How Can Cryptocurrency Accounts Pay Such High Interest?

As noted last week, I am happily receiving 9% interest in my new crypto account at BlockFi. How can they do that? The short answer is that BlockFi lends out my holdings to other parties, who pay somewhat more than 9% interest to BlockFi. This model is common to essentially all of the crypto brokers who pay out interest, but I will focus on BlockFi because (a) I have skin in the game there, and (b) they have been fairly transparent about their operations.

On the simplest level, this operates like a plain bank savings account does. A bank takes in funds from depositors, and (to oversimplify) lends those funds out to borrowers. The bank then pays to its depositors a portion of the interest it receives from its borrowers. Up until the last few years, this bank savings account model worked pretty well;  a depositor might receive something like 2-3% interest on a savings account or certificate of deposit. More recently, short term rates have been near zero, so depositors get almost nothing in a bank savings account.

As noted earlier, BlockFi pays up to 4.5% interest on Bitcoin and 5% on Ethereum. These are leading, high volume coins that are widely used in decentralized finance (defi). Here is how BlockFi describes the parties to which it lends (mainly) Bitcoin:

Who Borrows Crypto?

BlockFi works with institutional counterparties for trading and lending cryptocurrency. These counterparties look to us to help them provide liquidity for their businesses. But who are some of these borrowers?

( 1 ) Traders and investment funds who see a fragmented marketplace and discover arbitrage trading opportunities. Arbitrageurs need to borrow crypto in order to close mispricing between exchanges or dispersed markets. Similarly, margin traders need to borrow in order to execute their trading strategies. This is a simple example, but it demonstrates how arbitrage and margin trading activities facilitate price discovery, which is an essential component of developed markets.

( 2 ) Over the counter (OTC) market makers make money by connecting buyers and sellers who do not want to transact over public exchanges. OTC desks need to keep inventory on-hand to meet their client demand. Owning crypto outright is capital intensive and comes with the attendant risks of price fluctuations. Instead, they may prefer to borrow inventory in order to facilitate transactions. Liquidity is another essential component to healthy markets.

( 3 ) Businesses that require an inventory of crypto to provide liquidity to clients. This bucket includes companies like crypto ATMs. These businesses also need to be able to support withdrawals while keeping the vast majority of their crypto assets in cold storage. The liquidity we provide them helps with these basic and important functions.

A key piece of this lending is to require that the counterparty post adequate collateral for the loans. This is somewhat similar to a bank lending you money to buy a house, with the house as collateral for your loan. If you lose your job and cannot pay back the loan, the bank has the right to sell your house to recovery its money. Similarly, BlockFi wants to ensure that if something goes sour with their loan of your Bitcoin, they can get their funds back and make your account whole. Obviously, BlockFi customers like me are relying on BlockFi to manage this properly and to minimize lending losses. BlockFi goes on to reassure us:

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Something is going on with shoplifting

Organized shoplifting mobs! Retailers claiming increases in shoplifting! Journalists claiming its an overstated numbers grift! Other journalists saying we should ignore shoplifting because corporate theft! The conversation about shoplifting is often hysterical and occasionally stupid, but that doesn’t mean something isn’t actually happening. Some of the facts seem clear, others murkier, and the underlying causes, like everything during this pandemic, are no doubt complex and uncertain. Let’s see if we can organize our thoughts a bit.

What we know

Shoplifting has been on the rise across the United States, with increasing theft of both staples for survival and the goods most easily resold on the black market. More specifically, and perhaps even more certainly, a still wealthy San Francisco, where one would expect retailers to desperately want a presence, can only seem to watch as its retailers flee. CVS is out. Walgreens is out. One Target it out (but not the biggest one). And the reason they claim is not commercial real estate overhead costs or declining customer bases, but an overwhelming increase in shoplifting (or what the retail industry used to call “inventory shrink”). While obviously not the whole story, the effective decriminalization of theft under $950 in San Francisco seems a key component. It doesn’t take any clever or subtle theorizing to expect that if the cost of theft under a certain threshold is radically lowered, then all you have to do is disaggregate your theft events across time and people to yield a sufficiently lucrative use of time (especially for those who are struggling or already carry the far weightier burden of a felony record). You can’t lower the opportunity cost of labor (less jail time) in a field of endeavor (boosting consumer goods) and pretend to be shocked when supply increases (more theft).

What we think

I am sure there is no shortage of “greedy corporations are abandoning American cities” and other malice-based theories, but those aren’t particularly useful theories. Retailers want customers and cities have a lot lot of them. So the first possibility is that they are simply telling us, and their shareholders, the truth– theft has reduced the profitability of stores such that the optimal decision is to close the doors. It would be a pretty shocking development to look back one day and realize that shoplifting was what closed the book on brick and mortar retail. Not Amazon or delivery drones, but the favored hobby of bored delinquents and subsidy of struggling families.

To those ends, though, a meteoric rise in shoplifting nonetheless feels, if not convenient, then incomplete as an explanation. CVS isn’t just closing in San Francisco, it’s closing 900 stores and moving to a new “store format”. Perhaps the better way of framing these closures isn’t a “crime wave of shoplifting” but rather more evidence that the brick and mortar retail industry is incredibly fragile, where any unforeseen increase in costs immediately threatens profitability. In a composite of shoplifting, online competition, the unabated growth of Costco and other wholesale clubs, and the rise in reservation wages of labor all across the country, which story would you want to emphasize to your shareholders as you close shops in urban centers? That you can’t compete? That you can’t afford labor? Or that you are being forced out by the crumbling of civilization into Mad Max dens of wayward lawlessness? At least the last one holds out hope that your business model isn’t wholly obsolete.

Still, people definitely seem to be stealing a lot of stuff, and that just creates one more cost advantage for online competitors and venues that require membership for admission. Things are changing, perhaps at an accelerated rate thanks to the pandemic and it’s accompanying bundle of policy responses. When considering fundamental change, observation of chaos rarely offers evidence to the contrary.

What can or should we do?

There are lots of things we should decriminalize. Lots. But I am extremely confident that theft is not one of them. The consequences are obvious, and in the short run will be felt almost exclusively by the poorest, who depend on local retailers, particularly those on the public transportation routes they take to work. Further, this is a problem that can metastasize as people don’t just supplement their incomes with theft, but specialize in it. It will hollow out the largest retailers and the smallest bodegas. It will change the the entire structure of physical marketplaces. It will change how people interact with core components of our welfare system. It will poison another relationship, this time between seller and customer, where people are increasingly viewed as a threat.

So what should we do? Desperate people stealing rice and other staples is one more argument for an unconditional universal basic income. People opting for black market income is one more argument for wage subsidies to increase relative attractiveness of wages in the legal market. And people stealing because the price of getting caught approaches zero? That’s an argument for raising the price of theft. Not to new and cruel heights, but to the levels they were at before i.e. high enough that theft is nothing but a last resort. A very last one.

Book Review: Cronyism: Liberty versus Power in Early America, 1607–1849

For the past few weeks, economist Patrick Newman has been doing the rounds for his new book (i.e. in the title of this blog post) on American economic history from 1607 to 1849. Well, its not only about American economic history. Its a bit more about the institutional history of the United States before 1850 and how it relates to economic history. It is an amazing book. Unfortunately, I expect many economic historians to ignore or fail to notice it. I hope that this blog post will at least reduce the likelihood of this happening because Newman’s book holds strong explanatory power if one is interested in the link between growth and institutions.

Newman’s argument is actually quite simple. First, there are two broadly-defined camps: the forces of liberty and the forces of power. Already, some may balk at this dichotomy but I would advise them not to. There are many reasons to keep going. The first is that It invokes an older tradition in historical studies that starts with Lord Acton and has been continued by numerous historians on the left and right. The other reasons become evident as one moves along in the book.

The forces of liberty are those that seek to constrain the state and the exercise of power. The forces of power, for their part, are those that seek to be empowered by a strong, capable and relatively unconstrained state. The forces of power, however, invite cronyism because the empowerment also permits personal aggrandizement (e.g. legally protected monopolies such as charters, tariffs, subsidies, grants, patronage).

The founding of the United States was, according to Newman, a battle between both forces with the British being the forces of power. After the Revolution, the forces of power continued inside the Federalist Forces — who basically dominated the constitutional convention of 1787 and the first Congress. Acting a de facto (because that is the title I give him) heir to Murray Rothbard, Newman adopts the position that the foundation of the US was in fact a rent-seeking bargain thanks to the federalists forces (Newman notably edited the lost volume of Rothbard’s Conceived in Liberty on the early republic).

After that, antifederalists and republicans coalesced into a working coalition that reinterpreted the constitution in a way that backfired against the Federalists and led to the Jeffersonian revolution of 1800. Important reforms, which Newman credits as being beneficial to living standards, were adopted. However, the Jeffersonians rapidly became corrupted by power. And here is the second reason to not balk at Newman’s dichotomy of the forces of power/liberty: people can move between camps. In other words, ideological commitment is not inelastic. Some in one camp or the other can switch when the rewards to do so change. However, the key point that Newman makes is that commitment to the forces of liberty is far more elastic than the commitment to the forces of power (which is more inelastic). The Jeffersonians’ commitment to liberty waned and they eventually enacted relatively similar policies to those of the federalists. They too engaged in cronyism. The same ebb and flow reoccurred later with the Jacksonians.

And here comes the third reason not to balk at Newman’s dichotomy: it actually hold pretty decent explanatory power. One common argument among financial and economic historians is that the United States may have sounded like a Jeffersonian project but the policies of the Early Republic and Antebellum were distinctly Hamiltonian (i.e. Federalist). To be sure, there is some evidence to that effect — which is what someone could retort to Newman. However, the old adage that “one in a glass house should not throw bricks” applies here. Revisions to the historical estimates of living standards have gradually swung in favor of the predictions associated with Newman’s model of the forces of power/liberty.

Consider this new article in Historical Methods by Frank Garmon (of Christopher Newport University). Garmon took issue with data from 1798 used by many scholars. In 1798, Congress introduced the a direct property tax to prepare for the possibility of a war with France. As Garmon succintly summarizes: “The law creating the tax consisted of three elements: a flat tax on slaves per head, a progressive tax on houses with rates escalating based on value, and a proportional tax on land based on value to make up the difference in each state’s obligation”. Other scholars, such as my co-author Peter Lindert and Jeffrey Williamson, argued that these features invited corruption during the assessing of tax liabilities. This was particularly true in the south because of the flat tax per slave. Thus, if one tries to use the tax data to estimate economic activity circa 1800, one has to augment it to some degree to reflect the geographically varying levels of corruption. Garmon finds that corruption was not an issue. The disparities pointed out by others (which made sense at first glance) could be largely explained by normal economic factors such as population density (which would affect land valuations etc.). Thus, Garmon argues that there is no need to deflate. As a result, he finds that incomes were roughly 5% lower in the southern states in 1800 (a proportion that would have been smaller in northern states).

Why is Garmon’s result relevant to Newman’s claim? Because any lowering of the 1800-level of income is going to increase the rate of growth from there to 1840 when the commonly-used estimates (produced by R.A. Easterlin) become available. Any increasing in that rate of growth goes in favor of Newman’s model because his prediction because the era from 1800 to 1840 is predominantly occupied with pro-liberty forces (even though there are ebbs and flows).

I am not in full agreement with Newman’s book and his Rothbardian narrative (I am much less fond of Rothbard than he is notably because of the tendency for villains and heroes to exist in his narrative). However, the reality is that Newman’s description (and the Rothbardian narrative he imports and adapts) holds strong explanatory powers.

Covid-19 & The Federal Reserve

I remember people talking about Covid-19 in January of 2020. There had been several epidemic scare-claims from major news outlets in the decade prior and those all turned out to be nothing. So, I was not excited about this one. By the end of the month, I saw people making substantiated claims and I started to suspect that my low-information heuristic might not perform well.

People are different. We have different degrees of excitability, different risk tolerances, and different biases. At the start of the pandemic, these differences were on full display between political figures and their parties, and among the state and municipal governments. There were a lot of divergent beliefs about the world. Depending on your news outlet of choice, you probably think that some politicians and bureaucrats acted with either malice or incompetence.

I think that the Federal Reserve did a fine job, however. What follows is an abridged timeline, graph by graph, of how and when the Fed managed monetary policy during the Covid-19 pandemic.

February, 2020: Financial Markets recognize a big problem

The S&P begins its rapid decent on February 20th and would ultimately lose a third of its value by March 23rd.  Financial markets are often easily scared, however. The primary tool that the Fed has is adjusting the number of reserves and the available money supply by purchasing various assets. The Fed didn’t begin buying extra assets of any kind until mid-March. There is a clear response by the 18th, though they may have started making a change by the 11th.  One might argue that they cut the federal funds rate as early as the 4th, but given that there was no change in their balance sheet, this was probably demand driven.

https://fred.stlouisfed.org/graph/?g=JYVL
https://fred.stlouisfed.org/graph/?g=JYVy

March, 2020: The Fed Accommodates quickly and substantially.

In the month following March 9th, the Fed increased M2 by 8.3%. By the week of March 21st, consumer sentiment and mobility was down and economic policy uncertainty began to rise substantially – people freaked out. Although the consumer sentiment weekly indicator was back within the range of normal by the end of April, EPU remained elevated through May of 2020. Additionally, although lending was only slightly down, bank reserves increased 71% from February to April. Much of that was due to Fed asset purchases. But there was also a healthy chunk that was due to consumer spending tanking by 20% over the same period.

https://fred.stlouisfed.org/graph/?g=JYXj
https://fred.stlouisfed.org/graph/?g=JYYz

In the 18 months prior to 2020, M2 had grown at rate of about 0.5% per month. For the almost 18 months following the sudden 8.3% increase, the new growth rate of M2 almost doubled to about 1% per month. The Fed accommodated quite quickly in March.

April, 2020: People are awash with money

Falling consumption caused bank deposit balances to rise by 5.6% between March 11th and April 8th. The first round of stimulus checks were deposited during the weekend of April 11th. That contributed to bank deposits rising by another 6.7% by May 13th.

By the end of March, three weeks after it began increasing M2, the Fed remembered that it really didn’t want another housing crisis. It didn’t want another round of fire sales, bank failures, disintermediation, collapsed lending, and debt deflation. It went from owning $0 in mortgage-backed securities (MBS) on March 25th to owning nearly $1.5 billion worth by the week of April 1st. Nobody’s talking about it, but the Fed kept buying MBS at a constant growth rate through 2021.

May, 2020 – December, 2021: The Fed Prevents Last-Time’s Crisis

Jerome Powell presided over the shortest US recession ever on record. The Fed helped to successfully avoid a housing collapse, disintermediation, and debt deflation – by 2008 standards. The monthly supply of housing collapsed, but it had bottomed out by the end of the summer. By August of 2021, the supply of housing had entirely recovered. The average price of new house sales never fell. Prices in April of 2020 were typical of the year prior, then rose thereafter. A broader measure of success was that total loans did not fall sharply and are nearly back to their pre-pandemic volumes. After 2008, it took six years to again reach the prior peak. A broader measure still, total spending in the US economy is back to the level predicted by the pre-pandemic trend.

The Fed can’t control long-run output. As I’ve written previously, insofar as aggregate demand management is concerned, we are perfectly on track. The problem in the US economy now is real output. The Fed avoided debt deflation, but it can’t control the real responses in production, supply chains, and labor markets that were disrupted by Covid-19 and the associated policy responses.

What was the cost of the Fed’s apparent success? Some have argued that the Fed has lost some of its political insulation and that it unnecessarily and imprudently over-reached into non-monetary areas. Maybe future Fed responses will depend on who is in office or will depend on which group of favored interests need help. Personally, I’m not so worried about political exposure. But I am quite worried about the Fed’s interventions in particular markets, such as MBS, and how/whether they will divest responsibly.

Of course, another cost of the Fed’s policies has been higher inflation. During the 17 months prior to the pandemic, inflation was 0.125% per month. During the pandemic recession, consumer prices dipped and inflation was moderate through November.  But, in the 16 months since April of 2020, consumer prices have grown at a rate of 0.393% per month – more than three times the previous rate. Some of that is catch-up after the brief fall in prices.

Although people are genuinely worried about inflation, they were also worried about if after the 2008 recession and it never came. This time, inflation is actually elevated. But people were complaining about inflation before it was ever perceptible. The compound annual rate of inflation rose to 7% in March of 2021. But it had been almost zero as recent as November, 2020. That March 2021 number is misleading. The actual change in prices from February to March was 0.567%. Something that was priced at $10 in February was then priced at $10.06 in March. Hardly noticeable, were it not for headlines and news feeds.

What would a Great Reorganization look like?

In our eternal quest to never let go of any effective rhetorical device that can double as a headline, the last 12-18 months have been dubbed The Great Resignation. Within voluntary job separations, a sizable chunk of which appear to be early retirements, many are young people transitioning from low-paying jobs to those that have seen fit to adapt to the labor shortage faster, offering some combination of higher wages, better benefits, or a higher quality of life, often through the channel of relaxed educational or experience prerequisites.

Some, generally from the political left, are framing this as a shift in power from management to labor, particularly for those who hope this can be the moment that pushes unionization back to the forefront. Others, mostly from the political right, are framing this as a catastrophic undercutting of the incentive to work induced by the expanded welfare state. I tend to see these positions as frantic over-optimism or pessimism from those desperate for a sexy political narrative to sell.

I think the closer parallel, in terms of mechanism (not scale), isn’t the Great Depression or the New Deal era that followed, but rather the World War II draft-accelerated entry of women into the workforce. I think what we’re seeing is a massive reorganization of the US labor market. If this half-baked generalization were true, what would it look like?

  1. Education, Training, and Experience reconsidered

My guess is that managers in a range of fields have long had a itch in the back of their minds that they weren’t always hiring the right people. Specifically, they were eliminating large swaths of applicants from the pool of consideration because they lacked the minimum formal education or years of narrowly defined experience. A lot of these requirements, I suspect, existed not as tried and true markers of the subset of optimal candidates, but because they could be routinized through online job applications and human resources triage, largely in an effort to conserve on managerial and administrative time. Combined CYA incentives and other sources of herding behavior both within and across firms (i.e. no one gets fired for only hiring college graduates), these are exactly that kind of sub-optimal practices that can widely embed themselves when an economy is growing, but the labor market is relatively loose, so any suboptimality is lost in the wash.

A negative labor shock, be it a military draft or global pandemic, is exactly the kind of thing that rewards firms that begin hiring from whole strata of previously unconsidered job candidates. Not for nothing, that’s how you end learning all kinds of new things: the relative value of various degrees and training, the cross-applicability of job experience previously treated as irrelevant to an open position, and the marginal products of a firms employment portfolio.

2. Compensation bundles rebalanced

There’s plenty of fuss (rightly so) over the shift towards working from home. Yes, it saves on fixed costs, particularly in cities with sky-high commercial real estate costs, but I suspect the greater impact in the long run will be on the composition of wages+benefits+flexibility in employee compensation bundles, where flexibility is largely a catch-all for the quality of life component associated with any job. Maybe we already knew that health insurance and paid leave were valuable, but I think a lot of employees have discovered they were previously undervaluing the costs of commuting, schedule uncertainty, and existing “on call” for co-workers and superiors. Whether its working from home or as an independent contractor, many people are discovering that recapturing 10 hours a week of the rest of your life is worth a lot more than the wages being foregone. We already know that women are the future, but we also know that women value flexibility in work schedules more than men. A shift towards quality of life in compensation bundles was likely already in the cards, the pandemic just accelerated it.

For firms that have spent the last 20 years burning out the handful of key employees, rewarding their exceptional productivity by turning them into productivity bottlenecks, they are either going to have to find a way to spread the work thinner or recapture those key employees by finding other means of maintaining the quality of their employee lives.

3. The service industry is dead. Long live the service industry?

We’ve been eating on borrowed time. Through the combination of over-priced and over-valued higher education, a gratuitous over-stigmatization of non-violent criminal records, and the employment trap of limited human capital building, but lots of cash in hand, the service industry has been feeding us all on the cheap for a very long time now.

Turns out, though, that the relative frugality of diners has squeezed margins in restaurants razor thin, and has largely come at the expense of servers and kitchen staff. Came at the expense, I should say. I think we’re all going to have find a new normal where outsourcing meal preparation is, at the margin, slightly less of a staple and slightly more of a luxury. I still see Help Needed signs in lots of restaurants, and owners complaining in news stories that “No one wants to work“, but I’m also seeing new employees bring home higher salaries at McDonald’s after 90 days than fine dining cooks in their 3rd year working sauté. Eventually the new equilibrium will be reached, and I predict it’s going to involve higher salaries and better benefits for line cooks, but it’s also going to mean customers are going to have to get over there perceived $28 ceiling on entrees. Also, don’t expect your favorite restaurants to be open on Monday’s and Tuesdays, because it turns out everyone wants to have weekend.

4. The same, but different
What will the labor market look like in 5 years? Forecasting is a fool’s errand, but I never promised anyone I wasn’t a fool. Here’s my best guess:

I don’t expect a revival of unionization, but I do expect that employment will start taking on a lot of the attributes that pro-union people are currently agitating for. There will be more people with 3 and 4-day work weeks, though I suspect those people will be working 10 and 12 hour shifts. I think there will be a lot of flexible office-home work schedules, where firms coordinate their employees around days when everyone is in the office, the rest floating between the office and home as the work dictates. I expect there will be more independent contractors, but unlike previously self-employed people who bounced from contract to contract, they will instead be people who balance a portfolio of employment, with what amounts a small number of long term contracts. Rather than work for one person at a time 40 hours a week, they’ll work for 2 or 3, 8-10 hours each, building up enough firm-specific capital that contracts will last years, even decades, at a time.

I expect kitchens will remain hot, crowded, and loud. I expect chef’s will remain angry and owner’s tight-fisted with every penny. I expect that servers will still finish every shift with sore feet and stories of annoying customers. Maybe even more annoying than before, because those customer’s will be paying 15% more than the prices they already manage to complain about. But it’ll be okay, because everyone in that restaurant is going to be earning a much better living. They’ll have to, because otherwise they’re not coming back.

Car Prices and Quality

Inflation is on everyone’s mind. Everybody freaks out. You cannot do anything about it. As such, lets talk about something mildly related: how price indexes (those that we use to talk about inflation) deal with quality changes.

One big problem when we try to measure the cost of living is that the price information we collect does not reflect the same thing we consume. I know that sentence seems weird. After all, 1$ for a pound of bread is 1$ for a pound a bread. And if prices go up 10%, then the price per pound of bread is 1.10$!

If you think that, you’re wrong. Think about the following example from my native province of Quebec. In the 1990s, Quebec deregulated opening hours for grocery stores. The result was … higher prices at large superstores. Why? Before the reform, stores had shorter hours especially on sundays. This meant that stores were competing with each other on a smaller quality dimension which meant more price-based competition. With deregulation, some consumers were willing to pay slightly higher prices to shop at ungodly hours. What were these consumers consuming? Were they consuming only the breadloafs they bought or were they consuming those loafs and the flexible schedule of the grocery stores? The answer is the latter! Ergo, the change from 1$ per pound to 1.10$ per pound does not mean that the price of bread alone increased — it may have even fallen all else being equal!

So how do you adjust for that? There are many papers on how to do hedonic adjustments (hedonic is the fancy words we use to say “quality-adjusted”) and they are all a pain to read unless you are very familiar with real analysis, set theory and advanced calculus (and even there, its still a pain). Fortunately, I recently found a neat little application from an old econometrics graduate text from the 1960s (see image below) that allows me to teach this to my students (and now, you too!) in an easy-to-get format.

A neat book

The book has a neat chapter by one of the most famous econometricians of the 20th century, Zvi Griliches, titled “Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Change”. In the chapter, Griliches points out that from 1954 to 1960, car prices went up some 20% — well above the overall price index. From 1937 to 1950, prices for cars went up in line with inflation. Taken together, these two facts suggest that the real price of cars stayed constant from 1937 to 1950 and increased to 1960. But that suggestion is wrong Griliches points out because of our aforementioned quality issues. Up until 1960, there were considerable improvement in vehicle quality: better gears, better brakes, more horsepower, safer settings, automatic transmission, hardtops, switching to V-8 engines rather than 6 cylinders engines etc.

How do you account for these quality changes? Griliches simply went about consulting guide books for autobuyers. He collected price data for the cars as well the details regarding quality. And he used this very simple specification where the log of the nominal price is set as a dependent variable.

Griliches’ specification

The vector X is all the quality dimensions he could find (horsepower, shipping weight, length, V-8 engine, hardtop, automatic transmission, power steering, power brakes, compact car). All of these dimensions were statistically significant determinants of the price of cars (with the exception of V-8 engines which was not significant). Then, Griliches assumed that all quality dimensions were “unchanged” from 1954 to 1960 in order to see how prices would have evolved without any changes in quality. The result is the figure below. The blue line depicts the actual prices he collected where you can see the 20% increase to 1960 (which is a 30%+ increase to 1959). The orange line depicts the price holding quality constant. That orange line is unambiguous: quality-constant car prices didn’t change much during the 1950s. Adjusting for inflation during the period suggests a drop in 10% in the real price of a quality-constant car.

Image

Isn’t that a fascinating way to understand what we are actually measuring when we collect prices to talk about inflation? I find this to be an utterly fascinating example (and a useful teaching tool). Okay, I am done, you can go back to freaking out about inflation and how bad the Fed, Bank of Canada, ECB are.

The stakes have never been higher

These two tweets came through my feed today through secondhand channels

I am not suggesting that these two tweets are equivalent. The first is grotesque cosplay, the second a bit of hyperbole (possibly inspired by the first). Rather, I think they are both part of the same democratic mechanism – the belief that there are more votes to be gained from incentivizing turnout of the base rather than persuading those at the margin. The voters in your base have already decided you and your party are a better option than the rival option, so the only obstacle between you and their vote is the opportunity cost of their time relative to their chances of being decisive in the next election. None of this is new – this uncanny astuteness is how 24 of the last 3 failures of the Median Voter Theorem were predicted. If you want the base to show up, you don’t need to persuade them – you need to scare them.

You need people to vote, so you give them big stakes. Of course, mathematically no stakes short of global extinction are big enough to warrant voting in a national election. The thing about stakes, though, is that even short of extinction-level threats, they still increase the value of a vote that absolves your guilt if the other side wins. You can move on with your life because at least you tried.

Episode 1 Halloween GIF by The Simpsons

When you’re trying to bring out the base, stakes are everything. Problem is, people start to catch on when every election somehow manages to be the most important one ever. You need to recruit someone to convince your base that this election is the most important one ever. Someone credible. And that’s what politicians and activists have figured out. The most credible source for the potential terror that only our candidate can hold at bay is the opposition. Not their candidates or campaigns, mind you. Their base.

The most credible way to increase the stakes for your base is the rile up the rage and vitriol of the the opposition’s. If you want to truly convince your voters that the stakes are high, all you have to do is chum the water and let the craziest avatars of your political opposition do the work for you. They’ll wave their guns, call each other “comrade”, insult their religious faith, call them stupid, make veiled threats, make unveiled threats, all of which will make perfectly clear that if we don’t win this next election, these people will win. They will win and have power. They must be stopped.

This is the principal reason there has been such a meteoric rise of professional trolls and hyperbolic “reply-guys”. The trolls, your Tucker Carlson’s and Chapo Trap Houses chum the waters, and then an entire ecosystem of reply-guys respond, quote tweet, and record 30 second CNN/Fox News video commentaries. Politicians have discovered that truly horrific people, and the shrieking dystopia fetishists that swarm them, are amazing at bringing out political support, not through persuasion or direct signaling of group identity, but through the specter of the lunacy of the opposition, and the subtle implication that if you don’t signal your affinity for our group, you are by implication associated with the toxicity of our opposition.

Which is why when these sort of messages show up on social media or television sound bites, you can quickly see that they aren’t propaganda or even fan service. They’re bait.

Bait GIF

And just so you don’t get the wrong impression, I fall for this too. I try not to, but these people are professionals for a reason.

Look at me, promoting this image on social media. They played me like a fiddle. I knew exactly what its goal was, and it still put me in such a despairing rage that the rest of the world had to hear about it.

Just because I’m an economist, and one who studies political economy at that, that doesn’t mean I not still a sucker.

Economic freedom and income mobility

A few weeks ago, my friend James Dean (see his website here, he will soon be a job market candidate and James is good) and I received news that the Journal of Institutional Economics had accepted our paper tying economic freedom to income mobility. I think its worth spending a few lines explaining that paper.

In the last two decades, there has been a flurry of papers testing the relationship between economic freedom (i.e. property rights, regulation, free trade, government size, monetary stability) and income inequality. The results are mixed. Some papers find that economic freedom reduces inequality. Some find that it reduces it up to a point (the relationship is not linear but quadratic). Some find that there are reverse causality problems (places that are unequal are less economically free but that economic freedom does not cause inequality). Making heads or tails of this is further complicated by the fact that some studies look at cross-country evidence whereas others use sub-national (e.g. US states, Canadian provinces, Indian states, Mexican states) evidence.

But probably the thing that causes the most confusion in attempts to measure inequality and economic freedom is the reason why inequality is picked as the variable of interest. Inequality is often (but not always) used as a proxy for social mobility. If inequality rises, it is argued, the rich are enjoying greater gains than the poor. Sometimes, researchers will try to track the income growth of the different income deciles to go at this differently. The idea, in all cases, is to see whether economic freedom helps the poor more than the rich. The reason why this is a problem is that inequality measures suffer from well-known composition biases (some people enter the dataset and some people leave). If the biases are non-constant (they drift), you can make incorrect inferences.

Consider the following example: a population of 10 people with incomes ranging from 100$ to 1000$ (going up in increments of 100$). Now, imagine that each of these 10 people enjoy a 10% increase in income but that a person with an income of 20$ migrates to (i.e. enters) that society (and that he earned 10$ in his previous group). The result will be that this population of now 11 people will be more unequal. However, there is no change in inequality for the original 10 people. The entry of the 11th person causes a composition bias and gives us the impression of rising inequality (which is then made synonymous with falling income mobility — the rich get more of the gains). Composition biases are the biggest problem.

Yet, they are easy to circumvent and that is what James Dean and I did. We used data from the Longitudinal Administrative Database (LAD) in Canada which produces measures of income mobility for a panel of people. This means that the same people are tracked over time (a five-year period). This totally eliminates the composition bias and we can assess how people within that panel evolve over time. This includes the evolution of income and relative income status (which decile of overall Canadian society they were in).

Using the evolution of income and relative income status by province and income decile, we tested whether economic freedom allowed the poor to gain more than the rich from high levels of economic freedom. The dataset was essentially the level of economic freedom in each five-year window matching the LAD panels for income mobility. The period covered is 1982-87 to 2013-18.

What we found is in the table below which illustrates only our results for the bottom 10% of the population. What we find is that economic freedom in each province heavily affects income mobility.

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More importantly, the results we find for the bottom decile are greater than the results “on average” (for all the panel) or than for the top deciles. In other words, economic freedom matters more for the poor than the rich. I hope you will this summary here to be enticing enough to consult the paper or the public policy summary we did for the Montreal Economic Institute (here)

Getting hired by a bot is unsettling

Samford student Savanah Needham identified an interesting recent WSJ article about the use of AI in hiring. Savanah writes:

In The WSJ, we learn that AI is being used for hiring employees rather than a traditional hiring manager, thus job applicants fear that they must impress a robot instead of relying on human interaction to get their dream job. The writer argues that job applicants deserve to know ahead of time how the algorithm will judge them and ought to receive feedback if they are rejected. Her proposal highlights the uncertainty that job candidates face in the newly AI-augmented hiring world.

We desperately need such a system. AI’s widespread use in hiring far outpaces our collective ability to keep it in check—to understand, verify and oversee it. Is a résumé screener identifying promising candidates, or is it picking up irrelevant, or even discriminatory, patterns from historical data? Is a job seeker participating in a fair competition if he or she is unable to pass an online personality test, despite having other qualifications needed for the job?

Julia Stoyanovich, WSJ

Robots can look at social media postings, linguistic analysis of candidates’ writing samples, and video-based interviews that utilize algorithms to analyze speech content, tone of voice, emotional states, nonverbal behaviors, and temperamental clues (HBR 2019). In just a few quick seconds, AI uses all the data it has on you to jump to conclusions. AI uses tools that claim to measure tone of voice, expressions, and other aspects of a candidate’s personality to help “measure how culturally ‘normal’ a person is.”

You spend a large amount of time proving to employers that you are not like the others, you’re different/better than other candidates…but now we need to try and convince a robot that we are “normal.”  

Researchers predict that face-reading AI can soon discern candidates’ sexual and political orientation as well as “internal states” like mood or emotion with a high degree of accuracy. This can be worrisome if the face reader claims that one is “too emotional” or assigns someone to a certain political party. 

Forgiveness is Underprovided

Forgiveness is Important

Whether one might socially offend us or whether one commits a crime, we face a fundamental tension between punishment and forgiveness. Punishment is important because it acts as a deterrent to the initial offense or to subsequent offenses. But punishment is also costly. Severing social or commercial ties reduces the number of possible mutually beneficial transactions. We lose economies of scale and lose gains from trade when we exclude someone from the market. Forgiveness is important because it permits those who previously had conflict to acknowledge the sunk cost of the offense and proceed with future opportunities for trade. However, an excess of forgiveness risks failure to deter destructive behaviors.

In the US, we enjoy a state that can prosecute alleged offenders and enforce punishments regardless of the economic status of the offended. While not perfect, the state incurs great cost by being the advocate of those who could not enforce great retributive punishment by their own means. A victim may choose to press charges against an offender, or the state can press charges despite a permissive victim.

In fact, our system of prosecution is somewhat asymmetrical. The state can press charges against a suspect, regardless of the victim’s wishes. While a victim can’t compel an unwilling state to press charges, say if the evidence is scant, an individual can engage in litigation against the accused.

Most of the possible combinations of victim and state strategies result in some kind of prosecution of the alleged offender. Except for litigation, our punishments in the US tend not to be remunerative – the victim isn’t compensated for the evils of the offender. ‘Justice’ is often construed as a type of compensation, however.

Herein lies a problem.

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