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Measuring the Ampleness of Reserves

Over the past fifteen years, reserves in the banking system have grown from tens of billions of dollars to several trillion dollars. This extraordinary…

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Over the past fifteen years, reserves in the banking system have grown from tens of billions of dollars to several trillion dollars. This extraordinary rise poses a natural question: Are the rates paid in the market for reserves still sensitive to changes in the quantity of reserves when aggregate reserve holdings are so large? In today’s post, we answer this question by estimating the slope of the reserve demand curve from 2010 to 2022, when reserves ranged from $1 trillion to $4 trillion.

What Are Reserves? And Why Do They Matter?

Banks hold accounts at the Federal Reserve where they keep cash balances called “reserves.” Reserves meet banks’ various needs, including making payments to other financial institutions and meeting regulatory requirements. Over the past fifteen years, reserves have grown enormously, from tens of billions of dollars in 2007 to $3 trillion today. The chart below shows the evolution of reserves in the U.S. banking system as a share of banks’ total assets from January 2010 through September 2022. The supply of reserves depends importantly on the actions of the Federal Reserve, which can increase or decrease the quantity of reserves by changing its securities holdings, as it did in response to the global financial crisis and the COVID-19 crisis.

Reserves Have Ranged from 8 to 19 Percent of Bank Assets from 2010 to 2022

Sources: Federal Reserve Bank of New York; Federal Reserve Economic Data, FRED (“TLAACBW027SBOG”); authors’ calculations.

Why does the quantity of reserves matter? Because the “price” at which banks trade their reserve balances, which in turn depends importantly on the total amount of reserves in the system, is the federal funds rate, which is the interest rate targeted by the Federal Open Market Committee (FOMC) in the implementation of monetary policy. In 2022, the FOMC stated that “over time, the Committee intends to maintain securities holdings in amounts needed to implement monetary policy efficiently and effectively in its ample reserves regime.” In this ample reserves regime, the Federal Reserve controls short-term interest rates mainly through the setting of administered rates, rather than by adjusting the supply of reserves each day as it did prior to 2008 (as discussed in this post). In today’s post, we describe a method to measure the sensitivity of interest rates to changes in the quantity of reserves that can serve as a useful indicator of whether the level of reserves is ample.

The Demand for Reserves Informs Us about Rate Sensitivity to Reserve Shocks

To assess whether the level of reserves is ample, one needs to first understand the demand for reserves. Banks borrow and lend in the market for reserves, typically overnight. The reserve demand curve describes the price at which these institutions are willing to trade their balances as a function of aggregate reserves. Its slope measures the price sensitivity to changes in the level of reserves. Importantly, banks earn interest on their reserve balances (IORB), set by the Federal Reserve. Because the IORB rate directly affects the willingness of banks to lend reserves, it is useful to describe the reserve demand curve in terms of the spread between the federal funds rate and the IORB rate. In addition, we control for the overall growth of the U.S. banking sector by specifying reserve demand in terms of the level of reserves relative to commercial banks’ assets.

There is a clear nonlinear downward-sloping relationship between prices and quantities of reserves, consistent with economic theory. The chart below plots the spread between the federal funds rate and the IORB against total reserves as a share of commercial banks’ total assets.  When reserves are very low, the demand curve has a steep negative slope, reflecting the willingness of borrowers to pay high rates because reserves are scarce. At the other extreme, when reserves are very high, the curve becomes flat because banks are awash with reserves and the supply is abundant. Between these two regions, an intermediate regime–that we refer to as “ample”–emerges, where the demand curve exhibits a modest downward slope. The color coding of the chart reflects the shifts in the reserve demand curve over time. In particular, the curve appears to have moved to the right and upward around 2015 and then moved upward after March 2020, at the onset of the COVID pandemic.

Reserve Demand Has Shifted over Time

Sources: Federal Reserve Bank of New York; Federal Reserve Economic Data, FRED (“TLAACBW027SBOG,” “IOER,” and “IORB”); authors’ calculations.

This chart highlights two of the main challenges in estimating the slope of the reserve demand curve. First, the curve is highly nonlinear, which means that a standard linear estimation approach is not appropriate. Second, various long-lasting changes in the regulation and supervision of banks, in their internal risk-management frameworks, and in the structure of the reserve market itself have resulted in shifts in the reserve demand curve. A third challenge is that the quantity of reserves may be endogenous to banks’ demand for them. Therefore, to properly measure the reserve demand curve, one must disentangle shocks to supply from those to demand. As we explain in detail in a recent paper, our estimation strategy addresses all three of these challenges.

Estimating the Slope of the Reserve Demand Curve

Our approach provides time-varying estimates of the price sensitivity of the demand for reserves that can be used to distinguish between periods in which reserves are relatively scarce, ample, or abundant. The chart below presents our daily estimates of the slope of the demand curve, as measured by the rate sensitivity to changes in reserves. Although we do not have a precise criterion for when reserves are scarce versus ample, during two episodes in our sample, the estimated rate sensitivity is well away from zero. The first episode occurs early in our sample, in 2010, and the second emerges almost ten years later, in mid-2019. In two other periods—during 2013-2017 and from mid-2020 through early September 2022—the estimated slope is very close to zero, indicating an abundance of reserves. The remaining periods are characterized by a modest negative slope of the reserve demand curve, consistent with ample (but short of abundant) reserves. The overall pattern of these estimates is robust to changes in the model specification, such as including spillovers from the repo and Treasury markets or measuring reserves as a share of gross domestic product or bank deposits (instead of as a share of banks’ assets).

Rate Sensitivity Changed over Time, Following the Path of Reserves

Sources: Federal Reserve Bank of New York; Federal Reserve Economic Data, FRED (“TLAACBW027SBOG,” “IOER,” and “IORB”); authors’ calculations.

Interest Rate Spreads Alone Are Not Reliable Indicators of Reserve Scarcity

As we discuss in our paper, the time variation in the estimated price sensitivity in the demand for reserves is based on observations of small movements along the demand curve due to exogenous supply shocks. The location of the curve itself, however, also changes over time. That is, there is not a constant relationship between the level of reserves and the slope of the reserve demand curve.  

In our paper, we find evidence of both horizontal and vertical shifts in the reserve demand curve, with vertical upward shifts being particularly important since 2015. This finding implies that the level of the federal funds-IORB spread may not be a reliable summary statistic for the sensitivity of interest rates to reserve shocks, and that estimates of the price sensitivity in the demand for reserves provide additional useful information.

In summary, we have developed a method to estimate the time-varying interest rate sensitivity of the demand for reserves that accounts for the nonlinear nature of reserve demand and allows for structural shifts over time. A key advantage of our methodology is that it provides a flexible and readily implementable approach that can be used to monitor the market for reserves in real time, allowing one to assess the “ampleness” of the reserve supply as market conditions evolve.

Gara Afonso is the head of Banking Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

Gabriele La Spada is a financial research economist in Money and Payments Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.   

John C. Williams is the president and chief executive officer of the Federal Reserve Bank of New York.  

How to cite this post:
Gara Afonso, Gabriele La Spada, and John C. Williams, “Measuring the Ampleness of Reserves,” Federal Reserve Bank of New York Liberty Street Economics, October 5, 2022, https://libertystreeteconomics.newyorkfed.org/2022/10/measuring-the-ampleness-of-reserves/.


Disclaimer
The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).

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Government

NIH awards researchers $7.5 million to create data support center for opioid use disorder and pain management research

WINSTON-SALEM, N.C. – March 24, 2023 – Researchers at Wake Forest University School of Medicine have been awarded a five-year, $7.5 million grant…

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WINSTON-SALEM, N.C. – March 24, 2023 – Researchers at Wake Forest University School of Medicine have been awarded a five-year, $7.5 million grant from the National Institutes of Health (NIH) Helping End Addiction Long-term (HEAL) initiative.

Credit: Wake Forest University School of Medicine

WINSTON-SALEM, N.C. – March 24, 2023 – Researchers at Wake Forest University School of Medicine have been awarded a five-year, $7.5 million grant from the National Institutes of Health (NIH) Helping End Addiction Long-term (HEAL) initiative.

The NIH HEAL initiative, which launched in 2018, was created to find scientific solutions to stem the national opioid and pain public health crises. The funding is part of the HEAL Data 2 Action (HD2A) program, designed to use real-time data to guide actions and change processes toward reducing overdoses and improving opioid use disorder treatment and pain management.

With the support of the grant, researchers will create a data infrastructure support center to assist HD2A innovation projects at other institutions across the country. These innovation projects are designed to address gaps in four areas—prevention, harm reduction, treatment of opioid use disorder and recovery support.

“Our center’s goal is to remove barriers so that solutions can be more streamlined and rapidly distributed,” said Meredith C.B. Adams, M.D., associate professor of anesthesiology, biomedical informatics, physiology and pharmacology, and public health sciences at Wake Forest University School of Medicine.

By monitoring opioid overdoses in real time, researchers will be able to identify trends and gaps in resources in local communities where services are most needed.

“We will collect and analyze data that will inform prevention and treatment services,” Adams said. “We’re shifting chronic pain and opioid care in communities to quickly offer solutions.”

The center will also develop data related resources, education and training related to substance use, pain management and the reduction of opioid overdoses.

According to the CDC, there was a 29% increase in drug overdose deaths in the U.S.  in 2020, and nearly 75% of those deaths involved an opioid.

“Given the scope of the opioid crises, which was only exacerbated by the COVID-19 pandemic, it’s imperative that we improve and create new prevention strategies,” Adams said. “The funding will create the infrastructure for rapid intervention.”


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International

How They Convinced Trump To Lock Down

How They Convinced Trump To Lock Down

Authored by Jeffrey A. Tucker via Brownstone Institute,

An enduring mystery for three years is how…

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How They Convinced Trump To Lock Down

Authored by Jeffrey A. Tucker via Brownstone Institute,

An enduring mystery for three years is how Donald Trump came to be the president who shut down American society for what turned out to be a manageable respiratory virus, setting off an unspeakable crisis with waves of destructive fallout that continue to this day. 

Let’s review the timeline and offer some well-founded speculations about what happened. 

On March 9, 2020, Trump was still of the opinion that the virus could be handled by normal means. 

Two days later, he changed his tune. He was ready to use the full power of the federal government in a war on the virus. 

What changed? Deborah Birx reports in her book that Trump had a friend die in a New York hospital and this is what shifted his opinion. Jared Kushner reports that he simply listened to reason. Mike Pence says he was persuaded that his staff would respect him more. No question (and based on all existing reports) that he found himself surrounded by “trusted advisors” amounting to about 5 or so people (including Mike Pence and Pfizer board member Scott Gottlieb)

It was only a week later when Trump issued the edict to close all “indoor and outdoor venues where people congregate,” initiating the biggest regime change in US history that flew in the face of all rights and liberties Americans had previously taken for granted. It was the ultimate in political triangulation: as John F. Kennedy cut taxes, Nixon opened China, and Clinton reformed welfare, Trump shut down the economy he promised to revive. This action confounded critics on all sides. 

A month later, Trump said his decision to have “turned off” the economy saved millions of lives, later even claiming to have saved billions. He has yet to admit error. 

Even as late as June 23rd of that year, Trump was demanding credit for having followed all of Fauci’s recommendations. Why do they love him and hate me, he wanted to know. 

Something about this story has never really added up. How could one person have been so persuaded by a handful of others such as Fauci, Birx, Pence, and Kushner and his friends? He surely had other sources of information – some other scenario or intelligence – that fed into his disastrous decision. 

In one version of events, his advisors simply pointed to the supposed success of Xi Jinping in enacting lockdowns in Wuhan, which the World Health Organization claimed had stopped infections and brought the virus under control. Perhaps his advisors flattered Trump with the observation that he is at least as great as the president of China so he should be bold and enact the same policies here. 

One problem with this scenario is timing. The Oval Office meetings that preceded his March 16, 2020, edict took place the weekend of the 14th and 15th, Friday and Saturday. It was already clear by the 11th that Trump was ready for lockdowns. This was the same day as Fauci’s deliberately misleading testimony to the House Oversight Committee in which he rattled the room with predictions of Hollywood-style carnage. 

On the 12th, Trump shut all travel from Europe, the UK, and Australia, causing huge human pile-ups at international airports. On the 13th, the Department of Health and Human Services issued a classified document that transferred control of pandemic policy from the CDC to the National Security Council and eventually the Department of Homeland Security. By the time that Trump met with Fauci and Birx in that legendary weekend, the country was already under quasi-martial law. 

Isolating the date in the trajectory here, it is apparent that whatever happened to change Trump occurred on March 10, 2020, the day after his Tweet saying there should be no shutdowns and one day before Fauci’s testimony. 

That something very likely revolves around the most substantial discovery we’ve made in three years of investigations. It was Debbie Lerman who first cracked the code: Covid policy was forged not by the public-health bureaucracies but by the national-security sector of the administrative state. She has further explained that this occurred because of two critical features of the response: 1) the belief that this virus came from a lab leak, and 2) the vaccine was the biosecurity countermeasure pushed by the same people as the fix. 

Knowing this, we gain greater insight into 1) why Trump changed his mind, 2) why he has never explained this momentous decision and otherwise completely avoids the topic, and 3) why it has been so unbearably difficult to find out any information about these mysterious few days other than the pablum served up in books designed to earn royalties for authors like Birx, Pence, and Kushner. 

Based on a number of second-hand reports, all available clues we have assembled, and the context of the times, the following scenario seems most likely. On March 10, and in response to Trump’s dismissive tweet the day before, some trusted sources within and around the National Security Council (Matthew Pottinger and Michael Callahan, for example), and probably involving some from military command and others, came to Trump to let him know a highly classified secret. 

Imagine a scene from Get Smart with the Cone of Silence, for example. These are the events in the life of statecraft that infuse powerful people with a sense of their personal awesomeness. The fate of all of society rests on their shoulders and the decisions they make at this point. Of course they are sworn to intense secrecy following the great reveal. 

The revelation was that the virus was not a textbook virus but something far more threatening and terrible. It came from a research lab in Wuhan. It might in fact be a bioweapon. This is why Xi had to do extreme things to protect his people. The US should do the same, they said, and there is a fix available too and it is being carefully guarded by the military. 

It seems that the virus had already been mapped in order to make a vaccine to protect the population. Thanks to 20 years of research on mRNA platforms, they told him,  this vaccine can be rolled out in months, not years. That means that Trump can lock down and distribute vaccines to save everyone from the China virus, all in time for the election. Doing this would not only assure his reelection but guarantee that he would go down in history as one of the greatest US presidents of all time. 

This meeting might only have lasted an hour or two – and might have included a parade of people with the highest-level security clearances – but it was enough to convince Trump. After all, he had battled China for two previous years, imposing tariffs and making all sorts of threats. It was easy to believe at that point that China might have initiated biological warfare as retaliation. That’s why he made the decision to use all the power of the presidency to push a lockdown under emergency rule. 

To be sure, the Constitution does not allow him to override the discretion of the states but with the weight of the office complete with enough funding and persuasion, he could make it happen. And thus did he make the fateful decision that not only wrecked his presidency but the country too, imposing harms that will last a generation. 

It only took a few weeks for Trump to become suspicious about what happened. For weeks and months, he toggled between believing that he was tricked and believing that he did the right thing. He had already approved another 30 days of lockdowns and even inveighed against Georgia and later Florida for opening. He went so far as to claim that no state could open without his approval. 

He did not fully change his mind until August, when Scott Atlas revealed the whole con to him. 

There is another fascinating feature to this entirely plausible scenario. Even as Trump’s advisors were telling him that this could be a bioweapon leaked from the lab in China, we had Anthony Fauci and his cronies going to great lengths to deny it was a lab leak (even if they believed that it was). This created an interesting situation. The NIH and those surrounding Fauci were publicly insisting that the virus was of zoonotic origin, even as Trump’s circle was telling the president that it should be regarded as a bioweapon. 

Fauci belonged to both camps, which suggests that Trump very likely knew of Fauci’s deception all along: the “noble lie” to protect the public from knowing the truth. Trump had to be fine with that. 

Gradually following the lockdown edicts and the takeover by the Department of Homeland Security, in cooperation with a very hostile CDC, Trump lost power and influence over his own government, which is why his later Tweets urging a reopening fell on deaf ears. To top it off, the vaccine failed to arrive in time for the election. This is because Fauci himself delayed the rollout until after the election, claiming that the trials were not racially diverse enough. Thus Trump’s gambit completely failed, despite all the promises of those around him that it was a guaranteed way to win reelection.

To be sure, this scenario cannot be proven because the entire event – certainly the most dramatic political move in at least a generation and one with unspeakable costs for the country – remains cloaked in secrecy. Not even Senator Rand Paul can get the information he needs because it remains classified. If anyone thinks the Biden approval of releasing documents will show what we need, that person is naive. Still, the above scenario fits all available facts and it is confirmed by second-hand reports from inside the White House. 

It’s enough for a great movie or a play of Shakespearean levels of tragedy. And to this day, none of the main players are speaking openly about it. 

Jeffrey A. Tucker is Founder and President of the Brownstone Institute. He is also Senior Economics Columnist for Epoch Times, author of 10 books, including Liberty or Lockdown, and thousands of articles in the scholarly and popular press. He speaks widely on topics of economics, technology, social philosophy, and culture.

Tyler Durden Fri, 03/24/2023 - 17:40

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International

Could the common cold give children immunity against COVID? Our research offers clues

Certain immune cells acquired from a coronavirus that causes the common cold appear to react to COVID – but more so in children that adults.

Rawpixel.com/Shuttersock

Why children are less likely to become severely ill with COVID compared with adults is not clear. Some have suggested that it might be because children are less likely to have diseases, such as type 2 diabetes and high blood pressure, that are known to be linked to more severe COVID. Others have suggested that it could be because of a difference in ACE2 receptors in children – ACE2 receptors being the route through which the virus enters our cells.

Some scientists have also suggested that children may have a higher level of existing immunity to COVID compared with adults. In particular, this immunity is thought to come from memory T cells (immune cells that help your body remember invading germs and destroy them) generated by common colds – some of which are caused by coronaviruses.

We put this theory to the test in a recent study. We found that T cells previously activated by a coronavirus that causes the common cold recognise SARS-CoV-2 (the virus that causes COVID) in children. And these responses declined with age.


Read more: Does COVID really damage your immune system and make you more vulnerable to infections? The evidence is lacking


Early in the pandemic, scientists observed the presence of memory T cells able to recognise SARS-CoV-2 in people who had never been exposed to the virus. Such cells are often called cross-reactive T cells, as they stem from past infections due to pathogens other than SARS-CoV-2. Research has suggested these cells may provide some protection against COVID, and even enhance responses to COVID vaccines.

What we did

We used blood samples from children, sampled at age two and then again at age six, before the pandemic. We also included adults, none of whom had previously been infected with SARS-CoV-2.

In these blood samples, we looked for T cells specific to one of the coronaviruses that causes the common cold (called OC43) and for T cells that reacted against SARS-CoV-2.

We used an advanced technique called high-dimensional flow cytometry, which enabled us to identify T cells and characterise their state in significant detail. In particular, we looked at T cells’ reactivity against OC43 and SARS-CoV-2.

We found SARS-CoV-2 cross-reactive T cells were closely linked to the frequency of OC43-specific memory T cells, which was higher in children than in adults. The cross-reactive T cell response was evident in two-year-olds, strongest at age six, and then subsequently became weaker with advancing age.

We don’t know for sure if the presence of these T cells translates to protection against COVID, or how much. But this existing immunity, which appears to be especially potent in early life, could go some way to explaining why children tend to fare better than adults with a COVID infection.

A little boy sleeps with a teddy bear.
Children are less likely to get very sick from COVID than adults. Dragana Gordic/Shutterstock

Some limitations

Our study is based on samples from adults (26-83 years old) and children at age two and six. We didn’t analyse samples from children of other ages, which will be important to further understand age differences, especially considering that the mortality rate from COVID in children is lowest from ages five to nine, and higher in younger children. We also didn’t have samples from teenagers or adults younger than 26.

In addition, our study investigated T cells circulating in the blood. But immune cells are also found in other parts of the body. It remains to be determined whether the age differences we observed in our study would be similar in samples from the lower respiratory tract or tonsil tissue, for example, in which T cells reactive against SARS-CoV-2 have also been detected in adults who haven’t been exposed to the virus.


Read more: Colds, flu and COVID: how diet and lifestyle can boost your immune system


Nonetheless, this study provides new insights into T cells in the context of COVID in children and adults. Advancing our understanding of memory T cell development and maturation could help guide future vaccines and therapies.

Marion Humbert received funding from KI Foundation for Virus Research (Karolinsk Institutet, Sweden) and Läkare mot AIDS (Sweden).

Annika Karlsson receives funding from the Swedish Research Council (Dnr 2020-02033), CIMED project grant, senior (Dnr: 20190495), and Karolinska Institutet (Dnr: 2019-00931 and 2020-01599).

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