It’s unusual for the expert opinions on an issue to range all the way from zero to 100%.
Economists using an instrumental variable approach found that digital piracy did not hurt record sales in the 2000’s. Hammond (2014) found, incredibly, that file-sharing increased record sales. The picture above is of an article critiquing the Oberholzer-Gee and Strumpf (2007) conclusion that was published by a top journal.
Liebowitz reports that music industry professionals believed that digital piracy was the primary or complete cause of the decline of record sales. One would think that industry insiders have accurate data on the problem and a decent mental model relating the variables together.
The estimated effect of music file-sharing ranged from helping music sales to completely eliminating them. Where else can we find so much disagreement on the answer to a narrow empirical question?
Between 1850 and 1910, most US censuses asked whether an individual was deaf. There were four alternative descriptions among the combinations of deafness and dumbness. Seems straightforward enough. The problem is that these aren’t discrete categories, they’re continuous. That is, one’s ability to hear can be zero, very good, bad, or just middling. What constitutes the threshold for deafness? In practice, it was the discretion of the enumerator. Understandably, there was a lot of variation in judgement from one enumerator to another. A lot of older people were categorized as deaf, even if they had some hearing loss.
I recently bought a used desktop computer for what seemed like next to nothing. $240 for a machine more powerful than my much-more-expensive 2019 MacBook Pro, most notably due to its 32GB of RAM. Desktops have always been cheaper than equivalently powerful laptops, Windows computers cheaper than Macs, and used computers cheaper than new, so this isn’t totally shocking. But the extent of the difference still surprised me. For instance, buying a new desktop from Dell with similar specs to the used one I just got would cost $1399.
So why is the used discount so big right now? My guess is that its one more knock-on effect of work-from-home. Remote work has been the most persistent change from Covid, and there’s been a huge decline in the demand for office space, with occupancy rates still half of pre-Covid levels.
This means that offices are on sale relative to their pre-Covid prices. Office REITs are down 37% over the past year even after the Covid-induced drop of the previous two years. So it makes sense that all sorts of office equipment is on sale too. Offices tend to be full of employer-owned desktop computers, but when employees work from home they typically use their own machine or a company laptop. That means less demand for office desktops going forward, and a big overhang of existing office desktops that are being under-used. Employers realizing this may just sell them off cheap. Several things about the refurbished desktop I bought, such as its Windows Pro software, indicate that it used to be in an office.
Recently I was watching a lecture by historian Marcus Witcher which addressed the treatment of African Americans in the Jim Crow era. Witcher mentioned the “pig laws,” which were severe legal punishments given to Blacks in the South for what used to be petty crimes. Such as stealing a pig. He mentioned that the fines could be anywhere from $100 to $500, and then he asked me directly: how much is $100 adjusted for inflation today?
My initial, immediate answer was about $3,000. That turns out to be almost exactly correct for around 1880. But the more I thought about it, the more I realized that this wasn’t a satisfactory answer. We were trying to put $100 from a distant past year in context to understand how much of a burden this was for African Americans at the time. Does knowing that adjusted for inflation it’s about $3,000 give us much context?
So much has been happening in the banking world it is a little hard to keep track of it. See recent articles here by fellow bloggers Mike Makowsky, Jeremy Horpedahl, and Joy Buchanan. Here is a quick guide to all the drama.
Credit Suisse Takeover by UBS
Perhaps the biggest, newest news is a shotgun wedding between the two biggest Swiss banks announced over the weekend. Credit Suisse is a huge, globally significant bank that has suffered from just awful management over the last decade. Its missteps are a tale in itself. Its collapse would be an enormous hit to the Swiss financial mystique, and would tend to destabilize the larger western financial system. So the Swiss government strong-armed a takeover of Credit Suisse by the other Swiss bank behemoth, UBS, in an all-stock transaction. The government is providing some funding, and some guarantees against losses and liability. An unusual aspect of this deal is that Credit Suisse shareholders will get some value for their stock, but a whole class of Credit Suisse bonds Is being written down to zero. Usually bond holders have strong priority over stockholders, so this may make it more difficult for banks to sell unsecured bonds hereafter.
Silicon Valley Bank Collapse: Depositors Protected
The Silicon Valley Bank (SVB) collapse is old news by now. The mismanagement there is another cautionary tale: despite having a flighty tech/venture capital deposit base, management greedily reached for an extra 0.5% or so yield by putting assets into longer-term bonds that were vulnerable to a rise in interest rates instead of into stable short-term securities.
A key step back from the brink here was the feds coming to the rescue of depositors, brushing aside the existing $250,000 limit on FDIC guarantees. That was an important step, otherwise large depositors would stampede out of all the regional banks and take their funds to the few large banks that are in the too-big-to-fail category. It is true that this new level of guarantee encourages more moral hazard, since depositors can now be more careless, but the alternative to guaranteeing these deposits (i.e. the collapse of regional banks) was just too awful. Bank shareholders and most bondholders were wiped out. Presumably that will send a message to the investing community of the importance of risk management at banks.
The actual disposition of the business parts of SVB are still being worked out. The feds originally tapped the big, well capitalized banks to see if one of them would take over SVB as a going concern. That would have been a nice, clean, thorough resolution. But the big banks all declined. I suspect the actual responses in private were unprintable. Here’s why: in the 2008 banking crisis, the Obama-Biden administration went to the big banks and encouraged them to take over failing institutions like Countrywide Mortgage, who among other things had made arguably predatory loans to subprime borrowers who had poor prospects to keep up with the mortgage payments. The Obama administration’s Department of Justice promptly turned around and very aggressively prosecuted these big banks for the sins of the prior institutions. J. P. Morgan ended up paying something like 13 billion and Bank of America paid 17 billion. So when today’s Biden administration reached out to these big banks this month to see about taking over SVB, they got no takers.
Now the assets of SVB (renamed Silicon Valley Bridge Bank) are getting auctioned off, perhaps piecemeal, but how exactly that happens does not seem so critical.
Signature Bank: Shut Down, But Sold Off Intact
Crypto-friendly Signature Bank was shuttered by New York State officials on Sunday, March 12, making this the third largest (SVB was the second largest) bank failure in U.S. history. Forbes gives the whole story. At the end of last year, Signature had over $110 billion in assets and $88 billion in deposits. Spooked by Signature’s similarities to failed banks SVB and Silvergate, customers rushed to withdraw deposits, which the bank could not honor without selling securities at huge losses. As with SVB, the feds had the FDIC insure all deposits of all sizes at Signature.
Unlike SVB, Signature has received a bid for the whole business, from New York Community Bancorp’s subsidiary Flagstar Bank. Flagstar will take over most deposits and loans and other assets, and operate Signature Bank’s 40 branches.
First Republic: Teetering on The Brink, Propped Up by Banking Consortium
First Republic is in a somewhat different class than these other troubled institutions. Its overall practices seem reasonable, in terms of equity and assets. However, it caters to a wealthy clientele in the Bay Area, with a lot of accounts over the $250,000 threshold. In the absence of a rapid and decisive move by Congress to extend FDIC protection to all deposits at all banks, somehow (I haven’t tracked what started the stampede) depositors got to withdrawing huge amounts (like $70 billion) last week. This was a classic “run on the bank.” That would stress any bank, despite decent risk management. Once confidence is lost, it’s game over, since there are always alternative places to park one’s money. Ratings agencies downgraded First Republic to junk status, and the stock has cratered.
It is in the interest of the broader banking industry to forestall yet another collapse. If folks start to generally mistrust banks and withdraw deposits en masse, our whole financial system will be in deep trouble. In the case of First Republic, the private sector is trying to prop up it up, without a government takeover. So far this has mainly taken the form of depositing some $30 billion into First Republic, as deposits (not loans or equity), by a consortium of eleven large U.S. banks led by J. P. Morgan. This is was a quick and fairly unheroic intervention, since in the event of liquidation, depositors (including this consortium) have the highest claim on assets. This intervention will probably prove insufficient. Two potential outcomes would be a big issuance of stock to raise capital (which would dilute existing shareholders), or some large bank buying First Republic. The stock rose today on reports that Morgan’s Jamie Dimon was talking with other big banks about taking an equity stake in First Republic, possibly by converting some of the $30 billion deposit into equity.
Old News: Silvergate Bank Liquidation
Overshadowed by recent, bigger collapses, the orderly shutdown of the crypto-focused Silvergate Bank is old news. It was two weeks ago (March 8) that Silvergate announced it would shut down and self-liquidate. The meltdown of the crypto financing world led to excessive loss of deposits at Silvergate. Unlike SVB and Signature, it held a lot of its assets in more liquid, short-term securities, so its losses have not been as devastating – – all depositors will be made whole, though shareholders are toast (stock is down from $150 a year ago to $1.68 at Monday’s close).
Warren Buffett To the Rescue?
Banks generally operate on the model of borrow short/lend long: they “borrow” from depositors and buy longer-term securities. Normally, short-term rates are lower than long-term rates, so banks can pay out much lower interest on their deposits than they receive on their bond/loan investments. With the Fed’s rapid increases in short-term rates this past year, however, the rate curve is heavily inverted, which is disastrous for borrow short/lend long. Fortunately for banks, many depositors are too lazy to do what I have done, which is to move most of my immediate-need money out of bank accounts (paying maybe 1%) and into T-bills and money market funds paying 4-5%.
All this churn goes to highlight an inherent fragility of banks: there is typically a maturity mismatch between a bank’s deposits/liabilities (which are short-term and can be withdrawn at any time) and its assets (longer-term bonds it purchases, and loans that it makes which can be difficult to quickly liquidate). Runs on banks, where if you were late to panic you lost all your deposited money, used to be a real feature of life, as dramatized in classic films Mary Poppins and It’s a Wonderful Life. Eliminating this danger was a key reason for setting up the Federal Reserve system, and in general that has worked pretty well in the past hundred years. Banks in general (see chart above) are now carrying enormous amounts of unrealized losses on their portfolios of bonds and mortgage-backed securities (MBS) due to the increase in market rates. In addition to the existing “discount window” at which banks can borrow, the Fed has set up a new lending facility to help tide banks over if they (as in the case of SVB and Signature) get stressed by having to sell marked-down securities to cover withdrawal of deposits. Also, new measures reminiscent of 2008 were announced to extend dollar liquidity to central banks of other nations.
But when Gotham really has a problem, the Commissioner calls in the Caped Crusader. Warren Buffett has been in touch with administration officials about the banking situation. We wrote two weeks ago about Warren Buffett’s gigantic cash hoard from the float of his Berkshire Hathaway insurance businesses which allows him to quickly make deals that most other institutions cannot. Buffett rode to the rescue of large banks like Bank of America and Goldman Sachs in the 2008-2009 financial crisis. A lot of corporate jets have been noted flying into Omaha from airports near the headquarters of various regional banks. Buffett’s typical playbook in these cases is to have the troubled institution issue a special class of high yielding (with today’s regional banks, think: 9%) preferred stock that he buys, perhaps with privileges to convert into the common stock. That stock would count as much needed equity in the banks’ books.
An unwillingness to guarantee all the deposits would satisfy the desire to penalize businesses and banks for their mistakes, limit moral hazard, and limit the fiscal liabilities of the public sector. Those are common goals in these debates. Nonetheless unintended secondary consequences kick in, and the final results of that policy may not be as intended.
Once depositors are allowed to take losses, both individuals and institutions will adjust their deposit behavior, and they probably would do so relatively quickly. Smaller banks would receive many fewer deposits, and the giant “too big to fail” banks, such as JP Morgan, would receive many more deposits. Many people know that if depositors at an institution such as JP Morgan were allowed to take losses above 250k, the economy would come crashing down. The federal government would in some manner intervene – whether we like it or not – and depositors at the biggest banks would be protected.
In essence, we would end up centralizing much of our American and foreign capital in our “too big to fail” banks. That would make them all the more too big to fail. It also might boost financial sector concentration in undesirable ways.
To see the perversity of the actual result, we started off wanting to punish banks and depositors for their mistakes. We end up in a world where it is much harder to punish banks and depositors for their mistakes.
The problem is, what happens if PNC fails? PNC is the sixth largest bank in the country with over $500 billion in assets. That makes it dramatically smaller than the Big Four banks that are informally labeled “too big to fail” and formally classified as Global Systemically Important Banks (GSIBs).
Tyler wants to see more banks, and not just “Too big to fail” banks. In as many industries as possible, we prefer less concentration. More competition tends to be good for customers and leads to more innovation. Tyler is more comfortable in the messiness that midsize banks cause, or at least he presents that as a necessary evil.
Matt is arguing against more banks, because Silicon Valley Bank wasn’t pre-designated as too big to fail, and yet we are in crisis mode now.
Matt might say that I’m mischaracterizing his argument. Specifically, Matt said that tiny banks are fine because they are small enough for a private company to buy in times to distress. Matt does not explicitly call for fewer banks. However, I think the demise of the mid-size bank would almost certainly result in fewer banks total.
To give a full picture of the arguments being made this week, here’s someone arguing against bailing out SVB.
It covers the years 1990 to 2019 for every US state, and has life expectancy at birth, age 25, and age 65. It includes breakdowns by sex and by race and ethnicity, though the race and ethnicity breakdowns aren’t available for every state and year.
This is one of those things that you’d think would be easy to find elsewhere, but isn’t. The CDC’s National Center for Health Statistics publishes state life expectancy data, but only makes it easily available back to 2018. The United States Mortality DataBase has state life expectancy data back to 1959, but makes it quite hard to use: it requires creating an account, uses opaque variable names, and puts the data for each state into a different spreadsheet, requiring users who want a state panel to merge 50 sheets. It also bans re-sharing the data, which is why the dataset I present here is based on IHME’s data instead.
The IHME data is much more user-friendly than the CDC or USMDB, but still has major issues. By including lots of extraneous information and arranging the data in an odd way, it has over 600,000 rows of data; covering 50 states over 30 years should only take about 1,500 rows, which is what I’ve cleaned and rearranged it to. IHME also never actually gives the most basic variable: life expectancy at birth by state. They only ever give separate life expectancies for men and women. I created overall life expectancy by state by averaging life expectancy for men and women. This gives people any easy number to use, but a simple average is not the ideal way to do this, since state populations aren’t exactly 50/50, particularly for 65 year olds. If you’re doing serious work on 65yo life expectancy you probably want to find a better way to do this, or just use the separate male/female variables. You might also consider sticking with the original IHME data (if its important to have population and all cause mortality by age, which I deleted as extraneous) or the United States Mortality DataBase (if you want pre-1990 data).
Here’s an example of what can be done with the data:
If states are on the red line, their life expectancy didn’t change from 1990 to 2019. If a state were below the red line, it would mean their life expectancy fell, which done did (some state names spill over the line, but the true data point is at the start of the name). The higher above the line a state is, the more the life expectancy increased from 1990 to 2019. So Oklahoma, Mississippi, West Virginia, Kentucky and North Dakota barely improved, gaining less than 1.5 years. On the other extreme Alaska, California, New York improved by more than 5 years; the biggest improvement was in DC, which gained a whopping 9.1 years of life expectancy over 30 years. My initial thought was that this was mainly driven by the changing racial composition of DC, but in fact it appears that the gains were broad based: black life expectancy rose from 65 to 72, while white life expectancy rose from 77 to 87.
As you may have heard, there have been a few bank failures in the US in the past week. This has led ordinary people to start refreshing their memory about exactly what “deposit insurance” is and what it means for them. It has also led regulators, politicians, and economists to start refreshing their memory about the social purpose of deposit insurance, which is to stabilize the banking system. There are lots of aspects of the bank failures and deposit insurance to consider, but I think we can all agree that when ordinary people are thinking about this topic, bad things are going on.
While I can’t find a systematic survey of economists on this topic, my guess is that most economists would agree with the statement “on balance, deposit insurance promotes stability in the financial system.”
But there is a minority view, and one with (in my opinion) considerable historical support. Deposit insurance could potentially be destabilizing, since it has the potential (like any form of insurance) to create moral hazard. By lowering the cost of making mistakes, we would expect more mistakes. The cost need not be lowered all the way to zero for moral hazard to be a problem (bank owners still have some skin in the game), but the cost is certainly lower. These problems may be even more of a threat to the financial system than other areas of life covered by insurance.
That’s the title of a blockbuster new paper by Shikhar Singla. The headline finding is that increased regulatory costs are responsible for over 30% of the increase in market power in the US since the 1990’s. That’s a big deal, but not what I found most interesting.
One big advance is simply the data on regulation. If you want to measure the effect of regulation on different industries, you need to come up with a way to measure how regulated they are. The crude, simple old approach is to count how many pages of regulation apply to a broad industry. The big advance of Mercatus’ RegData was to use machine learning to identify which specific industry is being discussed near “restrictive words” in the Code of Federal Regulation that indicate a regulatory restriction is being imposed. But not all regulatory words (even restrictive ones) are created equal; some impose very costly restrictions, most impose less costly restrictions, and some are even deregulatory. Singla’s solution is to take the government’s estimates of regulatory costs and apply machine learning there:
This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period.
The government’s estimates of the costs are of course imperfect, but almost certainly add information over a word-count based approach. Both approaches agree that regulation has increased dramatically over time. How does this affect businesses? Here’s what’s highlighted in the abstract:
We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in con- centration is associated with lower elasticity of entry with respect to Tobin’s Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms
More from the paper:
an average small firm faces an average of $9,093 per employee in our sample period compared to $5,246 for a large firm
a 100% increase in regulatory costs leads to a 1.2%, 1.4% and 1.9% increase in the number of establishments, employees and wages, respectively, for large firms, whereas it leads to 1.4%, 1.5% and 1.6% decrease in the number of establishments, employees and wages, respectively for small firms when compared within the state-industry-time groups. Results on employees and wages provide evidence that an increase in regulatory costs creates a competitive advantage for large firms. Large firms get larger and small firms get smaller.
The fact that large firms benefit while small firms are harmed is what drives the increase in concentration and market power.
What I like and dislike most about this paper is the same thing: its a much better version of what Diana Thomas and I tried to do in our 2017 Journal of Regulatory Economics paper. We used RegData restriction counts to measure how regulation affected the number of establishments and employees by industry, and how this differed by firm size. I wish I had thought of using published regulatory cost measures like Singla does, but realistically even if I had the idea I wouldn’t have had the machine learning chops to execute it. The push to quantify what “micro” estimates mean for economy-wide measures is also excellent. I hope and expect to see this published soon in a top-5 economics journal.
Warren Buffett is referred to as “the legendary investor Warren Buffett” or “the sage of Omaha”. The success of his Berkshire Hathaway fund is remarkable. He is also a pretty nice guy, and every year writes (with help, I’m sure) a letter describing the activities of his fund, along with general observations on investing and the economy. His letter covering 2022 was published two weeks ago.
Buffett noted that he and his team invest in companies in two ways: by buying shares to become a partial “owner” along with thousands of other shareholders, and also by buying ownership of the whole company. They aim to hold American companies that have a good business model, and will keep growing profits for years or decades. They look for great businesses at great prices, but they would rather buy a great business at a good price, than to buy a (merely) good business at a great price.
He was refreshingly honest about his overall stock picking record:
In 58 years of Berkshire management, most of my capital-allocation decisions have been no better than so-so. In some cases, also, bad moves by me have been rescued by very large doses of luck. (Remember our escapes from near-disasters at USAir and Salomon? I certainly do.) Our satisfactory results have been the product of about a dozen truly good decisions – that would be about one every five years – and a sometimes-forgotten advantage that favors long-term investors such as Berkshire.
In 1994 they bought a then-huge stake ($ 1.3 billion) in Coca-Cola, and another $1.3 billion stake in American Express. As it turned out, these two companies had the staying power that Buffet had anticipated, and have grown enormously in value over the past three decades.
In addition to their wholesome stock-picking philosophy, the “secret sauce” of Berkshire Hathaway is having the available funds to make those great investments in those great companies. These funds came large from the “float” from their insurance businesses. In Buffett’s words:
In 1965, Berkshire was a one-trick pony, the owner of a venerable – but doomed – New England textile operation. With that business on a death march, Berkshire needed an immediate fresh start. Looking back, I was slow to recognize the severity of its problems. And then came a stroke of good luck: National Indemnity became available in 1967, and we shifted our resources toward insurance and other non-textile operations.
The insurance business is interesting, in that clients pay in money “now”, but it does not get paid out until “later”. The insurance company has the money to own and manage until there is some claim event (e.g., someone dies or gets their home flooded) perhaps many years later. The traditional, conservative way for insurance companies to manage this float money was to invest it in low-paying but ultra-safe investment grade bonds.
Buffett’s key secret to success was to realize that he could invest at least part of these float funds in stocks, which would (hopefully!) over time make much more money than bonds. That gave him the cash to make those great investments in Coke and Amex. And his fund continues to have billions in hand to make strategic investments. He has made a bundle bailing out good companies that fell into short term difficulties. In his words:
Berkshire’s unmatched financial strength allows its insurance subsidiaries to follow valuable and enduring investment strategies unavailable to virtually all competitors. Aided by Alleghany, our insurance float increased during 2022 from $147 billion to $164 billion. With disciplined underwriting, these funds have a decent chance of being cost-free over time. Since purchasing our first property-casualty insurer in 1967, Berkshire’s float has increased 8,000-fold through acquisitions, operations and innovations. Though not recognized in our financial statements, this float has been an extraordinary asset for Berkshire.
You, too, can participate in Buffett’s investing magic, by buying shares in Berkshire Hathaway. The stock symbol is BRK.B. (Disclosure: I own a few shares). Buffett has been skeptical of flashy tech stocks, and so BRK.B’s performance lagged the S&P 500 fund SPY in 2020-2021, but over the long term Berkshire (orange line in chart below) has crushed the S&P: