Abstract
This research examines gender disparities in STEM across education, entrance into the workforce, and within the labor force. Many datasets were collected from a multitude of sources on different components of gender discrimination and were then thoroughly examined and transformed for visualization. Then, six visualizations were developed to illuminate how test scores have evolved at the education level, how the share of men and women entering professional fields have evolved moving into the labor force, and how labor force participation, fertility rate, parental leave, earnings, and technical skills in the labor force compare for men versus women and change over time. Overall, this analysis finds that gender divides are still present but are diminishing in education, entrance into the labor force, and within the workplace. Potential reasons for the narrowing gap include lower fertility rates and improved parental leave policies.
Introduction
Gender discrimination is a prominent human rights issue that manifests itself via job segregation, employment inequity, pay gaps, and lack of freedom in career choice for women around the world. In fact, it is estimated that it will take another 14.2 years to close the educational attainment and health and survival gap, 267.6 years to close the economic participation and opportunity gap, 145.5 years to close the political empowerment gap, and 135.6 years to close the gender gap globally (Global Gender Gap Report 2021). Despite these dire statistics, over the last decade and mainly in the last few years, gender disparities have been at the forefront of the media, social justice movements, and political reforms both globally and in the United States. There have been many studies, publications, and analyses conducted on the different components of gender disparities, and the majority of these study one facet at a time.
This portfolio offers a more general toolset to examine gender inequities in education, entrance into the labor force, and within the labor force by location and over time. Developed by women in tech, this analysis specifically hones in on gender disparities in STEM and assesses how test scores, entrance into professional fields, labor participation, parental leave, and salaries differ for men and women. Through a variety of interactive visualizations, this platform enables students, women, policy-makers, and stakeholders to see the presence and evolution of gender disparities and drill-down into underlying factors to understand potential causes and solutions. Due to the widespread awareness on gender inequality, it is expected that this portfolio will elucidate a narrowing gender gap in education, entrance into the labor force, and in the labor force via the factors mentioned above, but also that the disparities are still present.
Methods
In order to closely examine this topic, six datasets were leveraged to visualize gender disparities and their underlying factors. To investigate the gap at an educational level, an OECD dataset of standardized test scores for boys and girls containing PISA test scores for each subject, country, gender, and year from 2000 to 2018 was collected from the OECD’s website. This dataset contained three separate files, one for each subject - reading, science, and math. PISA is the OECD’s Program for International Student Assessment which measures 15-year-olds’ ability to use reading, mathematics, and science knowledge to meet real-life challenges (PISA). To understand the disparities into the workforce, a dataset was downloaded from the OECD’s website that contains the distribution of graduates and new entrants by field from 2005 to 2019.
Then, to explore the more macro trends in the labor force, a separate dataset was collected from the OWID that contains the percentage of women active in the labor force and the average births per woman by country from 1960 to 2020. Additionally, an OECD dataset on length of parental leave in fifty countries from 1970 to 2018 that contains minimum government granted parental leave for four separate metrics was collected. To explore the inequities in STEM and non-STEM fields, a dataset from the United States Census Bureau was collected that contains 2019 median earnings for a series of different occupations. And, finally, a Kaggle dataset on women in data science was used to examine the technical skillset between men and women. This dataset looks at the gender divide in data science between 1990 and 2018 and contains attributes on men and women’s education levels, length of time coding, perceptions of ML/AI, and use of different technical tools.
Results
Figure 1
Figure 1 contains the PISA test scores for each subject and gender over time. From the default view that contains the average test scores for all countries, math test scores have decreased from 2003 to 2018 for boys and girls. In 2003, girls had an average 488.74 score and boys had an average 499.55 score, and in 2018, girls had an average of 486.73 while boys had an average of 491.76. Although average math scores have decreased, the gap between boys and girls has also decreased as evident by the difference in values. Simultaneously, reading test scores have decreased for boys and girls, contributing to an overall decrease in average test scores from 2000 to 2018. In 2000, girls had an average of 505.4 and boys had an average of 474.2, and in 2018, girls had an average of 502.17 and boys had an average of 472.44. Although average reading scores have decreased overall, the gap between boys and girls has decreased. Lastly, science test scores have decreased for all cohorts from 2006 to 2018. In 2006, girls had an average of 493.94 and boys had an average of 496.46 while, in 2018, girls had an average of 498.54 and boys had an average of 487.03. In this case, the gap between boys and girls has reversed where girls have higher science test scores than their counterparts. Drilling down, most countries emulate this narrowing gender gap amongst subjects, but their overall trends deviate significantly. For instance, Portugal (PRT) shows that math, reading, and science test scores have increased significantly over time.
Figure 2
Figure 2 illustrates labor force entrance rates across eleven professions, both non-STEM (Agriculture, forestry, fisheries and veterinary; Arts and humanities; Business administration and law; Education; Generic programmes and qualifications; Services; Social sciences, journalism, and information) and STEM (Engineering, manufacturing, and construction; Information and Communication Technologies; Health and welfare; Natural sciences, mathematics and statistics). Specifically, the difference between the share of men and women entering the professional sphere for each field is examined for OECD countries between 2010 and 2019. A few major trends have emerged.
First, the gender disparities in entrance rates for STEM fields remain higher than in non-STEM fields. Using Natural sciences, mathematics, and statistics as a proxy for STEM fields, the majority of countries saw 1-2 percentage points more men entering the workforce than women, as shades of blue indicate that the rate of entrance for men is higher than women. This trend remains consistent across time, with the exception of 2011 and 2016 where more women than men entered the field. Such anomalies indicate that progress has been made where more women are pursuing STEM occupations, but that the progress has not been linear. In contrast, most non-STEM fields see more women entering the workforce than men with the map shaded pink across almost all countries during this timeframe. Overall, the gender gap across fields is shrinking on average. For instance, in the United States, 10.95 percentage points more men were entering Natural sciences, mathematics, and statistics than women in 2010 and 1.48 percentage points more men were entering the field in 2018.
Figure 3
Figure 3 contains three figures that examine macro trends in the labor force for fertility rate and female labor force participation. From the scatter plot on the bottom right, the female labor force participation has increased over time; the darker and larger points are more concentrated at higher female labor force participation values. Additionally, the fertility rate has decreased over time; the darker and larger points are more concentrated at lower fertility rate values. Turkey appears to be an exception to this trend, which appears to have higher labor force participation at earlier time periods. From the box plot on the top right, the fertility rate distribution from 1960-2020 appears to be highest for the U.S. Virgin Islands, Turkey, Costa Rica, Columbia, Chile, and Mexico and lowest for Italy, Switzerland, Austria, Luxembourg, and Germany. And, from the bar plot on the left, the average labor force participation from 1960-2020 has been highest for Iceland, Sweden, and Norway and lowest for Turkey, Italy, Chile, Greece, Spain and Mexico. Though there are exceptions to this trend, many countries that have lower distributions of fertility rates have higher average female labor force participation and vice versa for countries with higher fertility rate distributions. One notable exception to this trend is Italy which has a low fertility rate distribution and a low average female labor force participation.
Figure 4
Figure 4 depicts the length of minimum government granted parental leave by country over time from 1970 to 2018. It is clear that the length of parental leave (maternity, paid paternity, and total with job protection) have all increased over time. Expectedly, the first eight steps in the animation have the largest increase in granted parental leave, since they are at five year increments and not yearly increments. During this timeframe, from 1970 to 2005, parental leave increased drastically for almost all of the OECD countries. Countries with no minimum parental leave requirements, like the United States and Australia, began to enforce requirements. After 2005, granted parental leave continued to increase for the majority of countries, but at a slower rate. Interestingly, New Zealand’s total parental leave with job protection requirements seem to have decreased from 2005 to 2018 from 40 to 45 weeks while other countries like the United States seemed to have remained constant at 12 weeks. Furthermore, granted paid paternity leave has increased over time. In 1970, almost no countries had paid paternity leave requirements. In 1995, six countries had minimum requirements. In 2005 a little less than half of all countries had minimum granted leave requirements. And, in 2018, almost 75% of countries had requirements in place. As of 2018, European countries like the UK and Greece grant the most maternity leave and Asian countries like Japan and Korea grant the most paid paternity leave. And, the United States continually is at the bottom of the pack for both.
Figure 5
Figure 5 depicts the relationship between the percentage of women employed in STEM occupations and their earnings as a percentage of mens’ in 2019. The purpose of this visualization is to understand which STEM occupations have the greatest gender inequity and how the wage disparity contributes to the divide. It is clear that women get paid less than men whether or not they dominate the workforce for that profession. For instance, computer occupations all have less women employed than men (under 50% on the y-axis), and all but one of the sub-occupations pay women less than men (under 100% on the x-axis). And, healthcare occupations have more women employed than men, and all but two sub-occupations pay women less than men. These two examples illustrate that women appear to be paid less than men in all types of STEM occupations. But, as evident by the position of points on the x-axis, computer occupations (hard sciences) pay women the least as compared to men while healthcare occupations (soft sciences) pay women the most as compared to men. Simultaneously, as evident by the position of points on the y-axis, healthcare occupations have the greatest percentage of women employed while computer and engineering occupations have the least. Figure 5 suggests occupations that have more women as compared to men pay women more on average as compared to other professions.
Figure 6
Figure 6 illustrates the percentage of survey respondents of each gender using different programming tools on a regular basis. The primary goal of this visualization is to explore whether or not male and female data science and analytics (STEM specialty) professionals have the same technical skills. This visualization illustrates a similar pattern in programming tool usage across male and female data analytics professionals. Python, SQL, and R are the three most common tools for both males and females used on a regular basis while Julia, Ruby, and Go are the least common. Over one quarter of male and female data analytics professionals use Python and over one tenth of them use SQL and R regularly. Amongst these three tools, slightly more men use Python than women while slightly more women use SQL and R than men. Additionally, slightly fewer females use Javascript and Bash than men while slightly more females use MATLAB and SAS.
Discussion
These findings highlight the more systemic gender gap present in education over time; girls have historically performed poorer in STEM fields than boys while boys have historically performed worse in non-STEM fields than girls. As of 2018, these patterns seem to be dissolving and in some cases reversing. But, it appears that this gap is reversing at the cost of lower average test scores overall. This may be indicative of a cultural paradigm to encourage more girls to focus on math and science due to the underrepresentation of the fields overall. Additionally, lower average test scores may be representative of larger concerns including educational access, segregation, instruction, and curriculum. These results reveal that a greater emphasis must be placed on improving education outcomes overall, with an emphasis on math for girls and reading for boys.
The share of women entering the workforce in STEM fields appears to be increasing globally while the share of men entering the workforce in non-STEM fields appears to be increasing. These trends largely emulate those in the educational sector which suggests that student performance in school manifests into career choice. However, unlike test scores, the gender equality progress is not linear. There are aberrant years across STEM and non-STEM fields where the dichotomies reverse and more women and men are entering the fields respectively. These years raise questions about what underlying factors are causing these shifts as well as what countries turn to as a model for equality and representation in STEM employment. As the gap continues to narrow, contributing factors to inequality will become more opaque and shift from quantifiable aspects like test scores to unquantifiable ideas around biases and social norms. Looking to countries like Slovenia, Poland, and Turkey that fairly consistently have minimal differences in the share of men and women entering STEM fields will shed light on ways to close these gender gaps.
Trends in the workforce reveal that there is an inverse relationship between female labor force participation and fertility rate over time. Female labor force participation has increased over time while the fertility rate has slightly decreased. Though a causal inference cannot be drawn, it is clear that there is a correlation between labor force participation and fertility rate. One could infer that because women today have more opportunities in the workforce and are able to be more independent than ever, there may be a reduced emphasis on having a large family. Or inversely, perhaps there is a larger emphasis on family planning today in order to participate in the workforce.
Correspondingly, the length of government guaranteed parental leave has increased in the majority of countries over time. While maternity leave has been in place for most countries, paid-paternity leave has more recently been adopted and increased by countries. This illuminates the fact that gender disparities are not only female dominated; men and fathers need time to support their children, wives, and families too. Also, by offering men paid leave, governments support women returning to work, as they do not have to be the sole caretakers of the family. This trend, in conjunction with the increase in maternity leave length, enables women to support their families both physically and financially. Interestingly, the total length of parental leave with job protection has been astronomical for some European countries, even up to three years in some. Though parents might not get paid for a large part of this leave, governments are committed to supporting parents and offering job security. The job security that results from improved parental leave policies may also contribute to the increasing trends in labor force participation.
Honing in on the most recent trends in STEM professions, the majority of occupations have skewed representations with men dominating the workforce. This suggests that the narrowing gender gaps in education and entrance into the labor force have not manifested into the labor force just yet. Additionally, for almost all these professions, men make more money than women. So, perhaps women are choosing not to work in these fields because they know they’ll be paid less. Conversely, because women are not equally represented, employers might not feel obliged to pay them equally. The latter seems more plausible given the findings from Figure 5, where occupations that comprise more women pay better than those that do not. It appears as though this gap persists in STEM professions despite men and women having similar skill sets. Specifically, in data science and analytics, men and women utilize the same programming languages regularly in the workplace, largely Python, SQL, and R. Regardless of why the pay gap exists, companies must make a concerted effort to pay employees with similar experiences equitably. By not doing so, they are stifling productivity, labor force growth, and overall gender equality.
Conclusion
This research has found that gender inequality is still present but has decreased over time. Regarding education, the disparities as assessed by PISA test scores have shrunk to where boys no longer significantly outperform girls in STEM fields and girls no longer significantly outperform boys in non-STEM fields. These trends largely persist into the workforce entrance post-education. From 2010 to 2019, STEM fields have historically had more men than women entering the labor force, but that gap is diminishing. And, non-STEM fields have more women than men entering the labor force which also appears to be closing over time. Regarding the labor force, female participation in the labor force has increased while fertility rates have decreased. Simultaneously, government granted maternity leave and paid paternity leave have both increased over time. Currently, male and female analytics professionals have the same technical skills, but women are less represented and paid significantly less in almost every STEM field. Therefore, the original hypothesis that the gender gap has narrowed over time but is still present can be corroborated.
In addition to understanding how the gender gap has changed over time, the intent of this analysis was to investigate the potential factors for its evolution and presence today. Given the inverse relationship between fertility rate and female labor force participation, one potential reason the gender gap may be diminishing is due to a lower percentage of women giving birth. Correspondingly, the length of minimum granted parental leave for both men and women have increased globally. With less women giving birth and more time allotted to families who opt to have children, women are able to participate and perform better in the workforce. Although no causal relationship can be drawn from this analysis alone, there is a relationship between the narrowing gender gap in the workforce, parental leave policies, and fertility rate. The current disparities in workforce earnings and representation are likely not attributed to test scores or technical skills. This indicates that there are likely more abstract and cultural causes that contribute to the current disparity. For instance, perhaps more girls and women should be encouraged to study STEM subjects and join STEM professions.
Overall, this research can be used to raise awareness of gender inequalities in STEM and to promote policies to combat it in the future. However, one limitation of this analysis was the inability to explore additional factors that contribute to the gender gap across STEM fields, such as access to education, job segregation, and medical care. Subsequent analyses can hone in on these factors in addition to the parental leave policies and fertility rates discussed in this paper to more comprehensively understand why the gender gap persists. Another limitation of this analysis was the inability to truly assess the current-state of gender disparities, as of 2022. The most recent data obtained for programming tool usage was from 2018 while the most recent data obtained for workforce earnings and representation was from 2019. As such, future research can iteratively add the most up to date information released by these sources or obtain new data altogether.
Works Cited
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