Korn Ferry · NYSE:KFY

Korn Ferry — Hay Group — Investor Presentation (September 2014)

35 slides from Korn Ferry's investor presentation, September 2014

1. Slide 1: Gender Pay Gap

   Gender Pay Gap SEPTEMBER 2014 TREVOR WARDEN

2. Slide 2: Introduction

   Introduction

3. Slide 3: Agenda

   Agenda 1 Introduction 2 Measuring the gap 3 The data 4 The real problem 5 What now? © 2014 Hay Group. All rights reserved 3

4. Slide 4: About Hay Group

   About Hay Group We help clients globally to solve their organisational and people related business problems. We help them to make their organisations work through our knowledge and insights into people, jobs and organisations.  Organisation transformation  Organisation Design  Talent management 

5. Slide 5: Why is Gender Pay Gap an issue?

   Why is Gender Pay Gap an issue? An overview of the issue:  Pay inequity means less lifetime earnings for women  Increasingly women are the breadwinners  The economy needs to attract women into the workforce  Companies with more women in top jobs perform better  A societal issue It is simply unf

6. Slide 6: Measuring the gap

   Measuring the gap

7. Slide 7: The data

   The data It is easy to be cynical when it comes to data…. Statistics are used much like a drunk uses a lamppost: for support, not illumination. Vin Scully Facts are stubborn, but statistics are more pliable. Mark Twain There are three kinds of lies: lies, damned lies, and statistics. Unknown (Attrib

8. Slide 8: Calculating the gap

   Calculating the gap Approaches  Average earnings over a period e.g. WGEA  Life time earnings  Equal pay for equal work © 2014 Hay Group. All rights reserved 8

9. Slide 9: WGEA Gender Pay Gap

   WGEA Gender Pay Gap What is the GPG according to WGEA? Workplace Gender Equality Agency (WGEA) : “…is the difference between women’s and men’s average weekly full-time equivalent earnings...” WGEA calculation - AWOTE Average weekly full-time equivalent earnings before tax, excluding  overtime  ben

10. Slide 10: Lifetime earnings

    Lifetime earnings Fully inclusive  populist approach  great headlines Considerations  complex calculations – not easily replicable  many assumptions © 2014 Hay Group. All rights reserved 10

11. Slide 11: Equal pay for work of equal value

    Equal pay for work of equal value Job sizing approach  apples-to-apples comparisons Considerations  no “official” (government) data  point in time  Most businesses understand method  Comprehensive - all financial valued remuneration aggregates/elements © 2014 Hay Group. All rights reserved 11

12. Slide 12: The data

    The data

13. Slide 13: The data in summary

    The data in summary WGEA  Australian Bureau of Statistics –  the average man working full-time earns 18.2% more than the average full-time working woman Lifetime earnings  AMP/NATSEM –  Women with Bachelor degree qualifications earn 42% less over their lifetime than male postgraduates Job size 

14. Slide 14: WGEA - 18.2%

    WGEA - 18.2% Accommodation and food services 8.0 Administrative and support services 17.9 Arts and recreation services 14.9 Construction 18.8 Education and training 12.0 Electricity, gas, water and waste services 12.3 Financial and insurance services 30.0 Health care and social assistance 30.7 Infor

15. Slide 15: Lifetime - 42% Bachelor degree

    Lifetime - 42% Bachelor degree $1.52M or a 42% difference over a lifetime from 25 – 59 years old with a Bachelor © 2014 Hay Group. All rights reserved 15

16. Slide 16: Job size Hay Group data - 5%

    Job size Hay Group data- 5% Fixed Fixed plus STI Fixed plus STI plus LTI 4.9% 2.8% 2.8% 3 years of progress? GPG Prevalence Averages of Grades Fixed plus STI Year Fixed Fixed plus STI Males Females plus LTI 2012 7.2% 6.5% 7.2% 70.6% 29.4% 2013 6.9% 5.4% 6.1% 70.7% 29.3% 2014 4.9% 2.8% 2.8% 69.8% 30.

17. Slide 17: What is the market doing?

    What is the market doing? Hay Group database as a proxy for the market # of Jobs % Total Database % Total Coded Total Jobs in database as at May 2014 257,462 100% Total Jobs with Gender Coded 201,891 78% 100% Number of Female 75,137 29% 37% Number of Male 126,754 49% 63% Distribution of Participants

18. Slide 18: The big picture on prevalence – 2014

    The big picture on prevalence – 2014 100% 80% 60% 40% 20% 0% 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 Female % Male% © 2014 Hay Group. All rights reserved 18

19. Slide 19: The prevalence picture over time

    The prevalence picture over time Percentage of Females – 3 years 2014 2013 2012 Senior Mgmt and 9% 17% 10% 19% 8% 17% Executives Middle Mgmt & Seasoned 26% 27% 26% 27% 26% 27% Professional Supervisory & Junior 39% 38% 39% 38% 41% 41% Professionals Clerical & Operational 44% 50% 34% 53% 42% 64% Avera

20. Slide 20: Tunnelling in on prevalence – job size

    Tunnelling in on prevalence – job size 2014 2013 2012 Employee Level Grade Female Male Female Male Female Male 33 0% 100% 0% 100% 0% 100% 32 0% 100% 0% 100% 0% 100% 31 0% 100% 0% 100% 0% 100% 30 0% 100% 0% 100% 0% 100% 29 10% 90% 8% 92% 0% 100% 28 11% 89% 14% 86% 14% 86% Senior Management and Execut

21. Slide 21: Job family prevalence

    Job family prevalence 2014 2013 2012 Job Family % Female % Male % Female % Male % Female % Male Administration Support Service 83% 17% 77% 23% 83% 17% Human Resources 67% 33% 66% 34% 69% 31% Corporate Affairs 65% 35% 60% 40% 60% 40% Legal 58% 42% 62% 38% 62% 38% Call Centre 55% 45% 54% 46% 54% 46% C

22. Slide 22: Sector prevalence

    Sector prevalence Female % Male % Sector 2012 2013 2014 2012 2013 2014 Pharmaceuticals and Healthcare 60% 79% 62% 40% 21% 38% Consumer Durables 57% 49% 62% 43% 51% 38% Not-for-Profit 59% 56% 61% 41% 44% 39% Public Sector 44% 53% 54% 56% 47% 46% Insurance 58% 53% 52% 42% 47% 48% FMCG - Other 45% 51%

23. Slide 23: What is the data showing?

    What is the data showing? Pay ratio by Hay reference level 140% $900,000 118% $800,000 120% 105% $700,000 100% 96% 98% 94% 95% 95% 93% 96% 93% 94% 92% 93% 92% 93% 91% 94% 93% 90% 87% $600,000 80% $500,000 60% $400,000 $300,000 40% $200,000 20% $100,000 0% $0 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

24. Slide 24: What is the pay data showing?

    What is the pay data showing? Pay ratio by Hay reference level Ratio of average female to male pay - 2014 Fixed plus annual plus long- Job size Fixed remuneration Fixed plus annual incentive term incentive 26 105% 136% 129% 25 87% 97% 98% 24 94% 96% 101% 23 90% 91% 91% 22 93% 95% 95% 21 96% 98% 97%

25. Slide 25: What is the data showing?

    What is the data showing? Pay ratio by Hay reference level Ratio of high female to male pay - 2014 Job size Fixed remuneration Fixed plus STI Fixed plus annual plus LTI Above RL 26 Small sample 26 45% 37% 44% 25 59% 62% 38% 24 72% 72% 81% 23 46% 75% 71% 22 79% 87% 89% 21 97% 99% 77% 20 87% 91% 73% 1

26. Slide 26: What is the data showing? Sectors

    What is the data showing? Sectors Male to female pay – 2014 difference Sector Fixed remuneration Fixed plus STI Fixed plus LTI All Orgs 5% 3% 3% Industrial Goods 12% 12% 12% Mining 8% 6% 5% Oil and Gas 8% 7% 7% Transportation 8% 8% 8% Utilities 8% 9% 10% Building Materials 7% 7% 7% Business Services

27. Slide 27: The real problem

    PROBLEMS SOLUTIONS

28. Slide 28: The problem

    The problem  Equal pay for work of equal value hides the bigger problem  Using the Hay Group database as a proxy for our workplaces  “Only” ~2.8% to 5% difference  Trending the right direction. Makes it look like a problem that will be easily solved! But…  Females not getting the big jobs  Zer

29. Slide 29: The solution: What are we seeing in the market?

    The solution What are we seeing in the market? “Those organisations that have made most progress in my experience are those where the CEO owns diversity and has genuinely bought in” “Targets relate to inputs though not outputs, e.g. shortlists for senior appointments need to be minimum of 50% women”

30. Slide 30: The solution: What are we seeing in the market? (2)

    The solution What are we seeing in the market? (2) Interventions include:  Unconscious bias training  Mentoring for high potential women  Job rotation for high potential women  Remove bias from selection processes  removing names & any sign of gender from resumes  more women to attend graduate

31. Slide 31: Regulatory interventions and targets work!

    Regulatory interventions and targets work! ASX Corporate Governance impact on mining sector 2010 Amendment -Principle 3.2 – 3.4 relate to gender diversity  Companies with women significantly outperformed:  ROE for all male Boards decreased by average 65.5%; at least one woman on board decreased by

32. Slide 32: Summary

    Summary Key points:  Australia has a Gender Pay Gap problem, regardless of how it is calculated  Focusing on the gap in pay for equal work is a good start - only  The glass ceiling still exists  Insufficient woman in higher paying job families and sectors  Not a business problem, a societal pro

33. Slide 33: What now?

    What now?

34. Slide 34: What now

    What now Have you set gender diversity targets by job level? Are you specifically targeting starting at the lower levels in order to increase your talent pool? Are you focusing on the BIG problems of glass ceiling and prevalence or solving the “relatively smaller” problem of equal pay for equal work

35. Slide 35: Thank you!

    Thank you! Trevor Warden – (03) 9667 2628 trevor.warden@haygroup.com © 2014 Hay Group. All rights reserved 35

Hay Group — Investor Presentation (September 2014) on 6ix