economy, media, sports etc.
A couple visualizations that were initiated but not completed are a line graph and a scatter plot of female representation in parliament in relation to total enrollment in education. The idea was to organize the axes with the values arranged in chronological order and have the visualization be filtered by country. So, if a country was selected, the graph would directly show how female representation changed over time with change in total education enrollment. One reason for not being able to complete this visualization was the inability to arrange the axes in chronological order while having them represent other values (e.g. total enrollment) in ascending order. Another reason was the education data would have been more relevant if it was represented as a percentage of the population.
As a tool, Tableau has certain limitations such as lagging or longer loading time for interactive visualizations and lack of customization of the user interface. However, the tool is easy to understand and navigate making it very user friendly. I found its dashboard function efficient for the purpose of my research.
There were some notable limitations of this research. One of them being that the datasets on education lacked information on certain countries for certain years. This is clearly reflected in the visualizations. Even though certain trends were able to be detected with the data that was available, consistent data would have highly improved the research. The second limitation, and a major one, is regarding time. It takes decades of education and other socioeconomic changes for countries and their cultures to progress. The data for education from one time period may be more likely to have an impact on female representation in parliament at a later time period, if at all. How might we find out how long it takes for that data to show its impact?
An overarching question for future research is developed: