Such Is Life (Insurance)

When David Graeber published Bullshit Jobs back in 2018 I expected a much bigger reaction from the defenders of the status quo. Here was someone with a relatively high profile (at least as far as academics go) making some pretty incendiary claims about how society operates. In essence Graeber was arguing that that the traditional ‘protestant work ethic’ has curdled into an ideology that reveres work for its own sake. At the same time, the idea of work as a moral duty has prompted the creation of countless new bullshit jobs to compensate for those lost due to technology and automation.

Moreover, Graeber argued that this pseudo-religious attitude toward work – combined with the meaninglessness of many white collar jobs – has resulted in an epidemic of psychological unrest. Despite the story we’re told by mainstream economists the reality appears to be that people don’t actually thrive when they minimise their efforts while maximising their income. Humans may hate working but we hate feeling like we’re wasting our time more.

Since the publication of Bullshit Jobs other researchers have come along and tested Graeber’s theory by conducting their own surveys of the workforce. These surveys have revealed that somewhere between a third and half of all white collar workers believe that their jobs serve no social purpose.

Even if we take the lower estimate that would still mean that an enormous amount of economic activity is, in some meaningful sense, ‘unproductive’. You’d think an accusation like this would put a few noses out of joint. After all, doesn’t the existence of a massive number of bullshit jobs contradict one of the basic tenets of capitalism? Isn’t the efficient allocation of human labour supposed to be a side-effect of free markets and competition? If half of all office workers are just spinning their wheels for 40 hours a week then don’t we have a glaring problem with our economic system?

These all seemed like important questions to me but, as far as I could tell, Graeber’s thesis didn’t get much pushback from the neoliberal economists and free market think tanks. Instead, the few dissenting opinions on Bullshit Jobs that were published generally acknowledged the fundamental problem while downplaying its scale. To bolster their case most of these articles (like this one from The Economist) pointed to a British paper published in 2021 which suggested that Graeber’s findings were exaggerated. 

That paper leaned heavily on the results of a survey conducted in 2015 which, among other things, asked respondents how often they experienced ‘the feeling of doing useful work’. According to this survey only about 4.8% of workers in the EU reported that they ‘rarely’ or ‘never’ felt that they were doing useful work. Moreover, the authors insisted that these workers weren’t actually demoralised by meaninglessness work but were instead just suffering due to poor management and the general sense of alienation that comes from being a small cog in a big machine. 

But the British paper never really addressed the fundamental issue that Graeber identified in Bullshit Jobs because the survey it relied on was more concerned with gauging day-to-day workplace morale. Its key question about the ‘feeling of doing useful work’ was also open to a range of interpretations. As one commenter on Hacker News pointed out:

“If I don’t “have the feeling of doing useful work”, does that mean my job is not useful, or my job is potentially useful but carried out inefficiently? Am I attempting useful work but blocked from doing so by some administrative or practical barrier? Do I consider myself insufficiently talented or qualified at my job to carry it out in a useful manner?”

By comparison, the case studies that Graeber compiled were from people who recognised that their work didn’t make any meaningful contribution to the world. He was trying to understand the psychological impact of toiling away at a job you thought shouldn’t exist in the first place. 

But aside from a handful of articles that attempted to minimise the problem there really wasn’t a lot of pushback from the Milton Friedman types. Even Australia’s foremost business lobby group – the Institute for Public Affairs – basically accepted Graeber’s premise that the labour market in the western world was gradually being subsumed by ‘layer upon layer of middle management’ – a process of bureaucratisation that Graeber referred to as ‘managerial feudalism’.

Of course if you keep clicking through to the tail end of the google search results you eventually find commentators who reject, on principle, the idea that a capitalist economic system could produce a whole lot of useless corporate jobs. These opinions mostly crop up on the fringes of online discourse – LinkedIn posts, Quora threads and (eugh) personal blogs.

Although these commentators, as individuals, may not have a massive audience I think it’s important to understand their perspective because their opinions are probably more representative of what we might call ‘conventional wisdom’. Despite all the corporate scandals and scams and all the press coverage of ‘zombie companies’ I get the sense that a lot of people still cling to the idea that the big businesses are lean and efficient and immune to the sorts of bureaucratic dysfunction that is often attributed to public institutions and government departments.

So in order to address this myth of efficiency I’ve chosen to focus on one particular essay written by freelance tech journalist Bryn Hobart. Under the headline ‘Bullshit Jobs is a terrible, curiosity-killing concept’ Hobart calls for his readers to show a ‘deeper appreciation for the complexity of the modern economy’. While he acknowledges that there are a lot of bullshit jobs he insists that we shouldn’t worry too much about them because ‘the world is full of mysterious economic phenomena’. We certainly shouldn’t use these quirks in the labour market to judge the economic system as a whole.

In order to maintain that view Hobart ignores several surveys – like the one conducted in Britain – which show that a sizeable percentage of workers believe that their jobs are essentially meaningless. He also ignores the case studies featured in Bullshit Jobs. According to Hobart, Graeber’s thesis is flawed because it’s based on testimony from the wrong people. Instead of asking the workers themselves whether they thought their work was useful he should have asked their bosses – because obviously the only people who can be trusted to understand the bigger picture are those signing the paycheques. Thus he dismisses all the self-submitted testimonials in Bullshit Jobs as standard gripes made by freewheeling hippies.

“surely after the tenth person told a story about living on a commune or partying with anarcho-syndicalists, Graeber should have wondered if this is a truly representative audience.”

I take exception to this line because I’m one of the people cited by Graeber in Bullshit Jobs and I’ve never lived on a commune or been invited to any anarcho-syndicalist parties (more’s the pity). In fact, based on what I can see on LinkedIn, Hobart’s work history looks fairly similar to my own. The big difference is that he doesn’t appear to have worked for any large corporations. And you can tell that he’s only seen big business from afar by the rose tinted glasses he puts on when he wanders downtown.

“The big wealth-generating industries tend to be intolerant of [inefficiencies]; when competition is fierce, it’s hard to justify arbitrarily accepting low margins, and when some roles require long hours, it’s demoralizing to employ people who are visibly going to shirk”

Look, it’s easy to laugh at these sorts of claims but I believed the same thing once upon a time and I think this basic assumption of efficiency is still fairly widespread. People who’ve only worked in small businesses tend to think the larger ones must be less dysfunctional and people who’ve only ever worked in the public sector tend to imagine that the grass must be greener (or at least more regularly mowed) in the private sector. Gross inefficiencies are hard to reconcile with what we’re told about how profit-driven companies are managed and led and when you’ve never never worked in a large corporation it’s easy to believe that all those people you see scurrying in and out of office towers in the CBD must be engaged in something productive.

Hobart’s critique is a bit scattered but his argument against Bullshit Jobs seems to be based on the mistaken idea that Graeber developed his theory by personally passing judgement on the social utility of various professions. Basically Hobart says that anthropologists are not qualified to assess whether a given job is bullshit or not and, to illustrate this, he seized on a footnote in which Graeber issued a half-hearted retraction regarding the social value of actuaries.

“Since the book was written to expand a previous article, he has room to backtrack on at least one of those [bogus jobs], conceding that actuaries may do something useful, something he learned through pushback. Here we have our first anthropological datapoint: he didn’t learn this by asking himself [‘is this job useful?’] … No, what happened according to Graeber, is that people who read his claims responded and set him straight.”

Hobart treats this very mild concession as a ‘gotcha’ moment – insisting that if Graeber was wrong about actuaries then perhaps he might be wrong about all those other bullshit jobs.

This is a bit of a red herring because Graeber never suggested that he had devised an objective way of measuring a job’s social utility. Neither did he present himself as the authority on which jobs should be considered bullshit. Rather, his definition of a bullshit job was based on the subjective assessment of the person doing it:

“A bullshit job is a form of paid employment that is so completely pointless, unnecessary, or pernicious that even the employee cannot justify its existence even though, as part of the conditions of employment, the employee feels obliged to pretend that this is not the case.”

This is a pretty fundamental point to miss so I probably should have just ignored Hobart’s essay in its entirety but I kept thinking about his decision to defend the role performed by actuaries. Because that’s something I might have done if I didn’t know better. Specifically, he poses this rhetorical question:

“Is there, after all, some kind of social utility in knowing how long someone is likely to live? In an advanced economy where people [are] working from the first moment they’re capable of it until they’re incapacitated or dead, might we expect such a job to exist, to create value, and to be paid accordingly?”

I think that’s a reasonable question. I also think Hobart’s assumption about what actuaries do is probably shared by a lot of people. Unfortunately it happens to be wrong – or, at least, outdated. So, in this essay, I’m going to try to explain why Graeber was right in the first place and why some actuarial jobs are bullshit. 

What is an Actuary?

Before we get into the weeds I probably need to describe what an actuary does. For those who aren’t familiar with this profession, an actuary is a type of statistician whose responsible for assessing the long term risks associated with financial decisions. On a good day they might describe their role as ‘analysing risk’ or ‘estimating the costs of uncertain events’. On a bad day they might quote the classic joke: 

“An actuary is a person who passes as an expert on the basis of their ability to produce incomprehensible figures calculated with minute precision from the vaguest of assumptions based on debatable evidence from inconclusive data derived by persons of questionable reliability for the sole purpose of confusing an already hopelessly befuddled group of persons who never read the statistics anyway.”

For some reason actuaries don’t feature very heavily in popular culture. The closest thing to a fictional actuary that I can think of is the unreliable narrator in Chuck Palahniuk’s novel Fight Club – who’s job sounds more like an insurance claims assessor with a bit of risk management thrown in. In an early passage he describes the gruesome results of a car crash caused by a manufacturing defect before laying out the brutal calculus of conducting a recall*.

“You take the population of vehicles in the field (A) and multiply it by the probable rate of failure (B), then multiply the result by the average cost of an out-of-court settlement (C).

A times B times C equals X. This is what it will cost if we don’t initiate a recall.
If X is greater than the cost of a recall, we recall the cars and no one gets hurt.
If X is less than the cost of a recall, then we don’t recall.”

Real actuaries deal with much more complex formulas. They generally work for banks and superannuation funds and government departments but the prototypical example (and the one that Hobart reached for in his essay) are those who work in the life insurance industry.

When you think about it life insurance is a fairly strange proposition. It’s sort of like gambling for pessimists. When you buy life insurance you’re effectively betting that you’re going to die before you get old (in which case your family gets a big payout). The insurance company, on the other hand, is betting you’re going to live through to retirement (in which case they get to keep all the fees you’ve paid). Traditionally, the task of life insurance actuaries has been to calculate the likelihood that a given person will expire before their insurance policy does. If, on average, their predictions are right, the company gets to keep making money. If, on average, their predictions are wrong, the company collapses like a ponzi scheme.

At least that was my understanding of the business model before I started working in the industry. Like most people I thought that life insurance companies were all about assessing the risks posed by illnesses and accidents. My naive assumption was that ‘innovation’ in the context of life insurance meant finding new ways to understand and anticipate human mortality. According to that logic the most successful life insurance providers must be the ones that have turned risk analysis into an exact science. Presumably their actuaries could simply glance at your vital statistics and calculate the exact date and time you’d drop dead from congenital heart failure.

Maybe one day, I figured, I might even get the chance to meet these mystical figures – these corporate Norns – secluded in their labs, weaving the fates of mankind in microsoft excel.

The reality was a bit of a let down. It turns out that the work that actuaries do is much closer to accounting than prophecy and their jobs are subject to the same social, political and bureaucratic forces as any other. While the discipline itself is sometimes referred to as ‘actuarial science’ what these workers do is only scientific in the broadest sense of the word. They certainly use mathematical methods but their calculations are only as good as their underlying data.

And reliable data is hard to come by because, as a general rule, life insurance companies do not have ties to research institutions. Their actuaries do not attempt to grapple with, say, the implications of the latest treatments for heart disease or the knock-on effects of improved vehicle safety features. For the most part these companies do not employ medical experts, sponsor long-term studies on mortality or create empirical models to predict life expectancy. And while the basic ‘life tables’ that the industry relies on are based on real demographic data, these figures are generally compiled by research institutions, collated by government departments, provided free of charge to insurance companies and glued together with folk wisdom handed down by previous generations of actuaries and underwriters.

Even in the best case scenario these formulas are always going to be somewhat unreliable because the variables that get used to price policies are socially determined. Take smoking as an example. A scientific consensus that cigarettes were a leading cause of lung cancer emerged during the 1930s and 40s but it took until the 60s and 70s – when public opinion towards smoking began to shift – for life insurance companies to begin pricing in the health risks associated with smoking. 

In the United States you can see the same lag between scientific consensus and insurance pricing when it comes to firearm ownership. Insurance companies in North America typically demand higher fees if your home has a pool or a trampoline but they don’t charge higher fees those who own firearms – despite the fact that gun owners are approximately four times more likely to be shot than non gun owners and about eight times more likely to commit suicide.

This lack of what you might call a scientific approach to risk became pretty obvious to me soon after I began working for one of these life insurance providers. One of the first projects I worked on was the digitisation of the company’s underwriting questionnaire. This is the test used to determine an applicant’s risk rating (and, by extension, the monthly ‘premium’ they’ll pay). Our job was to make this questionnaire available online but, in the process, we wanted to rearrange the order and reduce the number of questions to make it easier to follow and somewhat less intrusive.

Ultimately, we were looking for ways to improve the ‘user experience’ but, when we asked the actuaries about the significance and necessity of each question, they were generally pretty evasive. They didn’t want to make any changes – even apparently superficial ones – but they also didn’t want to tell us why everything had to remain the same. What we eventually realised was that they didn’t want to tamper with the questionnaire because they didn’t really know which questions – aside from the ‘big four’ variables (age, sex, smoking status and BMI) – were actually useful for determining how long someone would live. 

Underwriting, it turned out, was a bit of a black box. Answers went in and premiums came out but no one knew exactly what happened in between. The risk ratings assigned to certain occupations were a big part of this mysterious formula. The company I worked for had a master list of jobs with individual weightings which were supposedly meant to reflect the risks associated with that job. And while the broad categories seemed sensible (eg. white collar office jobs were considered low risk while blue collar jobs involving heavy machinery were considered high risk) the weightings differed for very similar jobs in slightly different industries and the assumptions about what each job involved was, even the actuaries admitted, very outdated. 

At the company I worked for, the most recent addition to the underwriting questionnaire was a section dedicated to adventure sports. This sub-questionnaire attempted to create a hierarchy of risk for everything from paragliding and rock fishing to downhill mountain biking. At the same time, the underwriting questionnaire totally ignored more routine but, presumably, much higher risk activities like commuting to work on two wheels or foraging for wild mushrooms.

The actuary on our team readily admitted that there wasn’t much science behind these weightings. In his opinion the main factor determining someone’s risk of accidental injury was probably not what they did for a job, or what they did on the weekends, but the way they behaved at home. By his estimation the biggest risk factor when it came to fatal accidents and injuries was overconfidence. When pressed on how we might improve the underwriting questionnaire he half-jokingly suggested we switch to behavioural questions. For example we might ask someone how they would respond if their gutters were overflowing during a rainstorm. If their first instinct is to call a handyman they’re probably going to live longer than someone who doesn’t think twice before hauling out the ladder.

Eventually I came to understand that most underwriting questions were put there simply to disqualify people for hereditary medical conditions or to filter out those employed in particularly high risk occupations (anything involving heights or depths).

As I discovered later, even the industry’s most relied-upon indicator of overall health – a person’s Body Mass Index (BMI) – doesn’t actually stand up to any sort of scientific scrutiny.

A quick detour back in time is required here. The concept of determining a person’s health by cross referencing their height and weight was first proposed in the early 19th century by a Belgian statistician by the name of Lambert Quetelet who was on a quest to discover the statistically ‘average man’. 

After Quetelet died his crude formula (divide your weight by your height squared) was more or less forgotten for the next century until it was revived and rebranded in the 1970s by the physiologist Ancel Keyes as an expedient way to measure obesity. To his credit Keyes maintained that BMI was better suited to measuring the health of populations rather than the health of individuals but this crucial caveat got lost in translation when the life insurance industry adopted BMI as a proxy for overall physical health. 

Since then BMI has worked its way into all sorts of medical screening exams and scientific studies. The only problem with the Body Mass Index is that it doesn’t account for the difference between muscle mass, bone density and body fat and so it doesn’t really tell you how healthy a person is. Moreover, it becomes less accurate when it’s applied to segments of the population considered ‘outliers’ by 19th Century Belgian philosophers – including women, children, the elderly, non-Europeans, very short people, very tall people and very fit people.

As you might expect, medical professionals have spent decades pushing to replace BMI with something more diagnostically accurate. Meanwhile life insurance providers – who are, in some sense, responsible for causing this mess – never kick up much fuss about the shortcomings of BMI.

In recent years insurance companies have begun looking into ways to leverage ‘big data’ to generate even more granular risk profiles. Their ultimate aim is to offer ‘personalised’ insurance policies by cross-referencing records of the applicant’s purchase history, driving performance, self-tracking fitness data and all sorts of other information collected by ‘smart’ devices. But it seems unlikely that all this additional information will bring us any closer to understanding the factors that affect human mortality because, rather than being studied by humans, all this data will inevitably be fed into machine-learning algorithms – another black-box process which flattens complexity and conflates cause and effect. As Jathan Sadowski wrote in a recent paper on ‘insurtech’:

“The ability to collect, analyse, and connect more data about more risk factors opens the way for scores and judgments based on seemingly arbitrary correlations. It doesn’t matter if insurance companies know why people who drink coffee after 5 p.m. or have low credit scores or are implicated by whatever other random factor may correlate with higher risk. What matters is that the pattern has been identified in the data and can be turned into ‘actionable insights’ that justify price discrimination.

…such practices fit well with how actuarial calculation already largely works. Both are built on an epistemology less concerned with knowing why a relationship might exist and more with showing a probabilistic connection. If you ever want to frustrate an actuary, start by asking them to explain the causal validity of factors used to assess, predict, rate, and price risk. Actuarial decisions by insurers do not need to be—and indeed very often are not—justified by clear causal relations or based only on objective facts.”

So much for actuarial science. 

Returning to Hobart’s question about whether there might be a social benefit to knowing how long people are likely to live. I think most people would agree that the answer is yes. But, if that’s the case, why aren’t insurance companies – and the actuaries that work for them – striving to create better statistical models of risk? Shouldn’t they be trying to better understand the threats to human health?

Theoretically at least, life insurance companies should in the best position to perform this service because life insurance is a multi-billion dollar industry and the leading providers have immense resources at their disposal. If they wanted to, they could probably come up with some robust models to determine which exercise regimes, eating habits, occupations and hobbies lead to longer lives.

But they don’t. At least not in my experience.

And the reason for this neglect becomes pretty clear once you understand how life insurance companies in Australia make money and what they’re incentivised to care about. But in order to understand the industry as it currently exists we need to understand how life insurance worked back in the olden days.

A quick history lesson:

Demand for life insurance began to emerge in the late 18th century as urban tradespeople and middle-class workers began to look for organisations that could provide some sort of social safety net for their families. The earliest providers of life insurance were mutual aid or ‘friendly societies’ that were run purely for the benefit of their members. Those that joined paid ongoing fees to the association and, in return, they received a guarantee that they, or their families, would be compensated if they became sick or died. These mutual societies did not have external shareholders and any surplus funds would typically be reimbursed to members.

By the late 19th century these organisations had become fixtures of the wider financial/welfare system. They were especially prominent in Australia where they received an early boost in terms of profitability because British mortality tables underestimated the lifespans of Australia’s relatively healthy colonial settlers. 

Nevertheless, during these early years, life insurance providers still had to pay close attention to their actuarial calculations because these organisations relied on membership fees to stay afloat. Misjudging mortality in the long term would result in premature claims – causing the fund to dry up before new members could be brought in to top up the pool.

However, in the mid 20th century, life insurance providers began abandoning this business model in pursuit of higher profits. In Australia, as in many other places in the Western world, deregulation of the financial services sector allowed insurance providers to behave more like investment funds. This meant that they were free to invest their surplus in stocks and bonds and other financial instruments while still being compelled to retain enough cash to pay claims and cover their administrative costs. In order to raise capital most of these legacy insurance firms ‘demutualised’ and became corporate entities – managed by bankers and accountable to shareholders. 

In Australia, the life insurance industry received another windfall in the 1980s with the introduction of compulsory superannuation schemes which allowed insurance providers to quietly attach life policies to those enrolled in superannuation funds. By offering these basic, non-underwritten policies to super fund members insurance providers could collect fees from millions of unwitting Australians without doing much in the way of admin or customer service. If the members of these funds couldn’t make claims because these policies were too narrowly circumscribed (or they didn’t know about them in the first place) then so much the better.

Within the life insurance industry this segment of the market is referred to as ‘group insurance’ and it accounts for the majority of the revenue collected by major insurance providers – dwarfing the premiums collected via ‘direct’ channels (eg. those funeral plan advertisements you see on daytime TV) and ‘retail’ channels (the traditional middle-class insurance policies purchased through a broker or financial advisor).

So, in 21st century Australia, what we’ve ended up with is a situation where the largest and most profitable life insurance companies make most of their money from a combination of investment returns and fees collected from junk insurance policies which have been piggybacked onto superannuation funds. 

This process of ‘financialisation’ has shifted the focus of life insurance providers from assessing risks to their policyholders to assessing risks to their investment portfolio. Over time, this has meant that the mortality-related work done by actuaries has become more and more distant from the profit centres of the companies they work for.

So does that mean that ‘life insurance actuary’ is a bullshit job?

Well yes and no. Actuaries still play a part in ensuring that the company doesn’t haemorrhage money in the form of premature claims from poorly underwritten policies. They also play a part in assessing the long term risks associated with the company’s investment strategy. But they’re no longer incentivised (if they never were) to uncover or understand the factors that determine life expectancy. Recall the assumption that Hobart made at the start of his essay:

“Is there…some kind of social utility in knowing how long someone is likely to live?…In an advanced economy where people [are] working from the first moment they’re capable of it until they’re incapacitated or dead, might we expect such a job to exist, to create value, and to be paid accordingly?” 

We should expect that job to exist. And it does. It’s the collective responsibility of hundreds of different scientists and doctors and public servants. Meanwhile the life insurance actuaries responsible for pricing policies are mainly there to ensure no one mucks around with their company’s sacred rulebook. In effect they have become custodians of the underwriting dogma that has been handed down to them – a mini cargo cult within the financial system.

So why haven’t these companies laid off most of their pricing actuaries? Why don’t they keep a few people on the books to plug the latest ABS statistics into their spreadsheet and ditch the rest? Surely that would save a lot of HR hassle and a certain amount of money in the form of staff salaries (associate actuaries earn upwards of $150k). This is what free-market enthusiasts like Hobart would assume any well-managed, profit-driven company would do if they found themselves saddled with a large cohort of highly-paid workers who were mainly shuffling .xls files.

And here’s where the definition of ‘usefulness’ gets a bit blurry. Because if we set aside the question of ‘social utility’ it becomes clear that actuaries actually do fulfil an important role within these organisations – even if it’s not the one laid out in the job description. The main purpose of these employees is to give the appearance of mathematical and scientific rigour to the legacy part of the business (selling insurance). They exist to signal to customers (and regulators) that their prices are the result of extensive number crunching and careful deliberation.

Whether you think this role is ‘bullshit’ or not probably depends on whether you think the industry itself – and the whole idea of a parallel, privatised welfare system – is socially valuable. 

According to Graeber’s Bullshit Jobs schema these workers would be considered ‘box-tickers’ – employees who exist ‘only or primarily to allow an organisation to be able to claim it is doing something that, in fact, it is not doing’ – in this case calculating health risks and predicting life expectancy. 

Final Thoughts:

When it comes to understanding the phenomenon of bullshit jobs the example of life insurance actuaries is quite instructive. In particular it tells us a few important things.

The first thing it tells us is that ‘real’ jobs can become bullshit jobs over time without anyone really noticing. If we look back over the long term shift in the industry from friendly societies to corporate insurance firms it would be hard to pinpoint the exact point at which the job of an actuary went from being mainly useful to mainly bullshit. In this case the shift was so gradual that even those working in the industry didn’t notice it happening. When it came to the digitisation project I was involved in, no one warned us that the underwriting process was poorly understood (even by those who administered it). We weren’t warned about this because the people who gave us the task weren’t aware of it themselves. That company didn’t employ anyone to audit the actuarial department and they probably won’t unless something goes drastically wrong.

Secondly, this case study tells us that bullshit jobs are not an all-or-nothing proposition. Within every job there will be tasks that are bullshit and tasks that are genuinely valuable – either to the organisation or to wider society. One of my first pointless jobs (mentioned in Chapter 4 of Bullshit Jobs and detailed in this post) was designing online banner ads. But in between working on those stupid little rectangles I also worked on various other projects that presumably prompted people to make purchases – which, in turn, made money for our clients. So I did provide some sort of economic value in that role.

On the other side of the equation you have the lucky few who’ve managed to secure jobs that have genuine social and economic value. Take, for example, emergency physicians – the front line doctors tasked with treating people who are critically ill or injured. Their work is both urgent and necessary but, like everyone else in the medical field, emergency physicians are also saddled with a whole lot of tedious ‘compliance training’ and administrative tasks that get in the way of providing patient care.

Clearly some jobs are mostly bullshit with a bit of valuable work thrown in while others jobs are mostly valuable work with a little bit of bullshit thrown in. Call this the Yin and Yang model of labour productivity.

When it comes to life insurance actuaries in Australia it seems as if the basic task of valuation – calculating the reserves necessary to cover claims and costs – is mostly useful. On the other hand, many of the tasks that relate to the pricing of insurance policies – like creating elaborate hierarchies of risk for adventure sports based on sketchy data – seem to be mostly bullshit.

Making sweeping claims about someone else’s line of work is obviously very fraught. Graeber’s approach was one of humility. Rather than render judgement from afar he believed that the best judge of a job’s meaningfulness was the person doing that job. If they think their job is bullshit then it probably is and, if they don’t, then it probably isn’t. 

So how do actuaries feel about what they do? It’s hard to know for sure but when Bullshit Jobs was published back in 2018 someone actually polled the members of the r/actuary subreddit on whether they thought the work they did contributed to society. For the guy who created the poll (who clearly took a certain amount of pride in his profession) the results were sobering. Fully 23% of those who responded said that their work provided ‘no real social value’. A similar number said that their work provided social value only 20-40% of the time. Bear in mind these responses came from actuaries based in the U.S. working in a range of different roles. I would have expected that most of those who responded would have been working in industries that were more competitive and less prone to bullshit than Australia’s hedge-fund-esque life insurance sector. And yet roughly half those surveyed figured that they spent most of their time doing meaningless work.

David Graeber closed out Bullshit Jobs with a chapter on the benefits of implementing some sort of universal basic income. Guaranteeing every person a living wage would immediately remove the need for the extensive bureaucracies in most Western countries – which are mostly there to apply means-test ing to welfare programs and, in Graber’s words, ‘make poor people feel bad about themselves’. This would also free up an enormous amount of people currently employed in all sorts of non-government and charitable organisations which currently exist to fill gaps in the social safety net. By extension a UBI would also reduce the need for private life insurance providers – allowing us all to enjoy the benefits of membership in a ‘mutual society’ which spanned class and gender divides.

More importantly, a guaranteed UBI would allow people who felt they were doing a bullshit job to tell their boss to kick rocks. If those surveys on worker attitudes are accurate then this sudden freedom to choose what one does with their days would result in a rapid transformation of society. More conservative economists instinctively assume that this would starve the economy of its productive labour force. One of the main criticisms of UBI is that it some critical percentage of the population would pursue activities that had no wider social value – leading to some sort of economic collapse. But as Graeber points out:

…right now, 37 to 40 percent of workers in rich countries already feel their jobs are pointless. Roughly half the economy consists of, or exists in support of, bullshit. And it’s not even particularly interesting bullshit! If we let everyone decide for themselves how they were best fit to benefit humanity, with no restrictions at all, how could they possibly end up with a distribution of labor more inefficient than the one we already have?

Graeber doesn’t delve into the practicalities of implementing a universal basic income scheme but it seems to me that it would involve constructing economic models, analysing demographic data and making careful risk assessments.

Sounds like the perfect job for an actuary.

*The recall formula featured in Fight Club is pretty typical of the sort of cost/benefit calculations made by manufacturers when they discover a defect with their product. The specific scenario described in Fight Club seems to be heavily inspired by the discussions that went on within Ford Motor Company in the 1960s following several fatal incidents involving their dangerously flammable Pinto hatchback.

References:
David Graeber (2018) – Bullshit Jobs: A Theory
Byrne Hobart (2024) – “Bullshit Jobs” is a Terrible, Curiosity-Killing Concept
Kaylee Boccalatte (2022) – The Burden Of Bulls**t Jobs
Magdalena Soffia et. al (2021) – Alienation Is Not ‘Bullshit’: An Empirical Critique of Graeber’s Theory of BS Jobs
Will Dahlgreen (2015) – 37% of British workers think their jobs are meaningless
Jathan Sadowski (2023) – Total life insurance: Logics of anticipatory control and actuarial governance in insurance technology
Ronald Mizen (2020) – ‘Paying for junk’: Super insurance gets mixed reviews
Monica Keneley (2001) – The Evolution of the Australian Life Insurance Industry
Brian J. Glenn (2003) – Postmodernism: The Basis of Insurance