Women in STEM Technology, career pathways and the gender pay gap. Julie Mercer, Harvey Lewis 24 October 2016

Transcription

1 Women in STEM Technology, career pathways and the gender pay gap Julie Mercer, Harvey Lewis 24 October 2016

2 Women in STEM Presenters 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 Harvey Lewis Harvey is a director in the technology consulting practice and the UK lead for cognitive computing. harveylewis@deloitte.co.uk

3 Women in STEM: a data analysis 3

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 4

5 Women vs men The proportion of female vs male students taking STEM subjects at various stages of their education remain approximately the same Percentage of students who are male Percentage of students who are female GCSE 51.8% 48.2% TOTAL: 2,330,000 A-Level 59.0% 41.0% TOTAL: 317,000 Degree 46.8% 53.2% TOTAL: 150,000 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 Destination of Leavers from Higher Education Survey 5

6 Percentage Women vs men How does this compare with other European countries? Proportion of women vs men studying STEM subjects at tertiary level 100 Percentage_Women Percentage_Men Note: STEM subjects in EF4,EF5, EF6 and EF7 chosen to best match UK equivalents. Source: Eurostat, Deloitte analysis 6

7 Women vs men But significant differences emerge when we consider individual STEM subjects, such as Computing/Computer Science Percentage of students who are male Percentage of students who are female GCSE 79.4% 20.6% TOTAL: 63,000 A-Level 85.7% 14.3% TOTAL: 7,000 Degree 83.2% 16.8% TOTAL: 12,900 Sources: 2016 GCSE and A-Level Computing results from UK Joint Council for Qualifications, Computer Science Higher Education data from the Destination of Leavers from Higher Education Survey 7

8 Women vs men and across all STEM subject areas at degree-level Percentage of students who are male Percentage of students who are female Subjects allied to medicine Veterinary science Agriculture & related subjects Biological sciences Medicine & dentistry Physical sciences Mathematical sciences Architecture, building & planning Computer science Engineering & technology 20% 24% 38% 39% 42% 61% 61% 68% 83% 86% 80% 76% 62% 61% 58% 39% 39% 32% 17% 14% Note: STEM subjects as defined by the Parliamentary Science and Technology Committee. Source: Destination of Leavers from Higher Education Survey 8

9 Percentage Women vs men How does this compare with other European countries? Proportion of women vs men studying Computing at tertiary level 100 Percentage_Women Percentage_Men Note: STEM subjects in EF48 (ISCED97). Source: Eurostat, Deloitte analysis 9

10 Percentage Women vs men How does this compare with other European countries? Proportion of women vs men studying Health at tertiary level 100 Percentage_Women Percentage_Men Note: STEM subjects in EF72 (ISCED97). Source: Eurostat, Deloitte analysis 10

11 Women vs men Women outperform men in STEM subjects at every level of education Percentage of students who are male Percentage of students who are female GCSE (A* - C) 63.1% 67.4% A-Level (A* - C) 75.6% 77.3% Degree (With honours) 56.8% 61.8% Sources: 2016 GCSE and A-Level results from UK Joint Council for Qualifications, Higher Education data from the Destination of Leavers from Higher Education Survey 11

12 Women vs men But very few women with STEM degrees go on to work in STEM occupations Percentage of students who are male Percentage of students who are female STEM occupations (not incl. occupations in medicine or dentistry) 29% 8% STEM occupations (incl. occupations in medicine, pharmacy and dentistry) 42% 50% Note: STEM occupations were based on SOC2010 occupational classifications and descriptions Source: Destination of Leavers from Higher Education Survey, employment six months after graduation 12

13 Women vs men Where do all the men go? Business & related associate professionals n.e.c. Design and development engineers Information technology and telecommunications professionals n.e.c. Architectural and town planning technicians Pharmacists 1.7 % 1.7 % 1.9 % 2.0 % 2.5% 3.1 % Engineering professionals n.e.c. 3.2 % Civil engineers Quantity surveyors 3.2 % Mechanical engineers Architects IT business analysts, architects and systems designers All STEM subjects 4.2 % Nurses University researchers, unspecified discipline 6.2 % Medical practitioners 7.0 % Programmers and software development professionals Note: STEM occupations were based on SOC2010 occupational classifications and descriptions Source: Destination of Leavers from Higher Education Survey, employment six months after graduation, all those for whom salary information was known 13

14 Women vs men Where do all the women go? Business & related associate professionals n.e.c. Health professionals n.e.c. Laboratory technicians Biochemists, medical scientists 1.5 % Medical radiographers 1.6 % 1.9 % Physiotherapists 1.9 % Occupational therapists 2.4 % Midwives Dental practitioners 3.1 % Pharmacists Other administrative occupations n.e.c. Welfare & housing associate professionals n.e.c. All STEM subjects 6.1 % Medical practitioners Therapy professionals n.e.c. 28.2% Nurses Note: STEM occupations were based on SOC2010 occupational classifications and descriptions Source: Destination of Leavers from Higher Education Survey, employment six months after graduation, all those for whom salary information was known 14

15 Men are paid more than women, even in top STEM destinations Pay gap top occupations for female STEM graduates 18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Pay gap top occupations for male STEM graduates 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 15

16 Routine Non-routine Routine and repetitive tasks are increasingly automatable Cognitive Manual E.g. Management consultants and business analysts E.g. Care workers and home carers IMPACT OF TECHNOLOGY: STRONG COMPLEMENTARITIES IMPACT OF TECHNOLOGY: LIMITED OPPORTUNITIES FOR SUBSTITUTION, SOME COMPLEMENTARITIES E.g. Bank and post office clerks E.g. Metal making and treating process operatives IMPACT OF TECHNOLOGY: SIGNIFICANT SUBSTITUTION IMPACT OF TECHNOLOGY: SIGNIFICANT SUBSTITUTION 16

17 The future of employment looks bleak for many occupations As forecast by Frey and Osborne in

18 Employment (Thousands) Automation will affect different occupations to different degrees Potential impact on UK occupations in the next years 90 Low probability of automation Strong complementarities Medium probability of automation Some complementarities High probability of automation Strong substitutive effects Probability of Computerisation Managers directors and senior officials Associate professional and technical occupations Skilled trades occupations Sales and customer service occupations Elementary occupations Professional occupations Administrative and secretarial occupations Caring leisure and other service occupations Process plant and machine operatives Source: ONS, Frey and Osborne, Deloitte 18

19 Change in employment (Thousands) We can already sense the shifts Change in employment in UK occupations, Low probability of automation Strong complementarities Medium probability of automation Some complementarities High probability of automation Strong substitutive effects Probability of Computerisation Managers directors and senior officials Associate professional and technical occupations Skilled trades occupations Sales and customer service occupations Elementary occupations Professional occupations Administrative and secretarial occupations Caring leisure and other service occupations Process plant and machine operatives Source: ONS, Frey and Osborne, Deloitte 19

20 What skills are needed by workers to help them remain employed? We asked 100 London-based businesses in 2014 The skills increasingly required by businesses and public sector organisations (weighted score) Digital know-how Management 15% 16% Creativity 13% Entrepreneurship 10% Problem solving Negotiation 9% 9% Professional qualifications 8% Processing, support and clerical 6% Social perceptiveness Persuasiveness 5% 5% Cultural know-how 4% Source: Deloitte survey of 100 London based businesses,

21 Now, we have built 366 separate occupation profiles for the UK, using 120 different skills, knowledge and abilities attributes Abilities Cognitive abilities Sensory abilities Psychomotor abilities Physical abilities Basic skills Content skills Process skills Cross-functional skills Social skills Complex problemsolving skills Technical skills Resource management skills Web design & development professional Systems skills Business and management Manufacturing and production Health services Education and training Knowledge Mathematics and science Law and public safety Arts and humanities Transportation Engineering and technology Communications 23

22 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) 25

23 Percentage change Percentage change Percentage change But the relative importance of these talents is changing health_services_knowledge arts_and_humanities_knowledge education_and_training_knowledge mathematics_and_science_knowledge systems_skills communications_knowledge process_skills social_skills Percentage change in knowledge 8 6 Percentage 4 change in skills resource_management_skills law_and_public_safety_knowledge complex_problem_solving_skills content_skills business_and_management_knowledge cognitive_abilities transportation_knowledge engineering_and_technology_knowledge sensory_abilities manufacturing_and_production_knowledge technical_skills physical_abilities pyschomotor_abilities business_and_management_knowledge manufacturing_and_production_knowledge 0 engineering_and_technology_knowledge mathematics_and_science_knowledge -2 health_services_knowledge Percentage change in abilities -4 education_and_training_knowledge arts_and_humanities_knowledge -6 law_and_public_safety_knowledge communications_knowledge content_skills process_skills transportation_knowledge social_skills complex_problem_solving_skills technical_skills systems_skills resource_management_skills cognitive_abilities pyschomotor_abilities physical_abilities sensory_abilities -15.0% -10.0% -5.0% 0.0% 5.0% 10.0% 15.0% 20.0% Percentage change in attribute importance (forecast) 26

24 Percentage change in employment share Percentage change in employment share Percentage change in employment share Percentage change in employment share Some attributes have a positive effect on employment Process skills Social skills Least important Job importance decile Most important Least important Job importance decile Most important High risk of automation 35 Complex problem-solving skills 40 Systems skills Medium risk of automation Low risk of automation Least important Job importance decile Most important -20 Least important Job importance decile Most important 27

25 Percentage change in employment share Percentage change in employment share Percentage change in employment share Percentage change in employment share While others have a negative effect Physical abilities Psychomotor abilities Least important Job importance decile Most important Least important Job importance decile Most important High risk of automation 20 Sensory abilities 25 Technical skills Medium risk of automation Low risk of automation Least important Job importance decile Most important Least important Job importance decile Most important 28

26 Importance of cognitive and social skills Low High Importance of cognitive and social skills Low High Women vs men Differences in the nature of employment in the UK Men Number of people employed 1.0m 0.5m 0.0m Women Number of people employed 1.0m 0.5m 0.0m Low High Low High Importance of technical skills Importance of technical skills 29

27 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? 30

28 What can we do to change the outlook for women in STEM? 31

29 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. 32

30 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 33

31 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 TeachFirst and school mentoring Blind applications Monitoring the numbers Flexible working environment Return to work programme Parental leave Respect and Inclusion Calling it out 34

32 Further resources 35

33 Further resources Forthcoming publications Hidden talents: The search for tomorrow s business stars Veterans Work: The benefits to UK businesses of employing military veterans 36

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