Hanover Special Report #19

Compensation Study - Catastrophe Modelling and Exposure Management

Michael Stefan

Last update: Aug 6, 2021

Introduction

This is Hanover’s fourth annual salary survey for the catastrophe modelling and exposure management field (for short I’ll refer to the field as “CatEx”). I am grateful, as always, to all respondents as well as the dozen or so reviewers who have provided excellent feedback over the years. For reference, you can still view the 2019 and 2020 editions online (no registration needed). The project grew out of a desire to have a more granular handle on pay and has been going since 2018. We also produce a very popular actuarial salary survey as well and you can view the 2021 edition here.

Pay (and in particular pay equality) has grown in importance over the last decade and whilst this survey does not claim to have all the answers, I hope it at least sheds some light on a very opaque area, especially in an area of the market where there is very wide spectrum of skill-sets. As in previous years, we have only surveyed UK based CatEx staff. We have surveyed staff at all levels, although our database is very heavily geared towards experienced practitioners, typically with 5 years experience or more, and often with Master and PhD degrees. We don’t recruit junior analysts, although I am connected to a some via Linkedin, and they were invited to participate too.

The set of questions has been updated to take into account feedback received last year. Whilst total pay is important, it is by no means the only factors that affects recruitment and retention; work/life balance, company culture, team and departmental dynamics, relationship with supervisors etc all impact on whether someone joins or leaves. The covid-19 crisis has also completely changed work-life dynamics; working from home has rendered “flexibility” meaningless and it will be extremely interesting to see what effect this has on salaries and pay in 2021.

Uses

I always find there are three types of “user” for my salary surveys:

The compensation study is meant as as a reference guide, meaning you do not need to read the whole document from start to finish. I will usually provide both average pay (usually referred to as mean) although I prefer to use median pay. Every compensation study I have completed has included a small number of outliers that unduly influence average pay. Median pay, on the other hand, is much more robust against outliers.

Last but not least, this guide is best read on a desktop, or perhaps a larger tablet. Please remember that this guide is constantly updated, so it may have changed slightly since the last time you viewed it (as new responses and new analysis might have taken place). As of the last update (Aug 6, 2021), the survey is based on 58 responses.

What’s new for 2021

For 2020, the focus was on simplicity and readability. The aim has always been to have a salary survey that can be understood by anyone with some level of insurance knowledge. To that extent I have removed most of the more statistical exhibits for this, the 2021 edition.

Prior to 2020’s survey, I used a combination of Excel, Tableau, PowerPoint, Google Sheets and Paint. I have now transitioned to the “R” programming language, using an integrated workflow called RMarkdown. The details of how Rmarkdown works are not important, however, the key advantages are that it will always reflect the latest data available, and I can create new analysis and exhibits with ease. As with last year’s results, I have chosen to host this presentation online rather than as a pdf. The advantage is that one perma-link will always lead to the presentation, and any changes can be incorporated anytime. As such, if you are reading this online, you are reading the most up to date version.

I also need your help! If you have filled out this year’s survey already, thank you. If you haven’t, I would urge you to complete it; here’s the link. The average completion time is around 4.5 mins, and by completing it you are helping to bring more clarity to a very opaque area. I am particularly keen to get more responses from women, as it will help to better quantify the gender pay gap.

Terminology

Total Earnings

This section will focus on total earnings (ie P60 + LTIPs), adjusted for 100% full time equivalent pay. I will cover the following:

Earnings are affected by many factors, and considering sector, gender, years experience etc individually in turn is the simplest way to identify any obvious gaps. That being said, analysing each of these factors on it’s own discards the other factors; ie if we just look at years experience, we are ignoring academic achievement, sector etc. I have a model that captures these factors (ie a linear regression model) but have chosen not to present it here as I have done previously and it’s never really seemed to be of interest to the audience.

The overall average for all respondents is £112,301 with a standard deviation of £63,202. The rather large standard deviation is due to the mix of experience levels, ranging from a few junior analysts to some very experienced individuals. The median pay for all respondents is £95,070.

Total earnings by sector

Please note that “Other” is comprised of one response from consulting and one from a regulator.

Sector Mean Minimum Median Top Quartile Maximum Mean Yrs Exp Responses
Broker £ 94,784 £ 42,884 £ 98,826 £ 109,875 £ 165,000 10 6
LLM £ 121,351 £ 33,000 £ 110,596 £ 170,750 £ 275,000 9 42
Other £ 132,000 £ 80,000 £ 132,000 £ 158,000 £ 184,000 12 2
Vendor £ 73,003 £ 42,800 £ 65,750 £ 89,003 £ 132,000 6 8
How does this compare with previous years? Looking at previous survey data we have the following table of earnings:
Year Mean Median
2018 £ 89,248 £ 77,686
2019 £ 107,006 £ 96,125
2020 £ 86,292 £ 75,000
2021 £ 112,301 £ 95,070

There is some variation in the figures, and to a large extent that’s down to noise (different respondents every year), leading to slightly different experience profiles. The figures are also presented as they were collected, ie not adjusted for inflation (not a big problem up to now but perhaps relevant in future years).

Total earnings by gender

All respondents:

Gender Mean Minimum Median Top Quartile Maximum Mean Yrs Exp Median Yrs Exp Responses
Female £ 98,381 £ 35,000 £ 88,225 £ 147,500 £ 215,000 8 6 17
Male £ 118,073 £ 33,000 £ 97,652 £ 160,000 £ 275,000 8 8 41

Respondents with 5 years experience or less:

Gender Mean Minimum Median Top Quartile Maximum Mean Yrs Exp Median Yrs Exp Responses
Female £ 44,199 £ 35,000 £ 44,967 £ 49,070 £ 52,947 3 3 8
Male £ 57,352 £ 33,000 £ 46,000 £ 78,000 £ 90,000 3 4 13

At first it seems that men’s median earnings are twice as high as women’s median earnings, however, as in previous years, the experience profile is different for male and female respondents and we have to take this into account. The median years experience for women is 4 (vs 9 for men). Likewise, the mean for women is c 78% of the mean for men, however, this is reflected in the fact they have a mean of 7 years experience vs 9 for men. If one was to have a ratio of “median earnings per year experience” or “mean earnings per year experience” the figures for men vs women are nearly identical (which is great news!). There is some divergence in the top quartile and maximum earnings though and this is very clear when looking at the respondents with less than 5 years experience, where mean and median experience levels for men and women are identical.

Earnings by gender per year worked

One way to look at the gender pay gap is to calculate how much each year of experience is worth (in other words, divide someone’s total earnings by the number of years experience they have, and do that for everyone). The results are very encouraging; as mentioned in the previous slide, the range, as well as the median amount that women and men get is nearly identical.

Total earnings by years experience

Mean Minimum Median Top Quartile Top Decile Max-Min Ratio 90-10 Ratio Respondents
0-2 years £ 46,733 £ 33,000 £ 43,942 £ 49,000 £ 56,650 2.6 1.6 10
3-5 years £ 64,823 £ 41,832 £ 56,473 £ 81,960 £ 89,784 2.4 2.0 12
6-8 years £ 116,844 £ 76,000 £ 101,192 £ 134,000 £ 168,000 2.6 2.1 13
9-11 years £ 143,687 £ 88,225 £ 139,750 £ 154,250 £ 181,801 3.1 2.0 10
12-14 years £ 182,618 £ 113,167 £ 177,270 £ 208,444 £ 239,000 2.4 2.0 7
15-17 years £ 216,154 £ 215,449 £ 216,154 £ 216,506 £ 216,717 1.0 1.0 2
18-20 years £ 186,333 £ 165,000 £ 184,000 £ 197,000 £ 204,800 1.3 1.2 3
21 years plus £ 42,800 £ 42,800 £ 42,800 £ 42,800 £ 42,800 1.0 1.0 1

What is the “Max-Min Ratio” and the “90-10 Ratio”? Very simply, “Max-Min” is the ratio of the highest pay disclosed by a respondent divided my the lowest disclosed by a respondent (for each respective category) and is a measure of dispersion (or variation, or perhaps you could say inequality). For example, the lowest age bands have relatively narrow ratios (a ratio of 1.6 means the highest respondent earned 1.5 times the lowest respondent). What is very interesting (to me anyway!) about this table is that the ratios (especially the 90-10 one) are very consistent going up to 14 years (after that, the low number of responses makes the ratios inconsistent). The ratios are reasonably similar to the actuarial profession where they are mostly in the 2.5-2.7 range. If you’d like to compare, please see the 2021 Actuarial Salary Survey.

Total earnings by gender and number of staff managed

Below we have charted the Total Earnings for every respondent, given how many staff they manage. The size of each bubble is proportional to how many years experience they have (the large the bubble, the more experienced). What is apparent is that for those individuals who do not manage others (call them “Individual Contributors” or “Non-Managers”), there is a wide variation in pay between men and women, even with similar number of years experience. In other words, female individual contributors appear to be paid less than male individual contributors.

Once we move into management (ie at least one staff member under management), it is harder to state that women are paid less than men, as there is significant variation (at some staff levels women are paid the highest, at others they are not). There is also a positive trend, as one would expect - the higher the staff under management, the higher total earnings.

Total earnings by academic level

HighestQual Mean Min Median Top Quartile Max Mean Yrs Exp Median Yrs Exp Responses
Pre-Bachelor £ 152,667 £ 33,000 £ 210,000 £ 212,500 £ 215,000 12 14 3
Bachelor £ 109,805 £ 35,700 £ 93,500 £ 155,250 £ 270,009 8 8 20
PGD&C and Master £ 112,642 £ 35,000 £ 99,326 £ 156,250 £ 216,858 9 8 28
PhD £ 100,773 £ 42,800 £ 78,000 £ 101,584 £ 275,000 6 5 7

Due to the low number of responses for A-level and GCSE holders, we have consolidated them into a category called “Pre-Bachelor”. We have also consolidated the responses for “Master” and “Post Graduate Diploma & Certificate”.

Can we conclude anything looking at academic levels alone? Given the number of responses and identical experience profiles (ie the same number of mean and median years experience, namely 8), we will concentrate on comparing Bachelor’s degree holders vs the holders of Master’s and PG Certificates/Diplomas. The median for Bachelor’s is quite a bit lower than the mean, suggesting a few outliers at the top end, whereas the mean and median for PGD/C/Master’s is nearly identical. It does suggest some premium for the latter, especially for the “typical” (ie median) respondent.

Total earnings by academic level (part 2)

One way to look at the academic pay differential is to calculate how much each year of experience is worth (in other words, divide someone’s total earnings by the number of years experience they have, and do that for everyone, segmenting by their highest qualification).

It is interesting is that employers don’t seem to really value academic achievement differently between those who only have a Bachelor vs those who have a PGDC/Master (the median pay per year is nearly identical). Median pay per year worked is higher for PhDs, although the highest median pay is for those who only have A-levels/GCSEs. With only three responses, and two of them very experienced, it is likely that these high results are due to the low number of responses in the “Pre-Bachelor” category.

Concluding comments

First of all, thank you for taking the time to read this report. The last year has been unusual in the sense that remote working has completely dominated although I feel it is still too early to really assess the pandemic’s long term impact on earnings. That being said, what have we from this year’s survey and how has it differed from last year?

Finally, I will repeat my appeal from earlier: if you have filled out this year’s survey already, thank you. If you haven’t, I would urge you to complete it; here’s the link again. Better still, if you can forward this on to others, it will help spread the word.

About me

I have spent the last 17 years recruiting for senior actuarial, catastrophe modelling and analytical positions in the UK, and abroad (principally the Americas and Bermuda). My client base ranges from consumer insurance, commercial lines and Lloyds of London to more unusual operations involved in insurance linked securities, private equity and broking. I also research and write virtually all of our research reports, including compensation surveys.

Prior to joining Hanover Search in 2010, I spent 6 years working as lead consultant for the insurance and financial services division of Hays, a global FTSE-250 listed recruitment group.

Prior to university I spent 2 years working in sales for Churchill Insurance, one of the legacy companies of Direct Line Group.

I have a degree in Economics and Mathematics from York University and have completed a number of development courses, including a Strategy and Finance module with INSEAD and a psychometrics certificate with Cambridge University. In the summer of 2020 I completed two R-based courses with Essex University’s Summer School in Quantitative Social Science.

I have 2 young children under 5 so I don’t really have any spare time, but when I get a moment, I usually spend it reading.

Get in touch

Other than my research work (eg salary surveys), I am an active member of Hanover’s search and selection team. I have significant experience of completing difficult searches, especially where the “candidate pool” is very small. My track record includes:

My recruiting philosophy can be summarised as very simply:

I am always happy to:

For all queries, please email me:

Other reports

Since early 2018, I have authored a number of reports/briefs and a selection is listed below. All are available to download directly from my cloud folder (right click, save as, no registration needed). -Demographic Analysis of Cat Modelling Teams in the London Market

-Market Conditions for Actuarial, Catastrophe and Data Science

-AI & ML Expertise in the major insurance Companies

Last but not least, if you recruit or manage actuaries, you will want to see my 2021 Actuarial Salary Survey