Independence, Structure, and Risks Ahead for Central Banks

- Do you wanna test the speed? Yes.

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- I know, thank you. I appreciate that.

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- How many water? Last name? Daley. She's

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- So I think the program site for, so you'll have 15 minutes and then we're gonna go q and a, you know, we'll see what, what happens. But, and there is a 15 minutes in case you need to time yourself. It's fine. Short is fine because everyone is going off

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- Actually some of the Es are giving time to the next speaker, which is a very gracious Yeah, gracious. Perfect. Okay, we're gonna start the final panel. So please take your seat. I'm Pa Apian, I'm a senior fellow here at Hoover and I'm really delighted to moderate the final panel of this conference. This is the panel that is with people that everyone has been talking about for the rest of the conference. So I want to first thank the organizing Michael, John and Valerie also commend them for doing a policy panel with the real policy makers, which is not always happening at conference. So we have a great a lineup. We're gonna go in alphabetical order. I'm gonna, you know, there's some very famous people, so I'm gonna briefly introduce them. Michelle Bowman, vice chair for supervision of the Federal Reserve Board, brings a combination of community banking experience, regulatory expertise, and is leading, I hope she's gonna talk a little bit about that. A more significant transformation in supervisory. Mary Daley, president of the Federal Reserve Bank of San, one of the clearest thinkers on how structural shifting labor market are reshaping some of the policy landscape. Austin Gouldsby, president of the Federal Reserve Board, the, the Federal Reserve of Chicago leading expert in innovation, public finance taxes. And finally, Christopher Waller, he's a governor of the Federal Reserve Board, one of the most rigorous monetary economist. He's also doing lots of heavy lifting. I learned recently he lifted 365 pounds for his birthday. So that's impressive. So we're gonna, I know lots of people have approached me saying that they have a lot of question for you guys, so, so we're gonna go, I'm not gonna do a lot of introduction. I'm gonna ask some question after. So we're gonna start with Michelle 15 minutes each and then we're gonna go to general discussion.

- Great. Well thank you Paula. It's wonderful to be here with so many esteemed economists and people we read about and have had the opportunity to work with some of you. But it's great to be here. I am pleased to visit Stanford. I think that this might be my fourth or fifth time, but I've only ever been able to introduce one person as a speaker. So this is my first time on the stage. Very exciting. So thank you for the invitation, Sean to do that. But good afternoon and thank you for the invitation to join you at the Hoover in Hoover's annual Monetary Policy Conference. As the Fed's vice chair for supervision, I oversee the safety and soundness of the banking institutions that we oversee with responsibilities that are closely linked to another of the fed's important roles, which is to safeguard financial stability. So instead of talking about monetary policy, my remarks will focus on this topic. And today I'll address a challenge that emerges at the intersection of these two responsibilities. That is when regulatory requirements become disproportionately burdensome relative to risk, and banks simply curtail the targeted activities, this leaves a deficit between demand for banking services and banks that are willing to provide them. When banks are no longer willing to provide specific services, non-banks step in to meet those needs. And activity is essentially pushed out of the regulated banking system. So this includes the migration of corporate lending from banks to non-banks. Therefore, my remarks will focus on private credit funds and business development companies and will consider the circumstances that lead to this out migration, the implications of banks exiting these services and the Federal Reserves policy response. Over the past 10 years, we have seen a shift in how credit reaches businesses in the real economy. Since 2015, the bank share of corporate lending decreased from 48% to 29% in 2025. The private credit market is a significant driver of this shift. In the United States, the private credit market has grown significantly and currently accounts for about $1.4 trillion, similar in size to both the leveraged loan market and the high yield bond market. But despite its recent rapid growth, private credit is still a small fraction of overall corporate borrowing in the United States, making up only about 10%. There is no mystery about what drove the shift in corporate lending away from banks. While the post 2008 financial crisis reforms strengthened bank capital and liquidity, which were necessary to promote the safety and soundness of banks and US financial stability. They did so with unintended consequences. Attempts to address the legitimate gaps resulted in some requirements becoming excessive relative to underlying risk, forcing banks to pair back on some corporate lending activities or to raise the cost of credit to borrowers. The effects of the current framework become clear when we examine the incentive structure that it creates. Current capital rules create a perverse incentive. Ironically, banks receive a more favorable treatment for lending to private credit funds than for lending directly to credit worthy corporations. This treatment encourages banks to finance intermediaries rather than directly serve and borrowers. The broad definition of NDFI includes an array of diverse entities like private credit funds, business development companies, insurance companies, private equity firms, and broker dealers. These entities differ in terms of the types of lending that they offer, the effectiveness of their underwriting and risk management, their ability or inability to work with borrowers under stress, the stability of their funding sources and their connections to the banking system. N DFI also rely on diverse funding structures with private credit funds and some BDCs, primarily attracting capital from institutional investors, including pension funds and insurance companies, and with other BDCs providing access to retail investors. ND advisors are also interconnected with the regulated banking sector through bank loans that typically involve revolving credit lines and term loans. But over the past decade, growth in bank lending to ND FIS has outpaced the growth in all other bank loan categories. Recent bankruptcies that impose losses on banks and N dfi have raised concerns about the quality of loans made by private credit providers. More recently worries about exposures to industries vulnerable to the implementation of ai. Like the software sector have compounded these concerns. We have already seen one potential channel for the transmission of risk within the financial system, the withdrawal of private credit funding sources as a result of private credit losses and the failure to achieve targeted investment returns. Some private credit funds have experienced a wave of redemptions concentrated among BDCs that are, that offer investors limited redemption rights. But despite these redemptions, banks have continued to extend credit to BDCs and other private credit vehicles with loan commitments and outstanding amounts growing significantly over the past year. These loans generally appear to be well collateralized, which should help protect against bank losses in the event of borrower distress or default. Even though NFIs generally lend to riskier borrowers default and loss rates would need to be abnormally high for banks to be at risk. Recognizing these challenges. Let's turn now to the federal reserves approach. Our current approach relies on three complimentary pillars. First, recently proposed changes to the Basel three framework addressed the punitive capital treatment imposed on traditional bank lending activity. Unfortunately, the banking, regulatory and supervisory framework has created an environment in which traditional bank lending activity has migrated outside of the banking system to non-banks. In the case of mortgage lending, our priority is to ensure that our capital regulations better align bank capital requirements with risk across multiple asset classes. For bank lending to corporations and businesses, the proposal would generally reduce the risk weight from 100% to 65% for corporates that are considered to be investment grade by the lending bank. These changes in the capital proposals will reduce the gap in risk weights between loans to non-financial businesses and loans to non-bank financial companies. This will increase competition in ways that benefit borrowers and reduce risks to financial stability. The proposal enables banks to compete more effectively with NDS in serving credit worthy businesses. When banks receive a more favorable treatment for lending to private credit funds than for lending directly to credit worthy corporations. That can result in an undersupply of credit to TRA to traditional bank business borrowers. By properly calibrating capital requirements, we can allow banks to more effectively compete in providing credit to private business. Banks have deep experience underwriting loans to these borrowers and they should not be excluded from serving their customers to meet this market demand. But to be clear, the capital proposal maintains the stro, the strong banking sector capital, while ensuring that regulatory requirements do not lead to activities leaving the regulated banking sector. Second, our approach recognizes that addressing the inappropriate risk weighting of certain activities doesn't mean eliminating private credit from the market. There is a role for both banks and nds in providing credit to private companies. ND ffis serve legitimate functions through their specialization in narrow market segments, speed of origination and the flexibility in their credit terms. They are well suited to provide long-term loans to borrowers that may be unsuitable for banks, typically smaller and riskier borrowers that are financed with locked in capital from institutional investors. BDCs, for instance, make most of their loans at spreads of 400 basis points or more, whereas large banks make most of their loans at 200 basis points or less. The optimal outcome preserves the division of credit provision private credit funds and banks can effectively serve different parts of the market. The long-term relationships that banks have established with corporate clients gives them an an advantage in underwriting and monitoring loans for their traditional customers. Banks are funded primarily by depositors and providers of short-term wholesale funding. N DFI have different expertise and may have a greater tolerance for risk. Their model relies on funding from investors who accept relative illiquidity in exchange for higher expected returns. The question is not whether N DFI should exist. Instead, we should ensure that the regulatory framework does not tilt the playing field to push activity outside of the regulated perimeter perimeter for reasons unrelated to risk or efficiency. Some lending to non by non-banks is riskier and is better kept outside of regulated financial institutions because it can be funded by investment structures that rely on more stable funding and less leverage. But credit worthy businesses that could be served by banks instead turn to private credit primarily because of excessive regulatory burden. We should consider whether our rules are appropriately calibrated. And the third pillar, finally, even as the Fed seeks to level the playing field and to preserve complementary roles for different segments of finance, we should improve our ability to understand the connections between these segments. One approach is to provide more transparency through regulatory reporting, measuring and monitoring risks in bank lending to ND ffis requires effective supervision and data that can be collected through regulatory reporting. Our current data reporting relies on industry classification codes that are too broadly, too broad to effectively measure these specific exposures. The current industry code for other financial vehicles includes hedge funds, private equity funds, BDCs, special purpose entities, and asset backed security insurers. Without further distinction, this lack of granularity makes it difficult to assess concentration risks, measure interconnectedness, or calibrate capital requirements to actual risk. Therefore, the board will update our regulatory reporting to ensure that supervisors have transparency into bank lending. To N dfi. The update requires the largest banks to report financial information about N DFI to which they extend credit, including total assets, net income and leverage that enables an analysis of credit underwriting and ongoing risk assessments. This enhanced transparency also supports other policy objectives. It will provide a better understanding of the risks associated with bank lending to N dfi relative to other types of bank lending. It will ensure that supervisory stress test models are appropriately calibrated for these exposures, which will also benefit activities and processes related to capital planning. It will allow supervisors to independently evaluate risks and assess the condition of these borrowers on an ongoing basis. This can increase consistency and eliminate the need for ad hoc data collections and other onsite supervisory efforts. These three elements, recalibrated capital requirements, preserved complementary roles for different segments of finance. And a new targeted data collection are part of an integrated approach to supervision that serves multiple objection objectives. An approach that supports economic growth by enabling banks to deploy capital efficiently supports financial stability by improving risk monitoring of bank investments in nds and it maintains safety and soundness by ensuring that banks remain well capitalized, providing supervisors with necessary information to comprehensively assess those risks. Together, these create a more efficient, stable financial system where banks return to providing traditional banking activities and credit and other risks migrate to entities and investors better suited to bear them. As we consider these issues, we should remember that regulation always involves choices and trade-offs. The federal reserve's rep approach represents an evolution in supervision and regulation. We recognize the changing landscape of credit intermediation, but preserve our commitment to safety and soundness by calibrating capital requirements more closely to actual risk. We enable banks to compete on a more level playing field with non-bank lenders and serving credit worthy borrowers Through targeted data collection, we can better understand and effectively supervise these relationships and their inherent risks. So I'll conclude by saying that I look forward to working together with my us, my colleagues in the other regulatory agencies as we continue to refine our approach to modernizing supervision of this evolving landscape. Thank you.

- Thank you Miki. Perfectly.

- Go ahead. Excellent. So the first time I was on the policy panel at Hoover, I came and I had I think one slide and a long text. And the person who was on the policy panel with me like this one was Jim Buller and he had no text in all slides and I thought there's a revelation. This is a place where I can do that. So I'm gonna follow suit. So I'm gonna talk about something that is on our minds. When I called Valerie or emailed Valerie and I said, what do you want us to talk about? She said something that's not narrowly focused on policy, something that's big and thinking about how we do macro and monetary policies strategically. So I'm gonna talk about shocks and monetary policy and importantly how to assess the conventional wisdom. So let me fix ideas by telling you what I mean by conventional wisdom. Where do I point? Okay, then knee out. Oh, there it's, no, that's, now I gotta go backwards. Okay, now I gotta go forwards. I did it. Hell. Okay, so the conventional wisdom I'm talking about is when you get a shock to the economy, then you want to understand as policymaker, whether it's a supplier demand driven shock because you want to understand what to look for and then you want to understand if it's persistent or if it's temporary. And if it's temporary, the standard conventional wisdom is you look through it and if it's persistent, well then you react, you respond in some way or at least you consider responding. So that all works until it doesn't. And so I'm gonna do what Marvin did in, in, you know, many slides. I'm gonna show this one slide where you can see that we had the pandemic inflation and in the pandemic inflation we continued to say that it was transitory and thought it was transitory. It wasn't just we're saying it and hoping there was a forecast that was transitory but it wasn't correct. And so then the subsequent runup in inflation, which doesn't look very much, it's another shock, it's two shocks actually. We had the tariff shock and then now the oil price shock. And so inflation picked up a bit and it doesn't look very different than some of the run-ups you saw in the great moderation or in the decade after the financial crisis. But in point of fact, it's got a lot more attention because the concern is, is this going to turn into another post pandemic surge or is it going to be the normal, hey look through an oil shock look through a tariff shock. So how do we know, how do we assess that? That's the question. And early on in my career, I was told by Alan Greenspan and then followed along by Janet Yellen, that you can't really use models to beat models when you're trying to forecast because you can use their history, but you don't know how they're gonna forecast in the go forward period. And you only know X post if they're, if they're wrong. So then what do you do? Well, we had a chance to challenge that, that logic when we went to the GFC. So this is a picture from after the global financial crisis and the debate at the time, if you like to read transcripts at the Fed, that, and many of the policy makers who had to grapple with that are, are here, are usually come here. You know, the labor market question was, did the enormous shock to the labor market that pushed unemployment considerably higher than we had seen in a long time. Did that result in through hysteresis or just displacement, a persistent shock that meant it was secular and the fed couldn't offset it? Or was it simply a long lasting cyclical shock that we could offset? And there was, there were estimates at the time, Arianna Kota estimated that the natural rate of unemployment had actually risen to 8.9%. And John Williams, who was the research director at the time, and I was on his team, we, we somewhat argued about this a lot, but he said it was as high as 7% from his models. And the problem was that these were models that were predicting this. Ana was saying you can't make construction workers into nurses. And John was saying, but Loock Williams tells you that you know, this is it. And the, and the problem is you can't settle that debate without more information. So we started producing more information. Now you're not gonna be able to read a word of this, I don't think reading is the point. The point is we started making what ultimately was called labor market dashboards, labor market heat maps. I've only got one part of the heat map. There's actually two, three pages of this. But the point was then you take lots of data, you take as much information about the labor market as you can possibly take, you figure out whether it's below or above its historic norms. And if you're really thinking about this hard, which we did, you're, you're putting, this is from a briefing I gave, but you can do this, many banks did this many reserve banks, the board staff and many academics. You're actually trying to understand which indicators lead, which indicators lag. When would I know we're on a cyclical upswing? How would I know? But even that's dissatisfying because then you just have more data. And what would happen is the debates would come to, you know, will I like this indicator better than that one? But we're still just guessing about which indicators are gonna tell us the truth, not guessing, but but anticipating or expecting estimating and arguing about that. So the, the next thing we did is we talked about, and many of them did this, I'm just gonna focus on this since I know it well. So I wrote a paper with Bart Hobe is Shaheen and Rob Valletta where, and for the JEP where we tackled this idea of we've got a lot of data. How do we discipline into models? So the question we wanted to know is what would move the job creation curve and what would move the beverage curve? And when they intersect, what would we learn about where the natural rate of unemployment is? And you, we were able to separate it with theory into cyclical factors, structural factors, and then score them as whether they would be normally in conventional wisdom transitory or more persistent. And then we could take that to the data. And if you're interested, you can read the paper on that and it'll tell you all the different ones, but I didn't have time to show everything. But the point isn't really about this paper. The point is about you take the analysis, you take the theory, you take the conventional wisdom, and then it points you to what to look for in the other sources of information you had. And ultimately we, our estimate was something closer to 5.6 for the natural rate of unemployment. The we, the policymakers used this and other analysis and all the different things that that academic research and policy research at the Federal Reserve was bringing to bear to change their views. And this is a picture of how, you know, the unemployment rate evolved, but also how the CBOs interest, natural rate of unemployment evolved and how the summary of economic projections measure of U star evolved. And it, they were lower than they had originally thought and in part they, we learned that from just seeing how the economy could go. But mostly we learned it from understanding that there was not a lot of evidence to suggest that it was going to be as high as it was. And there was more evidence suggesting that people can change industries. You might not make construction workers into nurses, but construction workers can definitely do other things. And it was a lot more mobility. So those were the pieces of information. And the point of this exercise is to say that when you don't know the answer between models or you don't know if it's or transitory, you start digging for more information. So then the question is, can we do the same thing for inflation? So let me remind you of the problem we had on inflation, which again, Marvin kind of showed, but I'm gonna show you in more detail using the summary of economic projections. So the same debate came up, is it temporary or persistent? I'm trying not to use the word transitory. So you know that temporary means that, but I'm not using it anymore. So we, so this is the, basically what we were grappling with was inflation's coming in, if you use conventional wisdom, it's a supply shock. And this is before it was really clear that people were gonna come out of their homes right after the COVID and then they were gonna stay outta their homes and buy a lot of things and they were gonna be supported by accommodative monetary policy and supportive fiscal policy. But for minute, put that to the side and say a standard supply shock you look through unless you think it's going to be persistent. And I don't think anyone knew at the time. Most people didn't that this was going to be, you know, one supply chain disruption after another. But that said, here's the picture, headline inflation, PCE inflation is the blue, the the March, 2021 yearend projections for inflation. These are the year end projections that, and this is the median of the SEP. And just out of respect for other policymakers, there were other people who had different dots, but this is the median of the SEP and then it, so what you can see is that the FMC put in March a marker on where inflation was when we had to make the projection. And then we also, and I was on the committee at the time so I I own this too, but we, and we had a little bit of an increase but then by September we moved it up but we still thought it's going to come down rapidly. And so obviously reality often is a forcing function for trying to figure out where you didn't see something. And so we dug in to what we didn't see and how much longer would it last. And again, many people in the federal reserve system did this work. I'm just gonna show you things that I know very well because I was, we were doing them in San Francisco so we, we recognized that these traditional inflation gauges, they weren't giving us anything. And if you look at inflation, you're getting backward answers. And so you're still in the forecasting of how long this will last. It's very unsatisfying to try to extrapolate from what's happened. You also have inflation expectations. But again, those were moving around and it was not as reassuring to say that medium run isn't really moving in long run, staying stable. You wanna make sure you're really sure of that. So we started making a dashboard really looking with the material that we had learned in the GFC. Many of us still had that experience of the, of the unemployment rate and trying to grapple with that. And so we started building an inflation dashboard or a heat map. Now I've put the whole thing up there, but what I want to start with is the, the columns. So the columns you'll notice don't look like traditional measures of inflation. They've got different pieces of analysis in them. These are ones that the San Francisco Fed did like trying to decompose inflation and demand driven versus supply driven cyclical versus a cyclical responses sector responses. We have a momentum, a shock momentum index which just asked historically what, how persistent or shocks that hit one sector versus another. And so what should we extrapolate from that? And then things like the vacancy to unemployment rate, we ratio, you know, that's not common, wasn't commonly used if you remember before the pandemic to forecast or think about labor market tightness. But it was very material in this period. So we didn't have this dashboard when we were doing the march SEP or the the September SEP, but we had it afterwards. And so I put it here just to ask the question, would it have helped us and from this red that was very present in 2021 and certainly by September of 2021 it indicates that a lot of the elements of inflation that would be persistent were flashing red or showing red. And it just gives you an idea that this dashboard might, would likely have been helpful in just understanding there was more there than what was obvious from the conventional wisdom or was obvious from, you know, taking the categories of inflation and trying to figure out if you used cars were gonna stay high or and keep rising or, or go back down. So those are the kinds of things that I think are relevant for assessing conventional wisdom and checking our work. So then the final thing I'll say in terms of the slide is let's apply this to the current moment. And it turned out to be helpful, at least in my own thinking in how I thought about the inflation shock of, I'll start with tariffs. So the conventional wisdom is tariffs are a one-off you, you raise them and then you go out, you then they go, they raise inflation but they don't keep raising inflation. So it goes back down as they roll through. So we were looking at all these different measures and many others, I'm just giving you the stylized dashboard here so that it can fit on a page. But looking at this measure, this dashboard and many others and only a small fraction of that dashboard was showing red or even showing pink. And it was just checking the work of is it really going to be a look through? And you do that and you know, we go out and talk to businesses too and ask them about how much they're passing through, et cetera. But this was the part that made it easier to have more confidence, not complete confidence, but more confidence that looking through was a reasonable strategy to have while you're being watchful that something else might change. So now we have the oil shock on top of that and this is, you know, we only have data through April, so there's still more data to watch, but it's, you're starting to see red in the areas that you'd expect. And one of the things I'm really keeping an eye on myself is not only the momentum index but the New York Fed's global supply chain index, which they also developed during the pandemic. And if an oil price shock turns out to limit the supply chains and then it takes, you know, what we've learned about supply chains is once they get clogged up, it takes a long time to bring them back. And so that's the things that could lead to more persistent pressure on inflation. And this doesn't say you're gonna have more persistent inflation, it says you're gonna have more infl persistent inflation pressure. So I, I offer this here and I'll conclude by saying that, you know, the, the message from all of this isn't that you can do perfectly by looking at more data. The message of this in my judgment is that the Federal Reserve and any policymaker really needs to think about how do you resolve the, the models are our benchmarks. They give us a a starting point. Conventional wisdom is critical I think in terms of what has history taught us, but then history has to be disciplined and what we understand from history with the incoming information. And if we do that well and keep looking, you know, under every rock for things and use analysis and models to discipline that, then we have a much better chance of seeing things before they get there. We won't see everything. I mean that's the unfortunate part of being central banking. You don't know everything until you know it, but you can know more than we would. And I think we've had a history of this that history for me started in the GFC when we really dug in, but it's extended now to the, to the inflation shocks that we face. So thank you very much. Thank you.

- This is really a easy job everyone on time. Austin, your turn.

- Okay, thank you. It seems like every time I come out to Stanford I've gonna talk about productivity and that's the, that's that's natural I guess. Thank you so much for having me and what a is what a fabulous conference it is been so far. Last time I was here was a, about a year and a half ago for spr and it was in this very room and it was at this very podium and I argued about the productivity data that although productivity data are always noisy, I thought that there, there had been a persistent increase in the productivity growth rate and some people were attributing that to a one time level shift coming out of COVID, maybe work from home and things like that. But I argue that based on some of the research done at the Chicago Fed, more of the productivity growth increase look to be at least related to tech industries and, and AI usage. And if so, might end up being sustained for multiple years rather than being a one-off. As the technology adoption worked its way through the economy though I said then two things. One, if this happens and it is sustained, it will make us richer, it will be wonderful. And I said there could be a attention when it comes to setting interest rates if that were to happen. 'cause there are forces going both ways. So in summary, the productivity growth has been sustained ever since then, higher than the, the trend was before COVID and the AI hype has grown even more than, than the productivity growth rate has. So I'm back out here at Stanford with some more specific follow up thoughts on how should we think of the increase in productivity growth, like what we saw in the 1990s or what we've seen in the last few years, or what might be coming down the pipe if AI is as great as, as they say, how might that affect monetary policy decisions? I think it's fair to say that a lot of the current discussion on that topic is, has referred back to the 1990s, it and the internet caused a surge in productivity growth. As we know, the annual productivity growth, the, the growth rate rose more than a full percentage point per year above the prior trend. Alan Greenspan was the fed chair. He decided that the higher productivity growth meant lower inflation, therefore rates could be lower without overheating the economy. So I wanna think, I want to think that through in some more detail, what are the conditions in which that argument holds? And I hope to convince you that it makes a big difference whether we're talking about unexpected increases in the productivity growth rate, which arrive without warning or expected increases in the productivity growth rate that are going to come in the future and everyone believes that they're going to come in the future because those two different cases can have totally opposite implications for the interest rate Before just walking through some simple scenarios. I wanna drill down a little bit more about the 1990s episode in a way that is relevant for this discussion. The productivity growth acceleration in the mid 1990s was not clear in real time it had not shown up in the data. Alan Greenspan argued that there were strong corporate profits plus declining unemployment with rising wages and falling inflation. And he imputed that that must mean that there is faster productivity growth that is not yet measured. And on that basis, he argued against raising rates. But by the late 1990s, the productivity growth rate had clearly confirmed. Chairman Greenspan's hunch and Greenspan himself began arguing that if everyone expects a rise in structural productivity, even if it's proven valid, that that could pull forward a bunch of aggregate demand before the productivity gains have actually arrived. And that at some point that would need to be met by tighter policy to prevent inflation. By 1999 going into 2000, as you saw on Mary's chart, inflation was rising pretty, pretty significantly. And the Fed raised rates six times in less than a year in 99 to 2000. So now let us move back into the land of theory at the Chicago Fed. We wanted to think about whether a productivity boom today that looked like the one in the 1990s, would it mean the same thing for monetary policy? So we, we weren't trying to reinvent the wheel, we just started with a standard basic new Keynesian representative agent model with sticky wages and sticky prices that we calibrated it to match the empirical slope of the Phillips curve and to match an average duration of one year between wage changes. We don't have any capital, there's constant returns to scale. Monetary policy follows a standard Taylor rule that tracks the natural rate of interest and responds to inflation and to an output gap. We're not, we're not trying to do anything sophisticated, we're just trying to understand the stylized impact in a normal model. And to it we added a productivity growth increase of one percentage point per year for 10 years. The length is arbitrary, we just add it. We say what happens to inflation, what happens to output, what happens to the natural rate? Now importantly, the entire productivity surge is a surprise. Okay, so in this graph from the top left, the blue, I have black, but that's not black is it? That's blue. The blue line is actual productivity growth for 40 quarters, it's gonna be plus one percentage point and then it's gonna go away. The yellow line is what the expected, so nobody knows it. Every year it drops in their lap and they're like, wow, I didn't expect this to be here. And when that happens, as you would expect, the productivity surges, inflation falls, there's stickiness. So the wages are slow to catch up with the higher productivity. So the marginal cost fall production ramps up, the productivity surge raises output higher than potential. So it's generating a positive output gap. But here in the lower right you can see the low inflation more than offsets the increase in the output gap. And the standard tailor rule says you should lower the nominal rate in response to that shock. And to me that feels very much like a mid 1990 scenario. The productivity growth lands on us, we, the proper fed response is to lower the rates. Okay, but now consider the identical situation. The productivity's gonna go up for 10 years, exactly the same magnitude, but everybody knows it. Okay, so the yellow line of expected, everybody says AI is amazing, we're gonna get one percentage point of year for 10 years. And now a weird thing happens. So the inflation drops pretty much like before and the output gap goes up pretty much like before, but something totally different happens in the interest rate. It rises instead of falling. So what the heck is happening? Inflation's going down, why? Why is inflation going up? And the answer is, since everyone now knows that the productivity growth is coming in the future and that it's going to make them rich, they want to pull their activity forward to today. It's just a wealth effect. The consumer's lifetime income is going up, so they want to raise their consumption now, but most of the productivity miracle is still yet to arrive. We just, we know it's coming, but it's not here yet. So capacity hasn't expanded and that shifting from the future threatens to overheat the current economy. So the central bank has to raise the return on savings to get that pulled forward, consumption shoved back out to where it needs to be. And if it didn't do that, then the economy would overheat today and inflation would blow up the natural rate rises. The standard Taylor rule says that for the expected productivity, the central bank has to raise the nominal rate. And there's a bit of a caution. Let's say you're old school and you're the central bank says, I don't even wanna have an opinion, I'm just gonna wait until there's actual inflation or actual overheating before we do anything. Now the problem is that can backfire and it illustrates the dynamic. Here's the same model with the same predicted, but the orange line, the blue line is exactly what it was before the orange line is what happens if the central bank doesn't pay attention to the way the productivity is changing? The natural rate doesn't incorporate it into the Taylor rule, doesn't try to offset the wealth effect of higher expected productivity and only responds once you get inflation and an output gap opening up. And what you see is that the wait for it strategy has much higher inflation and much high, much more overheated economy than when you take the natural rate into account count. Of course, once you see inflation and a big positive output gap, the Taylor rule says you gotta go raise the rates, but in the end you have to raise the rates substantially more than if you were paying attention as it was going along because this is the thing, if you don't react and you just let it overheat, it overheats and you and, and on average you have more, you have more inflation. Now as a, as a sad aside, I would like to say that one of the first pieces that that I knew of that emphasized the point that news about future productivity growth might force a higher nominal rate, even if inflation was lower, was this Simon Gilchrist and John Lahey paper in the JME back in 2002. As many of you know, John passed away suddenly from complications in a, in a household accident. He was 10 days before starting as the research director at the Chicago Fed. And it's really heartbreaking to think how much we we could have benefited from from his insight these days. As we walk through these three, the thing that I like about these, they're just finger exercises, but they make clear what the mechanism is. And I think to me they make clear it doesn't really depend on the model. Yes, we don't have capital. Yes, it's a very simple setting, but the fundamental thing is if output exceeding potential you think leads to inflation, and if the agents are forward looking, then the amount of inflation pressure is gonna depend on how people are reacting to these announcements about future income and future productivity growth. And I feel like back in the real world, it suggests what the policymakers should be on the lookout for. When, when, when we're thinking about this, we should be on the lookout for anything that looks like pulling activity from the future into the present. So wealth effects on consumer spending, some of it right out the window here in Palo Alto, that would be a bad sign. High investment in data centers driven by stock market valuations, driving up the cost of land and electricians and computer chips for non-AI industries. The bigger are these kind of effects, the more they suggest that productivity growth might be pushing up the ideal interest rate rather than down. And more importantly, just go measure how much productivity people expect to be coming down the pipe versus how much we've already experienced. The bigger the share of the total productivity boom that is still on the way, the more likely it is that rates are gonna need to rise. Now Ezra Car who's at the Chicago Fed has led a survey of three groups, economists, tech people, and the general public about the future implications of ai. And he asked them, how much do you think productivity's gonna go up? And interestingly, the median person in each of the three groups pretty much agreed that they thought it would be about one percentage point for the next 10 years, each year, for the next 10 years. The OECD and McKinsey have studies that project similar. All of those would mean that the lion's share of the productivity boom from AI is still to come. And that would likely mean there are gonna be a lot of incentives to pull it forward and and borrow off of the future. So there's a mixed message here. Productivity growth is still a massive boom for the economy. We want it. What it means for interest rates though is a little more subtle. I think it depends a lot on whether productivity's expected to come in the future. If it happens unexpectedly, rates should probably fall. The more it's predicted to be coming soon, the more it's gonna motivate shifting behavior by forward looking agents and the more likely it is to drive up the natural rate to prevent overheating. If the central bank reacts too slowly, it probably gets worse. The bigger the hype, the bigger the concern. And note I didn't say one word about bubbles. This whole thing was about the fundamentals. Thank you,

- Thank you.

- I hate following him, but I'm gonna take one second to be to discuss it. This wealth effect thing has been around for a long time in a lot of models. So like the forward guidance puzzle where we say we're gonna keep rates low into the future, you should get these huge consumption booms. We didn't see 'em when that happened. So what tends to happen if you take this model and make half of the consumer's hand to mouth to bring that wealth forward, you have to have no borrowing constraints. But if you can't borrow and bring it forward, it chokes all off. Or you take habit persistence models, which say people wanna adjust their consumption slowly. So there's a whole literature that tones down what's in there. So it'd be interesting to see what happens if you ran the models that way. All right, after that excitement, thank you for the opportunity to speak today, since I'm not gonna talk about monetary policy. So if anybody's watching this and they're waiting for me to talk about it, you know, you can go off to the pub. What I want to talk about is something related to the first two words on this slide, independence and structure. So what I want to talk about is Central bank independence, but applied to reserve bank operations. I know right now they're gonna say, here he goes again. The decentralized and regional design of the Federal Reserve helps reinforce our independence by ensuring that the full range of interest and views are represented in policy discussions and that is something that should be preserved. But much of the day-to-day operations of the reserve banks are not connected to monetary policymaking. And I recently gave a speech at the Brookings Institution where I suggested some improvements to the efficiency of reserve bank operations. I believe that these improvements would keep the Fed's commitment to wisely use public resources in serving the American people, and thus help support our independence in conducting monetary policy in the public's interest. Now, in that speech I asked two simple questions. First, what are the functions and activities that are unique to a reserve bank district and need to be done by a reserve bank in a manner tailored to the local economy and local needs? Second, what functions and activities can be done anywhere and in fact can be done better and more efficiently if standardized and exploit economies of scales across the entire Federal Reserve system? So that first question addresses functions where geography matters, and the second focus on functions for which geography just doesn't matter. Now it's clear that there are responsibilities that belong in a district and to be true to the genus of the federal reserve design, they should be locally run. The president's vote on monetary policy, having a research function to aid the president's community outreach, community development, supervision, and discount window operations. But I noted in my speech that most system employees are engaged in operations providing critical services to the banking system, the US Treasury, and ultimately the American Public Information Technology, human resources, financial management, enterprise risk management, and payments are essential to achieving those operational outcomes. But there is no obvious rationale to do these things in 12 different ways or done individually 12 different ways. These are certainly services needed by each reserve bank, but they do not need to be provided by each reserve bank. These are functions that can be done in a standardized and centralized way and provided uniformly a scale across the system, the system, and ultimately the taxpayer benefits from lower operating costs and better operating overall risk management with services delivered consistently across the reserve banks. So I propose centralizing and standardizing back office functions and having the reserve banks focus on the things they uniquely provide to their districts. Now toward that end, the presidents have developed a framework that shows how to reap the risk and efficiency benefits of standardization and centralization. And I wanna applaud their efforts. It is this tremendous step forward for the Federal Reserve System. Now, after I gave that speech, I have heard several comments that my proposal was somehow at odds with the fundamental and time tested design of the Federal Reserve Act with its emphasis on regional perspective and reserve bank independence. So let me address that concern. The Federal Reserve system was designed as a federated system expressly to meet the needs of a large and diverse country. And while avoiding the concentration of too much power of influence in places like Washington and Wall Street, the 12 reserve banks were designed to carry out most of the non-monetary policy functions and services with oversight by the Board of Governors. In this sense, the Reserve banks are able to make largely independent decisions on operations while being accountable to the board and the American public. Day-to-day control is not exercised in Washington, but by the reserve banks. This is how they have operated since the beginning of the fifth. But as I said, in that speech, technology and leg legislative changes are driving us to rethink how we provide those services in a cost efficient manner. So the question that the presidents and I have been facing is how is how do we exploit those efficiencies while maintaining the spirit of regionalism and reserve bank independence? That is at the heart of the Federal Reserve Act. Now the presidents have answered that question by developing a framework that has the Reserve Banks make independent decisions as a collective group as opposed to making decisions on a one by one bank by bank basis. Now there is still oversight of these decisions by the Board of Governors, but it's just that oversight, not decision making. Regionalism is preserved via the activities I listed above that are unique to each district and ensures that the spirit of the Federal Reserve Act is maintained. But functions such as human resources will now be centrally led by a single reserve bank who will then act like, for a lack of better word, a contractor for the rest of the reserve banks to provide services with appropriate service level agreements. Accountability is strengthened in the process, but the Reserve Bank responsible for a particular function will have the authority to allocate resources to operate in a cost efficient way that achieves operational excellence for the system as a whole. Now, individual banks must give up day-to-day decision rights over how the contractor bank provides those services. The board will maintain its oversight role to ensure that performance meets service expectations and costs are appropriate. But the key element of this design is that the reserve banks still have control over all operations. Their operational independence is not diluted in this framework. Furthermore, the President's plan distributes key responsibilities across the system so that each bank contributes in a manner consistent with its local expertise and capacity to benefit the system as a whole. But to make this new framework effective as a collective group tasked with improving operations and at a lower cost, there has to be a change in mindset and a change in governance. Bank presidents and first vice presidents need to adopt a system, first Bank. Second mindset. This is a change in mindset that I have been pushing since I was given my oversight role in 2022. Now, historically, there tended to be a bank first system, second philosophy, which was fine when everything was done locally, but times have changed and so must our mindset. What also needs to change is the governance model. While striving for consensus is a great model for making difficult policy decisions, it is not obviously successful when running a complex and critical when running complex and critical operations. Otherwise, one bank can halt actions that are needed to move the system forward. Again, in the past this was not uncommon, but moving to a model where consensus is not the modus operat operandi will require, we will require rethinking how decisions get made for the system. Banks will need to give up day-to-day control of many parts of their operations and delegate decision makings to a single bank that requires collective trust in the contractor bank and a commitment by that bank to deliver the services needed by all other banks. So to conclude, over the last six months, the board and the reserve banks have moved rapidly toward developing an approach that I am confident will modernize our operations to be more efficient while enhancing service delivery. There are still details to be worked out and all of us who play a role in system leadership understand the complexities of change management and execution. That is especially true given the criticality of the services that are provided by the reserve banks, including moving trillions of dollars in us in payments every day for commercial banks and the US Treasury. But the foundation is now in place for driving important transformation and I look forward to working with all the reserve bank presidents and first vice presidents to move this framework forward. Thank you very much.

- Thank you everyone. This was great. We have half an hour before the, so I'm maybe I'm gonna interspar some of the question later, but I'm gonna go to the public right away because I know that Yeah, and you, there are lots of people that have questions. I have. Can I have Mike over here? Amit? Oh, over there. Okay, that's fine. Yep.

- Thanks

- Steve. Yeah,

- Thanks. Great. That's on the great discussions. Comment on Austin's remarks and I wanna draw attention to another federal reserve survey that looks at expectations of productivity growth. That's one I'm involved in at the Atlanta Fed. We take a different approach than the one den describes. What we do is we survey CEOs, ask them about what they expect AI's impact to be on productivity growth in their own firms over the next three years. And then aggregate that yields a somewhat lower number than the one Austin described 75 basis points per year of extra productivity growth rather than a hundred. But it's a very different approach and it's broadly in line with what Austin described. So I find that reassuring. But something else, and this this relates to Chris Waller's comment on Austin. Chris briefly describes some mitigating factors, some complexifying factors in that it's also the case that if you look at the investment data, and I suspect this is true in the spending data as well, that the response to AI thus far is extreme, extremely skewed. So in the Atlanta Fed, we just released a blog post earlier this week that looks at AI spending, investment spending at the firm level. So just to give you an idea of how skewed this is, it's about the mean is 14, the mean employment weighted across firms is 14 times as large as the median. Okay. So to Chris's points about when you can bring wealth forward and how that might affect demand today here we have the added complexity that at least on the investment side, and I suspect, and this goes back to Austin's remark about what he sees driving around Palo Alto, that the, the wealth effects that are brought forward in today's spending on the consumption side and the investment side, I think are highly uneven in the economy. One last observation on this point. We have everybody from Silicon Valley enthusiasts who tell us AI is so great that no one will need to work in five years. That's how rich will be to i the the most recent Michigan consumer sentiment survey, which I not sure if you wanna take that with a grain of salt wraps, but it it, it's been running at the lowest levels on record. So there's this enormous heterogeneity and perceptions about whether things are good or times are good or bad. And I kind of invite Austin to comment on how that might affect his, his analytical approach to this issue.

- We're gonna take questions because, so there is Amit here.

- Thanks Amit. Who were institution, this is a question for Michelle. So thanks for laying out how you're thinking about the financial intermediation architecture with both banks and non-banks. And I appreciate how you're thinking about reducing regulatory costs on banks so that they could be competitive, especially in segments where they have a competitive advantage, maybe relative to non-banks. I was wondering if you would be willing to comment on something that's also happening in Palo that, that you've sort of alluded to earlier, not here, which is the architecture of supervision, like streamlining supervision, changing the models like camels and so on, that also gets to compliance costs. And I wonder when you've thought about reducing regulatory costs, if you've, if you've also taken into account the fact that the compliance costs are also going to go down in supervision and if one is overcorrecting, maybe by doing all of these things at the same time or how you're thinking about it.

- Pablo w asset management related question to Steven. So AI among the, among the general population, it's very unpopular. And if you look at service like the New York Fed, it says that people think they're gonna be losing their jobs. I mean, the probability, the perceived probability of losing a job is very high. My understanding is in, in Austin's model, this heavily relies the implication of the second model is that the, the, the a key assumption is that people are gonna be borrowing, but the people think they're gonna be losing their jobs. You could have actually the opposite effect in which the saving rate increases in preparation for AI taking over. How, how, how are you thinking about this in kind of in, in, in the context of your model? Is this something we should really be concerned about? Even if, and the consumers could be wrong and not lose their jobs, but it's a fact that they currently think they're gonna be losing their jobs. So how would that affect the, the implication from a policy perspective?

- Joan?

- Hello? Hello. I got a short one for each of you, Mickey. I don't understand why you'd wanna lower the capital requirements on anything. Private credit is not doing well because it has a lower capital requirement. They have a lot more capital and more long-term debt. They, I Why aren't you cheering that here? Here we have a, a way of funneling money to corporate lending that is, that is not funding itself from run prone liabilities in the end, instead of, of having deposits which are prone to runs going into corp corporate lending. Mary, that was great. I wonder what you think of Bob Hall and Marianna Klix observation that because inflation was completely flat. That means we were at the natural rate all along. Austin is absolutely wonderful. I think your model, you left out capital, which you kept it simple, which, but it seems that capital adds to the need. If you have to invest to get this productivity, you're gonna have a higher marginal product, higher a rate. You need to look, find the Elon Musk video where he says nobody should save for retirement. 'cause we're all gonna be rich in the future. And, and finally Chris.

- Oh damn. I was hoping I wouldn't get any

- Questions. Oh, no, no, no. The, the minute you say centralized for efficiency, kind of, you know, the Hoover instincts go off, you know, we should get rid of these stupid state governments and just have the federal government do it. You know, let's have a big conglomerate. The Soviets were, you know, oh, we only need one steel mill. Hoover has its own H hr, thank God we're not under Stanford hr. And thank god Stanford and Harvard and Yale and Princeton don't have one. Centralized hr. We'd never hire anyone in, in closing, by the way, I want to thank all of you. The previous policy panels were fantastic. You guys have hit it outta the park. This is the best one in the 16 years of this conference, so thank you.

- Hi, my name is Alejandra Edwards and I had a question, but for Austin, but I, I think it's gonna be a repetition and the question had to do with what happens if there is a lot of unemployment coming up from that productivity growth. And one for Mary, it's my own ignorance, but I don't understand how those little squares go from green to red in those tables. Could you please explain?

- Oh yeah, sure.

- Okay. So I have a one there and then one very patient person at the back.

- Yeah, I have a question for Chris Waller on the proposal. I'm just trying to understand the governance of such a change. So you are the governor in charge of reserve bank operations. You make this proposal, but the board doesn't need to vote on this proposal. That's a question. And then, so it's put out to the reserve banks and it sounds like they are jointly and cooperatively trying to come to a solution to implement their proposal. Are there any sort of required votes or governance structure?

- No. Yeah. Jim Ballard again, so this is for Austin at Purdue, we're always talking about AI all day, every day. So I thought you did a great job of framing up this issue, which is going to be very hot in monetary policy going forward as to whether, but you know, I guess I would just like to know, did you do any analysis about, you expect the productivity boom in the future, but it actually doesn't materialize versus you expect the productivity boom in the future and it does materialize or it even exceeds what you thought. So you could get the risk of getting it wrong. The policymaker getting it wrong on either side, and maybe you have just have intuition about that, but there's an awful lot of expectation out there and it's not clear it's gonna be enough profits out there to pay for all these data centers and so on. So it could go a lot of different directions.

- I think I'm, I'm gonna stop here and start giving you some time. Christopher, do you want to start? We're gonna and respond to the questions.

- Oh, well, no,

- That's it.

- And on centralization, I mean we're just, we're arguing about exploiting economies of scale and lowering cost to provide the same level of service. That's straightforward. Econ, I don't see that. Plus we're trying to be good stewards of taxpayer dollars. So, you know, whatever your psychological scars are from the word centralization, John, those are your demons, not mine.

- Does it? Can I, can I ask a follow up on that? I mean is like, as you were, is a follow up on John, but on a more positive side. So one of the, one of the, one of the concern that we often have with central banks, we think about the independence from government, but there is also independence from banks. Your from, from the, from, from the banking system. So your story to me seems positive on that dimension because to some extent, you know, a more structural way of organizing the regional banks should insulate them more from banking pressure. Are you thinking in that or are you just thinking about efficiency?

- No, I'm just talking about back office operations and how to do 'em in the best efficient way without reducing service.

- Okay, Austin.

- Okay, well there were a couple of, of different themes. One for the, for the ones that are what if there's a lot of unemployment or what if people are afraid that AI's going to going to take away their jobs in the future or in the space of if consumer sentiment is really bad, isn't the implication of this that things should be overheating now that's the danger. Yes, I think it is. So if any things that we have people's fear that they're going to lose their jobs in the future leads them to precautionary save that would be going the other way. And then, then we'd be less nervous. Don't convince yourself though that well, if 50% of consumers are hand to mouth and they can't borrow from the future, then we can't overheat that. Steve, I think the, my intuition was if you think there's a khap economy and equity values are going through the, the ceiling and the top of the K are spending like crazy on houses in Palo Alto out of their equity wealth. Nobody has to borrow anything. And it could still look very much like what, what I'm, what what I'm describing now with Steve Davis. Let me just observe what a coal family, my friend and colleague for 25 years and he calls the Atlanta Fed to do his survey. Yeah, thanks Steve. Then. Ah, good. Hit a blow. What a blow. What a blow. You've been talking to Chris too long. He's like, oh, efficiency. Atlanta knows how to do the surveys.

- Damn right. Yeah.

- I John, I thought you were gonna ask, and it is kind of related to this discussion about, I thought you were gonna say, Hey, if everybody thinks like Elon Musk and in the future we're gonna be risk, not only don't save for retirement, don't work now. And I I was thinking you were about to say that. I was saying in a weird way that is a form of consumption. So the shifting of consumption to the present of the form of leisure. I'm gonna take leisure because I know I'm gonna be so rich in the future. If you started to see labor, labor supply, labor force participation unexpectedly dropping for among the same group of people, that would also be a thing that should start making you nervous. The last thing I'll say we have can, can we pull the slides back up to Jim to your question of what if they get it wrong? I did have a slide where I did that. The only reason, well

- Is there anything,

- If it comes up, we'll we'll have it, but if not, the only reason I didn't present it in addition to you couldn't see it, but I finished with two seconds left in my time is it's a little less robust. So the thing about the, what I showed you is a very simplified model, but it's pretty robust. We tried different models and, and different production structure. This thing, it makes a big difference.

- Here it is.

- Okay, here it is. Let's see.

- You have to switch. Can see

- It Now what maybe you want where

- I have no idea where to point.

- Keep, keep going. Keep, where is it? Oh, oh, dang it. It just had a leg. It went, it went times.

- Keeps having a legs. I

- Hit too many. This is why the survey's in Atlanta.

- Okay, here's continually disappoints here. The, the, the only complication is, so here it falls off halfway through. So we, we, but everybody keeps thinking it's about to come back. And now that makes a huge difference. Do does when it falls off, does everybody realize it's done or are they still waiting? Wait a minute, it's gonna come back. Just wait one year. So I I said let's just have them keep thinking it's coming back. It's, they think it's gonna be 10 years. Interestingly, you can get it if you think through the intuition, the more you think is coming in the future, the more you're pulling it forward to today. So the bigger the disappointment's gonna be, the recession's gonna be bigger because there's stickiness and inflation is kind of persistent. All this shows you is you can easily get stagflation when that happens where you overheated the economy and you got inflation going and then you get the recession because you, you you overspent compared to what you were. And this is all fundamentals. This again, this is not a bubble, this is just, they made a mistake. I should have done it. But you should, I should have done it the other way too. What if we get a blessing? I think that goes back to the original one. What if you just got 10 years of productivity that you didn't expect? The rate goes down every everybody's happy. 'cause 'cause it, 'cause it goes great. But thank, thank you for these comments. That, that was really helpful

- Mary.

- Okay, so I'm gonna take the prerogative of having the microphone to say something about Austin's piece that relates to what I was trying to say in, in my work is that my presentation is, you know, I like where you ended Austin. And I think that's really important. We can sit here and it's fun to do 'cause we're at Hoover and deliberate the models and should we have capital in them or is is everybody's or there's too many hand to mouth people that we can't possibly get the result. But the thing that you said at the end is really the discipline, which is what would we look for that would prove to us whether we were there. We're not gonna to look to our star because that's up, hard to estimate. And you know, vaguely, vaguely understood in real time. So the the things you talked about was data center investment and consumer spending at the higher end. And do they keep going through that? And there's a whole list of other things. So I think that's the discipline for the model and trying to figure out whether the, the good scenario or the bad scenario would merge. Now on the questions, I'll start with the green and red since that's easy and I should have said it, it was just illustrative, but the green and red was really standard deviations away from your historical norms. So if you're two standard deviations away, your red, if you're one you're, you, you, you move up radiations of color, the real elements that got us to move forward with those measures. And many people do it. I mean the Kansas City Fed has a labor market index. Now the Atlanta Fed publishes regularly a spider chart that shows how far away or how close we are to conditions that would be recessionary or, or dynamic. And the the whole point of this is you learn early by doing this, which ones lead and which ones lag and which ones help and which ones don't. But it goes back to something that I learned in microeconomics. I'm trained as a labor economist and when the professor said, you'll never know anything for sure in micro. So you're going to have to have a preponderance of evidence. And so I'd see these, these questions that, that the models produce as a preponderance of evidence mentality. Okay, so are you talking about Bob, Paula and Mariana's work where they they're, so Mariana Klik is on the San Francisco Fed staff and we have vigorous debates about whether because inflation was pretty constant for a long time that we had reached the natural rate of unemployment. And there's something to think about there. But what we kept finding, and I had the same, you can have the same debates with many people. What you keep finding is the labor market is more flexible than what the natural rate estimates are. And that you can't always use inflation, especially when it's surprising that it's not moving. So we put, we put all these shocks onto the inflation and the great moderation and the GFC at the post GFC and nothing happened with inflation. It was stuck basically at 1.8. And so no matter what we did, and then it becomes an uninteresting measure or calibrating measure for where the natural rate of unemployment is. And so that's why in many of the, the discussions we had, we were learning about the natural rate of unemployment or how far the economy can go down without spurring inflation, you know, experientially and we could run hotter because we didn't have an inflation problem. That's, you know, ultimately, I'll tell you how I, and I always conclude with Bob Hall and Mariana is that it's really hard to tell Americans that you're taking, you're gonna constrain their job growth when we have price stability. And so the natural rate is a concept that's useful for benchmarking, but actually the data teaches us where the labor market can go and how fast it can run. I think that was the end of my question. Yeah, thank you.

- Thank you.

- So I'll start with John's question about what, why do we care about private credit and monitoring it? I think from the perspective of financial stability, we need to understand how all of these different activities work together and what the investments of the banking system are into those areas that are much less opaque. So in, in the, the unintended consequences, I think of Dodd-Frank, when it was implemented, it risk weighted a lot of activities that were really commonplace in the banking system in ways that made banks really not interested in continuing to do that. And it pushed it into the non-regulated space. And then we couldn't understand how those investments were being, were being utilized because we weren't collecting any data that could help us understand that. And two, we don't oversee the non non-data, non-bank financial sector. So it was difficult for us to understand what the spillovers could be, how we could think about what kind of a financial stability it, it could be, and at what point should we be really concerned about it. So I do think that we took the opportunity with the new Basel capital rules to bring a lot of that back activity back into the banking system because we far over calibrated on regular banking activities in that rule itself. So, and I think everyone knows that that many before me had tried to implement a response to the Basel three process that we, that we agreed to in 2017. And this was the, the right way, the right opportunity. And I think there is a shift to a mindset around the world on bank supervision and regulation that says it's time for us to stop thinking about what happened before we've overcorrected or corrected appropriately in, in some ways for those activities. What are, how should we be thinking about what's gonna happen in the next 10 years or the next 20 years, and how should we be positioning the banking system for that? So in our work to modernize the, the regulatory and the supervisory systems, what we're trying to better understand is how can, how can we bring some of that activity back in, but also allow for innovation in the banking system and recognize that, that those activities are going to change and we need to be positioned to be able to oversee those whether through regulation or for or through supervision. So your question was about supervision and we're making a lot of changes and a lot of those changes are related to, I serve also as the chair of the F-F-I-E-C right now. So when I, when I became chair, I had a big agenda and people that also were serving with me said, this is a very sleepy organization. And I said, well, it's not now

- Wake up.

- Exactly. So we've been doing a number of different things through the F-F-I-E-C and in the coming weeks we're going to be introducing a new update to camels among many other things that we've been working on that won't be quite as public, but, but these are things that have not been looked at or reviewed or updated in 50 years. And so when you're thinking about frameworks that worked for banks back in, you know, pre-farm crisis, maybe we're not looking at things or calibrating things, we're thinking about their inter interactions and how we should proportionately understand what activities or what, what, what supervisory constructs should comprise each one of the components of the camel's rating framework. And what we found was that oftentimes the m part, the the management rating had been abused in ways that was not transparent, that it integrated a lot of things that weren't related to necessarily management that then led to a, an opportunity to downgrade the financial institution from its composite perspective. So through that and recognizing having been a banker and implementing a lot of these rules and regulations and, and being subject to supervision, what I recognized was that in addition to watching some of the bank failures that we've, that occurred over the last few years was that we were not really looking at things in a way that would allow us to effectively and promptly understand what was happening in the banking system. And what I would point to in particular is that for some reason we stopped looking at reports like the report in the spring of 2022 that showed us that there were problems with Silicon Valley Bank and then later in the fall when those were exacerbated and other reports that we were not responding to as well. So one thing that I've done as the vice chair is to implement a, an independent review of the failure of Silicon Valley Bank. We're in the process of learning some of the shortcomings of the previous reports that have been done. One of those shortcomings is that people from outside of the Federal Reserve were not interviewed. It's kind of head scratcher, isn't it? So I think that there were a lot of opportunities for us to think about did we learn the right lessons? How can we learn the lessons that keep us from, from, from repeating the failures that led us to the one of the most costly bank failures in the country's history. So by focusing on material financial risk and recognizing that there were more than 30 Mr. MRAs that existed on Silicon Valley Bank, only a handful of those had anything to do with material financial risk, which is actually what brought the bank down. So when the bank doesn't understand how to prioritize what those findings are and how to mitigate those findings and make them and remedy them, is it any wonder why we weren't focusing on the right things to keep Silicon Valley Bank from, from failing? So in our review of MRA, so we've published this statement of supervisory operating principles. It's the first time the Federal Reserve has ever done anything like that to provide transparency to how we approach supervision and what we're focusing on and how we're thinking about things in very plain language. So we, we, we use MRAs, which are matters, requiring attention to identify areas of either violations or areas where we want a bank to, to fix things that we found to be problematic. We had Mr. MRIs on the books from 2001. Does that tell you that if we thought it was that important in 2001 decided as an MRA we weren't following up to make sure that that condition was mitigated? I think we had a problem with the way that we were implementing these tools and now what we're doing is taking a look back to try to make sure that one, that they were appropriate when they were issued. Two, if they've been mitigated, we need to make sure that they are lifted and that we understand and, and the bank understands when we have a problem and we've identified it, what is it, what's the condition that says you fixed it? And then how do we make sure that if you do the same thing again, you get another MRA instead of leaving one on the books for 26 years. So

- So you're making hopeful that interesting question, way more practical.

- It's

- Not that you're

- Underweight

- No,

- No, no, we're not, we're not doing anything less. What I would tell you is that we're focusing more on financial risks. We're, we're still doing all of the other components of our supervisory process that includes cyber risks. Obviously we know that there are a lot of cyber risks that exist in the financial system. We're still doing BSA and A ML, everything else that is, is a regular component of our supervision programs we're continuing to do. But what we wanna make sure that we're not missing is those things that actually lead to big failures like material financial risk.

- Okay. I think we are at the end of the time, I, Valerie will thank you for everyone.

- So thanks to a terrific panel. So I, we'd like to end with a few thanks to a number of people who really made this happen. So first of all, Marie Christine s flakey is, are you here, Marie? Christine? This would not happen without her. She is the one that corrals US organizers in the fall and reminds us what we were thinking, keeping track of all of our brainstorming. She basically does everything single handedly until the last part coming up to the actual conference. And then we have Hoover staff events, marketing media. But so we particularly wanna thank Maurice Christine, but then also all of the Hoover event staff. Also, we are very appreciative of the generous funding from the Bradley Foundation. They've been a long time supporter of this event and, and you know, we think it's a valuable event and so we're, we're very happy to have their support. I'd also, yes, and we wanna thank all the panelists and moderators. This was just a terrific day. You know, we kind of dream these things up in advance. We say, how is this gonna go? And you know, it was beyond our wildest expectations. So I wanna thank everybody and particularly the policy makers who we know are very, very busy. We really appreciate your coming here and sharing your views and giving us insight into what's going on in terms of trying to make policy even better. And then finally, thank you to all the attendees, the people who ask questions during the general discussion or had great conversations during breaks. We hope that you found this rewarding and I think we're ready, unless I've forgotten anything, to go out to the fair weather courtyard and have some cocktails. Alright.