According to a global software developer survey in 2022, the vast majority of developers are males, accounting for ***** percent of all respondents. Female developers amounted to only **** percent of all respondents, demonstrating the male-dominating reality of software development jobs. Gender imbalance in the tech industry The tech industry has an overwhelming gender imbalance, in which women at leading tech companies tend to be the minority. Consider Dell’s workforce and Intel’s workforce as an example. In both leading tech companies, women make up only around ** percent of the workforce. Computing related positions in the United States vary on gender breakdown, with computer hardware engineers ranking the lowest in terms of gender diversity as of late. Diversity & inclusion initiatives When tech industry CEOs and founders were asked whether diversity and inclusion (D&I) initiatives were effective, **** indicated that they were not effective. This comes at an interesting time, especially given the overall gender imbalance in the tech industry. Not to mention, the majority of senior management positions within the IT industry are dominated by men. Diversity and inclusion initiatives could help to resolve workplace harassment and unequal treatment that many female executives experience within the tech industry today.

In 2024, a global developer survey revealed that approximately ** percent of the developers identified as male, while the share of female developers globally stood at around ** percent. This majority representation of males in the developer community underscores the historical trend of male dominance in the tech industry and highlights the challenges and barriers that women may face in entering or advancing in this field.

In the Ukraine in 2020, ** percent of software developers are female. At the other end of the spectrum of European countries in terms of gender composition is Lithuania, where about ** percent of software developers are women. Notably, ***** from the top five countries with the highest share of women in software development are Baltic states. Generally, there are ***** women developers for every ten developers in Europe. Software developers are high in demand Software developers are high in demand on the job market. As software development is a broad category that includes a wide variety of specific tasks and skills to perform the job, there is a great gap between available talent and required staff. In general, software developers are involved in the development process of software, including the analysis of users’ needs or assessing the level of security to ensure that software projects meet requirements. Global gender imbalance in tech industry remains pervasive Overall, the gender composition of professionals employed in the tech industry remains imbalanced, as more males than females are generally employed by global tech companies. This observation can also be made when zooming in on technical positions. For example, women more commonly occupy user experience (UX) designer positions than developer positions.

Context. Gender diversity in software development teams impacts software quality and team climate; however, women are still underrepresented. To address this imbalance, we need to attract more women to the field and ensure they remain in these roles. One key aspect of retaining women in software development teams is their perception of career success, which can be influenced by numerous factors.
Objective. This study aims to understand factors that influence career success in Software engineering, with a particular focus on the subjective perception of career success and how gender impacts it.
Research Method. We employed a quantitative approach using open and publicly available datasets produced by other researchers. We began with a literature review to develop a theoretical model and formulate hypotheses. Afterward, we selected appropriate datasets and tested our hypotheses.
Results. We developed a model that includes career progress satisfaction (the dependent variable) and three categories of independent variables: human capital (experience, education), socio-demographic status (gender, age), and organizational environment (no organizational difficulties). We confirmed all proposed hypotheses, demonstrating that gender directly affects our dependent variable and moderates all proposed relationships.
Conclusions. This research presents a model that can be utilized by companies and individuals to define strategies for career progress. Additionally, the model can assist researchers in determining the focus of their investigations. We also provide an example of effectively reusing a publicly available dataset.

Two sets of 100k anonymized software developers from World of Code after applying the name-to-gender inference tool Wiki-Gendersort and quantifying each developer's number of contributions by commit, project, files; files are classified into 116 sub-categories according to language or development file type (e.g., programming, scripting, or markdown languages such as C, C++, Python, JavaScript, Assembly, HTML; build files such as Dockerfiles, CMake, etc; configuration files; documentation files such as README and Markdown; and libraries. One sample is stratified (50/50 split between male and female developers), and one sample is representative of the population. Wiki-Gendersort outputs M (male), F (female), UNK (unknown), INI (initialis), or UNI (unisex). The scripts used to generate the data set can be found on GitHub.

Worldwide Gender Differences in Public Code Contributions - Replication Package

This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, Worldwide Gender Differences in Public Code Contributions. In Software Engineering in Society (ICSE-SEIS'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3510458.3513011

This document comes with the software needed to mine and analyze the data presented in the paper.

Prerequisites

These instructions assume the use of the bash shell, the Python programming language, the PosgreSQL DBMS (version 11 or later), the zstd compression utility and various usual *nix shell utilities (cat, pv, ...), all of which are available for multiple architectures and OSs. It is advisable to create a Python virtual environment and install the following PyPI packages: click==8.0.3 cycler==0.10.0 gender-guesser==0.4.0 kiwisolver==1.3.2 matplotlib==3.4.3 numpy==1.21.3 pandas==1.3.4 patsy==0.5.2 Pillow==8.4.0 pyparsing==2.4.7 python-dateutil==2.8.2 pytz==2021.3 scipy==1.7.1 six==1.16.0 statsmodels==0.13.0

Initial data

swh-replica, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available at https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/. We retrieved these data from Software Heritage, in collaboration with the archive operators, taking an archive snapshot as of 2021-07-07. We cannot make these data available in full as part of the replication package due to both its volume and the presence in it of personal information such as user email addresses. However, equivalent data (stripped of email addresses) can be obtained from the Software Heritage archive dataset, as documented in the article: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli, The Software Heritage Graph Dataset: Public software development under one roof. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019. http://dx.doi.org/10.1109/MSR.2019.00030. Once retrieved, the data can be loaded in PostgreSQL to populate swh-replica.

names.tab - forenames and surnames per country with their frequency

zones.acc.tab - countries/territories, timezones, population and world zones

c_c.tab - ccTDL entities - world zones matches

Data preparation

Export data from the swh-replica database to create commits.csv.zst and authors.csv.zst sh> ./export.sh

Run the authors cleanup script to create authors--clean.csv.zst sh> ./cleanup.sh authors.csv.zst

Filter out implausible names and create authors--plausible.csv.zst sh> pv authors--clean.csv.zst | unzstd | ./filter_names.py 2> authors--plausible.csv.log | zstdmt > authors--plausible.csv.zst

Gender detection

Run the gender guessing script to create author-fullnames-gender.csv.zst sh> pv authors--plausible.csv.zst | unzstd | ./guess_gender.py --fullname --field 2 | zstdmt > author-fullnames-gender.csv.zst

Database creation and data ingestion

Create the PostgreSQL DB sh> createdb gender-commit Notice that from now on when prepending the psql> prompt we assume the execution of psql on the gender-commit database.

Import data into PostgreSQL DB sh> ./import_data.sh

Zone detection

Extract commits data from the DB and create commits.tab, that is used as input for the gender detection script sh> psql -f extract_commits.sql gender-commit

Run the world zone detection script to create commit_zones.tab.zst sh> pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt > commit_zones.tab.zst Use ./assign_world_zone.py --help if you are interested in changing the script parameters.

Read zones assignment data from the file into the DB psql> \copy commit_culture from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev ''\s$'''

Extraction and graphs

Run the script to execute the queries to extract the data to plot from the DB. This creates commits_tz.tab, authors_tz.tab, commits_zones.tab, authors_zones.tab, and authors_zones_1620.tab. Edit extract_data.sql if you whish to modify extraction parameters (start/end year, sampling, ...). sh> ./extract_data.sh

Run the script to create the graphs from all the previously extracted tabfiles. This will generate commits_tzs.pdf, authors_tzs.pdf, commits_zones.pdf, authors_zones.pdf, and authors_zones_1620.pdf. sh> ./create_charts.sh

Additional graphs

This package also includes some already-made graphs

authors_zones_1.pdf: stacked graphs showing the ratio of female authors per world zone through the years, considering all authors with at least one commit per period

authors_zones_2.pdf: ditto with at least two commits per period

authors_zones_10.pdf: ditto with at least ten commits per period

The intersection of ageism and sexism can create a hostile environment for veteran software developers belonging to marginalized genders. In this study, we conducted 14 interviews to examine the experiences of people at this intersection, primarily women, in order to discover the strategies they employed in order to successfully remain in the field. We identified 283 codes, which fell into three main categories: Strategies, Experiences, and Perception. Several strategies we identified, such as (Deliberately) Not Trying to Look Younger, were not previously described in the software engineering literature. We found that, in some companies, older women developers are recognized as having particular value, further strengthening the known benefits of diversity in the workforce. Based on the experiences and strategies, we suggest organizations employing software developers to consider the benefits of hiring veteran women software developers. For example, companies can draw upon the life experiences of older women developers in order to better understand the needs of customers from a similar demographic. While we recognize that many of the strategies employed by our study participants are a response to systemic issues, we still consider that, in the short-term, there is benefit in describing these strategies for developers who are experiencing such issues today.

A game developer survey in 2021 found that 61percent of responding game developers were men, while 30 percent were women. The share of female game developers is up from 21 percent in 2017. Approximately eight percent of game developers did not identify as either men or women.

Description

The research employed a mixed methods online survey to understand better the meaning, use, and development of academic research software at the University of Illinois Urbana-Champaign. Other objectives include understanding academic research software support and training needs to make projects successful at Illinois, as well as investigating the use of generative AI tools in using and creating research software.

At the beginning of the survey, all participants gave informed consent. The University of Illinois Urbana-Champaign Institutional Review Board (IRB Protocol no.: Project IRB24-0989) reviewed the study and gave it an exempt determination.

Data collection took place from August 2024 to October 2024. Prior to data analysis, identifiable respondent details were removed during the data cleaning process. Not Applicable and Unsure style responses were used for descriptive statistics, but these responses were excluded for inferential statistics.

Survey design

At the beginning of the online survey, a consent form was provided based on guidelines from the University of Illinois Institutional Review Board to the respondents stating the aims of the study, its benefits and risks, ethical guidelines, being a voluntary survey for participation and withdrawal, privacy and confidentiality, data security, estimated time for survey completion, and contact information of researchers for asking questions. Respondents clicked to indicate their consent. Survey questions were divided into four parts: demographic information, using software for research, creating software for research, and the protocol of citing software for research. The survey had to stop points, whereby not all questions applied to respondents, which led to different sample sizes at the stop points. At the opening of the survey, the number of respondents was 251 with the funding demographic question being answered by all respondents, while other demographic questions had between 225 and 228 respondents answering them. For the first stop question, using research software in their research, the total respondents was 212, and at the last stop question, respondents considering themselves to be research developers, the total number of respondents was 74. The last question of the survey was answered by 71 respondents. Respondents may also have left the survey for other reasons. The questions were primarily closed-type questions with single choice, multiple choice, or Likert scale, as well as a few open-ended questions. Likert scale responses were created utilizing validated scales from Vagias' (2006) Likert Type Scale Response Anchors.

Sampling

Survey Respondents’ Demographics

While most respondents were Tenure Track Faculty (34.7%, f=227), other key categories included Principal Investigator (22.4%, f=227) and Research Scientist (12.1%, f=227). Computer Science, Information Science, Mathematics, and Engineering fields combined for 16% (f=228) of the respondents surveyed, but it should be noted the remaining respondents were from various academic fields across campus from various arts, humanities, and social science fields (25%, f=228) to agriculture (10%, f=228), education (5%, f=228), economics (3%, f=228), medical sciences (4%, f=228), and politics and policy/law (1%, f=228). Most respondents were likely to receive funding from various government agencies. A more detailed breakdown of the demographic information can be found in the supplemental figures. Of the 74 respondents who answered whether they were a research software developer, most respondents did not consider themselves a research software developer, with respondents stating Not at All (39%, n=74) and Slightly (22%, n=74). In addition, open-ended questions asked for further detail about research software titles used in research, research software developer challenges, how generative AI assisted in creating research software, and how research software is preserved (e.g., reproducibility).

Table 1: Survey Respondents’ Demographics

Characteristics

Respondent (%)

Age

 18-24

 25-34

 35-44

 45-54

 55-64

 Over 64

 Preferred Not Answer

3%

14%

33%

27%

14%

7%

2%

Gender

 Woman

 Man

 Non-binary / non-conforming

 Prefer not to answer

49%

44%

2%

4%

Race

 Asian

 Black or African American

 Hispanic or Latino

 Middle Eastern or North African (MENA; new)

 White

 Prefer not to answer

 Other

12%

5%

6%

1%

67%

8%

1%

Highest Degree

 Bachelors

 Masters

 Professional degree (e.g., J.D.)

 Doctorate

6%

19%

5%

70%

Professional Title

 Tenure Track Faculty

 Principal Investigator

 Research Scientist

 Staff

 Research Faculty

 Other

 Teaching Faculty

 Postdoc

 Research Assistant

 Research Software Engineer

35%

22%

12%

8%

7%

4%

4%

4%

2%

2%

Academic Field

 Biological Sciences

 Other

 Agriculture

 Engineering

 Psychology

 Earth Sciences

 Physical Sciences

 Education

 Medical & Health Sciences

 Computer Science

 Library

 Chemical Sciences

 Human Society

 Economics

 Information Science

 Environment

 Veterinary

 Mathematical Sciences

 History

 Architecture

 Politics and Policy

 Law

18%

10%

10%

9%

8%

6%

6%

5%

4%3%

3%

3%

3%

3%

2%

2%

2%

2%

1%

1%

1%

0%

Years Since Last Degree

 Less than 1 Year

 1-2 Years

 3-5 Years

 6-9 Years

 10-15 Years

 More than 15 Years

4%

8%

11%

14%

24%

40%

Receive Funding

 Yes

 No

73%

27%

Funders for Research

 Other

 National Science Foundation (NSF)

 United States Department of Agriculture (USDA)

 National Institute of Health (NIH)

 Department of Energy (DOE)

 Department of Defense (DOD)

 Environmental Protection Agency (EPA)

 National Aeronautics and Space Administration (NASA)

Bill and Melinda Gates Foundation

Advanced Research Projects Agency - Energy (ARPA-E)

Institute of Education Sciences

Alfred P. Sloan Foundation

W.M. Keck Foundation

Simons Foundation

Gordon and Betty Moore Foundation

Department of Justice (DOJ)

National Endowment for the Humanities (NEH)

Congressionally Directed Medical Research Programs (CDMRP)

Andrew W. Mellon Foundation

22%

18%

18%

11%

9%

5%

4%

4%

2%

2%

1%

1%

1%

1%

1%

1%

0%

0%

0%

Table 2: Survey Codebook

QuestionID

Variable

Variable Label

Survey Item

Response Options

1

age

Respondent’s Age

Section Header:

Demographics Thank you for your participation in this survey today! Before you begin to answer questions about academic research software, please answer a few demographic questions to better contextualize your responses to other survey questions.

What is your age?

Select one choice.

Years

1-Under 18

2-18-24

3-25-34

4-35-44

5-45-54

6-55-64

7-Over 64

8-Prefer not to answer

2

gender

Respondent’s Gender

What is your gender?

Select one choice.

1-Female

2-Male

3-Transgender

4-Non-binary / non-conforming

5-Prefer not to answer

6-Other:

3

race

Respondent’s Race

What is your race?

Select one choice.

1-American Indian or Alaska Native

2-Asian

3-Black or African American

4-Hispanic or Latino

5-Middle Eastern or North African (MENA; new)

6-Native Hawaiian or Pacific Islander

7-White

8-Prefer not to answer

9-Other:

4

highest_degree

Respondent’s Highest Degree

What is the highest degree you have completed?

Select one choice.

1-None

2-High school

3-Associate

4-Bachelor's

5-Master's

6-Professional degree (e.g., J.D.)

7-Doctorate

8-Other:

5

professional_title

Respondent’s Professional Title

What is your professional title?

Select all that apply.

1-professional_title_1

Principal Investigator

2-professional_title_2

Tenure Track Faculty

3-professional_title_3

Teaching Faculty

4-professional_title_4

Research Faculty

5-professional_title_5

Research Scientist

6-professional_title_6

Research Software Engineer

7-professional_title_7

Staff

8-professional_title_8

Postdoc

9-professional_title_9

Research Assistant

10-professional_title_10

Other:

6

academic_field

Respondent’s most strongly identified Academic Field

What is the academic field or discipline you most strongly identify with (e.g., Psychology, Computer Science)?

Select one choice.

1-Chemical sciences

2-Biological sciences

3-Medical & health sciences

4-Physical sciences

5-Mathematical sciences

6-Earth sciences

7-Agriculture

8-Veterinary

9-Environment

10-Psychology

11-Law

12-Philosophy

13-Economics

14-Human society

15-Journalism

16-Library

17-Education

18-Art & Design Management

19-Engineering

20-Language

21-History

22-Politics and policy

23-Architecture

24-Computer Science

25-Information science

26-Other:

7

years_since_last_degree

Number of years since last respondent’s last degree

How many years since the award of your last completed degree?

Select one choice.

1-Less than 1 year

2-1-2 years

3-3-5 years

4-6-9 years

5-10-15 years

6-More than 15 years

8

receive_funding_for_research

Whether respondent received funding for research

Do you receive funding for your research?

1-Yes

0-No

9

funders_for_research

Respondent’s funding sources if they answered yes in Question 8

Who funds your research or work (e.g., NIH, Gates Foundation)?

Select all that apply.

1-funders_for_research_1

United States Department of Agriculture (USDA)

2-funders_for_research_2

Department of Energy (DOE)

3-funders_for_research_3

National Science

Gender disparities in science have become a salient concern for policy makers and researchers. Previous studies have documented a gender gap in research productivity and recognition in the sciences, and different reasons for this gap have been proposed. In this study, we examine four academic fields with different proportions of men and women in their population. We address the following questions: What is the relationship between the gendered make-up of a field and the productivity and recognition of men and women scientists in that academic field? What is the relationship between the publication patterns of men and women in different academic fields and their productivity and recognition? We find that gendered patterns of productivity and recognition favour men in man-dominated subfields (Mathematical Physics and Software Engineering), while women were more productive and highly cited in one woman-dominated subfield (Nursing), though not in another (Psychology). Nursing, a woman-gendered field, provides an interesting counterpoint to the most usual findings regarding gender disparities in academia. Our findings highlight the need to disaggregate academic fields and to bring to the forefront other disciplines that remain under investigated in analyses of gender gaps to potentially elucidate conflicting findings in the literature.

This repository contains the datasets, scripts, and materials used in the dissertation. The data is organized into three folders, each corresponding to a dissertation chapter.

📁 Chapter 3

This folder contains all files related to the analysis of grey literature on empathy in software development, based on articles retrieved from the DEV community.

Contents:

• dev_scraper.py and ImportJSON.gs: scripts used to collect articles from DEV.to.
• codebook.xlsx: Final version of the codebook used in the content analysis.
• coded_articles.xlsx: Annotated data with codes and themes.
• Additional files supporting the thematic analysis and coder agreement.

📁 Chapter 4

This folder contains data and scripts related to a survey study conducted within a software company to assess the perception and practice of empathy at work.

📂 Survey data – Empathy in SE workplaces

• Survey Empathy Workplaces Raw Data.xlsx: Raw survey data (in Portuguese) including closed and open-ended questions.
• Complete Survey Empathy Workplaces.pdf: Survey instrument with all questions and response options.
• Content analysis - Survey Empathy Workplaces: Coded responses to open-ended questions.

📂 Data analysis – Scripts

• data_cleaning.R: Script to preprocess and clean the raw data.
• Other scripts to generate descriptive statistics, non-parametric tests based on roles, gender, and other demographics, exploratory factor analysis and heatmap.

📁 Chapter 5 – Expert Survey and Framework Refinement

This folder contains the data and analysis from a second survey conducted with experts to validate and refine the empathy framework.

📂 Survey experts – data

• Complete Survey - Framework Assessment.pdf : Survey instrument with all questions and response options.
• Raw data responses.xlsx: Raw qualitative responses from the expert participants.
• Data analysis survey experts.xlsx: Thematic analysis and code application.

📂 Framework

• initial_framework: Original empathy framework model.
• frameworkv2.pdf: Second version of the empathy framework model.
• revised_framework: Final version after expert feedback.

📄 Citation

If you use this dataset in your research, please cite: Cerqueira, L. (2025) “Dissertation Empirical Package - Empathy-Guided Software Development: A Conceptual Framework of Empathy in Software Engineering”. Zenodo.

Contact

For questions or clarifications, feel free to contact me:
Lidiany Cerqueira
lidiany.cerqueira@ufba.br

Graph and download economic data for Employed full time: Wage and salary workers: Computer hardware engineers occupations: 16 years and over: Women (LEU0254692600A) from 2000 to 2024 about hardware, engineering, computers, occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

In 2024, the share of female Google employees worldwide in leadership positions amounted to 32.8 percent. The majority of leadership employees were men. Overall, about two thirds of Google employees worldwide were male.

As of June 2022, only 37.1 percent of all global Meta Platforms employees were women. The majority of employees were male. Overall, women made up 25.8 percent of tech roles and 60.5 percent of non-tech roles.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer hardware engineers occupations: 16 years and over: Women (LEU0254746000A) from 2000 to 2011 about hardware, engineering, second quartile, computers, occupation, females, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

The share of male informatics, computer science (CS), computer engineering (CE), and information technology (IT) new graduates is drastically higher than female graduates in all Europe, both for bachelor's and master's degrees. The gender distribution in Bulgaria, Romania, Estonia, and Turkey was slightly more balanced than the other countries — specially for master's graduates, where among themselves, these countries reached a rough average of ** percent males and ** percent females.

Description The Post-Pandemic Remote Work Health Impact 2025 dataset presents a comprehensive, global snapshot of how remote, hybrid, and onsite work arrangements are influencing the mental and physical health of employees in the post-pandemic era. Collected in June 2025, this dataset aggregates responses from a diverse workforce spanning continents, industries, age groups, and job roles. It is designed to support research, data analysis, and policy-making around the evolving landscape of work and well-being.

This dataset enables in-depth exploration of:

• The prevalence of mental health conditions (e.g., anxiety, burnout, PTSD, depression) across different work setups.
• The relationship between work arrangements and physical health complaints (e.g., back pain, eye strain, neck pain).
• Variations in work-life balance, social isolation, and burnout levels segmented by demographic and occupational factors.
• Salary distributions and their correlation with health outcomes and job roles.

By providing granular, anonymized data on both subjective (self-reported) and objective (hours worked, salary range) factors, this resource empowers data scientists, health researchers, HR professionals, and business leaders to:

• Identify risk factors and protective factors for employee well-being. Benchmark health impacts across industries and regions.
• Inform organizational policy and future-of-work strategies.

| Column Name Description Example Values | | | Survey_Date Date when the survey response was submitted (YYYY-MM-DD) 2025-06-01 Age Age of the respondent (in years) 27, 52, 40 Gender Gender identity of the respondent Female, Male, Non-binary, Prefer not to say Region Geographical region of employment Asia, Europe, North America, Africa, Oceania Industry Industry sector of the respondent Technology, Manufacturing, Finance, Healthcare Job_Role Specific job title or function Data Analyst, HR Manager, Software Engineer Work_Arrangement Primary work mode Onsite, Remote, Hybrid Hours_Per_Week Average number of hours worked per week 36, 55, 64 Mental_Health_Status Primary self-reported mental health condition Anxiety, Burnout, Depression, None, PTSD Burnout_Level Self-assessed burnout (categorical: Low, Medium, High) High, Medium, Low Work_Life_Balance_Score Self-rated work-life balance on a scale of 1 (poor) to 5 (excellent) 1, 3, 5 Physical_Health_Issues Self-reported physical health complaints (semicolon-separated if multiple) Back Pain; Eye Strain; Neck Pain; None Social_Isolation_Score Self-rated social isolation on a scale of 1 (none) to 5 (severe) 1, 2, 5 Salary_Range Annual salary range in USD $40K-60K, $80K-100K, $120K+ | --- | | | |

This statistic displays the results of a survey about the employability rate among IT engineering graduates across India in 2018, based on gender. The survey revealed that the role of associate in ITeS operations in hardware and networking had the highest employability rate for female graduates at **** percent. The lowest employability rate for females was found for the role of software engineer in IT product at **** percent compared to **** for male graduates.

In Canada, while some post-secondary programs are relatively gender-balanced, such as business and administration, and trades services natural resources and conservation, others are much less so. Indeed, while the proportion of women enrolled in post-secondary programs in engineering or mathematics and computer science has increased over the past decade, it remains far lower than that of men. In the ********* academic year, only **** percent of women were enrolled in engineering and engineering technology and only **** percent in mathematics and computer and information sciences.

Characteristics and median employment income of postsecondary graduates five years after graduation, by educational qualification (Classification of programs and credentials - professional degree variant), field of study (Classification of Instructional Programs (CIP) Canada 2016 - STEM (science, technology, engineering and mathematics and computer sciences) and BHASE (business, humanities, health, arts, social science and education) groupings), gender, age group and status of student in Canada (cross-sectional analysis).