Defining the Gender Gap with Data

A Closer Look at the Data Behind Global Gender Disparity

To increase awareness and inspire continuous change, SAP is looking to the power of Big Data. Utilizing technology such as the SAP HANA database, SAP launched the Data for Good Initiative, which is a series of data visualization articles being developed around the United Nations’ Sustainable Development Goals. Starting with Goal #5, “Gender Equality,” these interactive data visualizations are created by analyzing hundreds of complex data sets gathered from government agencies and social organizations. The analysis helps to facilitate a data-driven discussion of the sustainable development goals and potential solutions, while encouraging readers to interact with the data and think critically about the content.


The world is buzzing with gender disparity conversations. Some people are outraged, some people are in disbelief, but most are motivated. Across the globe, gender disparity gaps are shrinking but slow moving.

The United Nations created the Sustainable Development Goals to “end poverty, protect the planet and ensure prosperity for all.” The fifth goal set out by the UN is gender equality, which includes eliminating gender violence, providing equal opportunity and representation in leadership positions (government and private enterprise), and universal access to reproductive health.

Using data collected from the United Nations, the Organization for Economic Cooperation and Development, the World Bank, and the World Economic Forum, we compared the rate of change of women in the workforce with a variety of growth indicators, including women in managerial positions, women on boards, and wages. We hypothesized that although women are entering the workforce at greater numbers, the increase is not equally reflected in these other indicators.

Data: Disproving Misconceptions

The gender gap discussion is full of misconceptions, especially when trying to identify the root causes of the disparity. Many data sets are incomplete and don’t provide proper context, losing nuances and greater implications in the process. Many data sets show gender disparity improvement, but don’t take into account that there are more women in the workforce.

False: Fewer Women in Leadership is a Result of Fewer Women In the Workforce

While men outnumber women in the workforce by nearly a third (ratio of female to male labor force participation is 67.445),¹ many use that fact to explain why more women aren’t represented in private and public leadership positions. While this may have been true when the ratio of men to women was higher, the data shows that more women entering the workforce does not directly correlate with more women on boards.

We selected the five countries with the highest increase in percentage of women on boards (of publicly traded companies, for which data was available) and analyzed it against the percentage of women in the workforce. If there was a direct correlation, we would expect to see an increase of women on boards matching the increase of women in the workforce over time, but we don’t.

False: Men Are More Educated

In many countries, women earn more higher education degrees than men – especially when given the opportunity for education early in life. In fact, education is a good example of women surpassing men in earned achievements when given the opportunity.

When women are given the opportunity to enroll in primary education in greater numbers, there is a direct correlation to the number of women entering and completing tertiary education.

If the wage gap was mainly impacted by a lack of education, we would expect to see a correlation with the wage gap, women in managerial positions, and women on boards. But the gap is decreasing at a much slower pace.

Show Visualization

How to Use: Use the drop-down menu to select a country or set of countries to view the corresponding chart, or simply click on the map to select your desired country. You can also customize the chart data by selecting or deselecting parameters on the legend, or changing the scale on the x and y axis. Using the button in the upper right-hand corner of the chart, you can download the chart as an image, add annotations, or download the raw data.

About the Data: We collected data from the United Nations, the Organization for Economic Cooperation and Development, the World Bank, and the World Economic Forum to compile these charts. The data set used included: the wage gap, share of employed who are managers by sex, gross enrollment ratio in tertiary education by sex, women’s board seat percentage on publicly traded companies, labor force participation, country data, and global gender gap data. We filtered the data for those relevant to our study and standardized it for easy comparisons across all charts. The central tables used for analysis were countries and gender gap. The country data is for country name normalization and to join other data sets using various country identification strings. The gender gap data provides a basis for the year series and also provides a way to rank countries by best and worst gender gap scores.

The top indicated countries are those with the highest gender gap index rating assigned by the World Economic Forum in 2016. The higher the number, the better the country scored on the following indicators: health, economy, politics, and education.

Based on our analysis, we found that the wage gap is not impacted by a lack of education, because then there would be a correlation with the wage gap, women in managerial positions, and women on boards. We did not find such a correlation.

We’d also expect to see similar earnings between men and women at various levels of education, but men outearn women at every level.⁴ According to our data, women attend higher education at greater rates than men but still make less money and move into leadership positions in much smaller numbers.

Analysis: Why Aren’t We Making Faster Progress?

While the wage gap is shrinking and women are entering more leadership positions, progress is slow. Traditional gender norms still limit women’s earning potential and keep them from career growth.

Unpaid gendered responsibilities

There are two large factors to consider when looking at gender-specific responsibilities outside of work: childcare and everything else. Work done outside the paid workforce, while influenced by societal norms that vary country by country, is still dominated by women. Besides childcare, this includes things such as household chores, caring for family members, running errands, and so on.

Globally, women spend an average of 271 minutes on unpaid work per day.⁵ In the United States, women spend 243.2 minutes of unpaid work per day compared to men, who spend 150.2 minutes on average.⁶ That’s almost 11 hours more per week than men. At US$26.82 an hour (what the average American earns per hour),⁷ women would earn an extra $15,000 a year.

In Asia and the Pacific, men perform the lowest share of unpaid care work of all regions which is equal to 1 hour and 4 minutes.

The weight of additional work weighs heavily on women and affects the gender disparity in the workplace. What affects it most, however, is childbirth.

Often dubbed the “motherhood penalty,” women face the largest wage gap increase during their childbearing years (around ages 26–35).

One study analyzed Denmark’s economic earnings in relation to childbirth and showed that women had a severe drop-off in earnings after having their first child. The same study shows that men did not face the same penalty.⁸

Conclusions from this data are mixed. There are many factors that could contribute to this drop-off, including women choosing to leave the workforce, switching to shorter hours, or being perceived as less invested in their jobs. Maternity leave is often touted as a possible solution, but Denmark has much more progressive policies than most, with up to 52 weeks of partial paid leave available for parents.

Gender-specific jobs

Societal norms have a big influence on the type of jobs women get. Whether applying and not getting traditionally male-dominated positions, or not attempting to go into those fields at all, this greatly affects the women’s earning potential.

Male-dominated careers are often more valued than traditionally female-dominated careers that tend to be based in more nurturing or domestic roles. Despite the notion that these are unskilled jobs, who holds that position will actually change the jobs’ value. As demographics shift, so do wages – they drop when jobs shift from male-dominated to female-dominated.⁹

Solution: Progress Takes Effort

Knowing the root causes helps individuals and organizations come up with solutions to solve the problem. But beyond humanitarian reasons for equal pay, companies benefit from women in leadership positions. In a McKinsey report, companies in the top quartile for gender diversity were more likely to outperform on profitability and value creation. In fact, companies with the greatest proportion of women on their executive committees earned a return on equity 47% higher than did those with no female executive members.¹⁰ So, how are these shifts happening, and what can companies do to promote equal gender representation?

Organization-led shifts

In an effort to fill the gaps, organizations are stepping up and taking the lead. There are many organizations that exist to train and support women in typically male-dominated careers, such as STEM fields. Girls Who Code, Million Women Mentors, and many others provide training, encouragement, and mentorship for young women entering technical fields. Simply seeing that women are encouraged to diversify their career interests can be enough to sway young girls. But these organizations can’t create change without buy-in from the private sector.

Organizational shifts also start from the top. Promoting women into leadership has positive impacts on organizational health, including profits. Things such as improved maternity and paternity policies, flexible schedules, and remote work can improve the gender pay gap and create more supportive workplaces for women.

Societal shifts

In the United States, the gig economy and independent businesses are closing the wage gap faster than traditional industries. Women are able to work with their own schedules and set their own prices, instead of relying on companies to establish their earnings.

Government programs can also help minimize the wage gap. For example, many members of the European Union employ caregiver credits or pension credits to women who take time out of the workforce to care for children or other family members.¹¹ These credits seek to offset the fewer number of years women might spend in the workforce due to these unpaid obligations.

Data Tells the Real Story

This data tells a story, but there are still many blind spots that better data could fill. Without better data, many of the discussions around gender disparity are ill informed and incorrect. While women are becoming more involved and more educated, other equality indicators aren’t improving fast enough.

Taking advantage of new data platform technologies that allow data from various sources and of various formats (structured and unstructured – such as graph and text) enables us to analyze data more deeply and easily than before so we can make decisions based on facts and improve the future for gender equality and other UN 17 goals.

The insights in this article were brought to you by data analysis and modeling, powered by the SAP HANA database.

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