Many people think that “ivory tower” intellectuals make little difference in the world. But some of the highest impact people in history have been researchers, and if you have good personal fit with academic research, we think it can be one of the highest-impact paths.

Alan Turing was an academic – a mathematician, the ultimate ‘ivory tower discipline’ – yet he developed code breaking machines that allowed the Allies to be far more effective against Nazi U-boats in WW2. Some historians estimate this enabled D-Day to happen a year earlier than it would have otherwise.1 Since WW2 in Europe resulted in 5 million deaths per year, Turing’s work contributed substantially to saving about 5 million lives.

And he invented the computer.

In this article, we’ll cover why we think a career in academia has the potential to be very high impact in the right circumstances, how to figure out whether this option is for you, and how to maximise the impact you can have as an academic.

Summary

To work on some important fields of research that are hard to fund commercially, academia is the natural place to go. A single outstanding researcher can move a field forward and make a significant contribution to solving key global problems. Beyond research, academics also have other avenues for impact, such as by influencing government policy, the priorities within their field, and the culture of society at large.

On the other hand, entering academia is a lengthy process, involving 5-15 years of study and post-docs, or even more. Most people who make the attempt – already a talented bunch – are rejected or give up getting a permanent position. While it has many positives, it’s common for people to continue with academic research by default when they’d be better suited elsewhere, so we encourage you to be self-skeptical and consider other options before starting a PhD.

Raw intelligence, hard work and curiosity are the key indicators you may be able to beat the odds. Salaries are lower than elsewhere, though job satisfaction nevertheless seems high.

Choosing the right research questions is essential to maximise your expected social impact and we offer advice on how to do that, but PhD students often face a trade-off between working on the questions they think are most useful, and those which will best advance their career.

Pros

  • If you’re a good fit, there's a large potential for social impact by working on pressing research questions
  • Prestigious platform for advocacy
  • Autonomy and job satisfaction

Cons

  • High competition for a limited number of positions
  • Lengthy training time
  • You often have to teach, and academic incentives remain imperfectly aligned with having social impact

Ratings

Career capital: 

Direct impact: 

Earnings: 

Advocacy potential: 

Ease of competition: 

Job satisfaction:

Our reasoning for these ratings is explained below. You might also like to read about our approach to rating careers.

Key facts on fit  

High intelligence, conscientiousness and need for intellectual stimulation. Ability to work independently. Deep interest in the area and willingness to focus on particular questions for long periods.

Next steps

Explore different fields to find one you’re interested in which can be used to work on important problems. Get very good grades at the undergraduate level. Look for PhD submission dates and search for an academic supervisor working on valuable questions at a prestigious university. Work hard to publish in good journals during your PhD or during postdocs.

Recommended

If you are well suited to this career, it may be the best way for you to have a social impact.

Review status

Medium-depth career profile 

What is this profile based on?

This profile is based on 10 interviews with successful academics (including in statistics, economics, computer science and biomedicine), as well as numerous more informal conversations with people pursuing academic careers (including some of our colleagues and trustees who have an academic background), and looking into the careers of some of history’s most impactful academics. We’ve also looked for other sources of advice on maximising your research, as well as research into what makes a successful academic career, statistics of progression within academia, and the distribution of research impact.

Note that in this profile, we cover academia in general. We’ve also started to write profiles on specific PhD programmes (economics, computer science, machine learning, philosophy PhD) and subject areas (biomedical, AI strategy, AI technical). Refer to these for the subject-specific details.

So, let’s start – why can academia be high-impact?

If you choose well, you can do valuable research

Academic research seems to have been high-impact historically

Alan Turing is far from the only example of a researcher who had an extreme positive impact.

In the late 1960s, there was growing concern that food production seemed unable to keep up with the world’s rapidly growing population.

Over 15 million people had died in what’s now known as the “Great Chinese Famine” a few years earlier, and it was predicted there might be hundreds of millions of deaths in years to come.

At the same time, researchers like agronomist Norman Borlaug were working hard to develop and implement more effective methods of plant breeding.

Fields were being planted with new, higher-yielding strains of wheat and rice, which, combined with modern fertilizers, ushered in the “Green Revolution.” Wheat production in India and Pakistan almost doubled between 1965 and 1970, and formerly famine-wrecked countries were able to produce enough food for their entire populations. These agricultural developments have been credited with saving an estimated one billion people from famine.2

Again, the green revolution is just one of many cases where academic research has been high impact. If we think about what has led to the most gains in the modern world, many of the key ideas can be traced back to academic research: advances in medicine such as the development of vaccines against infectious diseases, developments in physics and chemistry that led to steam power and the industrial revolution, and the invention of the modern computer, an idea which was first proposed by Alan Turing in his seminal 1936 paper, On Computable Numbers.3

What’s more, innovations are cumulative — once an idea has been discovered, it’s added to our stock of knowledge and available to everyone. Even ideas that become outdated often speed up the important future discoveries that supercede it.

At the same time, if we look through history, comparatively few people have been researchers4 – well under 1% of the population. This suggests that the average impact per person has been high.

What’s more, most researchers don’t get rich, even if their discoveries are highly valuable, because the benefits of research often come a long time in the future and aren’t easy to monetise. Turing made no money from the discovery of the computer, even though today it’s a trillion dollar industry. This means there’s far too little monetary incentive to do the most important research. This means society is likely to underinvest in research, and that makes it a promising area for people who want to do good rather than make money.

Can we expect research to be high-impact in the future?

We might doubt whether research will continue to be as high-impact as it has been in the past. Research is much less neglected than it used to be, since many more people pursue it: there are nearly 25 times as many researchers today as there were in 1930.5 Research also has diminishing returns, so more and more effort is required to discover new ideas.6

However, research is still one of the key things needed to tackle many of the world’s most important problems. This means that if you choose the right field, we think doing research can be one of the highest-impact paths available.

For instance, to reduce the risk posed by engineered pandemics, we need researchers to identify the biggest biosecurity risks and to develop better vaccines and treatments.

To ensure that developments in artificial intelligence are implemented safely and for the benefit of humanity, we need technical experts thinking hard about how to design machine learning systems safely, and policy researchers to think about how governments and other institutions should respond.

And to decide which global priorities we should spend our limited resources on, we need economists, mathematicians and philosophers to do global priorities research. Read our problem profiles for more details.

Some of the fields we think are most promising to go into, given these global priorities, are:

  • Economics (for global priorities research, development economics, or policy research relevant to any cause area, especially global catastrophic risks)

  • Computer science, especially machine learning (for research into risks from artificial intelligence, or applying advances in AI to other areas such as improving healthcare)

  • Applied mathematics/statistics (this may be a good choice if you’re very uncertain, as it teaches you skills that can be applied to a whole range of different problems)

  • Biology, particularly synthetic biology, genetics, public health, epidemiology (for biomedical research and biosecurity)

  • International relations/political science, including security studies (for policy approaches to mitigating catastrophic risks)

  • Neuroscience/cognitive psychology/cognitive science (for improving decision-making – could also lead into AI research if you study computational cognitive science or neuroscience in a top group, though if this is your aim it’s probably better to study AI directly)

We’ll talk about how to choose a field and how to choose high-impact research questions later in the profile.

A great researcher in an important field can make a huge difference

Many academics claim that their fields are highly constrained by a lack of great researchers. This means that if you have greater than typical chances of being one of these top researchers, this path is much higher-impact.

For instance, when we surveyed biomedical researchers, they said that very good researchers were rare, and they’d be willing to turn down large amounts of money if they could get a good researcher for their lab.7 Professor John Todd, who works on medical genetics at Cambridge, told us “the best people are the biggest struggle. The funding isn’t a problem. It’s getting really special people… One good person can cover the ground of 5, and I’m not exaggerating.”

This makes sense if you think the distribution of research output is very wide – that the very best researchers have a much greater output than the average researcher. It’s hard to know exactly how spread out the distribution is, but there are several strands of evidence that suggest the variability is very high.

Firstly, most academic papers get very few citations, while a few get hundreds or even thousands. An analysis of citation counts in science journals found that ~47% of papers had never been cited, more than 80% had been cited 10 times or less, but the top 0.1% had been cited more than 1,000 times. A similar pattern seems to hold across individual researchers, meaning that only a few dominate – at least in terms of the recognition their papers receive.

Citation count is a highly imperfect measure of research quality, so these figures shouldn’t be taken literally. For instance, which papers get cited the most may depend at least partly on random factors and “winner takes all” effects — papers that get noticed early end up being cited by everyone to back up a certain claim, even if they don’t actually represent the research that most advanced the field.

However, there are other reasons to think the distribution of output is highly skewed.

Dean Simonton, a psychology professor who has spent decades studying scientific productivity with the aim of developing more objective measures of output, writes:8

A small percentage of the workers in any given domain is responsible for the bulk of the work. Generally, the top 10% of the most prolific elite can be credited with around 50% of all contributions, whereas the bottom 50% of the least productive workers can claim only 15% of the total work, and the most productive contributor is usually about 100 times more prolific than the least.

William Shockley, who won the Nobel Prize for the invention of the transistor, gathered statistics on all the research employees in national labs, university departments and other research units, and found that productivity (as measured by total number of publications, rate of publication, and number of patents) was highly skewed, following a log-normal distribution.

Shockley suggests that researcher output is the product of several (normally distributed) random variables – such as the ability to think of a good problem, to recognize when a worthwhile result has been found, to write adequately, to respond well to feedback, and so on. This would explain the skewed distribution: if research output depends on 8 different factors and their contribution is multiplicative, then a person who is 50% above average in each of the 8 areas will in expectation be 26 times more productive than average.9

While these differences in output are not fully predictable at the start of a career, the spread is so large that it’s likely still possible to predict differences in productivity with some reliability. And it will mean that for people who have somewhat higher-than-typical chances of being a top researcher, this path will be much more promising. By contrast, the majority of people, who don’t anticipate being among the most productive academics, will have much less expected social impact from this path.10

At the same time, the most productive academic researchers are rarely paid 10 times more than the median, since they’re on fixed university pay-scales. This means that the most productive researchers yield a large “excess” value to their field. For instance, if a productive researcher adds 10 times more value to the field than average, but is paid the same as average, they will be producing at least 9 times as much net benefit to society.

This suggests that top researchers are underpaid relative to their contribution, discouraging them from pursuing research, and making this path undersupplied with talent compared to what would be ideal.

It also suggests that top researchers will typically do more good through their research than they can earning and donating, so in this sense, non-commercialisable research is talent constrained by top researchers.

Is academia the best place to do valuable research?

We’ve argued that research has had a lot of impact historically, is central to solving some of the world’s biggest problems, and that a really talented researcher in an important field can make a huge difference. But why go into academic research in particular? Academia isn’t the only place where it’s possible to do research on important problems. Instead, you could do research in the private sector – as a biomedical researcher you could also work for a pharmaceutical company, or as a computer scientist you could do research at a big technology company like Google or Amazon, for example. As a social scientist, you could work for a think tank or as a researcher for government.

Academia has some advantages over research in industry or the public sector, as well as some disadvantages, as we’ll discuss below. It’s certainly not true that academia is always better than these alternatives – it will depend on your personal situation, opportunities, and priorities. However, we do think that in many cases it’s one of the best environments for doing valuable research.

One downside of academia is that it can be slow-moving: academics often report frustrations with the efficiency and speed of university administration, especially relative to the private sector. A second is that academics may have to spend a lot of time applying for grants to fund their research. A third commonly recognised challenge is that academics often have many other responsibilities beyond their research, such as teaching, which can take up a lot of time. A typical teaching load during term-time is 10-12 hours a week – though in reality it’s probably substantially more, as this doesn’t account for time spent preparing, marking, and supervising students. This seriously limits the amount of time you have to spend doing your own research.

However, it’s not clear the situation is necessarily any better in other research jobs. In industry or the public sector, researchers are likely to have more meetings and more short-term deadlines to meet, giving them less time to really explore questions deeply. Academia also gives you more time to focus on learning and research early in your career than any other job will. Graduate school gives you ~4-7 years (depending on your course and country) where you’re able to focus almost entirely on studying and learning to do research. It’s pretty impossible to get this in any other job, even if it’s research-focused – as there’s much more day-to-day pressure to deliver.

More generally, academia provides a huge amount of freedom to research what you choose. Nick Feamster, a computer science professor at Princeton who has worked at both universities and large industry labs, says of why he ultimately chose the academic path:

As a professor, you do not have quarterly deadlines to meet, monthly reports to file, or a boss you are regularly accountable for…. Freedom from the constraints of short-term deadlines and having to answer to others who are setting the agenda really presents the possibility of thinking about the “right” solution to a problem, rather than hacking together something that just works well enough to get the job done. In my opinion, this aspect of being a professor presents the greatest opportunity for adding value.

Academia is also the best place to do research that is unlikely to get commercial funding because the benefits are long-term or abstract. Academics often have a lot more freedom to pursue whatever research they think is most valuable (especially at later stages of an academic career) than you would get doing research in a commercial setting. “In most companies, research topics are largely chosen by the business and marketing departments… pressures to show immediate and positive results can also challenge the best ethical and professional practices”, says Jaime Teevan, a scientist at Microsoft Research.

The flipside of this is that it can be harder to get academic research applied in “the real world.” Explaining why he chose to leave Harvard and join Google, computer scientist Matt Welsh writes “Academics have a lot of freedom, but this comes at the cost of high overhead and a longer path from idea to application.” And Nick Feamster agrees: “I find that I spend a lot of time ‘evangelizing’ my ideas to industry to try to transfer them into practice… industrial researchers do not have this problem.” However, just because the impact of academic research is often less immediate or obvious than the impact of private or public sector research, doesn’t mean it’s necessarily less valuable.

Finally, academia attracts some of the world’s brightest minds, so you can find great collaborators to work with and learn from.11 What’s more, in many fields there’s a perception that the most serious researchers work in academia, and this means being in academia is an important signal of ability, putting you in a better position to find collaborators.

Finally, as we’ll cover in the next section, having academic credentials also opens up lots of other ways to have an impact.

Research isn’t the only way academics can have a large impact

When we think of academic careers, research is what first comes to mind, but academics have many other pathways to impact which are less often considered. Academics can also influence public opinion, advise policy-makers, or manage teams of other researchers to help them be more productive.

If any of these routes might turn out to be a good fit for you, then it makes the path even more attractive. We’ll sketch out some of these other paths:

1. Public outreach

Peter Singer’s career began in an ordinary enough way for a promising young academic, studying philosophy at Oxford University. But he soon started moving in a different direction from his peers, by seriously trying to change the views and behaviour of the general public on important moral issues.

Singer’s first book, Animal Liberation, is one of the most widely-read books published by any philosopher. Many consider the book to have given birth to the animal liberation movement.

Singer has also argued strongly that we have a moral obligation to donate much more to those living in poverty. His writings on global poverty inspired the creation of the organisation Giving What We Can, whose 3,000 members have donated over $10 million to the most cost-effective charities, and pledged over $1 billion of future donations.12

Early-career economist Max Roser made a website clearly presenting data on how the world is changing that now gets over a million visits a month and earned him introductions to politicians and philanthropists, as well as a large Twitter following.

Other academics venture far outside of their original fields of research. Noam Chomsky managed to convert an academic position focussed on linguistics into being one of the most prominent commentators on US foreign policy.13

As we argue in the career guide, advocacy seems like a promising approach in general to have a large social impact. And academia seems to be an especially good position from which to advocate for important ideas. Successful academics have developed the expertise required to understand complex questions, and the credibility to have people listen to what they say.

This is especially true if you want to advocate for issues concerning extinction risks, because some of them are emerging from scientific and technological progress itself. We need experts in the relevant fields to shape public understanding — to prevent the risks and benefits from being overhyped, while helping people to understand genuine concerns with, and the real potential of, these technologies.

However, it’s important to be careful with public outreach – it wouldn’t be that difficult to do harm by spreading slightly the wrong idea, in slightly the wrong way, to slightly the wrong audience. For example, writing popular articles about threats from artificial intelligence might lead to widespread fear and alarm, which could be counterproductive.

This suggests that getting the biggest possible platform for your ideas might not be the best route to impactful outreach – sometimes targeting communication at smaller, more influential groups might be a better approach.

For instance, academic macroeconomists have a great deal to say about appropriate use of monetary or fiscal policy, and are regularly appointed to decision-making roles in central banks. But communicating their ideas to the general public is difficult and likely to result in misunderstanding. As a result macroeconomists more often write books and papers aimed at a limited audience with the goal of influencing politicians or bureaucrats within government or central banks themselves.

2. Apply your research to problems outside of academia

John Beddington was a successful academic biologist, but his biggest impact seems to have come through his service as a respected science advisor to the government.

Beddington was the UK Government’s Chief Scientific Adviser from 2008 to 2013, and played a key role in helping the government to navigate the challenges of the Fukushima nuclear disaster, eruptions of the Icelandic volcanoes, and ash dieback disease in the UK. He also pushed to establish a network of “Chief Scientists” across all government departments.

Often the people in charge of making important decisions — about how to allocate funding, or what the appropriate policy response to a given challenge is — won’t necessarily have expertise in the most relevant fields. This means they need to call on external experts who do have this specialist knowledge, who can help them to make the best possible decisions.

These experts are very often academics, since academia is one of the best ways for people to develop the credentials needed to be taken seriously by policymakers.

And a need for guidance from academics isn’t limited to government – you could do a lot of good if you have the relevant expertise to advise NGOs like the Gates Foundation or World Bank, or tech companies that have a large influence like Google or Amazon.

Policymakers in many countries often seek the advice of experts in some relevant fields such as economics, data science and machine learning, and some areas of social science such as behavioural science. That demand means it’s not necessarily as hard to end up in a position of advising decision-makers as you might think.

For example, Prof Bruce Chapman was an academic economist at the Australian National University who consultanted for the Australian Minister for Employment, Education and Training in 1987. While there he helped to devise a system of government-backed ‘income contingent’ student loans which enabled students to pay for more of the costs of higher education without taking on any significant financial risk. He had a large influence over the design of the policy, while the public advocacy and largely left to others. This approach to student loans was later copied by the UK, Thailand and Ethiopia, among others.14

If you want to be Chief Scientist of a government department you’ll need to be a pretty senior and established researcher, but we know several people who have advised government early in their academic careers, even during a PhD, because they have valuable expertise and have been proactive about building connections and looking for opportunities.

For example, Miles Brundage, a PhD student in Human and Social Dimensions of Science and Technology at Arizona State University has been advising the UK government on developments in artificial intelligence.

If you’re also interested in working in the effective altruism community, then another reason in favour of going into academia is that the community seems constrained by a lack of specialist expertise on topics such as machine learning, biology, and economics.15

Some academics also notice that insights from their field could be applied in the world more concretely; they might be used to build new products or companies that help solve important problems.

This could lead to them leaving academia to do these projects, but you might also be able to do this kind of work while continuing to do research. For example, leading AI researcher Yoshua Bengio cofounded Element AI with an established entrepreneur, in order to apply cutting-edge AI research to tackling important business challenges. Google was founded by researchers at Stanford trying to improve online search.

Other academics have gone on to found valuable non-profits based on their research. For example, GiveWell recommended charity the Schistosomiasis Control Initiative emerged from a group of academics at Imperial College London, such as expert in tropical parasitology Prof Alan Fenwick, who coined the term ‘neglected tropical diseases’ to draw attention to the problem. Similarly the Centre for Pesticide Suicide Prevention emerged from academic researchers at the University of Edinburgh. You can learn about that history in our interview with one of its founders, Dr Leah Utyasheva. The credibility they gain from their work in academia appears very helpful in getting these projects off the ground.

Anyone with expertise can set up these kinds of non-profits – you don’t necessarily need to be an academic – but we think academia is a particularly good place to develop expertise for many people, as you get to work alongside some of the most knowledgeable people in an area, free from commercial pressure.

3. Influencing academia and research management

When Nick Bostrom was at university, he was expelled from the psychology department for trying to study too many subjects: he was taking classes from anthropology and literature all the way to science and psychology.

Bostrom’s highly interdisciplinary approach, and his tendency to take a step back and look at the “bigger picture”, has led him to ask questions almost no-one before him had, such as whether we might face a genuine threat of extinction in the next century, or how technology might lead to drastic changes in the human condition.

Ten years ago, he founded the Future of Humanity Institute at Oxford, an academic research department which he still runs full-time. Rather than just conduct his own research, he’s recruited and managed other researchers working on important topics, who would have struggled to find a place to do this work otherwise, or might not even have considered doing so.

Other academics have had a huge impact on a field by suggesting improvements to the methods used in a field. Psychologist Brian Nosek and statistician Andrew Gelman are two examples of academics who have led the push for more rigorous methods and openness in social science, which could vastly improve the quality of research in future.

The recent “replication crisis” in social science has found that many of the most widely accepted findings do not hold up under scrutiny – in many cases, repeating the original experiments does not yield the same result. This suggests that a huge amount of resources have been wasted on research using poor methods – fixing this could massively boost the reliability of all future work in the entire field.

Another way to have a large impact as an academic, then, is to influence the direction of other researchers’ work, and the trajectory of a field as a whole – maybe even establishing entirely new fields.

Influencing the direction of others’ research often follows from having amazing ideas yourself. But it’s also possible to increase the output of other researchers or a field in more subtle, less glamorous ways, if you’re more entrepreneurial.

We’ve written before about how jobs in research management can be incredibly valuable: if you can help a whole team of researchers to be a little more productive by doing the neglected work of management, then you can have more impact than you would alone. We recently made the case in more detail in our article on operations management.

Projects like keeping track of and prioritising research within a department, fundraising, managing researchers to enable them to be as productive as possible, and recruiting new, talented researchers, are really important but often neglected within academia.

The skill-set needed to do this kind of work effectively is also relatively rare: you need to have a thorough understanding of the relevant research, but also administrative and management skills. Many great researchers are uninterested in management and do as little of it as they can, so if you have this rare combination of skills and a willingness to focus more on the management side, you could be very valuable to a department.

Research management jobs can be part of an academic career – working as head of a department, for example – but sometimes they’re a separate job, such as being a project manager in a research group. This latter case could also be a good option if you decide that research isn’t quite the right path for you.

For these kinds of roles, you often need a PhD and it certainly helps to have experience in the kind of research you’re managing, but you don’t need to be a leading professor. After finishing his PhD in genomics, Seán Ó hÉigeartaigh decided he could likely have a much greater impact doing project management at the Future of Humanity Institute, which he believed was doing really valuable work.

Seán is now Executive Director at the Centre for the Study of Existential Risk in Cambridge, an organisation which almost certainly wouldn’t exist without him.

The career paths we have discussed above are competitive, and success is not guaranteed. It’s natural to doubt whether you’ll be able to do high-impact research, be in a position to advise policymakers, and so on, and this leads us on to:

If you’re in doubt, it’s often worth staying in academia

Keeping options open

If you haven’t yet done a PhD and are unsure whether an academic career is right for you, then it may be worth spending a year or two exploring other options. It’s not unusual for people to start a PhD in their mid-to-late twenties, and this shouldn’t hinder your career prospects much.

But if you already have a PhD and are uncertain about whether to continue in academia, it’s probably better to lean towards staying (especially if you don’t have clear alternative options.)

Spend a couple of years focused on building up a good publication record and applying for academic jobs, and then reassess based on your success and what other options are available to you.

Almost everyone in academia advises against taking a break once you’ve done your PhD, because it’s generally harder to enter or re-enter academia later. Postdoc and permanent positions are highly competitive and will go to those with the best track record and strongest publications. If you take a career break, your publication record may not be strong enough to get a good position, and it gives the impression you’re not serious about pursuing research.
It’s also generally easier to transition from academia to other industries than the reverse, as many jobs look positively upon academic experience, but academia tends to place little value on non-academic experience.

Good backup options

Moreover, even if you leave academia later, you’ll probably have good back-up options, especially if you’ve chosen certain fields and leave before your early 30s. The table below shows some good backup options if you’re coming from different academic backgrounds:

Academic areaBackup options
Quantitative fields e.g. maths, statistics, computer science, economics, physicsQuantitative skills are in-demand in most career areas, but some particular quantitative areas include:
- Effective non-profits
- Think tanks and government research
- Quantitative asset management
- Tech startups
- Data science
- Software engineering
Social sciences e.g. economics, political science, sociology, public policy - Government
- Think tanks
- Foundation grantmaker
Lab sciences e.g. biology, chemistry - Scientific research in industry e.g. drug development
- Tech startups with a specialist focus e.g. biotech
- Public health research for NGOs/foundations
- Consulting

We cover specific exit options in our profiles on individual PhDs.

The work can be very satisfying

Academic research can be a very satisfying if you’re a good fit and find the right position: there’s probably no other job that gives you the same degree of autonomy to do intellectually satisfying work, and you’ll be surrounded by other smart and curious people.

However, the amount of freedom you have to research what you think is most important will vary depending on the specific field and department, and is likely to be lower earlier in your career, when there’s more pressure to work on whatever is most likely to get you published.

One study on job satisfaction in academia suggests that satisfaction is higher among full professors than more junior faculty members, which is consistent with this.16

One downside of academia to be aware of is that sometimes it can be quite solitary. Especially during a PhD, if you’re not working on projects with other people regularly, you’ll be working on your own quite a lot. However, this varies a lot by field – in the humanities and some social sciences highly collaborative research groups are less common, whereas lab sciences are generally a lot more collaborative.

This means you’ll need to be happy working fairly independently, and be able to manage and motivate yourself to a large degree. However, you’ll at least be based in a university with lots of smart and interesting young people – so even if the work is solitary, there are likely to be good social opportunities.

Overall, having a large degree of autonomy seems to be a good thing – at least for people who enjoy research, want the freedom to pursue their own ideas – and so is another point in favour of academic careers.

Having a very engaged and supportive supervisor (so not necessarily the most busy and senior academic in the field!) can make a big difference, as can talking to other PhD students and postdocs regularly to share ideas. You’ll also want to make sure you’re working on questions you feel very intrinsically motivated to answer, since you’ll have to drive yourself to keep making progress with much less external pressure than most jobs.

What are the main downsides?

Highly competitive and low chances of progression

As we mentioned earlier, academia is highly competitive:

  • A study by the U.S. Bureau of Labor Statistics found that in 2010, less than 15% of new Ph.D.’s in science, engineering, and health-related fields found tenure-track positions within 3 years after graduation. For Ph.D.’s in the life sciences, the figure was a grim 7.6%.
  • The NSF estimated that, in 2010, only 11% of PhDs in the biological sciences held tenure-track positions 3 to 5 years after graduation, down from 55% in 1973.17
  • One way to measure how competitive an academic field is to look at its “reproduction rate”: what is the mean number of new PhD students a typical faculty member will graduate during his or her career? This gives us an estimate of how many PhD graduates there are per faculty position. One study found that there is roughly one tenure‐track position in the US for every 6.3 PhD graduates in the biomedical sciences.18
  • Another looking at engineering finds that “a professor in the US graduates 7.8 new PhDs during his/her whole career on average … This implies that [barring growth in academic positions], only 12.8% of PhD graduates can attain academic positions.”19
  • The situation seems similar in other countries. For instance, in the UK, for example, only 3.5% of people with a science PhD make it to permanent research positions in academia and just 0.45% of STEM PhD holders in the UK become tenured professors (though note that the title “professor” is awarded much less often in the UK as in the US, as it is reserved for senior academic staff). Only around 20% end up in any sort of research roles.20

However, these figures vary a lot by institution – a 2015 study of 19,000 faculty members in business, computer science and history found that 25% of institutions produced 71-86% of all tenure-track faculty depending on the field.21 This means that if you’re able to do your PhD at one of the most elite universities, your chances of getting tenure will be substantially higher than 15%. (If we assume that all these universities produce the same number of PhD graduates, and that 15% on average get academic roles, then around 47% of graduates from the top 25% of universities would be successful but only 4% from the remaining universities. If the top 25% of institutions graduate twice as many students as the rest, then the figures move to 29% and 5%. This suggests your changes are 2-3x higher than the overall average at top institutions.)

Over the last 20 years, it has also become increasingly common to do one or more “postdocs” – temporary non-tenure-track research positions, normally lasting 1-3 years each – before getting a faculty appointment. According to the National Research Council’s report, “Bridges to Independence”, the share of recent PhDs in postdoc positions rose from 13 to 34 percent between 1972 and 2003.22 Scientists doing postdocs in the US spend an average of 3 years in this holding pattern and only about 17% ultimately land tenure‐track positions.23 A typical postdoctoral research associate salary is $45-55,000.

This seems to be a result of the fact that the number of PhD graduates has dramatically increased – in 1994, 7,800 people received doctorates in the life sciences in the US, whereas by 2014 there were 11,335 – while the number of tenure track and tenured professorship positions has stayed constant.

All of this comes after a large fraction of people who start PhDs fail to complete them. Between 41 and 78% of people who start PhDs have finished them after ten years, depending on their discipline. Computer and Information Science has among the lowest completion rates at (41%), Economics is in the middle (52%) and life sciences fairly high (63%).24 Though note completion rates are better among the most prestigious institutions.

FieldSubfieldTen year completion rate (%)
EngineeringBiomedical Engineering62.9
Chemical Engineering63.3
Civil Engineering77.6
Electrical and Electronics Engineering55.5
Mechanical Engineering65.8
Total63.6
Life SciencesBiology59.4
Genetics, Molecular Genetics69.3
Microbiology and Immunology69.1
Molecular and Cellular Biology63.7
Neuroscience65.4
Total62.9
Mathematics & Physical SciencesChemistry61.6
Computer and Information Sciences41.5
Mathematics50.8
Physics and Astronomy59.3
Total54.7
Social SciencesAnthropology and Archaeology46.2
Communications66.8
Economics52.4
Political Science43.6
Psychology65.1
Sociology44.8
Total55.9
HumanitiesEnglish Language and Literature51.9
Foreign Languages and Literatures48.4
History47.2
Philosophy48.7
Total49.3

Competitiveness also means that you might not have that much flexibility to decide where you live and work, or even what research you do. It’s common to have to move to a different university at each different stage of your academic career, which might not suit some people, especially if they need to bring their families with them.

Academic incentives aren’t always aligned with doing valuable research

One of the biggest problems in academia is the “publish or perish” mindset. As we showed in the section above, academia is extremely competitive. Progression is mainly decided based on your publications, which means there are powerful incentives to publish many papers, to do so in the most prestigious journals, and to be as highly cited as possible.

These incentives make it hard to do the most valuable research within academia. The research that gets you lots of publications isn’t necessarily the research that’s most valuable. The research that’s most likely to get published often depends on what’s popular in the field at the time, or research that suggests novel, exciting results.

In psychology, for example, the pressure to publish exciting and novel results appears to have led to a slip in the standards of research methods, which has now become clear as many of the most popular findings fail replication.25 If you need to publish regularly, this also pushes towards producing lots of small, incremental findings, rather than deeper work that might result in more valuable breakthroughs.

If you think that the most valuable research questions are far higher impact than average, then having to work on other questions might significantly reduce the impact of this path.

However, we still think academia has a lot of advantages for doing high-impact research – as we discussed earlier, you’ll generally have more freedom than doing research for a company, and academia attracts some of the smartest people.

How easy it is to do valuable research can vary a lot by the field and lab/research group you’re working with. One way to make this easier is to explicitly try and work with academics and groups who you think have a track record of working on important topics in the past, or who are clearly in the middle of trying to solve an important problem. If you can go and work in an AI group that has been producing cutting-edge, well-respected research over the last few years, a biomedical research lab who have a track record of producing effective disease treatments, or a public policy group who regularly engage with and advise government, for example, the incentives you face on a day-to-day basis are much likely to align with doing valuable work. Some positions might carry fewer teaching responsibilities, and if you can get to a tenure-track position relatively quickly, you’ll have much more time and autonomy.

That said, if you can’t find a PhD opportunity with a research group working on an important problem, you may want to reconsider. We discuss this issue under How to establish your career early on.

Lower salaries

Academia has lower salaries than industry in general – for example, being an academic in a quantitative field is much less well paid than being a data scientist or programmer which requires similar skills. A Nature Jobs salary and career survey found “a big disparity in industry versus academic salaries”, with average industry salaries exceeding average academic salaries by 50% in Asia and by 40% in Europe and North America (though note that many academics get far more annual leave and flexibility than those in corporate jobs).26

These averages vary significantly by academic field – with disciplines such as computer science, economics, and law paying higher salaries than average. Professors in those fields can earn as much as you might by your 30s in software engineering or even some parts of finance.27 However, people capable of becoming computer science professors can often earn far more than average in industry, and rise up the ranks more quickly. All considered, most academics have the impression that they could have earned significantly more in the commercial sector.

Long training time

Training for an academic career takes a long time – at least 3 years for a PhD, and closer to 5-7 years in the US (though you generally get much more training in the US, which prepares you better for the academic job market.) Because of the competitive nature of academia we discussed earlier, getting from a PhD to an academic position can also take a long time – in biomedical research, for example, the age of first independent faculty appointments has risen from 34 in 1979 to 38 in 2003.28

Since these are some of your most potentially productive years, the opportunity cost of doing a PhD and then postdocs is pretty high if you don’t end up following an academic career.

Furthermore, many of the problems we recommend focussing on are urgent, and work on them today is more useful than work on them in the future. Delaying your directly valuable work by 7 years, is itself a cost.

For this reason we generally recommend people think thoroughly about whether to do a PhD before committing to one, and ideally spend some time exploring other options first, rather than just doing one as a default. We cover how to do that later.

Lower flexibility

Establishing yourself as a specialist in a specific academic discipline may make it harder for you to be flexible and change the cause area you work on later in your career, though this varies from field to field. Mathematicians and social scientists, for example, often have lots of flexibility to apply their expertise to different problems, whereas biologists are often more focused on a narrow area.

What’s more, since relevant expertise is so important in many of the top problem areas, we think it’s often worth giving up this flexibility in order to gain specialist expertise if you pick a promising field. This is especially true if you’re coordinating with a community – if everyone specialises in something, the community learns more and some of those bets are sure to pay off.

How to assess your personal fit

As we’ve discussed, research seems to be an area where the most successful people have far more impact than the rest. This means that personal fit is an even more important factor than normal in deciding whether to enter this path.

What are the entry requirements?

To get started down this path, you’ll need:

  • A strong undergraduate degree (at least a 2.1 in the UK, though most people recommend a first, or a GPA of 3.5 or higher) in a relevant field.
  • Many PhD programs require a Master’s degree first (especially in Europe), depending on the field and your background.
  • For US PhDs you’ll have to take the GRE, a standardised test required by most graduate schools, measuring verbal reasoning, quantitative reasoning, and analytical thinking. This blog post has data on the average GRE scores of applicants in different fields, which gives an idea of the kinds of scores you should be aiming for.

What does it take to excel?

Track record

The best predictor of success in academia, as in many fields, will be your existing track record. If you have managed to publish respectable papers before or during your PhD, especially as first author, that is a great indicator that you could succeed in academia. Similarly, your ranking in your classes or the competitiveness of the graduate school you got into are leading indicators.

Within biomedical research, researchers built a statistical model that predicts chances of becoming a principal investigator based on factors including publication record, gender, and university rank.

Below we will focus on other factors whose predictive value is not so obvious, and which don’t require you to already have pursued a PhD.

How important is intelligence?

In general, high IQ seems to provide a significant advantage in doing scientific research. A study of 64 eminent scientists (physicists, biologists, and social psychologists) by Harvard psychologist Anne Roe found that their median scores on tests of verbal, spatial and mathematical reasoning corresponded to IQ scores well above the median IQ of PhD scientists (though some have contested this, as we discuss below).29 If IQ were irrelevant beyond a threshold, we’d expect this group of successful scientists to have average scores similar to the average population of scientists which are already very high. Another line of support for this comes from the fact that intelligence is correlated with job performance more generally30, and the correlation is stronger for more complex jobs31. Since research is among the most complex careers, this suggests intelligence will be strongly predictive of success in research.32

Beyond this, more specific abilities – in verbal, quantitative, and spatial reasoning – seem to be important predictors of which fields a person is most likely to succeed in. A more recent longitudinal study of “mathematically precocious youth” over 35 years found that ability level (as measured by SAT scores aged 13) contributed significantly to academic accomplishments (securing a doctorate, tenure-track position, patents or noteworthy publications), but that ability tilt (the difference between math and verbal SAT scores) was highly predictive of the kind of domain these achievements occurred in. Subjects who reached high levels of achievement in the humanities were more likely to score high on the verbal SAT relative to the math SAT, and the reverse for those whose achievements were in the sciences.33

Results from the same 35-year study of talented youth found that spatial reasoning ability (the ability to match objects seen from different perspectives, judge what cross-section will result when an object is cut in different ways, etc.) is predictive of academic success in addition to verbal & quantitative reasoning. A 2013 analysis found that verbal & quantitative reasoning jointly accounted for about 11% of the variance in the number of patents & peer-reviewed publications a subject had, and that spatial ability accounted for an addition 7.6%.34 David Lubinski, one of the study’s co-directors, suggests that spatial reasoning “may be the largest known untapped source of human potential… no admissions directors I know of are looking at this, and it’s generally overlooked in school-based assessments.”35

However, this doesn’t mean that you need to have an IQ or test scores in the top 0.01% in order to have a chance of contributing valuable research. In A Question of Intelligence, Daniel Seligman reports that the correlation between IQ and elementary school grades is 0.65.36 This is a high correlation, but far from perfect – meaning how hard you work and other personality factors are also likely to be important, as we’ll discuss below. Seligman points out that if becoming a tenured professor is a one in a thousand level accomplishment, then we’d expect the average tenured professor to have an IQ of around 150 if the correlation between IQ and academic success were perfect. But if the correlation is 0.65, then we should expect the average tenured professor to have an IQ around 133, with quite a bit of variability around that.

There’s also reasonable variability by field – high intelligence, and high verbal/quantitative/spatial reasoning ability will be more important in some areas than others. Here are the average GRE scores of applicants to the following PhD courses:

  1. Physics (1899)
  2. Mathematics (1877)
  3. Computer Science (1862)
  4. Economics (1857)
  5. Chemical Engin. (1845)
  6. Material Science (1840)
  7. Electrical Engin. (1821)
  8. Mechanical Engin. (1814)
  9. Philosophy (1803)
  10. Chemistry (1779)
  11. Earth Sciences (1761)
  12. Industrial Engin. (1745)
  13. Civil Engin. (1744)
  14. Biology (1734)
  15. English Lang. / Lit. (1702)
  16. Religion / Theology (1701)
  17. Political Science (1697)
  18. History (1695)
  19. Art History (1681)
  20. Anthro. / Archaeol. (1675)
  21. Architecture (1652)
  22. Business (1639)
  23. Sociology (1613)
  24. Psychology (1583)
  25. Medicine (1582)
  26. Communication (1549)
  27. Education (1514)
  28. Public Administrat. (1460)

We worry about the reliability of this data, which is purportedly from 2002, and would like to find a better source, but so far it is the only one we have found.

We can also get a sense of how important IQ is in a field by looking at the age of peak performance in that field. Since IQ declines sharply with age, fields where researchers make their biggest breakthroughs early in their careers are likely to rely more on intelligence — in physics and pure mathematics, the age of peak output is around 30, for example, suggesting intelligence is highly important for contributions in these fields. In medicine and history, by contrast, the age of peak output is closer to 50 — suggesting that accumulated knowledge and effort play a much larger role in making contributions to these fields. Psychology falls somewhere in between.37

So while intelligence is important, and will certainly increase your chances of being able to contribute valuable research, it varies by field, and you don’t have to be a genius to contribute. While IQ does predict academic success on average, the correlations are weak enough that there are lots of exceptions, and measures of IQ aren’t perfect.38 This means you shouldn’t necessarily give up on research if you don’t get an off-the-charts IQ score, especially if you have other strengths or can find important but neglected areas of research.

How much difference does hard work make?

K. Anders Ericsson, a leading researcher studying expert performance, argues against the view that some people are “naturals” in an area, able to attain mastery with ease. His research suggests that the highest-performing people have all done a huge amount of focused practice, usually with top mentors: “even children considered to have innate gifts need to attain their superior performance gradually, by engaging in extended amounts of designed deliberate practice over many years.”39

This suggests that success in academic research may depend to a large degree on your ability to work in focused ways over long periods of time, with good feedback — which may in turn depend on your interest in and motivation to sustain work in an area, and your ability to find others to learn from.

However, the importance of more innate factors like intelligence/talent versus deliberate practice is still debated. One meta-analysis of studies of deliberate practice challenges whether Ericsson’s findings – which mostly look at performance in clearly-defined, predictable areas such as games and music – generalise to less-predictable domains such as science and education (where presumably it’s harder to get the high-quality feedback needed for fast improvement.)40

More generally, Ericsson’s research strongly suggests that world class performance in a domain requires 10 to 30 years of focused practice. But it may be that even this is not sufficient for just anyone to achieve excellence – you may still need to start out with certain genetic predispositions. Simonton, the psychologist who studies scientific productivity we mentioned earlier, suggests that “scientific achievement is not a matter of either talent or training but rather a matter of talent operating in the context of training.” To say that some people are genetically predisposed to be more likely to successful scientists is not to say they are born with some diffuse “gift” for science. Rather, there may be a number of important composite factors – both intellectual and personality characteristics – which are at least partially genetically determined, and which contribute significantly to scientific achievement.41

What about other personality factors?

There’s reason to think that personality factors contribute substantially to academic success in addition to intelligence and deliberate practice.

This makes sense if we consider Shockley’s point that we mentioned earlier: being a successful scientist requires combining multiple distinct skills, including the ability to think of a good problem, the ability to work hard on it, the ability to write well, and the ability to respond well to feedback and persist in making changes. Different personality variables are likely to contribute to these different abilities – creativity and openness might help you to think of a good problem and look for unusual solutions, whereas conscientiousness will help you to persist in working on a problem when it’s no longer exciting.

There’s some evidence on how personality factors influence academic and research performance to back this up.42 A meta-analysis of studies on predictors of academic performance found that “conscientiousness
added as much to the prediction of tertiary academic performance as did intelligence”.43 The same paper also reports weaker effects of agreeableness and openness to experience on academic performance.

Other research suggests that intellectual curiosity may be an important determinant of academic achievement (a position we’ve found echoed by almost everyone we’ve spoken to in the field). Another meta-analysis of studies of academic achievement looked at the predictive power of the personality construct Typical Intellectual Engagement (TIE) – a measure of enjoyment of intellectually demanding activities – alongside intelligence and effort. They found that intelligence accounted for the greatest variance in academic achievement, but that the combined effects of TIE and effort equalled those effects of intelligence.44

There’s also some moderate evidence that creativity is important for doing successful research, and in some cases high levels of creativity can compensate for lower levels of intelligence45.

Finally, we expect that advancement in fields which require managing teams of young researchers and applying for grant funding will benefit from strong social skills.

How to assess your personal fit at each stage of your career?

You have a few key opportunities to assess whether an academic career is for you:

  • During your undergraduate studies, aim to complete at least one summer research project. This will help with graduate school applications, while also giving you a taste of what research is like — it’s pretty different from studying a subject at undergraduate.

  • After you graduate, if you’re highly confident that academia is your top option (say 80%+ confidence), then aim to continue directly into graduate studies. If you’re unsure (say 40%+), then the 1-2 years between undergraduate and graduate study are a good time to experiment with other options you’re interested in. We recommend experimenting now rather than after your PhD because, as we explained earlier, it’s hard to take any break from academia after your PhD. This could also be a good time to consider a research assistant position or pre-doctoral fellowship, which can allow you to work in a research lab and test your fit for academia while also sometimes allowing you to take classes at your host institution, without committing to a PhD.

  • At that point, if you still think academia is for you, then apply to graduate studies. Again, if you can get into a top 20 school in your subject, that’s some indication of potential. For graduate schools in the US, you have to take the GRE (Graduate Record Examinations), and your scores in this also provide some indication of potential – a meta-analysis found that GRE test scores predict grade point averages in graduate school, faculty ratings, citation scores, and later career research productivity.46

  • During your studies, you might be able to experiment with some internships on the side, to keep learning about alternative options. For example, as a graduate student you might be able to get internships in government, think tanks, or industry, depending on your field.

  • Near the end of your PhD, you face a key decision-point: will you continue? This is a good opportunity to re-assess your fit. If you think there’s a reasonable chance academia is your top option (say over 50%), then it’s worth continuing to keep your options open. You can also apply to postdoc positions to see what you get. If you’re able to get a postdoc in a good department/group without a large teaching load, it’s usually worth taking. In our individual profiles on specific fields, we discuss specific signals of potential at this point — but the conventional advice is that you (i) have some reasonably good publications (ii) have an offer to do a postdoc at a top research centre.

  • The next reassessment point is when you start applying for permanent positions. See what you get, and if in doubt, continue with academia.

  • However, it’s also worth mentioning that many people find it difficult or scary to leave academia – even if they don’t enjoy it anymore, or no longer think what they’re doing is valuable. People on the academic track are not taught about, nor encouraged to value, options that compete with academia. One thing that might help with this is to think in advance what your “exit” conditions are – under what conditions you’ll decide to leave academia and try something else (e.g. if you struggle to get a postdoc position in a top group/university a couple of years after your PhD), and commit to reassessing your options at each new career stage (after a PhD, after your first postdoc, when you’re going up for tenure-track positions.) If you’re going to leave academia and transition to another area, it’ll probably be easier to do so before your mid thirties (as a very rough guideline).

As well as your fit for academia in general, you’ll also want to think about your personal fit for specific areas of research, which you might be able to test by:

  • Doing research projects or working as a research assistant (especially if you’re an undergraduate);
  • Finding out what the prerequisites/normal backgrounds of people who go into this field are, and comparing your skills and experience to them;
  • Reading key research papers, trying to contribute to discussions with other researchers, and getting feedback on your ideas;
  • Talking to professors or other successful researchers in a field and asking what they look for.

Maximising your impact within academia

Which field should you go into?

When choosing the best field to focus on, in general the three key things to think about are:

  1. Personal fit – what are your chances of being a top researcher in this field?
  2. Field relevance – how likely is it research in your field will contribute to solving pressing problems?
  3. Backup options – what options would you have outside of academia if you left this field early?

Personal fit

Personal fit is extremely important when choosing a field – even if you work on an important question, you won’t make much difference if you’re not particularly good at it or motivated to work on the problem.

If you’re deeply immersed in and committed to a topic, you’ll spend so much more of your time thinking about it – even in the back of your mind while in the shower or walking places – which can give you a big competitive edge.

Early in your career, such a large fraction of your effort goes into reaching and staying at the frontier of knowledge, that someone who invests 20% more time, could effectively double the time they spend on actual research.

Richard Hamming, a mathematician whose research made important contributions to computing, spent years trying to figure out what made especially productive scientists successful – and he strongly emphasised the importance of being “emotionally involved” with your research. He points out how extra time spent thinking about a problem can bring vast returns: “knowledge and productivity are like compound interest… the more you know, the more you learn, the more you learn, the more you can do, the more the opportunity…”

Balancing your personal fit against the importance of the field is difficult. On the one hand, especially when you take into account the other ways to have an impact in a field – e.g. doing outreach or advising policymakers – personal fit could become the most important factor.

On the other hand, we’ve found that people sometimes think too narrowly about what they can be good at and enjoy (and the education system, especially in the UK, tends to encourage narrowing in one or two subjects very early on.)

This can be harmful because it means people who could contribute to highly important research don’t even consider it. Most people might therefore benefit from considering a wider range of options of academic fields early in their career, and avoid being pigeonholed in a specific area.

One strategy would be to first narrow your options based on which fields seem relevant to solving important problems, and then choose from those already-filtered options based on personal fit.

Field relevance

If you think you have a good chance of excelling in a field, but are less sure how valuable progress is in that field, one question to ask yourself might be: how valuable does cutting-edge research in this field seem to be right now? Can you point to examples of recent research breakthroughs/progress that contribute to solving an important problem?

More generally, we suggest choosing a field by first asking what global problems seem most pressing, and then which fields of research seem most likely to contribute to solving those problems.

As mentioned earlier, some fields we think are particularly promising given our list of urgent global problems include computer science, applied mathematics/statistics, economics, biomedicine, international relations and political science, neuroscience and cognitive science. You could look at our current list of pressing global problems and how we think about choosing cause areas to get more ideas.

You might also ask yourself whether developing expertise in this field might allow you to apply that expertise to solve problems outside of academia. For example, you might aim to develop expertise in food science in order to develop meat substitutes, or in biotechnology in order to better understand and improve biosecurity. If you can see a clear way that better understanding a field of research would help you to contribute towards solving an important problem, it might be less important that you’re able to push research progress forwards in that field – it might still be useful to go into academia in order to develop a deep understanding of research in that field.

It’s worth noting that the importance of research within these fields varies hugely – lots of philosophy research has no relevance for real-world problems! – so it’s also important to think very carefully about how you choose research questions within a field (which we talk about in more detail below.)

Backup options

Finally, you’ll want to consider what backup options working in this field would give you if you left academia.

To assess your backup options, think about what skills you’ll be building in this academic area and what other career options they might open up to you.

Some academic fields will have much better backup options than others – lots of jobs value applied quantitative skills, so if you study a quantitative subject you may be able to transition into work in effective non-profits or government. A history academic, by contrast, has fewer clear backup options outside of academia.

Keep in mind that the US unemployment rate for people with graduate degrees is fairly stable at around 2%. If you’re capable enough to get a doctorate, you’re likely to be able to find skilled work, even if it’s not what you dreamed of.

How to establish your career early on

At the beginning of your career, most of the academics we spoke to recommended focusing on developing expertise and building up a good track record of publications. A good publication record will be essential for you to get the best academic jobs and funding later on, which in turn will give you the freedom to pursue whatever research questions you think are most important.

One study of the predictors of long-term academic success found that “by far the best predictor of long-term publication success is your early publication record.”

Learning and building up a good track record doesn’t have to be completely at odds with doing valuable research, though. The best people to learn from are likely to be those who you think are doing good work on important questions.

And while building up a good publication record is important, it would also be a mistake to publish in areas completely divorced from the kind of research you want to do long-term, as then you risk getting pigeonholed in the wrong area, and won’t necessarily be building the right expertise. If you want to have a big impact through your research in the long-run, it seems important to spend at least some time early in your career on projects you think could be extremely valuable.

Owen Cotton-Barratt, a mathematician at the University of Oxford and research fellow at the Future of Humanity Institute who works on global priorities research, suggests young researchers spend at least 20% of their attention on questions they believe are highly valuable.

Dr Cotton-Barratt on whether to research something unimportant but good for your career until you get tenure

I think that sometimes people entering into academia feel like they don’t even have permission and they’re not entitled to have opinions about what research is important. They just need to buckle down and do the work to get tenure and then eventually they’ll be able to burst out and start thinking, “Okay, now what do I really want to work on?”

I actually worry about this attitude … I’m not even sure it’s the thing which sets them up to do the most valuable research later. There are lots of things which set us up to be in positions to do valuable research. One of them is having a recognition that you are an established thinker in this field and that what you have to say is going to be worth listening to.

Another is just having well calibrated ideas about what is going to be valuable to work on. I think that choosing particularly important research questions is a skill. I think it’s a somewhat difficult skill, because the feedback loops are a bit messy, but there are some feedback loops there. Like many skills, I think that it’s one where the best way to get better at it is to practice…

It’s also the case that, if people are trying to do research on questions that feel particularly important to them, they are going to be more directed in the other things that they’re choosing to learn about and build up expertise in. They are likely to build expertise in topics, which are really crucial to the things that they think are important. Rather than building expertise in the topic, which is kind of adjacent maybe it’s a little bit like something that’s important, but not actually that close. …

A PhD is a long time investment and I know a lot of people who do PhDs and then think, “This isn’t what I want to be working on.” … I think that if you are talented, often PhD supervisors will want to work with you. And if you have ideas of, ‘actually, I think that this is a particularly valuable research topic’, you can quite likely find somebody who would be excited to supervise you doing a PhD on that. And there you’ve used some of your selection power on choosing the topic and going for things that seem particularly important. Then you can still spend a lot of your time on working out, okay, how do I actually just write good papers in this topic? …

Robert Wiblin: You don’t think the cost in terms of advancing your academic career is so severe that you should just play the game early on?

Owen Cotton-Barratt: I don’t think it’s so severe. I think that there is a cost. I think that a lot of academic advice is generally standardized to trying to help people maximize the chance of a successful career for them personally sense [rather than for social impact].

Read more in our full interview with Owen

Maximising your impact later on

1. Maximising the impact of your research

Richard Hamming, the mathematician we mentioned earlier, famously went around asking his colleagues, “what do you think are the most important problems in your field?” When they responded with a few specific ideas, he followed up with, “And why aren’t you working on them?”

This may not have made him many friends, but it demonstrated how many academics simply don’t ask themselves this question, and don’t actually spend a great deal of time thinking about how they can do important research.

In a famous speech on ‘why so few scientists make significant contributions’ Hamming reported his observation that:, “the average scientist, so far as I can make out, spends almost all his time working on problems which they believe will not be important.”

This raises an important point – in order to do important research, you need to spend a reasonable amount of time thinking about what questions are important.

As we mentioned previously, even within a field that seems highly relevant to important problems, some research questions will be much more valuable than others.

Many people seem to “fall into” research areas due to circumstantial reasons: they inherited a PhD topic from their supervisor, and then looked for research positions in related areas. This leads to crowding and ‘path dependence’ in what topics are addressed by academics.

There can also be a tendency in academia to focus on very narrow areas. That makes it easier to explain what your specific area of expertise is, but it also leads to a lack of big picture thinking.

How can you ensure you focus on important questions throughout your academic career? When thinking about how to choose research questions within a field, you can use the same framework we use to think about cause areas: looking for questions that are important, tractable, and uncrowded. For example, if you’re a biomedical researcher you might try to identify diseases that affect a lot of people, which not many researchers focus on, but where it seems like more research could yield effective treatments.

Here are some rules of thumb, based on the advice we heard in our interviews with top scientists, which may help you to identify high impact research questions:

  • Look for research questions that seem important but are short on talent. Ask why people aren’t working on these questions – is it because they’re intractable (good reason!) or because there aren’t good incentives to work on it, it’s not fashionable or doesn’t fit neatly into an academic field? (bad reasons!) Sometimes small findings and unglamorous innovations can make a big difference – not all valuable research is paradigm-shifting.

  • Work on really giant problems that are in the process of being solved – even if there’s a lot of attention on a problem, if it’s big enough additional effort could make a big difference, and if there’s a track record of valuable progress that’s promising. For example, global health research gets quite a bit of attention, but an extra researcher could still do really valuable work on developing treatments and vaccines, especially if focused on more neglected areas. Focusing on big problems that seem tractable, and then looking for neglected areas within those areas could be a good approach.

  • Bring new skills, perspectives or technology to an important area – for example, Daniel Kahneman brought findings from psychology to economics and ended up winning a Nobel Prize. This might also allow you to work on questions that don’t fit neatly in an academic field, that others might miss. Learning maths, statistics, and how to work with data seems likely to be useful in all fields, and might give you an edge or do work others can’t if you’re in a field that’s not typically quantitative.

  • Ask other experts in the field what they think the most important questions are (like Hamming) – academics who have a lot of experience in a field might be better able to spot important, neglected areas. However, the opposite could also sometimes be the case – if a professor has built their career working in a niche area, then they may well have a bias towards thinking that area is disproportionately important. If you can find more experienced researchers who seem to have thought hard about the most important questions in their field, learning from them could be really helpful.

Some of the researchers we spoke to also emphasised the value of developing the skill of identifying which research questions are important, and regularly trying to get feedback on whether your research is going in the direction you’d hoped. Anders Sandberg, a research fellow at the Future of Humanity Institute, told us that “being able to evaluate what you’re working on, having some kind of importance check, and setting your priorities straight is really important.” He even said he’d rather take a pill that gave him this skill than an intelligence enhancing pill.

2. Maximising your impact in academia beyond research

As we’ve covered, you might be able to have as much or even more impact in ways that go beyond your own research – by doing advocacy, applying your research outside of academia, advising policymakers, by managing or influencing other researchers.

Your opportunity to make a difference via these different routes will depend a lot on your personal strengths, preferences, and the specific opportunities in your field.

Perhaps the best way to maximise your impact in academia beyond research is to stay open to all these possible paths early on, and spend time exploring different opportunities early in your career to learn where you can have most impact.

For example, during a PhD or postdoc, keep an eye out for opportunities to communicate your research for a popular audience, and see whether you enjoy and are good at communicating complex ideas simply.

You might do this by looking for popular science publications that look for contributions from academics, or finding opportunities to give talks (which isn’t too difficult in academia – even talking about your research to a wider academic audience at conferences could be good practice.)

If you notice that other academics in your field or department seem to be applying their research beyond academia – advising companies or government, or involved in building products – then talk to them about how they got into this area.

Some companies and parts of government also offer internships specifically for PhD students, which could be a great way to explore the potential to apply research from your field outside of academia. For example, the main research councils in the UK run a “policy internship scheme” for students whose PhD research they fund.

Even if you think you might be able to have a large impact through outreach or applying your research, you’ll still probably want to focus on excelling as a researcher, and developing a good publication record, early in your career. This is because being successful in advocacy, as a governmental advisor or other similar paths, requires you to both have genuine expertise and strong credentials.

If you decide that you definitely want to focus on outreach over research – a “public intellectual”-type path, then you may want to focus on getting an academic position that gives you plenty of time and freedom as soon as possible.

Conclusion

It is not a great surprise that many of history’s most influential figures – like Adam Smith, Stephen Hawking, Rosalind Franklin and Jonas Salk – have worked in academia.

On the other hand, most people who set out to become academics will not make it, as the impediments to reaching a position in which you have discretion over what you study are substantial.

But for those who have the intelligence, conscientiousness and curiosity to succeed, working in academia provides an unusual opportunity to work on the problems they think are most pressing in the world – at least so long as they can find grants to fund them and journals to publish in.

Those who are interested can learn more about specific PhD options and find interviews with academics discussing how the path has worked out for them in the Further reading section.

Think you have a good shot at becoming an academic researcher focussed on a pressing problem?

We’ve helped dozens of people decide if this is the right path for them, and if so how to go about it. We can offer introductions and funding opportunities, or answer specific questions you might have. If you think you have what it takes, apply for our free coaching service.

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Further reading

80,000 Hours podcasts that cover the pros and cons of academic research or how to have a successful academic career

Notes and references

  1. Alan Turing: The codebreaker who saved 'millions of lives', BBC News, 19 June 2012.
  2. “Green Revolution technology saved an estimated one billion people from famine and produced more than enough food for a world population that doubled from three to six billion between 1960 and 2000.” http://www.fao.org/news/story/en/item/80096/icode/
  3. Turing, A. M. (1937). "On Computable Numbers, with an Application to the Entscheidungsproblem". Proceedings of the London Mathematical Society. 2. 42 (1): 230–265.
  4. Less than 0.1% of the population today are academics, and this proportion was likely much smaller throughout history.
  5. See Figure 1 of Bloom et al, (2017)
  6. “We present a wide range of evidence from various industries, products and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore’s law. The number of researchers required today to achieve the famous doubling every two years of the density of computer chips is more than 18 times larger than the number required in the early 1970s.” Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2017). Are ideas getting harder to find? National Bureau of Economic Research.
  7. Here is what three researchers we interviewed said about how valuable talented researchers were relative to research funding:

    Sir Andrew McMichael, leading HIV vaccine researcher

    For the good person who’s CV you just described, would you prefer their CV landing on your desk or an extra grant?

    “It’s not a simple choice. If they’re that good, they’ll probably get their own funding at some point. You can take them on without huge risk. I would always take the person.” How about if you could have half a million pound grant?

    “It’s hard to turn down half a million pounds. I wouldn’t know many groups who would. You could buy another machine or do another project that would be too expensive otherwise. It depends on how much money I’ve got there already. It’s fantastic to get good people though, no question.”

    Can good researchers always get funding?

    “Yes, reasonably easily. Everyone can get bad patches. It’s unusual to always be on top of everything. For instance, you can get a dip at the end of a line of work, while you’re getting ready to start something else. But on the whole they can.”

    John Todd, a Professor of Medical Genetics at Cambridge

    Would you prefer £100,000 per year or [a good person] working for you?

    “Definitely the guy”

    How about £0.5mn per year?

    “I’d still the take the person at £0.5mn. By £5mn, I’d prefer the money! There’s a cut off somewhere between the two.”

    Why would you pay so much?

    “It’s very difficult to find brilliant people who have the true grit to get things done, even if it takes a long time. Most of them end up in the city.”

    “The best people are the biggest struggle. The funding isn’t a problem. It’s getting really special people. I call them the one percenters…If you have a good person, it’s easy to get the grants for them. I don’t think there’s a really good researcher out there who couldn’t get funding from the MRC or Wellcome Trust.”

    “One good guy can cover the ground of 5, and I’m not exaggerating”

    Katie Ewer, a cellular immunologist

    Is your impression is that it’s harder to find good researchers or additional funding?

    “In order for research to progress, you need lots of different types of people within an organization. You need people who are very methodical in what they do and are capable of doing large volumes of high through-put work, and then you need a few people at the top with the creativity to pull ideas out of the sky that nobody else would ever think of and convince Bill Gates to give you £1 million. I guess if you have somebody like that in your institution who is that creative and has that amazing ability and insight, then you can probably convince people to give you £1 million. But funding is always limited. We could proceed our field more quickly if we had as much funding as the HIV field.”

    “If you are uniquely gifted in scientific research, then you should probably be a scientific researcher. But for the other 99.9% of the population, they’re probably best going and earning £1 million elsewhere and funding research.”
  8. Simonton, D. K. (1988). Age and Outstanding Achievement: What Do We Know After a Century of Research?. Psychological Bulletin, Vol. 104, No.2, 251-267
  9. “Differences in rates of scientific production are much bigger than differences in the rates of performing simpler acts, such as the rate of running the mile, or the number of words a man can speak per minute... a large number of factors are involved so that small changes in each, all in the same direction, may result in a very large change in output. For example, the number of ideas a scientist can bring into awareness at one time may control his ability to make an invention and his rate of invention may increase very rapidly with this number.” Shockley, W. (1957) On the statistics of individual variations of productivity in research laboratories. Proceedings of the IRE, 45(3), 279-290.
  10. One caveat here is that while the very best researchers may be much more productive than average, it may be very difficult to predict in advance who those researchers will be, even with a lot of information about a researcher’s personal fit and potential. This is because there is an element of luck here - two young researchers might have similar abilities, but one may have more early successes than the other due to luck, and then find it easier to get subsequent research funding, academic positions, publications etc. - in a way that’s self-fulfilling.
  11. See figures 7-12: Hauser, Robert Mason. Meritocracy, cognitive ability, and the sources of occupational success. Madison, Wis, USA: Center for Demography and Ecology, University of Wisconsin, 2002.
  12. Full disclosure: Giving What We Can is part of the Centre for Effective Altruism, which also serves as the parent charity of 80,000 Hours. Without Peter Singer, there’s a good chance 80,000 Hours would not exist, either!
  13. "Prospect/FP Top 100 Public Intellectuals Results". October 15, 2005.
  14. Wikipedia contributors. "Bruce Chapman (Australian economist)." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 12 Oct. 2017. Web. 25 Jul. 2018.
  15. A recent survey of leaders in the EA community asked what skills they wanted to see more in the community as a whole - and found that specialist expertise in subjects such as machine learning and biology were perceived to be of medium importance (though not as important as more generalist skills such as management and policy expertise.)
  16. Hesli, Vicki L., and Jae Mook Lee. "Job satisfaction in academia: Why are some faculty members happier than others?." PS: Political Science & Politics 46.2 (2013): 339-354.
  17. Julie Gould. The elephant in the lab. NatureJobs Blog 2015.
  18. Ghaffarzadegan, N., Hawley, J., Larson, R., & Xue, Y. (2015). A Note on PhD Population Growth in Biomedical Sciences. Systems Research and Behavioral Science, 23(3), 402–405. http://doi.org/10.1002/sres.2324
  19. Larson, Richard C., Navid Ghaffarzadegan, and Yi Xue. "Too many PhD graduates or too few academic job openings: the basic reproductive number R0 in academia." Systems research and behavioral science 31.6 (2014): 745-750.
  20. Taylor, Martin, Ben Martin, and James Wilsdon. The scientific century: securing our future prosperity. The Royal Society, 2010.
  21. Clauset, A., Arbesman, S., Larremore, D.B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances, 1, 1
  22. Bonetta, L. (2009) “The Evolving Postdoctoral Experience”. Science Magazine
  23. Andalib, Maryam A., Navid Ghaffarzadegan, and Richard C. Larson. "The Postdoc Queue: A Labour Force in Waiting." Systems Research and Behavioral Science (2016).
  24. Ph.D. Completion and Attrition: Analysis of Baseline Program Data from the Ph.D. Completion Project. Downloadable here.
  25. “Ten Famous Psychology Findings That It’s Been Difficult To Replicate” by Christian Jarrett, September 16, 2016. Archived link, retrieved 1-Oct-2018
  26. Russo, G. (2010). For love and money. Nature 465, 1104-1107
  27. “Study Finds Continued Large Gaps in Faculty Salaries, Based on Discipline.” Esports Quickly Expanding in Colleges, Scott Jaschik, Inside Higher Ed, www.insidehighered.com/news/2016/03/28/study-finds-continued-large-gaps-faculty-salaries-based-discipline.
  28. “According to the National Research Council's report "Bridges to Independence," the age of first independent faculty appointments for Ph.D.s has been rising steadily from 34 in 1979 to 38 in 2003”, Bonetta, L. (2009) “The Evolving Postdoctoral Experience”. Science Magazine
  29. Roe obtained difficult problems in verbal, spatial and mathematical reasoning from the Educational Testing Service, and created three tests which were administered to the 64 scientists. The same tests were also administered to a cohort of PhD students, who also took standard IQ tests, which were used to normalise the V, S, & M tests. The median “normalised” scores for verbal, spatial and mathematical reasoning amongst the 64 scientists were 166, 137 and 154 respectively - the median IQ of the PhD graduates tested was 141. The median scientists’ score on the spatial test was lower than the median score of PhD graduates, but Roe notes that spatial reasoning scores correlate with age - younger men are likely to get higher scores - so the comparison with PhD graduates is not so direct (i.e. the scientists may have scored higher than the average PhD student when they were the same age.)

    See Roe, A. (1952) The Making of a Scientist.
  30. Hunter, John E. “Cognitive ability, cognitive aptitudes, job knowledge, and job performance.” Journal of vocational behavior 29.3 (1986): 340-362.
  31. Hunter, John E, Frank L Schmidt, and Michael K Judiesch. “Individual differences in output variability as a function of job complexity.” Journal of Applied Psychology 75.1 (1990): 28.
  32. “Although GMA predicts performance in all jobs the more complex the job is13, the stronger the relationship between GMA and performance.14 And the more complex the job, the more variation there is between top performers and bottom performers.15 So if you have one of the highest levels of GMA in a highly complex job, you’ll have a high output compared to the average performer.” Intelligence matters more than you think for career success.
  33. Park, G., Lubinski, D., and Benbow, C. (2007). Contrasting Intellectual Patterns Predict Creativity in the Arts and Sciences: Tracking Intellectually Precocious Youth Over 25 Years. Psychological Science, 18, 11, pp. 948-952
  34. Lubinski, D., Benbow, C., Kell, H. (2014). Life Paths and Accomplishments of Mathematically Precocious Males and Females Four Decades Later. Psychological Science, 25, 12, pp.2217-2232
  35. Clynes, T. (2016) How to raise a genius: lessons from a 45-year study of super-smart children. Nature 537, 152-155
  36. This is the best place to understand the true relationship between IQ and academic performance rather than later in the education system, because at later stages lower-IQ people have already dropped out, so other factors beyond IQ will account for more of the variance.
  37. “At one extreme, some fields are characterized by relatively early peaks, usually around the early 30s or even late 20s in chronological units, with somewhat steep descents thereafter, so that the output rate becomes less than one quarter the maximum. This agewise pattern apparently holds for such endeavors as lyric poetry, pure mathematics, and theoretical physics, for example. At the contrary extreme, the typical trends in other endeavors may display a leisurely rise to a comparatively late peak, in the late 40s or even 50s chronologically, with a minimal if not largely absent drop-off afterward. This more elongated curve holds for such domains as novel writing, history, philosophy, medicine, and general scholarship, for instance.” From Simonton, Dean K. "Age and outstanding achievement: What do we know after a century of research?." Psychological Bulletin 104.2 (1988): 251.
  38. “On a population level, we see that the average doctor is 30 IQ points higher than the average janitor, that college professors are overwhelmingly high-IQ, and we think yeah, this is about what we would hope for from a statistic measuring intelligence. But on an individual level, we see that below-average IQ people sometimes become scientists, professors, engineers, and almost anything else you could hope for.” Against Individual IQ Worries, SlateStarCodex.
  39. Ericsson, K.A. (2006). The influence of experience and deliberate practice on the development of superior expert performance. The Cambridge handbook of expertise and expert performance 38, 685-705
  40. “We found that deliberate practice explained 26% of the variance in performance for games, 21% for music, 18% for sports, 4% for education, and less than 1% for professions. We conclude that deliberate practice is important, but not as important as has been argued.” Macnamara, B. N., Hambrick, D. Z., & Oswald, F. L. (2014). Deliberate Practice and Performance in Music, Games, Sports, Education, and Professions A Meta-Analysis. Psychological Science 25(8), 1608-1618
  41. “Rather than define talent as a mysterious phenomenon that operates independent of domain-specific expertise, talent is best conceived as a process that openly involves that expertise. In different terms, scientific achievement is not a matter of either talent or training but rather a matter of talent operating in the context of that training... A person is certainly not born with a diffuse ‘gift’ for science. Instead, the natural endowment most likely consists of a weighted composite of numerous and highly specific intellectual and personality characteristics.” Simonton, D.K. (2008). Scientific Talent, Training, and Performance: Intellect, Personality, and Genetic Endowment. Review of General Psychology 12(1), 28-46
  42. Chamorro‐Premuzic, Tomas, and Adrian Furnham. "Personality traits and academic examination performance." European journal of Personality 17.3 (2003): 237-250.
  43. Poropat, Arthur E. "A meta-analysis of the five-factor model of personality and academic performance." Psychological bulletin 135.2 (2009): 322.
  44. Von Stumm, S., Hell, B., & Chamorro-Premuzic, T. (2011). The hungry mind: Intellectual curiosity is the third pillar of academic performance. Perspectives on Psychological Science, 6(6), 574-588.
  45. “There are no significant differences in academic achievement between the High IQ - Low Creative and Low IQ - High Creative groups. This supports the findings reported by Getzels and Jackson (1962), Torrance (1959) and Yamamoto (1964a) of equivalent academic achievement among the highly intelligent and highly creative groups.” Palaniappan, A. K. (2007, July). Academic achievement of groups formed based on creativity and intelligence. In The 13th International Conference on Thinking Norrköping; Sweden June 17-21; 2007 (No. 021, pp. 145-151). Linköping University Electronic Press.
  46. Kuncel, N., Hezlett, S., Ones, D. (2001). A Comprehensive Meta-Analysis of the Predictive Validity of the Graduate Record Examinations: Implications for Graduate School Selection and Performance. Psychological Bulletin, 127, 1, 162-181