The Quantum Bomb Experiment

I have been reading about the history of quantum theory and how this history has shaped the different interpretations of it. ‘Helgoland’, by Carlo Rovelli, ‘Quantum’, by Manjit Kumar, and ‘What is real?’, by Adam Becker, are excellent books that discuss these issues. The weirdness of quantum physics is fascinating. And an experiment that illustrates this weirdness wonderfully is the quantum bomb experiment.

Imagine that you have several bombs that are activated—i.e., they explore—when a photon (the particle that light is made of) passes through them. You need to get a bomb for a mission, but some of the bombs do not work anymore: their photon detector is broken. The only way to check whether a bomb works is to send a photon and see what happens. Can you find a bomb that works without actually make it explode?

It turns out that using quantum physics you can. Intuitively, when the bomb works, it is a detector to observe where the photon is, so it forces the collapse of the wave function; but when it does not work, quantum superposition is maintained. Using this difference, one can construct a device that can tell you whether the bomb works even when the photon does not go through it. I am not going to explain the inner workings of this device, but I’ll leave a couple of videos that do so.

The Bomb Experiment, by Sabine Hossenfelder:

Elitzur-Vaidman Bombs, by Barton Zwiebach:

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Rationality, by Steven Pinker

Whenever Steven Pinker, cognitive scientist at Harvard, publishes a book, one should buy it and read it. This is part of living a good life. His books are not easy, although the prose is clear and concise, but they provide the reader with a breath of knowledge that is frankly extremely difficult to find elsewhere. Rationality, his latest book, is no exception.

In this book, Steven Pinker talks about rationality, or reason, why we are able to act rationality, why sometimes we do not, and how we apply rationality to different problems in life, such as establish causality, separate signal from noise, and learn. Contrary to many other books on the topic, it is not about how irrational humans are. There is some of it, of course: when one talks about rationality one has to deal with our heuristics and the situations where they fail. But it is more about how we apply rationality in so many different aspects, and principally a defence of doing so as the only tested way to understand reality and live better.

There is a type of experiment mentioned in the book that I found particularly interesting. First, there is a version of the experiment typically used to show the limits of human rationality. It is an experiment that asks the subjects to check a rule of the type “If P, then Q.” It goes as follows. Suppose that the coins of a country portrait a sovereign on one side and an animal on the other. They have the following rule: if the coin has a king on one side, it has a bird on the other. Now suppose that there are four coins on the table: one has a king, one has a queen, one has a duck (which, just in case, is a bird), and one has a moose. The question is: how many coins should you flip to check whether the rule is broken?

Most people say that the one with a king and the one with the duck. The answer is, of course, not that one: you need to flip the king and the moose. The one with the duck could have a queen on the other side and the rule would not be violated. If the moose had a king, however, the rule would not be correct. This seems yet another example of human folly. But does that mean we are unable to properly comprehend a rule such as “If P, then Q”? Not really. Another experiment demonstrates this.

Imagine that we have four envelopes in front of us from the Post Office. The rule goes as follows: If it is Express Mail, it needs a 10-dollar stamp. We can see the following: one envelop is Express, another one if normal, the third one has a 50-cent stamp, and the fourth one has a 10-dollar stamp. Which envelopes should we flip to check the rule? Now most people get it right: the Express one and the 50-cent one. Same type of experiment, but with a very different situation: we are trying to find a “cheater” in the second case. In this framework, our evolved brains use rationality properly to find whoever might be breaking the rules. We wouldn’t want this person in our tribe!

Rationality is a tool. We apply it, or not, depending on whether it is in our interest to do so. Sometimes it is, such as when some politician of the other team is distorting statistics about a sensitive issue. In other situations—for instance, when this politician is in our team—we are way less likely to apply rational thinking. “We evolved not as intuitive scientists but as intuitive lawyers”, as Pinker puts it in a wonderful Chapter 10 titled “What’s Wrong with People?”. Even if that is the case, he says, we should strive to use rationality more often. “We children of the Enlightenment embrace the radical creed of universal realism: we hold that all our beliefs should fall within the reality mindset.” Rationality is a sort of public good: “Each of us has a motive to prefer our truth, but together we’re better off with the truth.” For this reason, then, rationality “is not just a cognitive virtue but a moral one.”

Buy it and read it. Learn about rationality, about us, and about the case for rationality. It is very difficult to find so much knowledge, and so well written, in under 400 pages anywhere else. I attended an event a couple of months ago with him and Richard Dawkins, which is a great introduction to the book: it has been uploaded here. Enjoy.

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Four Thousand Weeks, by Oliver Burkeman

Four Thousand Weeks is a book that leans against a recent trend—in non-fiction literature but also in podcasts and Youtube videos—in finding hacks to boost our productivity. It is not really an anti-productivity book. The central message is that the feeling of being overwhelmed—at work, in life—cannot be solved using productivity-enhancing hacks—if anything, this hacks will worsen this feeling as one is packs even more work in the limited time we humans have. We have to deal with the feeling directly.

Four thousand weeks is the number of weeks in around 77 years. Given that life expectancy—for the US at least—is around this mark, it is a good way to visualise the finitude of human life. As I was talking to a friend of mine, upon thinking about four thousand weeks, his reply was: really, only 4,000? It is astonishing that this crucial piece of information is not stuck in our heads as we decide how to spend our time.

The book does a great job convincing us to start facing our finitude. In many passages, it seemed like the author was talking directly and personally to me. It remarks how people tend to live for a future where they think they’ll be able to live better… and of course when this future becomes present, they’ll be focusing on the future again. Here is where the productivity hacks are counterproductive: if only I could deal with e-mails faster, maybe I can block some time after a put the kids to sleep, maybe… and one ends up feeling like the day is just a succession of items in a long to-do list. Even putting the kids to sleep, something that can be enjoyable if one is present, becomes a chore with a clear time limit in it.

This uncomfortable feeling of not being in control of our time leads us to seek distractions. And, boy, there are so many! Here comes another batch of recent books trying to help you avoid distraction. Some of the hacks are useful. Reducing the availability of distractions help. But the need for a distraction is not removed by deleting Twitter from your phone. You can’t realistically remove all distractions. Understanding why one is feeling like this, facing this feeling and overcoming it—over and over again—is the real work. There is no future in which we are in control of our time. It is not our time. It is time—and we should focus on doing things that are meaningful to us without expecting to be in control of the agenda.

The more I think about how to broadly organise my days, the more I see the the right approach is probably a minimalist one—and that this is just the very beginning. The real work comes during the day, to withstand urges to get distracted, to switch to easier tasks, or to continue doing something that is working. It is incredibly hard. I can set my goal of working for two hours in a particular research project. This is a completely reasonable goal. But then, if I am not making that much progress, maybe I should get a coffee to refresh my mind? Maybe I can take a look at my phone—there could be some important emails from work. Maybe I can finish this other task that is not super important? Maybe, maybe, maybe… the truth is that none of this would help with the underlying feeling. It is just a short-term relief that makes things harder later on as I did not work enough on my research project. Distraction is a way to feel in control, even if for a short time, while making it harder to do what is meaningful to us, thus increasing our anxiety at the lack of progress and time later on.

So I liked the book. Do not go there if you look for tips on who to better organise your work. There are some, for sure, but that’s not the strength of the book. The focus is on stepping back and thinking about your finitude, and what it means for how you live your life, with a focus on the feeling of overwhelm that appears to be more and more common nowadays. I found myself underlying and writing down in my journal—yes, I have a journal—extensive parts of the book. I leave a couple of quotes here:

On our finitude: “We are a limited amount of time. A decision to do any given thing will automatically mean sacrificing an infinite number of potential alternative paths. Any finite life is, therefore, a matter of ceaselessly waving goodbye to possibilities.

On attention and distraction: “To describe attention as a resource is to subtly misconstrue its centrality in our lives… Attention just is life. The distracted person isn’t really choosing at all.”

On the present: “To treat all these moments solely as stepping stones to some future moment is to demonstrate a level of obliviousness to our real situation that would be jaw-dropping if it weren’t for the fact that we all do it, all the time.”

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The 10th Anniversary of the Contrarian Prize

A couple of days ago I attended a debate on free speech for the 10th Anniversary of the Contrarian Prize. This prize, founded by Ali Miraj, recognises the independence and courage of British public figures who challenge the status quo. At Bayes, we have hosted the Prize since its inception. The Panel was formed by Michael Crick, Sunetra Gupta, Andre Spicer, Peter Tatchell, and Michael Woodford, and was moderated by Claire Fox.

The debate was about whether it is becoming impossible to be a Contrarian. Almost none of the panellists directly addressed the question—to be sure, many emphasised the chilling effect that some cancellations have on one’s speech. But they did not really address the issue. It might be a little harder to dissent, to be a contrarian, in certain circumstances—for instance, in academia—but is it becoming impossible?

I would answer with a clear no. I don’t think that being a contrarian is becoming impossible. Just some decades ago, the price that one would pay by being a contrarian was much higher—the police could come knocking on your door. And of course in many other countries, this is still the case. And here lies the key issue: it is always the state that can make being a contrarian impossible. This is especially true in countries without a robust protection of free speech: basically any country apart from the US.

That is not to say that a culture hostile to free speech is not harmful for society. I firmly believe free speech is extremely important. And as mentioned earlier, there have been cases, few, to be sure, but sufficient to matter, of academics and public figures “cancelled” for expressing the wrong opinions. Yet I think that by far the most damaging aspect of “cancel culture” is that it makes it easier to legislate against free speech, particularly in countries without robust protections of this right—basically countries other than the US.

A lower appreciation of the importance free speech makes it more likely that the government passes legislation to curtail protests or prosecute “hate speech”. And if you think that cancellations of academics or journalists or public intellectuals—the case of Roger Scruton was really shameful both for the New Statesman journalists and the Tories—are chilling, wait until the Government starts cancelling people. We will miss the times when the worst that could happen was to have a Twitter pile-on.

Contrarian Prize 10th Anniversary Debate, at Bayes Business School, London.
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The impact of school closure on test scores

The great* Emily Oster tweeted yesterday that they got their article on the impact of school closures accepted in the American Economic Review: Insights. Their paper shows that, during 2021, students in districts with more in-person classes performed better in standardised tests. This is a result that should surprise no one but maybe it does: closing schools is bad for students’ learning.

Importantly, the effect is not homogeneous. As shown in their Figure 1 (below), the drop in test scores was higher in districts with a higher share of black students. When they estimate the impact of more in-person teaching, they show that the effect is higher when the areas have a higher share of black students. In other words, the benefits (in terms of test scores) of more in-person teaching are stronger in those areas. Again, it is not entirely surprising; it is more surprising to find that those more opposed to re-opening schools in the US have also been those that claim to care more about ethnic minorities.

Figure taken from Jack et al. (2022)

Cool paper, clear and concise. I really hope more and more people submit to AER: Insights, because we need part of economic research to be in the form of very good shorter (and much quicker!) articles.

(*) Great because she is a top researcher (I use the Oster (2019) test in all of my papers), because she has written several books on parenting and family management, and because she is not afraid to discuss controversial issues such as the ones in this paper.

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Seeds of Science: Why we got it so wrong on GMOs, by Mark Lynas

This is a book that I’ve had wanted to read for a long time. I do not remember when I read about it, but the outline of the book—an early anti-GMO activist explaining how the movement started and how he changed his mind—was very compelling. The opposition of GMOs by certain parts of the left has always been puzzling to me. I wanted to understand why a technology with so much potential to help farmers especially in developing countries was being attacked so ferociously.

I first became aware of the existence of a movement against genetically-modified organisms (GMOs) some years ago when reading about Golden Rice. This rice is genetically-modified to provide higher levels of Vitamin A. Vitamin A deficiency is a grave problem in South-East Asian and Sub-Saharan Africa, especially among very young kids. Golden Rice could very cheaply provide a source of Vitamin A for these populations. Yet, Greenpeace and other activist organisations are opposed to this product, and actively lobby to get governments to ban it.

The book explains the origins of the anti-GMO movement very well, which is not surprising since the author was part of it in the 90s. It also highlights the dangers of activism and its disconnection from evidence: when part of your identity becomes fighting against GMOs because of their health risks, how do you change your mind once the evidence is in? The cases described, especially in Africa, are striking: there’s a case of a UK organisation paying for radio ads claiming—against all evidence—the GMOs cause cancer and infertility. This in a region where so many people struggle to get food on the table. And this from the people that claim to care about them.

Nevertheless, there is a chapter that I found quite disappointing. It is a chapter trying to understand the psychological reasons for anti-GMO advocates not to change their minds when presented with evidence. This, of course, relates to how ethics and morality are formed. And when I was expecting a discussion on how one has to reason rather than follow their gut-feeling when deciding what is ethical and what is not, he basically does the opposite.

He explains how he heard about a project to create trees that could provide natural lighting. His reaction was disgust: humans should not be tempering with the essence of other organisms. He admits he has no rational motives for his reaction: even worse, he claims that the evidence-based pros and cons for this project are “besides the point“. He says: “But what are our ethics if not intuitive, emotion-driven responses to the challenges of the world around us?” What? I understand that someone’s morals are driven by the type of responses that he describes. But the point is that, when doing policy—for instance, when deciding whether to forbid the development of GMOs—one has to use reasons, not emotion.

The fact that Mark Lynas spends half a book discussing all the pros of GMOs and then, when asked about a project to create trees that provide natural light, he says “I don’t know why, but that is just wrong”, well, it is disappointing. You are an anti-GMO activist. It is true that at least you do not make up fake stories about it you say you don’t know. But still, it does feel like he is not learning from his past experience. Why aren’t the anti-GMO activists allow to do the same? “Look, I don’t know why, but we shouldn’t allow GMOs, it’s just, you know, wrong, we are playing God… my gut tells me so”.

The fact that Mark Lynas spends half a book discussing all the pros of GMOs and then, when asked about a project to create trees that provide natural light, he says “I don’t know why, but that is just wrong”, well, it is disappointing. You are an anti-GMO activist. It is true that at least you do not make up fake stories about it you say you don’t know. But still, it does feel like he is not learning from his past experience.

Anyhow, the book is very interesting. I’ve learned a lot. It did not change my mind about GMOs, as my opinion was already informed by evidence. It helps not to be very invested in the topic. “Hold your identity lightly” is really a great advise.

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Loan evergreening: does it “save” firms?

I have written about my paper on loan evergreening in Uruguay in a couple of previous posts (here and here). A paper that is coming soon, by the way. The strategy we study—providing a bullet loan to repay an existing amortising loan—could improve the prospects of the borrower. Therefore, a relevant empirical question is: when firms receive loan evergreening, are they less likely to be delinquent—i.e., less likely to be delayed in their loan repayment?

We analyse this question by looking at whether the firm is delinquent with the bank providing loan evergreening 12 months after receiving it. Here, delinquent means 60 days or more of repayment delay. We find in fact the opposite: firms receiving loan evergreening are actually more likely to be delinquent a year later.

Yet the interesting results do not stop there. When we use Firm x Month fixed effects, the results reverse: firms receiving loan evergreening are less likely to be delinquent later on. What explains this difference? Two factors: first, when we use Firm x Month FE, we restrict the sample to firms obtaining loans from two or more banks. This is because firms with just one banking relationship in a given month only have one observation for that particular month, and these FE perfectly capture its variation. This alone only gets rid of the positive result; to get the negative one, we need the actual FE.

How can we interpret this? In general, and this result is driven by single-banking-relationship firms, loan evergreening is associated to a higher probability of loan delinquency. When analysing firms borrowing from two—or more—banks, however, loan evergreening leads to fewer loan delinquencies or, in other words, when a firm obtains loan evergreening from one bank but not the other, it tends to become delinquent more frequently with the latter.

We also find that the positive association for single-relationship firms between loan evergreening and loan delinquency disappears for banks with higher solvency. In fact, for banks with high enough capital, the relationship reverses. This suggests that better-capitalised banks might use loan evergreening more to help borrowers withstand temporary financial stress and less exclusively to save in loan-loss provisioning.

What does this mean for policy makers? First, loan evergreening is used to temporarily reduce loan delinquency, but not only do firms end up delinquent anyway, they are more likely to end up delinquent than other firms under similar circumstances. Furthermore, this is more important for less-solvent banks, precisely those that we should worry about the most—hence, this is a practice that should be looked into by supervisors. More-solvent banks, however, might be actually helping borrowers with this strategy. In other words, supervisors should pay less attention to better-capitalised banks using this strategy. Finally, the dynamics are different for firms with multiple banking relationships: the strategy can be used to signal to the borrower that one bank is willing to help it more than the other, and hence the borrower should prioritise the repayment to the first.

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Loan evergreening: the role of bank solvency

In a previous post, I discussed how a couple of economists at the Banco Central del Uruguay and I are identifying instances of loan evergreening—when banks provide additional credit so that firms repay their previous loans—using very granular data. The first thing we do in the paper (coming soon!) is to understand what the determinants of this strategy are.

It turns out that the main bank-level determinant is bank solvency. This is not entirely surprising, since a main motive to engage in this strategy is to keep provisions low. Higher provisions—which happens when a firm is delayed in its loan repayment—mean lower profits, which mean lower capital. Yet we are focusing on one particular loan evergreening strategy; there can be more, which would make it more difficult to detect the relationship with solvency. Still, we do detect it.

How do we detect it? We look at all bank-firm relations with outstanding amortizing credit at the end of each month (we focus on the period of 2006 to 2018). We then create a dummy variable that equals 100 whenever a bank is providing loan evergreening to a firm, and 0 when it is not doing so. Loan evergreening stops when the firm repays the bullet loan. Then we run a linear probability model to explain this loan evergreening variable by using bank-level variables and other controls as independent variables.

A key part of our approach is the use of fixed effects. Fixed effects regressions have become very common on papers analysing credit registers as we do. In our case, we use Firm-Month fixed effects. Let me see if I can explain this well.

Each firm, because of its characteristics, will have a probability of receiving loan evergreening. Moreover, this probability can vary across time: when the firm is performing well, maybe it is less likely to need a bullet loan to repay an existing one. In general, the situation of the firm is (or can be) an important determinant of loan evergreening. And controlling for this situation in a regression might be difficult: for instance, one could control for firm profitability, but surely this does not provide a complete picture of its situation.

Is this a big problem to understand how bank characteristics matter for loan evergreening? It might be. If banks with low solvency lend to different firms compared to banks with high solvency—which seems entirely reasonable—then it is difficult to know whether the relation between solvency and loan evergreening is due to solvency or different characteristics of the borrowers. This is where the usefulness of Firm-Month fixed effects comes in: why don’t we focus on firms borrowing from two (or more) banks at the same time? This way we make sure their characteristics are fixed, and we focus on whether banks with less capital are more likely to provide loan evergreening to the same firm at the same time compared to banks with more capital.

We find that banks with higher solvency (capital) are less likely to provide loan evergreening. Interestingly, the coefficient does not vary much once we add the different fixed effects: it goes from -6.578 (we only explain 0.7% of the variation) to -6.117 with all fixed effects (explaining 45.5% of the variation). These regressions include five additional bank-level controls and five loan-level controls.

There is an additional empirical check that we do to understand how robust this result is. As I just said, we have ten additional independent variables in the model other than bank solvency. We could try different combinations of these controls and see if the result stands. Or, as Brodeur and co-authors suggest, we could try them all. This is what we do. We adapt their Stata algorithm to run one regression for every single combination of these ten controls. This amounts to 2 to the power of 10 and then minus 1: 1,023 regressions. We actually do this for all bank-level controls to see whether solvency is the most important bank characteristic to determine loan evergreening.

Ok, but then what do you do with 1,023 results? Again as suggested by Brodeur and co-authors, you can plot the resulting t-statistic—the metric that determines the statistical significance level—in a histogram and see where it lands. This is what we do in this figure:

Figure: Determinants of loan evergreening

As we can see, solvency is the only variable whose coefficient is consistently above the 10% significance-level threshold. Other variables are never or almost never significant. And one variable—return on assets—has around half the mass at very low values. Of course, one has to make sure that the coefficient of solvency is always negative. But again this is also easy to plot in much the same way as the t-statistics.

I mentioned before that the coefficient for solvency was -6.117. This tells you very little about the magnitude. How can we translate this in economic terms? Solvency is expressed as a number between 0 and 1: its standard deviation is 0.083 (or 8.3 percentage points). The dependent variable, loan evergreening, is either 0 or 100 (we scaled it for easier interpretation of the results). -6.117 x 0.083 = -0.508, which means that increasing bank solvency by one standard deviation implies a reduction of the likelihood of providing loan evergreening by 0.508 percentage points. This might not seem a lot, but actually the instances this type of loan evergreening in our sample are infrequent, just above 1%.

We find these results interesting as they provide a link—a very robust one—between solvency and loan evergreening using a very different approach to identify loan evergreening compared to the existing literature. In a future post I will talk about what happens to the firms that receive loan evergreening: are they better off, or do they end up defaulting anyway?

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Books Do Furnish a Life, by Richard Dawkins

Yep, I am catching up with book reviews. I have read several this year, although not at the same pace as last year, so there’s a lot of work to do. Let’s dive in.

Richard Dawkins, the eminent British biologist, just published a book, Books Do Furnish a Life, that collects many of his writings and conversations. These include book reviews, forewords, afterwords, and conversations with other thinkers on various topics such as biology, the role of science, secularism.

I must admit that, as much as I admire Dawkins, this is the first book I read by him. The Selfish Gene is in my bookcase looking down on me, both figuratively and literally, as if saying: you don’t have what it takes, do you? Apparently not, or at least not yet. However, Books Do Furnish a Life has been useful in expanding my reading on biology, so much so that I am starting to think about taking up the challenge of his first book. Even since I was a teenager I had very little difficulty in reading popular astrophysics and particle physics books: I devoured all Stephen Hawking‘s books, and I still recall the impact that Just Six Numbers, by Martin Rees, had on me; all before I finished high-school.

But biology is a different matter. Maybe it is the broader range of specific vocabulary. Maybe it is my inner interest in the topic. Maybe it is a combination of different factors. But I’ve always found it difficult. It is also, by the way, because evolution by natural selection is not extremely intuitive. I mean, the mechanism is intuitive; what it is difficult to grasp is the timescale at which it has worked. Not also this: the role of the genes, and how the genes manifest themselves in different behaviours that can even change the environment leading to higher survival changes—the extended phenotype. But then again, quantum mechanics is not the most intuitive phenomenon either.

The short writings collected in this book are very useful to further the knowledge on these topics. The way that Dawkins writes, providing analogies to the various mechanisms at work especially in evolution by natural selection, is a delight. I can’t emphasise this enough: the clarity, and the beauty, and intuitiveness—they are superb. And the enthusiasm! You can tell that Dawkins feels fascinated by what we have learned in the recent decades about how we came to be. The sheer breath of knowledge at the service of educating the reader is a gift for all of us. He follows the advise of Steve Pinker in The Sense of Style: write as if your reader is as intelligent as you are but lacks the particular knowledge of the topic.

And then we have the conversations with other thinkers. One of them is the aforementioned Pinker, someone I also greatly admire, talking about Darwin and evolutionary psychology. Others include Neil deGrasse Tyson, Matt Ridley, and the one and only Christopher Hitchens—for The Hitch that was the last interview he gave before his death, published in the NewStatesman: Never Be Afraid of Stridence. Interesting and absorbing in equal parts, these conversations introduce each section of the book.

I don’t think there’s an equal to Dawkins on science popular writing. Maybe Carlo Rovelli, writing about physics, gets close to him. But the amount of writing by Dawkins far exceeds that by Rovelli. Anyone interested in biology, evolution by natural selection, and secularism, will find this collection of short writings incredibly absorbing. Highly, highly, highly recommended.

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Loan evergreening

Loan evergreening is a situation where banks provide loans to firms in order to ensure that firms keep repaying the existing (previous) loans. It is a concept related to zombie lending, broadly defined as lending to non-viable firms. Loan evergreening is a usual strategy to provide zombie lending, although it can be used in other circumstances.

In the recent years, there has been an increase in economic research trying to understand zombie lending and its consequences. Some blame zombie lending for the low productivity in some European countries. But less attention has been put on loan evergreening. Partly, this reflects an issue with data availability: it is easier to obtain data on firms (and hence proxy for which firms appear non-viable or very weak) rather than loan-level information.

And even if one has loan-level information, how do you detect loan evergreening? It is not like banks tell us why they are providing the loan. In many cases, they can provide loan evergreening before any problems, such as repayment delays, arise in the firm. This is an issue that we aim to tackle in a very recent paper with Cecilia Dassatti and Rodrigo Lluberas from the Banco Central del Uruguay.

Let’s imagine the following situation. A firm has an amortising loan, that is, a loan that has to be repaid periodically. But the firm is facing some trouble that may lead it to delay a loan repayment. Banks do not like that: they face increased credit risk—the firm might outright default—and, even more importantly, the regulator makes them provision for a potential loss. Provisions are very annoying for bankers: they come right before computing profits, and therefore they have a direct—negative, in this case—impact on bonuses.

What can the bank do? Well, the bank could provide a bullet loan to the firm—that is, a loan that is repaid only at the end of its maturity—so that the firm is not delayed. This seems a clear case of loan evergreening. But how can we detect such cases? A possibility is to flag every time a bank provides a bullet loan to a firm that already has an amortizing loan. However, firms might need bullet loans for reasons other than repaying the previous loan.

What about flagging situations in which the amount of a new bullet loan is very similar to the amount that the firm repays of the existing loan? For instance, a situation in which a firm receives $100 in the form of a bullet loan from a bank and repays in the same month $100 of the existing loan with the same bank. If we can detect these situations, it seems that we have found cases of loan evergreening.

But are these cases common? To answer this question, we compare the amount received in a new bullet loan with the amount repaid of the existing amortizing loan. We plot the histogram of the ratio in the figure below. As you can see, there is a spike of values very close to 1; that is, situations in which the two amounts are very similar.

From Dassatti et al. (2021)

In the paper, which is in its last minutes in the oven, we proceed to understand which bank characteristics are more linked to the provision of loan evergreening (spoiler alert: solvency), what happens with credit supply after engaging in this strategy, whether this strategy makes firms more or less likely to default, and what this means for other firms. But a post about the results will come once the paper is out.

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