of the people doing ICT work in Switzerland are women, 2024.
- Studying ICT · 2025/26
- The difference
- 20.9%
- −7.1 pp
ICTinformation and communication technology
Women are a fifth of Swiss ICT students and a seventh of ICT jobs.
More women than ever qualify in tech: 20.9% of the people studying ICT at Swiss universities and universities of applied sciences are women (2025/26), and 26.7% of Matura graduates in physics and applications of mathematics (2025). Fewer do the work: 13.8% of people in an ICT job are women (2024). That is about where the lecture halls stood five to twelve years ago.
In briefWhat we can measure, we can make visible. What we can track, we can influence. And what we understand, we can change.
Five findings, and the case for a Women in Tech Index.
Read the summaryClose the summary+
What we can measure, we can make visible. What we can track, we can influence. And what we understand, we can change.
Five findings, and the case for a Women in Tech Index.
Switzerland’s technology sector carries much of the country’s competitiveness and innovation capacity, demand for qualified people keeps growing, and AI is reshaping the skills and roles inside it. Women’s participation in technology is therefore not only a question of equity — it is a question of talent, competitiveness and innovation.
This preliminary study sets out an evidence-based baseline of where Switzerland stands. The picture is encouraging and incomplete at the same time: the education pipeline is improving at every stage, and that progress is not yet translating into the workforce.
20.9% of the people studying ICT in Switzerland are women (2025/26) — 21.4% at the universities, 20.3% at the universities of applied sciences. In head counts that is 9.5× more women at the universities than in 1996, and 9.3× more at the universities of applied sciences than in 2000. The shares are still low; nothing in the series suggests the climb is flattening.
26.7% of Matura graduates taking physics and applications of mathematics are women (2025) — off the 29.4% peak of 2024, though the long-run trend is still clearly up. The technical vocational Matura, the other main route, sits at 15.0% (2025) and has stopped rising.
13.8% of the people doing an ICT job are women (2024, counted by occupation). Counted by sector instead — everyone employed at an ICT firm, sales and HR included — the share is 23.7% (2025). The two are never comparable in level, and neither is moving: the sector stood at 23.8% in 2011, 14 years earlier.
On the university route the largest single loss is at the door: 23.9% of PAM Matura graduates against 15.9% entering an ICT bachelor, −8.0 pp. A second loss comes between master graduation and work, 20.1% to 13.8%. The vocational route does better at the door — 17.3% entering an FH ICT bachelor against 13.2% of technical vocational Matura graduates, +4.1 pp — which suggests that more entry routes bring more women in. It loses them at the same last step.
Women are 13.8% of ICT occupations in 2024. Over the last twelve years that share has risen +0.33 pp a year, measured end to end on the pooled two-year windows; extended, it reaches 20% around 2043.
Extrapolation is method-dependent: a least-squares fit on the annual survey points over the same years is steeper, +0.51 pp a year, and reaches 20% around 2035. Neither is a forecast — both show only where the trend leads if nothing changes.
Sources: [1] [2] [3] [4] [7] [11] [13] — full list in Methods.
Why Switzerland needs a Women in Tech Index
The relevant data already exists, but it is fragmented across sources, classifications and time periods. Without a consistent, regularly updated benchmark it is hard to see where progress is happening, where talent is being lost, and where action would have the greatest effect.
The Index is intended as a shared reference point for business, education, policy and research — a way to track change over time and decide on evidence rather than assumption. Measurement is not the objective in itself; it is where informed action starts.
This preliminary study lays the analytical and methodological groundwork: what can already be learned from official statistics, a common baseline, and the areas worth following. It covers ICT and rests primarily on figures published by the Swiss Federal Statistical Office. Future editions are intended to widen the scope to STEM.
The data analysis and this study were carried out at the Lucerne University of Applied Sciences and Arts, with a working group of TechFace, Women in Tech Switzerland, Women in Robotics Switzerland and Women in Data Science Zurich.
Because when progress becomes measurable, it becomes actionable.
The share of women is rising on both routes.
There are two routes in. The academic one runs through the Matura with physics and applications of mathematics (PAM) as a core subject; the vocational one through a technical Berufsmaturität and an apprenticeship. The share of women is rising on both, and at every stage after them: from a low base. No national aggregate has yet reached one third. These two are not the only ways in — see §09 for the routes this analysis does not follow.
The PAM share rose, then dipped in 2025
From 21.0% in 2008 to 26.7% in 2025: real, measured progress that coincides with federal and cantonal MINT-promotion efforts. The climb is not smooth. The series reached 29.4% in 2024, its highest value in eighteen years, then gave back 2.7 pp — one year, on a national cohort of about 2,500, so read it as a wobble and not yet a turn. The long-run trend is still up, and every year of it sits below one in three.
Universities and UAS are converging
Universities: 3.1% of ICT students in 1980, 21.4% today. The UAS track started lower and is catching up; the gap between the two has narrowed from 6–7 points to about 2. Both curves steepen from roughly 2015.
The other route in: the technical Berufsmaturität
Pooled across both routes, the share grew from 12.2% in 2005 (449 of 3'678) to 15.0% in 2025 (497 of 3'317). It has stagnated since the 15.4% peak in 2022, and the rise flatters the picture: over these two decades the number of women grew by only 48 (+10.7%) while the number of men fell by 409 (−12.7%). The rising share reflects a shrinking male cohort as much as a growing female one. Women take the BM1 route, alongside the apprenticeship, more often than BM2 afterwards.
The PAM share varies widely by canton
Switch between the map and a ranked list, and choose a period: pooled 2017–2025 as the report shows it, or drag the slider to watch a single year move.
Pooled over 2017–2025, Uri leads the reliable cantons at 36.8% and Schaffhausen trails at 11.8%. As report Figure 3. Every canton clears 50 graduates once pooled, so none is hatched. Drag the slider for a single year, 2008–2025 — useful for the trend, but small cantons rest on very few graduates in any one of them. Candidates of the canton-independent Swiss Maturity Commission are counted in the national total but appear on no canton.
The ICT workforce has not changed.
Women’s share of ICT-sector employment, meaning everyone working at an ICT firm, is lower today than in 1991. It fell through the 1990s and 2000s and has sat between 23% and 24% since 2011. §03 counts the same workforce a second way, by occupation, and finds a much smaller number.
Education and employment, on one axis
Workforce = everyone employed at ICT firms (NOGA 61–63), including non-technical roles. §03 counts ICT jobs instead. Counted as employed persons, part-time and full-time alike, not full-time equivalents. The series is drawn to 2025, the last complete BESTA year; a Q1-only 2026 point exists in the source and is deliberately not plotted. 1991 itself covers Q3–Q4 only, and the first full year reads 30.1% (1992), so the reversal stands either way. Student and Matura lines as in §01.
Public administration, over the same years
In 1991 the two sectors employed women almost equally (31.5% vs 32.1%). Public administration crossed 50% in 2025; ICT went the other way. Same survey, same method: BFS BESTA, employed persons rather than full-time equivalents. ICT is NOGA 61–63, public administration NOGA 84. Drawn to 2025, the last complete year; 1991 covers Q3–Q4 only.
The share holds flat in every big canton
The four largest ICT cantons plus Lucerne, from register data (BFS STATENT, NOGA 61–63), employed persons rather than full-time equivalents. A different source than the survey series on the left, hence the slightly different national level (24.1% vs 23.7% in 2024). Fourteen years, and every line stays inside a two-point band.
Counted by occupation, the share is lower.
Everything in §02 counts people employed at an ICT firm, receptionists and recruiters included. Counting instead the people whose own job is an ICT job gives a very different number: 13.8% in 2024, against 23.7% by sector in 2025. Both are correct; they answer different questions, and the narrower one is the one about who actually builds the software.
Everyone employed at an ICT firm, whatever their job: HR, sales, finance and facilities included.
Everyone doing an ICT job, wherever they work, including the developer at a bank or a hospital.
The occupation share has stalled since 2021
1990–2024Flat through the nineties (8.8% in 1990, 8.6% in 2000), then growth from 10.1% in 2010 to 14.0% in 2021, and stagnant since: 13.8% in 2022, 14.4% in 2023, 13.8% in 2024. That is roughly where the share of women enrolled in ICT already stood at Swiss universities in 2012/13 and at the universities of applied sciences in 2019/20: the workforce today stands about where the lecture halls stood five to twelve years ago. The 1990 and 2000 points are census head counts of the whole resident population; from 2010 the series is the annual Structural Survey, a sample, so single years carry sampling error and read better as a trend.
Regional spread: 4.6 points
2020–2024Zurich leads at 15.8% and Lake Geneva region follows at 14.5%, while Eastern Switzerland trails at 11.2%. The spread of about 4.6 pp is wider than the cantonal spread in the sector-based measure, with the large ICT centres at the top. Five survey years are pooled into one value per region, because a single region-year of the Structural Survey is too thin a sample to read on its own.
Unemployment and position, for women already in ICT.
The women who do make it into ICT face a harder labour market: in every year of the series their registered unemployment rate exceeds men’s: 2.92% against 2.26% in 2024, a gap of 0.65 pp. These are computed, directly standardised estimates rather than official SECO figures; see Methods. The second half of this section follows the same women once they are employed: five years after graduating, they hold fewer positions with a management function than men do.
The gap in every year since 2011
2011–2024Women minus men, in percentage points; above zero means women are unemployed more often. The ICT line is positive in every year of the series. It starts at 0.70 pp in 2011, peaks at 1.00 pp in 2017, then widens sharply in the first pandemic year, from 0.54 pp in 2019 to 0.88 pp in 2020, because women’s rate rose from 2.36% to 3.11% while men’s rose from 1.82% to 2.23%. It narrows to 0.50 pp in 2022, the low of the series, and then widens again to 0.65 pp in 2024 — practically the gap it started with, thirteen years on. The rest of the Swiss economy stays at parity across the whole period — +0.13 pp in 2011, and never further from zero than 0.06 pp from 2013 on — while the ICT gap never falls below +0.50 pp. This is a trait of the sector, not of the Swiss labour market.
Higher in the west; widest gaps in Vaud and Lucerne
2024Women’s rate is higher than men’s in all seven cantons, and the language border shows plainly in the level of both: Vaud (5.39% / 4.10%) and Geneva (3.50% / 2.54%) sit well above Zurich (2.75% / 2.09%), Lucerne (2.71% / 1.47%), Bern (1.70% / 1.48%), St. Gallen (1.61% / 1.58%) and Ticino (1.51% / 1.26%). It no longer shows in the width of the gap. Vaud is still widest at 1.30 pp, but Lucerne now comes second at 1.24 pp — ahead of Geneva at 0.96 pp — and the rest run from 0.66 pp in Zurich down to 0.03 pp in St. Gallen, where the two rates are all but identical. Ticino, Italian-speaking but not French-speaking, sits with the German-speaking cantons on the level of its rates. These seven are the cantons the report draws, together 75.2% of ICT sector employment; smaller cantons swing far too widely on tiny ICT workforces to compare — across all 26 the widest gap is not Vaud’s. No publishable rate at all for Appenzell A.Rh., Appenzell I.Rh., Glarus, Jura, Obwalden, Uri: a suppressed division cell cannot be repaired in a standardised rate.
Fewer reach a management position.
Five years after graduating, women in ICT are less often in a position with a management function than men and more often in one without. Each gap is shown next to the same gap outside ICT, so it can be read as an ICT trait or as the general pattern at that kind of institution.
Gender gap by occupational position
Master degree · five years onPositive means women hold that position more often than men, negative less often. Cohorts 2016, 2018 and 2020 pooled, surveyed five years after graduation. The ICT cells rest on small sample sizes and must be read with caution. The report recommends running these surveys more often and at larger sample sizes.
Women are 2.3 pp less likely than men to hold a position with a management function five years after an ICT master: 21.3% of women against 23.6% of men.
Outside ICT the same gap is 5.0 pp, so here ICT is the better of the two, not the worse.
This is the strongest direct evidence in this whole study that the problem is not only who enters the field, but what happens to them once they are in it.
The two routes, stage by stage.
The stages are measured separately and back-dated, so they are not a cohort. On the university route the share of women drops most between the Matura and ICT bachelor entry, recovers at master level, and falls again on the step into an ICT job. The vocational route, through a technical Berufsmaturität and a UAS bachelor, starts from a lower base, holds its share through the degree, and loses it at the same last step.
The biggest drop is at bachelor entry
One line per master-graduation window (red = 2024, the newest). Stages are back-dated with fixed assumed durations: Matura and bachelor entry five years before master graduation, bachelor graduation and master entry two. They do not track the same individuals. The biggest drop is always the first, from the Matura into an ICT degree, at between 7.4 and 11.5 pp in every window. A smaller but equally consistent loss of 0.1 to 3.5 pp follows across the bachelor itself. The share then jumps at master entry, by 3.3 to 6.7 pp, plausibly because the master stage is far more international: over 2010–2024 international students took 31.8% of ICT bachelor degrees at Swiss universities but 49.5% of master degrees. The final step into the workforce crosses a change of definition: every education stage is ICT by field of study, the last one is ICT by occupation (§03), so read it as a step down in measure as well as in level.
On the vocational route the drop comes last
One line per UAS bachelor-graduation window (red = 2024, the newest). The technical Berufsmaturität and bachelor entry are back-dated three years; bachelor graduation is also the workforce year. The shape is the mirror image of the academic route: entry (17.3%) now runs above the technical Berufsmaturität (13.2%), and only 1.3 pp is lost between starting a bachelor and finishing one. The whole loss comes at the end, 16.0% of graduates against 13.8% of people doing ICT work. Entry and graduation have gained about 8 pp since 2012–13, while the Berufsmaturität moved less than 2 pp: the UAS route is recruiting women who did not come through a technical apprenticeship. The technical direction is a proxy for ICT, and broader than it (§01); the last stage is ICT by occupation (§03). At the pace of the last twelve years, roughly 0.3 pp a year, the ICT workforce would not reach one fifth women before the 2040s.
Entry share and degree share are close
The share of women starting a bachelor and the share finishing one are nearly identical on both routes: the pipeline’s leak sits before enrolment, not during it. These are two different groups of people measured three years apart, not one cohort followed through — an indicator, not a survival rate. Same ICT definition as §01.
Two leaks stand out. The first sits at the university gate: for the newest cohort window, the share of women falls from 23.9% among PAM Matura graduates to 15.9% among ICT bachelor entrants. The second is the step into work: after recovering to 19.1% at master entry and 20.1% at master graduation, the share drops to 13.8% among people actually doing ICT jobs. The education system keeps sending a growing share of women, and the workforce share does not respond. Each stage is measured independently from a different dataset and year; the stages do not track the same individuals.
Sources: [1] [7] [10] [11] [12] [13] [14] — full list in Methods.
What has moved, and how fast.
Every education series in the table has risen since 2008. The workforce series has not moved since 2022. More qualified women leave education each year, and the share of women doing ICT work has stayed flat; what happens between those two facts is not something these data can show.
Each series at 2008, 2022 and now
share of women, in %| Series | Start | 2022 | Latest | ∆ since 2022 | per 3 yr before |
|---|---|---|---|---|---|
ICT occupations CH-ISCO-19 - people doing ICT work | 10.12010 | 13.8 | 13.82024 | ±0.0 | +0.9 |
Academic Matura (PAM) physics & applications of mathematics | 21.02008 | 24.5 | 26.72025 | +2.2 | +0.7 |
Vocational Matura (technical) technical direction, a proxy for ICT | 11.72008 | 15.4 | 15.02025 | −0.4 | +0.8 |
Universities (ICT) enrolled students | 12.72008 | 19.8 | 21.42025 | +1.6 | +1.5 |
Universities of applied sciences (ICT) enrolled students | 6.82008 | 17.0 | 20.32025 | +3.3 | +2.2 |
The five rows are the five of report Table 1; the ICT sector, counted by economic sector, is §02’s measure and is not in it. The two windows are of unequal length, so the last column rescales the long-run change to a three-year span, which is what makes it directly comparable to “∆ since 2022”. Series do not all start or end in the same year; each row carries its own. The change columns are computed from the unrounded shares, as the report does, so subtracting the printed values does not always land on the printed change. Read across: the two student series and the PAM academic Matura are still moving, and PAM is now moving faster than it did over the fourteen years before. The one labour-market measure here, ICT occupations, has not moved at all.
The workforce today stands about where the lecture halls stood five to twelve years ago.
At the pace of the last twelve years, parity is decades away. The supply of qualified women is no longer the binding constraint: every education series has risen, two of them steeply, while the sector has moved 0.5 pp in seventeen years and still sits well below its 1991 level. What happens between graduation and promotion is where this study now points, and §04 shows women in ICT are less often in a management position five years out, on both the university and the UAS route.
This is a preliminary study: it establishes what the published statistics can and cannot show. The Women in Tech Index is the intended next step — a repeatable, versioned measure tracked over time. It does not exist yet, and nothing on this page should be read as one.
- Why women leave PAM. The share of women studying at university grows more slowly than the share graduating with PAM. A plausible reading is that many choose other STEM routes; no dataset tracks it.
- Which route into an FH ICT degree. Women appear to enter FH ICT programmes from vocational Matura directions other than the technical one, and from routes outside the Matura altogether (§09). If so, that is an entry channel worth widening deliberately.
- What happens after five years. The graduate survey stops there, and its ICT cells are small. Running it more often and at larger sample sizes would tell us where in a career the gap actually opens.
Get in touch.
This study was produced at the Lucerne University of Applied Sciences and Arts. Write to us about the data, a correction, or a collaboration. We read everything.
Project team
Prof. Dr. Aygul Zagidullina
Albin Uruqi
Sebastian Kühne
Advisory group
This study was shaped in conversation with the Swiss women-in-tech communities. These practitioners advised on scope, definitions and what the data needed to show. Their organisations, and many more, are in Get involved below.
School of Computer Science and Information Technology
Suurstoffi 1
6343 Rotkreuz
SwitzerlandHSLU campuses
Where to get involved
This directory lists 30 Swiss organisations that run networks, mentoring and school outreach, mostly on volunteer time. All were active within the last year, and all are open to new members, mentors and volunteers.
Each marker is a city: the seat an organisation names in its imprint or contact page, not where its events take place. Marker area shows how many organisations have their seat there, and the segments show their categories.
Women in AI – HSLU
A student-led community at the Lucerne School of Computer Science and Information Technology, supporting women interested in artificial intelligence, data and technology through peer exchange, networking, outreach, and connections with role models from industry and research.
The aim is a welcoming space where students can explore the many routes into AI, build confidence, make connections, and develop a real sense of belonging in the field.
We would be glad to hear from organisations and professionals who want to engage: sharing professional experience, hosting a company visit, offering early-career opportunities, or anything else that would help.
wai@hslu.chFaculty advisor: Shaelom Fischer, HSLU Informatik
How to read these numbers.
Definitions decide results, so they are stated here in full: which fields of study and which economic sectors count as ICT, how the unemployment rates are constructed, and what these data cannot say.
Definitions
Definitions decide results. Each one below states exactly what is counted, and what is left out.
Fields of study: universities of applied sciences+
| Computer science | Informatik |
| Electrical engineering | Elektrotechnik |
| Information technology | Informationstechnologie |
| Media engineering | Medieningenieurwesen |
| Medical informatics | Medizininformatik |
| Microtechnology | Mikrotechnik |
| Systems engineering | Systemtechnik |
| Telecommunications | Telekommunikation |
| Business information technology | Wirtschaftsinformatik |
| Business information systems | Business Information Systems |
The two catalogues are independent nomenclatures with different field names and different granularity, so the university and UAS selections are made separately and do not map one to one onto each other.
Fields of study: universities+
| Computer science / IT | Informatik |
| Communication systems | Kommunikationssysteme |
| Electrical engineering | Elektroingenieurwesen |
| Microtechnology | Mikrotechnik |
| Business informatics | Betriebsinformatik |
Business informatics is counted on both routes, so student, entry and degree figures share one definition and can be compared directly across §01, §04 and §05.
Economic sectors: NOGA 2008+
| 61 | Telecommunications | wired, wireless, satellite and other telecom activities |
| 62 | Computer programming & consultancy | programming, consultancy, facilities management, other IT services |
| 63 | Information services | data processing & hosting, web portals, news agencies, other information services |
Divisions 61–63, excluding manufacturing of ICT hardware. The workforce figures count everyone employed at these firms, including sales, HR and administration.
The Matura, and the routes into an ICT degree+
Matura is the Swiss upper-secondary leaving certificate that grants access to higher education. Two kinds appear on this page.
academic Matura (gymnasiale Maturität) is taken at a Gymnasium and admits the holder to a university. Each candidate chooses a Schwerpunktfach, a core subject; we follow the one called physics and applications of mathematics (PAM), because it is the closest thing in the Matura statistics to an early signal of an ICT path.
vocational Matura (Berufsmaturität, BM) is taken alongside an apprenticeship (BM1) or after it (BM2) and admits the holder to a university of applied sciences. It has no ICT direction, so we follow the technical direction as a proxy — broader than ICT, and described above.
Neither is the only way in. A specialised Matura (Fachmaturität), a different Schwerpunktfach, the Passerelle bridge from the vocational to the academic route, a higher professional diploma (Höhere Fachschule), an admission examination, and foreign qualifications all lead into ICT study as well. This analysis follows the two routes the published statistics can actually measure; it is not a census of entry paths, and the share of women on the routes we do not follow may differ. The report sets the same caveat out in appendix B.4.
How the unemployment rate is built+
rate = registered unemployed / (employed + registered unemployed)A computed estimate, not an official SECO figure. Registered unemployed come from SECO AMSTAT (NOGA 61–63), counted as the mean of the year’s twelve monthly figures; the employment denominator is BFS STATENT, which is why the series starts in 2011. The rates are directly standardised rather than pooled: pooling would weight each division by its share of that sex’s own labour force, so part of a gap would reflect where women and men work rather than how their rates differ inside a division. The cantonal chart applies one shared standard — the national combined-sex ICT mix of 2024 — so the cantons stay comparable with each other; the sector chart standardises each series on its own combined-sex mix, recomputed yearly, because ICT and the rest of the economy share no divisions and no common standard exists. Appendix B.6 of the report sets out the construction in full.
A standardised rate needs all three division cells for both sexes and cannot absorb a missing one, so canton-years with a confidentiality-suppressed cell carry no rate at all rather than an imputed one. Six cantons have no publishable 2024 rate on that basis; the chart names them. They are not the same six as in 2023 — Schaffhausen gained a rate, Jura lost one — so the list is read from the data rather than fixed.
Two ways to count ICT work+
By sector (NOGA 61–63) counts everyone employed at an ICT firm, whatever their job. It is the measure behind §02 and §04.
By occupation (CH-ISCO-19) counts everyone whose own job is an ICT job, wherever they are employed. It is the measure behind §03 and the final stage of §05.
The two differ by about ten percentage points in the same year and are never directly comparable in level. Every chart states which one it uses.
What the data cannot say, and does not claim
- 01No dataset traces individuals through the pipeline. Each stage is measured independently; the stages in §05 are aggregates from different years, not a cohort.
- 02The employment series’ edge years are partial: 1991 covers only Q3–Q4, 2026 only Q1. Full-year anchors (1992: 30.1%, 2025: 23.7%) tell the same story.
- 03Small cantons produce unstable shares. Cantons with fewer than 30 Matura graduates are hatched, and unemployment rates built on suppressed or near-zero counts are excluded.
- 04Sector-based workforce counts include every employee of ICT firms, not only technical staff. Occupation-based counts would differ.
- 05The two workforce sources differ by method: BESTA is a quarterly survey (national only), STATENT an annual register (cantonal). Their national levels differ slightly — 0.30 pp on average over the fourteen years both cover and never more than 0.62 pp (24.1% vs 23.7% women in 2024) — and each chart names its source. STATENT cells suppressed for confidentiality are imputed with the midpoint of the hidden range.
- 06One ICT field definition is used throughout, matching appendix A of the report: business-informatics programmes are included at every stage, so student, entry and degree figures are directly comparable. UAS entries merge the historical Diplom route with bachelor entries (inseparable at source; negligible in recent years).
- 07Gender is recorded as a binary. Every source used here — BFS, SECO, the graduate survey — records only “woman” or “man”. People who identify as neither are absent from the published tables or counted in one of the two. Nothing on this page can speak about them, and the word “share of women” should be read as “share recorded as women”.
- 08Nothing here is causal. Every series is descriptive. The page shows that the education shares rose while the workforce share did not; it does not show why, and it cannot attribute either to any policy, programme or initiative — including the ones running over the same period.
- 09The pipeline is not a cohort. No Swiss dataset follows individuals from Matura through graduation into work. The pipeline stages are separately measured groups, back-dated with fixed assumed durations. A drop between two stages shows where the share is lower, not that particular women left at that point.
- 10There is no pay data here. No ICT-specific gender pay gap is published for Switzerland. The figure often quoted (16.2%, BFS 2022) is economy-wide and cannot be narrowed to ICT, so pay is simply outside what this study can measure.
- 11Retention and exit are invisible. The data show who enters education and who is employed at a point in time. They never show who left the field, when, or why — so a flat workforce share is consistent with several very different stories, and this study cannot tell them apart.
Datasets
- [1]BFS: Matura certificates by core subject, sex and school cantonpx-x-1503020200_102§01 Matura figures
- [2]BFS/SHIS: Students at universities by field, sex and institutionpx-x-1502040100_106§01 student figures (universities)
- [3]BFS/SHIS: Students at universities of applied sciences by field, sex and institutionpx-x-1502040400_133§01 student figures (UAS)
- [4]BFS Employment statistics (BESTA): employees by economic division, employment grade and sexpx-x-0602000000_101§02 workforce & public-administration series
- [5]BFS Business structure statistics (STATENT): establishments and employees by canton and divisionpx-x-0602010000_101§02 canton workforce series · §04 unemployment denominator
- [6]SECO Labour market statistics (AMSTAT): registered unemployed by sector, canton and sexamstat.ch§04 unemployment counts (annual mean of twelve monthly figures)
- [7]BFS Structural Survey (SE) and census: occupation exercised (CH-ISCO-19) by sexStrukturerhebung / Volkszählung§03 ICT-occupation series · §05 pipeline workforce stage
- [8]BFS Structural Survey: occupation exercised (CH-ISCO-19) by sex and major region, special analysis for HSLUSE Sonderauswertung 2020–2024§03 major-region breakdown
- [9]BFS graduate survey (ASHS): occupational position by field group and sexashs · 2026-0076§04 management-function gaps (ICT vs non-ICT)
- [10]BFS/SHIS-SIUS: master entries in ICT fields by sex and institution (special analysis, not in STAT-TAB)SHIS Sonderauswertung 2010/11–2025/26§05 master-entry stage
- [11]BFS/SHIS: Bachelor entries at universities by field, sex and institutionpx-x-1502040100_121§05 pipeline & entries-vs-degrees (universities)
- [12]BFS/SHIS: Degrees at universities by level, field, sex and institutionpx-x-1503040100_101§05 pipeline & entries-vs-degrees (universities)
- [13]BFS/SHIS: Diploma/bachelor entries at universities of applied sciences by field, sex and institutionpx-x-1502040200_183§05 entries-vs-degrees (UAS)
- [14]BFS/SHIS: Degrees at universities of applied sciences by level, field, sex and institutionpx-x-1503040200_111§05 entries-vs-degrees (UAS)
Further sources
- BFS, MINT-Fächer an den Hochschulen (2024)
- KOF Bulletin, Women in STEM by canton (Oct 2020)
- OECD Education at a Glance 2025, Switzerland
- ETH Zurich Equality Monitoring 2024
- EPFL Gender Monitoring 2023 – 2024
- swissuniversities Gendermonitoring
- swissICT / BFS Employment Statistics
- BFS, Statistik der Unternehmensstruktur (STATENT), employment by sector & sex
- Advance & HSG, Gender Intelligence Report 2025 (Swiss HR data, 376,000 employees)
- McKinsey & QuantumBlack, Women in tech and AI in Europe: can the region close its gender gap? (March 2026)