Women in STEMTechnology, career pathways and the gender pay gapJulie Mercer, Harvey Lewis | 24 October 2016

2

Julie Mercer

• Julie is the Industry Leader for Education at Deloitte both for the UK and globally, working with government, private and third sector organisations involved in designing, regulating, delivering or supporting the education system.

• julieemercer@deloitte.co.uk

• +44 20 7007 8292

Women in STEM

Presenters

Harvey Lewis

• Harvey is a director in the technology consulting practice and the UK lead for cognitive computing.

• harveylewis@deloitte.co.uk

• +44 20 7303 6805

3

Women in STEM: a data analysis

4

Setting the scene

According to the Women’s Engineering Society:

• Only 9% of the UK’s engineering workforce is female, and only 6% of all registered engineers and technicians are women

• The UK has the lowest percentage of female engineering professionals in Europe, at less than 10%, while Latvia, Bulgaria and Cyprus lead with nearly 30%

• 14% of engineering and technology undergraduates in the UK are female

• The proportion of young women studying engineering and physics has remained virtually static since 2012

• In 2013/14, women accounted for only 3.8% of Engineering apprenticeship starts and 1.7% of Construction Skills starts

• Only around 20% of A Level physics students are girls and this has not changed in 25 years

• There is now very little gender difference in take up of and achievement in core STEM GCSE subjects

• 64% of engineering employers say a shortage of engineers in the UK is a threat to their business. 32% of companies across sectors currently have difficulties recruiting experienced STEM staff, and 20% find it difficult to recruit entrants to STEM

• The UK needs to significantly increase the number of people with engineering skills. In 2014, one report put the annual shortfall of STEM skills at 40,000. In 2015, the annual shortfall of the right engineering skills is 55,000 We need to double, at least, the number of UK based university engineering students

5

Women vs men

The proportion of female vs male students taking STEM subjects at various stages of their education remain approximately the same

48.2%51.8%

41.0%59.0%

53.2%46.8%

TOTAL: 2,330,000

TOTAL: 317,000

TOTAL: 150,000

Percentage of students who are male Percentage of students who are female

GCSE

A-Level

Degree

Note: STEM subjects at GCSE and A-Level include Mathematics, Biology, Chemistry, Physics, Economics, Statistics, Further Mathematics, Design & Technology, ICT, Computing, All Sciences. STEM subjects at degree level include Medicine & dentistry, Subjects allied to medicine, Biological sciences, Veterinary science, Agriculture & related subjects, Physical sciences, Mathematical sciences, Computer science, Engineering & technology, and Architecture, building and planning (STEM subjects as defined by the Parliamentary Science and Technology Committee)Sources: 2016 GCSE and A-Level results from UK Joint Council for Qualifications, Higher Education data from the 2014-15 Destination of Leavers from Higher Education Survey

6

Women vs men

How does this compare with other European countries?

Note: STEM subjects in EF4,EF5, EF6 and EF7 chosen to best match UK equivalents.Source: Eurostat, Deloitte analysis

0

10

20

30

40

50

60

70

80

90

100

Percen

tag

e

Proportion of women vs men studying STEM subjects at tertiary level Percentage_Women

Percentage_Men

7

Women vs men

But significant differences emerge when we consider individual STEM subjects, such as Computing/Computer Science…

20.6%79.4%

14.3%85.7%

16.8%83.2%

TOTAL: 63,000

TOTAL: 7,000

TOTAL: 12,900

Percentage of students who are male Percentage of students who are female

Sources: 2016 GCSE and A-Level Computing results from UK Joint Council for Qualifications, Computer Science Higher Education data from the 2014-15 Destination of Leavers from Higher Education Survey

GCSE

A-Level

Degree

8

Women vs men

…and across all STEM subject areas at degree-level

80%20%

Percentage of students who are male Percentage of students who are female

Subjects allied to medicine

76%24%Veterinary science

62%38%Agriculture & related subjects

61%39%Biologicalsciences

58%42%Medicine & dentistry

39%61%

39%61%

Physicalsciences

Mathematicalsciences

32%68%Architecture, building & planning

17%83%Computer science

14%86%Engineering & technology

Note: STEM subjects as defined by the Parliamentary Science and Technology Committee.Source: 2014-15 Destination of Leavers from Higher Education Survey

9

Women vs men

How does this compare with other European countries?

Note: STEM subjects in EF48 (ISCED97).Source: Eurostat, Deloitte analysis

0

10

20

30

40

50

60

70

80

90

100

Percen

tag

eProportion of women vs men studying Computing at tertiary level Percentage_Women

Percentage_Men

10

Women vs men

How does this compare with other European countries?

Note: STEM subjects in EF72 (ISCED97).Source: Eurostat, Deloitte analysis

0

10

20

30

40

50

60

70

80

90

100

Percen

tag

e

Proportion of women vs men studying Health at tertiary level Percentage_Women

Percentage_Men

11

Women vs men

Women outperform men in STEM subjects at every level of education

67.4%63.1%

77.3%75.6%

61.8%56.8%

Percentage of students who are male Percentage of students who are female

GCSE (A* - C)

A-Level (A* - C)

Degree (With honours)

Sources: 2016 GCSE and A-Level results from UK Joint Council for Qualifications, Higher Education data from the 2014-15 Destination of Leavers from Higher Education Survey

12

Women vs men

But very few women with STEM degrees go on to work in STEM occupations

8%29%

50%42%

Percentage of students who are male Percentage of students who are female

STEM occupations (not incl. occupations in medicine or dentistry)

STEM occupations (incl. occupations in medicine, pharmacy and dentistry)

Note: STEM occupations were based on SOC2010 occupational classifications and descriptionsSource: 2014-15 Destination of Leavers from Higher Education Survey, employment six months after graduation

13

Women vs men

Where do all the men go?

Note: STEM occupations were based on SOC2010 occupational classifications and descriptionsSource: 2014-15 Destination of Leavers from Higher Education Survey, employment six months after graduation, all those for whom salary information was known

All STEM subjects

7.0

%

6.2

% Medical practitioners

4.2

%

Programmers and software development professionals

Nurses

3.2

%Mechanical engineers

2.5%

Design and development engineers

2.0

%

Business & related associate professionals n.e.c.

1.9

%

Pharmacists

1.7

%

Information technology and telecommunications professionals n.e.c.

Quantity surveyors

Architects

University researchers, unspecified discipline

3.2

%Civil engineers

3.1

%

Engineering professionals n.e.c.

1.7

%

Architectural and town planning technicians

IT business analysts, architects and systems designers

14

Women vs men

Where do all the women go?

Note: STEM occupations were based on SOC2010 occupational classifications and descriptionsSource: 2014-15 Destination of Leavers from Higher Education Survey, employment six months after graduation, all those for whom salary information was known

All STEM subjects

28.2%

6.1

% Medical practitioners

3.1

%

Nurses

Pharmacists

2.4

% Midwives

1.9

%Occupational therapists

1.9

%

Physiotherapists1.6

%

Medical radiographers

1.5

%

Business & related associate professionals n.e.c.

Health professionals n.e.c.

Laboratory technicians

Biochemists, medical scientists

Dental practitioners

Other administrative occupations n.e.c.

Welfare & housing associate professionals n.e.c.

Therapy professionals n.e.c.

15

Men are paid more than women, even in top STEM destinations

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

Pay gap – top occupations for female STEM graduates

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Pay gap – top occupations for male STEM graduates

16

Routine and repetitive tasks are increasingly automatable

Cognitive Manual

No

n-r

ou

tin

eR

ou

tin

e

IMPACT OF TECHNOLOGY:STRONG COMPLEMENTARITIES

IMPACT OF TECHNOLOGY:LIMITED OPPORTUNITIES FOR SUBSTITUTION, SOME COMPLEMENTARITIES

IMPACT OF TECHNOLOGY:SIGNIFICANT SUBSTITUTION

IMPACT OF TECHNOLOGY:SIGNIFICANT SUBSTITUTION

E.g. Management consultants and business analysts

E.g. Care workers and home carers

E.g. Metal making and treating process operatives

E.g. Bank and post office clerks

17

The future of employment looks bleak for many occupations

As forecast by Frey and Osborne in 2013

18

Automation will affect different occupations to different degrees

Potential impact on UK occupations in the next 10-20 years

0

10

20

30

40

50

60

70

80

90

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Em

plo

ym

en

t (T

ho

usan

ds)

Probability of Computerisation

Managers directors and senior officials Professional occupations

Associate professional and technical occupations Administrative and secretarial occupations

Skilled trades occupations Caring leisure and other service occupations

Sales and customer service occupations Process plant and machine operatives

Elementary occupations

Low probability of automation Strong complementarities

Medium probability of automation

Some complementarities

High probability of automation

Strong substitutive effects

Source: ONS, Frey and Osborne, Deloitte

19

We can already sense the shifts

Change in employment in UK occupations, 2001-15

-10

-5

0

5

10

15

20

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ch

an

ge i

n e

mp

loym

en

t (T

ho

usan

ds)

Probability of Computerisation

Managers directors and senior officials Professional occupations

Associate professional and technical occupations Administrative and secretarial occupations

Skilled trades occupations Caring leisure and other service occupations

Sales and customer service occupations Process plant and machine operatives

Elementary occupations

Source: ONS, Frey and Osborne, Deloitte

Low probability of automation Strong complementarities

Medium probability of automation

Some complementarities

High probability of automation

Strong substitutive effects

22

What skills are needed by workers to help them remain employed?

We asked 100 London-based businesses in 2014

4%

5%

5%

6%

8%

9%

9%

10%

13%

15%

16%

Cultural know-how…

Persuasiveness

Social perceptiveness

Processing, support and clerical …

Professional qualifications

Negotiation

Problem solving

Entrepreneurship

Creativity

Management

Digital know-how

The skills increasingly required by businesses and public sector organisations

(weighted score)

Source: Deloitte survey of 100 London based businesses, 2014

23

Now, we have built 366 separate occupation profiles for the UK, using 120 different skills, knowledge and abilities attributes…

Abilities

Basic skills

Cross-functional

skills

Knowledge

Cognitive abilities

Sensory abilities

Psychomotor abilities

Physical abilities

Content skills Process skills

Social skills

Complex problem-

solving skills

Systems skills

Technical skills

Resource

management skills

Business and

management

Manufacturing and

production

Mathematics and

science

Health services

Education and

training

Arts and humanities

Law and public

safety

Engineering and

technology

Transportation

Communications

Web design & development professional

25

We’ve found that cognitive skills and abilities, and social skills are most important right now

Most important attributes Least important attributes

1. Customer and personal service knowledge 111. Food production knowledge

2. Oral comprehension (ability) 112. Repairing skills

3. Oral expression (ability) 113. Fine arts knowledge

4. English language knowledge 114. Glare sensitivity (ability)

5. Active listening skills 115. Sound localisation (ability)

6. Written comprehension (ability) 116. Peripheral vision (ability)

7. Problem sensitivity (ability) 117. Night vision (ability)

8. Speaking skills 118. Explosive strength (ability)

9. Near-vision (ability) 119. Installation skills

10. Critical thinking skills 120. Dynamic flexibility (ability)

26

But the relative importance of these talents is changing…

-15.0% -10.0% -5.0% 0.0% 5.0% 10.0% 15.0% 20.0%

pyschomotor_abilities

physical_abilities

technical_skills

manufacturing_and_production_knowledge

sensory_abilities

engineering_and_technology_knowledge

transportation_knowledge

cognitive_abilities

business_and_management_knowledge

content_skills

complex_problem_solving_skills

law_and_public_safety_knowledge

resource_management_skills

social_skills

process_skills

communications_knowledge

systems_skills

mathematics_and_science_knowledge

education_and_training_knowledge

arts_and_humanities_knowledge

health_services_knowledge

Percentage change in attribute importance 2001-30 (forecast)

-6

-4

-2

0

2

4

6

8

10

12

14

16

Perc

enta

ge c

hange

Percentage change in knowledge

business_and_management_knowledge

manufacturing_and_production_knowledge

engineering_and_technology_knowledge

mathematics_and_science_knowledge

health_services_knowledge

education_and_training_knowledge

arts_and_humanities_knowledge

law_and_public_safety_knowledge

communications_knowledge

transportation_knowledge

-6

-4

-2

0

2

4

6

8

Perc

enta

ge c

hange

Percentage change in skills

content_skills process_skills

social_skills complex_problem_solving_skills

technical_skills systems_skills

resource_management_skills

-10

-8

-6

-4

-2

0

2

4

Perc

enta

ge c

hange

Percentage change in abilities

cognitive_abilities pyschomotor_abilities

physical_abilities sensory_abilities

27

Some attributes have a positive effect on employment

-25

-20

-15

-10

-5

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Job importance decile

Process skills

-30

-20

-10

0

10

20

30

1 2 3 4 5 6 7 8 9 10

Job importance decile

Social skills

-20

-15

-10

-5

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10

Job importance decile

Complex problem-solving skills

-20

-10

0

10

20

30

40

1 2 3 4 5 6 7 8 9 10

Job importance decile

Systems skills

High risk of automation

Medium risk of automation

Low risk of automation

Most important

Least important

Most important

Least important

Most important

Least important

Most important

Least important

Perc

enta

ge c

hange in

em

plo

ym

ent share

Perc

enta

ge c

hange in

em

plo

ym

ent share

Perc

enta

ge c

hange in

em

plo

ym

ent share

Perc

enta

ge c

hange in

em

plo

ym

ent share

28

While others have a negative effect

-15

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Job importance decile

Sensory abilities

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Job importance decile

Psychomotor abilities

-20

-15

-10

-5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Job importance decile

Physical abilities

High risk of automation

Medium risk of automation

Low risk of automation

Most important

Least important

Most important

Least important

Most important

Least important

Most important

Least important

-20

-15

-10

-5

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Job importance decile

Technical skills

Perc

enta

ge c

hange in

em

plo

ym

ent share

Perc

enta

ge c

hange in

em

plo

ym

ent share

Perc

enta

ge c

hange in

em

plo

ym

ent share

Perc

enta

ge c

hange in

em

plo

ym

ent share

29

Women vs men

Differences in the nature of employment in the UK

1.0m 0.5m 0.0m

Number of people employed

1.0m 0.5m 0.0m

Number of people employed

Men WomenH

igh

Low

Import

ance o

f cognitiv

e a

nd s

ocia

l skills

HighLow

Importance of technical skills

Hig

hLow

Import

ance o

f cognitiv

e a

nd s

ocia

l skills

HighLow

Importance of technical skills

30

Summary of our analysis

According to our analysis

• Girls/women outperform boys/men at every level of STEM education

• Although similar numbers of girls and boys/women and men study STEM-related subjects, overall, very few women enter STEM occupations except in health and social care

• There are substantial differences in the proportion of women vs men taking different STEM subjects

• As a consequence, women with STEM qualifications are more likely than men to be working in low-skilled, low-paid occupations

• Even where men and women are working in the same occupations, men are typically paid more than women

• Across the UK’s workforce, women are more likely than men to be working in jobs that do not require technical skills

Challenges

• How can more women be persuaded to study non-medicine/biology-related STEM subjects?

• How can more women be persuaded to enter STEM occupations outside healthcare?

31

What can we do to change the outlook for women in STEM?

32

Unconscious bias and role models

I hadn’t been aware that there were doors closed to me until I started knocking on them. I went to an all-girls school. There were 75 chemistry majors…most were going to teach … When I got out and they didn’t want women in the laboratory, it was a shock . . . we’ve never had a woman in the laboratory before, and we think you’d be a distracting influence.’

33

The role of schools, university, business and society

In the 2015 nominee pool, 83% were male and 17% were female compared to ACS membership demographics of 71% male and 29% female.”

American Chemistry Society

• When do you start

• How we teach

• Can we describe the breadth and depth of STEM opportunities

• Careers advice and guidance

• Who are the role models and how are they profiled?

• And when do we stop

• What we teach

• Business mentors, profiling and engagement with schools and colleges

• Stemnet mentors 40% female and most under 35

• L'Oréal science awards

34

What can we do?

Like most organisations Deloitte recognised we had a problem and a role to play in addressing women in stem and in our business

Return to work

programme

Flexible working

environment

Blind applications

Monitoring the

numbers

TeachFirstand school mentoring

Parental leave

Respect and Inclusion

Calling it out

35

Further resources

36

Further resources

Forthcoming publications

• Hidden talents: The search for tomorrow’s business stars

• Veterans Work: The benefits to UK businesses of employing military veterans

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.co.uk/about for a detailed description of the legal structure of DTTL and its member firms.

Deloitte MCS Limited is a subsidiary of Deloitte LLP, the United Kingdom member firm of DTTL.

This publication has been written in general terms and therefore cannot be relied on to cover specific situations; application of the principles set out will depend upon the particular circumstances involved and we recommend that you obtain professional advice before acting or refraining from acting on any of the contents of this publication. Deloitte MCS Limited would be pleased to advise readers on how to apply the principles set out in this publication to their specific circumstances. Deloitte MCS Limited accepts no duty of care or liability for any loss occasioned to any person acting or refraining from action as a result of any material in this publication.

© 2016 Deloitte MCS Limited. All rights reserved.

Registered office: Hill House, 1 Little New Street, London EC4A 3TR, United Kingdom. Registered in England No 3311052.