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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So we are Bayes Business School now

Yesterday, The Business School (formerly Cass), which used to be—yes, you guessed it—Cass Business School, and that also used to be City University Business School, became Bayes Business School.

You may recall that, in the wake of the killing of George Floyd, it was revealed that Sir John Cass had been actively involved in slave trading back in his time (1661-1718). I don’t think that was a super-hidden secret; I mean, of course I didn’t know about it, but I didn’t know a single thing about the guy. Yet it is difficult to imagine that when the School decided to adopt this name (2004 or so) they did not encounter this information. Those were different times—the early 2000s I mean—but I think slavery was already considered immoral.

Anyway. As a result, we (and by we, I mean the University and the Business School) decided to change our name. After almost a year, we chose Bayes Business School. Our links with Thomas Bayes, who first formulated the Bayes’ Theorem, are “extremely strong”: the dude is buried in front of the Business School, in Bunhill Fields. And that would be it. I mean, the Bayes’ Theorem is very important in finance, but I guess that applies to any finance department.

How do I feel about the name, you asked? Well, I think it is a fantastic name. It is better than Cass Business School even if John Cass had been an exemplary man. In fact, it is a name that is better than the business school, at least in its current state. But I guess this is what you want, right? I name that points to where you want to reach, what you want to achieve. It is easier to revert a frankly negative trend with a name that is inspiring. Although it is still difficult, so let’s see how it goes!

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The Scout Mindset, by Julia Galef

I have known about Julia Galef for a while, as she hosts the podcast Rationally Speaking. I have talked about this podcast in the past. She used to co-host it with Massimo Pigliucci, a philosopher and authors of several books, including Live Like a Stoic. But that was years ago. And, truth to be told, I think she hosts much better by herself. She listens to the arguments of the others and engages with them—not with a strawman version of them, but with them. And she mentions how she changed her mind after the interviews, even if just slightly. She has a scout mindset. And now she has written a book about it, titled, yes, The Scout Mindset.

So what is a scout mindset? Julia uses this term as opposed to a soldier mindset. The soldier’s goal is to win. The scout’s goal is to obtain a more precise representation of reality. Many times, when we discuss heated topics, we enter into a soldier mindset. I mean, not me, of course, but everybody else does. Apparently, it is difficult to realise—or at least it is not straightforward—when you are in soldier mode. Yet if we really want to find out the truth, the soldier mindset is counterproductive. We should be willing to change our mind. The scout might think or hope that there is a bridge to cross the river; but if there is not, then he or she will update their map accordingly.

But, do we really want to find out the truth? Some might argue that we do not. Those that insist in being precise, in being as truthful as possible, in knowing all the facts, those people are just not willing to solve the problems. Let’s see an example where this reasoning can be applied:

Maybe we still have a couple of decades to adjust our CO2 emissions before a significant increase in temperature is irreversible. Maybe not. Would waiting for all the answers—being a scout—be the right approach, given what’s at stake? Or is it better to say that unless we act TODAY, the world is going to end this century?

Some people will say the latter is a better option. People have to react, and unless they feel the thread is imminent, they will not. Maybe. But maybe making strong statements that are not fully backed by the science also discredits a movement. Maybe discovering that scientists skeptic about climate change in the early part of this century (“climategate“) were shut down by other scientists made hesitant public opinion even more hesitant. To the extent one does not control the information that people can access, being truthful seems to pay off almost always.

A part of the book that I found particularly appealing is about “holding your identity lightly”. A problem when you strongly identify with a particular movement is that push back against the movement actions, for example, feel extremely personal to oneself. Even by people that share the ultimate goals. Or even more by these people. Validating oneself might become more important than the ultimate goal. Dunking on someone with a different opinion might feel very well—I am good, the other is bad—but might do very little to advance the cause. It can actually push other people towards the other side.

But at least we can rely on education and intelligence to avoid our soldier biases, right? Not really. Even though one could expect that the more educated one gets, the more one is able to formulate logically sound arguments without getting into fallacies, this appears to be wrong. One reason might be that better educated people are better able to rationalise—even if ex post—their actions, or be aware of some “evidence” that seems to confirm their beliefs. Anyone knows highly educated people that do not seem capable of changing their mind on certain issues, irrespective of the evidence. But to think that this is not the exception, but in a way the rule, that’s striking. The chart below, from Kahan et al. (2017), shows how many agree with the statement that “there is solid evidence of recent global warming due mostly to human activity such as burning fossil fuels“. In low levels of “ordinary science intelligence” (this is determined by a series of questions that participants have to answer before), there is no disagreement between liberals and conservatives. As their science intelligence increases, they diverge substantially. Whatever you think the right answer is (spoiler alert: it’s AGREE), at least one group moves away from it as they are more “intelligent”. Funny enough, this does not happen when we measure the science curiosity of the participants, which we could see as a proxy of having a scout mindset; if anything, both groups tend towards the right answer the more scientifically curious they are.

Kahan et al. (2017). Available at:

To wrap up, Julia has written a wonderful book, with funny anecdotes, great advice to be more of a scout, and a very positive attitude. She has been talking to several people about the book (and other things, such as the rationalist community). Here are some of her appearances: The Wright Show, Mindscape with Sean Carroll, and Julia’s YouTube channel. Recommended.

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30 books in 2020

So I read 30 books in 2020. I’m not sure how I did it, but I did it. You don’t believe me? Look at my Goodreads notification:

Almost 23 pages per day, which is around one hour of reading. I am happy with the achievement, but I don’t think I am going to repeat it this year. I might try to go for fewer books but a more detailed reading. Let’s see how it goes. But anyway, I just wanted to mention the five books that I consider to be more interesting:

  • Moral Tribes. Emotion, Reason, and the Gap between Us and Them. Joshua Greene.
  • Utilitarism. A Very Short Introduction. Katarzyna de Lazari-Radek and Peter Singer.
  • Self-portrait in Black and White. Unlearning Race. Thomas Chatterton-Williams.
  • The Precipice. Existential Risk and the Future of Humanity. Toby Ord.
  • Everybody Lies. What the Internet Can Tell Us About Who We Really Are. Seth Stephens-Davidowitz.

I even read a book in science fiction! The Three-Body Problem, by Cixin Liu. It was a gift, but I read it quite fast. I will try to add more fiction in my list… although the one I would like to read is The Doors of Stone. No news on that front yet…

For 2021, as I said, my goal is more modest: 12 non-fiction books, one per month, trying to write a bit about each of them. I won’t provide a tentative list to avoid the dopamine release that makes it less likely that I end up reading them.

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Draft No. 4, by John McPhee

It takes what it takes” (William Shawn, former editor of The New Yorker)

Get that boy from the Old Vic” (Winston Churchill, referring to Richard Burton)

Write on subjects in which you have enough interest on your own to see you through all the stops, starts, hesitations, and other impediments along the way” (John McPhee)

Draft No. 4. On the Writing Process is a must-read—an overly-used adjective that nevertheless finds a worthy subject here—for anyone that writes non-fiction. McPhee, its author, is an American writer considered one of the pioneers of creative non-fiction. He has been writing for The New Yorker since the 1960s.

In Draft No. 4, he explores different aspects of writing—structure, editors, omission, and more—drawing on his vast experience and with his unique style. It’s like peeping behind a magician’s stage, glimpsing, and even understanding, albeit just superficially, how the trick is done. Yes, I understand that you take the card and place it in your pocket; I am still unable to do it myself though.

Trying to say anything more would be doing a disservice to this book. Read it. Read it even if you do not know—I did not—who John McPhee is. Read it even if you are not very familiar—I am not—with The New Yorker. This book brings the concept of craft to a different level. Reading through the way McPhee composes a piece, one realises how much there is to improve. It is, in a sense, reassuring: there is no need to worry about reaching the destination any time in this life.

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Arbitrage in SME lending

One of the big concerns of the aftermath of the Covid-19 crisis is that the recovery might take much longer because many firms, particularly small and medium (SMEs), will have closed down for good. From the very beginning, different actions by governments and central banks tried to make sure that SMEs did not suffer a reduction in credit. For instance, the Bank of England put in place a Term Funding Scheme (TFS) to provide cheap funding to banks, and the amount that banks can borrow from this scheme is proportional to the amount of lending that they provide to SMEs. The idea is then that banks can borrow very cheap from the central bank if they in turn provide credit to SMEs.

But of course there are limits to this policy. Even if banks keep lending to SMEs, these firms will suffer losses—many of them are temporarily closed—and hence the risk that they are unable to pay back the credit, even when this credit stays constant, increases as the lockdown remains. Banks have to consider these risks: their provisions and capital should go up to cover the increase in potential losses. This has been a source of concern at the Bank of England.

Nevertheless, the UK government has introduced a different scheme, Bounce Back Loans. These loans provided by banks but fully guaranteed by the government—if firms cannot repay, the government will. Given the maximum size of the loans, £50,000, they are designed for small firms. These are, hence, additional incentives for banks to lend to SMEs.

How do both schemes interact? Well, in the last days the Bank of England has provided some interesting information on this regard. First, bounce back loans can have a maturity up to six years, while, originally, the maximum term in the TFS was four years; the Bank of England has announced that it will be possible to extend the funding to match the maturity of the bounce back loans.

This way, banks can borrow very cheaply from the Bank of England scheme and provide bounce back loans to small firms. But what about capital requirements? There is a part of capital regulation that covers credit risk mitigation, which is the situation where the borrower obtains guarantees from a third party to repay the loans. Exactly as bounce back loans. Again, here the Bank of England has clarified that this can be done and hence banks can provide these loans at essentially zero risk weights; in other words, these loans carry no capital requirements.

Yet the 2007-08 financial crisis showed the limits of risk-based capital regulation; for this reason, policy makers around the world introduced the leverage ratio, a capital regulation that does not depend on the riskiness of bank’s assets, only on its size. I have talked about it extensively (here, here, here, here). Even if risk weights on these loans are zero, they would still affect the leverage ratio. However, the Bank of England has announced that these loans will be exempt from this regulation; more precisely, these loans will not be added to the “Leverage Exposure Measure”, which is the denominator of the leverage ratio.

Therefore, banks have a fantastic arbitrage strategy that consists on providing loans to SMEs with the full guarantee of the government and fund these loans using funding from the Bank of England just slightly above the Bank Rate (at the moment, the Bank Rate is at 0.1%). The only limit to this strategy is the amount of eligible collateral of the bank. Other than that, capital and leverage requirements are not binding, and from the point of view of liquidity requirements, this strategy is also neutral (the funding is quite long-term).

What are the risks? For the banking sector, none, as long as the UK government maintains its solvency. And this is key. The current framework assumes that the main problem of non-financial corporations is liquidity, not solvency. But this is not at all clear. For sure, liquidity is an issue, but so is solvency, especially for some sectors. What happens if a big part of these guarantees end up realising? The government debt would increase even more. It is not unthinkable that the UK might lose the double-A credit rating in the near future. And all this while negotiating the new deal with the EU.

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Limiting borrowers leverage

In the last post I talked about the countercyclical capital buffer (CCyB), a new regulatory tool to increase banks’ capital requirements that most countries have not used but that could have been effective to mitigate the Covid-19 crisis. As I mentioned there, the UK did use it, albeit not to the full extent. But the UK has used other tools to try to limit the buildup of risks; one of the most important ones is a tool limiting the number of high loan-to-income mortgages.

Loan-to-income (LTI) indicates how big is the mortgage debt relative to the annual income of the borrower. If someone earning £60,000 a year takes a £240,000 mortgage to buy a house, then the LTI is 4. The higher the LTI, the higher the leverage borrowers are taking. And the higher the amount borrowed with respect to income, the more consumption responds to changes in aggregate conditions. The reason is that borrowers adjust their consumption in order to reduce the possibility of default, especially in countries with full-recourse, such as the UK. The chart below presents evidence that higher LTIs were associated with stronger reductions in consumption during the financial crisis (even when normalising by income). The chart is taken from the Bank of England’s December 2019 Financial Stability Report.


Therefore, reducing mortgages with high LTIs should lead to a smaller adjustment during a crisis. I am not going to discuss here the differences between the Covid-19 and the financial crises—for instance, consumption is going down mostly because of the quarantine, not because unemployment is going up. The point is to highlight another measure introduced by the Bank of England in 2014: a limit on the number of mortgages with LTI equal or above 4.5 that lenders can grant. If this measure indeed limited this type of mortgages, then this might improve consumption during the Covid-19 crisis.

The evidence from the distribution of LTIs before and after the policy was introduced is consistent with the idea that it limited high LTI mortgage lending. In the chart below (taken from the July 2016 FSR) we can see that although the distribution has shifted right (compared to 2014Q1), the frequency of mortgages with LTI above 4.5 has decreased. To the extent that this reduction would not have happened without the policy (luckily we will share some evidence backing this claim up soon) then the adjustment in consumption during this crisis might be lower than what would have happened without the policy.


In general, one of the issues with this type of intervention (which we broadly call “macroprudential policy”), is that one can only empirically assess the “costs” while the economy is growing well; costs, for instance, would be a reduction in mortgage lending. It is precisely only during a crisis, or at least a change in the cycle, where the benefits of this policy start to show. This is one of the reasons why we see a weakening of the regulatory framework as the economy recovers and grows healthily. This Covid-19 crisis might in fact stop this trend and reinforce the importance of macroprudential regulation.

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