Honeypot

2018 Women in Tech Index

Gender parity has been at the forefront of public, private and professional debate in the past several months, with events such as The Women’s March highlighting equal rights’ injustices around the globe. At Honeypot, we are committed to actively promoting women in tech. With the gender wage gap widely reported as affecting women across the world, we decided to research the specific role that gender parity plays in the technology landscape. In sharing this study, we aim to highlight the barriers and opportunities for women in the IT industry.

The study focuses on 41 countries in the OECD and EU, and offers comparable data relating to both the tech industry and the wage gap. The data covers areas such as:

  • Gender in the Overall Economy: factors such as percentage of women in work and the overall gender income parity.
  • Women in Tech: as measured by the number of women in IT positions compared to the overall numbers of people in tech.
  • Opportunities for Women in Tech: calculated by comparing the difference between the percentage share of women in the general workforce, and the percentage of women in the technology sector. In addition, the study took into account the percentage of female STEM graduates.
  • Tech Wage Gap: difference in gender wage gap between women working in the tech industry and the overall workforce at large.
  • Female Career Progression: as judged by the percentage of women in managerial and ministerial positions.

Finally, to pinpoint any potential barriers which might hinder a woman’s progression and to highlight the best opportunities for women, we looked at the Gender Inequality Index. This analyses women’s reproductive health, empowerment and labor market participation to conclude overall parity. To determine if equality has increased or decreased in recent history, we then calculated the difference between the current available wage gap data, as compared to five years previous.

     “Gender parity in the workplace is not just an ethical or moral issue, but also an economic one: McKinsey found that $12 trillion could be added to global GDP by 2025 by advancing women’s equality. As tech recruitment specialists, we are often confronted with the gender imbalances of the industry, which are fully exposed in this study.” says Emma Tracey, Co-Founder at Honeypot. “The results reveal the countries which have the most to offer women looking to progress in the tech industry, with Portugal, The United States and Latvia highlighted as the top three nations that have taken positive steps towards gender parity in the technology field in terms of fairer wages. However, with the proportion of female tech workers remaining under 30% across the board, we hope that this study will enrich the conversation concerning equality in this industry and inspire more women to seek out opportunities in tech.”

The table below is ranked in order to show which countries offer the best opportunity for women in tech by comparing the difference between the overall gender pay gap and the pay gap in the technology industry. A positive number demonstrates that income disparity in tech is less than the overall average in that country, indicating that female tech workers have been afforded fairer wages.

Total Workforce (Millions)
Female Workforce (Millions)
% Women
% Women Legislators, Senior Officials, and Managers
% Women in Parliament
% Women in Ministerial Positions
Overall Workforce Average Wage (in €)
Women's Average Wage (€)
Gender Pay Gap (%)
Tech Workforce (Thousands)
% Workforce in Tech
Female Tech Workforce (Thousands)
% Women in Tech
% Difference of Women in Workforce and Women in Tech
Female STEM Graduates (%)
Tech Average Wage (€)
Tech Average Wage for Women (€)
Gender Pay Gap in Tech (%)
% Difference of Overall Gender Pay Gap and Gender Pay Gap in Tech
Gender Inequality Index (Score, 0 = equality)
Gender Pay Gap 2010
Comparison of Gender Pay Gap From 2010 To 2015
 Workforce TodayTech Industry TodayInequality & Opportunity
#Country
1Portugal5,182,5348,78%32,4%34,6%28,6%19.86816.22118,36%108,802,10%17,5016,08%-32,69%30,56%31.49127.99611,10%7,26%0,09113,13%5,23%
2United States159,1974,4346,76%43,5%19,4%25,9%48.72539.52418,88%6049,113,80%1488,6424,61%-22,15%24,24%79.59570.15311,86%7,02%0,20318,81%0,07%
3Latvia0,990,5050,25%44,4%18,0%23,1%18.13515.05217,00%19,701,99%4,9024,87%-25,38%16,67%32.79229.21810,90%6,10%0,19117,28%-0,28%
4Finland2,681,2948,14%33,8%41,5%62,5%34.12328.09117,68%162,306,04%35,6021,93%-26,20%20,00%43.98538.04713,50%4,18%0,05619,59%-1,91%
5France29,5614,2248,13%31,5%25,9%50,0%34.82429.32115,80%1003,803,40%181,6018,09%-30,03%25,37%47.99642.33311,80%4,00%0,10212,36%3,44%
6Sweden5,282,5147,62%39,4%43,5%52,2%34.68129.82614,00%310,805,89%64,7020,82%-26,80%22,48%45.08240.30310,60%3,40%0,04814,86%-0,86%
7United Kingdom33,2315,5146,69%35,5%29,6%22,5%34.69628.12118,95%1608,204,84%260,6016,20%-30,48%31,03%47.90039.85316,80%2,15%0,13119,37%-0,41%
8Estonia0,690,3448,54%30,6%23,7%46,2%19.13313.98626,90%34,104,93%6,4018,77%-29,77%21,26%29.37121.88125,50%1,40%0,13127,75%-0,85%
9Spain22,8210,6146,48%31,5%40,1%30,6%30.24025.73414,90%557,602,44%85,8015,39%-31,10%25,93%39.32833.94013,70%1,20%0,08114,85%0,05%
10Cyprus0,400,1948,61%22,5%19,4%9,1%28.73524.71214,00%7,901,99%1,6020,25%-28,36%28,57%32.76028.33713,50%0,50%0,11616,80%-2,80%
11Iceland0,200,0946,87%38,3%41,2%44,4%45.34739.12713,72%7,703,92%1,7022,08%-24,79%27,54%45.34738.77214,50%-0,78%0,05116,01%-2,29%
12Turkey30,529,6331,55%13,0%15,3%3,8%18.62617.3446,88%245,200,80%24,309,91%-21,64%37,11%40.77237.3408,42%-1,54%0,3283,10%3,78%
13New Zealand2,601,2347,39%40,1%31,5%33,3%31.91229.3847,92%96,133,70%17,2417,93%-29,46%27,01%40.62536.6299,84%-1,92%0,1587,01%0,91%
14Netherlands8,974,1646,42%25,9%37,5%46,8%42.79535.90516,10%422,204,71%65,7015,56%-30,85%18,70%56.62746.20818,40%-2,30%0,04417,84%-1,74%
15Canada19,449,2047,32%35,5%25,9%30,6%39.20631.90318,63%900,304,63%224,0024,88%-22,44%24,81%54.49843.03421,04%-2,41%0,09818,98%-0,35%
16Croatia1,760,8146,04%23,1%15,3%20,0%19.31617.30810,40%52,302,98%6,9013,19%-32,84%26,47%27.83824.21913,00%-2,60%0,1415,70%4,70%
17Ireland2,190,9945,09%34,2%22,5%28,6%41.86235.92814,17%78,103,56%14,8018,95%-26,14%24,81%59.31349.05217,30%-3,13%0,12713,37%0,80%
18Austria4,492,1046,82%29,6%30,6%30,6%39.11931.54119,37%178,603,98%30,7017,19%-29,63%20,63%53.86541.63822,70%-3,33%0,07821,59%-2,22%
19Bulgaria3,311,5546,91%36,7%20,6%35,1%11.7559.94515,40%80,902,44%24,5030,28%-16,63%28,57%26.82921.67819,20%-3,80%0,22313,00%2,40%
20South Korea27,2511,5342,31%10,7%17,4%5,7%26.24316.48737,18%1008,153,70%161,4116,01%-26,30%21,26%33.40917.98141,17%-3,99%0,06739,61%-2,43%
21Mexico53,6820,5038,20%35,9%42,5%17,4%12.40210.33516,67%461,290,86%65,6114,22%-23,97%29,08%15.78812.52020,70%-4,04%0,34511,63%5,04%
22Norway2,771,3047,03%35,9%39,8%47,1%43.45138.66611,01%113,104,09%22,0019,45%-27,58%19,35%55.96747.34815,40%-4,39%0,05312,08%-1,07%
23Switzerland4,842,2646,60%35,1%32,0%42,9%48.70040.08017,70%216,304,47%31,6014,61%-31,99%21,88%61.53647.81422,30%-4,60%0,04018,93%-1,23%
24Chile8,683,5641,04%25,4%16,0%34,6%23.03218.18321,05%321,093,70%49,8615,53%-25,51%15,97%29.32021.65326,15%-5,10%0,32216,00%5,05%
25Israel3,931,8647,30%32,9%26,5%18,0%27.55921.54321,83%236,276,02%25,9911,00%-36,30%24,81%35.08425.57027,12%-5,29%0,10320,70%1,13%
26Belgium4,982,2946,11%32,4%39,4%23,1%40.16537.5556,50%193,803,89%27,3014,09%-32,02%18,03%49.86743.98211,80%-5,30%0,0738,62%-2,12%
27Germany43,0419,9546,35%29,1%36,3%33,3%37.57530.48618,87%1541,103,58%255,5016,58%-29,77%21,26%47.83535.87625,00%-6,13%0,06619,44%-0,57%
28Japan66,7328,9243,34%11,5%9,9%22,5%31.68223.52925,73%3136,354,70%404,8712,91%-30,43%15,25%40.33227.44131,96%-6,23%0,11628,68%-2,95%
29Denmark3,031,4347,30%27,0%37,5%26,5%42.59038.13110,47%119,403,94%23,9020,02%-27,28%28,57%57.47947.53517,30%-6,83%0,04112,40%-1,93%
30Australia12,675,8846,45%36,3%26,5%17,4%42.17136.68913,00%482,623,81%135,1328,00%-18,45%23,66%51.70641.36520,00%-7,00%0,12014,04%-1,04%
31Malta0,210,0839,17%27,0%13,0%7,4%27.68324.74810,60%7,103,35%0,8011,27%-27,91%20,00%37.49430.52018,60%-8,00%0,2177,20%3,40%
32Slovenia0,990,4646,62%37,5%36,7%43,8%28.32226.0288,10%32,103,23%5,6017,45%-29,18%21,88%41.82735.05116,20%-8,10%0,0530,95%7,15%
33Hungary4,592,1045,71%40,5%9,9%0,0%17.58615.51711,76%158,103,45%20,7013,09%-32,61%18,03%28.97522.60122,00%-10,24%0,25211,99%-0,23%
34Italy25,7710,9242,37%26,5%31,0%43,8%28.67227.0955,50%584,802,27%83,1014,21%-28,16%33,33%36.25429.94617,40%-11,90%0,0857,62%-2,12%
35Czech Republic5,352,3744,35%29,6%20,0%18,7%19.21515.47219,48%180,903,38%20,2011,17%-33,18%23,08%34.30123.15332,50%-13,02%0,12918,70%0,78%
36Romania9,163,9342,87%31,5%13,8%14,5%12.27511.5635,80%167,701,83%44,1026,30%-16,57%31,51%23.67019.14919,10%-13,30%0,3398,80%-3,00%
37Luxembourg0,280,1345,32%17,4%28,6%26,5%50.73547.9455,50%10,803,88%1,5013,89%-31,43%28,57%58.93247.73519,00%-13,50%0,0756,65%-1,15%
38Slovak Republic2,761,2545,24%31,5%20,0%0,0%19.04116.49413,38%73,202,65%6,809,29%-35,95%24,81%24.24117.52627,70%-14,32%0,17914,85%-1,47%
39Greece4,802,1544,70%25,9%20,0%9,9%20.35018.18810,62%51,201,07%6,5012,70%-32,01%30,07%25.55619.06525,40%-14,78%0,11913,59%-2,96%
40Lithuania1,480,7651,17%39,8%23,7%21,3%18.58915.94914,20%34,102,31%8,5024,93%-26,24%17,36%30.89221.77929,50%-15,30%0,12112,62%1,58%
41Poland17,267,7544,88%40,1%27,5%28,1%20.99619.3797,70%431,802,50%62,6014,50%-30,38%25,93%36.15226.93425,50%-17,80%0,1375,85%1,85%

METHODOLOGY

This study focuses on 41 countries which are part of the OECD and the EU, due to comparably collected data relating to the technology industry and the gender wage gap.

Criteria

  • Total Workforce (Millions): Annual labour force, Persons, in Millions. Source: OECD Statistics, Eurostat.
  • Female Workforce (Millions): Annual labour force, Persons, in Millions, Women. Source: OECD Statistics, Eurostat.
  • % Women: The number of women in the labour force, depicted as a percentage.
  • % Women Legislators, Senior Officials, and Managers: The percentage of women in senior or managerial positions, with a higher percentage indicating a higher level of parity in terms of career progression. Taken from the World Economic Forum Report: Female, male legislators, senior officials and managers (%): Major Group 1 of the International Standard Classification of Occupations (ISCO-08). Source: ILO, ILOSTAT database, employment by occupation, 2016, or latest available data.
  • % Women in Parliament: World Economic Forum Report: Percentage of women in the lower or single house.Source: the Inter-Parliamentary Union, Women in National Parliaments. Data reflects information provided by National Parliaments by 1 September 2016.
  • % Women in Ministerial Positions: World Economic Forum Report: Percentage of women holding ministerial portfolios, such as Prime Minister, and Minister of Finance. Some overlap between ministers and heads of state that also hold a ministerial portfolio may occur. Source: The Inter-Parliamentary Union, Women in Politics 2015, reflecting appointments up to 1 January 2015. Data is updated every two years.
  • Overall Workforce Average Wage (), Women's Average Wage (), Gender Pay Gap (%): The average wage, across all professions, for both men and women and the percentage difference between them, known as the Gender Pay Gap, using most current available data (2015). Sources: OECD, Eurostat. Average of both sources. Eurostat: difference between the average gross hourly earnings of men and women expressed as a percentage of the average gross hourly earnings of men. OECD: difference between median earnings of men and women relative to median earnings of men. Data refers to full-time employees and to self-employed workers.
  • Tech Workforce (Thousands): ICT Persons, Thousands. Sub-major group 25 of the International Standard Classification of Occupations (ISCO-08). Professionals: Information and communications technology professionals. By definition, the information and communication sector has been created, combining activities involving production and distribution of information and cultural products, provision of the means to transmit or distribute these products, as well as data or communications, information technology activities and the processing of data and other information service activities. The main components of this section are publishing activities, including software publishing (division 58), motion picture and sound recording activities (division 59), radio and TV broadcasting and programming activities (division 60), telecommunications activities (division 61) and information technology activities (division 62) and other information service activities (division 63). Source: Eurostat.
  • % Workforce in Tech: The percentage of people (of all genders) working in the information and communication technology sector out of the overall labour force.
  • Female Tech Workforce (Thousands): Number of women in the information and communication technology sector out of the total labour force.
  • % Women in Tech: The percentage of women working in the information and communication technology sector out of the total labour force working in ICT.
  • Female STEM Graduates (%): The percentage of STEM graduates who are female, taken from the World Economic Forum Report. Source: UNESCO’s Institute for Statistics database (accessed September 2016). Measures the percentage of female and male graduates in ISCED 5-8 programmes from Science, Engineering, Manufacturing and Construction (% of total number of graduates).
  • Tech Average Wage (): Average annual wage in ICT (as defined above). Annual in USD PPP (Purchasing power parities), adjusted to OECD PPP wage level and EU PPP wage level. Sources: OECD, Eurostat, local reports.
  • Tech Average Wage for Women (): Average annual wage in ICT (as defined above), women, in USD PPP (Purchasing power parities), taking into account the wage gap in ICT and average wage in ICT. Sources: OECD, Eurostat, local reports.
  • Gender Pay Gap in Tech (%): Wage gap in the ICT sector (as defined above). Sources: Eurostat, OECD, local reports.
  • Gender Inequality Index: A score of 0 = equality, the higher the score, the worse the inequality. The closer the score is to 0, the more equal a country is. Gender inequality Index (2015). Source: Human Development Report.
  • Gender Pay Gap 2010: Gender pay gap (as defined above), data from 2010. In the case of Chile: 2011 as 2010 was not available.
  • Comparison of Gender Pay Gap From 2010 to 2015: Difference between the wage gap from 2010 and the wage gap from most current available data 2015. A positive number implies a positive increase, I.E. the gender pay gap has increased. A negative number implies that the gender pay gap has decreased.
  • Exchange rate correct as of 26.02.2018. 1 USD =0.81 Euro, 0.71 British Pound.