Introduction

This is Hanover’s third 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 2018 editions.

The project grew out of a desire to have a more granular handle on pay. It has since expanded to other areas such as actuarial and risk management. 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.

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 analysts, although I am connected to a small number 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 foresee three types of uses for this salary survey:

• As an individual contributor, you can benchmark your own salary against the market, either to ensure you are “on track” or perhaps when evaluating a job offer you have received
• As a leader of CatEx staff, you can benchmark your staff against the wider market, or perhaps use it when evaluating the level of money you’d like to offer to people you are interviewing
• As a HR/compensation professional, you can use this survey to benchmark all your staff and also when it comes to putting together job offers

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 constanly updated, so it may have changed slightly since the last time you viewed it (as new responses and new analysis might have taken place).

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 this year was 4 mins 20 seconds, 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.

What’s new for 2020

For 2020, the focus is on simplicity and readability. The aim is to have a salary survey that can be understood by anyone with some level of insurance knowledge, and to that extent I have removed much of the more statistical exhibits from last year’s survey.

For this year, I have changed the way I analyse the data. In previous years I used a combination of Excel, Tableau, PowerPoint, Google Sheets and Paint. This year I have transitioned to the “R” programming language, using an integrated workflow called RMarkdown. The details of how Rmarkdown works are not important, however, the advantages are:

• Output can be presented in a number of ways, including a pdf or an online, web hosted version
• Text, data and analysis are integrated
• Output is dynamic and adjusts if new data comes in; for example, if extra responses are gathered, all summary statistics such as averages are updated automatically
• Future editions can be updated as code can be copied and pasted with minimum extra work; for example, the extra code required to convert this web presentation to a pdf is about 2 lines

In another break with previous editions, 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.

Terminology

• All figures are for the financial year 6th of Apr 2019 to 5th of Apr 2020.
• Any reference to previous years follows the same convention; ie reference to “2019” means the financial year Apr 2018 to Apr 2019 etc
• “P60” refers to the P60 earnings; for the vast majority, it comprises of base, allowances, cash bonus, deductions for pension contributions etc
• “LTIPs” refers to vested long term incentives
• “Earnings” refers to P60 + LTIPs, with the total adjusted for 100% full time equivalent pay; for example, if a respondent stated that they earned a P60 of £80k working 80% (4 days a week), with zero LTIPs, their full time equivalent pay is £100k
• Anyone with more than 20 years experience has had their years of experience set to 20 (this is to prevent potential identification of older staff members)
• “LLM” refers to the Lloyds and London Market; “Other” clusters together responses from the individuals working for the regulator, ILS and consultancy fields
• Later in the report I will talk about a concept I call “£ per year”, that is, the cost per year of experience. This is a very handy concept as it’s simple and essentially captures the value of each additional year of experience

Earnings

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

• Total earnings by sector
• Total earnings in previous years
• Total earnings by gender
• Total earnings by number of years experience
• Cost Ratio by gender and sector

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.

For those who are more statistically include, the Appendix includes two additional types of analysis: a linear regression and a regression tree. Both are fairly straightforward to explain to non-specialists, and I am particularly keen on regression trees as they capture non-linearities in an intuitive, graphical way.

Total earnings by sector

Sector	Mean	Minimum	Median	Top Quartile	Maximum	Avg Yrs Exp	Respondents
Broker	£ 79,151	£ 40,000	£ 83,250	£ 102,212	£ 111,125	8	6
LLM	£ 90,189	£ 16,446	£ 69,950	£ 115,500	£ 265,000	8	30
Other	£ 83,200	£ 55,000	£ 69,600	£ 97,300	£ 125,000	7	3
Vendor	£ 79,736	£ 41,111	£ 75,034	£ 92,602	£ 140,000	7	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	£ 86,555	£ 70,400

Although at first glance it appears that average earnings “spiked” in 2019, there is more to this story (see next slide). The reason is that every yearly survey will have a slightly different spread of respondents; thus, the average (ie mean) or median of the number of years experience for that year’s survey will differ, and we need to take this into account when comparing differences across years.

Total earnings in previous years

In the table below we have computed both the mean and median earnings per year of experience by dividing the mean and median earnings for 2018-20 by the mean and median years of experience for our respondents. This metric is called “£ per year”.

Year	Mean	Median	Mean Yrs Exp	Median Yrs Exp	Mean £/Year	Median £/Year
2018	£ 89,248	£ 77,686	8	7	£ 14,919	£ 12,330
2019	£ 107,006	£ 96,125	8	7	£ 18,064	£ 12,285
2020	£ 86,555	£ 70,400	8	7	£ 12,160	£ 11,455

Conclusion? The median earnings per year of experience (“Median £ Per Year”) has actually been decreasing (albeit gently) since 2018. This is unsurprising given the number of catastrophes in the market. The mean is more variable, but recall that the mean is NOT my preferred method of reporting salaries as means are always affected by outliers.

As a rule, people’s base salaries stay the same or even increase slightly year-on-year. Given that median earnings per year has been declining, I can only conclude that this primarily due to lower bonuses. In other words, the “typical” CatEx employee has seen their total earnings decline slightly since 2018, primarily through lower bonuses.

Total earnings by gender

All respondents:

Gender	Mean	Minimum	Median	Top Quartile	Maximum	Median Yrs	Median £ / Year	Respondents
Female	£ 57,924	£ 16,446	£ 47,117	£ 65,018	£ 119,000	4	£ 11,101	16
Male	£ 101,332	£ 34,125	£ 77,000	£ 127,705	£ 265,000	7	£ 12,706	31

Respondents with 5 years experience or less:

Gender	Mean	Minimum	Median	Top Quartile	Maximum	Median Yrs	Median £ / Year	Respondents
Female	£ 38,619	£ 16,446	£ 41,243	£ 47,233	£ 52,299	3	£ 13,744	9
Male	£ 52,334	£ 34,125	£ 50,250	£ 63,332	£ 73,000	4	£ 15,156	10

We have to consider that the median number of years experience for female respondents is less than men in both cases, but even accounting for that, there is a difference. Using the previous slides as a guide, when we look at all respondents, women earn c 87% of what men earn. When we consider respondents with less than 5 years experience, women earn c 90% of what men earn.

Please note this analysis ONLY takes into account the number of years experience, and ignores other factors such as academic achievement, team size managed etc. For those who are more statistically minded and wondering “could you just run a linear regression with all these factors plus gender?”, more can be found in the Appendix.

Total earnings by years experience

Years	Mean	Minimum	Median	Top Quartile	Maximum	Respondents
1	£ 16,446	£ 16,446	£ 16,446	£ 16,446	£ 16,446	1
2	£ 48,000	£ 45,000	£ 48,000	£ 49,500	£ 51,000	2
3	£ 42,479	£ 17,460	£ 42,622	£ 46,750	£ 66,110	8
4	£ 46,005	£ 36,000	£ 48,617	£ 50,375	£ 52,299	6
5	£ 71,300	£ 69,600	£ 71,300	£ 72,150	£ 73,000	2
6	£ 71,690	£ 52,691	£ 77,034	£ 77,801	£ 80,000	4
7	£ 70,253	£ 41,111	£ 69,950	£ 77,500	£ 100,000	4
8	£ 71,247	£ 47,000	£ 76,742	£ 83,371	£ 90,000	3
9	£ 106,627	£ 70,400	£ 108,054	£ 139,707	£ 140,000	4
10	£ 105,000	£ 105,000	£ 105,000	£ 105,000	£ 105,000	1
11	£ 122,553	£ 122,553	£ 122,553	£ 122,553	£ 122,553	1
12	£ 120,768	£ 111,125	£ 120,768	£ 125,589	£ 130,410	2
13	£ 116,761	£ 106,282	£ 119,000	£ 122,000	£ 125,000	3
14	£ 140,000	£ 140,000	£ 140,000	£ 140,000	£ 140,000	1
15	£ 102,000	£ 102,000	£ 102,000	£ 102,000	£ 102,000	1
16	£ 240,778	£ 240,778	£ 240,778	£ 240,778	£ 240,778	1
19	£ 170,000	£ 170,000	£ 170,000	£ 170,000	£ 170,000	1
20	£ 258,500	£ 252,000	£ 258,500	£ 261,750	£ 265,000	2

Total earnings by gender and number of staff managed

Please note:

• The size of the dots is proportional to the number of years experience the respondent has; thus, when looking at all the respondents with zero staff, it may look at first like men are paid more consistently, however, the male respodents also had a higher number of years experience
• There is an upward trend between staff managed and total earnings
• Earnings seem to be similar for similarly-sized teams (as well as that, the respondents running similarly-sized teams seemed to have similar levels of experience)

Total earnings by academic level

HighestQual	Mean	Minimum	Median	Top Quartile	Maximum	Median Yrs	Median £ / Year	Respondents
Pre-Bachelor	£ 115,030	£ 17,460	£ 70,000	£ 170,000	£ 265,000	7	£ 9,360	5
Bachelor	£ 82,895	£ 16,446	£ 70,400	£ 112,276	£ 240,778	8	£ 11,856	15
PGD&C and Master	£ 81,390	£ 34,125	£ 69,900	£ 95,000	£ 252,000	6	£ 12,261	19
PhD	£ 87,888	£ 51,000	£ 89,000	£ 109,462	£ 130,410	8	£ 14,239	8

Please note that due to the low number of responses for A-level and GCSE holders, we have consolidated them into a category called “Pre-Bachelor”. This category has a big outlier, so we have reported the mean, but, as always, we suggest using the median as a better reference point.

We have also consolidated the responses for “Master” and “Post Graduate Diploma & Certificate” as the latter only had two responses.

Can we conclude anything looking at academic levels alone? Looking at Median £ per Year (a concept defined in earlier slides), it is fairly clear that having some form of postgraduate qualification beyond a Bachelor degree confers around 3% extra per year. Whilst this might not seem like a large differential, extrapolated over (say) 20 years this would make a meaningful difference in earnings.

There is also an earnings differential for PhD holders, which is entirely consistent with human capital theory. Using Bachelor holders as a baseline, a PhD earns around 20% more per year. Using a Master/PGDC holder as a baseline, a PhD earns around 16% more. Again, compounded these will make a big difference over a lifetime of earnings.

Concluding comments

First of all, thank you for taking the time to read this report.

I concluded last year’s survey with a discussion of flexibility and work life balance as a differentiator. Little did I think that I would be writing the next year’s follow up against the backdrop of virtually 100% remote working. To some extent it is too early to really assess the pandemic’s long term impact on earnings.

What have we learned so far?

• Pay for a “typical” CatEx professional (ie median earnings) has declined slightly over the last two years, mainly as a result of lower bonuses
• There is a gender pay gap; although we haven’t discussed it so far, the Appendix uses linear regression to quantify the pay “penalty” for a woman (for all respondents in this year’s survey, being a woman means on average around £23k a year less).
• Broadly speaking, more formal education implies higher earnings per year of experience, although the gap between a Bachelor and a Master’s/Post Graduate Diploma/Certificate isn’t massive
• There is an upward trend between staff managed and total earnings

This year’s survey was sent in Apr 2020, at the early stages of the pandemic. Perhaps for that reason, the first draft of this report was based on only 40 responses. 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 15 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.

I am always happy to discuss the results and methodology of this survey: michael.stefan@hanoversearch.com

Appendix

I have always been very interested in being able to predict (to a reasonable degree of accuracy) what someone’s total earnings are. I have fielded numerous questions from both HR and team leaders over the years, usually in relation to benchmarking a role they want to recruit for (and sometimes benchmarking themselves or their own team members). When conducting a search, it’s also useful for me to estimate what someone might be on as it allows me to do a “package sense check” in my own mind before approaching them.

As such, I attempt a linear regression model every year, with the key caveat that I want a model that’s reasonably predictive whilst being as parsimonious as possible. There is of course a trade off here - the simplest model could have just one predictor: mean (average) earnings. Clearly this wouldn’t be best model for prediction but it is the simplest.

More complex models will include years experience, staff managed, the highest level of qualification etc. I will not list every combination of model that I’ve tried, but suffice to say that (unusually), this year’s adjusted R squared is quite high (> 0.8).

Regression diagnostics are on the next slide.

A Linear Model (part 1)

The variables Years, Staff and Female are all statistically significant. Female is a dummy variable (=1 if Female, =0 if Male). Regression diagnostics can be seen below. The first set of regression diagnostics includes HighestQual. The second does not, as HighestQual is also not statistically significant.

Observations	47
Dependent variable	Total
Type	OLS linear regression

F(6,40)	36.23
R²	0.84
Adj. R²	0.82

	Est.	S.E.	t val.	p
(Intercept)	15721.55	14779.21	1.06	0.29
Years	9745.54	922.87	10.56	0.00
Staff	145.87	1065.68	0.14	0.89
Female	-22939.92	7722.17	-2.97	0.01
HighestQualBachelor	2258.75	14069.30	0.16	0.87
HighestQualPGD&C and Master	2496.05	13940.41	0.18	0.86
HighestQualPhD	7944.03	15245.36	0.52	0.61
Standard errors: OLS

A Linear Model (part 2)

Observations	47
Dependent variable	Total
Type	OLS linear regression

F(3,43)	76.97
R²	0.84
Adj. R²	0.83

	Est.	S.E.	t val.	p
(Intercept)	18418.62	7603.67	2.42	0.02
Years	9811.20	887.39	11.06	0.00
Staff	-2.75	911.43	-0.00	1.00
Female	-22425.42	7306.08	-3.07	0.00
Standard errors: OLS

As can be seen, simply being female will lower earnings by over £23k a year. In this second model, the adjusted R squared is the same whiles Staff is not significant at all.

The conclusion? I mentioned it in last year’s salary survey too, but I have long thought that the cat modelling market primarily pays based on years of experience (ie longevity in the market). Whilst at first this is not a controversial statement, the logical outcome is that the market is more akin to the Japanese and Korean labour markets that reward people primarily on how long they’ve been in the job. That really bears some thinking about.

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 Box folder (right click, save as, no registration needed).

-Demographic Analysis of Actuarial Teams in the London Market

-Demographic Analysis of Cat Modelling Teams in the London Market

-Compensation Report 2018, UK General Insurance Actuaries

-Compensation Report 2018, Catastrophe & Exposure Modellers

-Compensation Report 2018, Risk Compliance and Governance

-2019 Market Conditions for Actuarial, Catastrophe and Data Science

-Salary Survey 2018, SIMF4 CROs

-AI & ML Expertise in the major insurance Companies

-Compensation Report 2019, UK General Insurance Actuaries

-Compensation Report 2019, Catastrophe Modelling & Exposure Management

I also maintain an up to date online Tableau viz of all “The Actuary” print magazine job ads in the GI sector (whether qualified or not). Whilst I won’t be developing the viz any further, I update the underlying data on a monthly basis when “The Actuary” arrives and you can view it here.

Last but not least, if you recruit or manage actuaries, you will want to see my 2020 Actuarial Salary Survey