Tuesday, 16 February 2021

Who is Nick J. Cox and why is he the most important person in science?


If I have seen further it is by standing on the shoulders of Giants

Issac Newton


This quote, possibly the first recorded humblebrag, is how we often think science progresses. It is demonstrated nicely in my favourite documentary ever, Simon Singh’s Fermat’s Last Theorem, which tells the story of how Andrew Wiles would go on to solve this centuries-old math problem. It is an amazing story in so many ways and it is really worth watching it all. However, one part in particular always stands out to me and it appears in the very last minute of the documentary, while the credits are rolling. A long list of names of all the great mathematicians - who Andrew Wiles uses in order to solve the problem - are simply, read out. 

I do, however, dislike this theory (not Fermat's, the giant's shoulder one) because I feel it places far too much emphasis on the importance of great individuals. This way of thinking is an important part of where I think science goes wrong. In order to explain this, I am going to spend the next 5 paragraphs talking about a statistical software package...

Many economists use data to answer questions about the world and we need some sort of statistical software to do so. One of the most common ones in use has been around since the 1980s is called Stata (definitely, not STATA*). It looks like an old Atari game** and I have spent many days and nights staring and swearing into this screen while getting endless replies of Syntax Error, R(198).

So let's say you get some data, load it into Stata, and you realise that the dates are in a weird format Y'68MDEC05, Y'65MJAND2 etc. Why on earth someone decided that this was the best way to write dates is anyone's guess, but it happens all the time. Perhaps because the East German who wrote it in 1978 was drunk on Mecklenbuger Punsch and had grown tired of life. His only solace was the knowledge that this decision would really screw over someone in 50 years time, perhaps someone living in a small cathedral city in the north of England. Who knows? All that matters is that it is your problem now.

You figure out that Y'68MDEC05 is actually December the 5th 1968 and you need to decode these dates into a format Stata will understand. So you decide you are going to manually change each one and a few hours later you realise that it might take a little longer than expected (manually decoding 95,325 birthdays of former DDR citizens takes quite a long time apparently).

Although you can quite easily explain to a person how to decode this date, in Stata (as in any computer language) it is much harder. No amount of pleading and begging will help (believe me, I have tried). The answer, like all things in life, is to google it.

You type into google "weird date stata help drunk stasi" and amazingly someone has asked a similar question to you before! Someone has even replied giving the code to solve the problem whilst simultaneously chastising them for not reading the help file! Over time, problems like this keep coming up and your random googling keeps giving you the answers - you notice a pattern. One person seems to be answering all these idiosyncratic detailed questions: Nick J. Cox.

Since 2014, Nick J. Cox has posted on Statalist, Stata's dedicated messageboard, over 20,000 times at a rate of nearly 10 posts a day (he has been posting before this on old servers so the number is likely far higher). He has answered questions from undergraduates to Professors, at all levels of difficulty, and as a result, has probably increased the productivity of countless academics. Remember its not just the people who he replies to that benefits but the people that view his replies, like the example above. And it is not just economists that use Stata but a whole host of social scientists and biostatisticians and even epidemiologists. 

If you look at his rank in terms of citations he is number 35 in economics, just behind two little known economists Paul Krugman and Ben Bernanke. He is ahead of many people on this list who have been awarded Noble prizes. So you would think, after all this, that Nick J. Cox would be showered with academic achievements and writing columns in the New York Times or being head of the US Federal Reserve.






The thing is, Nick J. Cox is not even an economist, he is an Associate Professor of Geography at Durham University***. He is not as famous as other people on that list for one reason: academia cares only about your publications. We reward the goal scorer and not the person who made the assist, let alone the defensive midfielder who does all the work and goes unnoticed. Sadly, the way academia currently works is the equivalent of putting 11 strikers in a football team, thinking it is the best way to win matches

Science is a process of discovery. If Sir Issac Newton hadn't discovered gravity, I am fairly certain someone else would. And in order to make scientific progress, we have to make this process as efficient as possible. People like Nick J. Cox are such a good example of this because he has increased the productivity of so many researchers (If anyone wants to work on a paper trying to show just how much he has had an effect, I would be interested). 

OK, Nick J. Cox may not be the most important person in science but I do think what he represents is: the idea that science is more than just end process, the individual who makes the discovery. We are not just standing on the shoulders of giants, we are standing on the shoulders of everyone involved in the scientific process, regardless of height advantage.


*Some people think it is spelt like this as a result of the old logo. But DO NOT spell it this way or you will get shouted at.

**OK, the new version looks a bit more modern with the white background but I was brought up on Classic view and I will die with Classic view.

***In the 5 years I have been in Durham I have never bumped into him, I am beginning to expect he is just a benevolent A.I. sent by the Stata God. I am also slightly worried if I bump into him he will tell me to read the help file (although, you really should read it before posting on the message board).

Wednesday, 6 January 2021

The face mask of certainty: a thought experiment to separate the moral from the empirical.

I was recently asked why I got into my research area. I can trace the exact moment to a 2nd-year political philosophy module at Manchester University by Stephen de Wijze. It was a lecture on Rawls’ theory of justice that had a lasting effect on me. Rawls produced an interesting thought experiment by asking what sort of society you would want to live in. But the kicker is that you don’t know where you will be in that society, you are under what he calls the veil of ignorance. If you picked one of extreme inequality, you could end up being really rich or extremely poor: it would be a gamble. He then goes on to argue that you would end up choosing a society where you would tolerate some inequality, as long as it doesn’t make the poorest in society worse off.

There are a number of criticisms about this theory but there was one thing that really got me thinking. His idea of what society we would choose rested on what I think is an empirical claim: the relationship between inequality and development. This is why I have spent most of my life trying to look at the relationship between the two.

Many moral questions are reliant on empirical claims. Is it morally wrong to push someone off a cliff? The empirical question which this all rests on is what happens if we push someone off a cliff. Most of us (hopefully) would all agree that the person would fall. We don’t have to spend time talking about this. But what if moral questions rest on more difficult to prove empirical claims?

There are a number of Covid sceptics that come from libertarian backgrounds. They dislike lockdowns as it takes away from their individual liberties. It is, however, a separate question from how the virus evolves, which is an empirical claim. The problem with this is the two often get conflated. Many libertarians also claim that lockdowns are ineffective in combatting the virus. But why should this be the case?

This is usually what we refer to when we say people are biased. We search for evidence that helps us support our own belief. But this something we find so easy to see in others, yet so hard to see in ourselves. There is a danger in thinking that you are the only objective person in the room. I do, however, believe that we should at least try and fight against our internal biases and aim towards this when looking at empirical questions. So what can we do?

We could try a very simple thought experiment which I will call the face mask of certainty. When you put on the face mask, you instantly know the probability of something working.* Let's say now, the face mask of certainty tells us that there is a 90% chance that lockdowns will suppress the virus, 10% it doesn’t work. Given the trade-offs, do you think we should go into a lockdown? Some of you reading this will think that lockdown sceptics would still choose not to lockdown. So what about if the probabilities were reversed, what if there was only a 10% chance that lockdowns work, and 90% it doesn’t? I am willing to bet some people will still think we should still go into lockdown.

You may find that you agree with people you otherwise thought you disagreed with. For example, some Covid sceptics may actually agree with you that IF lockdowns were X% effective then they would support lockdowns. They just don't think lockdowns work very well empirically. Most libertarians, however, will probably need a higher percentage of certainty for lockdowns to work than others. Which is a reasonable position to hold given that lockdowns have real trade-offs. It is important to argue about these sorts of moral questions, and I believe we should argue over them. But we don’t want the moral debate to spill over into the question of how empirically effective lockdowns are.


*By working I mean to suppress the virus with some known effectiveness over a specific time frame etc. This of course will affect peoples decisions but I am trying to keep the problem tractable.


Thursday, 17 December 2020

Home Economics: Why we are not learning how to cook even when we cook.

The weekend newspaper fans across the kitchen table and the glossy supplement catches your eye. As you are perusing, you come across a recipe and suddenly get the idea stuck in your head that you simply must make this tonight.

But, how do you go about it? Are you the type of person who worries about needing the specific type of mid-sized game bird, and go from shop to shop in search of the holy quail? Or perhaps you are a chancer, a substituter, a leaver-out-altogetherer – you live for culinary Jenga.

Most people learn to cook like this but I suggest this is not really learning. Or at least how we think about learning in many other areas of life.

When you first learn economics, you generally learn a collection of models (I could use other analogies, but this is a vaguely economics-related blog and I’m trying to protect the brand here). To really understand models, you need to play around with them. What happens if I increase this parameter, what if the situation was reversed? This is why exam questions are usually slight adjustments of the main model, to check you actually know what you are doing.

You may have heard of some chefs being “classically” trained. All this means is that they have learned to cook by making classic French dishes (think duck’a l'orange or coq au vin). It is often said that a chef needs to master the classics before you go on to create anything yourself. I don’t think this is strictly the case, but what it does do, is give the chef a collection of techniques and an understanding of why the recipe works. For example, orange works with duck as duck meat is strong and fatty, the orange provides sweetness and acidity to cut through the fat and provide freshness. This is why you often see duck paired with other fruit in restaurants: they are essentially variations on a classic recipe. It is the same with economics as you often take a basic model and tailor it to a specific situation.*

When you read most recipes, however, they don’t really give you an understanding of what each component does. One exception to this is Felicity Cloake’s “how to cook the perfect…” in the Guardian. The idea is quite simple: she reads lots of recipes and synthesises them to make what she deems to be the perfect version of a recipe. But the best thing about it is that she explains what each part of the recipe is actually doing, by noting the differences amongst recipes. It doesn't matter if it is the perfect recipe or not. By playing around with different variations and noting what happens when she tries means you actually learn something from her.

The reason why all this is so important is often (and you may have noticed this) things don’t always go according to plan in the kitchen. When you mess up it is incredibly frustrating. The pile of washing up grows as you have turned the bottom of the pan to carbon. You are stressed. You are hungry. The most frustrating thing of all, however, is if you don’t understand why the thing you have made went wrong. It just didn't work. Usually, this means never trying the recipe again.

I am here to tell you that it is not your fault, you just have a bad teacher: a recipe hardly ever tells you what happens when things go wrong. For example, google any meringue recipe and most will tell you the bare minimum. It doesn’t really tell you what the hell stiff peaks are (a David Lynch adult movie?), or why you need to add the sugar slowly, or what the sugar even does? It also doesn’t tell you that you can overwhip a meringue, what that looks like, or how to remedy it if it happens.

The thing is, when stuff goes wrong and you understand why it went wrong, you have actually learned something. It’s a radical new pedagogical concept called “learning from mistakes”. This is a good thing. However, you can only really learn from your mistakes if you understand what each component does. It is equivalent of changing loads of stuff in your model at the same time and wondering what made it crash. You need to change one bit at a time to see what happens. 


So how do you go about learning how to cook? Well, the way I earned was by setting out to learn different techniques and researching them before I attempted it. Recently, I have been trying to improve my pastry skills so I am making a lot of pies and tarts. I now know why you need to “rest” the pastry in the fridge, what it actually does to the dough, and why you can’t really skip it.

Although the learning curve is a bit steeper, it does end up saving you a lot of time if you enjoy cooking. You don’t have to rigidly stick to the same few recipes and it's much less stressful. Things go wrong less often, and you can also understand what stuff you can actually leave out of a recipe or substitute. If I do use recipes, it is usually for inspiration or to learn a new technique.


Cooking, it is my hobby, so I am going to spend a lot more time on it than the average person. But if you want to make cooking more enjoyable and less stressful, you got to do your research. Also, am happy to help if anyone wants advice :)

 

*I don’t want to enter this debate but you don’t have to be classically trained to be an amazing chef, and you don’t have to be neoclassically trained to be an amazing economist.

Friday, 11 December 2020

Why did some people think the UK/EU deal would be the “easiest in human history"?

A lot of people think free trade is all about tariffs. I think this is why some people thought the deal between the UK and EU would be the “easiest in human history”.

The logic for free trade is compelling because it is not immediately obvious. If you know the basic story, you may know a little bit more about trade than the average person. This may lead you to believe that anyone who disagrees with you is simply misguided. Cod economics, ya da ya da.

I am going to explain the Econ101 version of free trade which has become a bit of an Econ101ism. A lot of people may roughly understand the arguments in favour of free trade but haven’t got the complete picture.

So, a tariff is just a tax on a good that you import from another country. A tariff not only raises revenue for the government but also gives an advantage to home producers over foreign producers. Why does this happen? Let’s take a simple example.

You are in the UK and want to buy some chocolate. You see that the UK and US make similar quality* chocolate and are available at the same price, so it’s hard to decide which to buy. If, however, the UK imposed a tariff on chocolate coming into the country, then US chocolate would now be more expensive. The decision is easy, you buy chocolate made in the UK as the price is lower.

When governments put a tariff on imports like this, it is thought of as protecting home producers - hence the term protectionism.

So why is this a problem? Well let's say that US firms find a new way of making cheaper chocolate via some nuclear reaction or whatever. US chocolate would now be cheaper than the UK if it wasn’t for the tariff. What this means is that consumers in the UK are going to lose out (this is why some people think Brexit will mean cheaper food prices).

But this is not the main compelling argument for free trade. Reducing tariffs means that the whole of society gains by more, on average, than under tariffs. Tariffs impose what is known as a dead-weight loss on society. We can prove this with lots of nice graphs that involve little triangles. The upshot of all this is that even though UK chocolate makers may gain from tariffs, the overwhelming majority of people lose from them. In fact, because there is a net gain from free trade, the winners can compensate the losers – everyone wins.

And because we can rework this example from a US perspective, the same logic applies to the US. As the logic for free trade is undeniable, both countries sit down and agree not to impose tariffs over a cup of tea and it will be the easiest deal in history…

There are a number of important criticisms of this theory that are not as widely known as the above. Not least, the idea that the winners compensate the losers has not really happened in practice. This is related to the whole globalisation and inequality issue. But this is not what I am going to focus on as I want to talk about why trade deals are not actually so easy.

Tariffs are probably the most conceptually obvious way in which you can prevent free trade. But, they are not the only way.

Subsidies are another way you can distort the process of free trade. A subsidy is the opposite of a tax. Instead of taking away, the government gives. For example, rather than imposing a tariff on US chocolate, the UK government could subsidise UK chocolate. This would allow UK firms to charge a lower price than US firms, giving them a competitive advantage. Even though those subsidies** would actually give us cheaper chocolate than putting a tariff on US chocolate, we are going to have to pay higher taxes to fund those subsidies. So there is still a deadweight loss (despite chocolate lovers gaining).


Regulations are also another example of distortions. If the UK allows the use of less expensive graphite tips in their nuclear chocolate reactors and the US doesn’t, then the UK has a competitive advantage over the US.

Now at this point, you may be thinking “isn’t government regulation a market distortion in itself?”. You would be correct in thinking that. This was seen as a benefit of Brexit, to be free from the red tape of Brussels.

This, however, is where trade deals get complicated. This is because regulations are what sovereignty is all about. What is a law other than a regulation? One person's red tape is another person's necessary safety measures. You can make your own regulations that are different from other countries, but if this gives you an artificial competitive advantage over other countries, then you shouldn’t be surprised if that country won’t sign a trade deal with you.

To put it simply, there is a trade-off between sovereignty and free trade. 

You can’t have your chocolate cake and eat it too. This is literally baked into free trade theory. However, when you learn the basics of free trade you just assume (for pedagogical purposes) that the other country has the same preferences as yours. 

This does not mean, however, that Brexit was a bad thing. If the UK has different preferences to the rest of the EU then it makes sense to sacrifice some wealth (via trade) to satisfy those different preferences. The problem, however, is that many people who voted to leave not only had different preferences from the EU, but also different preferences from each other. Some wanted more protectionism/regulations, whereas others wanted less.

When the UK was inside the EU, they had a say in what regulations were imposed, but other countries could also influence these regulations. As the UK leaves, it can impose its own regulations, but other countries are still going to influence these regulations via trade deals.

You can’t really escape the fact that if you want to trade, and preferences differ, you are going to have to find some way of compromising over these regulations.

 The trade-off is real. This is why, sadly, it wasn’t so easy and was never going to be.



*If you are from the UK and have ever tried a Hershey’s kiss, this quality analogy might be a bit of a stretch. Perhaps this is why the US needs so much ice if they have to put wax in their chocolate to stop it from melting.

**The EU does actually allows a certain level of subsidies (or state aid). The crucial point though is that the rules have to be the same for everyone or else there is a risk of other countries having an unfair advantage.

Thursday, 10 December 2020

An economist's guide to Christmas (not).

After discussing the idiosyncratic way I buy presents (more of which later) with Tom Chivers, he said: “An economist's guide to Christmas would make a good book title”. To which I sent him back a vomit emoji (Tom and I are actually writing a book together that doesn’t actually make me want to vom).

I am a little weary of pop economics advice on how to improve your life. I think sometimes the advice is a bit of a stretch from what economic models or empirics tell us.

For example, if I were to write a book like this, I could point to evidence that finds that we tend to overvalue the gifts we give others. Or perhaps highlight the fact that people would not pay as much for the gift they are given if were they to buy it themselves. I would then triumphantly argue we should just give people money instead.

This is bad economics and bad advice. This reasoning doesn’t take into account the warm glow you feel from giving the gift or the fact that someone went to the trouble of buying you a gift makes you happy. After all, isn’t this the reason gifts exist in the first place?

If you were to take anything from this, it suggests that giving a gift that is difficult to put a direct value on may be worthwhile – a homemade gift for example. But it depends on lots of factors which I am sure you already consider when giving gifts such as… do you think they will like it?

Let's be honest - we have all received gifts that we have been disappointed with. I remember my Dad once told me about the time my Grandfather gave him some sort of electronics set for his 18th birthday. My Dad recalled thinking at the time “does he even know me?”. This is probably why giving a gift can be so stressful, the thought that the other person won’t like it and think badly of you.

I think the old saying of “it is the thought that counts” is extremely important here. If you accept that giving gifts is a good thing and want it to continue, you also need to accept that people are going to get it wrong occasionally: it doesn’t necessarily reflect how they feel about you.

So how do I give gifts? Well I really like buying gifts for people. But the way my wife and I give gifts to each other is as follows: we don’t. Well, strictly speaking, this isn’t true. If we find something nice that we think the other will like, we buy it (recently, my wife bought me Dirt, a fantastic book about a journalist training to be a chef in Lyon and I loved it).

However, we worked out that birthdays, anniversaries, and Christmases amounts to 3 gifts a year, each. Given our average life expectancies (and marriage expectancy?) we are looking at well over 200 gifts. Buying one, thoughtful gift for someone is hard enough. Repeating the process every few months makes it ever more difficult, so we decided to stop.

We also have an aversion to accumulating physical stuff (despite my love of kitchen gadgets). So instead of buying each other stuff on special occasions, we tend to just go to restaurants or visit places to celebrate. But when we do get each other gifts, we are usually really happy with them and it removes the stress involved with time constraints of birthdays, etc. It also has the added bonus of being a surprise!

However, and this is the crucial thing here, I AM NOT ADVISING YOU TO DO WHAT WE DO. If you enjoy giving and receiving presents with your partner, or anyone for that matter, continue doing so. If you read this and think you might like to do the same, great. If not, also great!

In economics, we often model decisions as utility maximisation (basically, doing what makes you happiest). But what makes you happy is down to your preferences which are extremely difficult to change. Although it may be the case that you just don’t know until you try, if you are happiest in what you are doing, then keep on doing you. Oh and if do want to buy someone a present for Christmas, buy my book.



Tuesday, 18 August 2020

You can’t blame an algorithm: A-levels and unintended consequences.

You are at a stag do and in charge of paying the bill and you need to do long division which literally no one knows how to do by hand. So, you attempt to divide £2344 by 24 (lads) into your calculator. But because the multiple rounds of black sambuca adds to your already poor levels of coordination you drunkenly mash the last digit and end up typing in 2344/26. Inevitable you get the wrong answer and argue with the waiter for half an hour.

Who is at fault here? Could it possibly be you? No, it is the calculator’s fault. The calculator, being a sentient and telekinetic being should have known you meant to type 4 and not 6, like you definitely did do with your actual physical finger.

The problem with the word "algorithm" is that it makes you think of complex mathematical equation that makes decisions for us. But this is not correct all. An algorithm is not sentient, it does not make the decisions, it just follows orders. When you type 2344/26 into your calculator the calculator is following an algorithm. It is taking your input of 2344 and splitting it up into 26 equal parts for you. It does exactly what you said, it does not look around the table and notice there are only 24 people (lads) and think "ah I know what he means here".

Even the most complex of algorithms, so called “artificial intelligence” follow the orders we give them. We may not be able to understand how they solve problems but that does not mean we didn’t design the rules which they follow (it is quite an interesting subject really, someone should probably write a book about it).

However, the “algorithm” used to assess A-level results was not anywhere near as complex as artificial intelligence. It wasn’t even a “black box”: a situation which describes not knowing precisely how the data in your algorithm is transformed (usually as a result of complexity). But it was trying to deal with a complex task of assigning grades to students without them sitting exams.

The problem of designing algorithms is similar to that of designing laws. After all, laws are basically simply algorithms without numbers.

For example, I think most people would agree that stealing is wrong, and we should have a law that prevents people from stealing. So, let’s say we write a law that says “do not steal” and hope that does the job.

But what if a situation occurs where someone was bleeding badly in the street. I run to the nearest chemist and grab some bandages from the shelf, run out of the shop and dutifully tend to the bleeding person. 

If we were to enforce our “do not steal” law here I would have committed an illegal act despite my heroic efforts and would have been stopped by a security guard. Perhaps I could have gone back in to pay for the item, but it isn’t really clear whether this is stealing or not. This is because haven’t really established what “stealing” technically is in our “do not steal” law.

Writing laws are particularly difficult because of this. We need caveats and exceptions, we need careful definitions. But even this process isn’t perfect. We still need judges and juries to interpret the law on a case by case basis. 

The problem with laws and algorithms is that they can have unintended consequences. The "do not steal" law did not intend for a person to bleed out on the street. The question which we should then ask is who is to blame for these unintended consequences? Can we really blame someone who did not intend for these things to happen when they were acting in good faith?  In the A-levels case the answer is yes and no.

Firstly, I do not think we should blame the designers of the algorithm for having unintended consequences. It is not like they didn’t think things through or create an overly simple law. They did carefully check for certain things such as if their algorithm was favouring certain groups.

What happened, however, is that their algorithm potentially favoured private schools since private schools tend to have small class sizes and the model relied heavily on predicted grades for groups smaller than 15. Now you may say this is “obvious” but we have the benefit of hindsight and they were also dealing with a number of complicated issues. The fact that something would have escaped their model is not surprising at all which is why I do not think you can blame the designers completely for this.

However, you can blame someone for not acknowledging the fact that their model will have unintended consequences. Because of the high likelihood that something will go wrong you need to discuss the model with as many informed people as possible to spot potential issues. The other thing you need to do is take it as given that there will be unintended consequences and work out ways to mitigate their impact. Essentially, planning to fail.

The thing is, just as we need laws, we also need algorithms to help us. Imagine the scenario where we just said that students’ grades would solely be based on teacher assessment. The amount of pressure on teaches to inflate grades would be extremely high. Even if they would not do it themselves, they may be worried that other teachers would do it and not inflating grades would put their students at an unfair advantage.

But when we make algorithms, we need to be sufficiently prepared to deal with unintended consequences.



Thursday, 11 June 2020

Why do we still have journals?

When academic journals first came into fashion, they were primarily a way to disseminate research. Unless you attended a lecture, there was no other way you could keep up to date with new findings in a field without seeing a physical written copy. One would think that the invention of the internet would revolutionise how this process happens but this has not been the case. In fact, it is deeply weird how much academia sticks to pretending the physical publication is still a thing. I have never seen a physical copy of any of my publication let alone held one.

We are all still pretending that lots of people read physical copies of journals. We even format papers designed to be read like they are still physically printed objects. It is the equivalent of logging into the New York Times website and there just being a bunch of pictures of the physical newspaper itself.
We do not need journals to tell people about our research. All we need is a website that we can upload papers to so people can search and access them. If only there was a way to publish a PAPER we are currently WORKING on? We really don’t have to waste time with font sizes or fiddle with how to present references.* And if you want to have a physical copy of them then you can cut down a tree and buy a printer yourself.

So if not dissemination, then journals must be of some other value right? There are two main reasons which often come up when defending journals: scientific rigour and quality. These both come from the process of peer review: where you send your paper to the editor of a journal who then sends them out to anonymous referees and they are rude to you.

In terms of scientific rigour, having a few people read your article closely for mistakes is a good thing. But you know what is better than a few people? Lots of people looking for mistakes. If I made a typo in this blog, due to the magic of the internet, I can quite easily change the typo. I can even post longer amendments and changes based on people's suggestions as they read my piece. If someone disagrees with me so much, they can write their own blog saying I am wrong.

Now some may argue that doing away with formal referees will mean we do not have anonymity and so may be afraid to speak the truth (or at least this is the view of wonk54 on twitter with an anime character as their profile pic). Personally, I am not massively convinced that this improves the process all much as it makes referees overly negative.

There is little incentive to referee for a journal other than academic duty, at the best of times. If there is an incentive, it is to signal to an editor that you have high quality standards. This, however, doesn’t necessarily lead to the overall goal we should all be working toward: increasing scientific knowledge.
The problem with the current approach is that a lot of the time reports are read more like how the referee would do the paper, rather than whether it is correct or not. Often reports don’t take into consideration whether the effort to re-run a study, to get one more data set etc is worth the marginal gain in improving the paper - they are not the ones having to do it after all.

The part I find the most frustrating about journals is the argument that we need to assign “quality”. In economics, we have 5 journals that are deemed the top in the field for historical reasons. I think calls to break up this oligopoly miss the point. We should be debating whether we need journals at all?

I am not against assigning quality to publications, but “quality” is not the same as saying the research isn’t publishable (don’t get me started on “not a good fit of this journal”). What you mostly end up doing is pinging about your research to journals in order to find one that would meet some quality threshold which can take years! Why doesn’t the first editor you send it to just say this if of X quality? Like having AER, and AER macro? Just say AER 1, 2, 3, 4. Or even the top X percent of AER submissions. Journals are not the only way to signal quality and we do not have to stick to this format. I think the film review site Rotten Tomatoes does a better and fairer job of assigning quality than academic journals.

I am not going to suggest exactly what replace journals with but alternatives already exist. For example, physics is already moving away from the journal format with something like https://arxiv.org/. Journals do not need to continue because we think this is how science has been done forever (peer-review isn’t actually that old). I am not really aware of any (peer-reviewed) evidence that journals are the best way to expand scientific knowledge???

The main thing I am worried about is we have a Lord of the Rings problem. We may all think that destroying journals is the best thing to do. But once people start publishing in these journals and become editors of these journals, they no longer want to lob them into the fiery pit of Mount Doom.
I am nowhere near good enough to be Frodo. But if people at the top are willing to get rid of journals, you will definitely have “my axe”.


*we still have to cite issue and page numbers as if we all have libraries in the east wing of our mansions. It is ridiculous.