Methods

Data

Data was obtained from the World Bank’s database. The following datasets were downloaded for both genders:

• Primary completion rate (a)
• Lower secondary completion rate (b)
• Gross graduation ratio, tertiary (c)
• Labor force participation rate (d)

For the geometry of the countries, the “World Countries Generalized” dataset published in 2022 was used as a geojson from ArcGIS (e).

Pre-processing

In order to minimize data gaps and improve comparability, it was decided to divide the analysis into time periods. This approach not only facilitates comparisons between different education and participation rates, but also allows for a more detailed examination of trends over time.

How we made our map

A white background is used and no base map is needed. This ensures that oceans in particular do not draw too much attention to themselves. A white background without labels works well to avoid distracting from the main focus. Labels are kept to a minimum to keep the focus on the data.

To compare data over time, radio buttons are added to toggle between time periods. This allows users to examine the dynamic changes in the selected time periods. Since only one time period can be displayed at a time, the choice of radio buttons makes sense.

To facilitate analysis over time and space, a classification system with as few classes as possible is used. This ensures consistency between variables and helps users efficiently identify changes without overwhelming them with too many classes. A percentile classification system is used. This means that all classes contain approximately the same amount of data. This is useful because the data ranges vary widely between different datasets, where a classification with, for example, equal intervals would not make sense.

As for the color scheme, the male and female records are distinguished by different colors. The male records are displayed in blue, while the female records are displayed in purple. This clear distinction makes it easier to identify and compare the genders. Different values of blue and purple are used for classification, with lighter values representing lower rates and darker values representing higher rates. This means that the colors can always be assigned to the corresponding genders. The color of the gender selection button is adjusted accordingly. A dark grey color has been chosen for countries with no data to ensure that they can be distinguished from countries with very light colors (low rates). A legend helps the user identify the data ranges that each color value represents and indicates the meaning of the dark grey color (no data).

Pop-up windows are used to give the user more information about each country. Originally, it was considered to display a pop-up graph below the map with all the data for the country to allow a direct comparison between the development of different levels of education and labor force participation. During implementation, it was quickly realized that the map would be too cluttered with the second graph. Therefore, it was decided to simplify the pop-up and display only the country name and the rate of the selected dataset. To allow comparison between different countries, there is a graph showing the evolution of the selected dataset for several selected countries. This should help the user to understand the spatial variations of the selected dataset.

To ensure that users stay within an appropriate range, it is important to set zoom limits and provide a way to reset the view to its original setting. These measures help improve usability and user experience. Setting zoom limits prevents users from zooming in or out too far, which can distort the view or make it unusable. Zoom limits define the maximum and minimum zoom factor that is allowed. In addition to zoom limits, it is important to provide a way for users to reset the view to its original setting. This can be accomplished with a "Reset" button. If users have lost the overview due to excessive zooming or panning, they can quickly and easily return to the original view.

Target audience

The target audience of this website, and this map in particular, are NGOs working in the field of education and gender equality. The map is designed with the assumption that users are already somewhat familiar with the topic. It is intended to serve as a tool to provide a broad overview of recent progress in women's educational attainment and labor force participation, and to allow for comparisons over time, space, and between women and men. As a quality check, the map was shown to an NGO for feedback. It was suggested that more detailed information about the data be included below the map so that the user knows, for example, exactly what proportion of the population the data refers to. This is important information for interpreting the map, especially from an NGO's point of view.

Limitation

Data

Due to the limited availability of data, it is important to emphasize that the rates reported for lower secondary and primary completion are approximations. They serve as upper estimates and should be interpreted in the context of their respective limitations.

The reasoning behind rates above 100% includes several factors. The numerator may include students who had to repeat a grade as well as those who started school early. The denominator, on the other hand, refers to the number of children at the entry age for the last grade of the respective level of education (a, b, c).

It should be noted that the International Labor Organization (ILO) collects data on the labor force in a variety of ways. These include labor force surveys, population censuses and enterprise censuses and surveys. Labor force surveys are considered to be the most comprehensive source of internationally comparable data on the labor force. They cover a wide range of people and occupations, including those with multiple occupations (d).
In contrast, population censuses often have limited information on the economic characteristics of individuals and can therefore provide less detailed insights. Establishment censuses and surveys focus mainly on the employed population and therefore cannot cover all aspects of the labor market, such as the unemployed or those working in the informal sector (d).
It is important to note that the data collected may vary depending on the method and scope of the survey. They should therefore be interpreted with caution and may not always provide a complete or consistent picture of a country's labor force (d).

Technical issue

A true-surface projection, such as the Robinson projection, was originally intended to be used as a map projection. The advantage of the Robinson projection is that it looks very natural. It ensures that all countries are shown according to their size, which is ideal for comparing countries based on their area. However, technical issues did not allow for the projection to be changed, so the map uses the WGS84 reference system with a Mercator projection.