Paying Charity CEOs large amounts isn't as bad as it looks

A turn-off, but not a scandal.

The Charity Commission warns that spiralling levels of chief executive pay risk bringing organisations and the wider charitable sector into disrepute, following news in the Telegraph that 30 charity chiefs are paid more than £100,000.

High levels of pay and administration costs can be a real turn off. This comes from a natural desire to ensure that money is well-spent, which for a number of people means as much money as possible goes directly to the front-line, to helping people most in need.

To begin with, if all the money goes to the frontline, but staff at the front-line are being ineffective because the strategy’s poor, then that money’s been badly spent. For us, there isn’t a level at which pay in the charity sector becomes too high; charities are trying to solve some of our most stubborn social problems and they need to attract talent to be able to do that.

Pay in charities is a much more finely balanced argument than is usually supposed. We’ve put together some advice on how donors can think about whether or not giving to a charity with high salaries should be a cause for concern:

First, and most importantly, it’s all about impact. Knowing that children have been sponsored or that schools have been built isn’t enough: you need to know exactly what difference the charity is making, and how this is happening. Action on Hearing Loss’s annual report provides a summary of what it has achieved against its aims, which helps donors decide whether the organisation spends their money well.

Second, you need to consider the complexity of the charity. The CEO of Oxfam is paid £120,000, and is responsible for a £360 million budget, 700 shops in the UK and 5,000 employees and 20,000 volunteers who work in over 90 countries across the world—some of them very risky places to be. £120,000 doesn’t feel like a lot in the context of that job description. The CEO of Next also runs 700 shops (but no humanitarian aid) and gets nearly £1.5m. Of course, this is all proportionate to the task and budget at hand: you don’t want a £500,000 income charity to spend £100,000 on its CEO’s salary.

Third, although its difficult to tell from the outside, what value is the CEO bringing? Have they increased the charity’s profile and fundraising? Have they devised a good strategy? If the case is that you need to pay up for talent, then supporters should be able to see the fruits of that talent.

Finally, it’s worth thinking about the quality of the staff throughout the organisation. If the charity is making an argument that they need to pay well to attract the best staff at the top, then you want them to apply the same logic to front-line staff. Medicins Sans Frontières has a rule that the chief executive can’t be paid more than three times the pay of the lowest paid member of staff.

By making the judgement call based on these factors — and not on gut feelings about pay — more money will be well spent. We’d like to see the impact of the UK’s leading aid charities make the headlines, instead of six-figure salaries that really say nothing on their own.

Angela Kail is head of Funder Effectiveness at New Philanthropy Capital, which helps donors choose effective charities

This piece first appeared on Spear's.

Photograph: Getty Images

This is a story from the team at Spears magazine.

OLIVER BURSTON
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How science and statistics are taking over sport

An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others.

In the mid-1990s, statistics undergraduates at Lancaster University were asked to analyse goal-scoring in a hypothetical football match. When Mark Dixon, a researcher in the department, heard about the task, he grew curious. The analysis employed was a bit simplistic, but with a few tweaks it could become a powerful tool. Along with his fellow statistician Stuart Coles, he expanded the methods, and in doing so transformed how researchers – and gamblers – think about football.

The UK has always lagged behind the US when it comes to the mathematical analysis of sport. This is partly because of a lack of publicly available match data, and partly because of the structure of popular sports. A game such as baseball, with its one-on-one contests between pitcher and batter, can be separated into distinct events. Football is far messier, with a jumble of clashes affecting the outcome. It is also relatively low-scoring, in contrast to baseball or basketball – further reducing the number of notable events. Before Dixon and Coles came along, analysts such as Charles Reep had even concluded that “chance dominates the game”, making predictions all but impossible.

Successful prediction is about locating the right degree of abstraction. Strip away too much detail and the analysis becomes unrealistic. Include too many processes and it becomes hard to pin them down without vast amounts of data. The trick is to distil reality into key components: “As simple as possible, but no simpler,” as Einstein put it.

Dixon and Coles did this by focusing on three factors – attacking and defensive ability for each team, plus the fabled “home advantage”. With ever more datasets now available, betting syndicates and sports analytics firms are developing these ideas further, even including individual players in the analysis. This requires access to a great deal of computing power. Betting teams are hiring increasing numbers of science graduates, with statisticians putting together predictive models and computer scientists developing high-speed software.

But it’s not just betters who are turning to statistics. Many of the techniques are also making their way into sports management. Baseball led the way, with quantitative Moneyball tactics taking the Oakland Athletics to the play-offs in 2002 and 2003, but other sports are adopting scientific methods, too. Premier League football teams have gradually built up analytics departments in recent years, and all now employ statisticians. After winning the 2016 Masters, the golfer Danny Willett thanked the new analytics firm 15th Club, an offshoot of the football consultancy 21st Club.

Bringing statistics into sport has many advantages. First, we can test out common folklore. How big, say, is the “home advantage”? According to Ray Stefani, a sports researcher, it depends: rugby union teams, on average, are 25 per cent more likely to win than to lose at home. In NHL ice hockey, this advantage is only 10 per cent. Then there is the notion of “momentum”, often cited by pundits. Can a few good performances give a weaker team the boost it needs to keep winning? From baseball to football, numerous studies suggest it’s unlikely.

Statistical models can also help measure player quality. Teams typically examine past results before buying players, though it is future performances that count. What if a prospective signing had just enjoyed a few lucky games, or been propped up by talented team-mates? An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others. In many sports, scoring goals is subject to a greater degree of randomness than creating shots. When the ice hockey analyst Brian King used this information to identify the players in his local NHL squad who had profited most from sheer luck, he found that these were also the players being awarded new contracts.

Sometimes it’s not clear how a specific skill should be measured. Successful defenders – whether in British or American football – don’t always make a lot of tackles. Instead, they divert attacks by being in the right position. It is difficult to quantify this. When evaluating individual performances, it can be useful to estimate how well a team would have done without a particular player, which can produce surprising results.

The season before Gareth Bale moved from Tottenham Hotspur to Real Madrid for a record £85m in 2013, the sports consultancy Onside Analysis looked at which players were more important to the team: whose absence would cause most disruption? Although Bale was the clear star, it was actually the midfielder Moussa Dembélé who had the greatest impact on results.

As more data is made available, our ability to measure players and their overall performance will improve. Statistical models cannot capture everything. Not only would complete understanding of sport be dull – it would be impossible. Analytics groups know this and often employ experts to keep their models grounded in reality.

There will never be a magic formula that covers all aspects of human behaviour and psychology. However, for the analysts helping teams punch above their weight and the scientific betting syndicates taking on the bookmakers, this is not the aim. Rather, analytics is one more way to get an edge. In sport, as in betting, the best teams don’t get it right every time. But they know how to win more often than their opponents. 

Adam Kucharski is author of The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling (Profile Books)

This article first appeared in the 28 April 2016 issue of the New Statesman, The new fascism